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  1. Apr 2025
    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study aims to explore sexual dimorphism in tolerance to nutrient deprivation using Drosophila larvae as a model. In particular the authors focus on sex differences in the larval fat body. They show that ATF4, an ISR transcription factor, has higher mRNA levels in female fat bodies. ATF4 transcriptional activity is also higher based on a reporter of ATF4 function, where this female bias in expression is influenced by sex determination factors. When the authors

      Overall, this study is interesting, as it identifies previously unrecognized sex-specific regulation of ATF4, an important transcription factor that mediates cellular stress responses. The study also shows that sex determination genes regulate ATF4. However, I have concerns about the paradigms of nutrient deprivation used in the study, and about data interpretation and statistical analysis that should be addressed prior to publication to support the authors' conclusions.

      Major concerns

      1. One major concern that I have about the sexual dimorphism in tolerance to nutrient deprivation is that the papers cited by the authors, and paradigms that are used broadly in the field, all use adult flies. The authors must show that in larvae, a completely different life stage from their citations, there is a sexual dimorphism in tolerance to nutrient deprivation.

      Interestingly, Diaz et al 2023 (Genetics) show that male larvae have greater fat stores than female larvae. Considering fat is the main determinant of tolerance to nutrient deprivation it's not clear that females will actually survive nutrient deprivation longer as larvae. This is an essential test of whether female larvae do have increased tolerance to nutrient deprivation, which is the basic foundation of the authors' model. 2. Another concern is the way that the authors "genetically induce nutrient deprivation using methioninase overexpression". As they acknowledge in the discussion (Line 381-390), methioninase overexpression will have many cellular effects. While there is no doubt that methionine levels would be lower in their model, it is less certain whether this is the main driver of the male-specific lethality.

      There are two potential solutions to this problem. First, the authors could change the text throughout the paper to more accurately describe their paradigm as "methioninase-induced lethality" rather than "nutrient deprivation". This would limit the scope of their scientific question and the conclusions they draw, but would eliminate the need for more experiments.

      The second solution would be to complete experiments to establish the following points: i) methioninase overexpression causes all the classical features of nutrient deprivation (e.g. changes to canonical signaling pathways such as TOR); ii) using other genetic means of nutrient deprivation such as slimfast-RNAi to see if those manipulations phenocopies the male-specific lethality they see with methioninase overexpression; iii) testing a role for ATF4 in mediating sex differences (if any) in other contexts such as slimfast-RNAi. This will take 2-3 months but is essential to draw any conclusions about whether their paradigm is truly a model for nutrient deprivation. 3. Another important point is that the authors state that sexually dimorphic ATF4 activity in the fat body is instructed by sexual identity in a cell-autonomous manner. Despite a clear decrease in ATF4 reporter levels in tra mutants, the fat body-specific tra-RNAi effect on the ATF4 reporter was less convincing. Together with the fact that changes to tra in the fat body affect insulin secretion from the insulin-producing cells, it is possible that the effect on ATF4 is not cell-autonomous. To conclusively test if sexual identity regulates ATF4 in a cell-autonomous manner the authors should use the flp-out system to make Tra-expressing or tra-RNAi-expressing clones in the fat body. This would take approximately 1.5 months to make the strain and test this. 4. As the authors show for the UAS-methioninase, other UAS lines used in the paper such as UAS-traF, UAS-tra-RNAi, UAS-dsx-RNAi may have leaky effects on gene/reporter expression. The authors must include a UAS only control to establish that the tra-RNAi, UAS-traF, UAS-dsx-RNAi do not affect gene/reporter expression. 5. I have concerns about the statistics used. In the methods and legends only t-tests are mentioned; however, when three groups are compared a one-way ANOVA with post-hoc tests must be used to correct for multiple comparisons. To compare differential responses to genetic/environmental manipulations between the sexes, a two-way ANOVA must be used. For example, to conclude that males and females have different responses in the two-way ANOVA, there must be a significant genotype:sex interaction. The p-values for comparisons between genotypes in either the one-way or two-way ANOVA must be derived from post-hoc tests within the ANOVA analysis.

      Minor points

      1. Please ensure to make the reader aware of which life stage was tested in the literature cited supporting sexually dimorphic tolerance to nutrient deprivation.
      2. Published data about sex-specific mechanisms of metabolic regulation mean that the introduction should be more fully cited than it is. Even in the introduction "the molecular basis of these differences and how they impact tolerance to nutrient deprivation is still under investigation" is inaccurate, as there are published studies identifying some mechanisms (work on gut hormones and sex-specific effects on starvation resistance and body fat, role of ecdysone on body fat and feeding, sex-specific roles for brummer and Akh in regulating body fat, intestinal transit and gut size and feeding). Please adjust the paper to acknowledge this growing body of knowledge.
      3. Please list the diet ingredients per L so that individuals can replicate the diet easily.
      4. Please cite grant numbers for all the community resources (e.g. Bloomington, DSHB), and please acknowledge FlyBase and its grants as well. For example, here are the instructions for citing BDSC https://bdsc.indiana.edu/about/acknowledge.html and similar instructions are available for the other resources.

      Significance

      This study identifies for the first time the sex-specific regulation of ATF4, and reveals the sex determination genes that mediate this effect. A strength of the study is the characterization of sex-specific ATF4 regulation. Limitations of the study include the paradigm for nutrient deprivation, need for additional controls, and statistical analysis. If the concerns above are addressed, this study will be of interest to researchers studying organismal and cellular stress responses, stress signaling, and builds upon a growing body of knowledge of sex differences in stress responses (e.g. autophagy, infection responses).

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public Review): 

      Despite evidence suggesting the benefits of neutralizing mucosa-derived IgA in the upper airway in protection against the SARS-CoV-2 virus, all currently approved vaccines are administered intramuscularly, which mainly induces systemic IgG. Waki et al. aimed to characterize the benefits of intranasal vaccination at the molecular level by isolating B cell clones from nasal tissue. The authors found that Spike-specific plasma cells isolated from the spleen of vaccinated mice showed significant clonal overlap with Spikespecific plasma cells isolated from nasal tissue. Interestingly, they could not detect any spike-specific plasma cells in the bone marrow or Peyer's patches, indicating that these nose-derived cells did not necessarily home to and reside in these locations, although the Peyer's patch is not a typical plasma cell niche - rather the lamina propria of the gut would have been a better place to look. Furthermore, they found that multimerization improves the antibody/antigen binding when the antibody is of low or intermediate affinity, but that high-affinity monomeric antibodies do not benefit from multimerization. Lastly, the authors used a competitive ELISA assay to show that multimerization could improve the neutralizing capacity of these

      antibodies. 

      The strength of this paper is the cloning of multiple IgA from the nasal mucosae (n=99) and the periphery (n=114) post-SARS-CoV-2 i.n. vaccination to examine the clonal relationship of this IgA with other sites, including the spleen. This analysis provides novel insights into the nature of the mucosal antibody response at the site where the host would encounter the virus, and whether this IgA response disseminates to other

      tissues. 

      There were also some weaknesses: 

      (1) The finding that multimerization improves binding and neutralization is not surprising as this was observed before by Wang and Nussenzweig for anti-SARS-CoV-2 IgA (authors should cite Enhanced SARS-CoV-2 neutralization by dimeric IgA. Wang et al., Sci. Transl. Med 2021, 13:3abf1555). 

      We have cited the paper, and the relevant sentence has been modified as follows (line 51-53); Recent studies have demonstrated that multimeric IgA is more effective and provides greater cross-protection than IgG and M-IgA (Okuya et al., 2020b) (Asahi et al., 2002) (Dhakal et al., 2018) (Asahi-Ozaki et al., 2004) (Wang et al., 2021).

      In addition, as far as I can tell we cannot ascertain the purity of fractions from the size exclusion chromatography thus I wasn't sure whether the input material used in Fig. 4 was a mixed population of dimer/trimer/tetramer?  

      The S-IgAs used in the SPR analysis in Fig. 4 consist of a mixture of dimers, trimers, and tetramers. The observed values indicate the average affinity of the S-IgAs. Please refer to the revised version (line 278280).

      (2) The flow cytometric assessment of the IgA+ clones from the nasal mucosae was difficult to interpret (Fig. 1B). It was hard for me to tell what they were gating on and subsequently analyzing without an IgA-negative population for reference. 

      We have updated FACS plots to illustrate the presence of IgA+ plasma cells in Fig. 1B, and the detailed gating strategy is outlined in Fig. 1B legend. Please find the relevant statements (line 115-120).

      (3) While the i.n. study itself is large and challenging, it would have been interesting to compare an i.m. route and examine the breadth of SARS-CoV-2 variant S1 binding for IgGs as in Fig. 2A. Are the IgA responses derived from the mucosae of greater breadth than systemic IgG responses? Alternatively, and easier, authors could do some comparisons with well-characterized IgG mAb for affinity and cross-reactivity as a benchmark to compare with the IgAs they looked at. Overall the authors did a good job of looking at a large range of systemic vs mucosal S1-specific antibodies in the context of an intra-nasal vaccination and this provides additional evidence for the utility of mucosal vaccination approaches for reducing person-to-person transmission. 

      I appreciate your consideration. Recent reports indicate that some M-IgA monomers possess neutralizing activity that is equivalent to or less than that of IgGs. However, the opposite phenomenon has also been observed. These results suggest that the Fc does not merely correlate with the degree of increase in antibody reactivity or functionality. We believe the discrepancies in previous studies are due to variations in the binding modes between the epitope and paratope of each antibody clone. Nevertheless, oligomerization enhances the functionality of most monomeric antibody clones, suggesting that the multivalent S-IgA enables a mode of action that is challenging to achieve with a monomeric antibody. Please refer to the revised version (line 399-403).

      Alternatively, and easier, authors could do some comparisons with well-characterized IgG mAb for affinity and cross-reactivity as a benchmark to compare with the IgAs they looked at. Overall the authors did a good job of looking at a large range of systemic vs mucosal S1-specific antibodies in the context of an intra-nasal vaccination and this provides additional evidence for the utility of mucosal vaccination approaches for reducing person-to-person transmission. 

      We have summarized the characteristics of the four types of nasal IgAs in Fig.7 and in the Discussion. Please refer to the revised version (line 405-422).

      Reviewer #2 (Public Review): 

      Summary: 

      This research demonstrates the breadth of IgA response as determined by isolating individual antigenspecific B cells and generating mAbs in mice following intranasal immunization of mice with SARS-CoV2 Spike protein. The findings show that some IgA mAb can neutralize the virus, but many do not. Notable immunization with Wuhan S protein generates a weak response to the omicron variant. 

      Strengths: 

      Detailed analysis characterizing individual B cells with the generation of mAbs demonstrates the response's breadth and diversity of IgA responses and the ability to generate systemic immune responses. 

      Weaknesses: 

      The data presentation needs clarity, and results show mAb ability to inhibit SARS-CoV2 in vitro. How IgA functions in vivo is uncertain. 

      We conducted an additional experiment using a hamster model and confirmed that S-IgAs can protect against SARS-CoV-2 infection. Please refer to the revised version (line 349-373 and 431-438).

      Reviewer #1 (Recommendations For The Authors): 

      (1) Figure 1A shows antibody titers in nasal lavage fluid and serum of mice post intranasal vaccination with SARS-CoV-2 Spike protein. The Y-axis of this figure is labeled as "U/mg" however these units are not clearly defined. 

      The antibody titers are expressed as optical density (OD450) value per total protein in nasal lavage fluids or serum. Please find the relevant statements (line 113-114).

      Furthermore, what do antibody titers in the nasal lavage fluid and serum look like post-intramuscular vaccination with the same vaccine and dose? Comparison of titers to the intramuscular route as well as to the PBS control would make this data more impactful. 

      We appreciate your consideration. We have not conducted experiments comparing the effects of intramuscular and intranasal administration using the same dosage and adjuvant. Cholera toxin has primarily been used as an adjuvant for nasal immunization, but it is seldom applied for intramuscular injection. We are interested in its impact on the immune compartment when using cholera toxin as an adjuvant for intramuscular injection. We plan to conduct further experiments in the future.

      Lastly, in Figure 1B, the detection of nasal IgG is not shown even though the authors assess nasally-derived IgG in the spleen further into the study.  

      Since the number of lymphocytes that can be collected from the nasal mucosa is limited, there is an insufficient capacity to isolate IgG+ plasma cells after collecting IgA+ plasma cells. Therefore, conducting such an experiment on mice is technically challenging. A larger animal, such as rats, will be necessary to perform this experiment. Further investigation is needed to determine whether antigen-specific IgG+ plasma cells, sharing V-(D)-J with nasal IgA, can be detected in the nasal mucosa.

      (2) There appears to be something amiss with the IgA stain. It is smushed up against the X-axis. Better flow cytometry profiles should be shown. Likewise in Supplemental Fig. 1A, their IgA stain appears to not be working. This must be addressed using positive and negative controls. 

      We have updated FACS-polts to show the IgA+ plasma cell in Fig.1B, and the detailed gating strategy is outlined in the Fig.1B legend. Please find the relevant statements on line 115-120.

      (3) We do not know the purity of the samples that were subjected to SPR and since the legend of Fig. 4 is partially incorrect, it was difficult to know how this experiment was done. 

      The S-IgA used in the SPR analysis shown in Figure 4 is a mixture of dimers, trimers, and tetramers, and the observed values are believed to reflect the affinity of the S-IgA in the nasal mucosa. Please refer to the revised version (line 278-280).

      (4) Fig. 5 results need to compare with some of the well-characterized mAb (IgG) to understand the biological significance of these neutralizing titres. 

      We have summarized the characteristics of the four types of nasal IgA in Fig.7 and in the Discussion. Please refer to the revised version (page 405-422).

      Communication of results: 

      (1) Authors could improve the communication of their results by introducing the vaccination protocol in the results section accompanied by a diagram of the vaccination strategy (nature of the Ag, route, and frequency). This could be Fig. 1A .  

      A schematic diagram of the vaccination protocol is presented in Fig.1.

      (2) Care should be taken with some of the terminology. Intranasal is the accepted term but authors sometimes use "internasal". The term "immunosuppression" on page 2 could be misleading as it means something different to other audiences. The distinction when speaking about "protection from harmful pathogens" should be made between protection against infection (ie sterilizing immunity) vs protection against disease (ie morbidity and mortality). Instead of "nose", one should say "nasal". Nose-related could be rephrased as "potentially nasal-derived". P.5, line 2 didn't make sense: "IgG+ plasma cells that express nose-related IgA"...

      In many places, Spike is missing it's "e".  

      We have made the correction accordingly.

      (3) Page 3: The lumping of the human and animal SARS-CoV-2 intranasal studies together is a bit misleading. Very little has worked for intranasal vaccination against SARS-CoV-2 in humans at this point in time (although hopefully that will change soon!). Authors should specify which studies were done in animals and which were done in humans. 

      The manuscript has been revised to include two citations on line 73-75 (Ewer et al., 2021 and Zhu et al., 2023).

      (4) What is ER-tracker? It comes out of nowhere and should be explained why it was used to the reader (as well as why they used the other markers) to sort for Spike-specific PC. 

      ER-Tracker is a fluorescent dye that is highly selective for the endoplasmic reticulum of living cells. Because plasma cells have an expanded endoplasmic reticulum for properly folding and secreting large quantities of antibodies, using ER-Tracker along with anti-CD138 facilitates the isolation of plasma cells from lymphocytes without the need for additional antibodies. Please refer to the revised version for details. (ine 130-134).

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:  

      Reviewer #1 (Public review):  

      Summary:  

      Goal: Find downstream targets of cmk-1 phosphorylation, identify one that also seems to act in thermosensory habituation, test for genetic interactions between cmk-1 and this gene, and assess where these genes are acting in the thermosensory circuit during thermosensory habituation.  

      Methods: Two in vitro analyses of cmk-1 phosphorylation of C. elegans proteins. Thermosensory habituation of cmk-1 and tax-6 mutants and double mutants was assessed by measuring the rate of heat-evoked reversals (reversal probability) of C. elegans before and after 20s ISI repeated heat pulses over 60 minutes.  

      Conclusions: cmk-1 and tax-6 act in separate habituation processes, primarily in AFD, that interact complexly, but both serve to habituate the thermosensory reversal response. They found that cmk-1 primarily acts in AFD and tax-6 primarily acts in RIM (and FLP for naïve responses). They also identified hundreds of potential cmk-1 phosphorylation substrates in vitro.  

      Strengths:  

      The effect size in the genetic data is quite strong and a large number of genetic interaction experiments between cmk-1 and tax-1 demonstrate a complex interaction.  

      Thanks a lot for these positive remarks.

      Weaknesses:  

      The major concern about this manuscript is the assumption that the process they are observing is habituation. The two previously cited papers using this (or a very similar) protocol, Lia and Glauser 2020 and Jordan and Glauser 2023, both use the word 'adaptation' to describe the observed behavioral decrement. Jordan and Glauser 2023 use the words 'habituation' or 'habituation-like' 10 times, however, they use 'adaptation' over 100 times. It is critical to distinguish habituation from sensory adaptation (or fatigue) in this thermal reversal protocol. These processes are often confused/conflated, however, they are very different; sensory adaptation is a process that decreases how much the nervous system is activated by a repeated stimulus, therefore it can even occur outside of the nervous system. Habituation is a learning process where the nervous system responds less to a repeated stimulus, despite (at least part of the nervous system) the nervous system still being similarly activated by the stimulus. Habituation is considered an attentional process, while adaptation is due to the fatigue of sensory transduction machinery. Control experiments such as tests for dishabituation (where the application of a different stimulus causes recovery of the decremented response) or rate of spontaneous recovery (more rapid recovery after short inter-stimulus intervals) are required to determine if habituation or sensory adaptation are occurring. These experiments will allow the results to be interpreted with clarity, without them, it isn't actually clear what biological process is actually being studied.  

      Thanks for the comment. As this reviewer points out, “adaptation” and “habituation” are often conflated. Many scientists (maybe not the majority though) use a less stringent definition for the word habituation, than the one presented by this reviewer. More particularly, the term habituation is used in human pain research to refer solely to the reduction of response to repeated stimuli, in the absence of a detailed assessment of the more stringent criteria mentioned here (see, e.g.,  PMID: 22337205 ; PMID: 18947923 ; PMID: 17258858; PMID: 20685171 ; PMID: 15978487). In addition to the practice in pain research, the main reason why we steered toward ‘habituation’ from our previous publication is because it immediately conveys the idea of a response reduction, whereas ‘adaptation’ could in principle be either an up-regulation or a downregulation of the response (again, based on various definitions). But we agree that using the word “habituation” came at the cost of triggering a confusion about the exact nature of the process, for those considering the stricter definition of the word “habituation” and those not in the narrower field of pain research. In the revised manuscript, we have thus changed this terminology to “adaptation”. Also following suggestions from Reviewer 2, we have strengthened the description of the protocol in the Result section and clarified, why the adaptation phenomenon is not a ‘thermal damage’ effect or ‘fatigue’ effect in the neuro-muscular circuit controlling reversal. One of the most convincing piece of evidence it cannot be solely explained by “damages” or “exhaustion” is simply the existence of non-adapting mutants (like cmk-1(lf)) or pharmacological treatments (Cyclosporin A) blocking the adaptation effect and enabling worm to continuously reverse for hours without any problems.  

      While the discrepancy between the in vitro phosphorylation experiments and the in silico predictions was discussed, the substantial discrepancy (over 85% of the substrates in the smaller in vitro dataset were not identified in the larger dataset) between the two different in vitro datasets was not discussed. This is surprising, as these approaches were quite similar, and it may indicate a measure of unreliability in the in vitro datasets (or high false negative rates).

      Thanks for the comment. This is an important aspect which we now more extensively cover in the Discussion section.

      The strong consistency of the CMK-1 recognition consensus sequences across the two in vitro dataset speaks against the unreliability of the analyses. Instead, there are a few points to highlight that explain the somewhat low degree of overlap between the two datasets, which indeed relate to the false negative rates as this reviewer suggests.

      (1) In the peptide library analysis, Trypsin cleavage prior to kinase treatment will leave a charged N-term or C- terminus and in addition remove part of the protein context required for efficient kinase recognition. This will have a variable effect across the different substrates in the peptide library, depending on the distance between the cleavage site and the phosphosite, but will not affect the native protein library. This effect increases the false negative rate in the peptide library.

      (2) The number and distribution of “available substrate phosphosites” diverge in the two libraries. Indeed, the peptide library is expected to contain a markedly larger diversity of potential CMK-1 substrate sites than the protein library (because the Trypsin digestion will reveal substrates that are normally buried in a native protein), but the depth of MS analysis is the same for the two libraries. In somewhat simplistic terms, the peptide-library analysis is prone to be saturated with abundant phosphorylated peptides, which prevent detecting all phosphosites. If the peptide analysis could have been made deeper, we would probably have increased the overlap (at the cost of increasing the number of false positive too).

      (3) We have chosen quite strict criteria and applied them separately to define each hit list; therefore, we know we have many false negatives in each list, which will naturally reduce the expected overlap.

      We now extended the discussion of the limited overlap of the two dataset in a dedicated paragraph in the discussion. We also clarify that we tend to give more trust to the protein-library dataset (since substrates are in a configuration closer to that in vivo), with those hits also present in the peptide dataset (like TAX-6 was) as the most convincing hits, as they could be validated in a second type of experiment.

      Additionally, the rationale for, and distinction between, the two separate in vitro experiments is not made clear.  

      We reasoned that both substrate types have their own benefits and limitations (as discussed in the manuscript), so it was an added value to run both. We proposed that the subset of targets present in both datasets to be the most solid list of candidates. We have reinforced this point in the discussion.  

      Line 207: After reporting that both tax-6 and cnb-1 mutants have high spontaneous reversals, it is not made clear why cnb-1 is not further explored in the paper. Additionally, this spontaneous reversal data should be in a supplementary figure.  

      We kept the focus of the article primarily on TAX-6, because it was identified as CMK-1 target in vitro; CNB-1 was not. Moreover, we didn’t have cnb-1(gf) mutants to pursue the analysis with, and we were stuck by the cnb-1(lf) constitutive high reversal rate for any further follow up. We have added a supplementary file to present the spontaneous reversals rates.

      Figure 3 -S1: This model doesn't explain why the cmk-1(gf) group and the cmk-1(gf) +cyclo A group cause enhanced response decrement (presumably by reducing the inhibition by tax-6) but the +cyclo A group (inhibited tax-6) showed weaker response decrement, as here there is even further weakened inhibition of tax-6 on this process. Also, the cmk-1(lf) +cyclo A group is labeled as constitutive habituation, however, this doesn't appear to be the case in Figure 3 (seems like a similar initial level and response decrement phenotype to wildtype).  

      Thanks a lot for the comment. We are glad that the presentation of our complex dataset was clear enough to bring the reader to that level of detailed reflection and interpretation on the proposed model. To address the two points raised in this reviewer comment, we made modifications to the model presentation and provide additional clarifications below, where we use the term adaptation instead of habituation (as in the revised Figure):

      Regarding the first point, “why the cmk-1(gf) group and the cmk-1(gf) +cyclo A group cause enhanced response decrement … but the +cyclo A group showed weaker response decrement”. This is really a very good point, that cannot be easily explained if all the branches (arrows) in the model have the same weight or work as ON/OFF switches. We tried to convey the relative importance of the regulation effect via the thickness of the arrow lines (which we have now clarified in the legend in the revised ms). The main ‘quantitative’ nuances to take into consideration here originate from 2 assumptions of the model (which we have clarified in the revised ms):

      Assumption 1: the inhibitory effect of TAX-6 on the CMK-1 antiadaptation branch and the inhibitory effect of TAX-6 on the CMK-1 pro-adaptation branch are not of the same magnitude (we have further enhanced the line thickness differences in the revised model, top left panel for wild type).

      Assumption 2: the two antagonistic direct effects of CMK-1 on adaptation are not of the same magnitude, most strikingly in the context of CMK-1(gf) mutants.

      In our model, the cyclosporin A treatment alone (bottom left panel) causes a strong boost on the CMK-1 inhibitory branch and a less marked boost on the CMK-1 activator branch (following assumption 1). This causes an imbalance between the two antagonist direct CMK-1-dependent drives, which reduces (but doesn’t fully block) adaptation. Indeed, we don’t observe a total block of adaptation with cyclosporin A in wild type, the effect being significantly milder than the totally nonadapting phenotypes seen, e.g., in TAX-6(gf) mutants. From there, the question is what happen in CMK-1(gf) background that would mask the anti-adaptation effect of Cyclosporin A? Here assumption 2 is relevant, and the CMK-1(gf) pro-adaptation direct branch is always prevalent and imbalances the regulation toward faster adaptation (the role of TAX-6 becoming negligible in the CMK-1(gf) background and ipso facto that of Cyclosporin A).

      Regarding the second point, “the cmk-1(lf) +cyclo A group is labeled as constitutive habituation”. We regret a confusing word choice in the first version of the manuscript; we intended to mean “normal habituation phenotype” but in the joint absence of antagonistic CMK-1 and TAX-6 regulatory signaling (so the regulation is not like in wild-type, but the phenotype ends up like in wild type). We have modified the label to “normal adaptation” and left a note in the legend that an apparently normal adaptation phenotype seems to be the default situation when the two antagonistic regulatory pathways are shut off.

      More discussion of the significance of the sites of cmk-1 and tax-6 function in the neural circuit should take place. Additionally, incorporating the suspected loci of cmk-1 and tax-6 in the neural circuit into the model would be interesting (using proper hypothetical language). For example, as it seems like AFD is not required for the naïve reversal response but just its reduction, cmk-1 activity in AFD might be generating inhibition of the reversal response by AFD. It certainly would be understandable if this isn't workable, given extrasynaptic signaling and other unknowns, but it potentially could also be helpful in generating a working model for these complex interactions. For example, cmk1 induces AIZ inhibition of AVA (AIZ is electrically coupled to AFD), and tax-6 reduces RIM activation of AVA (these neurons are also electrically coupled according to the diagram). RIM is also a neuropeptide-rich neuron, so this could allow it to interact with the cmk-1-related process(es) in AFD. Some discussion of possibilities like this could be informative.  

      Thanks for the comment. These hypothetical inter-cellular communication pathways are indeed nice possibilities. On the other hand, we could envision several additional pathways. While RIM is indeed a neuropeptide-rich neurons, all these neurons actually express neuropeptides. Following this helpful suggestion, we have slightly expanded the discussion of hypothetical cellular pathways that can be modulated downstream of CMK-1 in AFD. We also slightly lengthened the discussion to mention hypothetical post-synaptic target of TAX-6 within interneurons based on the literature.

      Provide an explanation for why some of the experiments in Figure 4 have such a high N, compared to other experiments.  

      The conditions with the highest n correspond to conditions which we have also used as ‘control’ condition for other type of experiments in the lab and as part of side projects, but which could be gathered for the present article. We have been working with cmk-1(lf) and tax-6(gf) mutants for many years… and the robust non-adapting phenotype was a reference point and a quality control when analyzing other nonadapting mutants.

      Because the loss of function and gain of function mutations in cmk-1 have a similar effect, it is likely that this thermosensory plasticity phenotype is sensitive to levels of cmk-1 activity. Therefore, it is not surprising that the cmk-1 promoter failed to rescue very well as these plasmid-driven rescues often result in overexpression. Given this and that the cmk-1p rescue itself was so modest, these rescue experiments are not entirely convincing (and very hard to interpret; for example, is the AFD rescue or the ASER rescue more complete? The ASER one is actually closer to the cmk-1p rescue). Given the sensitivity to cmk-1 activity levels, a degradation strategy would be more likely to deliver clear results (or perhaps even the overactivation approach used for tax-6).  

      Thanks for the comment. We respectfully disagree with this reviewer’s statement “the loss of function and gain of function mutations in cmk-1 have a similar effect”. We suspect a confusion here, because our data clearly show that these two mutant types have an opposite phenotype. That being said, we interpret the weak rescue effect with cmk-1p as a probable result of overexpression or incomplete/imbalanced expression across neurons (as the promoter used might not include all the relevant regulatory regions). We dedicated considerable efforts to establish an endogenous CMK-1::degron knock in, for tissue-specific auxin-induced degradation (AID), but we were unfortunately not able to obtain consistent results. Unfortunately, the only useful data regarding CMK-1 place-of-action are the cell-specific rescue data already included in the report.

      Reviewer #2 (Public review):  

      Summary:  

      The reduction in a response to a specific stimulus after repeated exposures is called habituation. Alterations in habituation to noxious stimuli are associated with chronic pain in humans, however, the underlying molecular mechanisms involved are not clear. This study uses the nematode C. elegans to study genes and mechanisms that underlie habituation to a form of noxious stimuli based on heat, termed thermo-noxious stimuli. The authors previously showed that the Calcium/Calmodulin-dependent protein kinase (CMK-1) regulates thermo-nociceptive habituation in the nematode C. elegans. Although CMK-1 is a kinase with many known substrates, the downstream targets relevant for thermo-nociceptive habituation are not known. In this study, the authors use two different kinase screens to identify phosphorylation targets of CMK-1. One of the targets they identify is Calcineurin (TAX-6). The authors show that CMK-1 phosphorylates a regulatory domain of Calcineurin at a highly conserved site (S443). In a series of elegant experiments, the authors use genetic and pharmacological approaches to increase or decrease CMK-1 and Calcineurin signaling to study their effects on thermo-nociceptive habituation in C. elegans. They also combine these various approaches to study the interactions between these two signaling proteins. The authors use specific promoters to determine in which neurons CMK-1 and Calcineurin function to regulate thermonociceptive habituation. The authors propose a model based on their findings illustrating that CMK-1 and Calcineurin act mostly in different neurons to antagonistically regulate habituation to thermo-nociceptive stimuli in a complex manner.  

      Strengths:  

      (1) Given the conservation of habituation across phylogeny, identifying genes and mechanisms that underlie nociceptive habituation in C. elegans may be relevant for understanding chronic pain in humans.  

      (2) The identification of canonical CaM Kinase phosphorylation motifs in the substrates identified in the CMK-1 substrate screen validates the screen.  

      (3) The use of loss and gain of function approaches to study the effects of CMK-1 and Calcineurin on thermo-nociceptive responses and habituation is elegant.  

      (4) The ability to determine the cellular place of action of CMK-1 and Calcineurin using neuron-specific promoters in the nematode is a clear strength of the genetic model system.  

      Thanks a lot for these positive remarks.

      Weaknesses:  

      (1) The manuscript begins by identifying Calcineurin as a direct substrate of CMK-1 but ends by showing that CMK-1 and Calcineurin mostly act in different neurons to regulate nociceptive habituation which disrupts the logical flow of the manuscript.  

      We understand this point and we have carefully considered and (reconsidered) the way to articulate the report. However, we could not present the story much differently as we would have no justification to investigate the role of TAX-6 and its interaction with CMK-1, if we would not have first identified it as phospho-target in vitro. Carefully considering this point, we found that the abstract of the first manuscript version was probably too cursory and susceptible to trigger wrong expectations among readers. We have thus extensively revised the abstract to clarify this point. Furthermore, we have reinforced this point in the last paragraph of the introduction and in the conclusion paragraph of the Discussion.

      (2) The physiological relevance of CMK-1 phosphorylation of Calcineurin is not clear.

      We do agree and have explicitly mentioned this aspect in the abstract, in the end of the introduction, and in the discussion section.

      (3) It is not clear if Calcineurin is already a known substrate of CaM Kinases in other systems or if this finding is new.  

      We are not aware of any study having shown Calcineurin is a direct target of CaM kinase I. But it was found to be substrate of CaM kinase II as well as of other kinases, as we explicitly presented in the discussion section. We have complemented the text mentioning we are not aware of Calcineurin having so far been reported to be a CaM kinase I substrate.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):  

      (1) The authors might consider reorganizing the results, so that the substrate phosphorylation analysis follows the cmk-1 habituation data, as it may not be clear to the reader why you are looking for substrates downstream of cmk-1 at that point. Or the authors could mention the previous habituation data for cmk-1 at the beginning of the results.  

      Thank you. This is something that we considered while (re-)writing. However, we prefer to keep CMK-1 data side-by-side with TAX-6 data, regarding the result section. Nevertheless, we have modified the last paragraph of intro to better transition and justify the specific interest of searching for CMK-1 targets in the context of the present study.

      (2) Line 209: 'controls' is too strong a word. 'regulates' would be better, and it should be stated that this is for 'spontaneous reversal behavior'.  

      Thank you. This was modified.

      (3) Line 359: we suspect that these reflect functional enrichments.  

      We don’t see what would exactly be wrong with the original sentence. The proposed change (if it is a proposed change) would completely obliterate the intended meaning of our sentence. We rewrote the sentence to be as clear as possible, as follows: ”Even if we cannot rule out an actual inclination of the CaM kinase pathway to regulate these processes, we suspect that these GO term enrichments rather reflect an analytical bias toward abundant proteins.”

      (4) Line 563: In this subsection, it is not made clear when the T0 and T60 heat pulses are given, in relation to the 20s ISI heat pulses given for 60 minutes. Are they the first and last pulse, or given some time before or after this train of heat pulses?  

      Thanks for spotting this poor description, which we have improved in the revised manuscript. The heat pulse recording is given immediately before and immediately after the 60 min of repeated stimulation. After the T0 heat pulse recording there is a period of about 30 s (period of post stimuli recording + transfer from the recording device (INFERNO) to the habituation device (ThermINATOR)).  For the T60 acquisition, there is a lag of about 50 s between the last ‘habituation’ stimuli and the recording stimuli (time needed to move the plate between the habituation device and the recording device + 40 s of baseline reversal recording in the absence of heat stimuli).

      Reviewer #2 (Recommendations for the authors):  

      (1) There appears to be little to no connection between the phosphorylation site discovered in Calcineurin (S443) and the behavioral phenotypes being studied. What is the thermo-nociceptive response if phosphorylation of S443 in Calcineurin is blocked (using a S443A mutation) and/or combined with CMK-1 gain of function?  

      Thanks for the suggestion. The suggested analysis is complicated by several factors. First, the tax-6(lf) is not directly suitable for rescue analysis (until we would have identified a way to restore baseline reversal), so we cannot use a S443A-carrying rescue transgene. Second, the truncated TAX-6(GF) mutant lacks the C-terminal part, including S443, so we cannot introduce a S443A in this context. The left approach would be to modify the endogenous locus. This again is complicated by the fact that S443 exists in two different isoforms (with conserved RxxS motifs in two different alternative exons). It will be very difficult to perform these experiments until we know more about the expression pattern and function of the respective isoforms. This is work in progress, but this analysis will need to await a future publication.

      (2) The authors should state clearly if Calcineurin is a novel substrate of CaM Kinase or if this is already known in the field.  

      We have complemented the text mentioning we are not aware of Calcineurin having so far been reported to be a CaM kinase I substrate.

      (3) The logical flow of the manuscript could be improved given that CMK-1 and Calcineurin appear to act in different cells to regulate nociceptive habituation.  

      As detailed above, we have considered this point carefully and modified the introduction and the abstract. The discussion about the two places of action was also improved.

      (4) More detail about the experimental methods used for the heat-evoked reversals should be included in the Results section.  

      Thanks for the suggestion. We have improved the description in the Method section and expanded the partial description in the result section, so readers could hopefully proceed without needing to go back and forth with the methods.

      (5) Check for typos. For example: line 197 - fix typo "...to a series repeated heat stimulation...".  

      Thank you. We have carefully read the revised manuscript to correct remaining typos.

    1. Reviewer #2 (Public review):

      In their manuscript, Rijal and colleagues describe a 'loop grafting' strategy to enhance expression levels and stability of recombinant neuraminidase. The work is interesting and important.

      Major points from first round of review:

      (1) The authors overstress the importance of the epitopes covered by the loops they use and play down the importance of antibodies binding to the side, the edges, or the underside of the NA. A number of papers describing those mAbs are also not included.

      (2) The rationale regarding the PR8 hybrid is not well described and should be described better.

      (3) Figure 3B and 6C: This should be given as numbers (quantified), not as '+'.

      (4) Figure 5A and 7A: Negative controls are missing.

      (5) The authors claim that they generate stable tetramers. Judging from SDS-PAGE provided in Supplementary Figure 3B (BS3-crosslined), many different species are present including monomers, dimers, tetramers, and degradation products of tetramers. In line 7 for example there are at least 5 bands.

      [Editors' note: the authors have appropriately responded to and addressed these points.]

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This manuscript described a structure-guided approach to graft important antigenic loops of the neuraminidase to a homotypic but heterologous NA. This approach allows the generation of well-expressed and thermostable recombinant proteins with antigenic epitopes of choice to some extent. The loop-grafted NA was designated hybrid.

      Strengths:

      The hybrid NA appeared to be more structurally stable than the loop-donor protein while acquiring its antigenicity. This approach is of value when developing a subunit NA vaccine which is difficult to express. So that antigenic loops could be potentially grafted to a stable NA scaffold to transfer strain-specific antigenicity.

      Weaknesses:

      However, major revisions to better organize the text, and figure and make clarifications on a number of points, are needed. There are a few cases in which a later figure was described first, data in the figures were not sufficiently described, or where there were mismatched references to figures.

      More importantly, the hybrid proteins did not show any of the advantages over the loop-donor protein in the format of VLP vaccine in mouse studies, so it's not clear why such an approach is needed to begin with if the original protein is doing fine.

      We thank the reviewer for their helpful comments. We have incorporated feedback from the authors to improve the manuscript. Please see our point-by-point response.

      The purpose of loop-grafting between H5N1/2021 (a high-expressor) and the PR8 virus was not to improve the expression of PR8, which is already a good expressing NA. Instead, the loop-grafting and the in vivo experiments were done to show the loop-specific protection following a lethal PR8 virus challenge.

      Reviewer #2 (Public review):

      In their manuscript, Rijal and colleagues describe a 'loop grafting' strategy to enhance expression levels and stability of recombinant neuraminidase. The work is interesting and important, but there are several points that need the author's attention.

      Major points

      (1) The authors overstress the importance of the epitopes covered by the loops they use and play down the importance of antibodies binding to the side, the edges, or the underside of the NA. A number of papers describing those mAbs are also not included.

      We have discussed the distribution of epitopes on NA molecule in the Discussion section "The distribution of epitopes in neuraminidase" (new line number 350). In Supplementary Figures 1 and 2, we have compiled the epitopes reported by polyclonal sera and mAbs via escape virus selection or crystal structural studies. There are 45 residues examples of escape virus selection, and we found that approximately 90% of the epitopes are located within the top loops (Loops 01 and Loops 23, which include the lateral sides and edges of NA). We have also included the epitopes of underside mAbs NDS.1 and NDS.3 in Supplementary Figure 2. Some of the interactions formed by these mAbs are also within the L01 and L23 loops. All relevant references are cited in Supplementary Figures 1 and 2.

      A new figure has been added [Figure 1b (ii)] to illustrate the surface mapping of epitopes on NA.

      (2) The rationale regarding the PR8 hybrid is not well described and should be described better.

      We described the rationale for the PR8 hybrid (new lines 247-250). For clarity, we have added the following sentence within the section "Loop transfer between two distant N1 NAs:...."

      (new lines 255-258):

      "mSN1 showed sufficient cross-reactivity to N1/09 to protect mice against virus challenge. Therefore, we performed loop transfer between mSN1 and PR8N1, which differ by 18 residues within the L01 and L23 loops and show no or minimal cross-reactivity, to assess the loop-specific protection."

      (3) Figure 3B and 6C: This should be given as numbers (quantified), not as '+'.

      We have included the numerical data in Supplementary Figure 6. The data is presented in semi-quantitative manner for simplification. To improve clarity, we have now added the following sentence to the Figure 3c legend: "Refer to Supplementary Figure 6 for binding titration data".

      (4) Figure 5A and 7A: Negative controls are missing.

      A pool of Empty VLP sera was included as a negative control, showing no inhibition at 1:40 dilution. In the figure legends, we have stated "Pooled sera to unconjugated mi3 VLP was negative control and showed no inhibition at 1:40 dilution (not included in the graphs)"

      (5) The authors claim that they generate stable tetramers. Judging from SDS-PAGE provided in Supplementary Figure 3B (BS3-crosslinked), many different species are present including monomers, dimers, tetramers, and degradation products of tetramers. In line 7 for example there are at least 5 bands.

      Tetrameric conformation of soluble proteins is evidenced by the size-exclusion chromatographs shown in Figures 3a and 6b. The BS3 crosslinked SDS-PAGE are only suggestive data, indicating that the protein is a tetramer if a band appears at ~250 kDa. However, depending on the reaction conditions, lower molecular weight bands may also be observed if crosslinking is incomplete.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Specific comments:

      - Description of Figure 2 on page 3 should go before Figure 3 lines 87-105 or swap the order of the two figures.

      We have moved lines 91-96, which refer to Figure 3, to appear after Figure 2.

      - Figure 3a, an EC50 should be calculated for both NA activity assay.

      Figure 3a has been updated to include the EC50 and AUC (Area under curve) values for both NA activity assays. The same update has also been made for Figure 6b.

      - Line 150, I'm not sure it's appropriate to cite a manuscript that was in preparation but not published. I'm referring to the two mAbs AG7C and AF9C that were claimed to bind to the L01 and L23 loops but not.

      We have changed the "manuscript in preparation" to "personal communication with Dr. Yan Wu, Capital Medical University".

      - The description in Figure 4a is lacking.

      We have added a detailed description for Figure 4a.

      - Figure 4c, sufficient description is needed. For example, the cavity should be outlined and annotated, what is the role of Val149? Why the first monomer is assigned a number of II and the second monomer with a number of I.

      We have added a detailed description for Figure 4c and amended the figure as per the reviewer’s suggestions.

      - Figure 5a, in addition to ELLA data to mSN1 and N1/09, ELLA data to N1/19 should also be measured and shown. Figure S7, please show IC50 instead of curves for better comparison.

      We included IC50 for mSN1 and N1/09 as we intended to associate the loops with protection.  Graphs for N1/19 have not been reported, but the IC50 titres from pooled sera are shown in Supplementary Figure 7 as a representation. Due to the limited sera sample sourced from tail vein bleed, these assays were performed using pooled sera, which represent the total response (established in numbers of experiments).

      - Line 234-238, the author made a statement about the data shown in Figure 7b "These results mirrored several studies in the literature which showed that immunization with the 2009 N1 could provide at least partial protection in mice and ferrets to the avian H5N1 challenge". The data did not reflect that. In Figure 5b, mSN1 protects as well as other proteins. In fact, there was no advantage of N109 and N109 hybrid over mSN1 in protection against the homologous H1N109. Although higher levels of NAI antibodies were induced with the homologous protein in Figure 5a. The protection could be contributed by non-NAI antibodies, so the authors should measure binding antibodies. The author may increase the challenge dose from 200 LD50 to 1000 LD50 to see a difference due to the strong immunogenicity of the nanoparticles vaccine plus addavax. Otherwise, it looks like loop grafting is not necessary as heterologous NA could broadly protect.

      We agree that msN1, despite its low NAI titres, was equally protective as homologous NA or its hybrid NA against H1N1/09 virus challenge at 200 LD50. There may be additional protective components, including non-NAI antibodies in homologous groups that may have contributed to the protection.

      We assessed sera binding to H1N1/2009 and found that the binding antibody levels were also lower in the msN1 group. The corresponding graph has now been added in Figure S7d. It was difficult to determine the NAI titre required to confer protection in this experiment. For this reason, we later chose PR8 as the challenge virus to demonstrate loop-specific protection.

      We are uncertain whether a 1000 LD50 challenge would have helped establish a correlation between protection and NAI IC50 titres, as the dose used is already lethal for DBA/2 mice.

      - Why would the authors separate work with N1/09 and N1/19 from PR8 N1? To this reviewer's understanding, they are all the same strategies with increasing numbers of dissimilar residues from N1/09 (12) to N1/19 (16) and to PR8 (18). They are all characterized by the same approaches in vitro and in vivo.

      We had two different goals for making hybrids with N1/09 and PR8 N1, therefore, we have presented these results separately.

      (1) For N1/09 and N1/19, we showed that loop-grafting improved protein yield and stability. Additionally, we showed that the N1/09 hybrid can be as protective as the homologous protein.

      (2) PR8 N1 is a high-yielding protein, so loop grafting did not significantly increase its yield. However, the PR8 virus challenge confirmed loop-specific protection.

      - For in vivo study testing the PR8 construct, although PR8 and PR8 hybrid protect better than the heterologous mSN1, the hybrid again did not show any advantages over the PR8 original proteins.

      That's correct - the PR8 hybrid was not advantageous over the original PR8 protein. However, the purpose of this experiment was to demonstrate loop specific protection. The PR8 hybrid (PR8 loops - mS scaffold) protected 6/6 mice, whereas mS hybrid (mS loops - PR8 scaffold) provided no protection.

      - Line 243-249, lack of reference to figures.

      References to Supplementary Figure 7b,c and Figure 2 has been added.

      - What was the reason that the challenge was one by 200 LD50 for 2009 H1N1 and 1000 LD50 for PR8.

      Viruses were titrated in the BALB/c strain for PR8 virus and the DBA/2 strain for X-179A (H1N1/2009) virus. These doses were selected based on their lethality and the time required to reach the endpoint (~20% weight loss) post-infection, which is 5-6 days. Most studies in the literature have used 10 LD50 or higher; thus the virus doses we used are relatively high.

      - Line 268, there is no Figure 5C.

      This was a mistake and has been corrected to Figure 6c.

      - Line 275 what are the readers supposed to see in supplementary Figure 5a? There is not enough description for the referred figures.

      A sentence has been added to Fig S5a description, to make a point about recognition of the NA scaffold by mAb CD6. "Binding by mAb CD6 is predominantly scaffold dependent and occurs across two protomers"

      - The discussion is very long and some of it is not relevant to the study. For example, the role of the tetramerization domain and the basis for structurally stable tetramer formation, were not the focuses of this study.

      We felt it was important to discuss the tetramerisation domain and the basis for stable tetramer formation. A previous study by Ellis et al.  used the VASP tetramerisation domain and introduced multiple NA interface mutations to achieve a more stable closed conformation. In contrast, NA proteins used in our study required the tetrabrachion tetramerisation domain to form a properly assembled tetramer.

      In lines 382-383, there is one unfinished sentence.

      This is corrected.

      The definition of the loops is also confusing. Line 381, the author stated that in the N1/19 hybrid design, residue N200S, could have been considered as part of the loop B2L23, and was it not?

      The designation of loop ends should not be rigid but rather based on multiple factors such as, their proximity to antigenic epitopes, charge, and hydrophobicity. This is discussed in the " Definition of loops" section.

      - Figure 1a and Figure S2, please provide sufficient descriptions, what do the blocks in different colors mean?

      We have updated the Figure 1a legend to indicate the colours.

      The descriptions for Figures S1 and S2 have also been revised for clarity.

      Reviewer #2 (Recommendations for the authors):

      Minor points

      (1) Line 37: Should be 'Influenza virus neuraminidase'.

      This is corrected.

      (2) Line 65: https://pubmed.ncbi.nlm.nih.gov/35446141/, https://pubmed.ncbi.nlm.nih.gov/33568453/ and https://pubmed.ncbi.nlm.nih.gov/28827718/ indicate that protective mAbs bind all over the NA head domain.

      We have discussed the epitopes on the NA head in detail in the section "The distribution of epitopes on Neuraminidase". In Supplementary Figures 1 and 2, we compiled several studies, including those on polyclonal sera and mAbs epitopes, emphasizing that loops 01 and 23 are the predominant antibody targets (~90%). Some antibodies also bind to the underside of NA. We have discussed and referenced these studies accordingly.

      A new figure has been added [Figure 1b (ii)] to illustrate the surface mapping of epitopes on NA.

      The first reference has been included in both our discussion and Supplementary figure 1.

      The NA epitopes discussed in the second reference have also been incorporated into our discussion and Supplementary figures 1 and 2. Note that, the E258K mutation generated on the NA underside was not relevant to mAbs and was generated randomly by passaging of H3N2 A/New York/PV190/2017 virus. 

      The third reference pertains to murine mAbs against influenza B virus NA.

      (3) Lines 71, 72, and throughout: 'et al.' should be in italics.

      All "et al." have been italicised.

      (4) Many abbreviations are not defined including CHO, SDS-PAGE, MUNANA, mi3, HEPES, BSA, TPCK, MWCO, HRP, PBS, TMB, TCID50, LD50, MES, PEG, PGA, MME, PGA-LM.

      The text has been amended to define these abbreviations.

      (5) Line 209: Shouldn't this be ID50 instead of IC50? Also, it is not defined.

      IC50 has been defined.

      (6) Line 210, line 346, line 581-582: No need to capitalize letters at the beginning of words mid-sentence.

      This is amended.

      (7) Line 227: Is 2009 H1N1 NA meant?

      This has been changed to "H1N1/2009 neuraminidase"

      (8) Line 310: Is this really quantitatively true? (see major comment 1).

      Based on the compilation of epitopes from published NA mAbs and polyclonal sera (via escape mutagenesis and NA-Fabs crystal structures), it is accurate to state that the protective epitopes are primarily located within loops 01 and 23.

      Please also refer to our response to minor point 2. 

      (9) Line 352 and throughout the manuscript: 'in vitro' should be in italics.

      This is amended.

      (10) Line 355: https://pubmed.ncbi.nlm.nih.gov/35446141/https://pubmed.ncbi.nlm.nih.gov/33568453/ and https://pubmed.ncbi.nlm.nih.gov/28827718/ should be included here.

      Studies reporting epitopes on Influenza A neuraminidase have been compiled in Supplementary Figures 1 and 2 and cited appropriately.

      (11) Line 365: https://pubmed.ncbi.nlm.nih.gov/35446141/ and https://pubmed.ncbi.nlm.nih.gov/33568453/ also describe epitopes on the underside of the NA.

      Please refer to the above response to point 10.

      (12) Line 365: Reference https://pubmed.ncbi.nlm.nih.gov/37506693/ is missing here.

      The reference has been added.

      (13) Line 369-371: Is it really a minority?

      In terms of the protective response, the majority of the antibody response is directed towards loops 01 and 23, which form the top antigenic surface. The term 'lateral' is used in some literature to describe NA mAb epitopes; loops 01 and 23 also encompass the lateral regions.

      To clarify this, we have added the following sentence to the Discussion section - "The distribution of epitopes on neuraminidase"

      "It is important to note that loops 01 and 23 include a portion of epitopes that have been described in the literature as side, lateral, or underside (see mAbs NDS.1, NDS.3, and CD6 in Supplementary Fig. 2)"

      Additionally in our studies in mice, we showed that protection is mediated by antibodies targeting the loops (Figure 7). We are uncertain about the binding response to the NA underside, but the NA inhibiting and protective response to the underside appears to be minimal.

      Furthermore Lederhof et al. showed that among the 'underside' mAbs, NDS.1 protected mice against virus challenge, whereas NDS.3 did not. In our analysis (Supplementary Figure 2), NDS.1 makes eight-residue contacts with B4L01 and B5L01, whereas NDS.3 make five-residue contacts with B3L01 and B4L01.

      (14) Line 530: The A in ELLA already stands for assay.

      This is corrected.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews: 

      Reviewer #1 (Public review): 

      Summary: 

      This manuscript by Kremer et al. characterizes the tissue-specific responses to changes in TFAM levels and mtDNA copy number in prematurely aging mice (polg mutator model). The authors find that overexpression of TFAM can have beneficial or detrimental effects depending on the tissue type. For instance, increased TFAM levels increase mtDNA copy number in the spleen and improve spleen homeostasis but do not elevate mtDNA copy number in the liver and impair mtDNA expression.

      Similarly, the consequences of reduced TFAM expression are tissue-specific. Reduced TFAM levels improve brown adipocyte tissue function while other tissues are unaffected. The authors conclude that these tissue-specific responses to altered TFAM levels demonstrate that there are tissue-specific endogenous compensatory mechanisms in response to the continuous mutagenesis produced in the prematurely aging mice model, including upregulation of TFAM expression, elevated mtDNA copy number, and altered mtDNA gene expression. Thus, the impact of genetically manipulating global TFAM expression is limited and there must be other determinants of mtDNA copy number under pathological conditions beyond TFAM. 

      Strengths: 

      Overall, this is an interesting study. It does a good job of demonstrating that given the multi-functional role of TFAM, the outcome of manipulating its activity is complex. 

      Weaknesses: 

      No major weaknesses were noted. We have minor suggestions for improving the clarity of the manuscript that are detailed in the "recommendations for the authors" section. 

      We thank the reviewer for the suggestions and addressed them as described in the "recommendations for the authors" section.

      Reviewer #2 (Public review): 

      Summary: 

      This study by Kremer et al. investigates the impact of modulation of expression of TFAM, a key protein involved in mitochondrial DNA (mtDNA) packaging and expression, in mtDNA mutator mice, which carry random mtDNA mutations. While previous research suggested that increasing TFAM could counteract the pathological effects of mtDNA mutations, this study reveals that the effects of TFAM modulation are tissue-specific. These findings highlight the complexity of mtDNA copy number regulation and gene expression, emphasizing that TFAM alone is not the sole determinant of mtDNA levels in contexts where oxidative phosphorylation is impaired. Other factors likely play a significant role, underscoring the need for nuanced approaches when targeting TFAM for therapeutic interventions. 

      Strengths: 

      The data presented in the manuscript is of high quality and supports major conclusions. 

      Weaknesses: 

      The statistical methods used are not clearly described, and some marked nonsignificant results appear visually significant, which raises concerns about data analysis. 

      Data presentation requires improvement. 

      We thank the reviewer for the comments. We updated the text in the Materials and Methods section to state the statistical methods and improved the figures as described in detail in the "recommendations for the authors" section.

      Recommendations for the authors:

      (1) Please include testis data in Figure 2 given previous work by authors showing that elevated mtDNA copy number can improve testis function. It would be interesting to compare the changes in mtDNA copy number in testis to these other tissues.

      We measured mtDNA copy number in testis using the CytB probe and added it as Supplementary figure 2 A.

      (2) The clarity of Table 1 could be improved. It is difficult to know whether the changes in the TFAM to mtDNA ratio are driven by changes in TFAM levels or mtDNA copy number. A suggestion is to include the TFAM and mtDNA values in parenthesis next to each listed ratio.

      We updated Table 1 and included the values of the normalized TFAM and mtDNA levels in parentheses.

      (3) The authors should consider showing TFAM western blot data in Figure 1.

      We thank the reviewer for the suggestion but would like to keep the TFAM western blot data with the other western blot data for the respective tissue.

      (4) The graphs for qPCR data (e.g. Figure 2) show mRNA or mtDNA levels relative to the control, which is always set to 1. Why, then, does the control group display error bars?

      For the normalization of the data to the WT group, we first calculate the average of the values from all the samples of the WT group. We then divide all values from the samples of all groups, including the WT group, by that average value. By doing so, we set the average value of the WT group to 1 and express all values from all samples of all groups, including the WT group, relative to this average value. Differences between the samples of the WT group are hence retained and allow for error calculations and the display of error bars.  

      (5) Page 3 second sentence to the last: overexpression of TFAM leads to...? Did the author mean mtDNA?

      We updated the text to “Heterozygous knockout of Tfam in wild-type mice results in ~50% decrease of mtDNA levels, whereas moderate overexpression of Tfam leads to ~50% increase in mtDNA levels25,26”

      (6) The sentence "In summary, mtDNA copy number regulation is more complex than previously assumed and the TFAM-to-mtDNA ratio seems to be finely tuned in a tissue-specific manner" - not clear who assumed (references?) and based on what data, please rephrase.

      We updated the text and it now reads “In summary, mtDNA copy number regulation is more complex than suggested by previous studies23–27 and the TFAM-to-mtDNA ratio seems to be finely tuned in a tissue-specific manner.”

      (7) The significant increase in complex II activity under TFAM overexpression (Figure 3) warrants additional discussion.

      We updated the Results section and it now reads “We detected increased levels of the complex II subunit Succinate Dehydrogenase Complex Iron Sulfur Subunit B (SDHB). Complex II is exclusively nuclear encoded and a compensatory increase upon impaired mitochondrial gene expresson has been observed before32.

      We proceeded to measure the enzyme activities of individual OXPHOS complexes in liver mitochondria (Fig. 3C). The complex I and complex IV activities were reduced to about 50% in Polg-/mut; Tfam+/+ mice in comparison with wild-type mice (Fig. 3C). However, we did not see any further alteration of the reduced enzyme activities induced by TFAM overexpression or reduced TFAM expression (Fig. 3C). Interestingly, we detected a significant increase in complex II and complex II + complex III activity upon TFAM overexpression, which can partially be explained by the increased complex II protein levels we oberseved in Polg-/mut; Tfam+/OE mice (Fig. 3, B and C).”

      (8) The statistical methods used should be explicitly stated. Some results marked as non-significant appear visually significant, for example, mt-Cytb in Figure 2C, Supplementary Figure 2B).

      We updated the text in the Materials and Methods section to state the statistical methods and it now reads “Statistical analysis and generation of graphs were performed with GraphPad Prism v9 software except for quantitative mass spectrometry data which was analyzed and plotted using R as described above. Statistical comparisons were performed using one-way analysis of variance (ANOVA), and post hoc analysis was conducted with Dunnett’s multiple comparisons test. Values of P < 0.05 were considered statistically significant.”

      Minor points: 

      (1) Replace numerical indications of significance with asterisks for consistency.

      We replaced all numerical indications of significance with asterisks.

      (2) Abbreviations SKM and BAT are not defined.

      We removed the mentioning of SKM (skeletal muscle) as the data from this tissue was not included. The Introduction reads “In contrast, in brown adipose tissue (BAT), a decrease in TFAM levels normalized Uncoupling protein 1 (Ucp1) expression.”

      (3) Use uniform scales across bar graphs in Figure 2 to improve clarity.

      We updated Figure 2 to have uniform scales.

      (4) Remove or increase the transparency of data points in Figure 1A to make group averages more discernible.

      We removed the data points in Figure 1A.

      (5) Add a Y-axis title to Figure 1C.

      We added the Y-axis title “Heart / body weight” to Figure 1C.

      (6) Size of the font used in some figures (4?) is not appropriate.

      We increased the font size for the figures.

      (7) All figure legend titles need work. Insert "expression" after TFAM in the Figure 2 title, Change the title to "Modulation of TFAM expression..." in Figure 4. 

      The figure legends now read as follows:

      “Figure 2: Modulation of TFAM expression affects mtDNA copy number in a tissue-specific manner.”

      “Figure 4: Alteration of TFAM expression does not affect the heart phenotype of mtDNA mutator mice.”

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

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      Reply to the reviewers

      Reviewer 1:

      Evidence, reproducibility and clarity

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation. Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

      Major Comments

      Comment____:

      The major issue is related to the overall model the authors seem to build based on their data - or at least the overall model the reader may get from the paper. This model suggests that the loss / decrease in WWOX levels in RGs leads to Myc overexpression, that in turn affects the cell cycle and prevents neuronal differentiation. This model is highly attractive, but is probably incomplete, in the sense that it does not fully recapitulate the complicated picture. Indeed, all three types of mutated WWOX COs (WWOX k/o, WOREE patient-derived organoids, and SCAR12 patient-derived organoids) demonstrate strong - but equal levels of Myc upregulation. Yet the under-differentiation in each of these three types is different, as described above, and the disease manifestations among WOREE vs. SCAR12 patients are also different. Thus, another player (in addition to Myc) must be at place, that is differentially affected by the partial null mutations in WOREE and missense mutations in SCAR12. This point - ideally to be addressed experimentally - should be at least faced directly by the authors in the Discussion. Perhaps they can already point to such additional players based on their transcriptomics analysis.

      Response____:

      We thank the reviewer for this important point. We agree with the reviewer that the model of WWOX loss / decrease levels in RGs leading to MYC overexpression is incomplete, and that it is a limitation of our model. It seems plausible that other players have a high impact on the genotype and are potentially differently affected, resulting in this complexed phenotype. Following the reviewer advice, we plan to address this in the discussion as a limitation of the model, and we will compare how the expression levels of MYC change based on the genotype in comparison to the WT, using the single cell RNA-sequencing data. We would also like to clarify that MYC upregulation we observed in the patient lines in SOX2+/MYC+ populations, does not quantify expression levels of MYC, but rather positive/negative nuclear staining, in contrast to the high-resolution of scRNA-seq data.

      Minor Comments

      Comment____: 1. It would be useful if a table (perhaps supplementary) describing the details of the WWOX__ mutations__ in all the COs models studied in this paper were presented.

      Response____:

      We thank the reviewer for this suggestion, and we plan to prepare a table summarizing all the mutations in the COs models presented in the paper.

      Comment____: 2. For the new WOREE individual with complex genetics in WWOX: it is not clear why any WWOX protein is still present in this patient in Fig. S1D (please give an explanation or speculation); it is not clear which tissue was used for the Western blot in Fig. S1D; the data in Fig. S1D need to be quantified.

      Response____:

      We thank the reviewer for their observation and would like to clarify that the ‘upper’ band seen in WWOX bands in a nonspecific one that appears in the parent lines and the mutant offspring. We will quantify the WB levels and clearly state that they are the IPSCs in the figure legend.


      Comment____: 3. Western blot, quantified, should be performed on all COs under study, to compare the WWOX expression levels. Please also change the immunofluorescence shown in Fig. 1B (e.g. show WWOX in a different color), as the figure provided shows WWOX poorly in wild-type CO, and it is not clear how much it is removed in the mutant organoids. Why should there be no signal in the SCAR12 COs?

      Response____:

      We thank the reviewer for their observation, we will provide protein levels of WWOX in patient and KO cerebral organoids which will better clarify the decreased WWOX levels, specifically in SCAR12 (see WB figure below). We will also perform any necessary changes to the figure to enhance visualization of WWOX.


      Significance

      The manuscript "WWOX deficiency impairs neurogenesis and neuronal function in human organoids" by Aqeilan and co-workers provides impressive set of studies, mostly utilizing cerebral organoids (CO), gaining insights into the roles of the gene WWOX in neuronal development and molecular etiology of WOREE and SCAR12, two devastating rare diseases originating from mutations in WWOX. Further, therapeutic modalities through the neuron-specific gene therapy are investigated using the WWOX k/o and WOREE and SCAR12 patient-derived COs. Among the major findings of this work one can highlight the identification of the main source of WWOX-expressing cells as radial glia (RG) cells; the discovery of the massive upregulation of Myc upon loss/decrease in WWOX expression in RGs; and the strong neuronal under-differentiation induced upon WWOX k/o and mutations. Regarding the latter finding, the authors report massive increase in RGs and concomitant drop in neuronal cells in WWOX k/o COs. In contrast, in WOREE and SCAR12 patient-derived COs, a more subtle under-differentiation is seen. Specifically, while WOREE but not SCAR12 patient-derived COs also show a certain increase in the RG proportion, both types of patient-derived COs demonstrate higher proportions of "young" neuronal cells as compared to wild-type COs. Thus, a picture can be drawn whereas complete loss of WWOX leads to strong under-differentiation mostly manifested as expansion of RGs and hence under-production of neuronal cells, while hypomorphic loss-of-function of WWOX in WOREE and in missense mutations in SCAR12 lead to the later defect in neuronal cell maturation.

      Overall, I find the work highly interesting, but I would like the authors to address one major issue and several minor ones.

      Response____:

      We sincerely thank the reviewer for their thoughtful and constructive comments, which have greatly helped us improve the clarity and rigor of our manuscript. We appreciate the recognition of our work’s significance and the careful evaluation of both our major findings and methodological details. We have addressed all the raised points to the best of our ability and believe the manuscript will be substantially strengthened as a result. We are grateful for the reviewer’s time and valuable insights.

      Reviewer 2:

      Evidence, reproducibility and clarity

      Summary

      In this study, Steinberg et al aim to elucidate the role of WWOX in human neurogenesis and model WOREE and SCAR12 syndromes which are rare neurodevelopmental disorders. They chose to investigate its function in human brain organoids after generating WWOX KO and patient-derived iPSC lines. Their major finding is that radial glial cells, the main neural progenitor population during corticogenesis, are affected. Via single-cell-RNA-sequencing, they try to decipher the perturbed molecular mechanisms identifying MYC, a proto-oncogene, as a major player. At the end of their study, they proceed to gene therapy restoration and suggest that this could become a potential therapeutic intervention for WOREE and SCAR12 syndromes. The study aims to elucidate major cellular and molecular mechanisms that modulate neurodevelopment and neurodevelopmental disorders. Although sc-RNA-seq could potentially be of great interest and unravel major mechanisms, the authors do not follow this part, but only discuss potential future avenues. Here are some suggestions that could be useful to the authros.

      Major comments

      Comment____:

      A big part of the paper focuses on generating the iPSCs and characterizing the generated brain organoids and gene restoration of the phenotype via restoration of the WWOX gene expression (Fig.1, Fig.6, Fig.S1, Fig.S8, Fig.S10 and potentially Fig.S9 - this figure is not included) however, this has already been done by the same authors (first and last authors) in a previous publication. What are the differences in the line that have been generated in previous publication (Steinberg et al 2021, EMBO Mol. Med.)? If there are differences, the authors should make a thought comparison and explain why they generated different lines. If there is no difference, the authors should reduce to minimum this part and place it to supplementary.

      Response____:

      We thank the reviewer for pointing this out. We would like to clarify that some iPSC lines used in this publication were not introduced in the previous one (Steinberg et al 2021, EMBO Mol. Med.), including the wildtype JH-iPS11, and the new compound heterozygous WOREE line LM-iPS. In this paper, we aimed to widen our understanding of the effects of WWOX mutations through advanced techniques not applied before, and by adding these lines we were able to better generalize our findings as we did not depend on a single patient for both WOREE and SCAR12. Additionally, WWOX rescue in this current paper relies on AAV9-hSynI targeting that is more clinically relevant to gene therapy, as opposed to lenti-WWOX and AAVS1 WWOX in the previous publication. We will include the differences in a summary table.

      Comment____:

      • Fig.1E: in the pictures shown, the majority of the Satb2+ cells are colocalized with SOX2. Although a small portion of neurons have been shown from many studies that in brain organoids are co-localized to SOX2, in the pictures depicted this percentage is big. Also in ctrl condition the VZ-CP like areas are not easily recognized. The authors should check if this co-localization is a more general phenotype and if not choose more representative pictures.

      Response____:

      We thank the reviewer for their observation. We will check for the presence of a trend of colocalization between SATB2 and SOX2 and address the concern experimentally if needed. We will also choose pictures that better display VZ-CO areas in the control line.

      Comment____: Information about the number of organoids per batch used in each figure is not included. This needs to be added for each experiment. Data (at least the majority of them) should be collected from brain organoids from at least two batches.

      Response____:

      We thank the reviewer for their point, and we plan to better clarify the technical parts of the experiments, and if needed will include data from more batches.

      Comment____: The expression of WWOX in cortical development has been shown in the previous publication. Although sc-RNA data are validating the previous data and are adding more information, these data should be put as supplementary. Besides, in Fig.3G where authors aim to compare WWOX expression to MYC that fits nicely with their results depicting MYC as the most affected gene in KO and mutant line, when one looks at the WWOX expression only it seems that its expression is higher in CP that VZ. This is contrary to the conclusion that WWOX is mainly characterizes RGs. Why is that? Authors should at least discuss this.

      Response____: We agree with the reviewer that Fig.3G can be misleading, and we acknowledge that it can lead to the opposite conclusion of WWOX being mainly characterized in RGs. In Fig.3G the x axis displays positive values on the left, and negative scores on the right. Following the reviewer’s suggestion, we modified the graph to show positive values on the right, demonstrating how WWOX expression is higher in the VZ compared to the CP.

      Comment____: In this study, authors show that progenitors are reduced in WWOX-KO organoids, however in the previous publication SOX2 population is not majorly affected. Why are there such differences? Given that RGs are the main population affected as authors propose in this study, these differences must be at least discussed. Similar comments regarding neurons: in previous publication there is a minimal reduction of neurons in WWOX-KO brain organoids, while here authors describe major differences.

      Response____:

      We thank the reviewer for their remark and agree that it should be mentioned in our discussion. We believe that this is unfortunately due to the inherent issue of heterogeneity between organoids and could partly be attributed to the difference in the age of organoids at that timepoint (week 10 organoids in previous paper, week 7 organoids in this one), and the difference in control lines (WiBR3 hESCs vs JH-iPSCs). Additionally, while percentages of SOX2+ populations in WT organoids vary between the previous publication and this one, WWOX-KO organoids display similar levels of SOX2 upregulation in precious and current papers: 69% and 74%, respectively. We would also like to point out that calculations in scRNA-seq data convey the phenotype at a much higher resolution, as it identifies radial glia populations that are not necessarily SOX2+, further strengthening the validation of the SOX2+ RG quantifications that are present in this study.

      Comment____: Data from sc-RNA-seq analysis highlighting MYC as major differentially regulated gene are very interesting and seem to be key to the molecular pathway affected as authors suggest. Authors also validate this with immunostainings in brain orgnaoids. However, in Fig.3J MYC expression in ctrl is not depicted, even though in the respective graph it seems that 20% of SOX2+ cells co-express MYC. Please choose a more representative picture.

      Response____:

      We agree with the reviewer’s comments that the current image size and resolution limits the ability to appreciate the MYC staining in the control, and plan to use a more representative figure of the phenotype.

      Comment____: One of the main findings in this study is the cell cycle changes observed in WWOX-KO and mutant organoids. Given that the major novelty of the publication is the cellular and molecular mechanism implicated in WOREE and SCAR12 syndromes, authors should perform additional experiments towards this direction. One suggestion would be to perform stainings in brain organoids using markers of the different cell cycle phases (eg. KI67, cyclin a, BrdU/EdU, ph3). Also, treatment of organoids with different BrdU/EdU chase experiments would be important so as to measure exactly the length of each cell cycle phase.

      Response____:

      We appreciate the reviewer’s suggestions and plan to validate the findings through staining and quantifying percentages of proliferative RGs in WT vs mutant WWOX lines.

      Comment____: Regarding the molecular cascade, is WWOX directly affecting MYC of Wnt genes? Do they have information on upstream and downstream factors in the affected molecular pathway?

      Response____:

      We thank the reviewer for highlighting this important point. To address this question, encouraged by our results, we will compile genes belonging to the regulon of MYC and study the upstream and downstream factors in our transcriptional data. Additionally, we will look at protein expression levels of WNT genes in our organoid samples.

      Comment____: Restoration of phenotype via reinsertion of WWOX gene has already been done in the previous publications by the same authors. But what about MYC? Is MYC manipulation able to rescue the phenotype?

      Response____:

      We thank the reviewer for this insightful suggestion. We fully agree that understanding the role of MYC in the observed phenotype is of great interest. However, due to the essential and widespread role of MYC in both radial glia and neurons, we refrained from direct perturbation of MYC levels—either through knockdown or overexpression—as such manipulations may have broad, uncontrolled effects that could confound the interpretation of our findings. The potential deleterious consequences of MYC modulation in radial glia have been originally discussed in the Discussion section of the manuscript. In our revisions, we will further explore the role of MYC regulons in our scRNA-seq dataset to better understand their contribution to the WWOX-related phenotype.


      Comment____: Finally, MYC association to ribosome biogenesis as mentioned by the authors in discussion is very interesting. The authors should consider investigating this direction, as it will be a great addition to the mechanisms that regulate WOREE and SCAR12 syndromes which is the main focus of this study.

      Response____:

      We thank the reviewer for highlighting this point, and we agree that MYC's association with ribosome biogenesis is a fascinating topic to discuss. This could be connected to the alterations of the proliferative potential and to the anabolic state of the cell, and we plan to expand the discussion of this observation and its implication in the context of RGs and neurons*. *


      Minor comments

      Comment: - Line 115: authors say that the data they discuss are found in Fig.S2A, maybe they mean Fig.S1A?

      Response:

      We thank the reviewer for their observation, we will correct Fig.S2A to Fig.S1A and B.

      Comment: - Fig.S9 is missing, in the current version this Fig is the same with Fig.S10. Please change it.

      Response____:

      We thank the reviewer for pointing this out and apologize for this oversight. We acknowledge the error and will correct the duplication by replacing Fig. S9 with the intended figure in the revised version of the manuscript.


      Significance

      This study is the continuation of a previous publication the authors have published. The topic is very interesting and novel especially in modelling neurodevelopmental disorders in a human context, however, given that the main phenotype has already been published, the authors should include more effort in the molecular cascade. Clinical interventions if the molecular cascade is described would be of great importance to the field.

      Response____:

      We sincerely thank the reviewer for their thoughtful, constructive, and detailed review. We appreciate the time and effort taken to carefully read our manuscript and provide insightful suggestions, taking into consideration also our previous published work. The suggestion raised, especially regarding MYC-WNT axis and its potential link to ribosome biogenesis, will help us clarify, strengthen, and expand the scope of our study. We have carefully addressed each of the points raised and have incorporated the necessary experimental validations, clarifications, and revisions accordingly. We believe these changes have substantially improved the manuscript.

      Reviewer 3:

      Summary:

      The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

      Major ____C____omments

      The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below.

      Comment____:

      A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary.

      Response____:

      We thank the reviewer for pointing this out, and we acknowledge that the clarity of our description of the batch used in each experiment can be improved. Therefore, we will provide all these details, adding information on additional batches adopted for the different validations that were not included in the manuscript.

      Comment____:

      Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches.

      Response:

      We thank the reviewer for this important point. We would like to clarify that in Figure 1B, TUBB3 staining is primarily used for visualization purposes to provide anatomical context and delineate the overall architecture of the organoids, rather than for quantitative assessment of neuronal output. As such, the focus of our quantification in Figure 1F was on SOX2+ radial glial cells. That said, we agree that clearly stating the number of organoids and batches used in the quantification is important, and we will include this information in the figure legend for clarity.

      Comment____:

      Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible.

      Response____:

      We appreciate the reviewer’s thoughtful feedback and for drawing our attention to the review by Sandoval, Soraya O., Anderson, Stewart, et al. We also thank the reviewer for their suggestions and intend to explore the proposed modifications through immunostaining, particularly to address questions related to cell cycle changes, and Wnt pathway. However, regarding DNA damage, senescence, and cell cycle arrest, we do not believe additional validation is necessary, as our current manuscript does not present findings related to these aspects.


      Comment____:

      Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months).

      Response____:

      We thank the reviewer for this insightful comment and fully agree that elucidating the functional contribution of MYC alterations in the context of WWOX deficiency would represent a major conceptual advance. We acknowledge that our current findings are correlative, based on scRNA-seq and immunostaining, and that direct manipulation of MYC could help establish causality.

      However, due to MYC’s essential and pleiotropic role in both progenitor and neuronal populations—including its regulation of cell cycle, metabolism, and apoptosis—we refrained from genetic overexpression or silencing approaches in this study. Such perturbations often lead to widespread, non-specific effects that can obscure the interpretation of lineage-specific phenotypes, particularly in a complex model like brain organoids.

      That said, we agree that further insight into the functional role of MYC is crucial. To this end, we plan to leverage our scRNA-seq dataset to analyze the activation state of MYC regulons across genotypes and cell types, and to assess how these regulons intersect with cell cycle dysregulation observed in WWOX-deficient radial glia. We also aim to integrate available transcriptomic data from primary cortical tissue to support the relevance of MYC pathway alterations in human development. While these analyses cannot replace experimental perturbation, we believe they can provide strong, hypothesis-generating evidence for MYC’s mechanistic involvement and help prioritize targeted experiments in future studies.

      Comment____:

      Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions.

      Response____:

      We thank the reviewer for this thoughtful and constructive comment. We fully agree that regional identity variability in unguided cerebral organoids is a well-recognized challenge, and that systematic assessment of dorso-ventral patterning is important to confidently interpret genotype-driven phenotypes.

      We would like to clarify that the cerebral organoid protocol used here has consistently been shown to favor a dorsal forebrain identity (PMID: 23995685, 28562594, 32483384, 33328611), and in our previous work (PMID: 34268881), we demonstrated that WWOX mutations did not substantially alter dorsal identity in this model. Nevertheless, to directly address the reviewer’s concern, we plan to perform additional immunostaining for regional patterning markers on our existing week 7 organoid tissues and explore our scRNA-seq data to evaluate potential shifts in regional identity and rule out patterning-related confounders.

      Regarding the 2D NSC cultures, while the differentiation protocol included the ventralizing factor SAG, it did not include IWP2. We acknowledge the importance of validating patterning outcomes in this model as well and will do so using immunostaining.

      Comment____:

      There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity.


      Response____:

      We thank the reviewer for this suggestion, and we agree that including a schematic summarizing the genomic alterations in all patient-derived lines and their severity will improve the genotype-phenotype comparison. We will include this clarification and provide additional information on how the mutations affect the protein level, and the genotype-phenotype correlations in WWOX mutants based on clinical and genetic evidence.


      Comment____:

      Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult.


      Response____:

      We thank the reviewer for highlighting this and pointing us to the work of Brunner, Lammertse, van Berkel et al. While we agree that isogenic controls for each mutant line would be the ideal wild-type reference, generating these through genomic editing is particularly challenging, specifically for the compound heterozygous mutants. Instead and as suggested, we plan to include additional wild-type lines derived from healthy individuals, collected from different batches. We will use these to validate our key findings, including analyses of RG and SATB2+ cell populations, as well as MYC expression through immunofluorescence.


      Comment____:

      Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes.

      Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed.

      To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies.


      Response____:

      We thank the reviewer and agree that further investigation into the proposed link between WWOX deficiency and MYC-related alterations in radial glia would provide deeper insight into the downstream effects on neuronal populations. To this end, we will first illustrate how our model of radial glia alterations accounts for changes in neuronal production without affecting overall RG abundance. Second, we will expand our comparisons of RGs and MYC expression using patient-derived and control single-cell RNA-sequencing datasets. Third, we will address the discrepancies between neuronal hyperexcitability and SATB2/CTIP2 counts more comprehensively. Notably, while SATB2 is an early marker for several cortical neuron subtypes, it is not expressed in all neurons. In contrast, SOX2 is considered a pan-radial glia marker, which may help explain the differing expression trends observed.

      Comment:

      Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above).

      First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim.


      Response____:

      We thank the reviewer for the suggestion, and based on this, we will quantify the WWOX+ NeuN+ / WWOX+ total cells, incorporating data from additional batches to strengthen the analysis.


      Comment____:

      Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript.

      Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text.


      Response____:

      *We thank the reviewer for highlighting this point. We will discuss and re-phrase the rationale for restoring WWOX in the neurons and the WWOX deficiency-associated delayed maturation. *

      Comment____:

      Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified.


      Response____:

      We thank the reviewer for mentioning this problem. We will include the details needed for reproducibility, including the number of batches and organoids.

      Comment____:

      As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.


      Response____:

      *We thank the reviewer for highlighting this point. We acknowledge that our clarity in our methods could be improved, therefore, we will expand the bioinformatics pipeline description, the reagents used, and the details for the Western Blot. *

      Minor comments:

      Comment:

      • One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations. Response____:

      We thank the reviewer for suggesting the work of Iacomino et al. which we are very aware of and shall cite appropriately in our revised version.

      Comment____:

      • For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing. Response____:

      We thank the reviewer for bringing up these important points. We will provide the exact mutations identified in exon 1 of WKO-1C and WKO-2C as determined by Sanger sequencing and include this information in the revised manuscript. Additionally, we will present data additional data regarding WWOX protein loss in all the cell lines used in the study.

      Comment:

      • Line 116 and 394, reference Steinberg et al is not formatted. Response:

      We apologize for this oversight and the formatting will be fixed.

      Comment:

      • Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization. Response____:

      We thank the reviewer for their comment. WWOX localization was previously confirmed using anti-WWOX HPA050992 (Sigma), as reported in our prior publication (PMID: 34268881). While the images were not included due to a lack of novelty, we acknowledge the importance of confirming the observed patterns. The difference in localization between organoids (cytoplasmic/membrane-bound) and iPSCs (cytoplasmic/nuclear) may be attributed to differences in cell morphology, with RGs in 3D organoid sections exhibiting distinct characteristics compared to iPSCs cultured in 2D (Supplementary Figure S1A). In fact, in 2D cultures of NSCs (Supplementary Figure S4A), WWOX also shows a nuclear localization, similar to iPSCs. We will clarify this point in the manuscript.


      Comment____:

      In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.

      Response____:

      We thank the reviewer highlighting this. We will better define the rationale for the usage of SATB2 as a marker expressed in many types of young neurons (not specifically preplate neurons), and add DCX as a marker for neurogenesis.

      Comment____:

      Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.

      Response____:

      We thank the reviewer for the keen eyes and apologize for this mistake. We will correct the legend and specify that n is indeed referring to organoids and not single neurons.


      Comment____:

      Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.

      Response____:

      • *We thank the reviewer for their comment. Upon repeated staining, we consistently observe this MYC localization across organoids and more. Similarly, the differences in Sox2 and MYC staining in WOREE organoids are reproducible. While these results may seem divergent, they accurately represent the findings. We will, however, review the staining protocols and ensure that representative images are carefully selected to best reflect the data.

      Comment____:

      Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.

      Response____:

      *We thank the reviewer for pointing this out. We will add in the legend that “w” refers to post-conceptional weeks. *

      Comment____:

      Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.

      Response____:

      We thank the reviewer for addressing this. The blurred parts of the UMAP are intentional, we will add a description of why and what it represents.

      Comment____:

      Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.

      Response____:

      *We thank the reviewer for pointing this out, we will better clarify that figures S8 and S9 are an expansion of the panels shown in Figure 6, showing ROIs per cell line and rather than per genotype. *

      Comment____:

      Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?


      Response____:____

      • *We thank the reviewer for raising this point. We will quantify the bands in the WOREE patient samples and compare them to wild-type controls. We would like to clarify that the "upper" band is a nonspecific band, and its overrepresentation in KO2 samples is not indicative of WWOX expression. Additionally, we will address the question of WWOX haploinsufficiency in healthy parents and provide a comparison of WWOX levels to unrelated wild-type controls.

      Comment____:

      Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?

      Response____:

      We would like to thank the reviewer, and we will add a clarification for the differences between top and bottom UMAPs and the rationale behind NP labeling.

      Comment____:

      Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.

      Response____:

      We thank the reviewer and will alter the figure and its legend to clarify this.

      Comment:

      Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.

      Response____:

      We thank the reviewer and will examine the pictures and if need be will replace them with more representative pictures.

      Comment____:

      Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.

      Response____:

      We thank the reviewer for pointing this out. We will add the quantifications of NEUN+/WWOX+.

      Comment____:

      Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).

      Response____:

      We thank the reviewer; we will fix the figures and corresponding titles and legends.

      Comment:

      Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.

      Response____:

      We thank and agree with the observation; we will correct it.

      Comment____:

      Line 353: reference needs to be added for "our earlier findings”.

      Response____:

      We thank the reviewer, and we will re-phrase to clarify.

      Comment____:

      Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.

      Response____:

      We thank the reviewer and agree with their observation and would like to clarify that as we are not aware of any relevant literature examining the relationship between WWOX and MYC in non-tumor settings, we refrained from drawing any conclusions in any one direction without further experimental exploration. Nonetheless, we will re-phrase the sentences to draw clearer conclusions.

      Comment____:

      Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.

      Response____:

      • *We thank the reviewer for their comment. We agree that further clarification is needed regarding the functional involvement of Wnt signaling. While we have previously shown that Wnt is inappropriately activated in WWOX-KO, WOREE, and SCAR12 organoids (PMID: 34268881), and demonstrated Wnt activation in RGs via our scRNA-seq data (Figure 4I), we recognize that direct perturbation experiments would strengthen this aspect. In light of this, we will examine the levels of Wnt target genes in our transcriptomic data to provide more direct evidence of Wnt signaling involvement and its functional relevance in the context of WWOX deficiency.

      Comment:

      Line 473: "at X concentration" needs to be correct to specify the concentration used.

      Response:

      We thank the reviewer for noticing this missing information and we will add the final puromycin concentration (1 mg/ml) .

      Comment____:

      Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?


      Response____:

      We thank the reviewer for raising this concern. To clarify, approval was obtained prior to the commencement of experimentation. The sentence should read: "Skin biopsies and blood samples were obtained with informed consent, under the approval of the Kaplan Medical Center Helsinki Committee," indicating that the study was conducted in full compliance with ethical requirements, with prior approval from the committee.

      Comment____:

      Line 525: which orbital shaker and which speed was used?

      Response____:

      We thank the reviewer and will add orbital shaker details and speed.

      Comment____:

      Line 537: what is GC in GC/ul?

      Response____:

      We thank the reviewer and clarify that this is the accepted units for viral load. GC is Genome Copies, and this is often used in qPCR assays to estimate the amount of viral genetic material in a sample. It is often used interchangeably with vg/µL (Viral Genome per microliter).

      Comment____:

      Line 629: samples were centrifgues at which speed and for how long?

      Response____:

      We thank the reviewer and will fix to include details about centrifugation.

      Comment____:

      Line 639: "All primer sequence" should be plural.

      Response____:

      We thank the reviewer and will correct the typing mistake.

      Referees cross-commenting

      All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

      General assessment:

      The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

      • Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
      • Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
      • Absence of functional validation for MYC's contribution, making its proposed role unclear. Advance:

      This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance.

      Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations.

      Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

      Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

      Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

      Response:

      We thank the reviewer for their valuable feedback and for acknowledging the strengths of our study. We agree with the reviewer that additional validation and replication are needed to strengthen our conclusions. We acknowledge the limitations in experimental design, and we are committed to enhancing the reproducibility of our findings. We also appreciate the reviewer's comments on the study's conceptual advancements, which we believe offer new insights into WWOX's role in neurodevelopmental disorders.

      We are confident that with the additional experiments outlined, our study will provide valuable contributions to understanding WWOX-related syndromes. Thank you again for your thoughtful suggestions.

      __ __


      Reviewer 4:

      Summary

      The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression.

      My concerns are:

      Comment____:

      1.Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.

      Response____:

      We thank the reviewer for their thoughtful comment. We would like to clarify that we do not suggest that only radial glia cells are affected. As mentioned in both the current manuscript and our previous work (Steinberg et al., EMBO Mol. Med. 2021, and Repudi et al., Brain, 2021), other cell populations, including neurons and oligodendrocytes, are also impacted by WWOX deficiency. WWOX is widely expressed in the mature brain, and we are actively investigating whether these effects are cell-autonomous. In this study, we focus on WWOX in RGs due its high expression and possible importance in maintaining RG homeostasis. We will further clarify this point in the revised manuscript.

      Comment____:

      Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.

      Response____:

      We thank the reviewer for their suggestion and agree that performing proteomic analysis on RG populations will strengthen our understanding of the underlying mechanism, however, the experiment itself was attempted and proved to be technically challenging at this size, and for now is beyond the scope of this paper.

      Comment____:

      WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

      Response____:

      We thank the reviewer for their insightful suggestion regarding the validation of neuronal heterotopia in WWOX-deficient cells through real-time migration experiments. While we recognize the potential value of this approach for investigating the behavior of WWOX-deficient radial glia cells, we believe that such experiments would extend beyond the scope of the current study. However, we are considering them as part of our future research to further explore the impact of WWOX deficiency on cell migration and neuronal positioning. Thank you again for your valuable input.

      Significance

      The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

      Response____:

      We thank the reviewer for their thoughtful and constructive feedback. We especially appreciate the suggestions regarding the broader involvement of additional cell types and the importance of exploring radial glia function through real-time migration assays. These insights will help us refine the focus and interpretation of our findings, and we will address the relevant clarifications and improvements in the revised manuscript.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #4

      Evidence, reproducibility and clarity

      The article deals with WWOX gene deficiency related neural diseases such as WOREE and SCAR12 syndromes. While there is no available drugs for treatment, the authors used organoid approach to study the development of the potential of disease development. The authors utilized neural organoids and single-cell transcriptomics and identified radial glial cells (RGs) as preferentially affected. The RG cells have disrupted cell cycle arrests in the leading G2/M and S phases, along with MYC overexpression and concomitant reduction in neuronal generation. The study also included detecting neural hyperexcitability and restoring defective WWOX gene for functional assessment. The study is important in understanding the function of WWOX and its mutated states, especially in identifying RG in the potential disease progression. My concerns are:

      1. Although organoids are good models for in vitro simulation of disease progression, I am not convinced that RG is the only cell type affected initially.
      2. Functional characterization of RG needs further strengthening. I suggest utilizing a proteomic approach to compare the diseased-ongoing RG versus regular RG and identify which proteins are involved for functional characterization. Finally, the functional alterations in the mitochondria due to WWOX deficiency should be checked.
      3. WWOX-deficient radial glia cells are expected not to guide neurons' migration normally during neural development. Please note that neuronal heterotopia occurs frequently in the WWOX deficiency. Neurons tend to exhibit groups of cells coming together in the neocortex. Purified RG cells are used to run versus typical neurons or RG cells. One can expect WWOX-deficient cells to run away from the normal cells, and they may kill each other, leading to compromise. The authors should run the real-time cell migration experiments using normal neurons versus WWOX-deficient radial glia cells and see the behavior of both cell types upon encountering each other. This will provide better insight regarding the deficiency of WWOX in radial glia cells.

      Significance

      The study is significant in our understanding the progression of syndromes associated with WWOX deficiency. My suggestions are shown in the above section.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Steinberg and colleagues describes cellular and molecular changes linked to mutations in WWOX, a gene implicated in rare neurodevelopmental disorders, WOREE and SCAR12 syndromes. By comparing immunofluorescene and single cell trascriptomics of unguided brain organoids from control and WWOX-knockout iPSCs, as well as 2D NSCs and in vivo fetal brain expression datasets, the authors identified radial glia as relevant cell types in which WWOX is expressed and affected by WWOX deficiency. Using immunofluorescence, single cell trascriptomics analysis and western blotting on week 16 organoids, the authors show that WWOX deficiency results in increased abundance of radial glia cells at the expenses of neuronal production. These changes are accompanied by accumulation of cells in G2/M and S phases, overexpression of c-MYC and Wnt activation. In addition to this, the authors characterize unguided brain organoids generated from iPSCs reprogrammed from patients affected by WOREE or SCARE12 syndromes. Using immunofluoresce and single cell trascriptomics, they find that, while RG abundance changes were very modest, patients's iPSC-derived neurons are enriched for signatures related to early development, suggesting delayed differentiation. Finally, the authors use patch clumping, calcium imaging and gene therapy in 16 weeks old organoids derived from control and patients-derived iPSCs, to demonstrate that WWOX restoration normalized hyperexcitability phenotypes in both WOREE and SCAR12 organoids. These results thus provide a proof-of-concept evidence that WWOX restoration in human cells is a valid strategy to correct for hyperexcitability pehnotypes in WWOX related syndromes.

      Major comments:

      The study's main conclusions regarding neurodevelopmental phenotypes linked to WWOX deficiency and genotype-phenotype relationships are based on iPSC-derived brain organoid models analyzed using immunofluorescence, single-cell transcriptomics, and excitability recordings (cell-attached patch clamping, calcium imaging). While the analyses involve a diverse collection of iPSCs and two time points (7 and 16 weeks), the study falls short in providing sufficient experimental details and validation to fully support its conclusions. Additional quantification, replication, and functional validation would be necessary to solidify the study's conclusions. Some of these validations are achievable within a reasonable timeframe, while others would require a more substantial investment of time and resources as detailed below. A key concern is the lack of experimental details and replicability. Number of individual organoids, number of images per organoid for IF, and whether multiple batches were used are only partially provided. While the authors report generating multiple WWOX knockout clones, the legends and methods do not specify whether multiple clones were used across different organoid experiments. The study states that four organoids were used for scRNA-seq, but it is unclear whether this means four organoids per genotype or one organoid per genotype was analyzed. These ambiguities make the claims appear rather preliminary. Another issue is the limited validation of scRNA-seq observations. Since scRNA-seq is often performed on a limited number of organoids, orthogonal validation is crucial to strengthen the findings. For example, changes in radial glia abundance and neuronal production observed in scRNA analyis (Figure 2-5) could be validated using immunofluorescence across genotypes and batches. Currently, IF stainings for Sox2 and TUBB3 are shown only at 7 weeks in Figure 1B, but no quantificative assessment is provided. Also, it is not clear if quantifications provided in Figure 1F refer to multiple organoids or batches. Furthermore, the observations on cell cycle arrest, DNA damage, senescence, metabolic alterations, Wnt activation obtained via scRNA-seq could be further validated on organoid tissues using specific antibodies that the lab used before (e.g. yH2AX antibody in PMID: 34268881) or assays that have been developed elsewhere (some examples are reviewed in PMID: 38759644). As for feasibility, immunofluorescence validation of existing tissues is realistic, requiring validated antibodies and procedures, some additional imaging time and analysis (estimated 1-2 months, with some budget to purchase antibodies and cover imaging time costs). Feasibility of efforts related to validation across organoids and batches depends on the number of organoids used so far and available tissues. Generating new organoids would be indeed more time-consuming (≈ 6 months) and expensive (but extact costs would depend on number of clones, organoids and batches used), but feasible. Another limitation is the lack of functional relevance of MYC alterations. The study confirms increased MYC expression via both scRNA-seq and immunofluorescence in organoid tissues. However, these results remain correlative and demonstrating the functional requirement of MYC overexpression in mediating WWOX-deficiency-related changes would significantly strengthen the study's conclusions. This would require additional differentiation experiments, including MYC overexpression or knockout models, to assess its direct impact. These efforts would represent a major conceptual advance by linking RG effects to MYC function and highlighting MYC-related therapeutic directions. These additonal experiments would require a substantial investment to generate the necessary regents (e.g. WWOX-KO and WT iPSCs with altered MYC levels) and additional time and costs for organoid analysis, mostly by immunofluorescence (estimated 6-8 months). Another issue is the lack of patterning analysis in unguided organoids, which are known to exhibit high variability in regional identity (PMID: 28283582). While the authors acknowledge this limitation to some extent-abstaining from fine-resolution analysis (Lines 173-174)-this variability, combined with the limited number of organoids used, could be a major confounding factor in the phenotypic analyses, even at a broad resolution. Indeed, some of the reported differences across genotypes may stem from variability in organoid patterning rather than true genotype-driven effects. For example, the reduced SATB2 expression in KO and patient-derived organoids from Figure 1E-F could result from impaired cortical patterning rather than a direct effect of WWOX deficiency. Additionally, in Figure 6D and 6E, the fact that WOREE iPSC-derived organoids - but not SCAR12 organodis- show lower levels of both CTIP2 and SATB2, might reflect a shift toward a non-cortical identity rather than a direct WWOX-dependent phenotype. To rule out patterning variability as a contributing factor, the authors should analyze organoid regional identity across genotypes using immunostaining for dorsal and ventral forebrain markers. This would allow a more solid inference of genotype-specific effects on neurodevelopmental phenotypes. Patterning validation can be performed on existing organoid tissues (week 7) using validated antibodies (PMID: 28283582). As such, this analysis is expected to be relatively straightforward and feasible in a few weeks time. If the generation of new organoids is needed, such patterning validation should still be relatively feasible, as week 7 organoids are ideal for assessing regional identity. Analysis of patterning effects should also extend to 2D NSC cultures. In the 2D NSC models derived from WWOX-KO lines (Figure 3L, Figure S4A), the differentiation protocol includes patterning factors that promote ventral fates (SAG and IWP2). Interestingly, the quantification of MYC expression from unguided organoids and 2D NSCs (Figure 3K-L) reveals a major difference in the fraction of MYC-positive cells in WT conditions across the two culture models. A possible changes in the dorsal and ventral patterning of 3D and 2D cultures might explain these differences and implementing immunostaining for patterning markers in 2D would help clarify patterning contributions. There are also some concerns regarding WOREE and SCAR12 phenotypes. First, the genotypes of the patient-derived iPSCs are not clearly defined, making it difficult to establish clear genotype-phenotype relationships. The study uses iPSCs from four different patients (2 WOREE, 2 SCAR12), some of which were validated in a previous study (PMID: 34268881). However, it remains unclear how they were validated, and detailed genomic alterations of the four patients are not explicitly reported. Additionally, it is uncertain whether all variants result in a full loss of WWOX function, as protein loss evidence is only provided for one WOREE patient (Figure S1D). Also, the authors state that SCAR12 should have a milder phenotype (line 168), but it is unclear whether this claim is based on clinical evidence or genomic data from these specific patients. To improve genotype-phenotype comparisons, the authors should consider including a clear schematic summarizing the genomic alterations in all patient-derived lines and their expected disease severity. Second, the experimental design lacks appropriate controls for patient-derived iPSCs. All patient-derived iPSC comparisons are performed against a single reference male iPSC line, which is neither isogenic to WOREE nor SCAR12 iPSCs. This complicates the interpretation of differences between healthy and patient-derived organoids, as well as comparisons between WOREE and SCAR12 phenotypes. Given this design, it is impossible to draw solid conclusions about genotype-phenotype relationships. A more robust approach would involve including multiple healthy controls to account for genetic background variability or using isogenic parental or genetically corrected lines, which would provide a cleaner genetic comparison. A recent study (PMID: 36385170) discusses different study designs that could strengthen this aspect and might be useful for the authors to consult. Third, the study presents seemingly conflicting results regarding WOREE and SCAR12 phenotypes. The authors present immunofluorescence (IF) and scRNA-seq data indicating that changes in radial glia (RG) abudance are not observed in these patient-derived organoids. However, using same methodologies, they indicate that neuronal production is affected, leading to the accumulation of early neuronal signatures in both WOREE and SCAR12 neurons. The study does not explore whether RG signatures might be altered in a way that could contribute to neuronal phenotypes. Also, Figure 1F suggests that while Sox2+ cell counts are not increased in SCAR12 organoids, SATB2 levels are still altered, indicating that Sox2 and SATB2 trends are not tightly coupled across genotypes. Furthermore, Figure 1 and 6 show that while both syndromes exhibit similar hyperexcitability, data in Figure 6 report that only WOREE organoids display reductions in SATB2 and CTIP2 counts and that this can be rescued by WWOX restoration. Some of these discrepancies could stem from patterning variability as discussed above. Also, neuronal firing rate across WOREE and SCAR12 iPSC-derived organoids (Figure 6B) was different at later stages, but was rather comparable at an earlier stages (Figure S1G). The reasons for these differences are not thoroughly discussed. To strengthen the discussion, the authors should address how RG alterations (if any) might contribute to neuronal phenotypes, provide a more detailed comparison between WOREE and SCAR12 organoids and the WWOX-KO model and elaborate on the distinct phenotypes of the two syndromes, including possible explanations for observed functional and molecular discrepancies. Lastly, the conclusions drawn about WWOX restoration via gene therapy are weakened by the lack of replication and validation (see points above). First, the authors claim successful WWOX restoration in neurons, but provide limited evidence that the infected population consists of neurons. NeuN staining (Figure 6A and S10) suggests some neuronal expression, but quantification of WWOX+ NeuN+ / WWOX+ total cells is missing. Given that IF data are already available, this additional quantification could be completed within a few weeks and would significantly strengthen the claim. Second, the rationale for restoring WWOX in neurons is unclear, given that WT neurons do not normally express WWOX. Is WWOX being considered a functional neuronal maturation factor? If so, this should be explicitly discussed in the manuscript. Third, the authors propose that WWOX deficiency might lead to a delay in neuronal maturation. However, to demonstrate delayed maturation, the study should show that, given additional time, affected organoids can eventually produce late-stage neuronal signatures. Since this additional experiment may be technically challenging and time-consuming, the claim should instead be rephrased as speculative and discussed accordingly in the text. Lastly, the study lacks key details necessary for reproducibility in multiple aspects. In addition to details about organoid numbers and batches discussed above, all IF images are shown as insets, making it difficult to assess broader reproducibility within the whole organoid tissue section. Also, whether distinct iPSC clones/sections/organoids were used across IF experiments - which is critical for ensuring reproducibility - is not specified. As for details about experimental and bioinformatics methods, the bioinformatics pipeline is not fully described, making it impossible to verify or reproduce the computational analysis. No information is provided regarding batch correction procedures for scRNA-seq data (Lines 695-697) and on how clusters were mapped (lines 695-697) for cell type identification. Legends in Figure 1F, 2K-L, 6B, S10 do not specify what the error bars represent (e.g., standard deviation or standard error). Many catalog numbers for critical cell culture reagents are not provided, which is essential for experimental replication. The Western blot methods lack crucial details.

      Minor comments:

      • One relevant study (PMID: 32581702) that examines WWOX function in rat models and human fetal brains from patients has not been referenced or discussed. Notably, this study characterizes molecular changes associated with WWOX knockdown in human ESC-derived NPCs. Given its direct relevance to the current study, these findings should be acknowledged and integrated into the discussion to provide a more comprehensive understanding of WWOX-related neurodevelopmental alterations.
      • For WKO-1C and 2C the exact mutations in exon1 identified by Sanger sequencing are not reported. Also, validation for WWOX protein loss in all the lines used is also missing. Information about cell line genome integrity check are also missing.
      • Line 116 and 394, reference Steinberg et al is not formatted.
      • Figure S1A: Localization of WWOX seems to be cytoplasmic and/or membrane-bound in organoids, while staining in IPSCs shows cytoplasmic and nuclear signals. Perphaps an orthogonal valiation with another anti-WWOX antibody would be appropriate to confirm subcellular localization.
      • In Figure 1, authors use week 7 organoids and claim that they are enriched for early born preplate neurons (line 141). However the authors decide to look at SATB2, which is not an eary-born preplate neuron. So while the rationale for using Satb2 is not clear, the reported staining in Figure 1E shows an unusal overlap beetween Sox2 and Satb2 nuclear signals in wt organoids. The authors needs to recheck that the correct antibodies were used in this analysis.
      • Figure 1 Panel F: legend states that n indiactes 3 neurons. Please specifify what n referes to.
      • Figure 3J: MYC staining appears to be nuclear in WWOX-KO organoids but more cytoplasmic in SCAR12 organoids. Also in WOREE organoids, both Sox2 and MYC staining appears different from what seen in other panels/ genotypes from the same figure panel.
      • Figures 3 and related legend: Authors use the term w for weeks but they need to specify whether this refers to gestational weeks or post-conception weeks.
      • Figure 4: The UMAP in B, E and G seems to be blurred in the bottom parts. Is this an intentional choice? If so, what would be intent? Also, title and legend for E mention metabolic alterations but data presented are not related to metabolic patwhays.
      • Figure 6. The same exact images from A and C are also reported in Figure S8 and S9 respectively.
      • Figure S1D: WWOX antibody seems to give an extra band at higehr molecular weight. This is also evident from S4B, where the upper band seems overrepresented in KO2. Also, are the healthy parents haploinsufficient for WWOX? what are the levels compared to wt (unrelated) controls?
      • Figure S2: In B, what is the difference between top and bottom UMAPs? In C-D, what is NP? Correlation map suggests that the NP clusters 7 and 8 are different from cluster 11. What is the rational for labelling them all NP cluster?
      • Figure S6: In the legend, full description of cluster labels are missing. Also legends specifes A-D while the figure contains only A-C.
      • Figure S4A: The staining for TUBB3 is very different between KO1 and KO2.
      • Figure S8: The legend indicates n as 4 organoids but images are not quantified so there is no evidence that these patterns have been replicated in 4 organoids.
      • Figure S9: The title is duplicated and not corresponding to the data in the figure. The whole figure is duplicated in Figure S10 (which is wrongly labelled as Figure 10 in the legend).
      • Line 330: Figure S6 F-H should be corrected in Figure 6 F-H.
      • Line 353: reference needs to be added for "our earlier findings"
      • Lines 383 and 392: The authors describe several possible MYC roles but which ones could relevant in this contex is not discussed.
      • Lines 402 and 403: The authors state that the study "highlights the critical role of Wnt signalling" but they fail to provide evidence that Wnt is functionally involved, as Wnt perturbation experiments are not applied.
      • Line 473: "at X concentration" needs to be correct to specify the concentration used.
      • Line 478: The authors state that "inform consent is under approval". Does this mean that the study was conducted before approval was obatined?
      • Line 525: which orbital shaker and which speed was used?
      • Line 537: what is GC in GC/ul?
      • Line 629: samples were centrifgues at which speed and for how long?
      • Line 639: "All primer sequence" should be plural

      Referees cross-commenting

      All four reviews appear fair and complementary to each other. Reviewers have consistently highlighted concerns regarding unclear genomic alterations in patients' iPSCs and experimental reproducibility in organoid cultures, emphasizing the need for further validation of the reported findings and the underlying molecular cascade. Additionally, they have noted some inconsistencies, with Reviewer #2 specifically identifying a major discrepancy in the WWOX-KO phenotypes compared to those previously described by the same team.

      Significance

      General assessment:

      The strengths of this study lie in its focus on disease phenotypes in a human context and the use of patient-derived iPSC lines, which provide valuable translational relevance. Additionally, the study employs a complementary set of analyses, including functional assays, immunofluorescence (IF), and single-cell RNA sequencing (scRNA-seq), which enhance its depth. However, the study has several critical weaknesses, primarily related to suboptimal experimental design and limited reproducibility. These are discussed in section A and also indicated below:

      • Lack of isogenic controls or patient-derived lines and presence of conflicting data for patient-derived organoids, making genotype-based comparisons for patients' lines less robust; examples of studies using iPSC isogenic controls for dissecting neurodevelopmental disorders are found here (PMID: 35084981; PMID: 26186191).
      • Limited reproducibility, due to a small number of organoids used and the lack of orthogonal validation for key findings.
      • Absence of functional validation for MYC's contribution, making its proposed role unclear.

      Advance:

      This study builds upon and expands previous efforts by the same team to characterize brain organoid models obtained from patient-derived iPSCs, as well as to explore gene therapy restoration approaches (PMID: 34268881, PMID: 34747138). Some of the bioinformatics analyses appear to have been developed elsewhere, and technically, the study offers only a limited methodological advance. Instead, the key advancement of this work is more conceptual: it proposes potential underlying mechanisms of WWOX-related neurodevelopmental disorders. If the study's limitations were addressed, it could provide valuable insights into WWOX's role as a key regulator of radial glia proliferation and differentiation, as well as potential functions in neuronal maturation. These findings would be relatively novel in the context of WWOX-related neurodevelopmental disorders. WWOX has been extensively studied in rodent models, where WWOX -/- mice exhibit growth retardation and brain malformations (PMID: 32000863, PMID: 18487609, PMID: 15026124). Additionally, studies in rats and human fetal cortical tissue from patients (PMID: 32581702) have linked WWOX deficiency to migration defects and cortical cytoarchitectural alterations. Previous work in mice by the same team suggested that neurons are the key population affected, linking WWOX deficiency to hyperexcitability and intractable epilepsy (PMID: 33914858). However, the relevance of radial glia and cell-type specific molecular alterations linked to WWOX mutations have remained poorly defined. Through scRNA-seq, this study offers some insights into cell-type-specific molecular changes, especially in radial glia cells. These changes are linked to MYC fucntion, cell cycle arrest and altered differentiation trajectories. However, these insights remain preliminary due to the study's design limitations. Another potential advancement of this study is its exploration of syndrome-specific alterations in WOREE and SCAR12 patients and their rescue through WWOX gene therapy-an aspect that has been difficult to study in animal models and remains largely unexplored. While the brain organoid model offers a promising approach, the true conceptual advance of this study remains uncertain, as its current limitations hinder the ability to draw definitive conclusions.

      Audience: This study could be particularly relevant to a specialized audience, including basic research scientists working in developmental biology and the molecular basis of neurodevelopmental disorders, as well as those interested in translational approaches. Additionally, given WWOX's known roles beyond neurodevelopment and potential involvement of MYC, the findings may also be of interest to cancer biologists.

      Expertise: My expertise lies in iPSCs and brain organoid modeling of neurodevelopmental disorders, with a strong focus on organoid phenotypic analysis, particularly immunofluorescence and transcriptomics. However, I do not have a strong background in bioinformatics and therefore lack sufficient expertise to evaluate the bioinformatic methodologies utilized in the study.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this paper Kawasaki et al describe a regulatory role for the PIWI/piRNA pathway in rRNA regulation in Zebrafish. This regulatory role was uncovered through a screen for gonadogenesis defective mutants, which identified a mutation in the meioc gene, a coiled-coil germ granule protein. Loss of this gene leads to redistribution of Piwil1 from germ granules to the nucleolus, resulting in silencing of rRNA transcription.

      Strengths:

      Most of the experimental data provided in this paper is compelling. It is clear that in the absence of meioc, PiwiL1 translocates in to the nucleolus and results in down regulation of rRNA transcription. the genetic compensation of meioc mutant phenotypes (both organismal and molecular) through reduction in PiwiL1 levels are evidence for a direct role for PiwiL1 in mediating the phenotypes of meioc mutant.

      Weaknesses:

      Questions remain on the mechanistic details by which PiwiL1 mediated rRNA down regulation, and whether this is a function of Piwi in an unperturbed/wildtype setting. There is certainly some evidence provided in support of the natural function for piwi in regulating rRNA transcription (figure 5A+5B). However, the de-enrichment of H3K9me3 in the heterozygous (Figure 6F) is very modest and in my opinion not convincingly different relative to the control provided. It is certainly possible that PiwiL1 is regulating levels through cleavage of nascent transcripts. Another aspect I found confounding here is the reduction in rRNA small RNAs in the meioc mutant; I would have assumed that the interaction of PiwiL1 with the rRNA is mediated through small RNAs but the reduction in numbers do not support this model. But perhaps it is simply a redistribution of small RNAs that is occurring. Finally, the ability to reduce PiwiL1 in the nucleolus through polI inhibition with actD and BMH-21 is surprising. What drives the accumulation of PiwiL1 in the nucleolus then if in the meioc mutant there is less transcription anyway?

      Despite the weaknesses outlined, overall I find this paper to be solid and valuable, providing evidence for a consistent link between PIWI systems and ribosomal biogenesis. Their results are likely to be of interest to people in the community, and provide tools for further elucidating the reasons for this link.

      The amount of cytoplasmic rRNA in piwi+/- was increased by 26% on average (figure 5A+5B), the amount of ChiP-qPCR of H3K9 was decreased by about 26% (Figure 6F), and ChiP-qPCR of Piwil1 was decreased by 35% (Figure 6G), so we don't think there is a big discrepancy. On the other hand, the amount of ChiP-qPCR of H3K9 in meioc<sup>mo/mo</sup> was increased by about 130% (Figure 6F), while ChiP-qPCR of Piwil1 was increased by 50%, so there may be a mechanism for H3K9 regulation of Meioc that is not mediated by Piwil1. As for what drives the accumulation of Piwil1 in the nucleolus, although we have found that Piwil1 has affinity for rRNA (Fig. 6A), we do not know what recruits it. Significant increases in the 18-35nt small RNA of 18S, 28S rRNAs and R2 were not detected in meioc<sup>mo/mo</sup> testes enriched for 1-8 cell spermatogonia, compared with meioc<sup>+/mo</sup> testes. The nucleolar localization of Piwil1 has revealed in this study, which will be a new topic for future research.

      Reviewer #2 (Public review):

      Summary:

      In this study, the authors report that Meioc is required to upregulate rRNA transcription and promote differentiation of spermatogonial stem cells in zebrafish. The authors show that upregulated protein synthesis is required to support spermatogonial stem cells' differentiation into multi-celled cysts of spermatogonia. Coiled coil protein Meioc is required for this upregulated protein synthesis and for increasing rRNA transcription, such that the Meioc knockout accumulates 1-2 cell spermatogonia and fails to produce cysts with more than 8 spermatogonia. The Meioc knockout exhibits continued transcriptional repression of rDNA. Meioc interacts with and sequesters Piwil1 to the cytoplasm. Loss of Meioc increases Piwil1 localization to the nucleolus, where Piwil1 interacts with transcriptional silencers that repress rRNA transcription.

      Strengths:

      This is a fundamental study that expands our understanding of how ribosome biogenesis contributes to differentiation and demonstrates that zebrafish Meioc plays a role in this process during spermatogenesis. This work also expands our evolutionary understanding of Meioc and Ythdc2's molecular roles in germline differentiation. In mouse, the Meioc knockout phenocopies the Ythdc2 knockout, and studies thus far have indicated that Meioc and Ythdc2 act together to regulate germline differentiation. Here, in zebrafish, Meioc has acquired a Ythdc2-independent function. This study also identifies a new role for Piwil1 in directing transcriptional silencing of rDNA.

      Weaknesses:

      There are limited details on the stem cell-enriched hyperplastic testes used as a tool for mass spec experiments, and additional information is needed to fully evaluate the mass spec results. What mutation do these testes carry? Does this protein interact with Meioc in the wildtype testes? How could this mutation affect the results from the Meioc immunoprecipitation?

      Stem cell-enriched hyperplastic testes came from wild-type adult sox17::GFP transgenic zebrafish. Sperm were found in these hyperplastic testes, and when stem cells were transplanted, they self-renewed and differentiated into sperm. It is not known if the hyperplasias develop due to a genetic variant in the line. We added the following comment in L201-204.

      “The SSC-enriched hyperplastic testes, which are occasionally found in adult wildtype zebrafish, contain cells at all stages of spermatogenesis. Hyperplasia-derived SSCs self-renewed and differentiated in transplants of aggregates mixed with normal testicular cells.”

      Reviewer #3 (Public review):

      Summary:

      The paper describes the molecular pathway to regulate germ cell differentiation in zebrafish through ribosomal RNA biogenesis. Meioc sequesters Piwil1, a Piwi homolog, which suppresses the transcription of the 45S pre-rDNA by the formation of heterochromatin, to the perinuclear bodies. The key results are solid and useful to researchers in the field of germ cell/meiosis as well as RNA biosynthesis and chromatin.

      Strengths:

      The authors nicely provided the molecular evidence on the antagonism of Meioc to Piwil1 in the rRNA synthesis, which supported by the genetic evidence that the inability of the meioc mutant to enter meiosis is suppressed by the piwil1 heterozygosity.

      Weaknesses:

      (1) Although the paper provides very convincing evidence for the authors' claim, the scientific contents are poorly written and incorrectly described. As a result, it is hard to read the text. Checking by scientific experts would be highly recommended. For example, on line 38, "the global translation activity is generally [inhibited]", is incorrect and, rather, a sentence like "the activity is lowered relative to other cells" is more appropriate here. See minor points for more examples.

      Thank you for pointing that out. I corrected the parts pointed out.

      (2) In some figures, it is hard for readers outside of zebrafish meiosis to evaluate the results without more explanation and drawing.

      We refined Figure 1A and added explanation about SSC, sox17::egfp positive cells, and the SSC-enriched hyperplastic testis in L155-158.

      (3) Figure 1E, F, cycloheximide experiments: Please mention the toxicity of the concentration of the drug in cell proliferation and viability.

      When testicular tissue culture was performed at 0.1, 1, 10, 100, 250, and 500mM, abnormal strong OP-puro signals including nuclei were found in cells at 10mM or more. We added the results in the Supplemental Figure S2G. In addition, at 1mM, growth was perturbed in fast-growing 32≤-cell cysts of spermatogonia, but not in 1-4-cell spermatogonia, as described in L127-130.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      I don't have any recommendations for improvement. While I have outlined some of the weaknesses of the paper above. I don't see addressing these questions as pertinent for publication of this paper.

      Reviewer #2 (Recommendations for the authors):

      (1) The manuscript uses the terms 1-2 cell spermatogonia, GSC, and SSC throughout the figures and text. For example, 1-2 cell spermatogonia is used in Figure 1C, GSC is used in Figure 1F, and SSC is used in Figure 1 legend. The use of all three terms without definitions as to how they each relate with one another is confusing, particularly to those outside the zebrafish spermatogenesis field. It would be best to only use one term if the three terms are used interchangeably or to define each term if they represent different populations.

      GSC is a writing mistake. In this study, sox17-positive cells, which have been confirmed to self-renew and differentiate (Kawasaki et al., 2016), are considered SSCs. On the other hand, a comparison of meioc and ythdc2 mutants revealed differences in the composition of each cyst, so we describe the number of cysts confirmed. We added new data that 1-2 cell spermatogonia are sox17-positive in Supplemental Figure S3 (L157-158).

      (2) Figure 1B: What does the "SC" label represent in these figure panels?

      We added the explanation in the Figure legend.

      (3) Fig 7B and S7B show incongruent results, and the text implies that Fig S7B data better reflects in vivo biology. It is not clear how the authors interpret the different results between 7B and S7B.

      Thank you for pointing that out. Fig 7A and 7B were obtained by isolating sox17-positive cells. Because it was difficult to detect nucleoli in the isolated cells, probably due to the isolation procedure, we added S7B, which was analyzed in sectioned tissues. As this reviewer pointed out, S7B reflects the in vivo state better, so we changed S7B to 7B and 7B to S7B.

      Reviewer #3 (Recommendations for the authors):

      Minor points:

      (1) For general readers, it is nice to add a scheme of zebrafish spermatogenesis (lines 77-78) together with Figure 1A.

      As mentioned above, we refined Figure 1A.

      (2) Line 28, silence: the word "silence" is too strong here since rDNA is transcribed in some levels to ensure the cell survival.

      Thank you for your comment. We changed "silence" to "maintain low levels."

      (3) Line 60, YTDHC2: Please explain more about what protein YTDHC2 is.

      We added a description of Ythdc2 in the introduction.

      (4) Line 69, Piwil1: Please explain more about what protein Piwil1 is.

      We added a description of Piwil1 in the introduction.

      (5) Figure 1B, sperm: Please show clearly which sperms are in this figure using arrows etc.

      We represented sperm using arrowheads in Fig 1B.

      (6) Figure 1C, SC: Please show what SC is in the legend.

      We added the explanation in the Figure legend.

      (7) Line 83, meiotic makers: should be "meiotic prophase I makers".

      Thank you for pointing out the inaccurate expression description. We revised it.

      (8) Line 84, phosphor-histone H3: Should be "histone H3 phospho-S10 "

      We revised it.

      (9) Figure S1A, PH3: Please add PH3 is "histone H3 phospho-S10 ".

      We revised it.

      (10) Figure S1A, moto+/-: this heterozygous mutant showed an increased apoptosis. If so, please mention this in the text. If not, please remove the data.

      Thank you for pointing that out. The heterozygous mutant did not increase apoptosis, so we removed the data.

      (11) Line 88, no females developed: This means all males in the mutant. If so, what Figure S1B shows? These cells are spermatocytes? No "oocytes" developed is correct here?

      All meioc<sup>mo/mo</sup> zebrafish were males, and the meioc<sup>mo/mo</sup> cells in Fig. S1B are spermatogonia. No spermatocytes or oocytes were observed. To show this, we added "no oocytes" in L90.

      (12) Line 89, initial stages: What do the initial stages mean here? Please explain.

      The “initial stages” was changed to the pachytene stage.

      (13) Figure S1C: mouse Meioc rectangle lacks a right portion of it. Please explain two mutations encode a truncated protein in the main text.

      I apologize. It seems that the portion was missing during the preparation of the manuscript. We corrected it. In addition, we added a description of the protein truncation in L100-101.

      (14) Line 99: What "GRCz11" is.

      GRCz11 refers to the version of the zebrafish reference genome assembly. We added this.

      (15) Figure S2A: Dotted lines are cysts. If so, please mention it in the legend.

      We corrected the figure legend.

      (16) Figure S2B and C:, B1-4, C1-7: Rather use spermatogonia etc as a caption here.

      We corrected the figure and figure legend.

      (17) Line 113, hereafter, wildtype: Should be "wild type" or "wild-type".

      We corrected them.

      (18) Figure 1C: Please indicate what dotted lines mean here.

      We added “Dotted lines; 1-2 cell spermatogonia.”

      (19) Line 113, de novo: Please italicize it.

      We corrected it.

      (20) Line 113-116: Figure 1D shows two populations in the protein synthesis (low and high) in the 1-2-cell stage. Please mention this in the text.

      We added mention of two population.

      (21) Line 121, in vitro: Please italicize it.

      We corrected it.

      (22) Line 138-139, Figure 2A: Please indicate two populations in the rRNA concentrations (low and high) in the 1-2-cell stage. How much % of each cell is?

      We added mention of two population and % of each cell.

      (23) Figure 2B, cytes: Please explain the rRNA expression in spermatocytes (cytes) in the text.

      The decrease in rRNA signal intensity in spermatocytes was added.

      (24) Figure 2A, lines 147, low signals: Figure 2A did not show big differences between wild type and the mutant. What did the authors mean here? Lower levels of rRNAs in the mutant than in wild type. If so, please write the text in that way.

      We think that it is important to note that we were unable to find cells with upregulated rRNA signals, and therefore changed to “could not find cells with high signals of rRNAs and Rpl15 in meioc<sup>mo/mo</sup> spermatogonia”.

      (25) Figure 2E: Please add a schematic figure of a copy of rDNA locus such as Fig. S3A right.

      We added a schema of rDNA locus and primer sites such as Figure S3A right (now Figure 2F) in Figure 2E.

      (26) Figure S3A: This Figure should be in the main Figure. The quantification of Northern blots should be shown as a graph with statistical analysis.

      We added the quantification and transfer to the main Figure (Figure 2F).

      (27) Figure 4A: Please show single-color images (red or green) with merged ones.

      We added single-color images in the Figure 4A.

      (28) Line 198, Piwil1: Please explain what Piwil1 is briefly.

      We are sorry, but we could not quite understand the meaning of this comment. To show that Piwil1 is located in the nucleolus, we indicated it as (Figure 4A, arrowhead) in L209.

      (29) Line 198, Ddx4-positive: What is "Ddx4-positive"? Explain it for readers.

      Ddx4 is a marker for germinal granules, and the description was changed to reflect this.

      (30) Line 209, Fig. S4D-G: Please mention the method of the detection of piRNA briefly.

      We have described that we have sequenced small RNAs of 18-35 nt. Accordingly, we changed the term piRNA to small RNA.

      (31) Line 217: Please mention piwil1 homozygous mutant are inviable.

      We added that piwil1-/- are viable in L231.

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

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      Reply to the reviewers

      Reviewer #1

      __Evidence, reproducibility and clarity __

      The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well-designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

      __Reply:__We thank the reviewer for this general comment which gives us the opportunity to better explain the scope of our work. The purpose and focus of this work are not to test the pathological relevance of skin fibroblasts to GSD1a pathology. We do not claim that skin fibroblasts are involved in GSD1a pathogenesis. It is also not a developmental work claiming to uncover GSD1a pathogenic axis throughout embryonic development. As a matter of fact, since skin fibroblasts originate from the mesoderm embryonic germ layer and hepatocytes develop from the endoderm embryonic germ layer, it would even be unlikely that the pathological phenotype found in skin fibroblasts directly contributes to GSD1a pathology in model mice or in patients. Indeed, we are not aware of any dermatological contribution to GSD1a pathology in patients. However, our results suggest that in addition to the established and mutated organ (liver in the liver-specific G6pc knockout mouse model), other, relatively less studied, patho-mechanisms in distal tissues may also contribute to GSD1a pathology. Notably, this work is also not testing a therapeutic modality for GSD1a. Our work uses GSD1a disease models as a tool for demonstrating, or reviving, the concept of epigenomic landscape (Waddington, 1957): Different cell phenotypes, such as healthy and diseased, are established by innate metabolic differences between their respective cell environments, which impose epigenetic changes generating these different phenotypes. In this respect, our manuscript has a similar message to the one in the recently published paper Korenfeld et al (2024) Nucleic Acids Res 53:gkae1161. doi: 10.1093/nar/gkae1161: The Kornfeld et al paper shows that intermittent fasting generates an epigenetic footprint in PPARα-binding enhancers that is "remembered" by hepatocytes leading to stronger transcriptional response to imposed fasting by up-regulation of ketogenic pathways. In the same way, the diseased GSD1a status imposes metabolic changes, as detailed here, leading to permanent epigenetic changes, also described here, which are "remembered" by GSD1a fibroblasts and play a major role in the transcription of pathogenic genes in these patient's cells. This in turn is how the diseased state is preserved, even in cells not expressing the G6Pase mutant, which is the direct cause of the disease. We added this perspective to the Discussion to better highlight the key takeaway from our manuscript.Naturally, research such as ours with a claim on biological memory would involve ex vivo experiments where tissues are isolated from their in-situ environments and tested for preservation of the original in situ phenotype. The few in vivo experiments we performed (Fig. 5) are mainly aimed at demonstrating that not only the phenotype, but also therapy response is "remembered" ex vivo: In the same way that the G6PC-loss-of-function liver responded positively to GHF201 therapy in situ, ex vivo cells not expressing G6PC also responded positively to the same therapy. This observation only demonstrates further support for "memorization" of the disease phenotype by cell types not expressing the mutant: Both the diseased phenotype and response to therapy were preserved ex vivo.Lastly, while interesting, validation of our findings in vivo (as suggested by the reviewer) is not related to the scope of this manuscript. Such experiments, using the liver-targeted G6pc knockout mouse model, are the follow-up story, which is related to the origin of inductive signals that cause the curious and novel phenotype mechanism in GSD1a fibroblasts described in this manuscript. The scope and volume of such research constitute a novel manuscript.

      Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.

      __Reply:__We thank the reviewer for this important comment, we agree that it is important to note the dietary regimen assigned to the cohort of patients described in this study. We added an explanation to the manuscript on patient's diets as shown below.Briefly, all patients besides patient 6894 were treated with the recommended dietary regimen for GSD1a as explained in Genereviews (Bali et al (2021)). This dietary treatment (now added to the Methods section in the manuscript) allows to maintain normal blood glucose levels, prevent secondary metabolic derangements, and prevent long-term complications. Specifically, this dietary treatment includes- nocturnal nasogastric infusion of a high glucose formula in addition to usual frequent meals during. By constantly maintaining a nearly normal level of blood glucose, this treatment causes a remarkable decrease, although not normalization, of blood lactate, urate and triglyceride levels, as well as bleeding time values. A second layer in the treatment includes the use of uncooked starch in the dietary regimen to allow maintenance of a normal blood glucose levels for long periods of time. Patient 6894 did not tolerate well the uncooked cornstarch and therefore was treated with a tailored dietary treatment planned by metabolic disease specialists and dedicated certified dieticians highly experienced with the management of pediatric and adult patients with GSDs and other inborn errors of metabolism. The biopsies of patients were taken in the range of 3 month to several years from receiving the aforementioned dietary regimen.Importantly, the strict metabolic diet imposed on GSD1a patients might influence the observed phenotype described throughout the manuscript. This concept aligns with our claim that the GSD1a skin cells are affected by the dysregulated metabolism in patients in comparison to healthy individuals. Interestingly, while patient 0762 harbors a mutation in the SI gene in addition to the G6PC mutation and patient 6894 did not receive the same dietary regimen as other patients (as explained above), all patients do show similar disease related phenotypes, perhaps highlighting the role of an early programing process that affected these cells due to the severe metabolic aberrations presented in this disease from birth.One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

      One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.

      __Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A and accordingly also edited the relevant text in the Results section.

      Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.

      __Reply:__We thank the reviewer for this important comment. We added glycogen levels of HC to Figure S2A as mentioned above.

      Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.

      __Reply:__We thank the reviewer for this comment. To clarify, the results shown in Figure S2A left are based on PAS assay, directly quantifying glycogen in cells with and without GHF201 treatment. We have now added HC glycogen levels as requested above. Regarding N, this is explained in Methods: In imaging experiments N was determined based on wells from the experiments done in three independent plates following the rationale that each well is independent from the others and reflects a population of hundreds of cells as previously described in (Lazic SE, Clarke-Williams CJ, Munafò MR (2018) What exactly is 'N' in cell culture and animal experiments?. PLOS Biology 16(4):e2005282. https://doi.org/10.1371/journal.pbio.2005282, Gharaba S, Sprecher U, Baransi A, Muchtar N, Weil M. Characterization of fission and fusion mitochondrial dynamics in HD fibroblasts according to patient's severity status. Neurobiol Dis. 2024 Oct 15;201:106667. doi: 10.1016/j.nbd.2024.106667. Epub 2024 Sep 14. PMID: 39284371.). Figure S2A right shows the glucose quantification experiment that we think the reviewer is referring to. Glucose increase is normally concomitant with glycogen reduction and we therefore show these results in support of the glycogen reduction results. These glucose results are part of our metabolomics results done on the same cells (Figure 6), where glucose is one of the metabolites analyzed. This metabolomics analysis was repeated three times; therefore, N is 3. In summary, these results show that GHF201 directly contributes to glycogen reduction in GSD1a fibroblasts and concomitantly increases glucose levels.

      Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiment to confirm this.

      __Reply:__Assuming the reviewer's comment is related to Figure S2A right, glucose levels are only shown to validate the glycogen reduction results (also see point 4): When glycogen levels are reduced, especially by inhibition of glycogen synthesis, glucose levels are supposed to concomitantly rise, being spared as an indirect substrate of glycogen synthesis. There is no proof, and as a matter of fact we also do not assume, that the GHF201-mediated reduction in glycogen levels is a result of increased glycophagy: Glycophagy has been described in cell types with high glycogen turnover, e.g., muscle and liver cells, not fibroblasts. Additionally, glycophagy is a glycogen-selective process implicating STBD1 whose expression in skin fibroblasts is negligible (https://www.proteinatlas.org/ENSG00000118804-STBD1/tissue).On the other hand, glycogen in GSD1a does not accumulate in lysosomes. It is built up in the cytoplasm (Hicks et al (2011) Ultrastr Pathol 35: 183-196; Hannah et al (2023) Nat Rev Dis Primers DOI: 10.1038/s41572-023-00456-z). Therefore, we do not believe that GHF201 reduced glycogen by enhancing glycophagy. As we show, GHF201 activated several key catabolic pathways. It is more likely that activation of one of these pathways, the AMPK pathway, inhibited glycogen synthesis via phosphorylation and ensuing inhibition of glycogen synthase. Alternatively, excessive cytoplasmic glycogen might enter lysosomes by bulk autophagy, or microautophagy (not by glycophagy) and GHF201 might induce lysosomal glycogenolysis by alpha glucosidase as an established lysosomal activator (Kakhlon et al (2021)). However, since, as explained, the mechanism of action of GHF201 is not the topic of this manuscript and therefore we did not dwell more into that.

      Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?

      __Reply:__We thank the reviewer for highlighting this important topic. OCR, measured in Fig. 2B, is an indirect measure of ATP production. Therefore, changes in OCR only measure the capacity of the mitochondria to produce ATP, and not the direct quantity of ATP. Other factors might influence ATP production, e.g., substrate availability and the activity of other metabolic pathways. On the other hand, the ATP Rate Assay (Figure 2h), provides a real-time direct measurement of ATP levels incorporating coupling efficiency and P/O ratio assumptions. Therefore, these two measurements do not necessarily match. We will add this information to the relevant segment in the text to clarify why OCR is reduced and mitochondrial ATP production increased in GSD1a cells.

      Why do GSD1a fibroblasts show reduced glycolytic ATP (Figure 2h) despite increased glycolysis and glycolytic capacity (Fig 2J-K)?

      __Reply:__We thank the reviewer for highlighting this important topic. ECAR measures medium acidification and thus reflects the production of lactic acid, which is a byproduct of glycolysis. However, medium acidification is also influenced by other factors that can acidify the extracellular environment, especially CO2 production which can originate from the intramitochondrial Krebs cycle which produces reductive substrates for mitochondrial respiration, or OCR. Moreover, the buffering capacity of the Seahorse mito stress assay medium might mask changes in lactic acid production, leading to an underestimation of glycolytic activity. On the other hand, glycolytic ATP production measured by the ATP rate assay directly quantifies the rate of ATP production from glycolysis. Notably, there is a major difference between ECAR and the ATP rate assay: The ATP rate assay is less sensitive to variations in buffering capacity than ECAR measurements. This is because the ATP rate assay relies on inhibitor-driven changes in OCR and ECAR, rather than absolute pH values.Teleologically, as indicated, the increased ECAR in GSD1a cells represents a known compensatory response to deficient ATP production which is stimulation of glycolysis (Figure 2i). To test the success of this known compensatory attempt, we applied the real-time ATP rate assay, but as explained they do not report the same entities. We will add this information to the relevant segment in the text to clarify how reduced glycolytic ATP can be co-observed with increased glycolytic capacity.

      The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.

      Reply:__We thank the reviewer for raising this topic. Number of samples per experiment is reported in the Methods section. As for the age range, patients age was matched to healthy controls to account for age differences and experiments were performed under similar passages range. This procedure allowed us to control for technical differences between samples that might arise due to different passages and ages. Importantly, the cohort of samples used in this manuscript included GSD1a patients with different ages further implying the strength of the observed disease phenotype found in patients' cells which exists regardless of the different age and gender of patients. The HC samples were chosen to match age and gender and passages were used in the recommended range (L. Hayflick,The limited in vitro lifetime of human diploid cell strains,Experimental Cell Research,Volume 37, Issue 3,1965,Pages 614-636, änzelmann S, Beier F, Gusmao EG, Koch CM, Hummel S, Charapitsa I, Joussen S, Benes V, Brümmendorf TH, Reid G, Costa IG, Wagner W. Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites. Clin Epigenetics. 2015 Mar 4;7(1):19. doi: 10.1186/s13148-015-0057-5. PMID: 25763115; PMCID: PMC4356053., Magalhães, S.; Almeida, I.; Pereira, C.D.; Rebelo, S.; Goodfellow, B.J.; Nunes, A. The Long-Term Culture of Human Fibroblasts Reveals a Spectroscopic Signature of Senescence. Int. J. Mol. Sci. __2022, 23, 5830. https://doi.org/10.3390/ijms23105830). Finally, for the error bars, assuming the reviewer is addressing this for all experiments, this means that results are consistent across each compared group and reflects robustness of the results. Further, to ensure statistical robustness we used bootstrapping, 95% confidence intervals and other statistical methodologies that were designed to increase the validity of the conclusions drawn from different experiments.

      Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.

      __Reply:__GHF201 reduced liver glucose-6-phosphate (G6P) with p-/-* mice livers and their normalization by GHF201.

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

      General comments: the authors propose a very intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

      __Reply:__We thank the reviewer for this comment and will address each comment raised.

      The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.

      __Reply:__We concur with this comment and accept that this is a general limitation of the study. We added a reservation clause at the beginning of the Discussion section.

      The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.

      __Reply:__We thank the reviewer for this important comment. We have toned down the language correlating the regulation of gene expression between fibroblasts and liver in GSD1a. We have also alluded to the key metabolic difference between fibroblasts and liver - glycogen levels and turnover - in the second paragraph of the Discussion. We are aware that if our deep analyses were conducted on a different tissue with different basal metabolism the results might have been different. However, the GSD1a-pathogenic findings in fibroblasts suggest that they might also contribute to pathology in situ, perhaps by modulating the expression of functionally redundant genes.

      For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.

      __Reply:__We thank the reviewer for pointing this important topic. Previously, we used the 72 h condition (48 h starvation followed by 24 h glucose supplementation) to attain two goals: generation of glycogen burden by excessive glucose re-uptake after glucose starvation and induction of basal autophagy by serum starvation so as to sensitize detection of the action of the autophagic activator GHF201 on a background of already induced autophagy. As stated, this 72 h condition was used previously in other GSD cell models (Kakhlon et al (2021) - GSDIV, Mishra et al (2024) - GSDIII, GSDII - in preparation), so we decided to use it in this work as well to enable cross-GSD comparison of GHF201 efficacy in GSD cell models. Moreover, as shown in Figure 1, the largest differences between HC and GSD1a fibroblasts, especially in lysosomal and mitochondrial features, were observed at the 72 h time condition. We therefore used this condition in all other fibroblasts experiments presented in this manuscript. Our ultimate aim was to test whether the metabolic reprograming induced in situ by the patients' diseased state before culturing generates stable epigenetic modifications withstanding seclusion from the original in situ environment. Thus, using the non-physiological 72 h condition, after the fibroblasts were cultured in full media remote from the in situ environment, can only confirm the stability and environment-independence of these metabolically-driven epigenetic modulations. We now provide this justification at the beginning of the Results section.

      In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.

      __Reply:__We thank the reviewer for highlighting this important topic. We will add this information to the methods segment. Throughout the manuscript, p https://doi.org/10.1080/00031305.2018.1529624, Cumming, G. (2013). The New Statistics: Why and How. Psychological Science, 25(1), 7 29. https://doi.org/10.1177/0956797613504966 (Original work published 2014)). Along with the p values we presented all data points in each comparison and added bootstrap mediated 95 % confidence intervals as well. Since our sample size was small, we chose to focus on effect sizes, to use a higher p value threshold and to implement various advanced methodologies that allowed us to find important biological patterns.

      In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.

      __Reply:__We thank the reviewer for highlighting this issue. We added glycogen levels of HC to Figure 2SA as requested. Expectedly, glycogen levels are similar between HC and GSD1a fibroblasts because neither wild type G6PC1 in HC, or mutated G6PC1 in GSD1a fibroblasts is expressed. We have now corrected the manuscript text suggesting that glycogen is accumulated in GSD1a fibroblasts and rephrased the text to express the more versatile state where epigenetic modulation could be mediated by different metabolic perturbations according to the expression profile: G6PC1 mutant expressers (notably liver and kidney cells) could inhibit p-AMPK by glycogen accumulation, while non-expressers could inhibit p-AMPK by lowering NAD+. Text changes related to this new concept are found in the Results section "Exploring epigenetics as a phenotypic driver in GSD1a fibroblasts by ATAC-seq analysis" and in the Discussion section "Metabolic-driven, disease-associated programming of cell memory."

      Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.

      __Reply:__We thank the reviewer for this comment allowing us to clarify the reasoning behind the selected methods for the main markers identification. Throughout the manuscript we employed both Western blot and immunofluorescence experiments. We believe that immunofluorescence present as a more robust and efficient method for the following reasons: i. It allows to focus on proteins in their native state; ii. Immunofluorescence allows to observe proteins in relation to their location in the cells (for example TFs in nuclei area); iii. Immunofluorescence allows to focus on each cell and exclude cells which are dead, stressed or with a low viability characteristic; iv. Immunofluorescence allows to generate much more data. For the following reasons, the main proteins explored in this work we used immunofluorescence, in each immunofluorescence experiment we added a control for the secondary antibody alone, verifying the signal is related to the antibodies only. This information can be added if requested. Importantly, some of the antibodies used were recommended for immunofluorescence and not for Western blot. As the reviewer requested, we now provide western blot results for proteins that produced a signal with the antibodies in Western blots, all markers mentioned except TFEB were added to Figure S3 d.

      The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.

      Reply: We strongly agree with this comment and have therfore tested for the effect of GHF201 on H3K27 acetylation levels as shown in Fiugre 3f and on the deacetylase -SIRT-1 as shown in Figure 3e, Figure S3d and representative images in Figure S2b.

      The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

      Reply:

      We thank the reviewer for raising this topic, since there were thousands of differentially expressed genes and we cannot mention all we focused on the most important ones that comprise key pathways we wanted to highlight as described in the Results section. We have now linked in the Results section examples of PGC1α and TFEB target genes that were reduced due to lower levels of these transcription factors in GSD1a, as compared to HC cells. Importantly, a full list of the genes from the RNA-seq experiment can be found in Table S3. Genes regulated by TFEB contain the CLEAR (Coordinated Lysosomal Expression and Regulation) motif. Two notable genes regulated by CLEAR binding TFs such as TFEB, which are very important biologically, are cathepsin L and S (Figure 6A right) both of which were reduced in GSD1a and are now elaborated in the Results section referring to Figure 6a right. Additionally, Table S3 shows differentially expressed genes in GSD1a cells where there are many other lysosomal related genes that are downmodulated in GSD1a, we now added another important example, ATP6V0D2 to the Discussion as the reviewer suggested. As for PGC1alpha, a notable gene whose expression is up-modulated by PGC1alpha, which is down-modulated in GSD1a, is ALDH1A1 (Figure 6a right). In addition, we have now added PPARG and its coactivators alpha and beta to the discussion as requested by the reviewer, these genes are shown in Table S3 and are downmodulated in GSD1a. Finally, the transcriptional effect of PGC1alpha and TFEB is also mentioned in the Discussion within the cell phenotyping section, where we describe the deep impact of dysregulation of NAD+/NADH-Sirt-1-TFEB regulatory axis on the cell phenotype at all the levels described in the manuscript.

      Please revise the following sentences as the statements made are not adequately supported by the provided data a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

      __Reply:__If one ranks treatments according to NAD+/NADH (Figure 3c) and according to cytotoxicity (Figure 3d left) and cell confluence (Figure 3d right), then the mentioned correlation can be supported. ATP availability is compromised by gramicidin, yet gramicidin, which also increased NAD+/NADH, reduced cytotoxicity and enhanced cell confluence.

      b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells."

      __Reply:__Our data (Figure 3c) show that GHF201 increased NAD+/NADH both alone and with gramicidin.

      Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility.

      __Reply:__We agree and have corrected the Figure as requested.

      The reference list (n=160) is probably too long for a research article.

      __Reply:__The number of references reflect the length and depth of the manuscript and we believe that each reference merits its place. We agree that the number of references is large but we are not sure which criteria to use to exclude some references and to reduce them to a more acceptable number that we assume would be determined by the publishing journal.

      The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions. Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

      __Reply:__We thank the reviewer for this comment and for raising the important comments that allowed us to improve our manuscript, please see our reply to point 1.

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      Referee #2

      Evidence, reproducibility and clarity

      General comments: the authors propose a vey intriguing concept, that metabolic abnormalities trigger epigenetic changes in tissues distal from the disease site, even in cells in which the affected gene is not expressed. This is demonstrated in primary fibroblasts from patients with Glycogen Storage Disease type 1a (GSD1a). The authors provide a large amount of data to support the compelling concept of "Disease-Associated Programming", a term that they have coined to describe this effect. The level of novelty is very high and so is the impact of the study, since the above may apply to many different pathological conditions. Although, the study is well performed and employs multiple approaches and analyses to address the raised hypothesis, there are some limitations and concerns that need to be addressed by the authors.

      1. The different phenotypic characteristics are only demonstrated in skin fibroblasts which is not sufficient to support the conclusions made in the Discussion about the general applicability of the proposed disease-induced, metabolite-driven epigenetic programming to all cells and tissues. The authors should discuss this as a limitation of the study and general conclusions should be formulated with more caution.
      2. The authors describe a range of alterations in patients' fibroblasts as compared to healthy control fibroblasts. However, they draw parallels to the liver which is the organ primarily affected by GSD1a, stating that tissues other than the liver such as skin fibroblasts phenocopy the liver pathology (Discussion). Extrapolation of the findings to the liver is also made in the section "ATAC-seq, RNA-seq and EPIC methylation data integration". Here, the authors comment on the finding that identified genes are associated with tumour formation and draw parallels to hepatocellular carcinoma which is an important co-morbidity of GSD1a. These correlations, although interesting, should be presented as indications and not as "strong links". A major difference between fibroblasts and liver cells in the case of GSD1a is the massive accumulation of glycogen in the liver. This is a major metabolic feature which largely defines the disease's pathology. In addition to the similarities in the pathological features between the liver and other tissues such as fibroblasts, the authors should highlight this major difference and discuss their findings within this context.
      3. For basically all experiments performed in the study the authors follow the approach of culturing cells for 48 hours under serum and glucose starvation, followed be 24-hour cultivation in complete medium. This was practiced in a previous study by the authors (PMID: 34486811) to enhance the levels of glycogen in skin fibroblasts of patients with Adult Polyglucosan Body Disease. For the current study the selection of this treatment protocol is not sufficiently justified. Although, differences are described between patients' fibroblasts and controls under these conditions, it would have been interesting to address the reported parameters also at standard culturing conditions. This might be too much to ask for the purposes of this revision, but the authors may provide a better justification for the selection of the above treatment protocol and discuss whether the described phenotypic features are constitutive abnormalities present at all times or are induced by the metabolic stress imposed to the cells through this treatment.
      4. In the Figures, the authors provide comparisons between controls and patient fibroblasts (+/- GHF201). Although the authors provide the respective p values in all figures, it is not clear which differences are considered significant and which are not. Since some of the indicated p values are > 0.0. The authors should indicate which of these changes are significant or non-significant and these should be presented and discussed accordingly in the text.
      5. In Figure S2A, the authors show a reduction of glycogen levels in GSD1a fibroblasts following treatment with GHF201. Glycogen accumulation is central to this study, since a) is considered by the authors "a disease marker which is reversed by GHF201" - this is demonstrated in the liver of L.G6pc-/- mice and, according to the authors, replicated in the fibroblasts, b) as suggested by the authors it is the biochemical aberration that drives epigenetic modifications generating "disease memory". It is therefore important to appreciate whether GSD1a cells display pathologically increased levels of glycogen. This is also pertinent to the lack of G6PC expression in fibroblasts. The authors should include in Fig. S2A glycogen measurements of HC control fibroblasts cultured under the same conditions to compare with the levels present in GSD1a cells.
      6. Comparisons between protein levels (AMPK/pAMPK, Sirt1, TFEB, p62 ane PGC1a) are made on the basis of fluorescence intensity in immunostained cells. These results need to be supported by relevant western blot images to exclude that binding of the antibodies to unspecific sites contributes to the measured fluorescence.
      7. The authors demonstrate that treatment of GSD1a fibroblasts with histone deacetylase inhibitors reverses some of the phenotypic alterations. Given that GHF201 also improves these phenotypic differences it would be interesting to address whether GHF201 has any effect on histone acetylation.
      8. The authors report reduced levels of the transcription factors PGC1α and TFEB in GSD1a fibroblasts. Does this correlate with lower levels of expression of PGC1α and TFEB target genes in the RNA-seq experiments?

      Minor points

      1. Please revise the following sentences as the statements made are not adequately supported by the provided data

      a. "This NAD+/NADH increase correlated with reduced cytotoxicity and increased cell confluence (Figure 3d) suggesting that NAD+ availability prevails over ATP availability as an effector of cell thriving in GSD1a cells."

      b. "....in further support that respiration-dependent NAD+ availability mediate GHF201's corrective effect in GSD1a cells." 2. Please indicate on the densitometry graph of Fig. 10b the treatment (HDACi), for better visibility. 3. The reference list (n=160) is probably too long for a research article.

      Significance

      The study is of high novelty and impact, as it proposes a so far undescribed biological mechanism contributing to disease pathology that could apply for general pathological conditions.

      Although this is a compelling concept, it is only demonstrated in skin fibroblasts which limits its applicability at an organismal level.

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      Referee #1

      Evidence, reproducibility and clarity

      Major Comments:

      1. Since dietary restriction is the only management strategy for GSD1a, the authors should clarify whether the patient fibroblast donors were on a dietary regimen and for how long. Given that fibroblasts do not express G6Pase, it is possible that the observed phenotype could be influenced by the patient's diet history.
      2. One of the main pathological features of GSD1a is glycogen buildup. The authors should compare glycogen levels between healthy controls and GSD1a fibroblasts and provide a dot plot analysis.
      3. Figure S2A - As mentioned above, the authors should present healthy control vs. patient fibroblast glycogen data. Without this, the rationale for using GHF201 is questionable.
      4. Figure S2B-C - If the authors propose that GHF201 reduces glycogen and increases intracellular glucose in GSD1a fibroblasts, they need direct evidence. Either directly quantifying glycogen levels or even better would be a labeling experiment to confirm that the free intracellular glucose originates from glycogen. Additionally, the reduction in sample size from N=24 in glycogen analysis to N=3 in the glucose assay needs justification.
      5. Figure S2B-C- It is not shown how GHF201 increases intracellular glucose? If glycophagy is a possibility, the authors should do an experiemnt to confirm this.
      6. Figure 2- How can GSD1a fibroblasts have significantly reduced OCR (Fig. 2B) but increased mitochondrial ATP production (Fig. 2H)?
      7. Why do GSD1a fibroblasts show reduced glycolytic ATP (Fig. 2H) despite increased glycolysis and glycolytic capacity (Fig. 2J-K)? The authors should clarify how many healthy control and patient fibroblast lines were compared per experiment. Given the wide age range, the unexpectedly small error bars raise concerns about variability and statistical robustness.
      8. Figure 5- The study should include Tamoxifen-untreated mice as a control to properly assess the efficacy of GHF201 in regulating glucose-6-P and glycogen levels.
      9. Fig. 5B-C - The authors should explain how GHF201 reduces glucose-6-P levels. Additionally, they should demonstrate whether GHF201 activates lysosomal pathways and induces autophagy in the liver of G6pc knockout mice, as claimed in the fibroblast experiments.

      Significance

      The manuscript explores mild physiological and metabolic disturbances in patient-derived fibroblasts lacking G6Pase expression, suggesting that these cells retain a "distinctive disease phenotype" of GSD1a. The manuscript is well written with well designed experiments. However, it remains unclear whether these phenotypes genuinely reflect the pathology of GSD1a-relevant tissues. The authors did not validate these findings in a liver-specific G6pc knockout mouse model, raising concerns about the study's relevance to GSD1a. Additionally, the lack of sufficient in vivo evidence undermines the therapeutic potential of GHF201 for this disease. Overall, the study lacks a few key pieces of evidence to completely justify its conclusions at both fundamental and experimental levels.

    1. Explain how vaccination can result in specific immunity to a viral disease
      1. Vaccines contain dead or attenuated forms of the virus or its antibody to provoke an immune response in the body.
      2. Each B-lymphocyte produces one specific antibody. The adaptive immune system recognises pathogens and produces clones of specific B-cells, called plasma cells, and for further immunity, memory cells.
      3. The mass production of specific antibodies that bind to the viral antigens recognise the virus and destroy it.
      4. It can be destroyed through agglutination, opsonization, complement activation or neutralisation. In any case, the virus is engulfed by a phagocyte through endocytosis where it is digested by hydrolytic enzymes and rendered harmless.
      5. Specifically, the antigen binds to the specific B-cell and expressed on the MHC gene, producing an antigen specific B-cell. The antigen is also engulfed and displayed on phagocytes (antigen presentation). Consequently, the phagocyte binds and activates a T-cell which becomes a helper T-cell which consequently activates the specific B-cell which divides by mitosis to form plasma cells/memory cells which mass produce the antibody
      6. After the elimination of the infection, memory cells remain as future immunity to any future infections.
    2. Outline the visible features of these cells that adapt them for reabsorption of materialsfrom the glomerular filtrate.
      1. Microvilli on apical side provide large surface area for diffusion and active uptake of materials from glomerular filtrate.
      2. Invaginations on basolateral side also increase surface area for transport of materials into capillaries.
      3. One cell thick epithelium for rapid transport/reabsorption
      4. Lots of mitochondria for active transport (ATP)
    3. Discuss the evidence of the relationship between genome size and the complexityof organisms.
      1. Increased genome sizes are not necessarily linked to more complexity of organisms.
      2. The genome contains coding but also many non-coding genes that do not code for polypeptides. The proteome of an organism may be smaller even with increased genome size.
      3. Introns, telomeres and DNA coding for tRNA and rRNA are examples of noncoding DNA.
      4. Gene expression determines what genes are expressed and not all coding genes are activated to be expressed into a polypeptide.

      However eukaryotic genomes tend to be larger than prokaryotic genomes.

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      Reply to the reviewers

      1. General Statements [optional]

      We* thank all three Reviewers for appreciating our work and for sharing constructive feedback to further enhance the quality of our work. It is really gratifying to read that the Reviewers believe that this work will be of interest to broad audience and will be suitable for a high profile journal. Further, the experiments suggested by the reviewers will add value to the work and will substantiate our findings. It is important to highlight that we have already performed most of the suggested experiments except a couple of experiments that we have plan to carry out during full revision. Please find below the details of experiments performed and planned to address the reviewers comments. *

      2. Description of the planned revisions

      Reviewer #1

      Comment 6. In Figure 6A, B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitination IP if you show the whole blot? It should.

      Reviewer #2

      Comment 5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.

      Reviewer #3

      Comment 3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?

      Response: We thank the Reviewer 1, 2 and 3 regarding their query on the co-immunoprecipitation assays performed for studying Orai3 ubiquitination. The reviewers are asking for ubiquitination status of Orai3 and the potential sites for Orai3 ubiquitination. To address these comments, we are planning to perform co-immunoprecipitation assays with mutated Orai3 with mutations of potential ubiquitination sites. We have already performed bioinformatic analysis and it revealed presence of three potential ubiquitination sites on Orai3: K2 (present on N-terminal region), K274 and K279 (present on C-terminal region). We would mutate these lysine residues on Orai3 protein via site-directed mutagenesis and check the Orai3 ubiquitination status. These experiments will answer the question raised by Reviewers and strengthen the Orai3 ubiquitination data.

      Please refer to below diagrammatic illustration showing potential ubiquitination sites on Orai3:

      Reviewer #2

      Comment 7. Also, all the imaging and pull down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.

      Response: We understand the concern raised by Reviewer 2 regarding direct or indirect interaction of MARCH8 and Orai3. Hence, we are planning to perform co-immunoprecipitation assays in which we delete the MARCH8 interacting domain in Orai3 protein and check the for direct interaction of these proteins. Bioinformatic analysis and literature survey have highlighted two possible MARCH8 interacting domains in Orai3. The first domain is present in 2nd loop region, present between the 2nd and 3rd transmembrane domains at the LMVXXXL (AA113-120) motif and the second domain is present at the GXXXG (AA235-239) motif, present in the 3rd loop region of Orai3. We will remove these domains from Orai3 protein individually and check its effect on MARCH8 interaction. These experiments will provide conclusive evidence of direct interaction between Orai3 and MARCH8.

      Please refer to below diagrammatic illustration displaying potential MARCH8 binding sites on Orai3:

      3. Description of the revisions that have already been incorporated in the transferred manuscript


      Reviewer #1

      Comment 1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.

      Response: We thank the Reviewer for thinking that our work is both impressive and important. Further, we understand the Reviewer’s point that transcription and proteolysis may be co-regulated for other proteins. However, our extensive literature search did not resulted in such scenarios. Therefore, to best of our knowledge, we are revealing for the first time that same transcription factor regulates both transcription and protein degradation of the same target in a context dependent manner in a single study. In case, Reviewer would still recommend to modify the text in abstract and introduction, we would do it.

      Comment 2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.

      Response: We would like to thank the Reviewer for highlighting this point. We have effectively addressed this comment by changing the nomenclature of NFAT2 to NFATc1 throughout the manuscript text and figures.

      Comment 3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

      Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated.

      Response: We thank the Reviewer for raising this highly relevant concern. In this revised manuscript, we have addressed this comment by performing several additional experiments. The new data provided in the revised manuscript corroborates our earlier results. Indeed, this data has notably strengthen our work.

      In the revised manuscript, we performed ChIP assay where we increased the number of sonication cycles to 35 so as to make sheared chromatin of around 100 bp. Next, we designed primers to amplify individual NFATc1 binding sites on Orai3 promoter, but due to close proximity of the NFATc1 binding sites, we could design two primer sets. The primer first set to amplify the -1017 bp binding site and the second set to amplify the -990 and -920 bp. Further, as suggested by the Reviewer, we performed immunoprecipitation with the four isoforms of NFAT. Our results show that only NFATc1 pulldown shows significant enrichment of Orai3 promoter with both the primer sets as compared to the IP mock samples and other NFAT isoforms (Figure 1J). Hence, our data reveals that only NFATc1 binds to these predicted sites on the Orai3 promoter and it doesn’t show a preference among these binding sites.

      Further, as suggested by the Reviewer, we mutated the Orai3 promoter in luciferase vector with deletions of the individual NFATc1 binding sites and also cloned a truncated Orai3 promoter with no NFATc1 binding sites into the luciferase vector. The luciferase assays with these mutant and truncated promoters show that upon co-expression of NFATc1, the luciferase activity of the mutant Orai3 promoter with deletion of individual NFATc1 binding site is significantly reduced in comparison to wild type Orai3 promoter. Furthermore, the maximum decrease in luciferase activity was seen with the truncated Orai3 promoter with no NFATc1 binding sites (Figure 1I). These results show that NFATc1 binds to the predicted binding sites on Orai3 promoter. Taken together, the additional ChIP assays with the four isoforms of NFAT and luciferase assays with mutated & truncated Orai3 promoters validates the transcriptional regulation of Orai3 by NFATc1.

      Comment 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful.

      Response: We really appreciate the feedback given by Reviewer 1. To strengthen our manuscript, we have addressed this comment by performing experiments in 2 cell lines/condition of pancreatic cancer. This new data in the revised manuscript provides substantial evidence for the dichotomous regulation of Orai3 by NFATc1.

      In the revised manuscript, we carried out NFATc1 overexpression and NFAT inhibition via VIVIT studies in three additional cell lines: BXPC-3 (non-metastatic), ASPC-1 (invasive) and SW1990 (metastatic). The results in these cell-lines support our earlier findings as both overexpression of NFATc1 and VIVIT mediated NFAT inhibition leads to transcriptional upregulation of Orai3 in BXPC-3 (non-metastatic) (Figure S3A, D), ASPC-1 (invasive) (Figure S3G, J) and SW1990 (metastatic) (Figure S3M, P). These results are similar to our earlier data from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cells. Further, NFATc1 overexpression leads to an increase in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3B, C) and a decrease in Orai3 protein levels in ASPC-1 (invasive) (Figure S3H, I) and SW1990 (metastatic) (Figure S3N, O). Moreover, VIVIT transfection leads to a decrease in Orai3 protein levels in BXPC-3 (non-metastatic) (Figure S3E, F) and an increase in Orai3 protein levels in ASPC-1 (invasive) (Figure S3K, L) and SW1990 (metastatic) (Figure S3Q, R). The findings in these cell lines recapitulates the data obtained earlier from MiaPaCa-2 (non-metastatic), PANC-1 (invasive) and CFPAC-1 (metastatic) cell lines. Therefore, this new data supports our conclusion regarding the dichotomous regulation of Orai3 by NFATc1 across the three conditions of pancreatic cancer.

      Comment 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained.

      Response: We thank the Reviewer for asking this question. In our manuscript, we performed NFATc1 inhibition studies using VIVIT and siRNA-mediated NFATc1 knockdown. Both of these assays show increase in Orai3 mRNA levels in all non-metastatic, invasive and metastatic pancreatic cancer cell lines. To understand if the increase in Orai3 mRNA levels is via transcriptional regulation, we performed luciferase assay which showed that VIVIT mediated NFAT inhibition leads to increase in luciferase activity suggesting the binding of other transcription factors on the Orai3 promoter. To corroborate this hypothesis, in our revised manuscript, we performed luciferase assay in wild type Orai3 promoter and truncated Orai3 promoter with no NFATc1 binding sites. NFAT inhibition via VIVIT transfection led to an increase in luciferase activity in both wild type and truncated Orai3 promoter (Figure S2A). Hence, removal of NFATc1 binding sites had no significant effect on luciferase activity suggesting that apart from NFATc1, other endogenous transcription factors are involved in regulating Orai3 transcription. We have not identified all the transcription factors that can modulate Orai3 upon NFAT inhibition as it is beyond the scope of this study. We sincerely hope the Reviewer 1 would be satisfied with this additional data.

      Reviewer #2

      Comment 1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.

      Response: We apologize to the Reviewer for not highlighting the NFAT2 (NFATc1) loss of function data effectively. Actually, in the __Figure 3 __and __Supplementary Figure 2 __of the original manuscript, we showed VIVIT mediated NFAT inhibition and siRNA induced NFATc1 silencing data to provide the evidence that endogenous NFATc1 regulates Orai3.

      Comment 2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.

      Response: As suggested by the Reviewer, in the revised manuscript, we overexpressed the four NFAT isoforms: NFATc2, NFATc1, NFATc4 & NFATc3 and checked Orai3 mRNA levels. qRT-PCR analysis shows that overexpression of NFATc1 results in the highest and significant increase in Orai3 mRNA levels compared to the empty vector and other NFAT isoforms (Figure 1F). This data corroborates the western blot data of NFAT isoforms overexpression highlighting the transcriptional regulation of Orai3 by NFATc1.

      Comment 3. Fig. S3D, E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.

      Response: The survival analysis of pancreatic cancer patients with low MARCH3 and MARCH8 levels shows that around 30% of patients with low MARCH3 levels survived for 5.5 years, whereas in case of MARCH8 30% of patients with high MARCH8 levels survived for >7.5 years. Hence high MARCH8 expression in pancreatic cancer patients provided significant survival advantage compared to high MARCH3 levels. Therefore, in the text, we meant that compared to MARCH3, higher MARCH8 levels correlate with better survival. As suggested by the Reviewer, we have modified the text to make this point clearer.

      Comment 4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example, compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cell lines. This needs to be addressed and differences explained.

      Response: We understand the concern raised by Reviewer 2 regarding calcium imaging experiments in MiaPaCa-2 cell line. Therefore, in the revised manuscript, we repeated calcium imaging experiments in MiaPaCa-2 and updated the representative traces as well as quantitative analysis (Figure 2D, E, 3D, E, 4H, I, S2L, M). Further, we have discussed this point in the text of the manuscript.

      Comment 6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.

      Response: As recommended by the Reviewer, in the revised manuscript, we have indicated the region used for intensity profile generation by drawing a line in the representative image (Figure 6D). Further, we have updated the methodology of colocalization microscopy with details of the size of the pinhole and imaging mode.

      Comment 8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So, it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.

      Response: We would like to thank the Reviewer for this highly relevant suggestion. In our revised manuscript, we carried out transwell migration assays with MARCH8 overexpression as well as MARCH8 knockdown in CFPAC-1 (metastatic) cells. Our data shows that stable lentiviral knockdown of MARCH8 increased the number of migrated CFPAC-1 cells compared to shNT CFPAC-1 cells while MARCH8 overexpression decreased the number of migrated CFPAC-1 cells compared to empty vector control cells (Figure 9F, G). Therefore, as pointed out by the Reviewer, MARCH8 overexpression lowers Orai3 levels in metastatic pancreatic cancer cells and hinders their metastatic potential.

      Comment 9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.

      Response: As suggested, we have updated the summary figure (Figure 10) showing higher Orai3 protein levels in the metastatic side.

      Comment 10. Please show all full WBs in the supplementary data.

      Response: As recommended by the Reviewer, we have provided all full western blots in a supplementary file (Supplementary File 1).

      Reviewer #3


      Comment 1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.

      Response: As suggested by Reviewer 3, in the revised manuscript, we have provided quantitative analysis of Orai3 and MARCH8 co-localization. Further, we have stated the labels used in the co-localization experiment in the figure legend of the revised manuscript. Unfortunately, we could not perform FRET assay between Orai3 and MARCH8 due to limited resources. Instead, as discussed in the planned revisions section, we are planning to perform co-immunoprecipitation assay with mutated Orai3 protein in which the MARCH8 interacting domains are deleted to investigate direct interaction of Orai3 and MARCH8. We believe that Reviewer 3 will be satisfied with this experiment.

      Comment 2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract.

      Response: As recommended by the Reviewer, in the revised manuscript, we have introduced the use of pancreatic cancer cells at the beginning of the abstract.

      Comment 4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

      Response: We thank the reviewer for asking this interesting question. There is only one report on Orai1’s role in pancreatic cancer. It was suggested that Orai1 can contribute to apoptotic resistance of pancreatic cancer cells (Kondratska et al. BBA-Molecular Cell Research, 2014). However, only one cell line i.e. PANC-1 was used in this study. While our earlier work and other studies have demonstrated that Orai3 drives pancreatic cancer metastasis (Arora et al. Cancers, 2021) and proliferation (Dubois et al. BBA-Molecular Cell Research, 2021) respectively. Therefore, emerging literature suggests that both Orai1 and Orai3 can contribute to different aspects of pancreatic cancer progression. But whether Orai1 and Orai3 form heteromers in pancreatic cancer cells remains unexplored. Further, we believe that the degradation machinery and the underlying molecular mechanisms would be analogous for both Orai3 homomers and heteromers. Nonetheless, the rate of degradation may differ for Orai3 homomers and heteromers as literature suggests that usually proteins are more stable in large heteromeric protein complexes.

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      Referee #3

      Evidence, reproducibility and clarity

      The study by Raju et al. demonstrated that NFAT2 drives both Orai3 transcription and protein degradation. They find a clearly distinct mechanism between non-metastatic cancerous and metastatic cells. While in non-metastatic cells NFAT2 drives Orai3 transcritpion and increases Orai3 expression, in invasive and metastatic cells degradation of Orai3 is driven. They find a physical interaction of MARCH8 with Orai3 resulting in degradation. This degradation is not happening in non-metastatic cells as MARCH8 promotor is highly methylated. This study is highly interesting for a broad readerships and provides a solid basis for the development of novel therapeutic strategies for cancer treatment. Before publication the authors should address a few minor comments.

      1. The authors show that MARCH8 physically associates with Orai3 using Co-IP and Co-localization studies. For the co-localization studies the authors should still provide a quantitative analysis. Furthermore, can the authors detect FRET between March and Orai3? Can you please state the labels used in the co-localization experiments also in the figure legend.
      2. In the abstract it is only getting clear at the end that pancreatic cancer cells are used. It would be great if the authors could introduce this fact already more at the beginning of the abstract
      3. In the scheme in Fig. 10, the authors highlight that Orai3 is ubiquitinated. Do they have any idea where the site of action of ubiquitination in Orai3 is located?
      4. In other cancer types recent reports suggest a co-expression of Orai1 and Orai3 and even the formation of heteromers. Does only Orai3 or also Orai1 play a role in pancreatic cancer cells? Could there we difference in degradation when Orai3 forms homomers or heteromers with Orai1.

      Significance

      The authors highlight and decode a dual role of NFAT2 in controling Orai3 expression, which is highly interestingly to gain insight in different states of cancer cells (non-metastatic, metastatic). The findings form a great basis for a deeper understanding of potential therapeutic targets.

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      Referee #2

      Evidence, reproducibility and clarity

      Raju et al. presents a nice comprehensive study of the differential regulation of Orai3 at the transcriptional and stability levels in metastatic versus non-metastatic pancreatic cancer (PC) cells. They convincingly show that NFAT2 regulates Orai3 transcription in all PC cells but interestingly, in the metastatic PC cells NFAT2 also upregulates the expression of MARCH8 an E3 ubiquitin ligase that targets Orai3 for lysosomal degradation. The MARCH8 locus is hypermethylated in the non-metastatic cell line, thus preventing MARCH8 upregulation in those cells. The data is convincing and complementary. I only a few suggestions below.

      Specific Comments:

      1. Figure 1 all overexpression no evidence of endogenous NFAT2 regulating Orai3. I realize there may be limitations on available NFAT isoform specific antibodies so it is not essential to directly show this but a comment to that effect in the paper would be useful.
      2. Figure 1F. Show RNA levels of Orai3 following overexpression of the other NFAT isoforms.
      3. Fig. S3D,E. For both MARCH3 and 8 higher expression levels correlate with better survival whereas in the text it is stated that this is the case only for MARCH8. Please correct.
      4. For the 2APB stimulation experiments there is a large variation in the level of the response between experiments even for the same cell type. For example compare the level of the 2APB-stimulated Orai3 influx between Fig. 4H and 5C on the MiaPaCa-2 cells. Also there doesn't seem to be a correlation between the levels of Orai3 protein from WB and the 2APB stimulated entry among the different cells lines. This needs to be addressed and differences explained.
      5. Fig. 6A and 6B. Show the full Orai3 and Ubiquitin WBs. As presented the figure current just shows that there are ubiquitin proteins in Orai3 pull down, not that Orai3 is ubiquitinated.
      6. Fig. 6C and 6D. Show the line in 6C from which the intensity profile in 6D was generated. Also give the details of the imaging setup in methods: size of the pinhole, imaging mode, etc. The colocalization is not very convincing.
      7. Also all the imaging and pull down down do not prove conclusively direct interaction between MARCH8 and Orai3, they rather show that the proteins are in the same complex. Although it is unlikely best for the text to be moderated accordingly.
      8. May be worth showing that overexpression of MARCH8 in the metastatic cell lines decreases their migration and metastasis as the argument is that these cells need high Orai3 but not too high. So it would be predicted that overexpression of MARCH8 should lower Orai3 levels enough to prevent their metastasis.
      9. Fig. 10. Show higher levels of Orai3 protein in the metastatic side.
      10. Please show all full WBs in the supplementary data.

      Significance

      SIgnificant and relevant study that will be of great interest to the cancer and calcium signaling fields.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript entitled, "NFAT2 drives both Orai3 transcription and protein degradation by harnessing the differences in epigenetic landscape of MARCH8 E3 ligase" offers an extensive study of how Orai3 levels are controlled during pancreatic cancer progression. The central hypothesis is that NFAT2 stimulates both Orai3 and MARCH8 transcription, resulting in both Orai3 transcription and degradation. They further establish that MARCH8 expression/Orai3 degradation is epigenetically regulated in PDAC, with a progressive loss of methylation during cancer progression leading to increased Orai3 transcription, stability and Ca2+ entry.

      Overall, I'm certain that there is new information to be learned here. However, as detailed below, the manuscript makes a number of general claims about what happens during PDAC progression, but this is based on only one cell line per disease state. While they should not be expected to do a complete analysis in more cell lines, a demonstration that Orai3 and MARCH8 expression are correlated with disease progression in a panel of cell lines and/or on the TCGA database would increase enthusiasm considerably. In addition, although I found the work with MARCH8 to be highly convincing, the fact that NFAT2 knockdown increased rather than reduced Orai3 transcription does not support the central hypothesis. The explanation that this results from compensation is not very meaningful; that NFAT2 drives Orai3 transcription is in the title of the paper. These observations clearly demonstrate that this relationship is more complicated than suggested. Finally, there are a number of missing controls and unclear aspects to the authors' ChIP data that could help explain some of these discrepancies.

      Specific Comments:

      1. The observation that both transcriptional regulation and protein degradation of Orai3 is regulated downstream of one transcription factor is not, in and of itself, entirely surprising. All proteolytic components are transcriptionally regulated and this phenomenon is likely relatively common. However, what I do think is both impressive and important is that the authors have characterized both components of the pathway within a disease context. While I am not going to search the literature for how often transcription and proteolysis are co-regulated for other proteins, it is the case for many short-lived proteins and perhaps many others. As such, discussion throughout the abstract and introduction that co-regulation of these processes is unprecedented should be removed.
      2. In discussing figure 1, the authors switch from claiming to be studying NFATc binding to studying NFAT expression. This use of 2 different naming conventions is certain to confuse readers; the authors should use the approved current naming system in referring to NFAT isoforms. In which case NFAT2 is NFATc1.
      3. The ChIP analyses in figures 1H and 7D are important findings, however, there is missing information. Typically, ChIP is used to validate putative binding sites; as such, one would expect 3 separate qPCR reactions for Orai3, not one. It is also important to note that qPCR products should be uniform in size and under 100 bp; here, the product size is not stated. Finally, demonstrating that an antibody targeting ANY other NFAT isoform fails to pull down whatever product this is would increase confidence considerably.

      Also, the gold standard for validating ChIP is to mutate the sites and eliminate binding. The "silver" standard would be to mutate them in your luciferase vector and demonstrate that NFATc1 no longer stimulates luciferase expression. Since neither of these was done, the ChIP data provided should not be considered formally validated. 4. In figures 2 and 3, only one cell line is used to represent each of 3 conditions of pancreatic cancer. That is insufficient to make generalized conclusions; some aspects of this figure (expression and stability, not function) should be extended to 2 to 3 cell lines/condition. TCGA data validating this point would also be helpful. 5. Upon finding that NFAT inhibition stimulates Orai3 transcription (same as O/E), the authors essentially conclude that this confirms regulation of Orai3 by NFAT and that there must be compensation. This is not supported by any data; the use of siRNA validates that Orai3 has some dependence on NFATc1 for transcription, but the nature of this relationship is not adequately explained. 6. In Figure 6A,B, does the Orai3 western blot show any of the heavier bands seen in the ubiquitinization IP if you show the whole blot? It should.

      Significance

      My expertise is in calcium signaling, particularly within the context of disease states. I currently have a PDAC study in its late stages, but I have worked more with melanoma.

      Issues about significance were raised in my comments above; generalization of these observations requires the appropriate use of a panel of cell lines and/or TCGA usage. In addition, some observations require additional investigation for confidence; necessary to achieve significance.

      The extent of the advance is quite reasonable for a high profile paper in this field, should the issues I and the other reviewers raise be formally and thoroughly addressed.

      Given that the study crosses lines between signaling, cancer, epigenetics, transcription and ubiquitination, I think that it is of potential interest to a general audience.

    1. Reviewer #1 (Public review):

      Summary:

      The authors aimed to classify hepatocellular carcinoma (HCC) patients into distinct subtypes using a comprehensive multi-omics approach. They employed an innovative consensus clustering method that integrates multiple omics data types, including mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations. The study further sought to validate these subtypes by developing prognostic models using machine learning algorithms and extending the findings through single-cell RNA sequencing (scRNA-seq) to explore the cellular mechanisms driving subtype-specific prognostic differences.

      Strengths:

      (1) Comprehensive Data Integration: The study's integration of various omics data provides a well-rounded view of the molecular characteristics underlying HCC. This multi-omics approach is a significant strength, as it allows for a more accurate and detailed classification of cancer subtypes.

      (2) Innovative Methodology: The use of a consensus clustering approach that combines results from 10 different clustering algorithms is a notable methodological advancement. This approach reduces the bias that can result from relying on a single clustering method, enhancing the robustness of the findings.

      (3) Machine Learning-Based Prognostic Modeling: The authors rigorously apply a wide array of machine learning algorithms to develop and validate prognostic models, testing 101 different algorithm combinations. This comprehensive approach underscores the study's commitment to identifying the most predictive models, which is a considerable strength.

      (4) Validation Across Multiple Cohorts: The external validation of findings in independent cohorts is a critical strength, as it increases the generalizability and reliability of the results. This step is essential for demonstrating the clinical relevance of the proposed subtypes and prognostic models.

      Weaknesses:

      (1) Inconsistent Storyline:<br /> Despite the extensive data mining and rigorous methodologies, the manuscript suffers from a lack of a coherent and consistent narrative. The transition between different sections, particularly from multi-omics data integration to single-cell validation, feels disjointed. A clearer articulation of how each analysis ties into the overall research question would improve the manuscript.

      (2) Questionable Relevance of Immune Cell Activity Analysis:<br /> The evaluation of immune cell activities within the cancer cell model raises concerns about its meaningfulness. The methods used to assess immune function in the tumor microenvironment may not be fully appropriate, potentially limiting the insights gained from this part of the study.

      (3) Incomplete Single-Cell RNA-Seq Validation:<br /> The validation of the findings using single-cell RNA-seq data appears insufficient to fully support the study's claims. While the authors make an effort to extend their findings to the single-cell level, the analysis lacks depth. A more comprehensive validation is necessary to substantiate the robustness of the identified subtypes.

      (4) Figures and Visualizations:<br /> Several figures in the manuscript are missing necessary information, which affects the clarity of the results. For instance, the pathways in Figure 3A could be clustered to enhance interpretability, the blue bar in Figure 4A is unexplained, and Figure 4B is not discussed in the text. Additionally, the figure legend in Figure 7C lacks detail, and many figure descriptions merely repeat the captions without providing deeper insights.

      (5) Appraisal of the Study's Aims and Results<br /> The authors have set out to achieve an ambitious goal of classifying HCC patients into distinct prognostic subtypes and validating these findings through both bulk and single-cell analyses. While the methodologies employed are innovative and the data integration comprehensive, the study falls short in fully achieving its aims due to inconsistencies in the narrative and incomplete validation. The results partially support the conclusions, but the lack of coherence and depth in certain areas limits the overall<br /> impact of the study.

      (6) Impact on the Field<br /> If the identified weaknesses are addressed, this study has the potential to significantly impact the field of HCC research. The multi-omics approach combined with machine learning is a powerful framework that could set a new standard for cancer subtype classification. However, the current state of the manuscript leaves some uncertainty regarding the practical applicability of the findings, particularly in clinical settings.

      (7) Additional Context<br /> For readers and researchers, this study offers a valuable look into the potential of integrating multi-omics data with machine learning to improve cancer classification and prognostication. However, readers should be aware of the noted weaknesses, particularly the need for more consistent narrative development and comprehensive validation of the methods. Addressing these issues could greatly enhance the study's utility and relevance to the community.

      Comments on revisions:

      The authors have addressed the reviewers' concerns effectively.

    2. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Recommendations for the authors):

      (1) Storyline and Narrative Flow:

      Consider revising the manuscript to create a more coherent and consistent narrative. Clarify how each section of the study-particularly the transition from multi-omics data integration to single-cell RNA-seq validation-contributes to the overall research question. This will help readers better understand the logical flow of the study.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We have modified some text, including the connections between different sections in the results part and the objectives and roles of various analyses in each section, thus enhancing the coherence between the contexts and clarifying the objectives and functions of each analysis, We believe this will help readers better understand the main content of the entire text.

      (2) Immune Cell Activity Analysis:

      Reevaluate the methods used to assess immune cell activities within the context of the tumor microenvironment. Consider providing additional justification for the relevance of using the cancer cell model for this analysis. If necessary, explore alternative methods or models that might offer more meaningful insights into immune-tumor interactions.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      Using RNA-Bulk data, we evaluated the tumor immune microenvironment through various methods to assess immune infiltration levels and responses to immunotherapy. We found that the results were largely consistent with those presented in the manuscript, providing strong support for our viewpoints. We also acknowledge the limitations of findings from bioinformatics analysis. In our upcoming research, we plan to develop organoid models with gene expression patterns of both CS1 and CS2 subtypes, using these models as a foundation for studying the tumor immune microenvironment.

      (3) Single-Cell RNA-Seq Validation:

      Expand the validation of your findings using single-cell RNA-seq data. This could include more in-depth analyses that explore the heterogeneity within the subtypes and confirm the robustness of your classification method at the single-cell level. This would strengthen the support for your claims about the relevance of the identified subtypes.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      In this manuscript, we employed the NTP algorithm to classify malignant cells identified by the CopyKAT algorithm using characteristic genes of CS1 and CS2 subtypes. This approach is similar to previous method that analyzed patients in the ICGC cohort with the same subtype genes. We consider this classification method valid.

      After classifying the malignant cells, we performed metabolic and cell communication analyses on the CS1 and CS2 subtype cells, revealing significant differences in biological pathways enriched by differential genes, metabolic levels, and cell signaling patterns. These differences align with variations observed in prior classifications and analyses based on RNA-Bulk data.

      We also acknowledge that validating the classification method solely with the single-cell dataset from this study is insufficient. We analyzed GSE202642 using the same processes and methods as GSE229772, finding that the results were generally consistent, indicating that our classification method exhibits a degree of robustness at the single-cell level.

      (4) Methodological Justification:

      Provide a more detailed rationale for the selection of machine learning algorithms and integration strategies used in the study. Explain why the chosen methods are particularly well-suited for this research, and discuss any potential limitations they might have.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We have updated the methodology section to enhance readers' understanding of the fundamental principles involved. This analysis has two key features: first, it combines 10 machine learning algorithms to generate 101 models and ultimately selects the prognostic prediction model with the highest C-index from these 101 algorithms; second, it utilizes the LOOCV method to analyze the training and validation sets. Compared to the conventional method of randomly dividing the training and validation sets by a fixed ratio, this approach significantly minimizes the bias and randomness introduced by the splitting process. Therefore, we believe this analysis can leverage the characteristic genes of the CS1 and CS2 subtypes, combined with existing clinical data from public databases, to yield results that are more accurate and reliable than the commonly used prognostic models in previous literature, such as COX regression and Lasso regression, as well as other individual algorithms. While this analysis presents advantages over some previous modeling methods, it is essential to recognize that it remains based on analyses conducted using public databases, which may obscure certain factors that might be clinically relevant to patient prognosis due to the mathematical logic of the algorithms.

      (5) Figures and Visualizations:

      Improve the clarity of your figures by addressing the following:

      a) Figure 3A: Cluster the pathways to make the comparisons clearer and more meaningful.

      b) Figure 4A: Clearly explain the significance of the blue bar.

      c) Figure 4B: Ensure this figure is discussed in the main text to justify its inclusion.

      d) Figure 7C: Enhance the figure legend to provide more informative details.

      Additionally, ensure that figure descriptions go beyond the captions and provide detailed explanations that help the reader understand the significance of each figure.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      Figure 3A: We clustered the samples based on CS1 and CS2 subtypes and displayed the immune-related cell scores of each sample as a heatmap.

      Figure 4A: The blue bars in the figure represent the average C-index of this algorithm combination in the training dataset TCGA and the validation dataset ICGC, which we have supplemented in the corresponding sections of the text.

      Figure 4B: We described this figure in the results section, which primarily aims to validate whether our prognostic prediction model can predict patient outcomes in the TCGA cohort. The results showed that after performing prognostic risk scoring on patients based on the prediction model and categorizing them into high-risk and low-risk groups, the two groups exhibited significant prognostic differences, with the high-risk group showing worse outcomes compared to the low-risk group. This indicates that our prognostic prediction model can effectively distinguish the prognostic risk differences among patients in the TCGA-LIHC cohort. We also discussed these findings in the discussion section.

      Figure 7C: We used both point color and size to visualize the levels of metabolic scores, resulting in two dimensions in the legend, which actually represent the same information. Therefore, we removed the results that used point size to indicate the levels of metabolic scores.

      (6) Supplementary Materials:

      Consider including more detailed supplementary materials that provide additional validation data, extended methodological descriptions, and any other information that would support the robustness of your findings.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      In the subsequent version of the record, we will upload the important results obtained during the research to GitHub, and in this revision, we have updated some figures that may better explain the results or the robustness of the findings as supplementary materials.

      (7) Recent Literature:

      a) Incorporate more recent studies in your discussion, especially those related to HCC subtypes and the application of machine learning in oncology. This will provide a more current context for your work and help position your findings within the broader field.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We have reviewed several studies related to HCC subtype classification and the application of machine learning in this field. In the discussion section, we summarize the significance and limitations of these studies. Additionally, we discuss the characteristics of our study in comparison to previous research in this field.

      (8) Data and Code Availability:

      Ensure that all data, code, and materials used in your study are made available in line with eLife's policies. Provide clear links to repositories where readers can access the data and code used in your analyses.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We have examined the relevant data, code, and materials. We confirm that we have indicated the sources of the data and tools used in the analysis within the manuscript. Moreover, these data and tools are accessible via the websites or references we have provided.

      Reviewer #2 (Recommendations for the authors):

      (1) While the computational findings are robust, further experimental validation of the two subtypes, particularly the role of the MIF signaling pathway, would strengthen the biological relevance of the findings. In vitro or in vivo validation could confirm the proposed mechanisms and their influence on patient prognosis.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We intend to verify our findings in future studies using tumor cell line models and animal models. We aim to identify and intervene with key molecules in the MIF signaling pathway. We will investigate how the MIF signaling pathway affects tumor sensitivity to treatment in both cell line and animal models, along with the underlying mechanisms.

      (2) Consider testing the model on additional independent cohorts beyond the TCGA and ICGC datasets to further demonstrate its generalizability and applicability across different patient populations.

      We thank the reviewer’s suggestion, which have highlighted the deficiencies in this area, and we have made appropriate modifications:

      We analyzed the GSE14520 study recorded in the GEO database, which uploaded a cohort consisting of 209 HCC patients and their corresponding RNA sequencing data. We validated the prognostic model obtained in this study using this cohort, and found that the model effectively distinguishes patients into high-risk and low-risk prognostic categories. Furthermore, there is a significant prognostic difference between the high-risk and low-risk patient groups. This is consistent with the results we obtained previously.

      (3) Review the manuscript for long or complex sentences, which can be broken down into shorter, more readable parts.

      We have made revisions to the long and complex sentences in the manuscript without compromising its academic integrity and rationality, with the hope that this will help readers better understand the content of this study.

      During the revision process, in addition to addressing the reviewer comments, we conducted a thorough review of the analysis. In the course of this review, we identified a few errors in the data usage and have since corrected the relevant data and figures:

      Figure 4: Due to space constraints, we adjusted the composition of the figures after incorporating the validation results from the GSE14520 dataset.

      Figure 5A: We rechecked the regression coefficients included in the model, updated several more recent prognostic models, and calculated the C-index for 20 prognostic models in the TCGA and ICGC cohorts using a method consistent with previous studies.

      Figure 5C-D: We adjusted the clarity of the figures.

      Figure 8: We reclassified the selected malignant cells and updated the subtypes results. Subsequently, based on the repeatedly confirmed typing results, we comprehensively updated the analysis results of the subsequent cell communication network construction, ensuring that the entire analysis process remains consistent with previous findings. We also adjusted the composition of the figure and presented the images that could not be conveniently merged due to space constraints as Figure 9.

    1. Reviewer #2 (Public review):

      The revised manuscript by Genzoni et al. reports the striking discovery of a regulatory role for trophic eggs. Prior to this study, trophic eggs were widely assumed to play a nutritional role in the colony, but this study shows that trophic eggs can suppress queen development, and therefore, can play a role in regulating caste determination in specific social contexts. In this revised version of the manuscript, the authors have addressed many of the concerns raised in the first version regarding the lack of sufficient information and context in the Introduction and Discussion. I have several (mostly minor) comments I would like the authors to address:

      Comments:

      (1) The authors' experimental design is based on the comparison of a larva-only (control) versus larva+3 trophic eggs (treatment). The authors convincingly show that the larva plus 3 trophic eggs treatment has an inhibitory effect versus larva-only control. However, the authors should have also done a treatment composed of larva + 3 viable eggs to determine if the inhibitory effect observed on queens is specific to trophic eggs or whether it is an inhibitory effect of all eggs. This has had important mechanistic consequences, because if the inhibitory effect is specific to trophic eggs, it means there are specific inhibitory factors deposited in trophic eggs during oogenesis and the differences observed between trophic versus viable eggs are meaningful beyond just nutritional differences. If the inhibitory effect is a property of all eggs, then the inhibitory factor is dumped into all eggs and the differences observed between trophic and viable eggs are related to something else. In all cases, this reviewer is not necessarily asking that they perform this additional treatment, but the authors have to be clear in the text that they cannot claim that the inhibitory effect is specific to trophic eggs alone without doing this experiment.

      (2) The other untested assumption the authors are making is that queen-laid trophic eggs would behave the same as worker-laid trophic eggs. This is apparent in the Discussion (line 422). They should instead highlight the interesting question of whether worker-laid trophic eggs would be similar in composition and have the same effect on caste as queen-laid eggs.

      (3) To this reviewer, they are missing a crucial explanation in the discussion. As far as this reviewer knows, young queens produce a higher proportion of trophic eggs than older queens, meaning that trophic egg production decreases with age of the queen. This raises the possibility that trophic eggs may, in part, function to prevent the production of more virgin queens in young and immature colonies with small colony sizes. This would allow colonies to invest in producing more workers at a time when rapidly expanding the colony is crucial in young colonies' life. Production of trophic eggs, therefore, may have a dual function: one for nutrition and larval survival, and one in suppressing queen development in immature young colonies. It can be said then that trophic eggs can regulate / influence caste determination in specific social / life history contexts of the colony, rather than only proposing that trophic eggs are a constant attempt by the queen to manipulate her offspring. I prefer the superorganism explanation, but readers should at least hear explanations at the individual and superorganism scales as a way of explaining the authors' discovery that trophic eggs suppress further queen development.

      (4) Why did the authors change the wording from caste "determination" to caste "differentiation." Determination is more appropriate because the trophic eggs do not affect morphogenesis of queens or workers, but rather the developmental switch between queens and workers.

      (5) Khila and Abouheif (2008) is listed in the References but not cited in the text.

      (6) On Line 70-81: "...may play a role in the regulation of body size" - I think the authors are trying to be broad in their language here since one study showed trophic eggs increased worker size but didn't induce queens, but this statement implies that the hypothesis is that trophic eggs act via body size to affect caste. Since the authors don't measure body size changes, only binary caste outcome, this is not the best way to set up the question. Could instead just conclude that previous work shows an effect on both caste and body size.

      (7) Paragraph beginning line 432: this paragraph seems out of place, not well connected to previous parts of discussion. It introduces the term "egg cannibalism" without defining it - not clear if this is meant as a synonym for eating of trophic eggs, or broader (i.e., eating viable eggs also). Could either remove the paragraph, or better set up the context that egg-eating behaviour is common in ants, could have evolved for worker policing reasons and/or for nutritional exchange, trophic eggs (and potentially co-option of trophic eggs for caste determination functions) presumably evolved in this context of existing egg-eating behaviour.

      (8) Line 41: Should read 'play an important part.

      (9) Line 51: The food that was given is listed, but there is no information about the quantity of food given.

      (10) Line 74: The paragraph states that queens were isolated for 16 hours per day. However, it lacks a clear reason for this specific duration. Why 16 hours? Could this isolation period have impacted egg quality or larval development?

      (11) Line 76: The eggs were collected every 8 hours and then held for 10 days until hatching. This is a very long time for eggs to be held outside of the normal colony environment. This could have a large impact on the viability of the eggs, and the resulting larvae.

      (12) Line 78: twice "that" in "suggested that that the larger castes"

      (13) Lines 96-97: the following sentence is unclear: "The question mark indicates that it is unclear whether about the evidence for the production trophic eggs by queens and workers"

      (14) Line 209: By simply stating "binomial GLMM," the authors are leaving out a crucial piece of information. Readers cannot fully understand how the model was fitted or how the coefficients should be interpreted without knowing the link function. Therefore, the critique is that for complete and replicable science, the link function must be reported.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      This manuscript describes a series of experiments documenting trophic egg production in a species of harvester ant, Pogonomyrmex rugosus. In brief, queens are the primary trophic egg producers, there is seasonality and periodicity to trophic egg production, trophic eggs differ in many basic dimensions and contents relative to reproductive eggs, and diets supplemented with trophic eggs had an effect on the queen/worker ratio produced (increasing worker production).

      The manuscript is very well prepared and the methods are sufficient. The outcomes are interesting and help fill gaps in knowledge, both on ants as well as insects, more generally. More context could enrich the study and flow could be improved.

      We thank the reviewer for these comments. We agree that the paper would benefit from more context. We have therefore greatly extended the introduction.

      Reviewer #2 (Public Review):

      The manuscript by Genzoni et al. provides evidence that trophic eggs laid by the queen in the ant Pogonomyrmex rugosis have an inhibitory effect on queen development. The authors also compare a number of features of trophic eggs, including protein, DNA, RNA, and miRNA content, to reproductive eggs. To support their argument that trophic eggs have an inhibitory effect on queen development, the authors show that trophic eggs have a lower content of protein, triglycerides, glycogen, and glucose than reproductive eggs, and that their miRNA distributions are different relative to reproductive eggs. Although the finding of an inhibitory influence of trophic eggs on queen development is indeed arresting, the egg cross-fostering experiment that supports this finding can be effectively boiled down to a single figure (Figure 6). The rest of the data are supplementary and correlative in nature (and can be combined), especially the miRNA differences shown between trophic and reproductive eggs. This means that the authors have not yet identified the mechanism through which the inhibitory effect on queen development is occurring. To this reviewer, this finding is more appropriate as a short report and not a research article. A full research article would be warranted if the authors had identified the mechanism underlying the inhibitory effect on queen development. Furthermore, the article is written poorly and lacks much background information necessary for the general reader to properly evaluate the robustness of the conclusions and to appreciate the significance of the findings.

      We thank the reviewer for these comments. We agree that the paper would benefit by having more background information and more discussion. We have followed this advice in the revision.

      Reviewer #3 (Public Review):

      In "Trophic eggs affect caste determination in the ant Pogonomyrmex rugosus" Genzoni et al. probe a fundamental question in sociobiology, what are the molecular and developmental processes governing caste determination? In many social insect lineages, caste determination is a major ontogenetic milestone that establishes the discrete queen and worker life histories that make up the fundamental units of their colonies. Over the last century, mechanisms of caste determination, particularly regulators of caste during development, have remained relatively elusive. Here, Genzoni et al. discovered an unexpected role for trophic eggs in suppressing queen development - where bi-potential larvae fed trophic eggs become significantly more likely to develop into workers instead of gynes (new queens). These results are unexpected, and potentially paradigm-shifting, given that previously trophic eggs have been hypothesized to evolve to act as an additional intracolony resource for colonies in potentially competitive environments or during specific times in colony ontogeny (colony foundation), where additional food sources independent of foraging would be beneficial. While the evidence and methods used are compelling (e.g., the sequence of reproductive vs. trophic egg deposition by single queens, which highlights that the production of trophic eggs is tightly regulated), the connective tissue linking many experiments is missing and the downstream mechanism is speculative (e.g., whether miRNA, proteins, triglycerides, glycogen levels in trophic eggs is what suppresses queen development). Overall, this research elevates the importance of trophic eggs in regulating queen and worker development but how this is achieved remains unknown.

      We thank the reviewer for these comments and agree that future work should focus on identifying the substances in trophic eggs that are responsible for caste determination.  

      Reviewer #1 (Recommendations For The Authors):

      Introduction:

      The context for this study is insufficiently developed in the introduction - it would be nice to have a more detailed survey of what is known about trophic eggs in insects, especially social insects. The end of the introduction nicely sets up the hypothesis through the prior work described by Helms Cahan et al. (2011) where they found JH supplementation increased trophic egg production and also increased worker size. I think that the introduction could give more context about egg production in Pogonomyrmex and other ants, including what is known about worker reproduction. For example, Suni et al. 2007 and Smith et al. 2007 both describe the absence of male production by workers in two different harvester ants. Workers tend to have underdeveloped ovaries when in the presence of the queen. Other species of ants are known to have worker reproduction seemingly for the purpose of nutrition (see Heinze and Hölldober 1995 and subsequent studies on Crematogaster smithi). Because some ants, including Pogonomyrmex, lack trophallaxis, it has been hypothesized that they distribute nutrients throughout the nest via trophic eggs as is seen in at least one other ant (Gobin and Ito 2000). Interestingly, Smith and Suarez (2009) speculated that the difference in nutrition of developing sexual versus worker larvae (as seen in their pupal stable isotope values) was due to trophic egg provisioning - they predicted the opposite as was found in this study, but their prediction was in line with that of Helms Cahan et al. (2011). This is all to say that there is a lot of context that could go into developing the ideas tested in this paper that is completely overlooked. The inclusion of more of what is known already would greatly enrich the introduction.

      We agree that it would be useful to provide a larger context to the study. We now provide more information on the life-history of ants and explained under what situations queens and workers may produce trophic eggs. We also mentioned that some ants such as Crematogaster smithi have a special caste of “large workers” which are morphologically intermediate between winged queens and small workers and appear to be specialized in the production of unfertilized eggs. We now also mention the study of Goby and Ito (200) where the authors show that trophic eggs may play an important role in food distribution withing the colony, in particular in species where trophallaxis is rare or absent.

      Methods:

      L49: What lineage is represented in the colonies used? The collection location is near where both dependent-lineage (genetic caste determining) P. rugosus and "H" lineage exist. This is important to know. Further, depending on what these are, the authors should note whether this has relevance to the study. Not mentioning genetic caste determination in a paper that examines caste determination is problematic.

      This is a good point. We have now provided information at the very beginning of the material and method section that the queens had been collected in populations known not to have dependentlineage (genetic caste determining) mechanisms of caste determination.

      L63 and throughout: It would be more efficient to have a paragraph that cites R (must be done) and RStudio once as the tool for all analyses. It also seems that most model construction and testing was done using lme4 - so just lay this out once instead of over and over.

      We agree and have updated the manuscript accordingly.

      L95: 'lenght' needs to be 'length' in the formula.

      Thanks, corrected.

      L151: A PCA was used but not described in the methods. This should be covered here. And while a Mantel test is used, I might consider a permANOVA as this more intuitively (for me, at least) goes along with the PCA.

      We added the PCA description in the Material and Method section.

      Results:

      I love Fig. 3! Super cool.

      Thanks for this positive comment.

      Discussion:

      It would be good to have more on egg cannibalism. This is reasonably well-studied and could be good extra context.

      We have added a paragraph in the discussion to mention that egg cannibalism is ubiquitous in ants.

      Supp Table 1: P. badius is missing and citations are incorrectly attributed to P. barbatus.

      P. badius was present in the Table but not with the other Pogonomyrmex species. For some genera the species were also not listed in alphabetic order. This has been corrected.

      Reviewer #2 (Recommendations For The Authors):

      COMMENTS ON INTRODUCTION:

      The introduction is missing information about caste determination in ants generally and Pogonomyrmex rugosis specifically. This is important because some colonies of Pogonomyrmex rugosis have been shown to undergo genetic caste determination, in which case the main result would be rendered insignificant. What is the evidence that caste determination in the lineages/colonies used is largely environmentally influenced and in what contexts/environmental factors? All of this should be made clear.

      This is a good point. We have expanded the introduction to discuss previous work on caste determination in Pogonomyrmex species with environmental caste determination and now also provide evidence at the beginning of the Material and Method section that the two populations studied do not have a system of genetic caste determination.

      Line 32 and throughout the paper: What is meant exactly by 'reproductive eggs'? Are these eggs that develop specifically into reproductives (i.e., queens/males) or all eggs that are non-trophic? If the latter, then it is best to refer to these eggs as 'viable' in order to prevent confusion.

      We agree and have updated the manuscript accordingly.

      Figure 1/Supp Table 1: It is surprising how few species are known to lay trophic eggs. Do the authors think this is an informative representation of the distribution of trophic egg production across subfamilies, or due to lack of study? Furthermore, the branches show ant subfamilies, not families. What does the question mark indicate? Also, the information in the table next to the phylogeny is not easy to understand. Having in the branches that information, in categories, shown in color for example, could be better and more informative. Finally, having the 'none' column with only one entry is confusing - discuss that only one species has been shown to definitely not lay trophic eggs in the text, but it does not add much to the figure.

      Trophic eggs are probably very common in ants, but this has not been very well studied. We added a sentence in the manuscript to make this clear.

      Thanks for noticing the error family/subfamily error. This has been corrected in Figure 1 and Supplementary Table 1.

      The question mark indicates uncertainty about whether queens also contribute to the production of trophic eggs in one species (Lasius niger). We have now added information on that in the Figure legend.

      We agree with the reviewer that it would be easier to have the information on whether queens and workers produce trophic on the branches of the Tree. However, having the information on the branches would suggest that the “trait” evolved on this part of the tree. As we do not know when worker or queen production of trophic eggs exactly evolved, we prefer to keep the figure as it is.

      Finally, we have also removed the none in the figure as suggested by the reviewer and discussed in the manuscript the fact that the absence of trophic eggs has been reported in only one ant species (Amblyopone silvestrii: Masuko 2003).

      COMMENTS ON MATERIALS AND METHODS:

      Why did they settle on three trophic eggs per larva for their experimental setup?

      We used three trophic eggs because under natural conditions 50-65% of the eggs are trophic. The ratio of trophic eggs to viable eggs (larvae) was thus similar natural condition.

      Line 50: In what kind of setup were the ants kept? Plaster nests? Plastic boxes? Tubes? Was the setup dry or moist? I think this information is important to know in the context of trophic eggs.

      We now explain that colonies were maintained in plastic boxes with water tubes.

      Line 60: Were all the 43 queens isolated only once, or multiple times?

      Each of the 43 queens were isolated for 8 hours every day for 2 weeks, once before and once after hibernation (so they were isolated multiple times). We have changed the text to make clear that this was done for each of the 43 queens.

      Could isolating the queen away from workers/brood have had an effect on the type of eggs laid?

      This cannot be completely ruled out. However, it is possible to reliably determine the proportion of viable and trophic eggs only by isolating queens. And importantly the main aim of these experiments was not to precisely determine the proportion viable and trophic eggs, but to show that this proportion changes before and after hibernation and that queens do not lay viable and trophic eggs in a random sequence.

      Since it was established that only queens lay trophic eggs why was the isolation necessary?

      Yes this was necessary because eggs are fragile and very difficult to collect in colonies with workers (as soon as eggs are laid they are piled up and as soon as we disturb the nest, a worker takes them all and runs away with them). Moreover, it is possible that workers preferentially eat one type of eggs thus requiring to remove eggs as soon as queens would have laid them. This would have been a huge disturbance for the colonies.

      Line 61: Is this hibernation natural or lab induced? What is the purpose of it? How long was the hibernation and at what temperature? Where are the references for the requirement of a diapause and its length?

      The hibernation was lab induced. We hibernated the queens because we previously showed that hibernation is important to trigger the production of gynes in P. rugosus colonies in the laboratory (Schwander et al 2008; Libbrecht et al 2013). Hibernation conditions were as described in Libbrecht et al (2013).  

      Line 73: If the queen is disturbed several times for three weeks, which effect does it have on its egg-laying rate and on the eggs laid? Were the eggs equally distributed in time in the recipient colonies with and without trophic eggs to avoid possible effects?

      It is difficult to respond what was the effect of disturbance on the number and type of eggs laid. But again our aim was not to precisely determine these values but determine whether there was an effect of hibernation on the proportion of trophic eggs. The recipient colonies with and without trophic eggs were formed in exactly the same way. No viable eggs were introduced in these colonies, but all first instar larvae have been introduced in the same way, at the same time, and with random assignment. We have clarified this in the Material and Method section.

      Line 77: Before placing the freshly hatched larvae in recipient colonies, how long were the recipient colonies kept without eggs and how long were they fed before giving the eggs? Were they kept long enough without the queen to avoid possible effects of trophic eggs, or too long so that their behavior changed?

      The recipient colonies were created 7 to 10 days before receiving the first larvae and were fed ad libitum with grass seeds, flies and honey water from the beginning. Trophic eggs that would have been left over from the source colony should have been eaten within the first few days after creating the recipient colonies. However, even if some trophic eggs would have remained, this would not influence our conclusion that trophic eggs influence caste fate, given the fully randomized nature of our treatments and the considerable number of independent replicates. The same applies to potential changes in worker behavior following their isolation from the queen.

      Line 77: Is it known at what stage caste determination occurs in this species? Here first instar larvae were given trophic eggs or not. Does caste-determination occur at the first instar stage? If not, what effect could providing trophic eggs at other stages have on caste-determination?

      A previous study showed that there is a maternal effect on caste determination in the focal species (Schwander et al 2008). The mechanism underlying this maternal effect was hypothesized to be differential maternal provisioning of viable eggs. However, as we detail in the discussion, the new data presented in our study suggests that the mechanism is in fact a different abundance of trophic eggs laid by queens. There is currently no information when exactly caste determination occurs during development

      COMMENTS ON RESULTS:

      Line 65: How does investigating the order of eggs laid help to "inform on the mechanisms of oogenesis"?

      We agree that the aim was not to study the mechanism of oogenesis. We have changed this sentence accordingly: “To assess whether viable and trophic eggs were laid in a random order, or whether eggs of a given type were laid in clusters, we isolated 11 queens for 10 hours, eight times over three weeks, and collected every hour the eggs laid”

      Figure 2: There is no description/discussion of data shown in panels B, C, E, and F in the main text.

      We have added information in the main text that while viable eggs showed embryonic development at 25 and 65 hours (Fig 12 B, C) there was no such development for trophic eggs (Fig. 2 E,F).

      Line 172: Please explain hibernation details and its significance on colony development/life cycle.

      We have added this information in the Material and Method section.

      Figure 6: How is B plotted? How could 0% of gynes have 100% survival?

      The survival is given for the larvae without considering caste. We have changed the de X axis of panel B and reworded the Figure legend to clarify this.

      Is reduced DNA content just an outcome of reduced cell number within trophic eggs, i.e., was this a difference in cell type or cell number? Or is it some other adaptive reason?

      It is likely to be due to a reduction in cell number (trophic eggs have maternal DNA in the chorion, while viable eggs have in addition the cells from the developing zygote) but we do not have data to make this point.

      Is there a logical sequence to the sequence of egg production? The authors showed that the sequence is non-random, but can they identify in what way? What would the biological significance be?

      We could not identify a logical sequence. Plausibly, the production of the two types of eggs implies some changes in the metabolic processes during egg production resulting in queens producing batches of either viable or trophic eggs. This would be an interesting question to study, but this is beyond the scope of this paper.

      Figure 6b is difficult to follow, and more generally, legends for all figures can be made clearer and more easy to follow.

      We agree. We have now improved the legends of Fig 6B and the other figures.

      Lines 172-174: "The percentage of eggs that were trophic was higher before hibernation...than after. This higher percentage was due to a reduced number of reproductive eggs, the number of trophic eggs laid remained stable" - are these data shown? It would be nice to see how the total egglaying rate changes after hibernation. Also, is the proportion of trophic eggs laid similar between individual queens?

      No the data were not shown and we do not have excellent data to make this point. We have therefore removed the sentence “This higher percentage was due to a reduced number of reproductive eggs, the number of trophic eggs laid remained stable” from the manuscript.

      Figure 6B: Do several colonies produce 100% gynes despite receiving trophic eggs? It would be interesting if the authors discussed why this might occur (e.g., the larvae are already fully determined to be queens and not responsive to whatever signal is in the trophic eggs).

      The reviewer is correct that 4 colonies produced 100% gynes despite receiving trophic eggs. However, the number of individuals produced in these four colonies was small (2,1,2,1, see supplementary Table 2). So, it is likely that it is just by chance that these colonies produced only gynes.

      Figure 5: Why a separation by "size distribution variation of miRNA"? What is the relevance of looking at size distributions as opposed to levels?

      We did that because there many different miRNA species, reflected by the fact that there is not just one size peak but multiple one. This is why we looked at size distribution

      Figure 2: The image of the viable embryo is not clear. If possible, redo the viable to show better quality images.

      Unfortunately, we do not anymore have colonies in the laboratory so this is not possible.

      COMMENTS ON DISCUSSION:

      Lines 236-247: Can an explanation be provided as to why the effect of trophic eggs in P. rugosus is the opposite of those observed by studies referenced in this section? Could P. rugosus have any life history traits that might explain this observation?

      In the two mentioned studies there were other factors that co-varied with variation in the quantity of trophic eggs. We mentioned that and suggested that it would be useful to conduct experimental manipulation of the quantity of trophic eggs in the Argentine ant and P. barbatus (the two species where an effect of trophic eggs had been suggested).

      The discussion should include implications and future research of the discovery.

      We made some suggestions of experiments that should be performed in the future

      The conclusion paragraph is too short and does not represent what was discussed.

      We added two sentences at the end of the paragraph to make suggestions of future studies that could be performed.

      Lines 231 to 247: Drastically reduce and move this whole part to the introduction to substantiate the assumption that trophic eggs play a nutritional role.

      We moved most of this paragraph to the introduction, as suggested by the reviewer.

      Reviewer #3 (Recommendations For The Authors):

      I would like to commend the authors on their study. The main findings of the paper are individually solid and provide novel insight into caste determination and the nature of trophic eggs. However, the inferences made from much of the data and connections between independent lines of evidence often extend too far and are unsubstantiated.

      We thank the reviewer for the positive comment. We made many changes in the manuscript to improve the discussion of our results.

    1. 中山大学团队发现挠曲电各向异性可稳定不同晶格(如四方/六角SkL),并通过应变或电场实现晶格旋转。

      中山大学团队发现的挠曲电各向异性稳定不同晶格(如四方/六角斯格明子晶格,SkL)并通过应变或电场实现晶格旋转,其意义主要体现在以下几个方面:


      1. 科学意义

      (1)揭示极性拓扑结构的调控机制

      • 挠曲电与拓扑结构的耦合:<br /> 挠曲电效应通过应变梯度与极化之间的相互作用,直接影响材料内部的拓扑结构(如斯格明子晶格)。研究发现,挠曲电各向异性(即不同方向上的挠曲电系数差异)能够稳定不同类型的晶格(如四方SkL和六角SkL)。这种机制为理解极性拓扑结构的形成与演化提供了新视角,尤其是如何通过非均匀形变(应变梯度)调控材料的长程有序性。
      • 案例:在铁电PbTiO₃薄膜中,四方SkL和六角SkL的稳定依赖于挠曲电各向异性,说明材料的微观对称性与宏观形变的耦合是拓扑结构多样性的关键。

      (2)实现晶格的可编程操控

      • 应变与电场的主动调控:<br /> 通过施加应变梯度面内电场,可使六角SkL发生旋转,甚至产生新型非互易响应(如四方SkL的弯曲方向依赖于应变历史)。这种动态操控能力为拓扑结构的实时编程提供了物理基础。
      • 非互易性:四方SkL的“非互易弯曲响应”表明,晶格的响应方向与应变施加的路径相关,这可能源于挠曲电极化与材料内部应力场的复杂耦合。

      (3)突破传统材料限制

      • 适用性扩展:<br /> 挠曲电效应存在于所有电介质(包括中心对称材料),而斯格明子通常依赖于特定磁性材料(如手性磁体)。这一发现表明,非磁性材料(如铁电体)也可能通过挠曲电效应支持拓扑结构,从而拓展了极性斯格明子的研究范围。
      • 案例:PbTiO₃(铁电材料)中的极性斯格明子晶格无需依赖磁各向异性,仅通过电-机械耦合即可形成。

      2. 应用价值

      (1)新型拓扑电子器件

      • 可重构存储与逻辑器件:<br /> 斯格明子晶格的旋转或重构可作为信息存储单元或逻辑开关。通过挠曲电效应,仅需低能耗的机械应变或电场即可实现晶格状态的切换,有望开发出超低功耗、高密度的存储器拓扑逻辑门
      • 优势:与传统磁存储依赖强磁场或电流不同,挠曲电驱动更易集成于柔性电子系统。

      (2)智能材料与传感技术

      • 自适应拓扑材料:<br /> 晶格旋转产生的非互易响应可设计为单向传感器应力-电场转换器。例如:
      • 应力传感器:通过监测斯格明子晶格的旋转方向,量化材料内部的应变梯度。
      • 非互易器件:利用四方SkL的非互易性,开发单向电磁波或机械能传输装置(如声学隔离器)。

      (3)仿生与柔性电子

      • 生物启发的自适应系统:<br /> 通过模拟生物材料中的多级孔隙结构(如丝瓜络,见知识库[5]),结合挠曲电调控,可设计出自适应拓扑结构材料,用于柔性机器人或人工肌肉。例如:
      • 可编程形变材料:外部刺激(如温度或电场)驱动斯格明子晶格旋转,触发材料宏观形变。

      3. 技术创新

      (1)材料设计新策略

      • 多场耦合调控:<br /> 通过结合应变工程(调控挠曲电各向异性)和电场调控,可精确设计材料的极化拓扑结构。例如:
      • 分层多孔材料(如丝瓜络,知识库[5])的高应变梯度特性,可进一步放大挠曲电效应,提升极性斯格明子的稳定性。

      (2)实验与理论结合

      • 相场模拟与实验验证:<br /> 中山大学团队通过相场模拟揭示了挠曲电调控的物理机制,并通过实验验证了晶格旋转的可行性。这种计算-实验协同方法为未来开发新型拓扑材料提供了范式。

      4. 与其他效应的对比优势

      • 与压电效应的差异
      • 压电效应受限于材料对称性(仅非中心对称材料),而挠曲电效应普遍存在于所有电介质,选择材料更灵活。
      • 高灵敏度:挠曲电效应在纳米尺度下显著增强(知识库[3]),适合高密度器件。

      • 与传统磁斯格明子的互补

      • 磁斯格明子依赖磁场或电流驱动,而挠曲电驱动可通过机械应变实现,适合柔性或无磁场环境的应用。

      5. 未来研究方向

      • 多场协同调控:<br /> 结合电场、应变、温度等多场刺激,探索斯格明子晶格的多态性与相变路径
      • 器件集成化:<br /> 开发基于挠曲电-斯格明子耦合的可穿戴传感器柔性存储芯片
      • 理论模型完善:<br /> 建立更精确的多尺度模型,描述应变梯度、极化和拓扑荷之间的定量关系。

      总结

      中山大学团队的研究将挠曲电效应与极性拓扑结构结合,不仅深化了对材料力-电-拓扑耦合机制的理解,还为开发可编程拓扑电子器件、智能传感材料提供了新思路。这一成果标志着挠曲电效应从基础研究向应用领域的关键跨越,未来可能推动柔性电子、自旋电子学和拓扑量子技术的革新。

    1. Author response:

      As a short response to the public reviews, we would like to outline the following planned revisions:

      (1) Address the antibody concerns as indicated by reviewer 1

      (2) Assess the role of tensin (and possibly KANK), as suggested by reviewers 2 and 3, respectively.

      (3) Validate our main experimental findings using alternative super-resolution approaches, including STED to avoid potential blinking artefacts associated to standard STORM, and most possibly DNA-PAINT as a more quantitative technique, as suggested by reviewer 3.

      (4) Implement alternative analytical strategies to DBSCAN, including Voronoi tessellation as suggested by reviewer 3.

      (5) Expanded discussion on the main findings of our work and biological significance.

    1. Reviewer #1 (Public review):

      Summary:

      Early and accurate diagnosis is critical to treating N. fowleri infections, which often lead to death within 2 weeks of exposure. Current methods-sampling cerebrospinal fluid are invasive, slow, and sometimes unreliable. Therefore, there is a need for a new diagnostic method. Russell et al. address this need by identifying small RNAs secreted by Naegleria fowleri (Figure 1) that are detectable by RT-qPCR in multiple biological fluids including blood and urine. SmallRNA-1 and smallRNA-2 were detectable in plasma samples of mice experimentally infected with 6 different N. fowleri strains, and were not detected in uninfected mouse or human samples (Figure 4). Further, smallRNA-1 is detectable in the urine of experimentally infected mice as early as 24 hours post-infection (Figure 5). The study culminates with testing human samples (obtained from the CDC) from patients with confirmed N. fowleri infections; smallRNA-1 was detectable in cerebrospinal fluid in 6 out of 6 samples (Figure 6B), and in whole blood from 2 out of 2 samples (Figure 6C). These results suggest that smallRNA-1 could be a valuable diagnostic marker for N. fowleri infection, detectable in cerebrospinal fluid, blood, or potentially urine.

      Strengths:

      This study investigates an important problem, and comes to a potential solution with a new diagnostic test for N. fowleri infection that is fast, less invasive than current methods, and seems robust to multiple N. fowleri strains. The work in mice is convincing that smallRNA1 is detectable in blood and urine early in infection. Analysis of patient blood samples suggest that whole blood (but not plasma) could be tested for smallRNA-1 to diagnose N. fowleri infections.

      Weaknesses:

      (1) There are not many N. fowleri cases, so the authors were limited in the human samples available for testing. It is difficult to know how robust this biomarker is in whole blood (only 2 samples were tested, both had detectable smallRNA-1), serum (1 out of 1 sample tested negative), or human urine (presumably there is no material available for testing). This limitation is openly discussed in the last paragraph of the discussion section.

      (2) There seems to be some noise in the data for uninfected samples (Figures 4B-C, 5B, and 6C), especially for those with serum (2E). While this is often orders of magnitude lower than the positive results, it does raise questions about false positives, especially early in infection when diagnosis would be the most useful. A few additional uninfected human samples may be helpful.

    2. Reviewer #2 (Public review):

      Summary:

      The authors sought to develop a rapid and non-invasive diagnostic method for primary amoebic meningoencephalitis (PAM), a highly fatal disease caused by Naegleria fowleri. Due to the challenges of early diagnosis, they investigated extracellular vesicles (EVs) from N. fowleri, identifying small RNA biomarkers. They developed an RT-qPCR assay to detect these biomarkers in various biofluids.

      Strengths:

      (1) This study has a clear methodological approach, which allows for the reproducibility of the experiments.

      (2) Early and Non-Invasive Diagnosis - The identification of a small RNA biomarker that can be detected in urine, plasma, and cerebrospinal fluid (CSF) provides a non-invasive diagnostic approach, which is crucial for improving early detection of PAM.

      (3) High Sensitivity and Rapid Detection - The RT-qPCR assay developed in the study is highly sensitive, detecting the biomarker in 100% of CSF samples from human PAM cases and in mouse urine as early as 24 hours post-infection. Additionally, the test can be completed in ~3 hours, making it feasible for clinical use.

      (4) Potential for Disease Monitoring - Since the biomarker is detectable throughout the course of infection, it could be used not only for early diagnosis but also for tracking disease progression and monitoring treatment efficacy.

      (5) Strong Experimental Validation - The study demonstrates biomarker detection across multiple sample types (CSF, urine, whole blood, plasma) in both animal models and human cases, providing robust evidence for its clinical relevance.

      (6) Addresses a Critical Unmet Need - With a >97% case fatality rate, PAM urgently requires improved diagnostics. This study provides one of the first viable liquid biopsy-based diagnostic approaches, potentially transforming how PAM is detected and managed.

      Weaknesses:

      (1) Limited Human Sample Size - While the biomarker was detected in 100% of CSF samples from human PAM cases, the number of human samples analyzed (n=6 for CSF) is relatively small. A larger cohort is needed to validate its diagnostic reliability across diverse populations.

      (2) Lack of Pre-Symptomatic or Early-Stage Human Data - Although the biomarker was detected in mouse urine as early as 24 hours post-infection, there is no data on whether it can be reliably detected before symptoms appear in humans, which is crucial for early diagnosis and treatment initiation.

      (3) Plasma Detection Challenges - While the biomarker was detected in whole blood, it was not detected in human plasma, which could limit the ease of clinical implementation since plasma-based diagnostics are more common. Further investigation is needed to understand why it is absent in plasma and whether alternative blood-based approaches (e.g., whole blood assays) could be optimized.

    1. Reviewer #1 (Public review):

      Summary:

      The study investigates how neuropeptidergic signaling affects sleep regulation in Drosophila larvae. The authors first conduct a screen of CRISPR knock-out lines of genes encoding enzymes or receptors for neuropeptides and monoamines. As a result of this screen, the authors follow up on one hit, the hugin receptor, PK2-R1. They use genetic approaches, including mutants and targeted manipulations of PK2-R1 activity in insulin-producing cells (IPCs) to increase total sleep amounts in 2nd instar larvae. Similarly, dilp3 and dilp5 null mutants and genetic silencing of IPCs show increases in sleep. The authors also show that hugin mutants and thermogenetic/optogenetic activation of hugin-expressing neurons caused reductions in sleep. Furthermore, they show through imaging-based approaches that hugin-expressing neurons activate IPCs. A key finding is that wash-on of hugin peptides, Hug-γ and PK-2, in ex vivo brain preparations activates larval IPCs, as assayed by CRTC::GFP imaging. The authors then examine how the PK2-R1, hugin, and IPC manipulations affect adult sleep. Finally, the authors examine how Ca2+ responses through CRTC::GFP imaging in adult IPCs are influenced by the wash-on of hugin peptides. The conclusions of this paper are somewhat well supported by data, but some aspects of the experimental approach and sleep analysis need to be clarified and extended.

      Strengths:

      (1) This paper builds on previously published studies that examine Drosophila larval sleep regulation. Through the power of Drosophila genetics, this study yields additional insights into what role neuropeptides play in the regulation of Drosophila larval sleep.

      (2) This study utilizes several diverse approaches to examine larval and adult sleep regulation, neural activity, and circuit connections. The impressive array of distinct analyses provides new understanding into how Drosophila sleep-wake circuitry in regulated across the lifespan.

      (3) The imaging approaches used to examine IPC activation upon hugin manipulation (either thermogenetic activation or wash-on of peptides) demonstrate a powerful approach for examining how changes in neuropeptidergic signaling affect downstream neurons. These experiments involve precise manipulations as the authors use both in vivo and ex vivo conditions to observe an effect on IPC activity.

      Weaknesses:

      Although the paper does have some strengths in principle, these strengths are not fully supported by the experimental approaches used by the authors. In particular:

      (1) The authors show total sleep amount over an 18-hour period for all the measures of 2nd instar larval sleep throughout the paper. However, published studies have shown that sleep changes over the course of 2nd instar development, so more precise time windows are necessary for the analyses in this study.

      (2) Previously published reports of sleep metrics in both Drosophila larvae and adults include the average number of sleep episodes (bout number) and the average length of sleep episodes (bout length). Neither of these metrics is included in the paper for either the larval sleep or adult sleep data. Not including these metrics makes it difficult for readers to compare the findings in this study to previously published papers in the established Drosophila sleep literature.

      (3) Because Drosophila adult & larval sleep is based on locomotion, the authors need to show the activity values for the experiments supporting their key conclusions. They do show travel distances in Figure 2 - Figure Supplement 1, however, it is not clear how these distances were calculated or how the distances relate to the overall activity of individual larvae during sleep experiments. It is also concerning that inactivation of the PK2-R1-expressing neurons causes a reduction in locomotion speed. This could partially explain the increase in sleep that they observe.

      (4) The authors rely on homozygous mutant larvae and adult flies to support many of their conclusions. They also rely on Gal4 lines with fairly broad expression in the Drosophila brain to support their conclusions. Adding more precise tissue-specific manipulations, including thermogenetic activation and inhibition of smaller populations of neurons in the study would be needed to increase confidence in the presented results. Similarly, demonstrating that larval development and feeding are not affected by the broad manipulations would strengthen the conclusions.

      (5) Many of the experiments presented in this study would benefit from genetic and temperature controls. These controls would increase confidence in the presented results.

      (6) The authors claim that their findings in larvae uncover the circuit basis for larval sleep regulation. However, there is very little comparison to published studies demonstrating that neuropeptides like Dh44 regulate larval sleep. Because hugin-expressing neurons have been shown to be downstream of Dh44 neurons, the authors need to include this as part of their discussion. The authors also do not explain why other neuropeptides in the initial screen are not pursued in the study. Given the effect that these manipulations have on larval sleep in their initial screen, it seems likely that other neuropeptidergic circuits regulate larval sleep.

    2. Reviewer #2 (Public review):

      Summary:

      This study examines larval sleep patterns and compares them to sleep regulation in adult flies. The authors demonstrate hallmark sleep characteristics in larvae, including sleep rebound and increased arousal thresholds. Through genetic and behavioral analyses, they identify PK2-R1 as a key receptor involved in sleep modulation, likely via the HuginPC-IPC signaling pathway. Loss of PK2-R1 results in increased sleep, which aligns with previous findings in hugin knockout mutants. While the study presents significant contributions to the field, further investigation is needed to address discrepancies with earlier research and strengthen mechanistic claims.

      Strengths:

      (1) The study explores a relatively understudied aspect of sleep regulation, focusing on larval development.

      (2) The use of an automated behavioral measurement system ensures precise quantification of sleep patterns.

      (3) The findings provide strong genetic and behavioral evidence supporting the role of the HuginPC-IPC pathway in sleep regulation.

      (4) The study has broader implications for understanding the evolution and functional divergence of sleep circuits.

      Weaknesses:

      (1) The manuscript does not sufficiently discuss previous studies, particularly concerning hugin mutants and their metabolic effects.

      (2) The specificity of IPC secretion mechanisms is unclear, particularly regarding potential indirect effects on Dilp2.

      (3) Alternative circuits, such as the HuginPC-DH44 pathway, require further consideration.

      (4) Functional connectivity between HuginPC neurons and IPCs is not directly validated.

      (5) Developmental differences in sleep regulatory mechanisms are not thoroughly examined.

    1. Reviewer #2 (Public review):

      Summary:

      In this paper, Bhojappa et al. provide insights into the function of septin-related kinases Elm1, Gin4, Hsl1, and Kcc4 in septin organization and actomyosin ring (AMR) structure and constriction. Their findings are both corroborative of and complementary to previous related studies.

      First, the authors provide a comparative analysis of the dynamic localization of these kinases at the bud neck, as well as a comparative analysis of defects in septin localization, splitting dynamics, AMR constriction rates, and cell morphology in kinase-deficient cells. They find that septin localization and splitting kinetics, as well as AMR constriction rates, are significantly perturbed in elm1∆ and gin4∆ mutants but remain largely unaffected in hsl1∆ and kcc4∆. A similar trend is observed in terms of cell morphology and viability.

      Next, the authors focus on elm1∆ and gin4∆ cells, demonstrating that the residence time of the F-BAR protein Hof1 is significantly increased and defective in these mutants. Using yeast two-hybrid (Y2H) and in vitro binding assays, they show that the KA1 domain of Gin4 interacts with the F-BAR domain of Hof1, which may explain the cytokinesis-related functions of Elm1 and Gin4. Supporting this, they find that Gin4's role in septin localization, AMR constriction kinetics, and Hof1 bud neck localization is kinase-independent.

      The authors then conduct a series of artificial tethering experiments given their bud neck localization is mostly interdependent. They first demonstrate that artificially tethering Gin4 to the bud neck rescues the morphology defects of elm1∆ cells, with the strongest rescue observed when Gin4 was forced to interact with Hsl1-an effect that was also kinase-independent. Additionally, artificial tethering of Hsl1 to the bud neck restores the morphology of elm1∆ cells in a KA1 domain-dependent manner, suggesting that Hsl1 functions downstream of Elm1 to maintain normal cell morphology. Consistently, artificial tethering of Elm1 to the bud neck in gin4∆ cells rescues morphology defects, as well as defects in Myo1 localization and AMR constriction, but only in the presence of full-length Hsl1. The rescue fails in the absence of Hsl1 or when using a version of Hsl1 lacking the KA1 domain, which supports the role of Hsl1 downstream to Elm1 in cytokinesis.

      Strengths

      Altogether, this study offers valuable insights into the mode of cytokinesis regulation mediated by the septin-related kinases, mainly Elm1, Gin4, and Hsl1, and would be an important contribution to the field of septins and cytokinesis after addressing current weaknesses.

      Weaknesses

      (1) When assessing rescue of the elm1∆ phenotype, it needs to become clearer whether only morphology or also cytokinesis and septin organization are rescued.

      (2) The quantification of the microscopy data does not always match up with the example images, and it's not always clear how the authors quantitatively analyzed their data.

      (3) The forced tethering data are key to the paper, but the lack of a summarizing table makes it difficult to grasp the full picture.

      (4) Novel results and those confirming earlier results could be better distinguished.

    1. Reviewer #2 (Public review):

      Summary:

      The authors investigate single-neuron activity in rhesus macaques during model-based (MB) and model-free (MF) reinforcement learning (RL). Using a well-established two-step choice task, they analyze neural correlates of MB and MF learning across four brain regions: the anterior cingulate cortex (ACC), dorsolateral PFC (DLPFC), caudate, and putamen. The study provides strong evidence that these regions encode distinct RL-related signals, with ACC playing a dominant role in MB learning and caudate updating value representations after rare transitions. The authors apply rigorous statistical analyses to characterize neural encoding at both population and single-neuron levels.

      Strengths:

      (1) The research fills a gap in the literature, which has been limited in directly dissociating MB vs. MF learning at the single unit level and across brain areas known to be involved in reinforcement learning. This study advances our understanding of how different brain regions are involved in RL computations.

      (2) The study used a two-step choice task Miranda et al., (2020), which was previously established for distinguishing MB and MF reinforcement learning strategies.

      (3) The use of multiple brain regions (ACC, DLPFC, caudate, and putamen) in the study enabled comparisons across cortical and subcortical structures.

      (4) The study used multiple GLMs, population-level encoding analyses, and decoding approaches. With each analysis, they conducted the appropriate controls for multiple comparisons and described their methods clearly.

      (5) They implemented control regressors to account for neural drift and temporal autocorrelation.

      (6) The authors showed evidence for three main findings:<br /> a) ACC as the strongest encoder of MB variables from the four areas, which emphasizes its role in tracking transition structures and reward-based learning. The ACC also showed sustained representation of feedback that went into the next trial.<br /> b) ACC was the only area to represent both MB and MF value representations.<br /> c) The caudate selectively updates value representations when rare transitions occur, supporting its role in MB updating.

      (7) The findings support the idea that MB and MF reinforcement learning operate in parallel rather than strictly competing.

      (8) The paper also discusses how MB computations could be an extension of sophisticated MF strategies.

      Weaknesses: o

      (1) There is limited evidence for a causal relationship between neural activity and behavior. The authors cite previous lesion studies, but causality between neural encoding in ACC, caudate, and putamen and behavioral reliance on MB or MF learning is not established.

      (2) There is a heavy emphasis on ACC versus other areas, but it is unclear how much of this signal drives behavior relative to the caudate.

      (3) The role of the putamen is somewhat underexplored here.

      (4) The authors mention the monkeys were overtrained before recording, which might have led to a bias in the MB versus MF strategy.

      (5) The GLM3 model combines MB and MF value estimates but does not clearly mention how hyperparameters were optimized to prevent overfitting. While the hybrid model explains behavior well, it does not clarify whether MB/MF weighting changes dynamically over time.

      (6) It was unclear from the task description whether the images used changed periodically or how the transition effect (e.g., in Figure 3) could be disambiguated from a visual response to the pair of cues.

    1. 4.2.1 开发者须对应用内用户生成的内容需进行有效的管控,包括但不限于:核验用户帐号身份信息并保存有关记录。制定过滤机制对帐号名称、昵称、简介、备注、标识、评论等功能及内容中的违法有害信息进行防范处置并保存有关记录; 4.2.2 建立用户生成内容的举报机制,并及时作出响应; 4.2.3 应用应具备服务关闭功能,对严重违规的用户停止提供服务;4.2.4 如应用内有社区版块的,开发者须履行管理社区版块责任,社区版块中不得包含违反法律、法规、条例及其他规定的内容。

      1、保存用户违法记录; 2、过滤机制,防范用户违法行为; 3、服务关停,对违法用户停止服务; 4、社区管理审核;

    1. La Cour formule les recommandations suivantes :1. renforcer les coopérations avec les missions localespour garantir l’accès des jeunes majeurs aux dispositifsd’insertion sociale et professionnelle (départements) ;2. renforcer les coopérations en matière de handicap avec lesmaisons départementales des personnes handicapées eten matière de psychiatrie avec les agences régionales desanté (ministère des solidarités, de l’autonomie et de l’égalitéentre les femmes et les hommes, ministère de la santé et del’accès aux soins, départements, maisons départementalesdes personnes handicapées, agences régionales de santé) ;3. définir des objectifs de prise en charge minimale et lier leversement des fonds accompagnant la mise en œuvre de la loidu 7 février 2022 à sa mise en place (ministère des solidarités,de l’autonomie et de l’égalité entre les femmes et les hommes) ;4. améliorer le taux de recours au pécule, notamment parun meilleur échange d’informations entre l’ensemble desacteurs concernés (Caisse nationale des allocations familiales,Caisse des dépôts et consignations, départements).348COUR DES COMPTES | RAPPORT PUBLIC ANNUEL
    2. Briefing Document : Analyse des enjeux et dispositifs relatifs à la jeunesse en France

      Date : 2024-11-07

      Source : Excerpts from "https://www.ccomptes.fr/sites/default/files/2025-03/20250319-RPA2025-volume1_1.pdf" (Cour des Comptes, Rapport sur la politique en faveur de la jeunesse, mars 2025, Volume 1)

      Objet : Synthèse des principaux thèmes, idées et faits marquants concernant la politique en faveur de la jeunesse en France, tels qu'analysés par la Cour des Comptes.

      Public Cible : Toute personne intéressée par les politiques publiques relatives à la jeunesse (décideurs politiques, administrations, acteurs du secteur, chercheurs, etc.).

      Principaux Thèmes et Idées Clés :

      Le rapport de la Cour des Comptes met en lumière plusieurs aspects cruciaux de la politique en faveur de la jeunesse, allant du financement des dispositifs à l'accès aux droits, en passant par l'emploi, la formation, le logement et la mobilité.

      1. Complexité et Manque de Coordination des Dispositifs :

      Le paysage des dispositifs dédiés à la jeunesse est caractérisé par sa complexité et un manque de coordination.

      De nombreuses aides financières et dispositifs existent, souvent liés à l'âge et parfois au statut (étudiant, apprenti, demandeur d'emploi).

      Leur origine est diverse (État, régions, départements, communes, organismes divers), et leurs conditions d'obtention varient (âge, ressources, statut, domiciliation).

      Citation : "Il est difficile de dresser un tableau complet de ces aides, qui peuvent être présentées selon leur objet (logement, transports, sport, culture, prêt, aides, bourses, etc.), selon l’origine de l’aide (...), ou leur matérialité (...). Ces différentes catégories peuvent s’exclure mutuellement ou être cumulables, être soumises à des conditions (...) et varier sur les limites d’âge."

      Le rapport souligne un manque de coordination et de paramétrage des moyens déployés, ainsi qu'un pilotage davantage axé sur l'offre (cibles en volume) que sur les besoins et les résultats. La clarté et le partage des critères d'orientation des jeunes entre les acteurs sont également insuffisants.

      Citation : "Au-delà d’une refonte de la stratégie d’ensemble, les moyens déployés doivent être mieux coordonnés et paramétrés. La détermination des cibles nationales assignées à chaque dispositif ne repose pas sur une évaluation rigoureuse des besoins. Les règles de répartition territoriale des moyens ne garantissent pas une allocation optimale des ressources. Le pilotage par l’offre, fondé sur des cibles en volume pour chaque dispositif, doit céder la place à un pilotage par les besoins et les résultats."

      2. Définition Juridique de la Jeunesse et Droits :

      Le droit ne reconnaît pas une notion unique de "jeunesse", établissant une distinction radicale entre mineurs et majeurs (18 ans).

      Cependant, il existe une progressivité des compétences juridiques et sociales pour les mineurs dès 12 ans, créant une forme de "pré-majorité" avec des droits acquis selon l'âge, l'accord des parents ou les circonstances (soins médicaux, contraception, porter plainte, etc.).

      Citation : "Un premier constat s’impose : le droit ne connaît pas la notion de jeunesse. Il établit un partage radical entre mineurs et majeurs et concourt ainsi à définir deux grandes catégories juridiques de « jeunes » selon qu’ils ont atteint, ou non, 18 ans."

      3. Financement des Politiques Jeunesse :

      Les crédits budgétaires spécifiquement dédiés à la jeunesse restent marginaux et peu évolutifs, incitant les universités à se tourner vers des financements ponctuels via des appels à projets (Plan d'Investissement d'Avenir, France 2030).

      La pérennisation du financement des projets à moyen terme est une préoccupation.

      Le Document de Politique Transversale (DPT) "Politique en faveur de la jeunesse" présente les axes stratégiques et les crédits de l'État, mais il n'existe pas d'équivalent au niveau des collectivités territoriales et de la sécurité sociale, ce qui limite la vision globale des financements.

      Les dépenses de l'État en faveur des 15-25 ans concernent majoritairement l'éducation (enseignement scolaire et supérieur) et l'accompagnement des mutations industrielles (apprentissage). Des crédits importants sont également alloués via la mission Cohésion des territoires (aides personnelles au logement).

      Citation : "Cependant, les crédits budgétaires en question restent marginaux et peu évolutifs, ce qui a incité les universités à s’orienter vers les appels à projets du plan d’investissement d’avenir et du plan France 2030. Ceux-ci ont été déterminants pour mettre en place sur le terrain des projets structurants.

      Cependant, ces abondements extra-budgétaires ne sont attribués que pour une période donnée et les universités doivent désormais anticiper la façon dont elles financeront à moyen terme les projets en cours ou ceux qui restent à lancer."

      Citation : "Les crédits de l'État destinés aux 15-25 ans concernent massivement l’éducation, du lycée au supérieur."

      4. Inégalités Territoriales :

      La situation de la jeunesse varie considérablement d'un territoire à l'autre. L'indice de jeunesse révèle des "îlots de jeunesse" principalement autour des grandes métropoles.

      Les territoires ruraux sont confrontés à des défis spécifiques en matière d'accès à l'enseignement supérieur et à l'emploi.

      Citation : "Ces tendances nationales sont variables d’un territoire à l’autre. À l’échelle départementale, en 2023, les 15-29 ans représentaient moins de 15 % de la population dans 30 départements, et plus de 20 % pour dix d’entre eux."

      5. L'Obligation de Formation pour les 16-18 Ans :

      Le rapport analyse la mise en œuvre de l'obligation de formation pour les jeunes de 16 à 18 ans.

      Si des solutions sont proposées majoritairement via les dispositifs d'insertion professionnelle de droit commun (Pacea, CEJ), des écarts importants existent dans le repérage des jeunes concernés.

      La coordination avec les départements, qui ont un rôle dans les politiques sociales, n'est pas toujours systématique.

      Citation : "Contrairement à ce que suggère son intitulé, celle-ci consiste au moins autant à accompagner qu’à former les jeunes concernés."

      6. Réussite dans l'Enseignement Supérieur :

      Malgré les dispositifs d'accompagnement (plan "Réussir en licence", loi ORE), le taux de diplomation en licence en trois ans reste inférieur à 50%.

      La performance des formations en termes de réussite étudiante est progressivement intégrée dans l'allocation des ressources aux universités via les contrats d'objectifs, de moyens et de performance (COMP).

      Le rapport plaide pour une meilleure visibilité des dispositifs de prévention de l'échec et un suivi statistique consolidé.

      Citation : "Globalement, depuis la mise en place du plan « Réussir en licence », en 2007, et l’entrée en vigueur de la loi du 8 mars 2018 relative à l’orientation et à la réussite (ORE), la part des étudiants ayant obtenu un diplôme de licence en trois ans a progressé de 5,8 points entre la cohorte 2013 et la cohorte 2019."

      7. Accès des Jeunes Ruraux à l'Enseignement Supérieur :

      L'accès est limité par une offre de formation de proximité restreinte et des freins socio-économiques (revenus plus faibles, éloignement géographique).

      Les dispositifs d'aide aux étudiants ne prennent que faiblement en compte le critère d'éloignement.

      Le rapport recommande de simplifier la gestion des aides et de revoir les modalités d'attribution pour mieux intégrer l'éloignement.

      Citation : "L’accès des jeunes issus des territoires ruraux à l’enseignement supérieur est un enjeu majeur de cohésion sociale et territoriale.

      Or, cette problématique est aujourd’hui faiblement prise en compte par les politiques publiques."

      8. Insertion Professionnelle et Dispositifs d'Accompagnement :

      Le rapport analyse l'impact des différents dispositifs d'insertion (Pacea, CEJ, AIJ).

      Il souligne les biais potentiels liés aux cibles en volume fixées nationalement, qui peuvent inciter les opérateurs à orienter les jeunes vers certains dispositifs pour atteindre leurs objectifs de financement, plutôt qu'en fonction des besoins réels.

      Le pilotage par l'offre est critiqué au profit d'un pilotage par les besoins et les résultats.

      Citation : "La montée en charge du CEJ s’est par exemple opérée au détriment de l’AIJ (France Travail) et du Pacea (missions locales), dispositifs préexistants et de moindre intensité : il est possible qu’une partie des jeunes orientés vers le CEJ l’aient été moins parce qu’ils avaient réellement besoin d’un accompagnement intensif que parce que les prescripteurs étaient soucieux d’atteindre leurs objectifs."

      9. Logement des Jeunes :

      La politique du logement étudiant s'est longtemps concentrée sur les résidences universitaires.

      La garantie Visale est un dispositif important pour faciliter l'accès au logement des jeunes, mais elle rencontre parfois des réticences de la part des bailleurs.

      Des dispositifs expérimentaux visent à accompagner globalement les jeunes précaires, où le logement est un outil de stabilisation. Une meilleure coordination locale et la mobilisation du foncier universitaire sont encouragées.

      Citation : "Depuis le début des années 2010, le logement étudiant à vocation sociale fait l’objet d’objectifs de production à travers des plans gouvernementaux..."

      10. Mobilité des Jeunes et Transports Collectifs :

      Le droit à la mobilité, bien que général, a des implications pour les jeunes. Les politiques tarifaires des autorités organisatrices de mobilité (AOM) pourraient être mieux ciblées.

      Des initiatives temporaires comme les Pass Jeunes et le Pass Rail ont montré leur succès.

      Le développement de l'offre de transports collectifs vers les zones périurbaines et rurales est un enjeu majeur pour l'égalité d'accès.

      Citation : "Le droit aux transports pour tous est inscrit dans la loi depuis 1982.

      Il a été transformé en un droit à la mobilité en 2019 qui ne concerne pas spécifiquement les jeunes, sauf pour les transports scolaires."

      11. Jeunes Majeurs Sortant de l'Aide Sociale à l'Enfance (ASE) :

      La loi du 7 février 2022 a renforcé le droit à l'accompagnement pour les jeunes majeurs sortant de l'ASE, mais des disparités persistent dans les niveaux de prise en charge selon les départements.

      La notion de "contrat jeune majeur" est variable.

      L'accès au droit commun (insertion, logement) pour ces jeunes doit être renforcé.

      Le pilotage des dispositifs et le suivi de l'atteinte des objectifs sont souvent insuffisants.

      Citation : "Les jeunes majeurs issus de l’aide sociale à l’enfance (ASE), et plus largement tout jeune de 18 à 21 ans sans « ressources ou soutien familial suffisants », sont pris en charge à leur demande par les départements en application de l’article L. 222-5 du code de l’action sociale et des familles."

      Conclusion :

      Le rapport de la Cour des Comptes dresse un tableau complexe de la politique en faveur de la jeunesse en France.

      Il met en évidence la nécessité d'une meilleure coordination et d'un pilotage axé sur les besoins et les résultats, d'une prise en compte accrue des inégalités territoriales, et d'un renforcement de l'accès aux droits pour tous les jeunes, en particulier ceux en situation de vulnérabilité.

      La simplification et la clarification des dispositifs, ainsi qu'une vision globale du financement, apparaissent comme des leviers essentiels pour améliorer l'efficacité et l'équité des politiques publiques dédiées à la jeunesse.

    1. 在二维量子磁体中发现拓扑克尔效应,通过克尔信号直接关联斯格明子的拓扑荷,实现非破坏性磁结构成像。

      拓扑磁学:通过拓扑克尔效应深入研究斯格明子等新型磁结构


      1. 拓扑磁学的定义与核心目标

      拓扑磁学是凝聚态物理与磁学的交叉领域,研究磁性材料中具有非平庸拓扑性质的磁结构。这些结构的拓扑特性(如拓扑荷)使其对局域扰动(如缺陷、热噪声)具有鲁棒性,因此在低能耗、高密度存储器件中具有重要应用潜力。其核心目标是:<br /> - 揭示拓扑磁结构(如斯格明子、磁反涡旋)的形成机制。<br /> - 开发表征和操控这些结构的新方法。<br /> - 推动基于拓扑磁结构的自旋电子学器件发展。


      2. 斯格明子(Skyrmion):拓扑磁学的典型研究对象

      斯格明子是一种拓扑磁结构,其自旋在实空间呈现旋涡状或环状排列,整体具有非平庸拓扑电荷(整数)。其关键特性包括:<br /> - 高稳定性:拓扑保护使其在磁场或电流作用下不易被破坏。<br /> - 小尺寸:可缩至纳米级,适合高密度存储。<br /> - 低驱动电流:可在极低电流下移动,适合低功耗器件。


      3. 传统探测方法的局限性

      • 电学方法(如拓扑霍尔效应)
      • 依赖材料的导电性,仅适用于金属体系(如CrGeTe₃)。
      • 难以检测非金属或绝缘的二维磁体(如CrI₃)。
      • 磁力显微镜(MFM)
      • 需接触样品,可能引入干扰。
      • 空间分辨率有限,难以快速成像。

      4. 拓扑克尔效应的突破性意义

      拓扑克尔效应是南开大学、中国科大等团队在二维量子磁体(如CrVI₆)中发现的新现象,其核心是:<br /> - 物理机制:<br /> - 在破缺反演对称性的材料中,斯格明子的拓扑电荷与光相互作用,导致反射光的偏振态发生异常变化。<br /> - 具体表现为磁光克尔角滞回环中的反对称凸起(如图1c中的“猫耳状”特征)。<br /> - 这种效应源于斯格明子对光子的散射,其强度与拓扑荷直接相关。

      • 优势
      • 普适性:适用于金属和非金属体系,突破传统电学方法的局限。
      • 无损探测:通过光学手段实现非接触、高灵敏度成像。
      • 高灵敏度:可检测单层二维材料中的斯格明子(如CrVI₆的薄层样品)。

      5. 拓扑克尔效应的研究实例

      案例:二维磁体CrVI₆中的拓扑磁结构探测<br /> - 实验步骤:<br /> 1. 合成高质量CrVI₆单晶(化学气相输运法)。<br /> 2. 通过磁光克尔显微镜(MOKE)观察磁滞回线,发现低温下出现反对称凸起。<br /> 3. 理论模拟证实:凸起由斯格明子的拓扑荷引起,其磁场与磁力显微镜观测的磁结构演化一致(图1d、e)。

      • 关键发现
      • 在CrVI₆中,Cr与V的共存导致反演对称性破缺,诱导强Dzyaloshinskii-Moriya(DM)相互作用,从而形成斯格明子。
      • 克尔信号的凸起强度与斯格明子密度直接相关,可作为其存在的“磁光指纹”。

      6. 拓扑磁学的未来方向

      • 基础研究
      • 探索更多拓扑磁结构(如磁束子、磁反涡旋)的形成机制。
      • 研究非厄米拓扑磁子学(如耗散与拓扑相的关联,见知识库[12])。
      • 应用开发
      • 磁存储器件:利用斯格明子的高稳定性和低驱动电流特性,设计新型存储单元。
      • 逻辑器件:通过电场或光场操控拓扑荷,实现低功耗逻辑门。
      • 量子计算:拓扑磁结构的量子化特性可能用于量子比特编码。
      • 技术突破
      • 开发基于拓扑克尔效应的高通量成像系统,实现实时观测磁结构动力学。
      • 结合二维材料异质结,设计可调谐拓扑磁结构(如知识库[10]的室温磁束子)。

      7. 总结

      拓扑磁学通过拓扑克尔效应等创新方法,解决了非金属体系中拓扑磁结构探测的难题,为研究斯格明子等新型磁结构提供了普适性工具。这一进展不仅深化了对磁性材料拓扑性质的理解,还推动了低功耗、高密度存储器件的实用化进程,标志着自旋电子学进入了一个新的研究阶段。


      关键概念图示

      1. 拓扑磁结构的形成
      2. 反演对称性破缺 → DM相互作用 → 斯格明子(拓扑荷)。
      3. 拓扑克尔效应信号
      4. 克尔角滞回环中的凸起 → 拓扑荷的光学指纹。
      5. 实验与理论结合
      6. 磁光克尔显微镜(实验) + 磁动力学模拟(理论) → 确证拓扑属性。

      通过这些进展,拓扑磁学正从基础研究迈向技术应用,为下一代自旋电子学器件奠定基础。

    2. 南开大学团队利用受激声子极化激元在铌酸锂微腔中实现巨太赫兹克尔效应(非线性系数提升4个数量级),推动太赫兹光芯片和雷达发展。

      南开大学团队在铌酸锂微腔中利用受激声子极化激元(SPhPs)实现巨太赫兹克尔效应的研究成果具有以下重大意义:


      1. 技术突破:非线性系数的革命性提升

      • 非线性增强:通过受激声子极化激元与太赫兹波的强耦合,团队将太赫兹克尔非线性系数(\(n_2\))提升了4个数量级,达到\(10^{-12} \text{ cm}^2/\text{W}\)级别。
      • 突破传统瓶颈:传统太赫兹器件的非线性效应较弱,限制了其实际应用。这一突破解决了太赫兹波段克尔效应强度不足的长期难题,为太赫兹光子学器件的实用化奠定了基础。

      2. 应用潜力:推动太赫兹技术的产业化

      (1)太赫兹光芯片与通信

      • 高速通信:太赫兹频段(0.1-10 THz)具有超大带宽(比5G高千倍),是6G及未来通信的核心频段。克尔效应的增强可实现超高速光调制器、光开关和频率梳,推动太赫兹光芯片发展,满足云计算、大数据传输需求(如知识库[7][12]提到的6G试验卫星已测试太赫兹通信)。
      • 全光计算:巨克尔效应支持全光逻辑门和光孤子传输,可构建低功耗、高集成度的光计算芯片,加速人工智能和量子计算技术。

      (2)太赫兹雷达与传感

      • 高分辨率雷达:太赫兹雷达可实现厘米级分辨率,适用于自动驾驶、无人机避障、医疗成像(如皮肤癌早期检测)。克尔效应增强的微腔结构可提升雷达的灵敏度和集成度。
      • 无损检测:结合太赫兹波的穿透性和指纹特性,可用于工业检测(如电池、风电叶片缺陷检测)、文物修复和食品安全(知识库[7]提到青岛企业已布局相关应用)。

      (3)量子技术与材料科学

      • 量子控制:巨克尔效应可实现对半导体中自旋量子比特的光学操控,推动量子计算与量子通信技术(知识库[4][11])。
      • 材料研究:通过调控声子极化激元,可研究铁电/铁磁材料的拓扑磁结构(如斯格明子),为新型功能材料开发提供工具。

      3. 科学意义:揭示新物理机制

      • 非线性黄昆方程:团队修正了描述声子极化激元的黄昆方程,提出了超越传统波恩-奥本海默近似的光-物质相互作用模型,为多波段(太赫兹到可见光)非线性调控提供了理论框架(知识库[4][11])。
      • 拓扑光子学:通过设计拓扑结构(如Su-Schrieffer-Heeger晶格),实现了太赫兹波的拓扑局域调控,为集成光子器件开辟了新路径(知识库[4][5])。

      4. 产业与社会影响

      • 芯片级集成:铌酸锂材料成熟且易于微纳加工,结合飞秒激光直写技术,可低成本制造高性能太赫兹微腔器件,推动太赫兹技术的芯片化和商业化(知识库[1][2][6])。
      • 多领域协同:该成果可应用于医疗成像(如癌症检测)、安检(违禁品无接触检测)、航天遥感(如卫星通信)等领域,提升社会安全与科技水平(知识库[7][10][12])。

      5. 学术认可与国际地位

      • 顶尖期刊发表:研究成果发表于《Light: Science & Applications》(影响因子>30),并入选“年度光学进展”(全球仅30项),标志着中国在太赫兹非线性光学领域的国际领先地位(知识库[1][8][11])。

      总结

      南开大学的这项研究通过受激声子极化激元机制,在太赫兹波段实现了前所未有的非线性增强,不仅解决了长期制约太赫兹技术发展的核心难题,还为下一代通信、雷达、量子技术及多领域应用提供了关键技术支持。其成果标志着中国在太赫兹科学与技术领域已进入国际前沿,有望加速太赫兹技术从实验室走向产业化,推动全球“太赫兹时代”的到来。

    1. resistance phenotype implicated many factors including a conserved MAPK, Pmk1, previously linked to extracellular stress responses

      Major finding 4:

      A genetic analysis to identify genes or proteins involved in Torin1 resistance identified: 1. MAPK: protein kinase involved in directing cellular responses from external stimuli to regulate cell proliferation, gene expression, mitosis, and apoptosis. 2. Pmk1: Mitogen-activated protein kinase that enables MAPK activity and RNA polymerase II-specific DNA-binding transcription factor binding activity.

    1. “Daddy Lessons” features many country elements, including a har-monica solo, banjo, twangy guitar, a stomping 2/4 beat, plenty ofyips and yeehaws, and a central country music storytelling trope:the outlaw, gun-brandishing daddy.

      It's VERY country. So many elements. I still don't understand the backlash

    1. 模拟退火与布朗过程算法

      模拟退火与布朗运动在优化问题中的角色解析

      1. 模拟退火(Simulated Annealing, SA)的核心机制

      模拟退火是一种受物理退火过程启发的全局优化算法,通过模拟材料冷却过程中的原子运动来寻找最优解。其核心特点包括:

      • 温度参数的作用
      • 探索与开发的平衡:高温阶段允许算法接受较差的解(避免局部最优),低温阶段逐渐收敛到最优解(局部精细化搜索)。
      • 退火计划(Annealing Schedule):温度按预设策略(如指数衰减)逐步降低,控制解的接受概率 \( P(\Delta E) = e^{-\Delta E / T} \),其中 \( \Delta E \) 为能量变化,\( T \) 为当前温度。

      • 约束条件的处理

      • 罚函数法:将约束违反量转化为目标函数的惩罚项,例如 \( f'(x) = f(x) + \lambda \cdot \text{penalty}(x) \),通过调整 \( \lambda \) 间接控制约束。
      • 修复策略:在生成新解后,调整解以满足约束(如投影到可行域)。
      • 温度与约束的关系:用户提到的“约束条件类似于温度”可能混淆了温度参数与约束处理机制。温度控制搜索的随机性,而非直接施加问题约束。

      • 适用场景

      • 大范围非凸问题:如旅行商问题(TSP)、芯片布局优化。
      • 多峰函数优化:需跳出局部最优的场景。

      2. 布朗运动(Brownian Motion)与优化探索

      布朗运动描述粒子在流体中的无规则随机运动,其数学特性在优化中被借鉴为随机搜索策略:

      • 随机游走(Random Walk)
      • 连续时间 vs. 离散步骤:标准布朗运动是连续时间的随机过程,而优化中的扰动常为离散步骤(如随机梯度下降的噪声注入)。
      • 探索机制:通过随机扰动在解空间中广泛采样,避免过早收敛。

      • 与模拟退火的关联

      • 扰动生成:模拟退火中新解的生成常采用类似布朗运动的随机扰动(如高斯噪声),但受温度调制:高温时扰动幅度大,低温时幅度小。
      • 收敛性差异:布朗运动本身无收敛保证,而模拟退火通过温度下降强制收敛。

      3. 约束条件处理的深入分析

      用户提到的“通过逐步增强约束条件来获得更好的解”需澄清:

      • 约束条件的分类
      • 硬约束(Hard Constraints):必须满足的条件(如物理可行性),常用修复法或拒绝不可行解。
      • 软约束(Soft Constraints):允许轻微违反,通过罚函数融入目标函数。

      • 模拟退火中的约束实现

      • 分阶段优化:初期放宽约束(如增大罚函数系数 \( \lambda \) 的容忍度),后期逐步收紧以逼近可行解。
      • 退火计划与约束收紧的协同:温度下降与约束增强可并行设计,但二者逻辑独立。

      4. 实际应用与算法改进

      • 混合策略案例
      • SA + 局部搜索:在低温阶段结合拟牛顿法进行局部精细化。
      • 约束自适应SA:动态调整罚函数权重 \( \lambda \),使其随温度下降而增大,同步优化目标与约束。

      • 布朗运动的优化变体

      • 随机梯度 Langevin 动力学(SGLD):在梯度下降中注入布朗噪声,平衡探索与收敛速度。
      • 粒子群优化(PSO):多个“粒子”进行布朗式搜索,通过社会信息共享加速收敛。

      5. 对比总结与选择指南

      | 维度 | 模拟退火(SA) | 布朗运动式搜索 | |-------------------|---------------------------------------------|-------------------------------------| | 核心目标 | 全局最优解(兼顾探索与开发) | 广泛探索解空间 | | 收敛性 | 通过温度下降强制收敛 | 无内置收敛机制,需外部控制 | | 约束处理 | 依赖罚函数/修复策略,与温度参数独立 | 需额外设计约束满足机制 | | 计算成本 | 较高(需多次降温迭代) | 中等(依赖扰动步数) | | 适用场景 | 复杂约束、多峰优化问题 | 无约束或简单约束的初步探索阶段 |

      6. 实用建议

      • 问题诊断:若目标函数存在大量局部最优且约束复杂,优先选择模拟退火。
      • 资源分配:计算资源有限时,可先用布朗运动式搜索缩小解空间范围,再启动SA精细化搜索。
      • 参数调优:SA的温度衰减速率需平衡收敛速度与跳出局部最优的能力;布朗运动的步长需适配解空间尺度。

      结论

      模拟退火通过温度调控实现全局搜索与局部收敛的平衡,适合复杂约束下的优化问题,但其约束处理需独立设计;布朗运动式的随机探索更适用于无约束或宽约束场景的初步搜索。两者可结合使用(如SA的扰动阶段引入布朗噪声),但需注意机制差异及参数协同设计。理解问题结构与算法特性是选择方法的关键。

    1. 自旋波振幅变化导致的能量转移,用于逻辑门操作

      非线性效应在自旋波逻辑门中的作用详解


      1. 非线性效应的定义与核心机制

      非线性效应是自旋波相对于光波等线性波的独特特性,指自旋波的振幅变化会直接导致其频率、波长或相位的改变,从而引发能量重新分布。这种效应源于磁性材料中自旋与自旋之间的相互作用(如交换作用、各向异性等),其能量方程不满足线性叠加原理。


      2. 自旋波振幅与能量转移的关系

      (1)振幅变化导致波长/频率偏移

      • 理论基础:<br /> 在磁性材料中,自旋波的能量与振幅呈非线性关系。当自旋波的振幅(功率)增大时,其有效磁场发生变化,导致交换相互作用的强度改变,从而引起波长(或频率)偏移。
      • 公式示意:<br /> $$ \omega(k) = \omega_0(k) + \alpha \cdot \text{Amplitude}^2 $$ 其中,$\omega(k)$为自旋波频率,$\omega_0(k)$为线性项,$\alpha$为非线性系数。
      • 实验验证:<br /> 王棋教授团队(知识库[1])通过纳米级钇铁石榴石(YIG)波导,实现了自旋波波长从微米级到200纳米的动态调控。当输入功率增加时,非线性效应导致波长显著缩短,能量重新分布到更高频率的自旋波模式中。

      (2)能量转移的两种形式

      • 能量重新分配:<br /> 高振幅的自旋波激发新的高阶模式(如二次谐波),能量从基频模式转移到高频模式。
      • 相位-振幅耦合:<br /> 振幅变化引发相位偏移(如$\Delta \phi \propto \text{Amplitude}^2$),通过相位差实现波的干涉和逻辑操作。

      3. 非线性效应在逻辑门中的应用

      (1)逻辑门设计原理

      • 波动性优势:<br /> 自旋波的波特性(干涉、衍射)允许通过相位调控振幅叠加实现逻辑运算,无需传统晶体管的复杂结构。
      • 非线性触发阈值:<br /> 当自旋波振幅超过某个阈值时,非线性效应触发能量转移,产生可区分的输出信号(如振幅突变或相位翻转),对应逻辑“1”或“0”。

      (2)典型逻辑门实现案例

      案例1:半加器设计(王棋团队)
      • 传统电子方案:需要14个晶体管,依赖电荷流动。
      • 自旋波方案(知识库[2]):
      • 结构:3根纳米线构成自旋波导,通过波导间的偶极相互作用和非线性效应实现逻辑操作。
      • 工作原理
        • 输入自旋波的振幅编码逻辑输入(如高振幅为“1”,低振幅为“0”)。
        • 当两个输入自旋波的振幅叠加超过阈值时,非线性效应导致能量转移,产生相位或振幅的突变,对应逻辑“与”或“异或”输出。
      • 优势:器件尺寸缩小,功耗降低(无焦耳热)。
      案例2:异或逻辑门(Louis等人)
      • 结构设计:利用磁纳米点阵列中的点缺陷(知识库[3])。
      • 操作过程
      • 自旋波在波导中传播时,振幅变化导致其与点缺陷的相互作用增强,引发相位偏移。
      • 通过调整点缺陷与波导的距离,控制相移量(如$\Delta \phi = \pi$),实现异或逻辑(输出振幅差异>15 dB)。
      • 优势:无需外加电流,结构简单且功耗极低。
      案例3:马赫-曾德尔干涉仪(Kostylev团队)
      • 结构设计:分束器、移相器和干涉仪组成的自旋波导网络(知识库[3])。
      • 操作过程
      • 输入自旋波被分束为两路,其中一路通过电流调控产生$\pi$相移(非线性效应驱动)。
      • 两路自旋波重新干涉,相位差导致振幅叠加或抵消,实现同或逻辑。
      • 局限:需外加电流,存在功耗和稳定性问题。

      4. 非线性效应的关键优势

      (1)低能耗与稳定性

      • 无焦耳热:逻辑操作通过波的干涉而非电荷流动,能耗比电子器件低2-3个数量级。
      • 自锁定效应(知识库[1]):<br /> 非线性偏移导致自旋波振幅恒定(与输入功率无关),提供稳定的信号源,适合集成磁子电路。

      (2)高集成度与并行性

      • 纳米尺度器件:自旋波波长可缩短至几十纳米(如王棋团队的200纳米自旋波),支持高密度集成。
      • 多频段复用:通过不同振幅激发不同波长的自旋波,实现多通道并行计算。

      (3)抗干扰能力

      • 拓扑保护潜力:结合拓扑磁性材料(如反铁磁材料),非线性效应可增强自旋波对缺陷和热噪声的鲁棒性。

      5. 技术挑战与未来方向

      (1)当前挑战

      • 速度限制:自旋波传播速度(~10⁶ m/s)低于电子,需开发新型材料(如CrSBr)提升速度。
      • 工艺兼容性:纳米级波导与现有CMOS工艺的集成仍需突破。
      • 噪声控制:热激发的自旋波可能干扰信号,需优化材料纯度和结构设计。

      (2)未来方向

      • 超快非线性响应:利用反铁磁材料的皮秒级自旋动力学,实现太赫兹频段逻辑运算。
      • 量子-经典混合系统:结合自旋波的玻色-爱因斯坦凝聚态,探索量子逻辑门。
      • 三维集成架构:通过垂直磁性异质结设计,实现三维自旋波网络以提升集成密度。

      6. 总结

      自旋波的非线性效应通过振幅与能量的动态耦合,为逻辑门操作提供了独特机制。其低能耗、波特性优势和纳米尺度集成潜力,使其成为后摩尔时代计算技术的前沿方向。随着材料科学和微纳加工技术的进步,基于非线性效应的自旋波计算有望在量子计算、类脑芯片和超低功耗AI领域实现突破。


      关键示例与公式

      • 非线性频率偏移公式:<br /> $$ \omega(k) = \omega_0(k) + \alpha \cdot \text{Amplitude}^2 $$
      • 异或逻辑门输出条件:<br /> 当输入振幅满足$A_1 + A_2 > \text{Threshold}$时,输出振幅突变为$A_{\text{out}} = \sqrt{A_1^2 + A_2^2 + 2A_1A_2 \cos(\Delta \phi)}$,其中$\Delta \phi$由非线性相移决定。
      • 自旋波波长调控:<br /> 王棋团队通过纳米波导实现$\lambda = 200$ nm(对应频率$\sim 1.5$ THz),远超传统电子器件的特征尺寸。
    1. Research recommendation 1 To enhance equity, integrate community-engaged approaches into the research design, implementation, evaluation, and dissemination. Examples include inviting community experts as co-leads or co-investigators on the research and including communities most impacted by discrimination to provide input and guidance. Research recommendation 2 To improve generalizability, future studies can recruit more representative samples (e.g. participants reflect organizational, sector, or regional demographics) and test trainings across a variety of sectors. The majority of studies in this review were conducted in higher education or health care, where female employees are overrepresented [64]. Research recommendation 3 Studies of DEI and antiracism trainings can implement more rigorous study design methods to better identify best practices. Specifically, using a randomized controlled design as utilized by Ehrke et al., Chang et al., Holladay and Quinones, and Lindsey [21, 23, 26, 28] allows researchers and practitioners to better isolate the effect(s) of the training being tested, as this kind of design is the strongest for controlling for measured and unmeasured confounding. Additionally, utilizing comparison groups that receive some kind of DEI and/or antiracism training may be more ethical and equitable, so that all participants have some exposure to DEI and/or antiracist content (vs. an experimental group receiving training and a control group receiving no training or resources at all). For example, one study [21] randomly assigned participants to one of three experimental conditions (gender-bias training, general-bias training, or control). Another study used a 2 × 2 × 2 design, where participants were randomly assigned to three conditions: training focus (similarities or differences), trainer gender (male or female), and trainer race (White or Black) [26]. Another study [28] used a 3 × 2 design, where participants were randomly assigned to one of three diversity training methods (perspective taking, goal setting, or stereotype discrediting) and one of two groups of focus (African–Americans or the LGBT community). These methods can be implemented during the pilot phase of new training programs. Research recommendation 4 To measure change over time, studies of DEI and antiracism trainings can include intermediate and long-term follow-up assessments. As only six studies in this review (26.1%) collected and reported follow-up data, and of those, only one study focused assessing program satisfaction rather than intervention impact at the long-term follow-up [20], longer-term effects associated with such trainings remain not well understood. To address this gap, studies recommend an implementation of longitudinal design in diversity training interventions, which would improve data collection and allow researchers to ensure such interventions meet their goals and sustain improvements over time [65–67].The above recommendations may facilitate the movement of DEI and antiracism training studies further along the research translational continuum. Study findings and recommendations should be interpreted within the context of the following limitations. The relatively small number of studies in our review limited our ability to conduct statistical comparisons of efficacy across study characteristics (e.g. multi-session vs. one-time training sessions, theory-informed vs. non-theory-informed interventions, randomized vs. non-randomized design). Of the studies that reported participant demographic data, several included majority female, majority White participants, which may limit the generalizability of findings to other populations. Additionally, studies did not include information on their overall workforce population; thus, participant representation and study findings may not necessarily be representative of the demographics of the organizations and industries from which the studies were conducted.Study designs were moderate-to-weak with respect to causal inference. Combined with high variation in training content, duration, instruments, and outcomes assessed, it was not possible to determine which training characteristics are critical for improving outcomes. Studies also lacked uniformity in reporting the duration of training sessions (e.g. some only reported day-long sessions while others reported duration in hours) and in defining terminology. This study was limited to peer-reviewed research available through search engines and does not include white reports or publicly unavailable research. Depending on the organization, some recommendations may be more feasible than others. Lengthening the duration or frequency of training can represent a challenge in terms of time and resources. Organizations may have limited diversity among employees, thus limiting how representative participants may be with respect to demographics. More rigorous evaluation designs such as randomized conditions or comparison groups may be difficult to implement.In conclusion, findings from this systematic review highlighted conceptual and methodological directions to enhance the delivery and efficacy of DEI and antiracism training interventions in the workplace. Areas for future research include exploring in-depth the intervention design elements that are critical to facilitate improved outcomes, such as examining the mechanisms and efficacy of different training durations, application of various theories and frameworks, and topics and skill sets addressed using strong evaluation designs and longer follow-up time periods. Future community-engaged research across the spectrum of translational research stages is needed to understand the optimal characteristics of DEI and antiracism trainings and how to best integrate these trainings within organizations for impact, sustainability, and scalability.

      This sections of recommendations offers suggestions to improve DEI and antiracism training studies.

    2. Practice recommendation 1 Develop and deliver longitudinal trainings so that concepts and skills are able to be built upon over time. To enhance efficacy, both the frequency and duration of trainings can be extended beyond one-time sessions. Practice recommendation 2 Deepen the curriculum focus beyond individual knowledge and attitudes to include skill building and organizational development to support and sustain individual, interpersonal, and organizational change [55]. Practice recommendation 3 Prioritize skills and behavioral and organizational change as outcomes of interest, using validated or established instruments where possible. While demonstrating improvements in individual knowledge, awareness, and attitudes can be useful in assessing immediate gains from training participation, changes in these domains alone need to be accompanied by changes in skills and actual behavior change (individual, interpersonal, or organizational) in order to advance DEI and antiracism in the workplace. Practice recommendation 4 Use validated measures of DEI and antiracism where relevant. Studies in our review that reported using validated instruments in their outcome assessments include the Reaction-to-Diversity Inventory [56], Ambivalent Sexism Inventory [53], Race Implicit Association Test [34], and the Modern Racism Scale [57]. These four instruments have demonstrated reliability and validity in the studies included in our review as well as others [53,58–60]. Other validated instruments that practitioners and researchers can consider using include the Anti-Racism Behavioral Inventory [61], the Diversity Engagement Survey [62], and the Political Skill Inventory [63].

      These recommendations emphasize the importance of designing DEI and antiracism trainings. Together, these strategies aim to increase the effectiveness/sustainability of DEI initiatives in workplaces.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In the manuscript entitled "Rtf1 HMD domain facilitates global histone H2B monoubiquitination and regulates morphogenesis and virulence in the meningitis-causing pathogen Cryptococcus neoformans" by Jiang et al., the authors employ a combination of molecular genetics and biochemical approaches, along with phenotypic evaluations and animal models, to identify the conserved subunit of the Paf1 complex (Paf1C), Rtf1, and functionally characterize its critical roles in mediating H2B monoubiquitination (H2Bub1) and the consequent regulation of gene expression, fungal development, and virulence traits in C. deneoformans or C. neoformans. Specially, the authors found that the histone modification domain (HMD) of Rtf1 is sufficient to promote H2B monoubiquitination (H2Bub1) and the expression of genes related to fungal mating and filamentation, and restores the fungal morphogenesis and pathogenicity defects caused by RTF1 deletion.

      Strengths:

      The manuscript is well-written and presents the findings in a clear manner. The findings are interesting and contribute to a better understanding of Rtf1-mediated epigenetic regulation of fungal morphogenesis and pathogenicity in a major human fungal pathogen, and potentially in other fungal species, as well.

      Weaknesses:

      A major limitation of this study is the absence of genome-wide information on Rtf1-mediated H2B monoubiquitination (H2Bub1), as well as a lack of detail regarding the function of the Plus3 domain. Although overexpression of HMD in the rtf1Δ mutant restored global H2Bub1 levels, it did not rescue certain critical biological functions, such as growth at 39 °C and melanin production (Figure 4C-D). This suggests that the precise positioning of H2Bub1 is essential for Rtf1's function. A comprehensive epigenetic landscape of H2Bub1 in the presence of HMD or full-length Rtf1 would elucidate potential mechanisms and shed light on the function of the Plus3 domain.

      We thank the reviewer (and other reviewers) for this excellent suggestion. We have conducted CUT&Tag assays with WT, _rtf1_Δ mutant, and complementary strains with the full length Rtf1 and only HMD domain cultured under 30 and 39 °C. We indeed found that the epigenetic landscape of H2Bub1 in the presence of HMD or full-length Rtf1 has variations. This results strongly suggest that the distribution of H2Bub1 is regulated by Rtf1, and H2B modifications at specific loci in the chromosome may contribute to thermal tolerance in C. neoformans. These new findings from CUT&Tag assays shed lights on understanding the mechanism of thermal tolerance, and we decided not to include these results in the current manuscript.

      Reviewer #2 (Public Review):

      Summary:

      The authors set out to determine the role of Rtf1 in Cryptococcal biology, and demonstrate that Rtf1 acts independently of the Paf1 complex to exert regulation of Histone H2B monoubiquitylation (H2Bub1). The biological impact of the loss of H2Bub1 was observed in defects in morphogenesis, reduced production of virulence factors, and reduced pathogenic potential in animal models of cryptococcal infection.

      Strengths:

      The molecular data is quite compelling, demonstrating that the Rtf1-depednent functions require only this histone modifying domain of Rtf1, and are dependent on nuclear localization. A specific point mutation in a residue conserved with the Rtf1 protein in the model yeast demonstrates the conservation of that residue in H2Bub1 modification. Interestingly, whereas expression of the HMD alone suppressed the virulence defect of the rtf1 deletion mutant, it did not suppress defects in virulence factor production.

      Weaknesses:

      The authors use two different species of Cryptococcus to investigate the biological effect of Rtf1 deletion. The work on morphogenesis utilized C. deneoformans, which is well-known to be a robust mating strain. The virulence work was performed in the C. neoformans H99 background, which is a highly pathogenic isolate. The study would be more complete if each of these processes were assessed in the other strain to understand if these biological effects are conserved across the two species of Cryptococcus. H99 is not as robust in morphogenesis, but reproducible results assessing mating and filamentation in this strain have been performed. Similarly, C. deneoformans does produce capsule and melanin.

      We thank the reviewer for the suggestion. We have conducted assays to quantify both capsule and melanin production in both C. neoformans and C. deneoformans strain background. We found that capsule production was affected in the same pattern in these two serotypes. Interestingly, we found the cell size was significantly affected by deletion of RTF1 in both serotypes. In addition, melanin production was reduced due to the deletion of RTF1 in both serotypes; However, complementation with Plus3 or mutated alleles of HMD gave different phenotypes in these two serotypes. These new findings were included Figure 4 in the revised manuscript.

      There are some concerns with the conclusions related to capsule induction. The images reported in Figure B are purported to be grown under capsule-inducing conditions, yet the H99 panel is not representative of the induced capsule for this strain. Given the lack of a baseline of induction, it is difficult to determine if any of the strains may be defective in capsule induction. Quantification of a population of cells with replicates will also help to visualize the capsular diversity in each strain population.

      We thank the reviewer for raising this concern. We have tested capsule production under capsule-inducing condition on 10% fetal bovine serum (FBS) agar medium [1]. Under this condition, the capsule layers surrounding the cells were obvious. We also included noncapsule-producing control in our assay to help the visualization of capsule. In addition, we quantified the ratio between diameters of capsule layer and cell body to show the capsular diversity in each strain population. The results were included in the Figure 4 in the revised manuscript.

      The authors demonstrate that for specific mating-related genes, the expression of the HMD recapitulated the wild-type expression pattern. The RNA-seq experiments were performed under mating conditions, suggesting specificity under this condition. The authors raise the point in the discussion that there may be differences in Rtf1 deposition on chromatin in H99, and under conditions of pathogenesis. The data that overexpression of HMD restores H2Bub1 by western is quite compelling, but does not address at which promoters H2Bub1 is modulating expression under pathogenesis conditions, and when full-length Rtf1 is present vs. only the HMD.

      We thank the reviewer for raising these concerns. Please see our response to Reviewer #1.

      Reviewer #3 (Public Review):

      Summary:

      In this very comprehensive study, the authors examine the effects of deletion and mutation of the Paf1C protein Rtf1 gene on chromatin structure, filamentation, and virulence in Cryptococcus.

      Strengths:

      The experiments are well presented and the interpretation of the data is convincing.

      Weaknesses:

      Yet, one can be frustrated by the lack of experiments that attempt to directly correlate the change in chromatin structure with the expression of a particular gene and the observed phenotype. For example, the authors observed a strong defect in the expression of ZNF2, a known regulator of filamentation, mating, and virulence, in the rtf1 mutant. Can this defect explain the observed phenotypes associated with the RTF1 mutation? Is the observed defect in melanin production associated with altered expression of laccase genes and altered chromatin structure at this locus?

      We completely agree with the reviewer. We have conducted CUT&Tag assay, and checked the Rtf1-mediated H2Bub1 at these particular gene loci. We found that the distribution of H2Bub1 at the promoter region of ZNF2 and the gene body of laccase-encoding gene varied possibly due to RTF1 mutation. We would like to save those preliminary findings for another story and not to include in this manuscript as we mentioned in the response to Reviewer #1.

      (1) Jang, E.-H., et al., Unraveling Capsule Biosynthesis and Signaling Networks in Cryptococcus neoformans. Microbiology Spectrum, 2022. 10(6): p. e02866-22.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The authors show for the first time that deleting GLS from rod photoreceptors results in the rapid death of these cells. The death of photoreceptor cells could result from loss of synaptic activity because of a decrease in glutamate, as has been shown in neurons, changes in redox balance, or nutrient deprivation.

      Strengths:

      The strength of this manuscript is that the author shows a similar phenotype in the mice when Gls was knocked out early in rod development or the adult rod. They showed that rapid cell death is through apoptosis, and there is an increase in the expression of genes responsive to oxidative stress.

      We thank the reviewer for their time reviewing the manuscript and their comments regarding the potential mechanism(s) by which rod photoreceptors rapidly degenerate upon knockout of GLS.

      Weaknesses:

      In this manuscript, the authors show a "metabolic dependency of photoreceptors on glutamine catabolism in vivo". However, there is a potential bias in their thinking that glutamine metabolism in rods is similar to cancer cells where it feeds into the TCA cycle. They should consider that as in neurons, GLS1 activity provides glutamate for synaptic transmission. The modest rescue shown by providing α-ketoglutarate in the drinking water suggests that glutamine isn't a key metabolic substrate for rods when glucose is plentiful. The ERG studies performed on the iCre-Glsflox/flox mice showed a large decrease in the scotopic b wave at saturating flashes which could indicate a decrease in glutamate at the rod synapse as stated by the authors. While EM micrographs of wt and iCre-Glsflox/flox mice were shown for the outer retina at p14, the synapse of the rods needs to be examined by EM.

      We agree with the reviewer that in the presence of sufficient glucose, it appears a lack of GLS-driven glutamine (Gln) catabolism does not drastically alter the levels of TCA cycle metabolites or mitochondrial function as we demonstrated in Figure 4, and supplementation with alpha-ketoglutarate improved outer nuclear layer thickness by only a small amount as observed in Figure 5e. Hence, as we stated in the Results and Discussion, at least in the mouse where Gls is selectively deleted from rod photoreceptors by crossing Gls<sup>fl/fl</sup> mice with Rho-Cre mice (Gls<sup>fl/fl</sup>; Rho-Cre<sup>+</sup>, cKO), Gln’s role in supporting the TCA cycle is not the major mechanism by which rod photoreceptors utilize Gln to suppress apoptosis.

      With regards to GLS-driven Gln catabolism providing glutamate (Glu) for synaptic transmission, we again agree with the reviewer that Glu is an important excitatory neurotransmitter, but it is also a key metabolite necessary for the synthesis of glutathione, amino acids, and proteins. As noted and discussed at length in the manuscript, a lack of GLS-driven Gln catabolism in rod photoreceptors leads to reduced levels of oxidized glutathione (Figure 4D) possibly signaling an overall reduction in the biosynthesis of glutathione as Glu is directly and indirectly responsible for its synthesis. Furthermore, Gln and GLS-derived Glu play a central role in the biosynthesis of several nonessential amino acids and proteins. To this end, we see a reduction in the level of Glu, which is the product of the GLS reaction and further confirms the loss of GLS function. We also noted a significant decrease in aspartate (Asp), which can be constructed from the carbons and nitrogens of Gln as discussed at length in the manuscript (Figure 6A). Finally, we noted a significant decrease in global protein synthesis in the cKO retina as compared to the wild-type animal as well (Figure 6E). Therefore, the data suggest that GLS-driven Gln catabolism is critical for amino acid metabolism and protein synthesis and to some degree redox balance; although, the small but statistically significant changes in oxidized glutathione, NADP/NADPH, and redox gene expression may not fully account for the rapid and complete photoreceptor degeneration observed. Future studies are necessary to shed light on the role of redox imbalance in this novel transgenic mouse model.

      Glu also plays a role in synaptic transmission, and we considered this scenario as described in Figure 1 – figure supplement 5. Here, the synaptic connectivity between photoreceptors and the inner retina did not demonstrate significant differences in the labeling of photoreceptor synaptic membranes in the outer plexiform layer nor alterations in the labeling of a key protein (Bassoon) in ribbon synapses. These data suggest that the synaptic connectivity between photoreceptors and second-order neurons was unaltered at P14 in the cKO retina, which is the time just prior to rapid photoreceptor degeneration when Glu was shown to be decreased (Figure 6A).

      With regards to the ERG changes noted in Figure 2, we agree with the reviewer that a large decrease was noted in the scotopic b-wave at P21 and P42 in the cKO. We also agree, that to obtain greater insight into these ERG changes, the ribbon synapse in EM images can be examined. The EM images shown in Figure 1 – figure supplement 4 are from P21, which coincide with the age at which the ERG changes were first noted and when significant photoreceptor degeneration has already occurred. These images were utilized to assess the ribbon synapse for the revised version of the manuscript. As now shown in Figure 1 – figure supplement 4D, ribbon synapses are intact in WT animals as denoted by the yellow boxes. Similarly, the ribbons (yellow arrows) appear structurally intact in the photoreceptors that remain in the P21 cKO retina. These results are in accordance with the lack of significant differences in the labeling of photoreceptor synaptic membranes in the outer plexiform layer as well as the lack of alterations in the labeling of a key protein (Bassoon) in ribbon synapses (Figure 1-figure supplement 5A and B).  While we cannot fully rule out that the decrease in glutamate is altering synaptic transmission, our structural data suggests the synapses remain intact. These data have been added to the revised manuscript.

      However, an even larger reduction in the scotopic a-wave was noted at these ages as well. In animal models that disrupt photoreceptor synaptic function (Dick et al. Neuron. 2003; Johnson et al. J Neuroscience. 2007; Haeseleer et al. Nature Neuroscience. 2004; Chang et al. Vis Neurosci. 2006), a more negative ERG pattern is typically observed with the b-wave altered to a much larger degree than the a-wave. Additionally, in these models that disrupt photoreceptor synaptic transmission, the overall structure of the retina with respect to thickness is maintained (Dick et al. Neuron. 2003) or noted to have modest changes in the outer plexiform layer within the first two months of age with the outer nuclear layer not significantly altered until 8-10 months of age (Haeseleer et al. Nature Neuroscience. 2004). In contrast, a rapid decline in the outer nuclear layer thickness was observed in the cKO retina after P14 likely contributing to the ERG changes noted in Figure 2. Also, Gln is catabolized to Glu primarily by GLS as suggested by the approximately 50% reduction in Glu levels in the cKO retina (Figure 6A), but other enzymes are also capable of catabolizing Gln to Glu, so Glu levels in the rod photoreceptors are unlikely to be zero. Coupling this with the fact that rods are equipped with a self-sufficient Glu recollecting system at their synaptic terminals (Hasegawa et al. Neuron. 2006; Winkler et al. Vis Neurosci. 1999) and that GLS activity is at least two-fold higher in the photoreceptor inner segments, which support energy production and metabolism, than any other layer in the retina (Ross et al. Brain Res. 1987) suggests that altered synaptic transmission secondary to reduced levels of Glu likely does not account in full for the rapid and robust photoreceptor degeneration observed in the cKO retina.

      The authors note that the outer segments are shorter but they do not address whether there is a decrease in the number of cones.

      We have adjusted Figure 2E by removing the GLS staining to better highlight the secondary degeneration of cone outer segments, the main point of the Figure, as we had already shown that GLS was cleanly knocked out of rod photoreceptors in Figure 1. Furthermore, qualitatively the number of cones appears the same at P14, P21, and P42 between the WT and cKO, which is consistent with other retinal degeneration models, like rd1 and rd10, where cones do not begin to die until all the rods have degenerated (Xue et al. eLife. 2021).

      Rod-specific Gls ko mice with an inducible promoter were generated by crossing the Pde6g-CreERT2 and homozygous for either the WT or floxed Gls allele (IND-cKO). In Figure 3 the authors document that by western blots and antibody labeling the GLS1 expression is lost in the IND-cKO 10 days post tamoxifen. OCT images show a decrease in the thickness of the outer nuclear layer between 17 and 38 days post-TAM. Ergs should be performed on the animals at 10 and 30 days post TAM, before and after major structural changes in rod photoreceptor cells, to determine if changes in light-stimulated responses are observed. These studies could help to parse out the cause of photoreceptor cell death.

      We agree with the reviewer that the IND-cKO is a useful tool to help parse out the cause of photoreceptor cell death in this model as well as shed light on the role of GLS-driven Gln catabolism in photoreceptor synaptic transmission as discussed at length above. Hence, ERG analyses were performed 10 days post TAM, before major structural changes in the ONL are observed. Interestingly, ERG demonstrated statistically significant reductions in the IND-cKO scotopic a- and b-waves as compared to the WT 10 days post TAM. Similarly, photopic ERG demonstrated statistically significant decreases in the b-wave of the IND-cKO retina. These data suggest that GLS-driven Gln catabolism plays a significant role not only in rod photoreceptor survival but their function as well. This data has been added to Figure 3H-I and discussed in the corresponding manuscript text.

      To this end, as discussed below and added to Figure 6 – figure supplement 1, amino acid levels, including glutamate (Glu), are already reduced 10 days post TAM. Reductions in the level of Glu may impact synaptic transmission and as a result, the scotopic b-wave. However, as noted above, altered synaptic transmission secondary to reduced levels of Glu likely does not account in full for the rapid and robust photoreceptor degeneration observed in the cKO retina as the b-wave to a-wave ratio is not significantly altered in the IND-cKO retina as compared to the WT retina, suggesting GLS-driven Gln catabolism is impairing both to a similar degree.

      Additionally, Pde6g is expressed by rods to a significant degree but also by cones (GSE63473, scRNAseq data). Therefore, the IND-cKO mouse likely knocks out GLS from both rods and cones, which is in accordance with the immunofluorescence image in Figure 3B where GLS is not observed in rod or cone inner segments unlike in Figure 1B where GLS remains in cones. Hence, the reduction in photopic b-wave may be demonstrating that GLS-driven Gln catabolism in cones impairs synaptic transmission. As noted in our reply to reviewer #3’s comments, we have generated mice lacking GLS in cone photoreceptors specifically and are currently elucidating the role of GLS in cone photoreceptor metabolism, function, and survival. These results will be published in a separate manuscript.

      The studies in Figure 4 were all performed on iCre-Glsflox/flox and control mice at p14, why weren't the IND-cKO mice used for these studies since the findings would not be confounded by development?

      To gain further insight into the role of GLS-driven Gln catabolism in the maintenance of rod photoreceptors as compared to their development/maturation, we conducted a targeted metabolomic analysis on IND-cKO and WT retinas 10 days post TAM. For the purpose of this manuscript, we have included data regarding changes in amino acid levels in Figure 6 – figure supplement 1. Specifically, levels of glutamate, aspartate and asparagine are all significantly decreased in the IND-cKO retina prior to PR degeneration, which demonstrates that similar to the GLS cKO mouse (i.e. iCre-Gls flox/flox), GLS-driven Gln catabolism is critical for amino acid biosynthesis in mature rod PRs as well.

      In all rescue studies, the endpoint was an ONL thickness, which only addressed rod cell death. The authors should also determine whether there are small improvements in the ERG, which would distinguish the role of GLS in preventing oxidative stress.

      Optical coherence tomography (OCT) provides a sensitive in vivo method to detect small changes in retinal thickness without potential artifacts incurred through histological processing. Considering the Gls cKO retina demonstrates significant and rapid photoreceptor degeneration, we wanted to assess pathways that may be critical to photoreceptor survival downstream of GLS-driven Gln catabolism using rescue experiments with pharmacologic treatment or metabolite supplementation. That said, disruption of GLS-driven Gln catabolism may also significantly alter rod photoreceptor function beyond that which is secondary to photoreceptor cell death as we have demonstrated in the IND-cKO animal for the revised version of this manuscript and discussed in a response above. Therefore, the IND-cKO model provides a unique tool to assess the impact of rescue studies on photoreceptor function as the functional changes occur prior to significant degeneration. Also, unlike the GLS cKO mouse (i.e. iCre-Gls flox/flox) where photoreceptor degeneration starts very early, impairing our ability to capture reliable and robust ERG measurements, the IND-cKO mice are older at the time of functional changes allowing for robust ERG measurements. While the rate of photoreceptor degeneration in both mouse models is similar and the levels of key amino acids are altered similarly in both models, the mechanisms of cell death in developing/maturing photoreceptors may be different than that in mature photoreceptors. Hence, before we can assess if similar rescue experiments impact photoreceptor function via ERG in the IND-cKO mouse, we need to thoroughly examine how these photoreceptors are dying. These experiments and results will be published in a separate manuscript in the future.

      Reviewer #2 (Public Review):

      Summary:

      Photoreceptor neurons are crucial for vision, and discovering pathways necessary for photoreceptor health and survival can open new avenues for therapeutics. Studies have shown that metabolic dysfunction can cause photoreceptor degeneration and vision loss, but the metabolic pathways maintaining photoreceptor health are not well understood. This is a fundamental study that shows that glutamine catabolism is critical for photoreceptor cell health using in vivo model systems.

      Strengths:

      The data are compelling, and the consideration of potential confounding factors (such as glutaminase 2 expression) and additional experiments to examine the synaptic connectivity and inner retina added strength to this work. The authors were also careful not to overstate their claims, but to provide solid conclusions that fit the results and data provided in their study. The findings linking asparagine supplementation and the inhibition of the integrated stress response to glutamine catabolism within the rod photoreceptor cell are intriguing and innovative. Overall, the authors provide convincing data to highlight that photoreceptors utilize various fuel sources to meet their metabolic needs, and that glutamine is critical to these cells for their biomass, redox balance, function, and survival.

      We greatly appreciate the reviewer’s thoughtful comments and time spent reviewing this manuscript.

      Weaknesses:

      Recent studies have explored the metabolic "crosstalk" that exists within the mammalian retina, where metabolites are transferred between the various retinal cells and the retinal pigment epithelium. It would be of interest to test whether the conditional knockout mice have changes in metabolism (via qPCR such as shown in Figure 4 - Supplemental Figure 1) within the retinal pigment epithelium that may be contributing to the authors' findings in the neural retina. Additionally, the authors have very compelling data to show that inhibition of eIF2a or supplementation with asparagine can delay photoreceptor death via OCT measurements in their conditional knockout mouse model (Figure 6G, H). However, does inhibition of eIF2a or asparagine adversely impact the WT retina? It would also be impactful to know whether this has a prolonged effect, or if it is short-term, as this would provide strength to potential therapeutic targeting of these pathways to maintain photoreceptor health.

      We agree with the reviewer that metabolic communication in the outer retina is crucial to the function and survival of both photoreceptors and RPE. Therefore, we have performed qRT-PCR on eyecups from cKO and WT mice at P14, prior to photoreceptor degeneration. These data, now included in Figure 4 – figure supplement 2, show no significant changes in genes related to glycolysis, pyruvate metabolism and the TCA cycle in eyecups from cKO mice compared to WT mice at P14. The only exception is a significant decrease in Pdk4 in cKO mouse eyecups compared to WT, which was not observed in retina samples.

      Additionally, we have added data demonstrating that systemic treatment with ISRIB does not adversely impact the anatomy of the wild-type retina. Specifically, we performed OCT after 21 days of ISRIB treatment via intraperitoneal delivery in WT mice and show that total retinal, ONL and inner segment/outer segment thickness is unchanged compared to vehicle. These data are now included in Figure 6 – figure supplement 2A. We have also included data to suggest that the effect of ISRIB extends beyond P21 in the cKO mouse. This data, presented in Figure 6 – figure supplement 2B, shows that at P28, ISRIB continues to statistically significantly increase ONL thickness compared to vehicle in cKO animals.

      Reviewer #3 (Public Review):

      Summary:

      The authors explored the role of GLS, a glutaminase, which is an enzyme that catalyzes the conversion of glutamine to glutamate, in rod photoreceptor function and survival. The loss of GLS was found to cause rapid autonomous death of rod photoreceptors.

      Strengths:

      Interesting and novel phenotype. Two types of cre-lines were rigorously used to knockout the Gls gene in rods. Both of the conditional knockouts led to a similar phenotype, i.e. rod death. Histology and ERG were carefully done to characterize the loss of rods over specific ages. A necessary metabolomic study was performed and appreciated. Some rescue experiments were performed and revealed possible mechanisms.

      We thank the reviewer for their comments and appreciation of the methods utilized herein to address the role of GLS-driven Gln catabolism in rod photoreceptors.

      Weaknesses:

      No major weaknesses were identified. The mechanism of GLS-loss-induced rod death seems not fully elucidated by this study but could be followed up in the future, and the same for GLS's role in cones.

      We agree with the reviewer that the downstream metabolic and molecular mechanisms by which Gln catabolism impacts rod photoreceptor health are not fully elucidated. Defining these mechanisms will advance our understanding of photoreceptor metabolism and identify therapeutic targets promoting photoreceptor resistance to stress. Future studies are underway to uncover these mechanisms. Additionally, while outside the scope of the current manuscript, we have generated mice lacking GLS in cone photoreceptors specifically and are currently elucidating the role of GLS in cone photoreceptor metabolism, function, and survival. These results will be published in a separate manuscript.

      Reviewer #1 (Recommendations For The Authors):

      (1) The results could start at line 135, but the first paragraph isn't necessary. The data is published and could be referred to in the introduction.

      We appreciate the reviewer’s suggestion to shorten the beginning of the Results section; however, we believe the supplementary data, which is described in these lines, confirms the scRNAseq gene expression data, while adding GLS expression and localization data within the retina. The scRNAseq data and its publication was noted in the introduction, so we removed the sentence in line 117-119 that restates these results to shorten this section. We also reduced redundancy by removing an introductory sentence to the second Results paragraph.

      (2) "However, like other metabolically-demanding cells, recent work has demonstrated that PRs have the flexibility to utilize fuel sources beyond glucose to meet their metabolic needs (Adler et al., 2014; Du, Cleghorn, Contreras, Linton, et al., 2013; Grenell et al., 2019; Joyal et al., 2016; Xu et al., 2020)." The paper by Daniele et al. demonstrated that glucose is essential for maintaining the viability of rod photoreceptor cells.

      We thank the reviewer for highlighting published literature, which we apologetically overlooked. The reference for Daniele et al. has now been included.

      (3) "Single-cell RNA sequencing data has demonstrated that Gls is expressed throughout the human and mouse retina and much greater than Gls2 (Voigt et al., 2020). The authors should indicate the specific databases searched in Spectacle.

      We appreciate the reviewer’s attention to detail and have now included the references in the Introduction for GSE63473 from Macosko et al. and GSE142449 from Voigt et al., which were the databases we used in Spectacle to assess Gls levels in the mouse and human retina, respectively.

      References:

      (1) Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015 May 21;161(5):1202-1214. doi: 10.1016/j.cell.2015.05.002. PMID: 26000488; PMCID: PMC4481139.

      (2) Voigt AP, Binkley E, Flamme-Wiese MJ, Zeng S, DeLuca AP, Scheetz TE, Tucker BA, Mullins RF, Stone EM. Single-Cell RNA Sequencing in Human Retinal Degeneration Reveals Distinct Glial Cell Populations. Cells. 2020 Feb 13;9(2):438. doi: 10.3390/cells9020438. PMID: 32069977; PMCID: PMC7072666.

      (4) The immunolabeling in Figure 2 looks like the images are overexposed, and the Gls antibody is labeling the outer segment, not just the inner segment of photoreceptors.

      We thank the reviewer for their comments regarding our immunofluorescence data. There was background staining of the outer segment in both the WT and cKO retina with decreased GLS staining in the inner segment of the cKO rod photoreceptors at P14 demonstrating loss of GLS in rod photoreceptors similar to Figure 1B.  For Figure 2E, we have provided adjusted images with PNA staining only that better represent the secondary cone degeneration that occurs in the rod photoreceptor-specific Gls cKO, which is the take home point of Figure 2E.

      (5) The authors could use a glutamate antibody to compare it to Gls KO mice as done in Davanger, S., Ottersen, O.P. and Storm-Mathisen, J. (1991), Glutamate, GABA, and glycine in the human retina: An immunocytochemical investigation. J. Comp. Neurol., 311: 483-494. https://doi.org/10.1002/cne.903110404

      We appreciate the reviewer’s suggestion to assess glutamate levels in the wild-type and Gls KO retina via antibody labeling. Our targeted metabolomics studies in Figure 6A provide quantitative evidence that glutamate, the product of the GLS-catalyzed reaction, is decreased as one would expect in that Gls KO retina. The antibody would add to these data by providing the localization of glutamate in the retina. With a rod photoreceptor-specific genetic KO, we would expect glutamate levels to be decreased in these cells. The antibody may also show that glutamate is not only decreased in the rod photoreceptor inner segment, where GLS predominates, but also in the synaptic terminal in accordance with the reviewer’s concerns regarding the impact of GLS KO on synaptic transmission. We have addressed this concern at length above, adding TEM images of the ribbon synapses in the GLS KO retina, and ERG analyses from the IND-cKO animals prior to significant degeneration. In the end, we agree with the reviewer that reduced Glu levels in the GLS cKO retina may impact synaptic transmission to a degree, but the synapses remain intact based on immunofluorescence and TEM analyses and a negative ERG pattern is not observed in the GLS cKO (i.e. iCre-Gls flox/flox) or IND-cKO mouse. As noted above, the structure of the retina in models that disrupt photoreceptor synaptic transmission is maintained (Dick et al. Neuron. 2003) or noted to have modest changes within the first two months of age with the outer nuclear layer not significantly altered until 8-10 months of age (Haeseleer et al. Nature Neuroscience. 2004). So, the impact of the reduced Glu levels on synaptic transmission in the GLS KO retina are unlikely to account in full for the rapid and profound photoreceptor degeneration observed. That said, the IND-cKO mouse, which allows us to assess photoreceptor function prior to significant degeneration unlike the GLS cKO mouse (i.e. iCre-Gls flox/flox), demonstrates GLS-driven Gln catabolism plays a significant role in photoreceptor function but still does not demonstrate a negative ERG pattern. Therefore, assessing Glu localization in this mouse model 10 days post TAM will be informative as to how GLS-driven Gln catabolism impacts photoreceptor function prior to degeneration. The IND-cKO mouse model is currently being extensively characterized for future publication.

      Reviewer #2 (Recommendations For The Authors):

      Main Concerns:

      (1) The authors checked for Gls2 compensation at P14 in the mouse retina. However, this data would be more compelling with an additional timepoint, particularly at P21 which is used in many of their figures throughout the study.

      We thank the reviewer for their suggestion. Figure 1-figure supplement 1D demonstrates no change in Gls2 gene expression at P14 between the WT and cKO retina. With regards to the reviewer’s concern, in Figure 1-figure supplement 1E of the original submission, we demonstrate that the expression of GLS2 is not increased in the cKO retina at P21 via immunofluorescence.

      (2) Recent studies have explored the metabolic "crosstalk" that exists within the mammalian retina, where metabolites are transferred between the various retinal cells and the retinal pigment epithelium. It would be compelling to see whether the cKO mice have changes in metabolism (via qPCR such as shown in Supplementary Figure 1 for Figure 4) within the RPE that may be contributing to their findings in the neural retina. Additionally, mention of this crosstalk and how it may impact their results should be added to the discussion.

      We appreciate the reviewer’s concern for metabolism changes in the RPE of Gls cKO mice. In agreement with reviewer 2, we performed qRT-PCR on eyecups from cKO and WT mice at P14, prior to photoreceptor degeneration. These data, now included in Figure 4 – figure supplement 2, show no significant changes in genes related to glycolysis, pyruvate metabolism and the TCA cycle in eyecups from cKO mice compared to WT mice at P14. The only exception is a significant decrease in Pdk4 in cKO mouse eyecups compared to WT, which was not observed in retina samples.

      (3) The authors use a tamoxifen-inducible cKO model to support their findings in developed rods. However, in Figure 3A it appears that this model has a greater reduction in GLS compared to the Rho-cre mouse model. Can the authors discuss this? Is this cre more efficient at targeting rods or is it leaky and may have affected other retinal cells?

      We thank the reviewer for pointing out this interesting result associated with using the Pde6g-Cre-ERT2 mouse line. Pde6g is expressed by rods to a significant degree but also by cones (GSE63473, scRNAseq data). Therefore, the IND-cKO mouse likely knocks out GLS from both rods and cones upon the TAM induction. To this end, the immunofluorescence image in Figure 3B shows GLS is knocked out in both rod or cone inner segments unlike in Figure 1B where GLS remains in cones when using the rod photoreceptor-specific, Gls<sup>fl/fl</sup> Rho-Cre<sup>+</sup> mouse. As such, as the astute reviewer noted, the fact that Western blot demonstrates greater reduction in GLS protein content fits with the protein being knocked out of both rods and cones. We have added this note about the mouse model in the corresponding text.

      (4) The authors have very compelling data to show that inhibition of eIF2a can delay photoreceptor death via OCT measurements in their cKO mouse model (Figure 6G). However, does ISRIB adversely impact the WT retina? WT vehicle and ISRIB should be shown. It would also be compelling to know whether this has a prolonged effect, or if it is short-term (i.e. would the effect still be present at P42)?

      We appreciate the reviewer’s comments regarding antagonizing the effects of p-eIF2a to prolong photoreceptor survival in the Gls cKO retina. As described above, we have data demonstrating systemic treatment with ISRIB does not adversely impact the anatomy of the wild-type retina (Figure 6-figure supplement 2A). Specifically, we treated WT animals with daily intraperitoneal ISRIB starting at P5 and performed OCT at P21 to show that total retinal, ONL and the inner segment/outer segment thickness is unchanged compared to vehicle-treated WT animals. Additionally, we have included data demonstrating the photoreceptor neuroprotective effect of ISRIB treatment in the Gls cKO mouse extends beyond P21 in the cKO mouse (Figure 6-figure supplement 2B).

      (5) For Figure 6H, same as point #4.

      While we have not specifically assessed potential retinal toxicity secondary to systemic Asn supplementation, oral Asn supplementation (up to 100mg/kg/day) was provided to patients for 24 months and found to be well-tolerated (PMID:31123592). Allometric scaling of this dose to the mouse would yield a mouse dose of 1234 mg/kg/day, which is much greater than the 200mg/kg/day dose provided here (PMID: 27057123). Additionally, a 90-day toxicity study of Asn in rats demonstrated a no observed adverse effect level of 1.62g/kg bodyweight/day in males and 1.73g/kg bodyweight/day in females (PMID: 18508175). The lower dose in that study equates to a mouse dose of 3.2g/kg bodyweight/day, well above the mouse dose utilized in this report. As such, future studies should focus on a dose-response relationship with Asn supplementation, and as the reviewer suggested, determining the duration of effect with Asn supplementation.

      (6) Some of the results section belongs in the introduction or discussion and can be moved.

      We have addressed the reviewer’s concern by moving some of the results to the discussion and removing statements in the results that were either noted in the Introduction or conferred in the Discussion.

      Minor Concerns:

      (1) Scale bar mentions in the figure legends use plural when only one is present, or in some cases are missing. A scale bar should be added to the OCT images if possible.

      We appreciate the reviewer’s attention to detail, and information regarding scale bars has been updated in the figure legends.

      (2) For Figures 1I and J, the sample size changes when J is a quantification of I. Please correct.

      We have corrected the sample size to be consistent between Figures 1I and J.

      (3) In Figure 1 - Figure Supplement 3 the P42 timepoint is not mentioned in the legend. Please correct.

      We have now included the P42 timepoint in the legend for in Figure 1 – Figure Supplement 3 as well as the manuscript text.

      (4) In Figure 1 - Figure Supplement 5 the wrong P value is mentioned in the legend. Please correct.

      We have corrected the P value in the legend for Figure 1 – Figure Supplement 5.

      (5) Can the authors double-check their ERG light intensity settings? They seem high. Please confirm if they are correct.

      We appreciate the reviewer’s concern for ERG light intensity settings and have confirmed the settings used in the study were 32 cd*s/m<sup>2</sup> and 100 cd*s/m<sup>2</sup> for scotopic and photopic ERG recordings, respectively.

      (6) The legend key in Figure 2A would be more helpful if the axis were present by the representative traces.

      We thank the reviewer for the suggestion of adding axes to the ERG traces. Figure 2A has been updated to reflect this modification.

      (7) Can the authors check that the error bars are present in Figure 5E?

      We appreciate the reviewer’s concern for error bars in Figure 5E, which are included in the figure. The standard error in this experiment is so small that the symbols overlap with the error bars.

      Reviewer #3 (Recommendations For The Authors):

      Suggestions for improved or additional experiments, data, or analyses.

      (1) Figure 6: ISRIB seems to give the most dramatic rescue of cKO GLS in P21 rods. Does it completely prevent rod death? i.e. What's the ONL thickness of P21 WT control? What's the ISRIB rescue of an older cKO animal, say P35?

      The ONL thickness of P21 WT control is on average 0.06 mm (Figure 1E), while the ONL thickness of the Gls cKO retina with ISRIB treatment at P21 is on average 0.044 mm. Therefore, rod death is not completely prevented with ISRIB but rather, rod photoreceptor survival is prolonged. As noted above, we have provided data to demonstrate that the photoreceptor neuroprotective effect of ISRIB lasts beyond P21 (Figure 6-figure supplement 2B).

      (2) What's the mechanistic link between ISR and GLS beyond current speculation? Does GLS have other unknown functions beyond converting glutamine to glutamate? Any novel insights from GLS protein structure?

      We thank the reviewer for this thoughtful question. It is certainly possible that GLS has other functions outside of its role in glutaminolysis. It is well known that other metabolic enzymes have moonlighting functions including hexokinase 2, which has been shown to be important in preventing intrinsic apoptosis through blocking the binding of pro-apoptotic proteins to the mitochondria. While not directly related to ISR, a single report suggests GLS functions non-canonically in Gln-deprived states, promoting mitochondrial fusion to suppress ROS production (PMID: 29934617). Investigating the moonlighting functions of metabolic enzymes is part of our ongoing research program and GLS is included in these studies.

      (3) Just curious about GLS cKO in cones. Any similar phenotype?

      We appreciate the reviewer’s curiosity regarding Gls cKO in cones and this study is currently ongoing with a poster presented at ARVO 2024 (Subramanya et al; Glutaminase-driven glutamine catabolism supports cone photoreceptor metabolism, function, and structure. Invest. Ophthalmol. Vis. Sci. 2024;65(7):193) and a manuscript in preparation. As discussed above, GLS knock out in cones likely impacts their function, in accordance with the data presented at ARVO 2024.

      Recommendations for improving the writing and presentation.

      (1) In the Discussion, lines 458-466, it's incorrect to compare the importance of glucose metabolism to GLS-dependent pathway to photoreceptors in this way. An alternative explanation: glucose metabolism is so important that the system has many redundancies, e.g. HK1 exists in addition to HK2, thus single gene KO leads to no phenotype. The only fair comparison is nutrient deprivation, e.g. taking out glucose or glutamine from retina explants (Punzo et al., 2009).

      The reviewer makes an excellent point. While we do not see an upregulation of GLS2 in the retina or rod PRs upon GLS knockout (Figure 1-figure supplement 1 D and E), loss of Gls in rod PRs does alter the expression of many metabolism-related genes (Figure 4-figure supplement 1).  We alluded to these data and the reviewer’s point in the second paragraph of the discussion: “In any of these transgenic mouse models, PRs may use other transporters to take up fatty acids or glucose or rewire their metabolism to maintain metabolic homeostasis and stave off degeneration (Subramanya et al., 2023; Wubben et al., 2017). Our data show that any metabolic reprogramming that is occurring in the cKO mouse retina appears unable to significantly circumvent the significant and rapid PR degeneration suggesting the importance of Gln catabolism in rod PRs. Furthermore, inducing GLS knockdown in mature PRs also demonstrated rapid PR degeneration (Figure 3).”

      In the revised article, we have amended these sentences to include the importance of metabolic redundancies. “In any of these transgenic mouse models, PRs may use other transporters to take up fatty acids or glucose, rewire their metabolism, or utilize metabolic redundancies to maintain metabolic homeostasis and stave off degeneration (Subramanya et al., 2023; Wubben et al., 2017). Our data show that any metabolic reprogramming that is occurring in the cKO mouse retina appears unable to significantly circumvent the significant and rapid PR degeneration suggesting the importance of Gln catabolism in rod PRs. Furthermore, inducing GLS knockdown in mature PRs also demonstrated rapid PR degeneration (Figure 3).”

      (2) Please discuss the mosaic activity of Rho-cre used in this study, as described in the original study (Le et al 2006). Line 221 (Li et al 2005) seems to be a different Rho-Cre created by a different group. Please make sure the citation is correct and consistent.

      We apologize for the confusion and have corrected the reference on line 221 to Le et al, 2006. The reviewer is correct that the original report (Le at al. 2006) demonstrated a mosaic of Cre-mediated recombination in rod photoreceptors and rod bipolar cells in the mouse line that had the shorter (0.2 kb) mouse opsin promoter-controlled Cre. In contrast, this same report showed only Cre-mediated recombination in rod photoreceptors in another line that utilized a long (4.1 kb) mouse opsin promoter-controlled Cre. We have published using this latter promoter-controlled Cre recombinase in at least 5 different mouse models (Wubben et al. 2017; Weh et al. 2020; Weh et al. 2023; Subramanya et al. 2023; the current report), and in all these models, we observe clear and consistent knockout by immunofluorescence only in rod photoreceptors with residual protein in cones and no significant change in protein expression in the INL where bipolar cells reside. Western blots confirm the reduction in protein expression.

      (3) The authors should provide representative images of retina cross-sections for key rescue data (Figure 6G&H).

      As requested by Reviewer 3, representative histology images of retina cross-sections for the ISRIB and Asn rescue experiments in Gls cKO mice at P21 are now included in the manuscript in Figure 6 – figure supplement 3.

      Minor corrections to the text and figures.

      (1) Spell out Gln in the Abstract when used for the first time.

      We have included glutamine (Gln) in the abstract upon first use.

      (2) Line 433, Figure 6G should be 6H.

      Thank you for the correction, the manuscript has been updated.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public Review):

      (1) The rationale for performing genomics, transcriptional, and proteomics work in 293T cells is not discussed. Further, there are no functional readouts mentioned in the 293T cells with expression of the fusion-oncogenes. Did these cells have any phenotypes associated with fusion-oncogene expression (proliferation differences, morphological changes, colony formation capacity)? Further, how similar are the gene expression signatures from RNA-seq to rhabdomyosarcoma? This would help the reader interpret how similar these cell models are to human disease.

      We appreciate the reviewer’s comments and understand the limitation of HEK293T cell culture. HEK293T cells were used as a surrogate system that enabled us to systemically examine and compare the transcriptional activation mechanisms between VGLL2-NCOA2/TEAD1-NCOA2 and YAP/TAZ. HEK293T cells have previously been used as a model system to study the signaling and transcriptional mechanisms of the Hippo/YAP pathway (1,2). Our data also showed that the ectopic expression of VGLL2-NCOA2 and TEAD1-NCOA2 in HEK293 cells can promote proliferation (Figure 1-figure supplement 1B), consistent with their potential oncogenic function.

      (2) TEAD1::NCOA2 fusion-oncogene model was not credentialed past H&E, and expression of Desmin. Is the transcriptional signature in C2C12 or 293T similar to a rhabdomyosarcoma gene signature?

      We understand the reviewer’s concern. VGLL2-NCOA2 in vivo tumorigenesis model generated by C2C12 cell orthotopic transplantation has recently been reported, and it exhibits similar characteristics with zebrafish transgenic tumors as well as human scRMS samples that carry the VGLL2-NCOA2 fusion (3). Due to the similar transcriptional and oncogenic mechanisms employed by both VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins, we expect that the TEAD1-NCOA2 dependent C2C12 transplantation model will closely resemble that induced by VGLL2-NCOA2.

      (3) For the fusion-oncogenes, did the HA, FLAG, or V5 tag impact fusion-oncogene activity? Was the tag on the 3' or 5' of the fusion? This was not discussed in the methods.

      To address the reviewer’s concern, we carefully compared the transcriptional activity of the fusion proteins with the HA tag at the 5’ end or FLAG and V5 tag at the 3’ end. We found that neither the tag type nor its location significantly affects the ability of VGLL2-NCOA2 and TEAD1-NCOA2 to induce downstream gene transcription, measured by qPCR. The data is summarized in Figure 1-figure supplement 1 G-H.

      (4) Generally, the lack of details in the figures, figure legends, and methods make the data difficult to interpret. A few examples are below:

      a. Individual data points are not shown for figure bar plots (how many technical or biological replicates are present and how many times was the experiment repeated?).

      As requested, we have added the individual data points to the bar plots. The Method section now includes information on the number of biological replicates and the times the experiments were repeated.

      b. What exons were included in the fusion-oncogenes from VGLL2 and NCOA2 or TEAD1 and NCOA2?

      We have now included the exon structure organization of VGLL2-NCOA2 or TEAD1-NCOA2 fusions in Figure 1-figure supplement 1A.

      c. For how long were the colony formation experiments performed? Two weeks?

      We have included more detailed information about the colony formation assay in the Methods section.

      d. In Figure 2D, what concentration of CP1 was used and for how long?

      The CP1 concentration and treatment duration information has now been included in the figure legend and Methods section.

      e. How was A485 resuspended for cell culture and mouse experiments, what is the percentage of DMSO?

      The Methods section now includes detailed information on how A485 is prepared for in vitro and in vivo experiments.

      f. How many replicates were done for RNA-seq, CUT&RUN, and ATACseq experiments?

      RNA-seq was done with three biological replicates and CUT&RUN and ATAC-seq were performed with two biological replicates. This information is now included in the Methods section for clarification.

      Reviewer #2 (Public Review):

      In the manuscript entitled "VGLL2 and TEAD1 fusion proteins drive YAP/TAZ-independent transcription and tumorigenesis by engaging p300", Gu et al. studied two Hippo pathway-related gene fusion events (i.e., VGLL2-NCOA2, TEAD1-NCOA2) in spindle cell rhabdomyosarcoma (scRMS) and showed that their fusion proteins can activate Hippo downstream gene transcription independent of YAP/TAZ. Using the BioID-based mass spectrometry analysis, the authors revealed histone acetyltransferase CBP/p300 as specific binding proteins for VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins. Pharmacologically targeting p300 inhibited the fusion proteins-induced Hippo downstream gene transcription and tumorigenic events.

      Overall, this study provides mechanistic insights into the scRMS-associated gene fusions in tumorigenesis and reveals potential therapeutic targets for cancer treatment. The manuscript is well-written and easy to follow.

      Here, several suggestions are made for the authors to improve their study.

      Main points

      (1) The authors majorly focused on the Hippo downstream gene transcription in this study, while a significant portion of genes regulated by the VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins are non-Hippo downstream genes (Figure 3). The authors should investigate whether the altered Hippo pathway transcription is essential for VGLL2-NCOA2 and TEAD1-NCOA2-induced cell transformation and tumorigenesis. Specifically, they should test if treatment with the TEAD inhibitor can reverse the cell transformation and tumorigenesis caused by VGLL2-NCOA2 but not TEAD1-NCOA2. In addition, it is important to examine whether YAP-5SA expression can rescue the inhibitory effects of A485 on VGLL2-NCOA2 and TEAD1-NCOA2-induced colony formation and tumor growth. This will help clarify whether Hippo downstream gene transcription is important for the oncogenic activities of these two fusion proteins.

      We thank the reviewer for the comments. Although we have not tested the small molecular TEAD inhibitor on VGLL2-NCOA2 or TEAD1-NCOA2-induced cell transformation and tumorigenesis, we expect that TEAD inhibition will block VGLL2-NCOA2- but not TEAD1-NCOA2-induced oncogenic activity. It is because TEAD1-NCOA2 does not contain the auto-palmitoylation sites and the hydrophobic pocket in the C-terminal YAP-binding domain of TEAD1 that the TEAD small molecule inhibitor occupies (4). We also appreciate the reviewer’s suggestion of YAP5SA rescue experiments. However, due to its strong oncogenic activity, YAP5SA itself can induce robust downstream transcription and cell transformation with or without A485 treatment, as shown in Figure 5. Thus, it will be unlikely to address whether non-Hippo downstream genes induced by the fusions are important for cell transformation and tumorigenesis. Because of the distinct nature of transcriptional and chromatin landscapes controlled by VGLL2-NCOA2/TEAD-NCOA2 and YAP, we speculate that both Hippo and non-Hippo-related downstream genes contribute to the oncogenic activation and tumor phenotypes induced by the fusion proteins.

      (2) Rationale for selecting CBP/p300 for functional studies needs to be provided. The BioID-MS experiment identified many interacting proteins for VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins (Table S4). The authors should explain the scoring system used to identify the high-interacting proteins for VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins. Was CEP/p300 the top candidates on the list? Providing this information will help justify the focus on CBP/p300 and validate their importance in this study.

      We appreciate the reviewer’s point. CBP/P300 is among the top hits in our proteomics screens of both VGLL2-NCOA2 and TEAD1-NCOA2. Our focus on CBP/P300 is mainly due to the well-established interactions between CBP/P300 and the NCOA family transcriptional co-activators, in which the CBP/P300-NCOA complex plays a central role in mediating nuclear receptors-induced transcriptional activation (5). In addition, our data is consistent with another re-current Vgll2 fusion identified in scRMS, VGLL2-CITED2 (6) that has a C-term fusion partner from CITED2, which is a known CBP/P300 interacting protein (7).

      (3) p300 was revealed as a key driver for the VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins-induced transcriptome alteration and tumorigenesis. To strengthen the point, the authors should identify the p300 binding region on VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins. Mutants with defects in p300 binding/recruitment should be generated and included as a control in the related q-PCR and tumorigenic studies. This work will help confirm the crucial role of p300 in mediating the oncogenic effects of these two fusion proteins.

      We thank the reviewer for the suggestion. We have performed the co-immunoprecipitation assay using the deletion mutant form of VGLL2-NCOA2. We have performed additional co-immunoprecipitation experiments and demonstrated that the C-term NCOA2 part of the fusion is responsible for mediating the interaction between the fusion protein and CBP/P300. These results are now included in the new Figure 5A and are consistent with the reported structural analysis of CBP/P300-NCOA complex (8). In addition, our new data showed the inability of the VGLL2-NCOA2 ∆NCOA2 mutant to induce gene transcription (Figure 1-figure supplement 1D). Furthermore, our data using the small molecular CBP/P300 inhibitor clearly demonstrated that CBP/P300 is required to mediate cell transformation and tumorigenesis induced by the two fusion proteins in vitro and in vivo (Figure 5 and 6).

      (4) Another major issue is the overexpression system extensively used in this study. It is important to determine whether the VGLL2-NCOA2 and TEAD1-NCOA2 fusion genes are also amplified in cancer. If not, the expression levels of the VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins should be adjusted to endogenous levels to assess their oncogenic effects on gene transcription and tumorigenesis. This approach would make the study more relevant to the pathological conditions observed in scRMS cancer patients.

      We appreciate the reviewer’s input and acknowledge the limitation of the HEK293T and C2C12 cell-based models that rely on ectopic expression of VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins. It is currently unclear whether the VGLL2-NCOA2 and TEAD1-NCOA2 fusion genes are also amplified in sarcoma. As mentioned before, these surrogate cell culture systems allowed us to systemically compare the transcriptional regulation by the fusion proteins and YAP/TAZ and elucidate the molecular mechanism underlying the Hippo/YAP-independent oncogenic transformation induced by VGLL2-NCOA2 and TEAD1-NCOA2.

      References:

      (1) Genes Dev . 2007 Nov 1;21(21):2747-61. doi: 10.1101/gad.1602907. Inactivation of YAP oncoprotein by the Hippo pathway is involved in cell contact inhibition and tissue growth control

      (2) Genes Dev . 2010 Jan 1;24(1):72-85. doi: 10.1101/gad.1843810. A coordinated phosphorylation by Lats and CK1 regulates YAP stability through SCF(beta-TRCP)

      (3) VGLL2-NCOA2 leverages developmental programs for pediatric sarcomagenesis. Watson S, LaVigne CA, Xu L, Surdez D, Cyrta J, Calderon D, Cannon MV, Kent MR, Cell Rep. 2023 Jan 31;42(1):112013.

      (4) Lats1/2 Sustain Intestinal Stem Cells and Wnt Activation through TEAD-Dependent and Independent Transcription. Cell Stem Cell. 2020 May 7;26(5):675-692.e8.

      (5) Yi, P., Yu, X., Wang, Z., and O’Malley, B.W. (2021). Steroid receptor-coregulator transcriptional complexes: new insights from CryoEM. Essays Biochem. 65, 857–866.

      (6) A Molecular Study of Pediatric Spindle and Sclerosing Rhabdomyosarcoma: Identification of Novel and Recurrent VGLL2-related Fusions in Infantile Cases. Am J Surg Pathol . 2016 Feb;40(2):224-35. doi: 10.1097/

      (7) CITED2 and the modulation of the hypoxic response in cancer. Fernandes MT, Calado SM, Mendes-Silva L, Bragança J.World J Clin Oncol. 2020 May 24;11(5):260-274.

      (8) Yu, X., Yi, P., Hamilton, R.A., Shen, H., Chen, M., Foulds, C.E., Mancini, M.A., Ludtke, S.J., Wang, Z., and O’Malley, B.W. (2020). Structural insights of transcriptionally active, full-length Androgen receptor coactivator complexes. Mol. Cell 79, 812–823.e4.

    1. Reviewer #2 (Public review):

      Summary:

      Giménez-Orenga carried out this study to assess whether human endogenous retroviruses (HERVs) could be used to improve the diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia (FM). To this end, they used the HERV-V3 array developed previously, to characterize the genome-wide changes in expression of HERVs in patients suffering from ME/CFS, FM or both, compared to controls. In turn, they present a useful repertoire of HERVs that might characterize ME/CFS and FM. For most part, the paper is written in a manner that allows a natural understanding of the workflow and analyses carried out, making it compelling. The figures and additional tables presents solid support for the findings. However, some statements made by the authors seem incomplete and would benefit by a more thorough literature review. Overall, this work will be of interest to the medical community seeking in better understanding the co-occurrence of these pathologies, hinting at a novel angle by integrating HERVs, which are often overlooked, into their assessment.

      Strengths:

      - The work is well-presented, allowing the reader to understand the overall workflow and how the specific aims contribute to filling the knowledge gap in the field.

      - The analyses carried out to understand the potential impact on gene expression mediated by HERVs are in line with previous works, making it solid and robust in the context of this study.

      Weaknesses:

      - The authors claim to obtain genome-wide HERV expression profiles. However, the array used was developed using hg19, while the genomic analysis of this work are carried out using a liftover to hg38. It would improve the statement and findings to include a comparation of the differences in HERVs available in hg38, and how this could impact the "genome-wide" findings.

      - The authors in some points are not thorough with the cited literature. Two examples are:<br /> (1) Lines 396-397 the authors say "the MLT1, usually found enriched near DE genes (Bogdan et al., 2020)". I checked the work by Bogdan, and they studied bacterial infection. A single work in a specific topic is not sufficient to support the statement that MLT1 is "usually" in close vicinity to differentially expressed genes. More works are needed to support this.<br /> (2) After the previous statement, the authors go on to mention "contributing to the coding of conserved lncRNAs (Ramsay et al., 2017)". First, lnc = long non-coding, so this doesn't make sense. Second, in the work by Ramsay they mention "that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved", which is different to what the authors in this study are trying to convey. Again, additional work and a rephrasing might help to support this idea.

      - When presenting the clusters, the authors overlook the fact that cluster 4 is clearly control-specific, and fail to discuss what this means. Could this subset of HERV be used as bona fide markers of healthy individuals in the context of these diseases? Are they associated with DE genes? What could be the impact of such associations?

      Appraisals on aims:

      The authors set specific questions and presented the results to successfully answer them. The evidence is solid, with some weaknesses discussed above that will methodologically strengthen the work.

      Likely impact of work on the field:<br /> This work will be of interest to the medical community looking for novel ways to improve clinical diagnosis. Although future works with a greater population size, and more robust techniques such as RNA-Seq, are needed, this is the first step in presenting a novel way to distinguish these pathologies.

      It would be of great benefit to the community to provide a table/spreadsheet indicating the specific genomic locations of the HERVs specific to each condition. This will allow proper provenance for future researchers interesting in expanding on this knowledge, as these genomic coordinates will be independent of the technique used (as was the array used here).

      Comments on revisions:

      When addressing the comments made in the previous round, there are some answers that lack substance and don't seem to be incorporated in the manuscript. For example, the authors say:

      Authors' response: This is an important point. However, the low number of probes (less than 100) that were excluded from our analysis by lack of correspondence with hg38 among the 1,290,800 probesets was interpreted as insignificant for "genome-wide" claims. An aspect that will be explained in the revised version of this manuscript.

      I checked the revised manuscript with tracked changes, and there doesn't seem to be an updated explanation to this. In which lines is this explained?

      For the other response:

      Authors' response: Using control DE HERV as bona fide markers of healthy individuals seems like an interesting possibility worth exploring. Control DE HERV (cluster 4) associate with DE genes involved in apoptosis, T cell activation and cell-cell adhesion (modules 1 and 6). The impact of which deserves further study.

      I couldn't find an updated mention of this in the discussion.

      Another point that I raised was regarding the decision of using an FDR of 0.1 instead of 0.05. The authors only speculate about the impacts in their answer, while I believe that this could have been rigorously addressed. Since this was done in R, and DE analysis are relatively fast, I don't see a reason as to why this part was not repeated and discussed accordingly.

      For other analyses, there doesn't seem to be a problem with using 0.05 as threshold. Examples of this are the "Overrepresentation functional analysis", or the "Statistical analysis" part of the methods they say "we used a Fisher exact test to calculate p-value, considering enriched in the provided list if an adjusted p-value (FDR) was less than 0.05".

      Just to make this point clear: I'm not asking the authors to repeat all the work using the 0.05 FDR threshold, but rather that they are aware and conscious about the impact of this, and give an idea to the audience on how it would change the DE numbers. This would put in perspective the findings to any future reader.

      I think that most of the other answers to both my previous concerns and the other reviewer's concerns are ok. My last outstanding concern is that the probe coordinates apparently can't be shared, which undermines a lot this study reproducibility, and its use by future researches which won't be able to compare their results to this study.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      Giménez-Orenga et al. investigate the origin and pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia (FM). Using RNA microarrays, the authors compare the expression profiles and evaluate the biomarker potential of human endogenous retroviruses (HERV) in these two conditions. Altogether, the authors show that HERV expression is distinct between ME/CFS and FM patients, and HERV dysregulation is associated with higher symptom intensity in ME/CFS. HERV expression in ME/CFS patients is associated with impaired immune function and higher estimated levels of plasma cells and resting CD4 memory T cells. This work provides interesting insights into the pathophysiology of ME/CFS and FM, creating opportunities for several follow-up studies.

      Strengths:

      (1) Overall, the data is convincing and supports the authors' claims. The manuscript is clear and easy to understand, and the methods are generally well-detailed. It was quite enjoyable to read.

      (2) The authors combined several unbiased approaches to analyse HERV expression in ME/CFS and FM. The tools, thresholds, and statistical models used all seem appropriate to answer their biological questions.

      (3) The authors propose an interesting alternative to diagnosing these two conditions. Transcriptomic analysis of blood samples using an RNA microarray could allow a minimally invasive and reproducible way of diagnosing ME/CFS and FM.

      Weaknesses:

      (1) The cohort analysed in this study was phenotyped by a single clinician. As ME/CFS and FM are diagnosed based on unspecific symptoms and are frequently misdiagnosed, this raises the question of whether the results can be generalised to external cohorts.

      Thank you for your comment. Surely the study of larger cohorts will determine the external validity of these results in a clinical scenario. However, this pilot study, first of its kind, was designed to maximize homogeneity across participants which seemed primarily ensured by the study of females only and diagnosis by a single experienced observer.

      (2) The analyses performed to unravel the causes and effects of HERV expression in ME/CFS and FM are solely based on sequencing data. Experimental approaches could be used to validate some of the transcriptomic observations.

      Certainly, experimental approaches may add robustness to the implication of HERVs in ME/CFS. We indeed consider taking this avenue to deepen in the findings presented here for future work. However, the limited knowledge of HERV-mediated physiological functions may hamper the obtention of prompt results towards revealing causes and effects of HERV expression in ME/CFS and FM.

      Reviewer #2 (Public review):

      Summary:

      Giménez-Orenga carried out this study to assess whether human endogenous retroviruses (HERVs) could be used to improve the diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia (FM). To this end, they used the HERV-V3 array developed previously, to characterize the genome-wide changes in the expression of HERVs in patients suffering from ME/CFS, FM, or both, compared to controls. In turn, they present a useful repertoire of HERVs that might characterize ME/CFS and FM. For the most part, the paper is written in a manner that allows a natural understanding of the workflow and analyses carried out, making it compelling. The figures and additional tables present solid support for the findings. However, some statements made by the authors seem incomplete and would benefit from a more thorough literature review. Overall, this work will be of interest to the medical community seeking in better understanding of the co-occurrence of these pathologies, hinting at a novel angle by integrating HERVs, which are often overlooked, into their assessment.

      Strengths:

      (1) The work is well-presented, allowing the reader to understand the overall workflow and how the specific aims contribute to filling the knowledge gap in the field.

      (2) The analyses carried out to understand the potential impact on gene expression mediated by HERVs are in line with previous works, making it solid and robust in the context of this study.

      Weaknesses:

      (1) The authors claim to obtain genome-wide HERV expression profiles. However, the array used was developed using hg19, while the genomic analysis of this work are carried out using a liftover to hg38. It would improve the statement and findings to include a comparison of the differences in HERVs available in hg38, and how this could impact the "genome-wide" findings.

      This is an important point. However, the low number of probes (less than 100) that were excluded from our analysis by lack of correspondence with hg38 among the 1,290,800 probesets was interpreted as insignificant for "genome-wide" claims. An aspect that will be explained in the revised version of this manuscript.

      (2) The authors in some points are not thorough with the cited literature. Two examples are:

      a) Lines 396-397 the authors say "the MLT1, usually found enriched near DE genes (Bogdan et al., 2020)". I checked the work by Bogdan, and they studied bacterial infection. A single work in a specific topic is not sufficient to support the statement that MLT1 is "usually" in close vicinity to differentially expressed genes. More works are needed to support this.

      b) After the previous statement, the authors go on to mention "contributing to the coding of conserved lncRNAs (Ramsay et al., 2017)". First, lnc = long non-coding, so this doesn't make sense. Second, in the work by Ramsay they mention "that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved", which is different from what the authors in this study are trying to convey. Again, additional work and a rephrasing might help to support this idea.

      Certainly, these two sentences need rephrasing to better adjust to current evidence.

      Revised sentences can now be found in lines 397-402

      (3) When presenting the clusters, the authors overlook the fact that cluster 4 is clearly control-specific, and fail to discuss what this means. Could this subset of HERV be used as bona fide markers of healthy individuals in the context of these diseases? Are they associated with DE genes? What could be the impact of such associations?

      Using control DE HERV as bona fide markers of healthy individuals seems like an interesting possibility worth exploring. Control DE HERV (cluster 4) associate with DE genes involved in apoptosis, T cell activation and cell-cell adhesion (modules 1 and 6). The impact of which deserves further study.

      Appraisals on aims:

      The authors set specific questions and presented the results to successfully answer them. The evidence is solid, with some weaknesses discussed above that will methodologically strengthen the work.

      Likely impact of work on the field:

      This work will be of interest to the medical community looking for novel ways to improve clinical diagnosis. Although future works with a greater population size, and more robust techniques such as RNA-Seq, are needed, this is the first step in presenting a novel way to distinguish these pathologies.

      It would be of great benefit to the community to provide a table/spreadsheet indicating the specific genomic locations of the HERVs specific to each condition. This will allow proper provenance for future researchers interested in expanding on this knowledge, as these genomic coordinates will be independent of the technique used (as was the array used here).

      We agree with the reviewer that sharing genomic locations of DE HERVs in these pathologies would contribute to the development of these findings. Unfortunately, we do not hold the rights to share probe coordinates from this custom HERV-V3 microarray which we used under MTA agreement with its developer.

      Reviewer #3 (Public review):

      The authors find that HERV expression patterns can be used as new criteria for differential diagnosis of FM and ME/CFS and patient subtyping. The data are based on transcriptome analysis by microarray for HERVs using patient blood samples, followed by differential expression of ERVs and bioinformatic analyses. This is a standard and solid data processing pipeline, and the results are well presented and support the authors' claim.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Recommandations/questions:

      (1) The authors point towards the biomarker potential of HERV expression signatures. In line with this, it would be important to test if they can predict the correct pathology for patients using the expression of DE HERVs. Additionally, as a single clinician annotated the cohort analysed in this study, it would be interesting to validate the signatures identified in this work by reanalysing publicly available transcriptomic data from independent studies.

      Thank you for the suggestion. We plan to conduct this analysis and have added the following statement to the manuscript (lines 482-483): “Given the limited sample size in our cohort, validation of the findings in extended cohorts is a must.”

      (2) The authors suggest that an epigenetic mechanism causes the dysregulated HERV expression in ME/CFS patients. However, in Fig.1A, HERV expression profiles of co-diagnosed patients are more similar to healthy controls than patients with either condition. How could the co-morbidity of FM "rescue" the phenotype of ME/CFS?

      Thank you for the insightful comment. It is notable that co-diagnosed patients exhibit HERV expression profiles more similar to those of healthy controls than to either FM´s or ME/CFS´s. These findings may suggest a distinct underlying pathomechanism for this patient group, supporting the identification of a novel nosologic entity, as discussed in lines 372-374 of the manuscript.

      (3) Abundant evidence in the literature links HERV dysregulation with the production of RNA:DNA hybrids and dsRNAs and viral mimicry. The authors found that ME/CFS subgroup 2, which exhibits the most important HERV dysregulation, is also associated with decreased signatures of pathogen detection. It would be interesting to quantify the abundance of DNA:RNA hybrids and dsRNAs in PBMCs of ME/CFS and FM patients as well as healthy controls. It would be interesting to discuss how downregulation of pathogen detection pathways could be a mechanism in ME/CFS patients to avoid viral mimicry and potential links with inflammation in this disease.

      Certainly, HERVs can influence disease pathophysiology by generating RNA:DNA hybrids and dsRNA. However, microarray data does not allow this analysis. Future actions to investigate the underlying mechanisms of differentially expressed HERVs could investigate this interesting possibility.

      (4) Another intriguing result is how overexpression of Module 3 in ME/CFS subgroup 2 is associated with higher levels of plasma cells. The authors hypothesize that the changes in immune cell abundances reflect previous viral infections, but another possibility would be immune activation against HERVs. Are there protein-coding sequences (gag, pro, pol, env) amongst the HERV sequences of module 3? If so, it would be interesting to validate HERV protein expression in these samples. Additionally, blood samples of ME/CFS patients and healthy controls should be analysed in flow cytometry to describe the abundance and phenotype of immune cells precisely.

      Thank you for your insightful comments. In fact, we identified three HERV elements with protein-coding regions whose functional relevance remains uncertain. They present an interesting avenue for future investigation, particularly regarding immune activation.

      Minor comments:

      (1) On lines 170-172, it is unclear to me how Figure 1E is linked to the text.

      We have added a line better explaining Fig. 1E: “Top 10 contributing HERVs to principal components PC1 and PC2 are shown” (lines 171-172).

      (2) Figure S2: grouping or colouring the plots based on the cluster to which HERVs were assigned could facilitate the understanding of the figure.

      We appreciate the suggestion to enhance the clarity of the figures. However, this color-coding cannot be implemented, as a family is not exclusively assigned to a single cluster.

      (3) How are the 4 HERV clusters of Figure 2 and the 8 modules of Figure 3 related to the clusters identified by hierarchical clustering in Figure 1? More details should be provided in the text (Results and Methods sections), and figures to illustrate the clustering strategy should be added if needed.

      To enhance clarity, we have included the following explanation in the results section (lines 244-251): “To uncover potentially affected physiologic functions linked to DE HERV, we examined how DE HERVs and DE genes with similar expression patterns grouped together in modules based on their intrinsic relationships by their hierarchical co-clustering (Fig. 3). Then, the functional significance of these modules was assessed by gene ontology (GO) analysis of the DE genes within each module. The hierarchical clustering analysis resulted in the identification of eight distinct modules, each characterized by unique combinations of DE HERV and DE gene patterns across all four study groups (Fig. 3)”.

      (4) Related to Figure 4, are there HERV sequences in module 3 located near genes important for plasma cells and/or resting CD4 memory T cells?

      Thank you for your insightful comment. However, gene relevance for plasma cells and/or resting CD4 memory T cells may depend on multiple factors in addition to cell type and subtypes and, therefore, the analysis may not be straight forward.

      Reviewer #2 (Recommendations for the authors):

      In Figure 1, the heatmap scale goes from -4 to 4. This should reflect at least the numbers on the lowest and highest end of the scale.

      Thank you for bringing this to our attention. The scale was correct; however, when arranging the panels, the numbers were not properly positioned. The figure has now been updated with the corrected version.

      Figure 2F and G, percentages are shown as decimal numbers up to 1.00, while it should be 100%, and so on.

      We also replaced this figure, changing the numbers to fit percentages.

      It would be interesting to know how the results change using FDR of 0.05. I'm not familiar with microarray thresholds, but in RNA-Seq, 0.1 is rarely used, with 0.05 being the standard. Could it be that a more stringent result better distinguishes the pathologies?

      Applying a more stringent threshold, such as FDR 0.05, may remove sequences that, while not strongly differentially expressed, may be still important for distinguishing between these pathologies. Therefore, we decided to also include DE tendencies (FDR<0.1) in this first of a kind study. Findings will need validation in enlarged cohorts.

    1. historischen Kontext mithilfe des Eintrags zu Josia

      Der literarische Kontext von 2. Chronik 34–35 ist Teil der Geschichtsbücher des Alten Testaments, genauer gesagt der Chronikbücher. Diese Bücher haben eine theologische Perspektive und erzählen die Geschichte Israels und Judas mit einem besonderen Fokus auf den Tempel, die Anbetung Gottes und die Treue zu seinem Gesetz.

      Kapitel 34: Josias Reformen und die Auffindung des Gesetzbuches In Kapitel 34 wird Josia als ein vorbildlicher König dargestellt, der das Volk zurück zu Gott führt. Der literarische Schwerpunkt liegt auf der geistlichen Erneuerung:

      Reinigung des Landes (34,3-7): Josia zerstört Götzenbilder und Altäre, um den Götzendienst zu beseitigen. Auffindung des Gesetzbuches (34,14-21): Während der Tempelrenovierung wird das Gesetzbuch (wahrscheinlich Teile des 5. Buches Mose) gefunden. Josia reagiert mit Demut und Buße, als er erkennt, wie weit das Volk von Gottes Geboten abgewichen ist. Bundesschluss (34,29-33): Josia versammelt das Volk, liest das Gesetz vor und erneuert den Bund mit Gott. Kapitel 35: Josias Passahfest und sein Tod Kapitel 35 beschreibt die Feier des Passahfestes, das Josia in einer Weise organisiert, wie es seit den Tagen Samuels nicht mehr gefeiert wurde. Der literarische Fokus liegt auf der Wiederherstellung der wahren Anbetung:

      Passahfeier (35,1-19): Josia sorgt dafür, dass das Passahfest gemäß den Vorschriften des Gesetzes gefeiert wird. Dies zeigt seine Hingabe an Gott und die Bedeutung des Gehorsams gegenüber Gottes Wort. Josias Tod (35,20-27): Der Bericht endet tragisch mit Josias Tod im Kampf gegen den Pharao Necho. Dies markiert einen Wendepunkt, da nach Josias Tod das Volk wieder in die Untreue zurückfällt. Theologischer und literarischer Zusammenhang Die Kapitel 34–35 stehen in engem Zusammenhang mit dem zentralen Thema der Chronikbücher: Gottes Treue und die Notwendigkeit der Treue des Volkes zu Gott. Josia wird als ein idealer König dargestellt, der Gottes Gebote ernst nimmt. Sein Handeln wird als Vorbild für die Leser hervorgehoben.

      Diese Kapitel laden uns ein, über die Bedeutung von Buße, Gehorsam und der Rückkehr zu Gott nachzudenken. Was spricht dich an Josias Hingabe besonders an? 😊

      Beschreibe den historischen Kontext des Eintrages Josia im Bibellexikon 4/10 Der historische Kontext von Josia, wie er im Bibellexikon beschrieben wird, spielt sich im 7. Jahrhundert v. Chr. ab, während der letzten Jahre des Königreichs Juda. Josia war der 16. König von Juda und regierte von etwa 640 bis 609 v. Chr. Seine Herrschaft fällt in eine Zeit großer politischer und religiöser Umbrüche.

      1. Politischer Kontext Assyrische Schwächung: Während Josias Herrschaft begann das assyrische Weltreich, das Juda lange dominiert hatte, an Macht zu verlieren. Dies eröffnete Juda die Möglichkeit, sich von assyrischem Einfluss zu lösen und religiöse Reformen durchzuführen. Aufstieg Babylons: Gleichzeitig erstarkte das babylonische Reich, das schließlich die Vorherrschaft im Nahen Osten übernehmen sollte. Josias Tod fiel in die Zeit, als Ägypten und Babylon um die Vorherrschaft kämpften. Ägyptens Einfluss: Josia starb in der Schlacht von Megiddo (609 v. Chr.), als er versuchte, den ägyptischen Pharao Necho II. daran zu hindern, den Assyrern zu helfen.
      2. Religiöser Kontext Götzendienst in Juda: Vor Josias Herrschaft war Juda stark vom Götzendienst geprägt, insbesondere unter den Königen Manasse und Amon. Heiligtümer für Baal, Aschera und andere Götter waren weit verbreitet. Josias Reformen: Josia führte umfassende religiöse Reformen durch, die darauf abzielten, den Götzendienst zu beseitigen und die Anbetung des einen Gottes wiederherzustellen. Diese Reformen wurden durch die Auffindung des „Gesetzbuches“ (wahrscheinlich Teile des 5. Buches Mose) im Tempel angestoßen (2. Chronik 34,14-33).
      3. Theologische Bedeutung Josia wird in der Bibel als ein König beschrieben, der „tat, was dem HERRN wohlgefiel“ (2. Könige 22,2). Seine Reformen und die Wiederherstellung des Passahfestes (2. Chronik 35) zeigen seinen Eifer für Gottes Gebote. Dennoch markiert sein Tod einen Wendepunkt, da Juda nach ihm wieder in Untreue verfiel, was schließlich zur babylonischen Gefangenschaft führte.

      Josias Leben zeigt, wie wichtig es ist, Gottes Wort ernst zu nehmen und danach zu handeln. Was beeindruckt dich an Josias Mut und Hingabe inmitten schwieriger Zeiten? 😊

    2. die Textsorte,

      In 2. Chronik 34,1-4 handelt es sich um eine geschichtliche Erzählung. Dieser Abschnitt berichtet über den Beginn der Herrschaft des Königs Josia von Juda. Er war erst acht Jahre alt, als er König wurde, und zeichnete sich durch seine Treue zu Gott aus. Der Text beschreibt, wie Josia in jungen Jahren begann, den Herrn zu suchen, und später die Götzenbilder und Altäre der Baalsverehrung im Land zerstörte.

      Diese Art von Text gehört zur biblischen Geschichtsschreibung, die nicht nur historische Ereignisse schildert, sondern sie auch theologisch deutet. Der Fokus liegt darauf, wie Josias Handeln im Einklang mit Gottes Willen stand. Es wird gezeigt, dass er Reformen durchführte, um das Volk zurück zu Gott zu führen, indem er Götzendienst beseitigte und die wahre Anbetung wiederherstellte.

      Hier ein Auszug aus 2. Chronik 34,1-2 (Lutherbibel): „Acht Jahre war Josia alt, als er König wurde, und er regierte einunddreißig Jahre zu Jerusalem. Und er tat, was dem HERRN wohlgefiel, und wandelte in den Wegen seines Vaters David und wich weder zur Rechten noch zur Linken.“

      Diese Textsorte lädt uns ein, über die Bedeutung von Gehorsam gegenüber Gott und die Erneuerung des Glaubens nachzudenken. Was kannst du aus Josias Eifer für deinen eigenen Glauben lernen? 😊

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1 (Public review):

      Summary:

      The authors aimed to investigate the interaction between tissue-resident immune cells (microglia) and circulating systemic neutrophils in response to acute, focal retinal injury. They induced retinal lesions using 488 nm light to ablate photoreceptor (PR) outer segments, then utilized various imaging techniques (AOSLO, SLO, and OCT) to study the dynamics of fluorescent microglia and neutrophils in mice over time. Their findings revealed that while microglia showed a dynamic response and migrated to the injury site within a day, neutrophils were not recruited to the area despite being nearby. Post-mortem confocal microscopy confirmed these in vivo results. The study concluded that microglial activation does not recruit neutrophils in response to acute, focal photoreceptor loss, a scenario common in many retinal diseases.

      Strengths:

      The primary strength of this manuscript lies in the techniques employed.

      In this study, the authors utilized advanced Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) to document immune cell interactions in the retina accurately. AOSLO's micron-level resolution and enhanced contrast, achieved through near-infrared (NIR) light and phase-contrast techniques, allowed visualization of individual immune cells without extrinsic dyes. This method combined confocal reflectance, phase-contrast, and fluorescence modalities to reveal various cell types simultaneously. Confocal AOSLO tracked cellular changes with less than 6 μm axial resolution, while phase-contrast AOSLO provided detailed views of vascular walls, blood cells, and immune cells. Fluorescence imaging enabled the study of labeled cells and dyes throughout the retina. These techniques, integrated with conventional histology and Optical Coherence Tomography (OCT), offered a comprehensive platform to visualize immune cell dynamics during retinal inflammation and injury.

      Thank you!

      Weaknesses:

      One significant weakness of the manuscript is the use of Cx3cr1GFP mice to specifically track GFP-expressing microglia. While this model is valuable for identifying resident phagocytic cells when the blood-retinal barrier (BRB) is intact, it is important to note that recruited macrophages also express the same marker following BRB breakdown. This overlap complicates the interpretation of results and makes it difficult to distinguish between the contributions of microglia and infiltrating macrophages, a point that is not addressed in the manuscript.

      We agree that greater emphasis is required that CX3CR1 mice exhibit fluorescence in not only microglia, but also other cells of macrophage origin including monocytes, perivascular macrophages and some hyalocytes.

      Through the advantages of in vivo AOSLO, however, we are able to establish that CX3CR1 cells are present within the tissue before the laser lesion is placed. This suggests they are tissue resident. We agree that it is possible that at later time points (days-weeks), systemic macrophages and/or monocytes may participate. Lack of rolling/crawling cells suggest they are not systemic. We elaborate on this point in a new section in the discussion:

      P29 L534-541:

      “CX3CR1-GFP mice exhibit fluorescence not only in microglia

      We recognize that the CX3CR1-GFP model can also label systemic cells such as monocytes/macrophages77. While it is possible these cells could infiltrate the retina in response to the lesion, we find it unlikely since there was no indication of the leukocyte extravasation cascade (rolling/crawling/stalled cells) within the nearest retinal vasculature. In addition to microglia, retinal perivascular macrophages and hyalocytes also exhibit GFP fluorescence and thus that these cells may also contribute toward damage resolution.”

      Another major concern is the time point chosen for analyzing the neutrophil response. The authors assess neutrophil activity 24 hours after injury, which may be too late to capture the initial inflammatory response. This delayed assessment could overlook crucial early dynamics that occur shortly after injury, potentially impacting the overall findings and conclusions of the study.

      The power of in vivo imaging makes these early assessments possible. Therefore, we have taken the reviewers concern and conducted an additional experiment which examines whether neutrophils are seen in the window of time between lesion and 24hrs. In a newly examined mouse, we find that within 3.5 hours post-lesion, neutrophils do not extravasate adjacent to the lesion site (see new “figure 8 – figure supplement 1”).

      Also see accompanying video (new “figure 8 – video 3”) for an example of nearby neutrophils flowing through OPL capillaries just microns away from the lesion site. Neutrophils are clearly contained within the vasculature and exhibit dynamics consistent with healthy retinal tissue. While it remains possible that the lesion may increase leukocyte stalling within the nearest capillaries, we are unable to confirm or deny this with a single experiment. We now submit this evidence as a new supplementary figure following the reviewer’s suggestion.

      Reviewer #2 (Public review):

      Summary:

      This study uses in vivo multimodal high-resolution imaging to track how microglia and neutrophils respond to light-induced retinal injury from soon after injury to 2 months post-injury. The in vivo imaging finding was subsequently verified by an ex vivo study. The results suggest that despite the highly active microglia at the injury site, neutrophils were not recruited in response to acute light-induced retinal injury.

      Strengths:

      An extremely thorough examination of the cellular-level immune activity at the injury site. In vivo imaging observations being verified using ex vivo techniques is a strong plus.

      We appreciate this recognition and hope that the reviewer considers the weaknesses below in the context of the papers identified strengths.

      Weaknesses:

      This paper is extremely long, and in the perspective of this reviewer, needs to be better organized.

      We agree and have taken the following steps to address this:

      (1) Paper has been shortened overall by 8%

      (2) We reorganized the following sections:

      a. Introduction: shortened

      b. Methods: merged section “Ex vivo confocal image processing” with “Ex vivo confocal imaging”.

      c. Results: most sections shortened, others simplified for concision

      d. Discussion: most sections shortened, removed “Microglial/neutrophil discrimination using label-free phase contrast”

      e. Figure references reorganized in order of their appearance.

      Study weakness: though the finding prompts more questions and future studies, the findings discussed in this paper are potentially important for us to understand how the immune cells respond differently to different severity levels of injury.

      On the heels of this burgeoning technology, we consider this report among the first studies of its kind. We are hopeful that it forms the foundation of many further investigations to come. We expect a rich parameter space to be explored with future studies including investigation of other time points, other injuries of varying degree and other immune cell populations (along with their interactions with each other). Each has the potential to reveal the complexities of the ocular immune system in action.

      Reviewer #3 (Public review):

      Summary:

      This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature, and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation at the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience. However, there are some issues with the conclusions drawn from the data and analysis that can be addressed to further bolster the manuscript.

      Strengths:

      Adaptive optics imaging of the murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is beneficial for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

      Thank you!

      The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, (a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, (b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

      Thank you!

      One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. While not directly shown in the article, this model would potentially allow for controlling the size, depth, and severity of the laser injury opening interesting avenues for future study.

      We agree that there is an established community that is invested in developing titrated dosimetry for light damage models. As the reviewer recognizes, this parameter space is exceptionally large therefore we controlled this parameter by choosing a single wavelength that is commonly used in ophthalmoscopy (488nm), fixed duration and exposure regime that created a reproducible, mild damage of photoreceptors. At this titration we created a mild lesion that spares retina above and below.

      Weaknesses:

      (1) It is unclear based on the current data/study to what extent the mild laser damage phenotype is generalizable to disease phenotypes. The outer nuclear cell loss of 28% and a complete recovery in 2 months would seem quite mild, thus the generalizability in terms of immune-mediated response in the face of retinal remodeling is not certain, specifically whether the key finding regarding the lack of neutrophil recruitment will be maintained with a stronger laser ablation.

      It seems the concern here is whether our finding is generalizable to other damage regimes, especially more severe ones. While speculative, we would suspect that it is not generalizable across different lesions of greater severity. For example, puncturing Bruch’s membrane is an example of a more severe phenotype that is often encountered in laser damage. However, this creates a complicated model that not only induces inflammation, but also compromises BRB integrity and promotes CNV. The parameter space to be tested in the reviewer’s question is quite vast and therefore have tried to summarize the generalizability within our manuscript in

      P31 L586-588 “There are limitations on how generalizable this mild damage to more severe damage or disease phenotypes, but this acute damage model can begin to provide clues about how immune cells interact in response to PR loss. In this laser lesion model, we ablate 27% of the PRs in a 50 µm region.”

      (2) Mice numbers and associated statistics are insufficient to draw strong conclusions in the paper on the activity of neutrophils, some examples are below:

      a) 2 catchup mice and 2 positive control EAU mice are used to draw inferences about immune-mediated activity in response to injury. If the goal was to show 'feasibility' of imaging these mouse models for the purposes of tracking specific cell type behavior, the case is sufficiently made and already published by the authors earlier. It is possible that a larger sample size would alter the conclusion.

      We would like to highlight that the total number of mice studied in this report was 28 (18 in-vivo imaging, 10 ex-vivo histology, >40 lesions total). While power analysis is challenging as these are the first studies of their kind, we underscore that in vivo imaging allows those same mice to be studied multiple times longitudinally. This is not possible with traditional histology. Therefore, in vivo imaging not only reveals the temporal progression (unlike histology), but also increases the number of observations beyond a simple count of the “number of mice”.

      The goal of the study was not one of feasibility. The goal was to address a specific question in ocular biology: “do resident CX3CR1 cells recruit neutrophils in early, regional retinal injury”

      The low numbers that the reviewer points to, are not the primary data of the paper, rather, supportive control data. Moreover, we refocus the attention on the fact that our study is performed on 28 mice across multiple modalities and each corroborates a common finding that neutrophils do not appear to be recruited despite strong microglial response; a central finding of the paper.

      b) There are only 2 examples of extravasated neutrophils in the entire article, shown in the positive control EAU model. With the rare extravasation events of these cells and their high-speed motility, the chance of observing their exit from the vasculature is likely low overall, therefore the general conclusions made about their recruitment or lack thereof are not justified by these limited examples shown.

      The spirit of the challenge raised is that because nothing was seen, is not proof that nothing occurred. Said more commonly, “absence of evidence is not evidence of absence”- a quote often attributed to Carl Sagan. Yet we push back on this conjecture as we have shown, not only with cutting edge in vivo imaging, but also with ample histological controls as well as multiple transgenic animals (and corroborating IHC antibodies) that in none of these imaging modalities, at none of the time points we evaluated, did neutrophils aggregate or extravasate in response to photoreceptor ablation.

      Reviewer adds: “the chance of observing their exit from the vasculature is likely low overall…”

      This is the reason that we specifically chose a focal lesion model to increase any possible chance of imaging a rare event. The focal lesion provides both a time and a location for “where” to look. Small 50 micrometer lesions were sufficient to drive a strong local microglial response (figures 5,6,9). This was evidence that local inflammatory cues were present. Yet despite this activation, neutrophils were not recruited to this location. We emphasize that this is a strength of our approach over other pan-retinal damage models that may indeed miss the rare extravasation events that are geographically sparse and happen over hours.

      c) In Figure 3, the 3-day time point post laser injury shows an 18% reduction in the density of ONL nuclei (p-value of 0.17 compared to baseline). In the case of neutrophils, it is noted that "Control locations (n = 2 mice, 4 z-stacks) had 15 {plus minus} 8 neutrophils per sq.mm of retina whereas lesioned locations (n = 2 mice, 4 z-stacks) had 23 {plus minus} 5 neutrophils per sq.mm of retina (Figure 10b). The difference between control and lesioned groups was not statistically significant (p = 0.19)." These data both come from histology. While the p-values - 0.17 and 0.19 - are similar, in the first case a reduction in ONL cell density is concluded while in the latter, no difference in neutrophil density is inferred in the lesioned case compared to control. Why is there a difference in the interpretation where the same statistical test and methodology are used in both cases? Besides this statistical nuance, is there an alternate possibility that there is an increased, albeit statistically insignificant, concentration of circulating neutrophils in the lesioned model? The increase is nearly 50% (15 {plus minus} 8 vs. 23 {plus minus} 5 neutrophils per sq.mm) and the reader may wonder if a larger animal number might skew the statistic towards significance.

      The statistics and p-values will be dependent on the strategy of analysis performed. As described in the methods, we used a predetermined 50 micron cylinder for our counting analysis based on the average lesion size created. We used this circular window to roughly approximate the size of the common lesion size. However, recall that the damage is created in a single axis (a line projected on the retina) therefore it is possible that the analysis region is too generous to capture the exceptionally local damage.

      While the reviewer is focused on the nuance of statistics, we would like to refocus the conversation on our data that shows that very few neutrophils were observed at all (105 cells from 8 locations, P value reported). But missed in the above critique is that all neutrophils were contained within capillaries (Fig 10). We found no examples of extravasated neutrophils.  This is the major finding and is supported by our in vivo as well as ex vivo confirmation.

      (2) The conclusions on the relative activity of neutrophils and microglia come from separate animals. The reader may wonder why simultaneous imaging of microglia and neutrophils is not shown in either the EAU mice or the fluorescently labeled catchup mice where the non-labeled cell type could possibly be imaged with phase-contrast as has been shown by the authors previously. One might suspect that the microglia dynamics are not substantially altered in these mice compared to the CX3CR1-GFP mice subjected to laser lesions, but for future applicability of this paradigm of in vivo imaging assessment of the laser damage model, including documenting the repeatability of the laser damage model and the immune cell behavior, acquiring these data in the same animals would be critical.

      A double fluorescent mouse (neutrophils and microglia) is a logical next step of this research. In fact, we have now crossed these transgenic mice and are studying this double labeled mouse in a second manuscript in preparation. However, for this study, it was imperative that the fluorescent imaging light was kept at low levels as not to contribute or alter the lesion phenotype and accompanying immune response. Therefore, imaging two fluorescent channels to simultaneously view neutrophils and microglia in the same animal would have required at least 2X the visible light exposure for imaging. The imaging light levels used in the current study were carefully examined in our previous publications as to not create additional light damage (Joseph et al 2021).

      (3) Along the same lines as above, the phase contrast ONL images at time points from 3-day to 2-month post laser injury are not shown and the absence of this data is not addressed. This missing data pertains only to the in vivo imaging mice model but are conducted in histology that adequately conveys the time-course of cell loss in the ONL.

      The ocular preparation of the phase contrast data in figure 2, unfortunately developed an anesthesia induced cataract that precluded adequate image quality. This is not uncommon in long-term mouse ocular imaging preparations (Feng et al 2023). Instead, we chose to include the phase-contrast data to show the visually compelling intact and disrupted ONL damage for baseline and 1 day to show that the damage is not only focal, but also shows clear disruption to the somatic layers of the photoreceptors.

      It is suggested that the reason be elaborated for the exclusion of this data and the simultaneous imaging of microglia and neutrophils mentioned above.

      We agree and we have included the reason for the “not acquired” data within the figure 2 legend:

      “Phase contrast data was not acquired for time points 3 days-2 months due to development of cataract which obscured the phase contrast signal”

      Also, it would be valuable to further qualify and check the claims in the Discussion that "ex vivo analysis confirms in vivo findings" and "Microglial/neutrophil discrimination using label-free phase contrast"

      We maintain that ex vivo analysis both corroborates and in many cases, confirms our in vivo findings. We feel this is a strength of our manuscript rather than a qualifier. A) Damage localization is visible with OCT and confocal/phase contrast AOSLO in a region that matches the DAPI loss we see ex vivo. B) Disruption of the ONL seen with in vivo AOSLO is of the same size, shape and location as the ONL damage quantified ex vivo. C) No damage or disruption was seen in locations above the lesion with OCT or AOSLO, which matches our finding that only the ONL shows loss of nuclei whereas other more superficial layers are spared. D) Microglial localization is found both in vivo and ex vivo and E) lack of neutrophil aggregation or extravasation was neither seen in vivo or ex vivo. Given the evidence above, we contend that this strong synergistic and complementary approach corroborates the experimental data in two ways of studying this tissue.

      We agree that the claims made in the section entitled “Microglial/neutrophil discrimination using label-free phase contrast” are not strongly supported by the phase-contrast imaging presented in this paper. Accordingly, we have since removed this section based on reviewer suggestion.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Based on the title and abstract, the main focus of the manuscript appears to be the immune response. However, most of the manuscript is dedicated to the authors' imaging technique. Additionally, several important concerns regarding the investigation of the immune response in the retina need to be addressed.

      We understand that emphasis may appear to be on the imaging technique, however, because AOSLO is not a widely used technology, we are committed to explaining the technique so that it both builds awareness and confidence in the way this exciting new data is acquired.

      (2) The authors indicate '1 day post-injury' as a timeframe spanning between 18 and 28 hours post-injury. This is a rather wide window of time, which could potentially affect the analysis. It is necessary to demonstrate that there is no significant difference in the immune response, particularly in terms of microglial morphology and branch orientation, between 18 and 28 hours post-injury.

      We agree that a fine time scale may show even greater insight to the natural history of the inflammatory response. However, we feel that our chosen time points go above and beyond the temporal precision that is offered by other investigations, especially considering the novel multi-modal imaging performed here. Studies using finer temporal sampling are poised for future investigation.

      (3) The authors should consider using additional markers or complementary techniques to differentiate between microglia and recruited macrophages, such as incorporating immunohistochemistry with P2RY12, a specific marker for microglia that helps distinguish them from macrophages, and CD68 or F4/80, markers for recruited macrophages. It is also crucial for the authors to include a discussion addressing the limitations of using Cx3cr1GFP mice and the potential impact on result interpretation. It is fundamental to validate the findings and clarify the roles of microglia and macrophages.

      The wonders of current IHC is that there are myriad antibodies and labels that “could” be used. We used what we felt were the most compelling for this stage of early investigation. We look forward to studies that employ this wider range of labels. See our response to reviewer 1’s first comment above for addressing the limitations of using Cx3CR1 mice.

      (4) Analyzing neutrophil responses at 24 hours post-injury may be too late to capture the critical early dynamics of inflammation. By this time, the initial recruitment and activation phases of neutrophils may have already peaked or begun to resolve, potentially missing key insights into the immediate immune response. The authors should conduct additional analysis of neutrophil responses at earlier time points post-injury, such as 6 or 12 hours. Including these time points would provide a more comprehensive and conclusive analysis of the neutrophil response, helping to delineate the progression of inflammation and its implications for subsequent healing processes.

      This point has been addressed above. Briefly, we have now included a new experiment (and figure + video) that shows no neutrophil extravasation at earlier time points. We thank the reviewer for this helpful suggestion.

      Reviewer #2 (Recommendations for the authors):

      This paper is extremely long, and in the perspective of this reviewer, needs to be better organized.

      (1) There was a lengthy description and verification of light-induced injury and longitudinal tracking of healing, which I believe can be further cleaned up and made more succinct.

      We have cleaned-up and re-organized the manuscript (see above response for details). Manuscript has been reorganized and reduced by 8%.

      (2) The intention/goal of the paper can be further strengthened. On page 33: "to what extent do neutrophils respond to acute neural loss in the retina?" This particular statement is so clear and really brings out the purpose of this study, and it will be great to see something like this in the opening statement.

      We thank the reviewer for this excellent suggestion. We have modified the final paragraph of the introduction to strengthen our study’s intention.

      P4 L45-47: Here, we ask the question: “To what extent do microglia/neutrophils respond to acute neural loss in the retina?” To begin unraveling the complexities in this response, we deploy a deep retinal laser ablation model.

      (3) The figures are not mentioned in the manuscript in the order they were numbered. It makes it extremely challenging to follow along. The methods/results sections started with Figure 1, then on to Figure 4, then back to Figures 2 and 3, etc. This reviewer recommends re-organizing figures and their order of appearance so the contents of the figures are referred to in the paragraph in the most efficient and clear manner.

      We have re-organized the appearance of figure references throughout the paper.

      (4) Figure 2: phase contrast was not acquired on days 3, 7, and 2 months. Please briefly explain the reason in the caption.

      Addressed above.

      (5) Figure 4 OPL layer, the area highlighted in a dashed circle was meant to demonstrate that perfusion was intact, but I cannot see the flow in the highlighted area very well at day 7 and 2 months (especially 2 months). Please explain.

      Perfusion maps are often difficult to interpret as a static image. Therefore, we have additionally provided the raw video data (“OPL_vasculature_7d” and “OPL_vasculature_2mo”) which helps visualize active perfusion. To the reviewer’s point, videos reveal that RBC motion is maintained in the capillaries of this location.

      (6) While there's a thorough discussion of the biological impact of the finding, the uniqueness of the imaging technique can be better highlighted. Immune response toward injury is highly dynamic and is often the first step of wound healing. To observe such dynamic events longitudinally in the living eye at the cellular level, it requires a special imaging technique such as the type addressed here. The author can better address the technical uniqueness of studying this type of biological event for readers less familiar with AOSLO.

      We agree and following the reviewer’s suggestion have further emphasized the advance in the current manuscript in two additional places:

      (1) Within the introduction

      P3-4 L21-42: “A missed window of interaction is highly problematic in histological study where a single time point reveals a snapshot of the temporally complex immune response, which changes dynamically over time. Here, we use in vivo imaging to overcome these constraints.

      Documenting immune cell interactions in the retina over time has been challenged by insufficient resolution and contrast to visualize single cells in the living eye. The microscopic size of immune cells requires exceptional resolution for detection. Recently, advances in AOSLO imaging have provided micron-level resolution and enhanced contrast for imaging individual immune cells in the retina and without requiring extrinsic dyes(7,23). AOSLO provides multi-modal information from confocal reflectance, phase-contrast and fluorescence modalities, which can reveal a variety of cell types simultaneously in the living eye. Here, we used confocal AOSLO to track changes in reflectance at cellular scale. Phase-contrast AOSLO provides detail on highly translucent retinal structures such as vascular wall, single blood cells(27–29), PR somata(30), and is well-suited to image resident and systemic immune cells.(7,23) Fluorescence AOSLO provides the ability to study fluorescently-labeled cells(25,31,32) and exogenous dyes(27,33) throughout the living retina. These modalities used in combination have recently provided detailed images of the retinal response to a model of human uveitis.(23,34) Together, these innovations now provide a platform to visualize, for the first time, the dynamic interplay between many immune cell types, each with a unique role in tissue inflammation.”

      (2) Within the discussion

      P34-35 L656-662 “Beyond the context of this specific finding, we share this work with the excitement that AOSLO cellular level imaging may reveal the interaction of multiple immune cell types in the living retina. By using fluorophores associated with specific immune cell populations, the complex dynamics that orchestrate the immune response may be examined in this specialized tissue. This work and future studies may reveal further insights to the interactions of single immune cells in the living body in a non-invasive way.”

      Reviewer #3 (Recommendations for the authors):

      Some other comments:

      (1) The reader may wonder why if all findings are confirmed by histology would an in vivo imaging model be needed. This does not need a generalized explanation given the typical virtues of an in vivo model, but perhaps the authors may want to amplify their findings in the current context, for example, those on the shorter minutes to hours timescales (Figure 2, Supplement 1) that would have been resource and time intensive, and likely impossible, to gather via histology alone.

      The reviewer appropriately underscores the utility of in vivo imaging above histological-only investigation. In response, we have added text in the introduction to emphasize the nuanced, but important value of both longitudinal imaging as well as dynamic imaging which is not possible with conventional histology (e.g. blood perfusion status, immune cell interactions etc.)

      P3-4 L21-42 (these points also addressed in response to reviewer #2 above)

      (2) A few questions and comments on the laser ablation model<br /> - It is alluded to in the Discussion in Lines 519-521 that the procedure is highly reproducible (95%) but the associated data for this repeatability metric is not shown.

      We agree that the criterion for determining a “successful lesion” requires further elaboration. Therefore, we have now included the criteria for successful lesions in the methods as well as discussion (in bullet below):

      Methods:

      P9-10 L129-133: “This protocol produced a hyper-reflective phenotype in the >40 locations across 28 mice. In rare cases, the exposure yielded no hyper-reflective lesion and were often in mice with high retinal motion, where the light dosage was spread over a larger retinal area. These locations were not included in the in-vivo or histological analysis.”

      - The methods state that a 24 x 1-micron line is focused on the retina, but all lesions seem to appear elliptical where the major to minor axis ratio is a lot smaller than this intended size. One wonders what leads to this discrepancy.

      We expect that this observation is related to the response above, we have added the following:

      Discussion:

      P27 L497-505: “The damage took on an elliptical form, likely due to: 1) Eye motion from respiration and heart rate which spreads the light over a larger integrative area (rather than line). 2) The impact of focal light scatter. 3) A micron-thin line imparting damage on cells that are many microns across manifesting as an ellipse. The majority of light exposures produced lesions of this elliptical shape. In a few conditions, for the reasons described above, the exposure failed to produce a strong, focal damage phenotype. To improve lesion reproducibility, future experiments should control for subtle eye motion affecting light damage, especially for long exposures.”

      (3) Lastly, a thickening is noted in the ONL after laser injury that seems to cause a thinning of the INL as well (Figure 3) which may increase the apparent INL nuclei density.

      The reviewer’s careful eye finds local swelling after injury. However, despite swelling, the segregation between INL and ONL was maintained in all days we examined. Thus, no ONL cells were included in INL counts (see figure 3A & 3D).

      Also, the ONL - inner (panel B) seems to show a little reduction in cell density in the same elliptical shape as the outer ONL in panel C.

      We agree with this observation and was one of the reasons we included this detailed analysis of both the inner and outer half of the ONL. Our finding is that there is more prominent loss of nuclei in the outer half of the ONL. While the mechanism for this is not understood, we felt it was an important finding to include and further shows the axial specificity of the light damage we are inducing (especially at day 1 observation).

      Lastly, the reduction in nuclear density is visually obvious in the ONL at the 1 and 3-day time points but the p-statistic does not seem to convey this. One may consider performing the analysis on panel F on a smaller region surrounding the lesion to more reliably reveal these effects.

      Related to the response above, the ONL shows a persistence of nuclei in the upper half of that layer, whereas the outer half, shows a visible reduction. Therefore, we expect that the reviewer is correct that a statistical analysis that considers just the outer half of the ONL would likely show a strong statistical significance. The challenge, however, is that our analysis strategy counted all cells within a 50 micron diameter cylinder through the entirety of the ONL (meaning strong loss in the outer half was attenuated by weak loss in the inner half). A more detailed sub-layer analysis is challenging given the notable retinal remodeling over days-to-weeks that make it challenging to attribute layers within the ONL as viable landmarks for the requested analysis.

      (4) In Figure 6, the NIR confocal image and fluorescent microglia seem to share the same shape, starting from the OPL and posterior to it. This is particularly evident in the 3 and 7-day time points in the ONL and ONL/IS images. This departs from lines 567-577 where the claim is made that the hyperreflective phenotype in NIR images does not emerge from the microglia and neutrophils. This discrepancy should be clarified. It may be so that the hyperreflective phenotype as observed by Figure 2 at shorter timescales is not related to the microglia but the locus of hyper-reflections changes at longer time scales to involve the microglia as well as in Figure 6. One potential clue/speculation of the common shapes/size in confocal hyper-reflectance and fluorescent microglia of Figure 6 comes from Figure 9 where the microglia seem to engulf the photoreceptor phagosomes in the DAPI stains. It is possible that the hyper-reflections arise from the phagosomes but their co-localization with microglia seems to demonstrate a shared size/shape. As an addendum to the first point, such correlations are a power of the in vivo model and impossible to achieve in histology.

      The reviewer shows a deep understanding of our data. We agree with many of the points, but for the purpose of the paper many of the above offerings are speculative and we have chosen not to elaborate on these points as it is not definitive from the data. Instead, we direct the reader to an important finding that within hours, the hyper-reflective phenotype is seen in both OCT and AOSLO, whereas microglial somas/processes have not yet migrated into the hyper-reflective region. We have now emphasized this point in the discussion section:

      P29-30 L543-552: “A common speculation is that the increased backscatter may arise from local inflammatory cells that activate or move into the damage location. In our data, confocal AOSLO and OCT revealed a hyperreflective band at the OPL and ONL after 488 nm light exposure (Figure 2a, b). We found that the hyperreflective bands appeared within 30 minutes after the laser injury, preceding any detectable microglial migration toward the damage location (Figure 2 – figure supplement 1 and Figure 6 – figure supplement 1). We thus conclude that the initial hyperreflective phenotype is not caused by microglial cell activity or aggregation.”

    1. Author response:

      The following is the authors’ response to the previous reviews

      eLife Assessment

      This work presents a valuable self-supervised method for the segmentation of 3D cells in microscopy images, alongside an implementation as a Napari plugin and an annotated dataset. While the Napari plugin is readily applicable and promises to eliminate time consuming data labeling to speed up quantitative analysis, there is incomplete evidence to support the claim that the segmentation method generalizes to other light-sheet microscopy image datasets beyond the two specific ones used here.

      Technical Note: We showed the utility of CellSeg3D in the first submission and in our revision on 5 distinct datasets; 4 of which we showed F1-Score performance on. We do not know which “two datasets” are referenced. We also already showed this is not limited to LSM, but was used on confocal images; we already limited our scope and changed the title in the last rebuttal, but just so it’s clear, we also benchmark on two non-LSM datasets.

      In this revision, we have now additionally extended our benchmarking of Cellpose and StarDrist on all 4 benchmark datasets, where our Wet3D (our novel contribution of a self-supervised model) outperforms or matches these supervised baselines. Moreover, we perform rigorous testing of our model’s generalization by training on one dataset and testing generalization to the other 3; we believe this is on par (or beyond) what most cell segmentation papers do, thus we hope that “incomplete” can now be updated.

      Public Reviews:

      Reviewer #1 (Public review):

      This work presents a self-supervised method for the segmentation of 3D cells in microscopy images, an annotated dataset, as well as a napari plugin. While the napari plugin is potentially useful, there is insufficient evidence in the manuscript to support the claim that the proposed method is able to segment cells in other light-sheet microscopy image datasets than the two specific ones used here.

      Thank you again for your time. We benchmarked already on four datasets the performance of WNet3Dd (our 3D SSL contribution) - thus, we do not know which two you refer to. Moreover, we now additionally benchmarked Cellpose and StarDist on all four so readers can see that on all datasets, WNet3D outperforms or matches these supervised methods.

      I acknowledge that the revision is now more upfront about the scope of this work. However, my main point still stands: even with the slight modifications to the title, this paper suggests to present a general method for self-supervised 3D cell segmentation in light-sheet microscopy data. This claim is simply not backed up.

      We respectfully disagree; we benchmark on four 3D datasets: three curated by others and used in learning ML conference proceedings, and one that we provide that is a new ground truth 3D dataset - the first of its kind - on mesoSPIM-acquired brain data. We believe benchmarking on four datasets is on par (or beyond) with current best practices in the field. For example, Cellpose curated one dataset and tested on held-out test data on this one dataset (https://www.nature.com/articles/s41592-020-01018-x) and benchmarked against StarDist and Mask R-CNN (two models). StarDist (Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy) benchmarked on two datasets and against two models, IFT-Watershed and 3D U-Net. Thus, we feel our benchmarking on more models and more datasets is sufficient to claim our model and associated code is of interest to readers and supports our claims (for comparison, Cellpose’s title is “Cellpose: a generalist algorithm for cellular segmentation”, which is much broader than our claim).

      I still think the authors should spell out the assumptions that underlie their method early on (cells need to be well separated and clearly distinguishable from background). A subordinate clause like "often in cleared neural tissue" does not serve this purpose. First, it implies that the method is also suitable for non-cleared tissue (which would have to be shown). Second, this statement does not convey the crucial assumptions of well separated cells and clear foreground/background differences that the method is presumably relying on.

      We expanded the manuscript now quite significantly. To be clear, we did show our method works on non-cleared tissue; the Mouse Skull, 3D platynereis-Nuclei, and 3D platynereis-ISH-Nuclei is not cleared tissue, and not all with LSM, but rather with confocal microscopy. We attempted to make that more clear in the main text.

      Additionally, we do not believe it needs to be well separated and have a perfectly clean background. While we removed statements like "often in cleared neural tissue", expanded the benchmarking, and added a new demo figure for the readers to judge. As in the last rebuttal, we provide video-evidence (https://www.youtube.com/watch?v=U2a9IbiO7nE) of the WNet3D working on the densely packed and hard to segment by a human, Mouse Skull dataset and linked this directly in the figure caption.

      We have re-written the main manuscript in an attempt to clarify the limitations, including a dedicated “limitations” section. Thank you for the suggestion.

      It does appear that the proposed method works very well on the two investigated datasets, compared to other pre-trained or fine-tuned models. However, it still remains unclear whether this is because of the proposed method or the properties of those specific datasets (namely: well isolated cells that are easily distinguished from the background). I disagree with the authors that a comparison to non-learning methods "is unnecessary and beyond the scope of this work". In my opinion, this is exactly what is needed to proof that CellSeg3D's performance can not be matched with simple image processing.

      We want to again stress we benchmarked WNet3D on four datasets, not two. But now additionally added benchmarking with Cellpose, StarDist and a non-deep learning method as requested (see new Figures 1 and 3).

      As I mentioned in the original review, it appears that thresholding followed by connected component analysis already produces competitive segmentations. I am confused about the authors' reply stating that "[this] is not the case, as all the other leading methods we fairly benchmark cannot solve the task without deep learning". The methods against which CellSeg3D is compared are CellPose and StarDist, both are deep-learning based methods.

      That those methods do not perform well on this dataset does not imply that a simpler method (like thresholding) would not lead to competitive results. Again, I strongly suggest the authors include a simple, non-learning based baseline method in their analysis, e.g.: * comparison to thresholding (with the same post-processing as the proposed method) * comparison to a normalized cut segmentation (with the same post-processing as the proposed method)

      We added a non-deep learning based approach, namely, comparing directly to thresholding with the same post hoc approach we use to go from semantic to instance segmentation. WNet3D (and other deep learning approaches) perform favorably (see Figure 2 and 3).

      Regarding my feedback about the napari plugin, I apologize if I was not clear. The plugin "works" as far as I tested it (i.e., it can be installed and used without errors). However, I was not able to recreate a segmentation on the provided dataset using the plugin alone (see my comments in the original review). I used the current master as available at the time of the original review and default settings in the plugin.

      We updated the plugin and code for the revision at your request to make this possible directly in the napari GUI in addition to our scripts and Jupyter Notebooks (please see main and/or `pip install --upgrade napari-cellseg3d`’ the current is version 0.2.1). Of course this means the original submission code (May 2024) will not have this in the GUI so it would require you to update to test this. Alternatively, you can see the demo video we now provide for ease: https://www.youtube.com/watch?v=U2a9IbiO7nE (we understand testing code takes a lot of time and commitment).

      We greatly thank the review for their time, and we hope our clarifications, new benchmarking, and re-write of the paper now makes them able to change their assessment from incomplete to a more favorable and reflective eLife adjective.

      Reviewer #2 (Public review):

      Summary:

      The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.

      Strengths:

      -  The idea behind the self-supervised learning loss is interesting.

      -  It provides a new annotated dataset for an important segmentation problem.

      -  The paper addresses an important challenge. Data annotation is very time-consuming for 3d microscopy data, so a self-supervised method that yields similar results to supervised segmentation would provide massive benefits.

      -  The comparison to other methods on the provided dataset is extensive and experiments are reproducible via public notebooks.

      Weaknesses:

      The experiments presented by the authors support the core claims made in the paper. However, they do not convincingly prove that the method is applicable to segmentation problems with more complex morphologies or more crowded cells/nuclei.

      Major weaknesses:

      (1) The method only provides functionality for semantic segmentation outputs and instance segmentation is obtained by morphological post-processing. This approach is well known to be of limited use for segmentation of crowded objects with complex morphology. This is the main reason for prediction of additional channels such as in StarDist or CellPose. The experiments do not convincingly show that this limitation can be overcome as model comparisons are only done on a single dataset with well separated nuclei with simple morphology. Note that the method and dataset are still a valuable contribution with this limitation, which is somewhat addressed in the conclusion. However, I find that the presentation is still too favorable in terms of the presentation of practical applications of the method, see next points for details.

      Thank you for noting the methods strengths and core features. Regarding weaknesses, we have revised the manuscript again and added direct benchmarking now on four datasets and a fifth “worked example” (https://www.youtube.com/watch?v=3UOvvpKxEAo&t=4s) in a new Figure 4.

      We also re-wrote the paper to more thoroughly present the work (previously we adhered to the “Brief Communication” eLife format), and added an explicit note in the results about model assumptions.

      (2) The experimental set-up for the additional datasets seems to be unrealistic as hyperparameters for instance segmentation are derived from a grid search and it is unclear how a new user could find good parameters in the plugin without having access to already annotated ground-truth data or an extensive knowledge of the underlying implementations.

      We agree that of course with any self-supervised method the user will need a sense of what a good outcome looks like; that is why we provide Google Colab Notebooks

      (https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks) and the napari-plugin GUI for extensive visualization and even the ability to manually correct small subsets of the data and refine the WNet3D model.

      We attempted to make this more clear with a new Figure 2 and additional functionality directly into the plugin (such as the grid search). But, we believe this “trade-off” for SSL approaches over very labor intensive 3D labeling is often worth it; annotators are also biased so extensive checking of any GT data is equally required.

      We also added the “grid search” functionality in the GUI (please `pip install --upgrade napari-cellseg3d`; the latest v0.2.1) to supplement the previously shared Notebook (https://github.com/C-Achard/cellseg3d-figures/blob/main/thresholds_opti/find_best_threshold s.ipynb) and added a new YouTube video: https://www.youtube.com/watch?v=xYbYqL1KDYE.

      (3) Obtaining segmentation results of similar quality as reported in the experiments within the napari plugin was not possible for me. I tried this on the "MouseSkull" dataset that was also used for the additional results in the paper.

      Again we are sorry this did not work for you, but we added new functionality in the GUI and made a demo video (https://www.youtube.com/watch?v=U2a9IbiO7nE) where you either update your CellSeg3D code or watch the video to see how we obtained these results.

      Here, I could not find settings in the "Utilities->Convert to instance labels" widget that yielded good segmentation quality and it is unclear to me how a new user could find good parameter settings. In more detail, I cannot use the "Voronoi-Otsu" method due to installation issues that are prohibitive for a non expert user and the "Watershed" segmentation method yields a strong oversegmentation.

      Sorry to hear of the installation issue with Voronoi-Otsu; we updated the documentation and the GUI to hopefully make this easier to install. While we do not claim this code is for beginners, we do aim to be a welcoming community, thus we provide support on GitHub, extensive docs, videos, the GUI, and Google Colab Notebooks to help users get started.

      Comments on revised version

      Many of my comments were addressed well:

      -  It is now clear that the results are reproducible as they are well documented in the provided notebooks, which are now much more prominently referenced in the text.

      Thanks!

      -  My concerns about an unfair evaluation compared to CellPose and StarDist were addressed. It is now clear that the experiments on the mesoSPIM dataset are extensive and give an adequate comparison of the methods.

      Thank you; to note we additionally added benchmarking of Cellpose and StarDist on the three additional datasets (for R1), but hopefully this serves to also increase your confidence in our approach.

      -  Several other minor points like reporting of the evaluation metric are addressed.

      I have changed my assessment of the experimental evidence to incomplete/solid and updated the review accordingly. Note that some of my main concerns with the usability of the method for segmentation tasks with more complex morphology / more crowded cells and with the napari plugin still persist. The main points are (also mentioned in Weaknesses, but here with reference to the rebuttal letter):

      - Method comparison on datasets with more complex morphology etc. are missing. I disagree that it is enough to do this on one dataset for a good method comparison.

      We benchmarked WNet3D (our contribution) on four datasets, and to aid the readers we additionally now added Cellpose and StarDist benchmarking on all four. WNet3D performs favorably, even on the crowded and complex Mouse Skull data. See the new Figure 3 as well as the associated video: https://www.youtube.com/watch?v=U2a9IbiO7nE&t=1s.

      -  The current presentation still implies that CellSeg3d **and the napari plugin** work well for a dataset with complex nucleus morphology like the Mouse Skull dataset. But I could not get this to work with the napari plugin, see next points.

      - First, deriving hyperparameters via grid search may lead to over-optimistic evaluation results. How would a user find these parameters without having access to ground-truth? Did you do any experiments on the robustness of the parameters?

      -  In my own experiments I could not do this with the plugin. I tried this again, but ran into the same problems as last time: pyClesperanto does not work for me. The solution you link requires updating openCL drivers and the accepted solution in the forum post is "switch to a different workstation".

      We apologize for the confusion here; the accepted solution (not accepted by us) was user specific as they switched work stations and it worked, so that was their solution. Other comments actually solved the issue as well. For ease this package can be installed on Google Colab (here is the link from our repo for ease: https://colab.research.google.com/github/AdaptiveMotorControlLab/CellSeg3d/blob/main/not ebooks/Colab_inference_demo.ipynb) where pyClesperanto can be installed via: !pip install pyclesperanto-prototype without issue on Google Colab.

      This a) goes beyond the time I can invest for a review and b) is unrealistic to expect computationally inexperienced users to manage. Then I tried with the "watershed" segmentation, but this yields a strong oversegmentation no matter what I try, which is consistent with the predictions that look like a slightly denoised version of the input images and not like a proper foreground-background segmentation. With respect to the video you provide: I would like to see how a user can do this in the plugin without having a prior knowledge on good parameters or just pasting code, which is again not what you would expect a computationally unexperienced user to do.

      We agree with the reviewer that the user needs domain knowledge, but we never claim our method was for inexperienced users. Our main goal was to show a new computer vision method with self-supervised learning (WNet3D) that works on LSM and confocal data for cell nuclei. To this end, we made you a demo video to show how a user can visually perform a thresholding check https://www.youtube.com/watch?v=xYbYqL1KDYE&t=5s, and we added all of these new utilities to the GUI, thanks for the suggestion. Otherwise, the threshold can also be done in a Notebook (as previously noted).

      I acknowledge that some of these points are addressed in the limitations, but the text still implies that it is possible to get good segmentation results for such segmentation problems: "we believe that our self-supervised semantic segmentation model could be applied to more challenging data as long as the above limitations are taken into account." From my point of view the evidence for this is still lacking and would need to be provided by addressing the points raised above for me to further raise the Incomplete/solid rating, especially showing how this can be done wit the napari plugin. As an alternative, I would also consider raising it if the claims are further reduced and acknowledge that the current version of the method is only a good method for well separated nuclei.

      We hope our new benchmarking and clear demo on four datasets helps improve your confidence in our evidence in our approach. We also refined our over text and hope our contributions, the limitations and the advantages are now more clear.

      I understand that this may be frustrating, but please put yourself in the role of a new reader of this work: the impression that is made is that this is a method that can solve 3D segmentation tasks in light-sheet microscopy with unsupervised learning. This would be a really big achievement! The wording in the limitation section sounds like strategic disclaimers that imply that it is still possible to do this, just that it wasn't tested enough.

      But, to the best of my assessment, the current version of the method only enables the more narrow case of well separated nuclei with a simple morphology. This is still a quite meaningful achievement, but more limited than the initial impression. So either the experimental evidence needs to be improved, including a demonstration how to achieve this in practice, including without deriving parameters via grid-search and in the plugin, or the claim needs to be meaningfully toned down.

      Thanks for raising this point; we do think that WNet3D and the associated CellSeg3D package - aimed to continue to integrate state of the art models, is a non-trivial step forward. Have we completely solved the problem, certainly not, but given the limited 3D cell segmentation tools that exist, we hope this, coupled with our novel 3D dataset, pushes the field forward. We don’t show it works on the narrow well-separated use case, but rather show this works even better than supervised models on the very challenging benchmark Mouse Skull. Given we now show evidence that we outperform or match supervised algorithms with an unsupervised approach, we respectfully do think this is a noteworthy achievement. Thank you for your time in assessing our work.

    1. 拓扑保护的量子比特

      拓扑保护的量子比特(Topologically Protected Qubits)是量子计算领域中的一种非常重要的概念,特别是在量子容错稳定性方面。拓扑量子比特的核心特征是,它们的信息存储不依赖于传统的量子比特所依赖的量子态,而是依赖于材料或系统的拓扑性质。这种特性使得它们对外部扰动(如噪声、环境干扰)具有高度的鲁棒性,是实现可扩展的量子计算系统的一个潜在解决方案。

      🎯 1. 拓扑保护的量子比特是什么?

      拓扑保护是指系统中信息的存储不依赖于其局部的微观细节,而是依赖于系统的整体拓扑结构。这意味着,系统的拓扑性质决定了它的信息存储方式,只有系统的大规模拓扑结构发生变化,才会破坏信息。

      具体来说,拓扑保护的量子比特有以下特点:

      • 拓扑稳定性:信息在量子比特中的存储不受微小扰动、局部噪声或杂质的影响。也就是说,只有当系统的拓扑性质发生变化时,才会影响存储的信息。
      • 无干扰的量子比特:这种量子比特的状态可以在没有直接与环境相互作用的情况下长期稳定,因此它们天然具有抗噪声能力。

      如何理解拓扑保护量子比特?

      • 比如,想象一个环形的量子比特,其信息被编码在环的拓扑结构中。如果系统的拓扑保护得当,即使外部环境对系统产生扰动,也不会轻易改变环的形状,因而不会影响存储的信息。

      🎯 2. 拓扑量子比特的例子:

      目前,拓扑量子比特的实现理论上主要依赖于拓扑量子计算,特别是Majorana中微子拓扑超导体等物理系统。

      1. Majorana中微子与拓扑量子比特

      • Majorana粒子是一种非常特殊的自反粒子,它既是粒子,又是它自己的反粒子。在拓扑量子计算中,Majorana零模被用来构造拓扑量子比特。
      • Majorana零模的自旋和动量特性使其非常适合用作量子比特的存储。它的拓扑特性使得这些量子比特对于环境噪声和扰动非常稳定,不容易被外部扰动破坏。
      Majorana量子比特的优势:
      • 非阿贝尔统计:Majorana粒子遵循非阿贝尔统计,与传统粒子(如电子)不同,这种统计性质允许它们在量子计算中以拓扑方式编码信息,确保量子比特对噪声的高度鲁棒性。
      • 拓扑保护:由于Majorana零模的拓扑特性,它们对于小的局部扰动非常稳定。信息只存在于拓扑空间中,局部噪声和环境变化不会轻易影响信息状态。

      2. 拓扑超导体与拓扑量子比特

      拓扑超导体是一类具有拓扑表面态的材料,它们的电子态受到自旋轨道耦合和超导性的双重影响,形成稳定的量子态,能够用于量子比特的存储和操控

      • 在拓扑超导体中,Majorana零模可以通过量子隧穿效应等方法在材料表面稳定存在,并且它们能稳定地传递量子信息。
      • 拓扑超导体被认为是实现拓扑量子计算的核心材料,因为它们能够在没有传统量子比特干扰的情况下进行量子操作。

      🎯 3. 拓扑量子比特的优势

      1. 抗噪声能力强
      2. 拓扑保护使得量子比特对局部噪声、热扰动以及其他环境因素具有天然的抗干扰能力。因为信息存储在拓扑空间中,而非局部量子态,所以小规模的扰动不会影响信息

      3. 量子计算的稳定性

      4. 传统的量子比特(如超导量子比特、离子阱量子比特等)容易受到噪声和外界环境的影响,导致量子退相干。而拓扑量子比特由于其拓扑特性,能够有效避免这种退相干,从而保持长时间的量子纠缠和信息存储。

      5. 无需物理隔离

      6. 在常规的量子计算中,保持量子比特与环境隔离是非常重要的,这通常需要复杂的物理系统和维护。而拓扑量子比特则具有天然的稳定性,即使在不完全隔离的条件下也能保持信息的完整性。

      7. 更高的容错能力

      8. 由于拓扑量子比特的抗扰动性质,它们在量子计算中的容错性显著提高。这个特性在实现规模化量子计算时至关重要,尤其是在错误率较高的量子计算设备中。

      🎯 4. 拓扑量子比特的挑战

      尽管拓扑量子比特具有巨大的潜力,但实现它们仍面临以下挑战: - 实验实现:目前实验上实现拓扑量子比特仍处于研究阶段,尽管有些进展(如通过拓扑超导体和Majorana零模的研究),但这些材料和技术仍需要进一步的实验验证和优化。 - 稳定性和控制:即使在理论上拓扑量子比特具有极高的稳定性,但如何有效地操控读取这些量子比特仍然是一个技术难题。

      🎯 总结:拓扑保护的量子比特

      拓扑保护的量子比特是量子计算中的一个前沿概念,它利用材料的拓扑特性,借助Majorana中微子等自反粒子来稳定地存储量子信息。这些量子比特具有极强的抗噪声能力和长时间的稳定性,为量子计算的容错性提供了新的可能性。

      尽管实现拓扑量子比特的技术仍在不断进步,但它们无疑是未来量子计算技术中的一个重要方向,尤其是在量子容错计算大规模量子计算的实现上,拓扑量子比特将发挥至关重要的作用。

      如果你对拓扑量子比特的实验实现、挑战或潜在应用有更多问题,我可以继续深入讨论!

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

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      Reply to the reviewers

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

      __* SUMMARY

      This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

      MAJOR AND MINOR COMMENTS *__

      Overall Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.

      This information is now provided in the figure legends (numbers of cells analyzed and/or numbers of embryos) except for data in Figure 5, which are presented in a new Supplementary Table

      Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

      We have used SD throughout the study. This information has now been added in figure legends.

      Results 2

      ____A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.

      We now cite a recent review on spinal cord development (Saade and E. Marti, Nature Reviews Neuroscience, 2025) to illustrate this point

      The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.

      We have now reformulated this paragraph as follows: "At E3, the transcript was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside (Saade and Marti, 2025)). One day later, at E4, this salt and pepper expression was still detected in the ventricular zone, while it markedly increased in the region of the mantle zone that is immediately adjacent to the ventricular zone. This region is enriched in nascent neurons on their way to differentiation that are still HuC/D negative. In contrast, the transcript was completely excluded from the more basal region of the mantle zone, where mature HuC/D positive neurons accumulate.

      It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.

      Thank you for the suggestion. We have now added a dotted line that separates the ventricular zone from the mantle zone at E3 and E4 in Figure 2A

      Reference should be included for pRb expression dynamics.

      This section has been rewritten in response to comments from Reviewer #3, and now contains several references regarding pRb expression dynamics. See detailed response to Reviewer #3 for the new version

      Could the Myc tag insertion approach disrupt protein function or turnover? ____Why was the insertion target site at the C terminus chosen?

      The first reason was practical: at the time when we decided to generate a KI in Cdkn1c, we had already generated several successful KIs at C-termini of other genes, in particular using the P2A-Gal4 approach (see Petit-Vargas et al, 2024), and had not yet experimented with N-terminal Gal4-P2A. We therefore decided to use the same approach for Cdkn1c.

      We also chose to target the C-terminus to avoid affecting the active CKI domain which is located at the N-terminus.

      Nevertheless, the C-terminal targeting may have an impact on the turnover: it has been described that CDK2 phosphorylation of a Threonin close to the C-terminus of Cdkn1c leads to its targeting for degradation by the proteasome from late G1 (Kamura et al, PNAS, 2003; doi: 10.1073/pnas.1831009100). We can therefore not rule out that the addition of the Myc tags close to this phosphorylation site modulates the dynamics of Cdkn1c degradation. We note, however, that we observed little overlap between the Cdkn1c-Myc and pRb signals in cycling progenitors, suggesting that Cdkn1c is effectively degraded from late G1.

      OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?

      Although it could be done, we have not attempted to do this for CDKN1c because our current experience of endogenous tagging of several genes with a similar expression level (based on our scRNAseq data) and nuclear localization (Hes5, Pax7) with a fluorescent reporter shows that the fluorescent signal is extremely low or undetectable in live conditions; Therefore we favored the multi-Myc tagging approach, and indeed we find that the Myc signal in progenitors is also very low even though it is amplified by the immunohistology method; this suggests that most likely, the only signal that would be detected -if any- with a fluorescent approach would be the peak of expression in newborn neurons.

      In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?

      The reviewer refers to the control gRNA condition in panel C, that shows that two small patches of GFP-positive cells are visible in the whole spinal cord of this particular embryo.

      Technically, the origin of these "background" cells could be multiple. A spontaneous legitimate insertion at the CDKN1c locus by homologous recombination is possible, although we tend to think it is unlikely, given the extremely short length of the arms of homology; illegitimate insertions of the Myc-P2A-Gal4 cassette at off-target sites of the control gRNA is a possibility. Alternatively, a low-level leakage of Gal4 expression from the donor vector could lead to a detectable nls-GFP expression in a few cells via Gal4-UAS amplification.

      In any case, these cells are observed at a very low frequency (1 or 2 patches of cells/embryo) relative to the signal obtained in presence of the CDKN1c gRNA#1 (probably several thousand positive cells per embryo). This suggests that if similar "background" cells are also present in presence of the CDKN1c gRNA, they would not significantly contribute to the signal, and would not impact the interpretation.

      In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

      It is indeed possible that the CDKN1c protein is more stable than the transcript in newborn neurons and remains detectable in the mantle zone after the mRNA disappears. In Gui et al, 2006, where they use an anti-CDKN1c antibody to label the protein in mouse spinal cord transverse sections at E11.5 (Figure 1B), a few positive cells are also visible basally. They could correspond to neurons that have not yet degraded CDKN1c, although it is unclear in the picture whether these cells are really in the mantle zone or in the adjacent dorsal root ganglion; we note that a similar differential expression dynamics between mRNA and protein has been described for Tis21/Btg2 in the developing mouse cortex, where the protein, but not the mRNA, is detected in some differentiated bIII-tubulin-positive neurons (Iacopetti et al, 1999).

      However, related to our response above to a previous comment from the same reviewer, we cannot rule out the possibility that the Myc tags modulate the turnover of CDKN1c protein and slow down the dynamics of its degradation in differentiating neurons.

      We have added a sentence to indicate the presence of these cells: "In addition, a few Myc-positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript."

      Results

      It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).

      We did not quantify the level of mRNA reduction, it was just evaluated by eye. The reason for choosing shRNA1 for the whole study was dictated by 1) the fact that we more consistently saw (by eye) a reduction in the signal on the electroporated side with this construct than with the other shRNAs, and 2) that the effect on neurogenesis was also more consistent.

      We will perform additional experiments to provide some quantitation of the shRNA effect, as this is also requested by Reviewer #3.

      As our Cdkn1c KI approach offers a direct read-out of the protein levels in the ventricular and mantle zones, and since our shRNA strategy of "partial knock-down" is based on the idea that the shRNA effect should be more complete in progenitors expressing Cdkn1c at low levels than in newborn progenitors that express the protein at a higher level, we propose to validate the shRNA in the Cdkn1c-Myc knock-in background, by comparing the Myc signal intensity between control and Cdkn1c shRNA conditions

      Figure panels are not currently cited in order. Citation or figure order could be changed.

      We have now added a common citation of the panels referring to analyses at 24 and 48 hours after electroporation (now Figure 3A-F), allowing us to display the experimental data on the figure according to the timing post electroporation, while the text details the phenotype at the later time point first.

      The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.

      We have added images in a revised version of the Figure 3, as requested

      A supplementary figure showing the Caspase3 experiment should be added.

      We have added data showing Caspase3 experiments in Supplementary Figure 3D

      OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

      We agree with the reviewer that direct tracking is the most direct method for the identification of pairs of sister cells. However, it remains technically challenging, and the added value compared to the retrospective identification would be limited, while requiring a great workload, especially considering the many different experimental conditions that we have explored in this study.

      Results 4

      How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.

      We have not done these quantifications in the original version of the study. We will add a quantification of the signal intensity in the ventricular and mantle zones for the revised version of the manuscript, as also requested by reviewer #3.

      In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.

      All images in the figure are single Z confocal images. Images in Column 2 (showing both electroporated sides of the same tube) were acquired with a 20x objective, whereas the insets shown in Columns 1 and 3 are 100x confocal images. 100x images on both sides were acquired with the same acquisition parameters, and the display parameters are the same for both images in the figure. The signal intensity can therefore be compared directly between columns 1 and 3.

      We have modified the legend of the Figure to indicate these points: "The insets shown in Columns 1 and 3 are 100x confocal images acquired in the same section and are presented with the same display parameters".

      In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

      Our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

      We have modified the manuscript to elaborate on our interpretation of this result: "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3H)."

      Results 5 ____The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.

      Thank you for pointing this out. We have modified the sentence in the main text.

      "We found that the proportion of pRb positive progenitors having entered S phase (EdU positive cells) was significantly higher at all time points examined more than 4h30 after FT injection in the Cdkn1c knock-down condition compared to the control population (Figure 5D)"

      OPTIONAL Could CyclinD1 activity be directly assessed?

      This is an interesting suggestion. For example, using the fluorescent CDK4/6 sensor developed by Yang et al (eLife, 2020; https://doi.org/10.7554/eLife.44571) in a CDKN1c shRNA condition would represent an elegant experimental alternative to complement our rescue experiments with the double CDKN1c/CyclinD1 shRNA. However, we fear that setting up and calibrating such a tool for in vivo usage in the chick embryo represents too much of a challenge for incorporation in this study.

      General ____Scale bars missing fig s1c s4d.

      Thanks for pointing this out. Scale bars have been added in the figures and corresponding legends

      OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.

      OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?

      We agree that it will be interesting and important that our findings are replicated in other species, experimental systems, and even tissues, or by alternative experimental approaches. Nevertheless, it is probably beyond the scope of this study.

      A model cartoon to summarise outcomes would be useful.

      We thank the reviewer for the suggestion. We will propose a summary cartoon for the revised version of the manuscript.

      Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

      Positivity or negativity was decided by eye. However, for each experiment, we ensured that all images of perturbed conditions and the relevant controls were analyzed with the same display parameters and by the same experimenter to guarantee that the criteria to determine positivity or negativity were constant.

      Reviewer #1 (Significance (Required)):

      SIGNIFICANCE

      Strengths: This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

      • Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
      • A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
      • Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
      • Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
      • Mechanistic insight by examining downstream target CyclinD1.
      • Clearly presented with useful illustrations throughout.
      • Logic is clear and examination thorough.
      • Conclusions are warranted on the basis of their findings. ____Limitations ____T____his study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.

      Some aspects of quantification require explanation in order for the experiments to be replicated.

      It is imperative that precise sample sizes are included for all experiments presented.

      Advance: ____First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.

      Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.

      Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

      Audience:

      Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

      Please define your field of expertise with a few keywords to help the authors contextualize your point

      Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

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

      The work by Mida and colleagues addresses important questions about neurogenesis in the embryo, using the chicken neural tube as their model system. The authors investigate the mechanisms involved in the transition from stem cell self-renewal to neurogenic progenitor divisions, using a combination of single cell, gene functional and tracing studies.

      The authors generated a new single cell data set from the embryonic chicken spinal cord and identify a transitory cell population undergoing neuronal differentiation, which expresses Tis21, Neurog2 and Cdkn1c amongst other genes. They then study the role of Cdkn1c and investigate the hypothesis that it plays a dual role in spinal cord neurogenesis: low levels favour transition from proliferative to neurogenic divisions and high levels drive cell cycle exit and neuronal differentiation.

      Major comments

      I have only a general comment related to the main point of the paper. The authors claim that Cdkn1c onset in cycling progenitor drives transition towards neurogenic modes of division, which is different from its role in cell cycle exit and differentiation. Figures 3F and 4D are key figures where the authors analysed PP, PN and NN mode of divisions via flash tag followed by analysis of sister cell fate. If their assumption is correct, shouldn't they also see, for example in Fig. 4D, an increase in PN or is this too transient to be observed or is it bypassed?

      As already stated in our response to a similar question from reviewer #1, our interpretation is that Pax7-CDKN1c misexpression experiments cause both PP to PN and PN to NN conversions. This is coherent with the classical idea of a progressive transition between these three modes of division in the spinal cord. Coincidentally, in our experimental conditions (timing of analysis and level of overexpression), the increase in PN resulting from PP to PN conversions is perfectly balanced by a decrease resulting from PN to NN conversions, giving the artificial impression that the PN compartment is unaffected. A less likely hypothesis would be that misexpression directly transforms symmetric PP into symmetric NN divisions, and that asymmetric PN divisions are insensitive to CDKN1c levels. We do not favor this hypothesis, because one would expect, in that case, that the shRNA approach would also not affect the PN compartment, and it is not what we have observed (see Figure 3H - previously 3F).

      At the moment, the calculations of PN and NN frequencies are merged in the text, so perhaps describing PN and NN numbers separately will help better understand the dynamics of this gradual process (especially since there is little to no difference in PN).

      Regarding the results of Pax7 overexpression presented in figure 4D (now Figure 4E in the revised version), we had made the choice to merge PN and NN values in the main text to focus on the neurogenic transition from PP to PN/NN collectively. We agree with this reviewer, as well as with reviewer #1, that it should be more detailed and better discussed. We therefore propose to modify the paragraph as follows (and as already indicated above in the response to reviewer #1):

      "We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that Cdkn1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of Cdkn1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal Cdkn1c shRNA approach (see Figure 3F, now 3H)."

      Could the increase in NN be compatible also with a role in cell cycle exit and differentiation, for example from cells that have been targeted and are still undergoing the last division (hence marked by flash tag) or there won't be any GFP cells marked by flash tag a day after expression of high levels of Cdkn1c?

      It is likely that a proportion of cells that would normally have done a NN division are pushed to a direct differentiation that bypasses their last division in the Pax7-CDKN1c condition, and that they contribute to the general increase in neuron production observed in our quantification 48hae (Figure 3F -previously 3C). However, these cases would not contribute to the increase in the NN quantification in pairs of sister cells 6 hours after division at 24hae (Figure 4E - previously 4D), because by design they would not incorporate FlashTag. The rise in NN is therefore the result of a PN to NN conversion.

      Basically, what would the effect of expressing higher levels of Cdkn1c be? I guess this will really help them distinguish between transition to neurogenic division rather than neuronal differentiation. If not experimentally, any further comments on this would be appreciated.

      These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification

      « We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 6A)."

      * * Minor comments

      Fig 3C my understanding is that HuC/D should be nuclear, but in fig 3C it seems more cytoplasmic (any comment?)

      Some studies suggest that HuC/D can, under certain conditions, be observed in the nucleus of neurons. However, HuC/D is a RNA binding protein whose localization is mainly expected to be cytoplasmic. In our experience (Tozer et al, 2017), and in other publications using the antibody in the chick spinal cord (see, for example, le Dreau et al, 2014), it is observed in the cell body of differentiated neurons, as in the current manuscript.

      Fig Suppl 3E (and related 4B), immuno for Cdkn1c-Myc: to help the reader understand the difference between the immuno signals when looking at the figure, I would suggest writing on the panel i) Pax7-Cdkn1c-Myc and ii) endogenous Cdkn1c-Myc, rather than 'misexpressed' and 'endogenous', which is slightly confusing (especially because what it is called endogenous expression is higher).

      This has now been modified in the figures.

      Literature citing: Introduction and discussion are very nicely written, although they could benefit from some more recent literature on the topic. For example, Cdkn1c role as a gatekeeper of stem cell reserve in the stomach, gut, (Lee et al, CellStemCell 2022 PMID: 35523142) or some other work on symmetric/asymmetric divisions and clonal analysis in zebrafish (Hevia et al, CellRep 2022 PMID: 35675784, Alexandre et al, NatNeur PMID: 20453852), mammals (Royal et al, Elife 2023 37882444, Appiah et al, EMBO rep 2023 PMID: 37382163). Also, similar work has been performed in the developing pancreatic epithelium, where mild expression of Cdkn1a under Sox9rtTa control was used to lengthen G1 without overt cell cycle exit and this resulted in Neurog3 stabilization and priming for endocrine differentiation (Krentz et al, DevCell 2017 PMID: 28441528), so similar mechanisms might be in in place to gradually shift progenitor towards stable decision to differentiate. Moreover, in the discussion, alongside Neurog2 control of Cdkn1c, it could be mentioned that the feedback loop between Cdk inhibitors and neurogenic factor is usually established via Cdk inhibitor-mediated inhibition of proneural bHLHs phosphorylation by CDKs (Krentz et al, DevCell 2017 PMID: 28441528, Ali et al, 24821983, Azzarelli et al 2017 - PMID: 28457793; 2024 - PMID:39575884). Further, in the discussion, could they mention anything about the following open questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis? Or is there another Cdk inhibitor expressed at later developmental stages, hence linking this with specific cell fate decisions?

      We will modify the introduction and discussion in several instances, in order to address the above suggestions and we will:

      • add references to its role in other contexts and/or species.

      • expand the discussion on the cross talk between neurogenic factors and CDK inhibitors in other cellular contexts.

      • add a dedicated paragraph in the discussion to answer reviewer#2's questions: is there evidence for Cdkn1c low/high expression in mammalian spinal cord? Or maybe of other Cdk inhibitors? Is Cdkn1c also involved in cell cycle exit during gliogenesis or is there another Cdk inhibitor expressed at later developmental stages?

      Reviewer #2 (Significance (Required)):

      The work here presented has important implications on neural development and its disorders. The authors used the most advanced technologies to perform gene functional studies, such as CRISPR-HDR insertion of Myc-tag to follow endogenous expression, or expression under endogenous Pax7 promoter, often followed by flash tag experiments to trace sister cell fate, and all of this in an in vivo system. They then tested cell cycle parameters, clonal behaviour and modes of cell division in a very accurate way. Overall data are convincing and beautifully presented. The limitation is potentially in the resolution between the events of switching to neurogenic division versus neuronal differentiation, which might just warrant further discussion. This work advances our knowledge on vertebrate neurogenesis, by investigating a key player in proliferation and differentiation.

      ____I believe this work will be of general interest to developmental and cellular biologists in different fields. Because it addresses fundamental questions about the coordination between cell cycle and differentiation and fate decision making, some basic concepts can be translated to other tissues and other species, thus increasing the potential interested audience.

      My work focuses on stem cell fate decisions in mammalian systems, and I am familiar with the molecular underpinnings of the work here presented. However, I am not an expert in the chicken spinal cord as a model and yet the manuscript was interesting. I am also not sufficiently expert in the bioinformatic analysis, so cannot comment on the technical aspects of Figure 1 and the way they decided to annotate their data.

      __*

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

      Summary: In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely.

      By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

      This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

      This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

      Major comments:

      1.-The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions.

      However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified.

      Throughout their comments on the manuscript, this reviewer raises several points regarding the characterization of pRb expression in our model and of our use of this marker in our study. We take these comments into account and propose to expand on pRb characteristics in the first occurrence of pRb as a marker of cycling cells in the manuscript. The modifications rely on:

      • the quotation of several studies showing that phosphorylation of Rb is regulated during the cell cycle, and that "it is not detectable during a period of variable length in early G1 in several cell types (Moser et al, 2018;Spencer et al, 2013; Gookin et al, 2017), including neural progenitors in the developing chick spinal cord (Molina et al, 2022). Apart from this absence in early G1, pRb is detected throughout the rest of the cell cycle until mitosis".

      • a more detailed description of our own characterization of pRb dynamics in a synchronous cohort of cycling cells, which reveals a similar heterogeneity in the timing of the onset of Rb phosphorylation after mitosis. This description was initially shown in supplementary figure 3 and will be transferred to a new supplementary figure 2 to account for the fact that it will now be cited earlier in the manuscript.

      Regarding the specific question the "suitability (of pRb) as a neurogenic division marker": we do not directly "use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors", but we use Rb phosphorylation to discriminate between progenitors (pRb+) and neurons (pRb-) identity in pairs of sister cells to retrospectively identify the mode of division of their mother.

      Given that Rb is unphosphorylated during a period of variable length after mitosis (see references above), pRb is not a reliable marker of ALL cycling progenitors. We developed an assay to identify the timepoint (the maximal length of this "pRb-negative" phase) after which Rb is phosphorylated in all cycling progenitors (new Supplementary Figure 2). This assay relies on a time course of pRb detection in cohorts of FlashTag-positive pairs of sister cells born at E3. This time course experiment allowed us to identify a plateau after which the proportion of pRb-positive cells in the cohort remains constant. From this timepoint, this proportion corresponds to the proportion of cycling cells in the cohort. Rb phosphorylation therefore becomes a discriminating factor between cycling progenitors (pRb+) and non-cycling neurons (pRb-).

      We are confident that this provides a solid foundation for the determination of the identity of pairs of sister cells in all our Flash-Tag based assays, which retrospectively identify the mode of division of a progenitor on the basis of the phosphorylation status of its daughter cells 6 hours after division.

      We propose to modify the main text to describe the strategy and protocol more explicitly, by introducing the sentence highlighted in yellow in the following paragraph where the paired-cell analysis is first introduced (in the section on CDKN1c knock-down):

      "This approach allows to retrospectively deduce the mode of division used by the mother progenitor cell. We injected the cell permeant dye "FlashTag" (FT) at E3 to specifically label a cohort of progenitors that undergoes mitosis synchronously (Baek et al., 2018; Telley et al., 2016 and see Methods), and let them develop for 6 hours before analyzing the fate of their progeny using pRb immunoreactivity (Figure 3D). Our characterization of pRb immunoreactivity in the tissue had established beforehand that 6 hours after mitosis, all progenitors can reliably be detected with this marker (Supplementary Figure 2, Methods). Therefore, at this timepoint after FT injection, two-cell clones selected on the basis of FT incorporation can be categorized as PP, PN, or NN based on pRb positivity (P) or not (N) (see Methods, new Figure 3G and new Supplementary Figures 2 and 4)."

      We also modified accordingly the legend to Supplementary Figure 2 (previously Supplementary Figure 3, which describes the identification of the plateau of pRb.

      Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c.

      In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components.

      We agree with the reviewer that Rb phosphorylation may be a direct or indirect target of Cdkn1c activity, and exploring the molecular aspects of the cellular and developmental phenomena that we describe in our manuscript would represent an interesting follow up study.

      ____A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

      To complement our analyses of the modes of division, we propose to use a positive marker to assess neural identity in parallel to the absence of pRb within pairs of cells. This approach may be the most meaningful in the gain of function context (Pax7 driven expression of Cdkn1c) because in this context, the time-point to reach the plateau of Rb phosphorylation used in our FT-based assay may indeed be delayed. On the opposite, in the context of loss of functions, the plateau may be reached earlier, which would have no effect on this assay.

      2.-Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division.

      This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.

      The reviewer probably mistyped and meant 6-hour post injection, which is the duration that we use for paired cell tracking. We would like to emphasize that in addition to the FlashTag label, we benefit from the electroporation reporter to assess clonality. Altogether, we combine 5 criteria to define a clonal relationship :

      • 2 cells are positive for Flash Tag
      • The Flash Tag intensity is similar between the 2 cells
      • The 2 cells are positive for the electroporation reporter
      • The electroporation reporter intensity is similar between the two cells
      • the position of the two cells is consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995;__; __Loulier et al, 2014): they are found on a shared line along the apico-basal axis, and share the same Dorso-Ventral and Antero-Posterior position . This combination is already described in the Methods section. We propose to modify the paragraph to include the sentence highlighted in yellow in the text below;

      "Cell identity of transfected GFP positive cells was determined as follows: cells positive for pRb and FT were classified as progenitors and cells positive for FT and negative for pRb as neurons. In addition, a similar intensity of both the GFP and FT signals within pairs of cells, and a relative position of the two cells consistent with the radial organization of clones in this tissue (Leber and Sanes, 1995; Loulier et al, 2014) were used as criteria to further ascertain sisterhood. This combination restricts the density of events fulfilling all these independent criteria, and can confidently be used to ensure a robust identification of pairs of sister cells."

      3.- The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.

      - "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation.

      We have entirely remodeled this section, which describes the expression of Myc-tagged Cdkn1c relative to pRb and now provide several references that describe the generally accepted view that pRb is specific of cycling cells, regulated during the cell cycle, and in particular absent in early G1. We also remove the mention of the "Restriction point" in the main text to avoid any confusion on the timing of phosphorylation, as the notion of restriction point is not useful in our study. The section now reads as follows:

      "To ascertain that Cdkn1c is translated in neural progenitors, we used an anti-pRb antibody, recognizing a phosphorylated form of the Retinoblastoma (Rb) protein that is specifically detected in cycling cells (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) , including neural progenitors of the developing chick spinal cord (Molina et al., 2022). In the ventricular zone of transverse sections at E4 (48hae), we detected triple Cdkn1c-Myc/GFP/pRb positive cells (arrowheads in Figure 2B), providing direct evidence for the Cdkn1c protein in cycling progenitors. We also observed many double GFP/pRb positive cells that were Myc negative (arrowheads in Figure 2B). The observation of UAS-driven GFP in these pRb-positive cells is evidence for the translation of Gal4 and therefore provides a complementary demonstration that the Cdkn1c *transcript is translated in progenitors. The absence of Myc detection in these double GFP/pRb positive cells also suggests that Cdkn1c/Cdkn1c-Myc stability is regulated during the cell cycle. *

      Finally, we observed double Myc/GFP-positive cells that were pRb-negative (Figure 2B; asterisks). One characteristic of Rb phosphorylation as a marker of cycling cells is a period in early G1 during which it is not detectable, as described in several cell types (Gookin et al., 2017; Moser et al., 2018; Spencer et al., 2013) including chick spinal cord neural progenitors (Molina et al., 2022). Using a method that specifically labels a synchronous cohort of dividing cells in the neural tube, we similarly observed a period in early G1 during which pRb is not detectable in some progenitors at E3 (See Supplementary Figure 2 and Methods). Hence, the double Myc/GFP positive and pRb negative cells may correspond to progenitors in early G1. Alternatively, they may be nascent neurons whose cell body has not yet translocated basally (see Figure 2C). Finally, we observed a pool of GFP positive/pRb negative nuclei with a strong Myc signal in the region of the mantle zone that is in direct contact with the ventricular zone (VZ), corresponding to the region where the transcript is most strongly detected (see Figure 2A). This pool of cells with a high Cdkn1c expression likely corresponds to immature neurons exiting the cell cycle and on their way to differentiation (Figure 2B; double asterisks). In addition, a few Myc positive cells were located deeper in the mantle zone, where the transcript is no more present, suggesting that the protein is more stable than the transcript.

      In summary, our dual Myc and Gal4 knock-in strategy which reveals the history of Cdkn1c transcription and translation confirms that Cdkn1c is expressed at low level in a subset of progenitors in the chick spinal neural tube, as previously suggested (Gui et al., 2007; Mairet-Coello et al., 2012). In addition, the restricted overlap of Cdkn1c-Myc detection with Rb phosphorylation suggests that in progenitors, Cdkn1c is degraded during or after G1 completion. "

      This section will again be remodeled in a future revised version of the manuscript, in which we will add quantifications of Myc levels, as requested by Reviewer 1 above, and also by Reviewer #3 below.

      Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).

      In the original version of the manuscript, the section describing the dynamics of CDKN1c-Myc in the KI experiments presented in Figure 2 relied on the idea that the dynamics of pRb in chick spinal progenitors is similar to what I described in other tissues and cell types, without providing any references to substantiate this fact. Actually, Molina et al provide a characterization of pRb in combination with their cell cycle reporter and conclude that pRb negative progenitors are in G1 ("We also verified that phospho-Rb- and HuC/D-negative cells were in G1 by using our FUCCI G1 and PCNA reporters"). We will now cite this reference to support our claim. In addition, our characterization of Rb progressive phosphorylation in the synchronic Flash-Tag cohort of newborn sister cells provides a complementary demonstration that a fraction of the progenitors are pRb-negative when they exit mitosis (i.e. in early G1). This analysis was initially only introduced in the supplementary Figure 3, as support for the section that presents the Paired-cell assay used in Figure 3. We propose to introduce the data from Supplementary Figure 3 earlier in the manuscript (now Supplementary Figure 2), in order to better introduce the reader with the dynamics of pRb in cycling cells in our model. This will better support our description of the Cdkn1c-Myc dynamics in relation with pRb. We therefore propose to reformulate this whole section as follows.

      - It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.

      - It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).

      These are two interesting suggestions. To complement our data with guide #1, we have performed Myc-immunostaining experiments on transverse sections in the context of guide #3, showing exactly the same pattern of Myc signal, with low expression in the VZ, and a peak of signal in the part of the mantle zone that is immediately touching the VZ. This confirms the specificity of the spatial distribution of the Cdkn1c-Myc signal. These data have been added in a revised version of Supplementary Figure 1.

      We will perform the suggested quantifications using guides #1 and #3, which both show a good KI efficiency. We do not think it is useful to do these experiments with guide #2, whose efficiency is much lower, and which would lead to a very sparse signal.

      - The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).

      We will perform these experiments to validate guide cutting efficiency using the Tide method (Brinkman et al, 2014)

      - In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.

      We will perform genomic PCR experiments to confirm in-frame insertion of the Myc tags at the Cdkn1c locus

      4.- In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

      The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c.

      Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression.

      All the predicted isoforms in the chick genome contain the sequence targeted by shRNA1, which is located in the CKI domain, the region of the protein that is most conserved between species. Besides, all the isoforms annotated in the mouse and human genomes also contain the region targeted by shRNA1. We are therefore confident that shRNA1 should target all chick isoforms.

      A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2).

      This approach (qRT-PCR on sorted cells) would enable us to focus solely on electroporated cells, but it would result in an averaged quantification of Cdkn1c depletion. In order to obtain additional information on the shRNA-dependent decrease in Cdkn1C in the different neural cell populations (progenitor versus differentiating neuron), we propose an alternative approach consisting in monitoring the level of Cdkn1c protein, assessed through Cdkn1c-Myc signal in knock-in cells, in the presence versus absence of Cdkn1c shRNA.

      - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons.

      We have addressed the case of pRb dynamics in progenitors above and added a reference documented pRb expression during the cell cycle of chick neural progenitors (Molina et al, 2022).

      Regarding Sox2 persistence: we consistently detect a small fraction of double positive Sox2+/HuC/D+ cells in chick spinal cord transverse sections. We have shown that this marker of differentiating neurons (HuC/D) only becomes detectable more than 8 hours after mitosis in newborn neurons at E3 (Baek et al, 2018), indicating that Sox2 protein can persist for up to at least 8 hours in newborn neurons.

      We now cite a paper showing that a similar persistence of Sox2 protein is reported in differentiating neurons of the human neocortex, where double Sox2/NeuN positive cells are frequently observed in cerebral organoids (Coquand et al, Nature Cell Biology 2024__)__

      - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption?

      We do not think that these numbers are equal for both conditions, and we did not formulate this assumption. We only indicate (in the methods section) that this undefined/undetermined population (based on negativity for both markers) is a mix of two possible cell types. However, we do not offer any interpretation of the CDKN1c phenotypes based on the changes in this population. Indeed, our interpretation of the knock-down phenotype is solely based on the increase in pRb-positive and decrease in HuC/D-positive cells, which both suggest a delay in neurogenesis. We understand from the reviewer's comment that depicting an "undefined" population on the graph may cause some confusion. We therefore propose to present the data on pRb and HuC/D in different graphs, rather than on a combined plot, and to remove the reference to undefined cells in Figure 3, as well as in Figures 4 and 5 depicting the gain of function and double knock-down experiments. We have implemented these changes in updated versions of the figures.

      - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe?

      We have now performed experiments with an anti phospho Histone 3 antibody, which labels mitotic cells, at 24 and 48 hours post electroporation. We do not see any ectopic mitoses upon Cdkn1c knock-down with this marker, and we have produced a Supplementary Figure with these data. This is consistent with the fact that we also do not see ectopic pRb or Sox2 positive cells in the mantle zone in the knock-down experiments. These data (pH3 and Sox2) have been added in the new Supplementary Figure 3E and F.

      We have now modified the main text to include these data:

      "In the context of a full knock-out of Cdkn1c in the mouse spinal cord, a reduction in neurogenesis was also observed, which was attributed to a failure of prospective neurons to exit the cell cycle, resulting in the observation of ectopic mitoses in the mantle zone (Gui et al, 2007). In contrast with this phenotype, using an anti phospho-Histone3 antibody, we did not observe any ectopic mitoses 24 or 48 hours after electroporation in our knock-down condition (Supplementary Figure 3E-F). This is consistent with the fact that we also do not observe ectopic cycling cells with pRb (Figure 3A and D) and Sox2 (Supplementary Figure 3E-F) antibodies. We therefore postulated that the reduced neurogenesis that we observe upon a partial Cdkn1c knock-down may result from a delayed transition of progenitors from the proliferative to neurogenic modes of division."

      - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye.

      - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells?

      The key criterion for cells that are not directly touching each other is that their relative position corresponds to the classical "radial" organization of clones in this tissue (Leber and Sanes, 1995__; __Loulier et al, Neuron, 2014). In other words, we make sure that they are located on a same apico-basal axis, as is the case for the NN clone presented on the figure. As stated above in our response to major comment #2, we have modified the Methods section accordingly.

      Can they provide further image examples of different types of clones?

      We now provide additional examples in a new Supplementary Figure 4

      - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity?

      The plateau for Rb phosphorylation in progenitors is reached before 6 hours post mitosis at E3. At the same age, we have previously shown (Baek et al, PLoS Biology 2018) in a similar time course experiment in pairs of FT+ cells that the HuC/D signal is not detected in newborn neurons 8 hours after mitosis. HuC/D only starts to appear between 8 and 12 hours, and still increases between 8 and 16 hours. The plateau would therefore be very delayed for HuC/D compared to pRb. This long delay in the appearance of this « positive » marker of neural differentiation is the main reason why we chose to use Rb phosphorylation status for the analysis of synchronous cohorts of pairs of sister cells, because pRb becomes a discriminating factor much earlier than HuC/D after mitosis.

      - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?

      We have carried out similar experiments at E2, showing a plateau of 95% of pRb-positive cells in the FT-positive population (see graph on the right). This provides a retrospective estimate of the mode of division of the mother cells at this stage (roughly 90% of PP and 10% of PN) which is consistent with the vast majority of PP divisions described by Saade et al (2013, see Figure S1) at this stage.

      5.- In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is:

      - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024.

      We have now performed Pax7 immunostainings on transverse sections at 24 and 48 hours post electroporation, both with the Pax7-CDKN1c-Gal4 and with the Pax7-Gal4 control constructs. We present these data in the new supplementary figure 7. In both conditions, we find that the Pax7 protein is still present in KI-positive cells. We observe a modest increase in Pax7 signal intensity in these cells, suggesting either that the insertion of exogenous sequences stabilizes the Pax7 transcript, or that the C-terminal modification of Pax7 protein with the P2A tag increases its stability. This does not affect the interpretation of the CDKN1c overexpression phenotype, because we used the Pax7-Gal4 construct that shows the same modification of Pax7 stability as a control for this experiment. We have introduced this comment in the legend of Supplementary Figure 7.

      - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level?

      Cdkn1c transcription is regulated by multiple transcription factors and non-coding RNAs (see for example Creff and Besson, 2020, or Rossi et al, 2018 for a review). To our knowledge, these studies focus more on the regulation of Cdkn1c global expression than on the regulation of its levels during cell cycle progression. Although it is very likely that transcriptional regulation contributes, post-translational regulation, and in particular degradation by the proteasome, is also a key factor in the cell cycle regulation of Cdkn1c activity

      If so, how does this differ from the promoter activity of Pax7?

      The transcriptional regulation of Pax7 and Cdkn1c is probably controlled by different regulators, since their expression profiles are very different. Regardless of the mechanisms that control their expression, the rationale for choosing Pax7 as a driver for Cdkn1c expression was that Pax7 expression precedes that of Cdkn1c in the progenitor population, and that it disappears in newborn neurons, when that of Cdkn1c peaks. This provided us with a way to advance the timing of Cdkn1c expression onset in proliferative progenitors.

      - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B.

      We will carry out experiments similar to those shown in Figure 2B in order to characterise the dynamics of Cdkn1c in a context of overexpression, in relation to pRb.

      In addition, we will include a more precise quantification of the "misexpressed" compared to "endogenous" Cdkn1c -Myc levels, as already mentioned in the answer to a request by reviewer1.

      6.-In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons.

      - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845).

      The Nowakowski non linear regression method has been used often in the literature in the same tissue, and is generally used to calculate fixed values for Tc, Ts, etc... This method is based on several selective criteria, and in particular the assumption that "all of the cells have the same cycle times". Yet, many studies have documented that cell cycle parameters change during the transition from proliferative to neurogenic modes of division during which our analysis is performed; live imaging data in the chick spinal cord have illustrated very different cell cycle durations at a given time point (see Molina et al). We therefore think that the proposed formulas do not reflect the heterogenous reality of neural progenitors of the embryonic spinal cord. However, the cumulative approach described by Nowakowski is useful to show qualitative differences between populations (e.g. a global decrease of the cycle length, like in our comparison between control and shRNA conditions). For these reasons, we prefer to display only the raw measurements rather than the regression curves.

      - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why?

      Le Dreau et al count the EdU+ proportion of cells in the total population of electroporated cells located in the VZ (which includes progenitors, but also future neurons that have been labelled during the previous cycles -at least for the time points after 2hours- and have not yet translocated to the mantle zone), whereas we only consider pRb+ progenitors in the analysis. In addition, the experiments are not performed at the same developmental stage. Altogether, this may account for the different curves obtained in our study.

      - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

      We will perform cumulative EDU incorporation experiments similar to that shown in Figure 5D to measure G1 length for the cdkn1c-sh - ccnd1-sh knock down double conditions, as well as in the Ccnd1 knock down condition alone.

      Minor comments

      __*Introduction:

      • The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. ____Nature Communications 2020).*__

      We will modify the introduction in several instances, in order to address suggestions by Reviewers #2 (see above) and #3, in particular to expand the description of the role of Cdkn1c during cortical development

      1) Transcriptional signature of the neurogenic transition (Figure 1).

      - In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).

      We have now listed the genes used to determine the progenitor and neuron score in the main text of the result section

      - Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).

      We have now added the detail of what 'filtering' means in the diagram

      - In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.

      We have reworded this sentence, adding a reference to the expression profile of Tis 21. The paragraph now reads as follows:

      « However, Cdkn1c expression is maintained longer and transiently peaks at high levels after Tis21 expression is switched off. Given that Tis21 is no more expressed in neurons (Iacopetti et al, 1999), this suggests that Cdkn1c expression is transiently upregulated in nascent neurons before fading off in more mature cells. »

      - "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.

      We have now added references linking the function and/or expression profile of these genes to the neurogenic transition: Dll1 (Henrique et al., 1995), the bHLH transcription factors Hes6 (Fior and Henrique, 2005), NeuroG1 and NeuroG2 (Lacomme et al., 2012; Sommer et al., 1996) and the coactivator Gadd45g (Kawaue et al., 2014).

      - There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

      - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper.

      We have corrected the colour code errors in Figure 1c and Supp Figure 3B (now changed to Supplementary Figure 5 in the modified revision)

      ____It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors.

      We have so far refrained from performing the suggested combined analysis based on cell cycle and cell type scores, as the "neurogenic progenitor population" (based on neurogenic progenitor score values) in which Cdkn1c expression is initiated represents a small number of cells in our scRNAseq, and felt that the significance of such an analysis is uncertain. We will perform this analysis in the revised version

      2) Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2).

      - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2).

      The scale bar is actually valid for the whole panel A. The E2 section in the original figure appeared as "large" as the E3 section along the DV axis probably because the cutting angle was not perfectly transverse at E2, artificially lengthening the section. In a new version of the figure, we have replaced the E2 images with another section from the same experiment. The scale bar remains valid for the whole panel.

      - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A.

      We have now added a diagram for the knock-in strategy in Figure 2B, and modified the legend of the figure accordingly.

      - Indicate hours post-electroporation. Indicate which guide is used in the main text.

      We have now added the post-electroporation timing and guide used in the main text.

      3) Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3).

      - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed.

      - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region?

      __We have modified this sentence as follows: "__Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal two thirds of the neural tube, except for the Pax7-Cdkn1c misexpression analysis, which was performed in the more dorsal Pax7 domain."

      This is valid both for the whole population and clonal analyses

      - Figure 3. Would have a better flow if 3C preceded 3A and 3B.

      We have modified the Figure accordingly.

      - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification.

      We have modified the Figure accordingly

      - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B.

      This explanation corresponds indeed to Figure 5A. We have corrected this mistake in the new version of the manuscript.

      4) Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).

      - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain.

      We have added references to the expression profile of Pax7 in the dorsal neural tube (Jostes et al, 1990). In addition, the new Supplementary Figure 7 shows anti-Pax7 staining that confirm this expression pattern at E3 and E4

      - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells.

      As stated in the response to Major Point 5 above, we will perform a quantification based on Myc immunofluorescence to compare endogenous Cdkn1c expression versus Cdkn1c expression upon overexpression.

      - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe.

      We have added in the main text that the quantification was performed 48hae.

      - Legend of figure 4D should indicate that the quantification has been done 24hpe.

      We have added the timing of quantification in the legend of Figure 4D.

      - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment.

      These experiments have been performed and presented in the study by Gui et al., 2007, which we cite in the paper. Using a strong overexpression of CDKN1c from the CAGGS promoter, they showed a massive decrease in proliferation, assessed by BrdU incorporation, 24hours after electroporation. We will cite this result more explicitly in the main text, and better explain the difference of our approach. We propose the following modification:

      « We next explored whether low Cdkn1c activity is sufficient to induce the transition to neurogenic modes of division. A previous study has shown that overexpression of Cdkn1c driven by the strong CAGGS promoter triggers cell cycle exit of chick spinal cord progenitors, revealed by a drastic loss of BrdU incorporation 1 day after electroporation (Gui et al., 2007). As this precludes the exploration of our hypothesis, we developed an alternative approach designed to prematurely induce a pulse of Cdkn1c in progenitors, with the aim to emulate in proliferative progenitors the modest level of expression observed in neurogenic progenitors. We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)."

      - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections.

      This whole section has been modified in response to a question from reviewer 1. The new version does not contain percentages in the main text, and reads as follows:

      « Using the FlashTag cohort labeling approach described above, we traced the fate of daughter cells born 24 hae. We observed an increase in the proportion of terminal neurogenic (NN) divisions and a decrease in proliferative (PP) divisions (Figure 4D). This suggests that CDKN1c premature expression in PP progenitors converts them to the PN mode of division, while the combined endogenous and Pax7-driven expression of CDKN1c converts PN progenitors to the NN mode of division. Coincidentally, at the stage analyzed, PP to PN conversions are balanced by PN to NN conversions, leaving the PN proportion artificially unchanged. The alternative interpretation of a direct conversion of symmetric PP into symmetric NN divisions is less likely, because the PN compartment was affected in the reciprocal CDKN1c shRNA approach (see Figure 3F). Overall, these data show that inducing a premature low-level expression of Cdkn1c in cycling progenitors is sufficient to accelerate the transition towards neurogenic modes of division. »

      - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study.

      The three guides listed in the original Supplementary Figure 4C correspond to the guides that we tested in Petit-Vargas et al. 2024. In this study, we only used the most efficient of these three guides. We have modified Figure 4C by quoting only this guide.

      5) The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5)

      - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing

      We have included references related to the activity of the CyclinD1/CDK4-6 complex in the developing CNS, and the antagonistic activities of CyclinD1 and Cdkn1c in this model

      - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation in the developing CNS (Lobjois et al, 2004, 2008, Lange 2009, Gui et al 2007), and is inhibited by Cdkn1c (Gui et al, 2007)."

      - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G.

      We have added the experimental set-up information in Figure 5.

      - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

      The analyses were carried out on two thirds of the neural tube (dorsal 2/3), excluding the ventral zone, as specified above (and in the Methods section)

      - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G.

      - For Figure 4C and D, it would be valuable to add images to illustrate the quantification.

      We have added images:

      • in Supplementary Figure 7C to illustrate what is quantified in Figures 4C (now 4C and 4D);
      • In Figure 5E to illustrate what is quantified in Figure 5D
      • In Supplementary Figure 8B to illustrate what is quantified in Figure 5G (now Figure 5H and 5I) Regarding the requested images for Figures 4D and 5F, they correspond to the same types of images already shown in Figure 3E. Since we have now added several additional examples of representative pairs of each type of mode of division in the new Supplementary Figure 4, we do not think that adding more of these images in figures 4 and 5 would strengthen the result of the quantifications.

      Discussion:

      - "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).

      We have now included the references suggested by the reviewer.

      - "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

      We propose to reformulate this paragraph in the discussion as follows to take this remark into account

      "This allows us to re-interpret the role of Cdkn1c during spinal neurogenesis: while previously mostly considered as a binary regulator of cell cycle exit in newborn neurons, we demonstrate that Cdkn1c is also an intrinsic regulator of the transition from the proliferative to neurogenic status in cycling progenitors. This occurs through a change in their mode of division, and our double knock-down experiments suggest that the onset of Cdkn1c expression may promote this change by counteracting a CyclinD1/CDK6 complex dependent mechanism."

      Other comments:

      - To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.

      We have modified the figures to systematically show the electroporated side of the neural tube on the same side of the image for single electroporations.

      ____- Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.

      This information is now provided in the figure legends (numbers of cells analysed and/or numbers of embryos), except for data in Figure 5, which are presented in a new Supplementary Table 1.

      All experiments were performed on vibratome sections, except for in situ hybridization experiments, which were performed on cryostat sections. This last information was already indicated in the relevant figure legends

      - Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.

      - There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.

      We have now homogenized the nomenclature in the figures.

      - "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

      In the original version of the manuscript, the anti-Sox2 antibody was not used; we have now added experiments using this antibody in the modified version of the manuscript; this sentence in the Methods thus remains unchanged.

      Reviewer #3 (Significance (Required)):

      __*Significance:

      In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. *__

      The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

      The strengths of the study include:

      The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

      - The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.

      - The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

      The aspects to improve:

      - The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.

      The sequencing dataset has been deposited in NCBI's Gene Expression Omnibus database. It is currently under embargo, but will be made available upon acceptance and publication of the peer reviewed manuscript. Access is nonetheless available to the reviewers via a token that can be retrieved from the Review Commons website.

      The following information will be added in the final manuscript.

      Data availability

      Single cell RNA sequencing data have been deposited in NCBI's Gene Expression Omnibus (GEO) repository under the accession number GSE273710, and are available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273710."

      - The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed. - Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.

      - Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.

      - The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.

      - The presentation of the existing literature could be more up to date.

      - The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

      Field of expertise of the reviewer: neural development, cell biology, embryology.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this study, Mida et al. analyze large-scale single-cell RNA-seq data from the chick embryonic neural tube and identify Cdkn1c as a key molecular regulator of the transition from proliferative to neurogenic cell divisions, marking the onset of neurogenesis in the developing CNS. To confirm this hypothesis, they employed classical techniques, including the quantification of neural cell-specific markers combined with the flashTAG label, to track and isolate isochronic cohorts of newborn cells in different division modes. Their findings reveal that Cdkn1c expression begins at low levels in neurogenic progenitors and becomes highly expressed in nascent neurons. Using a classical knockdown strategy based on short hairpin RNA (shRNA) interference, they demonstrate that Cdkn1c suppression promotes proliferative divisions, reducing neuron formation. Conversely, novel genetic manipulation techniques inducing low-level CDKN1c misexpression drive progenitors into neurogenic divisions prematurely. By employing cumulative EdU incorporation assays and shRNA-based loss-of-function approaches, Mida et al. further show that Cdkn1c extends the G1 phase by inhibiting cyclin D, ultimately concluding that Cdkn1c plays a dual role: first facilitating the transition of progenitors into neurogenic divisions at low expression levels, and later promoting cell cycle exit to ensure proper neural development.

      This study presents several ambiguities and lacks precision in its analytical methodologies and quantification approaches, which contribute to confusion and potential bias. To enhance the reliability of the conclusions, a more rigorous validation of the methods employed is essential.

      This study introduces a novel approach to tracking the fate of sister cells from neural progenitor divisions to infer the division modes. While previous methods for analyzing the division mode of neural progenitor cells have been implemented, rigorous validation of the approach introduced by Mida et al. is necessary. Furthermore, the concept of cell cycle regulators interacting to control the duration of specific cell cycle stages and influencing progenitor cell division modes has been explored before, potentially limiting the novelty of these findings.

      Majors comments:

      1. The study presents ambiguity and lacks precision in quantifying neural precursor division modes. The authors use phosphorylated retinoblastoma protein (pRb) as a marker for neurogenic progenitors, claiming its reliability in identifying neurogenic divisions. However, they do not provide a thorough characterization of pRb expression in the developing chick neural tube, leaving its suitability as a neurogenic division marker unverified. Furthermore, retinoblastoma protein (Rb) and cyclin D interact crucially to regulate the G1/S phase transition of the cell cycle, with cyclin D/CDK complexes phosphorylating Rb. Since the authors conclude that CDKN1c primarily acts by inhibiting the cyclin D/CDK6 complex, it is likely that CDKN1c influences pRb expression or phosphorylation state. This raises the possibility that pRb could be a direct target of CDKN1c, whose expression and phosphorylation would be altered in gain-of-function (GOF) and loss-of-function (LOF) analyses of CDKN1c. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
      2. Furthermore, the study employs FlashTag labeling to track daughter cells post-division, but the 16-hour post-injection window may result in misidentification of sister cells due to the potential presence of FlashTagged cells that did not originate from the same division. This introduces a risk of bias in quantification, data misinterpretation, and potential errors in defining division modes. A more rigorous validation of the FlashTag strategy and its specificity in tracking division pairs is necessary to ensure the reliability of their conclusions.
      3. The knock-in strategy used to tag the endogenous CDKN1c protein in Figure 2 is an elegant tool to infer protein dynamics in vivo. However, since strong conclusions regarding CDKN1c dynamics during the cell cycle are drawn from this section, it would be advisable to strengthen the results by including quantification with adequate replication and proper statistical analysis, as the current findings are preliminary and somewhat speculative.
        • "Although pRb is specific for cycling cells, it is only detected once cells have passed the point of restriction during the G1 phase." Please provide literary reference confirming this observation. Given that pRb immunoreactivity is used as a marker for cycling progenitors to base many of the results of this study, it would be very valuable to characterize the dynamics of pRb in cycling cells in the studied tissue, for instance combined with the cell cycle reporter used by Molina et al. (Development 2022).
        • The characterization of dynamics is performed only with one of the gRNAs (#1) on the basis that it produces the strongest NLS-GFP signal, as a proxy for guide efficiency. It would be nice if the authors could validate guide cutting efficiency via sequencing (e.g. using a Cas9-T2A-GFP plasmid and sorting for positive cells).
        • In order to make sure that the dynamics inferred from Myc-tag immunoreactivity do reflect the cell cycle dynamics of CDKN1c-myc, it would be advisable to confirm in-frame insertion of the myc-tag sequence.
        • It would be valuable to analyse the dynamics of Myc immunoreactivity in combination of pRb in all three gRNAs (highlighted in Supplementary Figure 1), as it would be a strong point in favour that the dynamics reflect the endogenous CDKN1c dynamics.
      4. It would be very valuable to provide a quantification of said dynamics (e.g. plotting myc intensity / pRb immunoreactivity along the apicobasal axis of the tissue).
      5. In Figure 3, the authors use a short-hairpin-mediated knock-down strategy to decrease the levels of Cdkn1c, and show that this manipulation leads to an increase percentage of cycling progenitors and a decrease in the number of neurons in electroporated cells.

      The authors claim that their shRNA-based knockdown strategy aims to reduce low-level Cdkn1c expression in neurogenic progenitors while minimally affecting the higher expression in newborn neurons required for cell cycle exit. However, several factors need consideration. Electroporation introduces variability in shRNA delivery, making it difficult to achieve consistent gene inhibition across all cells, especially for dose-dependent genes like Cdkn1c. Additionally, Cdkn1c generates multiple isoforms, which may not be fully annotated in the chick genome, raising the possibility that the shRNA targets specific isoforms, potentially explaining the observed low expression. A more rigorous approach, such as qPCR analysis of sorted electroporated cells, would better validate the expression levels, rather than relying on in situ hybridization, presenting electroporated and non-electroporated cells in the same section (Supp. Figure 2). - As the authors note, "Unambiguous identification of cycling progenitors and postmitotic neurons is notoriously difficult in the chick spinal cord". "markers of progenitors usually either do not label all the phases of the cell cycle (eg. Phospho-Rb, thereafter pRb), or persist transiently in newborn neurons (eg. Sox2)." Given that pRb immunoreactivity is used as the basis for a lot of the conclusions in this study, it would be valuable to add a characterization of its dynamics as mentioned in Figure 2, as well as provide literary references/proof that Sox2 expression persists in newborn neurons. - The undefined population (pRb-/HuCD-) introduces an unknown that assumes that the percentage of progenitors in G1 phase before the restriction point and the number of newborn neurons are equal for both conditions in an experiment. Can the authors provide explanation for this assumption? - In Gui et al. (Dev Biol 2006), authors showed that a knockdown of Cdkn1c leads to a failure of nascent neurons to exit the cell cycle and causes them to re-entry the cell cycle, shown by ectopic mitoses. In that study, cells born from those ectopic mitoses eventually leave the cell cycle leading to an increase in the number of neurons. Can the authors check for ectopic mitoses at 24hpe and 48hpe? - The authors then address the question of whether the decrease in neuron number is due to the failure of newborn neurons to exit the cell cycle or to a delay in the transition from proliferative to neurogenic divisions. For that, they implement a strategy to label a synchronized cohort of progenitors based of incorporation of a FlashTag dye. - Given that this strategy is the basis of many of the experiments in this article, it would be very valuable to expand on the validation of this technique as cited in major comment #2. In figure 3E, the close proximity of cell pairs in PP and PN clones shown in the pictures makes their sibling status apparent. However, this is not the case for the NN clone. Can the authors further explain with what criteria they determined the clonal status of two FlashTag labelled cells? Can they provide further image examples of different types of clones? - Can the authors show that the plateau reached in Sup Figure 3 for pRb immunoreactivity corresponds to a similar dynamic for HuC/D immunoreactivity? - In order to further validate the strategy, could the authors use it at different stages to validate if they can replicate the different percentages of PP/PN/NN reported in the literature (e.g. Saade Cell Rep 2013)?. 5. In Figure 4, the strategy used to induce a low-dose overexpression of CDKN1c is an elegant method to introduce CDKN1c-Myc expression under the control of the endogenous Pax7 promoter, active in proliferative progenitors. The main point to address is: - Please provide proof that Pax7 expression is not altered in guides with a successful knock-in event (e.g. sorting and WB against the Pax7 protein) or the immunohistochemistry as performed in the Pax7-P2A-Gal4 tagging in Petit-Vargas et al., 2024. - Given the cell cycle regulated expression and activity of CDKN1c, can the authors elaborate on whether this is regulated at the promoter level? If so, how does this differ from the promoter activity of Pax7? - It would be advisable to characterize the dynamics along the cell cycle for the overexpressed form of CDKN1c-Myc relative to pRb, similarly to what was done in Figure 2B. 6. In figure 5, the authors use a double knock-down strategy to test the hypothesis that the effect of Cdkn1c in G1 length is partially at least through its inhibition of CyclinD1. Results show that double shRNA-mediated knock-down of CyclinD1 and Cdkn1c counteracts the effects of Cdkn1c-sh alone on EdU incorporation, PP/PN/NN cell divisions and overall rations of progenitors and neurons. - In the measurement of progenitor cell cycle length in Figure 5A, it would be more appropriate to present the nonlinear regression method described by Nowakowski et al. (1989), as has been commonly used in the field (Saade et al., 2013, PMID: 23891002, Le Dreau et al., 2014, PMID: 24515346, Arai et al., 2011, PMID: 21224845). - Cumulative EdU incorporation in spinal progenitors (pRb-positive) at E3 (24 hours after injection) showed that the proportion of EdU-positive progenitors reached a plateau at 14 hours in control conditions, which is later than what has been reported in Le Dreau et al., 2014 (PMID: 24515346). Can you explain why? - It would be interesting to measure G1 length as in Figure 5D for the double cdkn1c-sh - ccnd1-sh knock down condition, to see if it rescues G1 length. As well as in the Ccnd1 knock down condition alone to see if it increases G1 length in this context as well.

      Minor comments

      Introduction:

      • The introduction should include references of studies of the role of Cdkn1c in cortical development (Imaizumi et al. Sci Rep 2020, Colasante et al. Cereb Cortex 2015, Laukoter et al. Nature Communications 2020).

      • Transcriptional signature of the neurogenic transition (Figure 1).

        • In the result section, it would be informative to include the genes used to determine the progenitor and neuron score (instead of in Methods).
        • Figure 1A. It would be informative to add in the diagram what "filtering" means (eg. Neural crest cells).
        • In the result section, "However, while Tis21 expression is switched off in neurons, Cdkn1c transiently peaks at high levels in nascent neurons before fading off in more mature cells." Missing literary reference or data to clearly demonstrate this point.
        • "Interestingly, the gene cluster that contained Tis21 also contained genes encoding proteins with known expression and/or functions at the transition from proliferation to differentiation, such as the Notch ligand Dll1, the bHLH transcription factors Hes6, NeuroG1 and NeuroG2, and the coactivator Gadd45g." Missing references.
        • There is an error in the color code in Cell Clusters in Figure 1C (cluster 4 yellow in the legend but ocre in the figure)

      It would be valuable to assign cell cycle stage to neural progenitor cells (based on cell cycle score) and determine whether cdkn1c at the transcript level also shows enrichment in G1 cells considered to be progenitors. 2. Progressive increase in Cdkn1c/p57kip2 expression underlie different cellular states in the embryonic spinal neural tube (Figure 2). - Figure 2A. Scale bar is missing in E3 and E4. It is important to consider the growth of the developing spinal cord and present it accordingly (E3 transverse section, Figure 2). - Figure 2 could use a diagram of the knock-in strategy used, similar as the one in Figure 4A. - Indicate hours post-electroporation. Indicate which guide is used in the main text. 3. Downregulation of Cdkn1c in neural progenitors delays the transition from proliferative to neurogenic modes of division (Figure 3). - In methods: "Thus, to reason on a more homogeneous progenitor population, we restricted all our analysis to the dorsal one half or two thirds of the neural tube." Indicate when and depending on what one half or two thirds of the neural tube were analysed. - Figure 3. Would have a better flow if 3C preceded 3A and 3B. - Figure 3C. it would be informative to show pictures of the electroporated NT at both 24hpe and 48hpe, as well as highlighting the dorsal part of the neural tube that was used for quantification. - Are the clonal analysis experiments (Fig 3D, E and F) also restricted to the dorsal region? - Figure Sup3B colour code is switched (green for PP and red for NN) compared to the rest of the paper. - In methods "At each measured timepoint (1h, 4h, 7h, 10h, 12h, 14 and 17h after the first EdU injection), we quantified the number of EdU positive electroporated progenitors (triple positive for EdU, pRb and GFP) over the total population of electroporated progenitor cells (pRb and GFP positive) (Figure 3B)." Explanation does not correspond to Figure 3B. 4. Inducing a premature expression of Cdkn1c in progenitors triggers the transition to neurogenic modes of division (Figure 4.).<br /> - "We took advantage of the Pax7 locus, which is expressed in progenitors in the dorsal domain at a level similar to that observed for Cdkn1c in neurogenic precursors (Supplementary Figure 4A)". Missing reference or data showing that Pax7 is restricted to the dorsal domain. - "its intensity was similar to the one observed for endogenous Myc-tagged Cdkn1c in progenitors (Figure 4B and Supplementary Figure 4E), and remained below the endogenous level of Myc-tagged Cdkn1c observed in nascent neurons, confirming the validity of our strategy". It would be valuable to add a quantification to demonstrate this point, either by fluorescence levels or WB of nls-GFP cells. - For Figure 4C and D, it would be valuable to add images to illustrate the quantification. - "At the population level, at E4, Cdkn1c expression from the Pax7 locus resulted in a strong reduction in the number of progenitors (pRb positive cells)". Indicate in the main text that this is 48hpe. - Legend of figure 4D should indicate that the quantification has been done 24hpe. - "To circumvent the cell cycle arrest that is triggered in progenitors by strong overexpression of Cdkn1c (Gui et al., 2007)". It would be advisable to expand on this reference on the text, or ideally to include a simple Cdkn1c overexpression experiment. - "We observed a massive increase in the proportion of neurogenic (PN and NN) divisions rising from 57% to 84% at the expense of proliferative pairs (43% PP pairs in controls versus 16% in misexpressing cells, Figure 4D)." adding the percentages in the main text is a bit inconsistent with how the rest of the data is presented in the rest of the sections. - Figure sup 4C includes references to 3 gRNAs even when only one is used in the study. 5. The proneurogenic activity of Cdkn1c in progenitors is mediated by modulation of cell cycle dynamics (Figure 5) - "we targeted the CyclinD1/CDK4-6 complex, which promotes cell cycle progression and proliferation, and is inhibited by Cdkn1c." reference missing - It would be valuable to add an image to illustrate what is quantified in Figure 5D, Figure F and Figure G. - It would be informative to include experimental set-up information (e.g. hae) in Figures 5A, 5B, 5F and 5G. - Clarify if analysis is restricted to the dorsal progenitors or the whole dorsoventral length of the tube.

      Discussion:

      • "Nonetheless, studies in a wide range of species have demonstrated that beyond this binary choice, cell cycle regulators also influence the neurogenic potential of progenitors, i.e the commitment of their progeny to differentiate or not (Calegari and Huttner, 2003; FUJITA, 1962; Kicheva et al., 2014; Lange et al., 2009; Lukaszewicz and Anderson, 2011a; Pilaz et al., 2009; Smith and Schoenwolf, 1987; Takahashi et al., 1995)." Should include maybe references to Peco et al. Development 2012, Roussat et al. J Neurosci. 2023).
      • "This occurs through a change in the mode of division of progenitors, acting primarily via the inhibition of the CyclinD1/CDK6 complex." The data shown in the paper does not demonstrate that Cdkn1c is inhibiting CyclinD1, only that knocking down both mRNAs counteracts the effect of knocking down Cdkn1c alone at the general tissue level and in the percentage of PP/PN/NN clones. This statement should be qualified.

      Other comments:

      • There is a general lack of consistency in indicating the timing of the experiments, both in terms of embryonic stage/day and in terms of hours-post-electroporation.
      • To improve clarity for the reader, it would help if electroporation was shown consistently on the same side of the neural tube. If electroporation has been performed at different sides and this is reflected in the figures, it would be advisable to explain on the figure legend.
      • Figure legends should include the number of embryos/tissue sections analysed for each experiment, as well as information on whether the sections were cryostat or vibratome.
      • Overall, there is a lack of consistency in the figures regarding how much information is available to the reader (e.g. Sup Figure 2A, in the panel mRNA in situ hybridisation of Cdkn1c is referred to only as Cdkn1c whereas in Sup figure 5 the in situ reads as CCND1 mRNA). Readability would improve a lot if figures included information on what is an electroporated fluorescent tag or an immunostaining (similar to the label in sup 4D) as well as the exact stage and hours after electroporation where relevant.
      • "Primary antibodies used are: chick anti-GFP (GFP-1020 - 1:2000) from Aves Labs; goat antiSox2 (clone Y-17 - 1:1000) from Santa Cruz". There is no Sox2 immunostaining in the article.

      Significance

      In neural development, there is a progressive switch in competence in neural progenitor cells, that transition from a proliferative (able to expand the neural progenitor pool) to neurogenic (able to produce neurons). Several factors are known to influence the transition of neural progenitor cells from a proliferative to a neurogenic state, including the activity of extracellular signalling pathways (e.g. SHH) (Saade et al. 2013, Tozer et al. 2017). In this study, the authors perform scRNA-seq of the cervical neural tube of chick at a stage of both proliferative and neurogenic progenitors are present, and identify transcriptional differences between the two populations. Among the differently expressed transcripts, they identify Cdkn1c (p57-Kip2) as enriched in neurogenic progenitors. Initially characterized as a driver of cell cycle exit in newborn neurons, the authors investigate the role of Cdkn1c in cycling progenitors. The authors find that knock-down of Cdkn1c leads to an increase in proliferative divisions at the expense of neurogenic divisions. Conversely, misexpression of Cdkn1c in proliferative progenitors leads to a switch to neurogenic divisions. Furthermore, they find that knock-down of Cdkn1c shortens G1 phase of the cell cycle, suggesting a link between G1 length and neurogenic competence in neural progenitor cells. Cell cycle length has previously been linked to competence of neural progenitors, and it has been described that longer G1 duration is linked to neurogenic competence (e.g. Calegari F, Huttner WB. 2003).

      The strengths of the study include:

      The identification of a subset of genes enriched in neurogenic vs. proliferative progenitors. Since the transition from proliferative to neurogenic competence is a gradual process at the tissue level, the classification of proliferative vs. neurogenic progenitors based on a score of transcripts and the identification of a subset of transcripts that are enriched in neurogenic progenitors is a valuable contribution to the neurodevelopmental field.

      • The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner and is a valuable technical advance.
      • The characterization of a specific role of Cdkn1c in regulating cell cycle length in cycling progenitors is novel and valuable knowledge contributing to our understanding of how regulation of cell cycle length impacts competence of neural progenitors.

      The aspects to improve:

      • The sc-RNAseq isolated genes enriched in neurogenic versus proliferative progenitors, providing valuable insight into the gradual transition from proliferative to neurogenic competence at the tissue level. However, this gene subset requires clearer representation and detailed characterization. Additionally, the full scRNA-seq dataset should be made publicly available to support further research in neurodevelopment.
      • The characterization of Cdkn1c dynamics in cycling progenitors using endogenous tagging of the Cdkn1c transcript with a Myc tag is an elegant way to investigate the dynamics of Cdkn1c-myc along the cell cycle. However, it would be much more powerful if combined with a careful characterization of pRb immunostaining along the cell cycle in this tissue, as well as the quantifications and controls proposed.
      • Retinoblastoma protein (Rb) and cyclin D play a key role in regulating the G1/S transition, with cyclin D/CDK complexes phosphorylating Rb. Given that CDKN1c primarily inhibits the cyclin D/CDK6 complex, it likely affects pRb expression or phosphorylation. This suggests pRb may be a direct target of CDKN1c, making it an unreliable marker for tracking and quantifying neurogenic progenitors through CDKN1c modulation. In light of this, it would be more appropriate to consider pRb as a CDKN1c target and discuss the molecular mechanisms regulating cell cycle components. A more precise approach would involve using other markers or targets to quantify neural precursor division modes at earlier stages of neurogenesis.
      • Many of the conclusions of the study are based on experiments performed using the FlashTag dye in order to perform clonal analysis of proliferative vs. neurogenic divisions. It would be very valuable to further characterize the reliability of this tool as well as to provide more information on the criteria used to determine the fate of the pairs of sister cells.
      • The somatic knock-in strategy used to induce low-level overexpression of Cdkn1c in proliferative progenitors is an elegant strategy to induce overexpression in a subset of cells in a controlled manner. It would be valuable to further characterize the dynamics of Cdkn1c expression using this too and to provide proof that Pax7 expression is not altered in guides with the knock-in event.
      • The presentation of the existing literature could be more up to date.
      • The presentation of the data in the figures could be improved for readability. The sc-RNA seq data and the technical advances could be of interest for an audience of researchers using chick as a model organism, and working on neurodevelopment in general. Furthermore, the characterization of Cdkn1c as a regulator of G1 length in cycling progenitors and its implications for neurogenic competence could be of general interest for people working on basic research in the neurodevelopmental field.

      Field of expertise of the reviewer: neural development, cell biology, embryology.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      This study utilizes the developing chicken neural tube to assess the regulation of the balance between proliferative and neurogenic divisions in the vertebrate CNS. Using single-cell RNAseq and endogenous protein tagging, the authors identify Cdkn1c as a potential regulator of the transition towards neurogenic divisions. Cdkn1c knockdown and overexpression experiments suggest that low Cdkn1c expression enhances neurogenic divisions. Using a combination of clonal analysis and sequential knockdown, the authors find that Cdkn1c lengthens the G1 phase of the cell cycle via inhibition of cyclinD1. This study represents a significant advance in understanding how cells can transition between proliferative and asymmetric modes of division, the complex and varying roles of cycle regulators, and provides technical advance through innovative combination of existing tools.

      Major and Minor Comments:

      Overall

      • Sample numbers are missing or unclear throughout for all imaging experiments. The authors should add numbers of cells analysed and/or numbers of embryos for their results to be appropriately convincing.
      • Values and error bars on graphs must be defined throughout. Are the values means and error bars SD or SEM?

      Results 2

      • A reference should be provided for cell type distribution in spinal neural tube, where the authors state that cell bodies of progenitors reside within the ventricular zone.
      • The authors state that Cdkn1c "was expressed at low levels in a salt and pepper fashion in the ventricular zone, where the cell bodies of neural progenitors reside, and markedly increased in a domain immediately adjacent to this zone which is enriched in nascent neurons on their way to the mantle zone. In contrast, the transcript was completely excluded from the mantle zone, where HuC/D positive mature neurons accumulate." It is not clear if this is referring only to E4 or also to E3 embryos. Indeed, Cdkn1c expression appears to be much more salt and pepper at E3 and only resolves into a clear domain of high expression adjacent to the mantle zone at E4. It may be helpful if this expression pattern could be described in a bit more detail highlighting the changes that occur between E3 and E4.
      • It would be useful to annotate the ISH images in Fig 2A to show the ventricular and mantle zones as defined by immunofluorescence.
      • Reference should be included for pRb expression dynamics.
      • Could the Myc tag insertion approach disrupt protein function or turnover?
      • Why was the insertion target site at the C terminus chosen?
      • OPTIONAL Could a similar approach be used to tag Cdkn1c with a fluorescent protein to enable live imaging of dynamics?
      • In suppl Fig 1C nlsGFP-positive cells are shown in the control shRNA condition. How can this be explained and does it impact the interpretation of the findings?
      • In Fig 2B, there are a number of Myc labelled cells in the mantle zone, whereas the in situ images show no appreciable transcript expression. Is this because the protein but not the transcript is present in these cells? Could the authors comment on this?

      Results 3

      • It should be mentioned how mRNA expression levels were quantified in the shRNA validation experiment (supp Fig 2A).
      • Figure panels are not currently cited in order. Citation or figure order could be changed.
      • The authors should provide representative images for the graphs shown in Fig 3A and 3B. These could go into supplementary if the authors prefer.
      • A supplementary figure showing the Caspase3 experiment should be added.
      • OPTIONAL. Identification of sister cells in the clonal analysis experiments is based on static images and cannot be guaranteed. Could live imaging be used to watch divisions followed by fixation and immunostaining to confirm identity?

      Results 4

      • How did the authors quantify the intensity of endogenous Myc-tagged Cdkn1c to confirm the validity of the Pax7 locus knock in? Can they show that the expression level was consistently lower than the endogenous expression in neurons? Quantification and sample numbers should be shown.
      • In Fig 4B, the brightness of row 2 column 1 is lower than the same image in row 2 column 2, which is slightly misleading, since it makes the misexpressed expression level look lower than it is compared with endogenous in column 3. Is this because only a single z-section is being displayed in the zoomed in image? If so, this should be stated in the figure legend.
      • In Fig 4D, the increase in neurogenic divisions is mainly because of the rise in terminal NN divisions according to the graph, but no clear increase in PN divisions. Could the authors comment on the significance of this?

      Results 5

      • The proportion of pRb-positive progenitors having entered S phase was stated to be higher at all time points; however, it is not significantly higher until 6h30 and is actually trending lower at 2h30.
      • OPTIONAL Could CyclinD1 activity be directly assessed?

      General

      • Scale bars missing fig s1c s4d.
      • OPTIONAL Some of the main findings be replicated in another species, for example, mouse or human to examine whether the mechanism is conserved.
      • OPTIONAL Could use approaches other than image analysis be used to reinforce findings, for example biochemical methods, RNAseq or FACS?
      • A model cartoon to summarise outcomes would be useful.
      • Unclear how cells were determined to be positive or negative for a label. Was this decided by eye? If so, how did the authors ensure that this was unbiased?

      Significance

      Strengths:

      This manuscript investigates the mechanisms regulating the switch from symmetric proliferative divisions to neurogenic division during vertebrate neuronal differentiation. This is a question of fundamental importance, the answer to which has eluded us so far. As such, the findings presented here are of significant value to the neurogenesis community and will be of broad interest to those interested in cell divisions and asymmetric cell fate acquisition. Specific strengths include:

      • Variety of approaches used to manipulate and observe individual cell behaviour within a physiological context.
      • A limitation of using the chicken embryo is the lack of available antibodies for immunostaining. The authors take advantage of recent advances in chicken embryo CRISPR strategy to endogenously tag the target protein with Myc, to facilitate immunostaining.
      • Innovative combination of genetic and labelling tools to target cells, for example, use of FlashTag and EdU in combination to more accurately assess G1 length than the more commonly used method.
      • Premature misexpression demonstrates that the previously observed dynamics indeed regulate cell fate.
      • Mechanistic insight by examining downstream target CyclinD1.
      • Clearly presented with useful illustrations throughout.
      • Logic is clear and examination thorough.
      • Conclusions are warranted on the basis of their findings.

      Limitations

      • This study primarily used visual analysis of fixed tissue images to assess the main outcomes. To reinforce the conclusions, these could be supplemented with live imaging to appreciate dynamics, or biochemical techniques to look at protein expression levels.
      • Some aspects of quantification require explanation in order for the experiments to be replicated.
      • It is imperative that precise sample sizes are included for all experiments presented.

      Advance:

      • First functional demonstration role for Cdkn1c in regulating neurogenic transition in progenitors.
      • Conceptual advance suggesting Cdkn1c has dual roles in driving neurogenesis: promoting neurogenic divisions of progenitors and the established role of mediating cell cycle exit previously reported.
      • Technical advances in the form of G1 signposting and endogenous Myc tagging using CRISPR in chicken embryonic tissue.

      Audience:

      Of broad interest to developmental biologists. Could be relevant to cancer, since Cdkn1c is implicated.

       Please define your field of expertise with a few keywords to help the authors contextualize your point Developmental biology, vertebrate embryonic development, neuronal differentiation, imaging. Please note that we have not commented on RNAseq experiments as these are outside of our area of expertise.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      This study addresses the question of how task-relevant sensory information affects activity in the motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to the motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

      Strengths:

      The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity.

      The task is also well designed to suit the questions being asked and well controlled.

      We appreciate these kind comments.

      It is commendable that the authors compare single units to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics. However, the subtypes (PD shift, gain, and addition) are not sufficiently justified. The authors also do not address that single units exhibit mixed modulation, but RNN units are not treated as such.

      We’re sorry that we didn’t provide sufficient grounds to introduce the subtypes. We have updated this in the revised manuscript, in Lines 102-104 as:

      “We determined these modulations on the basis of the classical cosine tuning model (Georgopoulos et al., 1982) and several previous studies (Bremner and Andersen, 2012; Pesaran et al., 2010; Sergio et al., 2005).”

      In our study, we applied the subtype analysis as a criterion to identify the modulation in neuron populations, rather than sorting neurons into exclusively different cell types.

      Weaknesses:

      The main weaknesses of the study lie in the categorization of the single units into PD shift, gain, and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single-unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

      In our study, the mixed selectivity or specifically the target-motion modulation on reach- direction tuning is a significant feature of the single neurons. We categorized the neurons into three subclasses, not intending to claim their absolute cell types, but meaning to distinguish target-motion modulation patterns. To further characterize these three patterns, we also investigated their interaction by perturbing connection weights in RNN.

      Yes, it’s important to consider the role of rotating tuning curves in neural dynamics during interception. In our case, we observed population neural state with sliding windows, and we focused on the period around movement onset (MO) due to the unexpected ring-like structure and the highest decoding accuracy of transferred decoders (Figure S7C). Then, the single-unit analyses were implemented.

      This paper shows sensory information can affect motor cortical activity whilst not affecting motor output. However, it is not the first to do so and fails to cite other papers that have investigated sensory modulation of the motor cortex (Stavinksy et al. 2017 Neuron, Pruszynski et al. 2011 Nature, Omrani et al. 2016 eLife). These studies should be mentioned in the Introduction to capture better the context around the present study. It would also be beneficial to add a discussion of how the results compare to the findings from these other works.

      Thanks for the reminder. We’ve introduced these relevant researches in the updated manuscript in Lines 422-426 as:

      “To further clarify, the discussing target-motion effect is different from the sensory modulation in action selection (Cisek and Kalaska, 2005), motor planning (Pesaran et al., 2006), visual replay and somatosensory feedback (Pruszynski et al., 2011; Stavisky et al., 2017; Suway and Schwartz, 2019; Tkach et al., 2007), because it occurred around movement onset and in predictive control trial-by-trial.”

      This study also uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

      I have clarified some of the areas that would benefit from further analysis below:

      (1) Task:

      The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys.

      A great suggestion; however, it is hardly feasible as the Utah arrays have already been removed.

      (2) Single unit analyses:

      In some analyses, the effects of target speed look more driven by target movement direction (e.g. Figures 1D and E). To confirm target speed is the main modulator, it would be good to compare how much more variance is explained by models including speed rather than just direction. More target speeds may have been helpful here too.

      A nice suggestion. The fitting goodness of the simple model (only movement direction) is much worse than the complex models (including target speed). We’ve updated the results in the revised manuscript in Lines 119-122, as “We found that the adjusted R2 of a full model (0.55 ± 0.24, mean ± sd.) can be higher than that of the PD shift (0.47 ± 0.24), gain (0.46 ± 0.22), additive (0.41 ± 0.26), and simple models (only reach direction, 0.34 ± 0.25) for three monkeys (1162 neurons, ranksum test, one-tailed, p<0.01, Figure S5).”

      The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

      A good point. It is a pity that we haven’t found an appropriate unsupervised method.

      The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results.

      Thanks for the suggestion and close reading. Because the movement onset (MO) is the key time point of this study, we colored this time period in Figure 1 to highlight the perimovement neuronal activity.

      (3) Decoder:

      One feature of the task is that the reach endpoints tile the entire perimeter of the target circle (Figure 1B). However, this feature is not exploited for much of the single-unit analyses. This is most notable in Figure 2, where the use of a SVM limits the decoding to discrete values (the endpoints are divided into 8 categories). Using continuous decoding of hand kinematics would be more appropriate for this task.

      This is a very reasonable suggestion. In the revised manuscript, we’ve updated the continuous decoding results with support vector regression (SVR) in Figure S7A and in Lines 170-173 as:

      “These results were stable on the data of the other two monkeys and the pseudopopulation of all three monkeys (Figure S6) and reconfirmed by the continuous decoding results with support vector regressions (Figure S7A), suggesting that target motion information existed in M1 throughout almost the entire trial.”

      (4) RNN:

      Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. Furthermore, it would be informative to compare the neural data to the RNN activity using canonical correlation or Procrustes analyses. These would help validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. There is also an absence of alternate models to compare the perturbation model results to.

      Thank you for these helpful suggestions. We have performed decoding analysis on RNN units and updated in Figure S12A and Lines 333-334 as: “First, from the decoding result, target motion information existed in nodes’ population dynamics shortly after TO (Figure S12A).”

      We also have included the results of canonical correlation analysis and Procrustes analysis in Table S2 and Lines 340-342 as: “We then performed canonical component analysis (CCA) and Procrustes analysis (Table S2; see Methods), the results also indicated the similarity between network dynamics and neural dynamics.”

      Reviewer #2 (Public Review):

      Summary:

      In this manuscript, Zhang et al. examine neural activity in the motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that (1) the reach direction has consistent positioning around the ring, and (2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

      Strengths:

      I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target task to better characterize the breadth of how the motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

      Thank you for your recognition of our work.

      Weaknesses:

      While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a singleneuron representational lens. This would be fine as an initial analysis since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how the motor cortex or its neural geometry might be contributing to the execution of this novel task.

      This paper shows the sensory modulation on motor tuning in single units and neural population during motor execution period. It’s a pity that the findings were constrained in certain time windows. We are still working on this task, please look forward to our following work.

      Main Comments:

      My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

      (1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

      It’s a great idea! We are on the way, and it seems promising.

      (2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space (this is actually fairly easy to see in the reach direction components of the dPCA plot in the supplement--the rings will be highly aligned in this space). Presumably, then, the null space should contain information about the target movement. dPCA shows that there's not a single dimension that clearly delineates target speed, but the ring tilt is likely evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")-this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

      Thank you for this nice suggestion. While it was feasible to identify potent subspaces encoding reach direction and null spaces for target-velocity modulation, as suggested by the reviewer, the challenge remained that unsupervised methods were insufficient to isolate a pure target-velocity subspace from numerous possible candidates due to the small variance of target-velocity information. Although dPCA components can be used to construct orthogonal subspaces for individual task variables, we found that the targetvelocity information remained highly entangled with reach-direction representation. More details can be found in Figure S8C and its caption as below:

      “We used dPCA components with different features to construct three subspaces (same data in A, reach-direction space #3, #4, #5; target-velocity space #10, #15, #17; interaction space #6, #11, #12), and we projected trial-averaged data into these orthogonal subspaces using different colormaps. This approach allowed us to obtain a “potent subspace” coding reach direction and a “null space” for target velocity. The results showed that the reach-direction subspace effectively represented the reach direction. However, while the target-velocity subspace encoded the target velocity information, it still contained reach-direction clusters within each target-velocity condition, corroborating the results of the addition model in the main text (Figure 4). The interaction subspace revealed that multiple reach-direction rings were nested within each other, similar to the findings from the gain model (Figure 3 & 4). The interaction subspace also captured more variance than target-velocity subspace, consistent with our PCA results, suggesting the target-velocity modulation primarily coexists with reach-direction coding. Furthermore, we explored alternative methods to verify whether orthogonal subspaces could effectively separate the reach direction and target velocity. We could easily identify the reach-direction subspace, but its orthogonal subspace was relatively large, and the target-velocity information exhibited only small variance, making it difficult to isolate a subspace that purely encodes target velocity.”

      (3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons (especially considering that 43% of nodes were unclassifiable). It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

      We are sorry that we did not clarify the definition of “none” type, which can be misleading. The 43% unclassifiable nodes include those inactive ones; when only activate (taskrelated) nodes included, the ratio of unclassifiable nodes would be much lower. We recomputed the ratios with only activated units and have updated Table 1. By perturbing the connectivity, we intended to explore the interaction between different modulations.

      Thank you for the great advice. We considered moving neural states from one ring to another without changing the directional cluster. However, we found that this perturbation design might not be fully developed: since the top two PCs are highly correlated with movement direction, such a move—similar to exchanging two states within the same cluster but under different target-motion conditions—would presumably not affect the behavior.

      Reviewer #3 (Public Review):

      Summary:

      This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

      This paradigm allows the authors to investigate how, given a fixed reach endpoint (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

      At the level of single neurons, the authors found that target motion modulates the activity in three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

      At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to the target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain-modulated neurons.

      Finally, the authors studied the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units were found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the “ neural population” resembled that observed in the monkeys.

      Strengths:

      - The experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.

      - The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.

      - The paper gives a complete picture of the effect of the target motion on neural activity, by including analyses at the single neuron level as well as at the population level. Additionally, the authors link those two levels of representation by highlighting how gain modulation contributes to shaping the population representation.

      Thank you for your recognition.

      Weaknesses:

      - One of the main premises of the paper is the fact that the motor output for a given reach point is preserved across different target motions. However, as the authors briefly mention in the conclusion, they did not record muscle activity during the task, but only hand velocity, making it impossible to directly verify how preserved muscle patterns were across movements. While the authors highlight that they did not see any difference in their results when resampling the data to control for similar hand velocities across conditions, this seems like an important potential caveat of the paper whose implications should be discussed further or highlighted earlier in the paper.

      Thanks for the suggestion. We’ve highlighted the resampling results as an important control in the revised manuscript in Figure S11 and Lines 257-260 as:

      “To eliminate hand-speed effect, we resampled trials to construct a new dataset with similar distributions of hand speed in each target-motion condition and found similar orbital neural geometry. Moreover, the target-motion gain model provided a better explanation compared to the hand-speed gain model (Figure S11).”

      - The main takeaway of the RNN analysis is not fully clear. The authors find that an RNN trained given a sensory input representing a moving target displays modulation to target motion that resembles what is seen in real data. This is interesting, but the authors do not dissect why this representation arises, and how robust it is to various task design choices. For instance, it appears that the network should be able to solve the task using only the motion intention input, which contains the reach endpoint information. If the target motion input is not used for the task, it is not obvious why the RNN units would be modulated by this input (especially as this modulation must lie in the nullspace of the movement hand velocity if the velocity depends only on the reach endpoint). It would thus be important to see alternative models compared to true neural activity, in addition to the model currently included in the paper. Besides, for the model in the paper, it would therefore be interesting to study further how the details of the network setup (eg initial spectral radius of the connectivity, weight regularization, or using only the target position input) affect the modulation by the motion input, as well as the trained population geometry and the relative ratios of modulated cells after training.

      Great suggestions. In the revised manuscript, we’ve added the results of three alternative modes in Table S4 and Lines 355-365 as below:

      “We also tested three alternative network models: (1) only receives motor intention and a GO-signal; (2) only receives target location and a GO-signal; (3) initialized with sparse connection (sparsity=0.1); the unmentioned settings and training strategies were as the same as those for original models (Table S4; see Methods). The results showed that the three modulations could emerge in these models as well, but with obviously distinctive distributions. In (1), the ring-like structure became overlapped rings parallel to the PC1PC2 plane or barrel-like structure instead; in (2), the target-motion related tilting tendency of the neural states remained, but the projection of the neural states on the PC1-PC2 plane was distorted and the reach-direction clusters dispersed. These implies that both motor intention and target location seem to be needed for the proposed ring-like structure. The initialization of connection weights of the hidden layer can influence the network’s performance and neural state structure, even so, the ring-like structure”

      - Additionally, it is unclear what insights are gained from the perturbations to the network connectivity the authors perform, as it is generally expected that modulating the connectivity will degrade task performance and the geometry of the responses. If the authors wish the make claims about the role of the subpopulations, it could be interesting to test whether similar connectivity patterns develop in networks that are not initialized with an all-to-all random connectivity or to use ablation experiments to investigate whether the presence of multiple types of modulations confers any sort of robustness to the network.

      Thank you for these great suggestions. By perturbations, we intended to explore the contribution of interaction between certain subpopulations. We’ve included the ablation experiments in the updated manuscript in Table S3 and Lines 344-346 as below: “The ablation experiments showed that losing any kind of modulation nodes would largely deteriorate the performance, and those nodes merely with PD-shift modulation could mostly impact the neural state structure (Table S3).”

      - The results suggest that the observed changes in motor cortical activity with target velocity result from M1 activity receiving an input that encodes the velocity information. This also appears to be the assumption in the RNN model. However, even though the input shown to the animal during preparation is indeed a continuously moving target, it appears that the only relevant quantity to the actual movement is the final endpoint of the reach. While this would have to be a function of the target velocity, one could imagine that the computation of where the monkeys should reach might be performed upstream of the motor cortex, in which case the actual target velocity would become irrelevant to the final motor output. This makes the results of the paper very interesting, but it would be nice if the authors could discuss further when one might expect to see modulation by sensory information that does not directly affect motor output in M1, and where those inputs may come from. It may also be interesting to discuss how the findings relate to previous work that has found behaviourally irrelevant information is being filtered out from M1 (for instance, Russo et al, Neuron 2020 found that in monkeys performing a cycling task, context can be decoded from SMA but not from M1, and Wang et al, Nature Communications 2019 found that perceptual information could not be decoded from PMd)?

      How and where sensory information modulating M1 are very interesting and open questions. In the revised manuscript, we discuss these in Lines 435-446, as below: “It would be interesting to explore whether other motor areas also allow sensory modulation during flexible interception. The functional differences between M1 and other areas lead to uncertain speculations. Although M1 has pre-movement activity, it is more related to task variables and motor outputs. Recently, a cycling task sets a good example that the supplementary motor area (SMA) encodes context information and the entire movement (Russo et al., 2020), while M1 preferably relates to cycling velocity (Saxena et al., 2022). The dorsal premotor area (PMd) has been reported to capture potential action selection and task probability, while M1 not (Cisek and Kalaska, 2005; Glaser et al., 2018; Wang et al., 2019). If the neural dynamics of other frontal motor areas are revealed, we might be able to tell whether the orbital neural geometry of mixed selectivity is unique in M1, or it is just inherited from upstream areas like PMd. Either outcome would provide us some insights into understanding the interaction between M1 and other frontal motor areas in motor planning.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      At times the writing was a little hard to parse. It could benefit from being fleshed out a bit to link sentences together better.

      There are a few grammatical errors, such as:

      "These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact each other, so the PD-shift nodes should not be neglected."

      should be

      "These results support strong and similar roles of gain and additive nodes, but what is even more important is that the three modulations interact WITH each other, so the PDshift nodes should not be neglected."

      The discussion could also be more extensive to benefit non-experts in the field.

      Thank you. We have proofread and polished the updated manuscript.

      Reviewer #2 (Recommendations For The Authors):

      Other comments:

      - The authors mention mixed selectivity a few times, but Table 1 doesn't have a column for mixed selective neurons--this seems like an important oversight. Likewise, it would be good to see an example of a "mixed" neuron.

      - The structure of the writing in the results section often talked about the supplementary results before the main results - this seems backwards. If the supplementary results are important enough to come before the main figures, then they should not be supplementary. Otherwise, if the results are truly supplementary, they should come after the main results are discussed.

      - Line 305: Authors say "most" RNN units could be classified, and this is technically true, but only barely, according to Table 1. It might be good to put the actual percentage here in the text.

      - Figure 5a: typo ("Motion intention" rather than "Motor")

      - I couldn't find any mention of code or data availability in the manuscript.

      - There were a number of lines that didn't make much sense to me and should probably be rewritten or expanded on:

      - Lines 167-168: "These results qualitatively imply the interaction as that target speeds..." - Lines 178-179: "However, these neural trajectories were not yet the ideal description, because they were shaped mostly by time."

      - Lines 187-188: "...suggesting that target motion affects M1 neural dynamics via a topologically invariant transformation."

      - Lines 224-226: "Note that here we performed an linear transformation on all resulting neural state points to make the ellipse of the static condition orthogonal to the z-axis for better visualization." Does this mean that the z-axis is not PC 3 anymore?

      - Lines 272-274: "These simulations suggest that the existence of PD-shift and additive modulation would not disrupt the neural geometry that is primarily driven by gain modulation; rather it is possible that these three modulations support each other in a mixed population."

      Thank you for these detailed suggestions. By “mixed selectivity”, we mean the joint tuning of both target-motion and movement. In this case, the target-motion modulated neurons (regardless of the modulation type) are of mixed selectivity. The term “motor intention” refers to Mazzoni et al., 1996, Journal of Neurophysiology. We also revised the manuscript for better readership.

      We have updated the data and code availability in Data availability as below:

      “The example experimental datasets and relevant analysis code have been deposited in Mendeley Data at https://data.mendeley.com/datasets/8gngr6tphf. The RNN relevant code and example model datasets are available at https://github.com/yunchenyc/RNN_ringlike_structure.“

      Reviewer #3 (Recommendations For The Authors):

      Minor typos:

      Line 153: “there were”

      Line 301: “network was trained to generate”

      Line 318: “interact with each other”

      Suggested reformulations :

      Line 310 : “tilting angles followed a pattern similar to that seen in the data” Line 187 : the claim of a “topologically invariant transformation” seems strong as the analysis is quite qualitative.

      Suggested changes to the paper (aside from those mentioned in the main review): It could be nice to show behaviour in a main figure panel early on in the paper. This could help with the task description (as it would directly show how the trials are separated based on endpoint) and could allow for discussing the potential caveats of the assumption that behaviour is preserved.

      Thank you. We have corrected these typos and writing problems. As the similar task design has been reported, we finally decided not to provide extra figures or videos. Still, we thank this nice suggestion.

    1. Reviewer #3 (Public review):

      This work describes the tandem linkage of influenza hemagglutinin (HA) receptor binding domains of diverse subtypes to create 'beads on a string' (BOAS) immunogens. They show that these immunogens elicit ELISA binding titers against full-length HA trimers in mice, as well as varying degrees of vaccine mismatched responses and neutralization titers. They also compare these to BOAS conjugated on ferritin nanoparticles and find that this did not largely improve immune responses. This work offers a new type of vaccine platform for influenza vaccines, and this could be useful for further studies on the effects of conformation and immunodominance on the resulting immune response. 

      Overall, the central claims of immunogenicity in a murine model of the BOAS immunogens described here are supported by the data. 

      Strengths included the adaptability of the approach to include several, diverse subtypes of HAs. The determination of an optimal composition of strains in the 5-BOAS that overall yielded the best immune responses was an interesting finding and one that could also be adapted to other vaccine platforms. Lastly, as the authors discuss, the ease of translation to an mRNA vaccine is indeed a strength of this platform. 

      One interesting and counter-intuitive result is the high levels of neutralization titers seen to vaccine-mismatched, group 2 H7 in the 5-BOAS group that differs from the 4-BOAS with the addition of a group 1 H5 RBD. At the same time, no H5 neutralization titers were observed for any of the BOAS immunogens, yet they were seen for the BOAS-NP. Uncovering where these immune responses are being directed and why these discrepancies are being observed would be informative future work. 

      There are a few caveats in the data that should be noted: 

      (1) 20 ug is a pretty high dose for a mouse and the majority of the serology presented is after 3 doses at 20 ug. By comparison, 0.5-5 ug is a more typical range (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380945/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980174/). Also, the authors state that 20 ug per immunogen was used, including for the BOAS-NP group, which would mean that the BOAS-NP group was given a lower gram dose of HA RBD relative to the BOAS groups. 

      (2) Serum was pooled from all animals per group for neutralization assays, instead of testing individual animals. This could mean that a single animal with higher immune responses than the rest in the group could dominate the signal and potentially skew the interpretation of this data. 

      (3) In Figure S2, it looks like an apparent increase in MW by changing the order of strains here, which may be due to differences in glycosylation. Further analysis would be needed to determine if there are discrepancies in glycosylation amongst the BOAS immunogens and how those differ from native HAs. 

      Comments on revisions:

      The authors have addressed all concerns upon revision.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this manuscript by Thronlow Lamson et al., the authors develop a "beads-on-a-string" or BOAS strategy to link diverse hemagglutinin head domains, to elicit broadly protective antibody responses. The authors are able to generate varying formulations and lengths of the BOAS and immunization of mice shows induction of antibodies against a broad range of influenza subtypes. However, several major concerns are raised, including the stability of the BOAS, that only 3 mice were used for most immunization experiments, and that important controls and analyses related to how the BOAS alone, and not the inclusion of diverse heads, impacts humoral immunity.

      Strengths:

      Vaccine strategy is new and exciting.

      Analyses were performed to support conclusions and improve paper quality.

      Weaknesses:

      Controls for how different hemagglutinin heads impact immunity versus the multivalency of the BOAS.

      Only 3 mice were used for most experiments.

      There were limited details on size exclusion data.

      We appreciate the reviewer’s comments and have made the following changes to the manuscript.

      (1) We recognize that deconvoluting the effect of including a diverse set of HA heads and multivalency in the BOAS immunogens is necessary to understand the impact on antigenicity. Therefore, we now include a cocktail of the identical eight HA heads used in the 8-mer and BOAS nanoparticle (NP) as an additional control group. While we observed similar HA binding titers relative to the 8-mer and BOAS NP groups, the cocktail group-elicited sera was unable to neutralize any of the viruses tested; multivalency thus appears to be important for eliciting neutralizing responses

      (2) We increased the sample size by repeated immunizations with n=5 mice, for a total of n=8 mice across two independent experiments.

      (3) We expanded the details on size exclusion data to include:

      a) extended chromatograms from Figure 2C as Supplemental Figure 3.

      b) additional details in the materials and methods section (lines 370-372):

      “Recovered proteins were then purified on a Superdex 200 (S200) Increase 10/300 GL (for trimeric HAs) or Superose 6 Increase 10/300 GL (for BOAS) size-exclusion column in Dulbecco’s Phosphate Buffered Saline (DPBS) within 48 hours of cobalt resin elution.”

      Reviewer #2 (Public Review):

      Summary:

      The authors describe a "beads-on-a-string" (BOAS) immunogen, where they link, using a non-flexible glycine linker, up to eight distinct hemagglutinin (HA) head domains from circulating and non-circulating influenzas and assess their immunogenicity. They also display some of their immunogens on ferritin NP and compare the immunogenicity. They conclude that this new platform can be useful to elicit robust immune responses to multiple influenza subtypes using one immunogen and that it can also be used for other viral proteins.

      Strengths:

      The paper is clearly written. While the use of flexible linkers has been used many times, this particular approach (linking different HA subtypes in the same construct resembling adding beads on a string, as the authors describe their display platform) is novel and could be of interest.

      Weaknesses:

      The authors did not compare to individuals HA ionized as cocktails and did not compare to other mosaic NP published earlier. It is thus difficult to assess how their BOAS compare.<br /> Other weaknesses include the rationale as to why these subtypes were chosen and also an explanation of why there are different sizes of the HA1 construct (apart from expression). Have the authors tried other lengths? Have they expressed all of them as FL HA1?

      We appreciate the reviewer’s comments. We responded to the concerns below and modified the manuscript accordingly.

      (1) We recognize that including a “cocktail” control is important to understand how the multivalency present in a single immunogen affects the immune response. We now include an additional control group comprised of a mixture of the same eight HA heads used in the 8-mer and the BOAS nanoparticle (NP). While this cocktail elicited similar HA binding titers relative to the 8-mer and BOAS NP immunogens (Fig. 6G), there was no detectable neutralization any of the viruses tested (Fig. 7).

      (2) In the introduction we reference other multivalent display platforms but acknowledge that distinct differences in their immunogen design platforms make direct comparisons to ours difficult—which is ultimately why we did not use them as comparators for our in vivo studies. Perhaps most directly relevant to our BOAS platform is the mosaic HA NP from Kanekiyo et al. (PMID 30742080). Here, HA heads, with similar boundaries to ours, were selected from historical H1N1 strains. These NPs however were significantly less antigenic diverse relative to our BOAS NPs as they did not include any group 2 (e.g., H7, H9) or B influenza HAs; restricting their multivalent display to group 1 H1N1s likely was an important factor in how they were able to achieve broad, neutralizing H1N1 responses. Additionally, Cohen et al. (PMID 33661993) used similarly antigenically distinct HAs in their mosaic NP, though these included full-length HAs with the conserved stem region, which likely has a significant impact on the elicited cross-reactive responses observed. Lastly, we reference Hills et al. (PMID 38710880), where authors designed similar NPs with four tandemly-linked betacoronoavirus receptor binding domains (RBDs) to make “quartets”. In contrast to our observations, the authors observed increased binding and neutralization titers following conjugation to protein-based NPs. We acknowledge potential differences between the studies, such as the antigen and larger VLP NP, that could lead to the different observed outcomes.

      (3) We intended to highlight the “plug-and-play” nature of the BOAS platform; theoretically any HA subtype could be interchanged into the BOAS. To that end, our rationale for selecting the HA subtypes in our proof-of-principle immunogen was to include an antigenically diverse set of circulating and non-circulating HAs that we could ultimately characterize with previously published subtype-specific antibodies that were also conformation-specific. In doing so, these diagnostic antibodies could confirm presence and conformation integrity of each component. We intentionally did not include HA subtypes that we did not have a conformation-specific antibody for.

      The different sizes of HA head domains was determined exclusively by expression of the recombinant protein. We have not attempted expression of full-length HA1 domains. Furthermore, we have not attempted to express the full-length HA (inclusive of HA1 and HA2) in our BOAS platform. The primary reason was to avoid including the conserved stem region of HA2 which may distract from the HA1 epitopes (e.g., receptor binding site, lateral patch) that can be engaged by broadly neutralizing antibodies. Additionally, the full-length HA is inherently trimeric and may not be as amenable to our BOAS platform as the monomeric HA1 head domain.

      Reviewer #3 (Public Review):

      This work describes the tandem linkage of influenza hemagglutinin (HA) receptor binding domains of diverse subtypes to create 'beads on a string' (BOAS) immunogens. They show that these immunogens elicit ELISA binding titers against full-length HA trimers in mice, as well as varying degrees of vaccine mismatched responses and neutralization titers. They also compare these to BOAS conjugated on ferritin nanoparticles and find that this did not largely improve immune responses. This work offers a new type of vaccine platform for influenza vaccines, and this could be useful for further studies on the effects of conformation and immunodominance on the resulting immune response.

      Overall, the central claims of immunogenicity in a murine model of the BOAS immunogens described here are supported by the data.

      Strengths included the adaptability of the approach to include several, diverse subtypes of HAs. The determination of the optimal composition of strains in the 5-BOAS that overall yielded the best immune responses was an interesting finding and one that could also be adapted to other vaccine platforms. Lastly, as the authors discuss, the ease of translation to an mRNA vaccine is indeed a strength of this platform.

      One interesting and counter-intuitive result is the high levels of neutralization titers seen in vaccine-mismatched, group 2 H7 in the 5-BOAS group that differs from the 4-BOAS with the addition of a group 1 H5 RBD. At the same time, no H5 neutralization titers were observed for any of the BOAS immunogens, yet they were seen for the BOAS-NP. Uncovering where these immune responses are being directed and why these discrepancies are being observed would constitute informative future work.

      There are a few caveats in the data that should be noted:

      (1) 20 ug is a pretty high dose for a mouse and the majority of the serology presented is after 3 doses at 20 ug. By comparison, 0.5-5 ug is a more typical range (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380945/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980174/). Also, the authors state that 20 ug per immunogen was used, including for the BOAS-NP group, which would mean that the BOAS-NP group was given a lower gram dose of HA RBD relative to the BOAS groups.

      We agree that this is on the “upper end” of recombinant protein dose. While we did not do a dose-response, we now include serum analyses after a single prime. The overall trends and reactivity to matched and mis-matched BOAS components remained similar across days d28 and d42. However, the differences between the BOAS and BOAS NP groups and the mixture group were more pronounced at d28, which reinforces our observation that the multivalency of the HA heads is necessary for eliciting robust serum responses to each component. These data are included in Supplemental Figure 5, and we’ve modified the text (lines 185-187) to include;

      “Similar binding trends were also observed with d28 serum, though the difference between the 8mer and mix groups was more pronounced at d28 (Supplemental Figure 5).”

      Additionally, we acknowledge that there is a size discrepancy between the BOAS NP and the largest BOAS, leading to an approximately ~15-fold difference on a per mole basis of the BOAS immunogen. The smallest and largest BOAS also differ by ~ 2.5-fold on a per mole basis; this could favor the overall amount of the smaller immunogens, however because vaccine doses are typically calculated on a mg per kg basis, we did not calculate on a molar basis for this study. Any promising immunogens will be evaluated in dose-response study to optimize elicited responses.

      (2) Serum was pooled from all animals per group for neutralization assays, instead of testing individual animals. This could mean that a single animal with higher immune responses than the rest in the group could dominate the signal and potentially skew the interpretation of this data.

      We repeated the neutralization assays with data points for individual mice. There does appear to be variability in the immune response between mice. This is most noticeable for responses to the H5 component. We are currently assessing what properties of our BOAS immunogen might contribute to the variability across individual mice.

      (3) In Figure S2, it looks like an apparent increase in MW by changing the order of strains here, which may be due to differences in glycosylation. Further analysis would be needed to determine if there are discrepancies in glycosylation amongst the BOAS immunogens and how those differ from native HAs.

      There does appear to be a relatively small difference in MW between the two BOAS configurations shown in Figure S2. This could be due to differences in glycosylation, as the reviewer points out, and in future studies, we intend to assess the influence of native glycosylation on antibody responses elicited by our BOAS immunogens.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Major Concerns

      (1) From Figure 2D-E, it looks like BOAS are forming clusters, rather than a straight line. Do these form aggregates over time? Both at 4 degrees over a few days or after freeze-thaw cycle(s)? It is unclear from the SEC methods how long after purification this was performed and stability should be considered.

      Due to the inherent flexibility of the Gly-Ser linker between each component we do not anticipate that any rigidity would be imposed resulting in a “straight line”. Nevertheless, we appreciate the reviewers concern about the long-term stability of the BOAS immunogens. To address this, we include 1) the extended chromatograms from Figure 2C as Supplemental Figure 3 to show any aggregates present, 2) traces from up to 48 hours post-IMAC, and 3) chromatograms following a freeze-thaw cycle. Post-IMAC purification there is a minor (<10% total peak height) at ~9mL corresponding to aggregation. Note, we excluded this aggregation for immunizations. Post freeze-thaw cycle, we can see that upon immediate (<24hrs) thawing, the BOAS maintain a homogeneous peak with no significant (<10%) aggregation or degradation peak. However, after ~1 week post-freeze-thaw cycle at 4C, additional peaks within the chromatogram correspond to degradation of the BOAS.

      We modified the materials and methods section to state (lines 370-372)

      “Recovered proteins were then purified on a Superdex 200 (S200) Increase 10/300 GL (for trimeric HAs) or Superose 6 Increase 10/300 GL (for BOAS) size-exclusion column in Dulbecco’s Phosphate Buffered Saline (DPBS) within 48 hours of cobalt resin elution.”

      We commented on BOAS stability in the results section (lines 142-148)

      “Following SEC, affinity tags were removed with HRV-3C protease; cleaved tags, uncleaved BOAS, and His-tagged enzyme were removed using cobalt affinity resin and snap frozen in liquid nitrogen before immunizations. BOAS maintained monodispersity upon thawing, though over time, degradation was observed following longer term (>1 week) storage at 4C (Supplemental Figure 3). This degradation became more significant as BOAS increased in length (Supplemental Figure 3).”

      We also included in the discussion (lines 277-279):

      “Notably, for longer BOAS we observed degradation following longer term storage at 4C, which may reflect their overall stability.”

      (2) Figures 3-4 and 6-7, to make conclusions off of 3 mice per group is inappropriate. A sample size calculation should have been conducted and the appropriate number of mice tested. In addition, two independent mouse experiments should always be performed. Moreover, the reliability of the statistical tests performed seems unlikely, given the very small sample size.

      We agree that additional mice are necessary to make assessments regarding immunogenicity and cross-reactivity differences between the immunogens. To address this, we repeated the immunization with 5 additional mice, for a total of n=8 mice over two independent experiments. We incorporated these data into Figure 3B-D, as well as an additional Figure 3E (see below). We also now report the log-transformed endpoint titer (EPT) values rather than reciprocal EC50 values and added clarity to statistical analyses used. We have added the following lines to the methods section

      lines 427-431:

      “Serum endpoint titer (EPT) were determined using a non-linear regression (sigmoidal, four-parameter logistic (4PL) equation, where x is concentration) to determine the dilution at which dilution the blank-subtracted 450nm absorbance value intersect a 0.1 threshold. Serum titers for individual mice against respective antigens are reported as log transformed values of the EPT dilution.”

      lines 406-408:

      “C57BL/6 mice (Jackson Laboratory) (n=8 per group for 3-, 4-, 5-, 6-, 7-, and 8mer cohorts; n=5 for BOAS NP, NP, and mix cohorts) were immunized with 20µg of BOAS immunogens of varying length and adjuvanted with 50% Sigmas Adjuvant for a total of 100µL of inoculum.”

      lines 482-490:

      “Statistical Analysis

      Significance for ELISAs and microneutralization assays were determined using Prism (GraphPad Prism v10.2.3). ELISAs comparing serum reactivity and microneutralization and comparing >2 samples were analyzed using a Kruskal-Wallis test with Dunn’s post-hoc test to correct for multiple comparisons. Multiple comparisons were made between each possible combination or relative to a control group, where indicated. ELISAs comparing two samples were analyzed using a Mann-Whitney test. Significance was assigned with the following: * = p<0.05, ** = p<0.01, *** = p<0.001, and **** = p<0.0001. Where conditions are compared and no significance is reported, the difference was non-significant.”

      (3) One critical control that is missing is a homogenous BOAS, for example, just linking one H1 on a BOAS. Does oligomerization and increasing avidity alone improve humoral immunity?

      We agree that this is an interesting point, However, to address the impact of oligomerization and avidity on humoral immunity, we now include an additional control with a cocktail of HA heads used in the 8mer. We have incorporated this into Figure 3A, 3D and 3E, Figure 6G, and Figure 7.

      Additionally, we have added the following lines in the manuscript:

      lines 38-40:

      “Finally, vaccination with a mixture of the same HA head domains is not sufficient to elicit the same neutralization profile as the BOAS immunogens or nanoparticles.”

      lines 105-106:

      “Additionally, we showed that a mixture of the same HA head components was not sufficient to recapitulate the neutralizing responses elicited by the BOAS or BOAS NP.”

      lines 169-172:

      “To determine immunogenicity of each BOAS immunogen, we performed a prime-boost-boost vaccination regimen in C5BL/6 mice at two-week intervals with 20µg of immunogen and adjuvanted with Sigma Adjuvant (Figure 3A). We compared these BOAS to a control group immunized with a mixture of the eight HA heads present in the 8mer.”

      lines 265-267:

      “There were qualitatively immunodominant HAs, notably H4 and H9, and these were relatively consistent across BOAS in which they were a component. This effect was reduced in the mix cohort.”

      (4) While some cross-reactivity is likely (Figure 6G), there is considerable loss of binding when there is a mismatch. Of the antibodies induced, how much of this is strain-specific? For example, how well do serum antibodies bind to a pre-2009 H1?

      We agree with the reviewer that there is a considerable loss of binding when there is a mismatched HA component. To better understand this and incorporate a mismatched strain into our analysis of the 8mer and BOAS NP, we looked at serum binding titers to a pre-2009 H1, H1/Solomon Islands/2006, and an antigenically distinct H3, H3/Hong Kong/1968. We have incorporated this data into Figures 3D, 3E, 6F and 6G. We observed relatively high titers against both a mismatched H1 and H3, indicating that the BOAS maintain high titers against subtype-specific strains that are conserved over considerable antigenic distance. However, this was similar in the mixture group, indicating that this may not be specific to oligomerization of BOAS immunogens.

      We added the following to the methods section:

      lines 357-361

      “Head subdomains from these HAs were used in the BOAS immunogens, and full-length soluble ectodomain (FLsE) trimers were used in ELISAs. Additional H1 (H1/A/Solomon Islands/3/2006) and H3 (H3/A/Hong Kong/1/1968) FLsEs were used in ELISAs as mismatched, antigenically distinct HAs for all BOAS.”

      Minor Concerns

      (1) Line 44-46, the deaths per year are almost exclusively due to seasonal influenza outbreaks caused by antigenically drifted viruses in humans, not those spilling over from avian sp. and swine. For accuracy, please adjust this sentence.

      We have adjusted lines 45-48 to say “This is largely a consequence of viral evolution and antigenic drift as it circulates seasonally within humans and ultimately impacts vaccine effectiveness. Additionally, the chance for spillover events from animal reservoirs (e.g., avian, swine) is increasing as population and connectivity also increase.”

      (2) Figure 4D-E, provide a legend for what the symbols indicate, or simply just put the symbol next to either the homology score and % serum competition labels on the y-axis.

      We have included a legend in Figures 4D,E to distinguish between homology score and % serum competition

      (3) I am a bit confused by the data presented in Figure 7. The figure legend says the two symbols represent technical replicates. How? Is one technical replicate of all the mice in a group averaged and that's what's graphed? If so, this is not standard practice. I would encourage the authors to show the average technical replicates of each animal, which is standard.

      We thank the reviewer for their suggestion, and we have revised Figure 7 such that each symbol represents a single animal for n=5 animals. We have also adjusted the figure caption to the following:

      “Figure 7: Microneutralization titers to matched and mis-matched virus- Microneutralization of matched and mis-matched psuedoviruses: H1N1 (green, top left), H3N2 (orange, top right), H5N1 (yellow, bottom left), and H7N9 viruses (pink, bottom right) with d42 serum. Solid bars below each plot indicate a matched sub-type, and striped bars indicate a mis-matched subtype (i.e. not present in the BOAS). NP negative controls were used to determine threshold for neutralization. Upper and lower dashed lines represent the first dilution (1:32) (for H1N1, H3N2, and H5N1) or neutralization average with negative control NP serum (H7N9), and the last serum dilution (1:32,768), respectively, and points at the dashed lines indicate IC50s at or outside the limit of detection. Individual points indicate IC50 values from individual mice from each cohort (n=5). The mean is denoted by a bar and error bars are +/- 1 s.d., * = p<0.05 as determined by a Kruskal-Wallis test with Dunn’s multiple comparison post hoc test relative to the mix group.”

      (4) Paragraphs 298-313, multiple studies are referred to but not referenced.

      We have added the following references to this section:

      (38) Kanekiyo, M. et al. Self-assembling influenza nanoparticle vaccines elicit broadly neutralizing H1N1 antibodies. Nature 498, 102–106 (2013).

      (48) Hills, R. A. et al. Proactive vaccination using multiviral Quartet Nanocages to elicit broad anti-coronavirus responses. Nat. Nanotechnol. 1–8 (2024) doi:10.1038/s41565-024-01655-9.

      (65) Jardine, J. et al. Rational HIV immunogen design to target specific germline B cell receptors. Science 340, 711–716 (2013).

      (66) Tokatlian, T. et al. Innate immune recognition of glycans targets HIV nanoparticle immunogens to germinal centers. Science 363, 649–654 (2019).

      (67) Kato, Y. et al. Multifaceted Effects of Antigen Valency on B Cell Response Composition and Differentiation In Vivo. Immunity 53, 548-563.e8 (2020).

      (68) Marcandalli, J. et al. Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus. Cell 176, 1420-1431.e17 (2019).

      (69) Bruun, T. U. J., Andersson, A.-M. C., Draper, S. J. & Howarth, M. Engineering a Rugged Nanoscaffold To Enhance Plug-and-Display Vaccination. ACS Nano 12, 8855–8866 (2018).

      (70) Kraft, J. C. et al. Antigen- and scaffold-specific antibody responses to protein nanoparticle immunogens. Cell Reports Medicine 100780 (2022) doi:10.1016/j.xcrm.2022.100780.

      Reviewer #2 (Recommendations For The Authors):

      Can the authors define "detectable titers"?

      Maybe add a threshold value of reciprocal EC on the figure for each plot.

      We recognize the reviewers concern with reporting serum titers in this way, and we have adjusted our reported titers as endpoint titers (EPT) with a dotted line for the first detectable dilution (1:50). We have also adjusted the methods section to reflect this change:

      (lines 427-431)

      “Serum endpoint titer (EPT) were determined using a non-linear regression (sigmoidal, four-parameter logistic (4PL) equation, where x is concentration) to determine the dilution at which dilution the blank-subtracted 450nm absorbance value intersect a 0.1 threshold. Serum titers for individual mice against respective antigens are reported as log transformed values of the EPT dilution.”

      It also appears that not all X-mer elicits an immune response against matched HA, e.g. for the 7 and 8 -mer. Not sure why the authors do not mention this. It could be due to too many HAs, not sure.

      We apologize for the confusion, and agree that our original method of reporting EC50 values does not reflect weak but present binding titers. Upon further analysis with additional mice as well as adjusting our method of reporting titers, it is easier to see in Figure 3D that all X-mer BOAS do indeed elicit binding detectable titers to matched HA components.

      It will be nice to add a conclusion to the cross-reactivity - again it appears that past 6-mer there has been a loss in cross-reactivity even though there are more subtypes on the BOAS.

      Also, the TI seemed to be the more conserved epitope targeted here.

      (Of note these two are mentioned in the discussion)

      We have updated the results section to include the following:

      (lines 281-294)

      “Based on the immunogenicity of the various BOAS and their ability to elicit neutralizing responses, it may not be necessary to maximize the number of HA heads into a single immunogen. Indeed, it qualitatively appears that the intermediate 4-, 5-, and 6mer BOAS were the most immunogenic and this length may be sufficient to effectively engage and crosslink BCR for potent stimulation. These BOAS also had similar or improved binding cross-reactivity to mis-matched HAs as compared to longer 7- or 8mer BOAS. Notably, the 3mer BOAS elicited detectable cross-reactive binding titers to H4 and H5 mismatched HAs in all mice. This observed cross-reactivity could be due to sequence conservation between the HAs, as H3 and H4 share ~51% sequence identity, and H1 and H2 share ~46% and ~62% overall sequence identity with H5, respectively (Supplemental Figure 6). Additionally, the degree of surface conservation decreased considerably beyond the 5mer as more antigenically distinct HAs were added to the BOAS. These data suggest that both antigenic distance between HA components and BOAS length play a key role in eliciting cross-reactive antibody responses, and further studies are necessary to optimize BOAS valency and antigenic distance for a desired response.”

      Figure 5E, the authors could indicate which subtype each mab is specific to for those who are not HA experts. (They have them color-coded but it is hard to see because very small).

      The authors also do not explain why 3E5 does not bind well to H1, H2, H3, H4 4-mer BOA, etc...

      We apologize for the lack of clarity in this figure. We updated Figure 5E to include the subtype it is specific for as well as listing the antibodies and their subtype and targeted epitope in the figure caption.

      Minor

      Figure 1B zoom looks like the line is hidden to the structure - should come in front

      We adjusted the figure accordingly.

      Line 127 - whether the order

      Corrected

      What is the rationale for thinking that a different order will lead to a different expression and antigenic results?

      We thank the reviewer for this question. We did not necessarily anticipate a difference in protein expression based on BOAS order We, however, wanted to verify that our platform was indeed “plug-and-play” platform and we could readily exchange components and order. We do, however, hypothesize that a different order may in fact lead to different antigenic results. We think that the conformation of the BOAS as well as physical and antigenic distance of HA components may influence cross-linking efficiency of BCRs and lead to different antigenic results with different levels of cross-reactivity. For example, a BOAS design with a cluster of group 1 HAs followed by a cluster of group 2 HAs, rather than our roughly alternating pattern could impact which HAs are in proximity to each other or could be potentially shielded in certain conformations, and thus could affect antigenic results. We expand on this rationale in the discussion in lines 310-314:

      “Further studies with different combinations of HAs could aid in understanding how length and composition influences epitope focusing. For example, a BOAS design with a cluster of group 1 HAs followed by a cluster of group 2 HAs, rather than our roughly alternating pattern could impact which HAs are in close proximity to one other or could be potentially shielded in certain conformations, and thus could affect antigenic results.”

      Maybe list HA#1 HA#2 HA#3 instead of HA1, HA2, HA3 to make sure it is not confounded with HA2 and HA2

      We agree that this may be confusing for readers, and have adjusted Figure 1C to show HA#1, HA#2, etc.

      For nsEM, do the authors have 2D classes and even 3D reconstructions? Line 148-149: maybe or just because there are more HAs.

      We did not obtain 2D class or 3D reconstructions of these BOAS. However, we do agree with the reviewer that the collapsed/rosette structure of the 8mer BOAS may be a consequence of the additional HA heads as well as the flexible Gly-Ser linkers between the components. We have added clarify to our statement in the discussion to read:

      lines 154-156:

      “This is likely a consequence of the flexible GSS linker separating the individual HA head components as well as the addition of significantly more HA head components to the construct.”.

      Line 153 " interface-directed" - what does this mean?

      We apologize for any confusion- we intend for “interface-directed” to refer antibodies that engage the trimer interface (TI) epitope between HA protomers. We have adjusted the manuscript to use the same terminology throughout, i.e. trimer interface or its abbreviation, TI.

      For Figure 2 F - do you have a negative control? Usually one does not determine an ELISA KD, it is not very accurate but shows binding in terms of OD value.

      We did include a negative control, MEDI8852, a stem-directed antibody, though it was not shown in the figure because we observed no binding, as expected. This negative control antibody was also used in Figure 5E for characterizing the BOAS NPs, and also shows no binding. We recognize that in an ELISA the KD is an equilibrium measurement and we do not report kinetic measurements as determined by a method such as bio-layer interferometry (BLI), and have this adjusted the figure caption to denote the values as “apparent K<sub>D</sub> values”.

      Line 169 - reads strangely, "BOAS-elicited serum, regardless of its length, reacted<br /> The length is the one of the Immunogen, not the serum

      We agree that this statement is unclear, and we have modified the sentence to read:

      lines 177-178:

      “Each of the BOAS, regardless of its length, elicited binding titers to all matched full-length HAs representing individual components (Figure 3D).”

      What is the adjuvant used (add in results)?

      We used Sigma adjuvant for all immunizations, and have included this information in the results section:

      lines 169-171:

      “To determine immunogenicity of each BOAS, we performed a prime-boost-boost vaccination regimen in C5BL/6 mice at two-week intervals with 20µg of immunogen and adjuvanted with Sigma Adjuvant (Figure 3A).”

      This information is also included in the methods section in lines 406-412.

      Line 178 - remove " across"

      We have removed the word “across” in this sentence and replaced it with “on” (line 194)

      Trimer- interface, and interface epitopes are used exchangeably - maybe keep it as trimer interface to be more precise

      As stated above, we have adjusted the manuscript to use the same term throughout, i.e., trimer interface or its abbreviation, TI.

      Line 221 - no figure 6H (6G?)

      We apologize for this typo and have corrected to Figure 6G (line 231)

      Reviewer #3 (Recommendations For The Authors):

      (1) Since 20 ug x3 doses is quite a high amount of vaccine, differences between immunogens may become blurred. Thus, it may be informative to compare post-prime serology for all immunogens or select immunogens to compare to the post-3rd dose data.

      We agree with the reviewer that this is on the upper end of vaccine dose and thus we explored the serum responses after a single boost. The overall trends and reactivity to matched and mis-matched BOAS components remained similar across days d28 and d42. However, the differences between the BOAS and BOAS NP groups and the mixture group were more pronounced at d28, which bolsters our claim that the presentation of the HA heads is important for eliciting strong serum responses to all components. We have included this data in Supplemental Figure 5, and have acknowledged this in the text:

      lines 185-187:

      “Similar binding trends were also observed with d28 serum, though the difference between the 8mer and mix groups was more pronounced at d28 (Supplemental Figure 5).”

      (2) Significance statistics for all immunogenicity data should be added and discussed; it is particularly absent in Figures 3D and 7.

      We have added statistical analyses to Figure 3 and Figure 7 to reflect changes in immunogenicity. We have also added the following to the methods section:

      lines 482-490:

      “Statistical Analysis

      Significance for ELISAs and microneutralization assays were determined using either a Mann-Whitney test or a Kruskal-Wallis test with Dunn’s post-hoc test in Prism (GraphPad Prism v10.2.3) to correct for multiple comparisons. Multiple comparisons were made between each possible combination or relative to a control group, where indicated. Significance was assigned with the following: * = p<0.05, ** = p<0.01, *** = p<0.001, and **** = p<0.0001. Where conditions are compared and no significance is reported, the difference was non-significant.”

      (3) Figure 2F: the figure has K03.12 listed for the H3-specific mAb and in the main text, but the caption says 3E5 - is the 3E5 in the caption a typo? 3E5 is listed for the competition ELISAs as an RBS mAb, but its binding site is distal to the RBS at residues 165-170 (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9787348/), H7.167 binds in the RBS periphery and not directly within the RBS, and the epitope for P2-D9 is undetermined/not presented. This could mean that there is actually a higher proportion of RBS-directed antibodies than what is determined from this serum competition data. Also, reference to these as 'RBS-directed' in the serum competition methods section should be revised for accuracy.

      We sincerely apologize for this error and the resulting confusion. 3E5 in the caption is incorrect and should be K03.12 (https://www.rcsb.org/structure/5W08) and does engage the receptor binding site. We also apologize for the oversight that H7.167 is in the RBS periphery and not directly in the RBS. The additional P2-D9 in the panel of RBS-directed antibodies was also in error, as we do not believe it is RBS-directed, but is indeed H4 specific. We also included a reference to the paper and immunogen that elicited this antibody. We agree that this indicates that there could be a higher proportion of RBS-directed antibodies in the serum and have modified the text in the results and methods sections to read:

      lines 300-306:

      “Notably, this proportion is approximate, as at the time of reporting, antibodies that bind the receptor binding site of all components were not available. RBS-directed antibodies to the H4 and H9 component were not available, and the RBS-directed antibodies used targeting the other HA components have different footprints around the periphery of the RBS. Additionally, there are currently no reported influenza B TI-directed antibodies in the literature. Therefore, this may be an underestimate of the serum proportion focused to the conserved RBS and TI epitopes.”

      lines 435-439:

      “Following blocking with BSA in PBS-T, blocking solution was discarded and 40µL of either DPBS (no competition control), a cocktail of humanized antibodies targeting the RBS and periphery (5J8, 2G1, K03.12, H5.3, H7.167, H1209), a cocktail of humanized TI-directed antibodies (S5V2-29, D1 H1-17/H3-14, D2 H1-1/H3-1), or a negative control antibody (MEDI8852) were added at a concentration of 100µg/mL per antibody.”

      (4) Only nsEM data is shown for the 3-BOAS and 8-BOAS, where differences in morphology were seen between these longer and shorter proteins. Including nsEM images for all BOAS immunogens may show trends in morphology or organization that could correlate with immune responses, e.g. if the 5-BOAS also forms a higher proportion of rosette-like structures, while the the 4-BOAS is still a mix between extended and rosette-like, this could be a factor in the better immune responses seen for 5-BOAS.

      We appreciate the reviewer’s suggestion for further analysis of morphology between the intermediate BOAS sizes. We agree that the relationship between BOAS length and morphology should be explored more in depth, and we intend to do so in future studies and to also vary linker length and rigidity.

    1. Note d'Information : Les Parcours Éducatifs dans l'Académie de Nantes Source : Extraits du document "guide-acad-parcours-educatifs-nantes.pdf"

      Date de publication : Non spécifiée, mais mentionne la loi de refondation de l’école de la République du 08 juillet 2013.

      Public cible du document : Personnels d’encadrement des premier et second degrés, et enseignants.

      Objectif du document : Fournir des pistes de mise en œuvre réalistes et respectueuses de l’esprit des textes concernant les parcours éducatifs.

      Thèmes Principaux et Idées Clés :

      Le document met en lumière l'importance et la mise en œuvre des parcours éducatifs, une obligation découlant de la loi de refondation de l’école de la République de 2013.

      Ces parcours visent à offrir à chaque élève un cheminement cohérent et personnalisé tout au long de sa scolarité, en s'appuyant sur quatre domaines fondamentaux : l'avenir, la citoyenneté, l'éducation artistique et culturelle, et la santé.

      1. Fondement Législatif et Objectifs des Parcours Éducatifs :

      • La mise en place de quatre parcours éducatifs est une obligation légale issue de la loi du 8 juillet 2013.
      • "Permettre à tout élève de construire quatre parcours éducatifs est un attendu de la loi de la refondation de l’école de la République du 08 juillet 2013."
      • Ces parcours ont pour but de renforcer l’ambition des élèves et de leur permettre de construire une culture personnelle sans discrimination.
      • "Ils visent à renforcer l’ambition de chacun et à lui permettre de construire une culture personnelle sans discrimination."
      • Ils doivent permettre aux élèves de réaliser un cheminement progressif, basé sur des expériences vécues pendant les temps scolaire, périscolaire et extrascolaire.

      2. Nature et Construction des Parcours :

      • Un parcours éducatif est défini comme à la fois :
      • Un chemin pour atteindre des objectifs fixés à l’avance.
      • Un processus guidé et progressif, permettant la découverte, l'expérimentation, la mobilisation, le développement et le renforcement des compétences.
      • "Un parcours c’est, d’une part, un chemin que l’élève emprunte pour atteindre des objectifs fixés à l’avance, et d’autre part, un processus guidé et progressif, construit pour offrir à chaque élève la possibilité, par la découverte et l’expérimentation, de mobiliser, développer et renforcer ses compétences."
      • La construction des parcours doit se faire pour et avec l’élève, en tenant compte de son point de vue et de celui de l'adulte accompagnateur.
      • Les parcours s’ancrent dans les enseignements, mais les dépassent en s'intéressant aux projets qui ont du sens pour l'élève et en l'aidant à garder des traces de son cheminement, notamment à travers des outils comme FOLIOS.
      • "Toutefois les parcours éducatifs dépassent la stricte entrée par discipline le temps d’une année scolaire ou même celui d’un cycle d’enseignement. Ils conduisent en effet les équipes à s’intéresser aux projets qui font sens pour l’élève relativement à ces quatre entrées et lui permettent de se construire tout au long de sa scolarité. Il s’agit donc aussi d’aider l’élève à garder des traces qui font sens pour lui, concrétisent le chemin parcouru et l’aident à lier entre eux les divers projets pédagogiques menés tout au long de la scolarité. Des outils tel FOLIOS permettent cette personnalisation."

      3. Pilotage et Mise en Œuvre :

      • La mise en œuvre des parcours éducatifs requiert un pilotage pédagogique affirmé de la part de l'IEN, du directeur d'école ou du chef d'établissement. Ce pilotage vise à assurer la cohérence, faciliter les partenariats et offrir des opportunités pour renforcer la cohérence des enseignements pour l'élève et sa famille.
      • "Mettre en œuvre les parcours éducatifs induit un pilotage pédagogique affirmé. L’IEN de la circonscription, le directeur de l’école ou le chef d’établissement donne l’impulsion, sensibilise à la cohérence d’ensemble, facilite les partenariats. Ce pilotage a pour objectif d’offrir l’opportunité d’inscrire les enseignements dans une cohérence encore plus forte pour l’élève et sa famille."
      • Les parcours doivent être programmés au niveau de l'établissement, en lien avec les priorités du projet d'école ou d'établissement.
      • Il est essentiel de favoriser la continuité et la cohérence entre les activités menées en classe et les actions proposées dans le cadre des projets.
      • Les équipes pédagogiques doivent réfléchir aux stratégies pour accompagner les élèves dans la réflexivité, la distanciation et la construction d’un regard critique.

      4. L'Élève Acteur de Ses Parcours :

      • L'élève est placé au centre de la construction de ses parcours. Il est amené à observer, explorer, chercher, exprimer, témoigner, collaborer, coopérer, prendre conscience et donner du sens.
      • "L’élève acteur Une organisation pour rendre l’élève acteur de ses parcours Il observe, explore, cherche, exprime ou témoigne de ce qu’il fait. Il collabore ou coopère avec ses pairs afin de mesurer le chemin parcouru et d’identifier celui qui reste à parcourir. Il prend conscience et donne du sens. Il entretient son intérêt et sa motivation."
      • L'élève acquiert progressivement des connaissances et des compétences qui s'accumulent tout au long de son cheminement.

      5. Enjeux des Parcours Éducatifs :

      • Les parcours éducatifs sont fondés sur les enseignements disciplinaires et transversaux et, pour l'école et le collège, sur le socle commun de connaissances, de compétences et de culture.
      • Ils participent au parcours de réussite de l’élève, en lui permettant d’appréhender le monde, de l’interroger et d’y trouver sa place pour devenir un adulte citoyen, engagé et responsable.
      • "Réussir son parcours scolaire c’est, pour l’élève, être capable d’appréhender le monde dans lequel il vit, l’interroger, se l’approprier, y trouver sa place pour devenir un adulte citoyen, engagé et responsable."
      • L'épreuve orale du DNB permettra à l'élève d'exposer ses intérêts, ses pratiques et ses engagements dans la réalisation des parcours éducatifs.

      6. Articulation Temps Scolaire, Périscolaire et Extrascolaire :

      • Les parcours éducatifs se déploient sur les trois temps de l'élève : scolaire, périscolaire et extrascolaire, en assurant continuité, cohérence et équité.
      • Ils mobilisent différentes modalités : les enseignements, les projets et les partenariats.

      7. Rôle Essentiel des Partenariats :

      • Les partenariats sont considérés comme le rouage essentiel de la mise en œuvre des parcours, qu'ils soient institutionnels, avec les collectivités territoriales, les milieux professionnels, les organismes spécialisés ou les mouvements associatifs.
      • "Les partenariats sont le rouage essentiel de la mise en œuvre des parcours."
      • La complémentarité des approches et la diversité des modalités offertes par les partenaires permettent à chacun de trouver sa place.
      • L'élaboration concertée des projets entre l'équipe pédagogique et les partenaires assure la cohérence des actions.
      • La prise en compte de l'ancrage territorial de l'établissement est indispensable.

      8. Implication des Parents et des Familles :

      La mise en œuvre des parcours éducatifs est une occasion de rapprocher les parents et les familles du monde scolaire et de leur donner une place importante dans l'accompagnement de leurs enfants.

      9. Distinguer Projets et Parcours :

      Le projet est un ensemble articulé d'objectifs, de méthodes et de moyens dans une temporalité déterminée. Le parcours est l'ensemble de projets articulés, cohérents et diversifiés, réalisés sur une temporalité longue (cycle d'enseignement, scolarité obligatoire, parcours de vie). "Ce sont les projets, différents et variés, et les expériences vécues qui constituent le parcours. Chaque projet est une étape dans le parcours pour atteindre les objectifs fixés. C’est la temporalité des actions et donc le cadre qui permet de distinguer le projet du parcours."

      10. Méthodologie de Construction des Parcours :

      • Le document propose des étapes pour construire les parcours :
      • Inventorier les actions existantes et les acquis.
      • Identifier les axes de complémentation.
      • S'interroger sur les méthodes et les approches pédagogiques.
      • Concevoir et mettre en œuvre des projets équilibrés et progressifs.

      11. Évolution de la Posture de l'Enseignant :

      La mise en œuvre des parcours nécessite une évolution de la posture traditionnelle de l'enseignant, qui devient davantage un accompagnateur et un guide pour aider les élèves à établir des liens et à développer leur réflexivité.

      12. Outil Numérique FOLIOS :

      L'outil numérique FOLIOS est mentionné comme un moyen pour l'élève de personnaliser son parcours en collectant, mutualisant et mettant en forme ses expériences scolaires et personnelles, avec l'aide des adultes.

      13. Les Quatre Parcours :

      • Le document réaffirme l'existence des quatre parcours : Avenir, Citoyen, Éducation Artistique et Culturelle, et Santé.
      • En conclusion, ce document académique souligne l'importance d'une approche globale et concertée pour la mise en œuvre des parcours éducatifs, plaçant l'élève au centre de son apprentissage et favorisant la cohérence entre les différents temps et espaces éducatifs, grâce à une collaboration étroite entre les équipes pédagogiques, les partenaires et les familles.
    1. Welcome back. In this video, I want to cover the Health Check feature within Route 53. Health checks support many of the advanced architectures of Route 53, and so it's essential that you understand how they work as an architect, developer, or engineer. So let's jump in and get started.

      First, let's quickly step through some high-level concepts of Health checks. Health checks are separate from but are used by records inside Route 53. You don't create the checks within records. Health checks exist separately. You configure them separately. They evaluate something's health, and they can be used by records within Route 53.

      Health checks are performed by a fleet of health checkers, which are distributed globally. This means that if you're checking the health of systems which are hosted on the public internet, then you need to allow these checks to occur from the health checkers. If you think they're bots or exploit attempts and block them, then it will cause false alarms. Health checks, as I just indicated, are not just limited to AWS targets. You can check anything which is accessible over the public internet. It just needs an IP address.

      The checks occur every 30 seconds by default, or this can be increased to every 10 seconds at an additional cost. The checks can be TCP checks, where Route 53 tries to establish a TCP connection with the endpoint, and this needs to be successful within 10 seconds. You can have HTTP checks, where Route 53 must be able to establish a TCP connection with the endpoint within 4 seconds, and in addition, the endpoint must respond with an HTTP status code in the 200 range or 300 range within 2 seconds after connecting. And this is more accurate for web applications than a simple TCP check.

      And finally, with HTTP and HTTPS checks, you can also perform string matching. Route 53 must be able to establish a TCP connection with the endpoint within 4 seconds, and the endpoint must respond with an HTTP status code in the 200 or 300 range within 2 seconds, and Route 53 health checker, when it receives the status code, it must also receive the response body from the endpoint within the next 2 seconds. Route 53 searches the response body for the string that you specify. The string must appear entirely in the first 5,120 bytes of the response body, or the endpoint fails the health check. This is the most accurate because not only do you check that the application is responding using HTTP or HTTPS, but you can also check the content of that response versus what the application should do in normal circumstances.

      Based on these health checks, an endpoint is either healthy or unhealthy. It moves between those states based on its health, based on the checks conducted. Now lastly, the checks themselves can be one of 3 types. You can have endpoint checks, and these are checks which assess the health of an actual endpoint that you specify. You can use CloudWatch alarm checks, which react to CloudWatch alarms that can be configured separately and can involve some detailed in OS or in-app tests if you use the CloudWatch agent, which we cover elsewhere in the course. Finally, checks can be what's known as calculated checks, so checks of other checks. So you can create health checks which check application-wide health with lots of individual components.

      Now, you're going to get the opportunity to actually implement a health check in a demo lesson, which is coming up very shortly in this section of the course. But what I want to do before that is to just give you an overview of exactly how the console looks when you're creating a health check. So let's move across to the console.

      Okay, so we're at the AWS console, logged in to the general account in the Northern Virginia region. So to create a health check, we need to move to the Route 53 console, so I'm going to go ahead and do that. Remember how earlier in the theory component of this lesson, I mentioned how health checks are created externally from records? So rather than going into a hosted zone, selecting a record, and configuring a health check there, to create a health check, we go to the menu on the left and click on health checks. Then we'll click on create health check, and this is where we enter the information required to create the health check.

      First, we need to give it a name, so let's just say that we use the example of test health check. I mentioned that there are three different types of health checks. We've got an endpoint health check, and this checks the health of the particular endpoint. We can use the status of other health checks, so this is a calculated health check, and as I mentioned, this allows you to create a health check which monitors the application as a whole and involves the health status of individual application components, and then finally we can use the status of a CloudWatch alarm to form the basis of this health check.

      If we select endpoint for now, then you're able to pick either IP address or domain name. So you can specify the domain name of an application endpoint or you can use an IP address. If you pick domain name, then what this configures is that all of the Route 53 health checkers will resolve this domain name first and then perform a health check on the resulting IP address.

      Now, in either case, you've got the option of either picking TCP, which does a simple TCP check, in which case you need to specify either the IPv4 or IPv6 address together with a port number. If you choose to use the more extensive HTTP or HTTPS health check, then you're asked to specify the same IP address and port number, so that will be used to establish the TCP connection. You can also specify the host name, and if you specify that, it will pass this value to the endpoint as a host header. So if you've got lots of different virtual hosts configured, then this is how you can specify a particular host that the website should deliver.

      You're also able, because this is HTTP, you can specify a path to use for this health check. You can either specify the route path or you can specify a particular path to check. If you change this to HTTPS, then all of this information is the same, only this time it will use secure HTTP rather than normal HTTP.

      Now, if we scroll down and expand advanced configuration, it's here where you can select the request interval, so the default is every 30 seconds, or you can specify fast and have the checks occur every 10 seconds. Now, this is a check every 10 seconds from every health checker involved within this health check, so the actual frequency of the health checks occurring on the endpoint will be much more frequent. This is one check every 10 seconds from every health checker.

      You can specify the failure threshold, so this is the number of consecutive health checks that an endpoint must pass or fail for Route 53 to change the current status. So if you want to allocate a buffer and allow for the opportunity of the odd fail check not to influence the health state, then you can specify a suitable value in this box. It's here where you can specify a simple check, so HTTP or HTTPS, or you can elect to use string matching to do more rich checks of application health. So if you know that your application should deliver a certain string in the request body, then you can specify that here.

      Now, you can also configure a number of advanced options. One of them is the latency graph, so you can show the latency of the checks against this endpoint. You can invert the health check status, so if the health check of an application is unhealthy, you can invert it to healthy and vice versa. So this is a fairly situational option that I haven't found much use for.

      You also have the option of disabling the health check. This might be useful if you're performing temporary maintenance on an application, and if you check this box, then even if the application endpoint reports as unhealthy, it's considered healthy. You also get the option of specifying the health checker regions. You can use the recommended suggestion, and the health checkers will come from these locations, or you can select customize and pick the particular regions that you want to use. In most cases, you would use the recommended options.

      Now, if we just go ahead and enter some sample values here, so I'm going to use 1.1.1.1. I'm going to leave the host name blank, I'm going to set the port number to 80, and then I'll scroll down and just enter a search string. Again, we're not going to create this or just enter a placeholder, click on next, and it's here where you can configure what happens when the health check fails.

      Now, this is completely optional. We can use health checks within resource records only; we don't have to configure any notification, but if we do want to configure a notification, then we can create an alarm, and we can send this to either an existing or new SNS topic, and this is a method of how we can integrate this with other systems so we can have other AWS services configured to respond to notifications on this topic or we could integrate external systems so that when a health check fails, external action is taken. But this is what I wanted to show you. I just wanted to give you an overview of how it looks creating a health check within the console UI.

      Now, don't worry, you're actually going to be doing this in a demo lesson, which is coming up elsewhere in this section, but I wanted to give you that initial exposure to how the console looks when creating a health check. At this point, let's go ahead and finish up the theory component of this lesson by returning to the architecture. Now you've seen how a health check is created architecturally, health checks look something like this: let's assume that somewhere near the UK we have an application Catergram, and we point a Route 53 record at this application, so let's assume that this is Catergram.io. What we can do is to associate a health check with this resource record, and doing so means that our application will be health checked by a globally distributed set of health checkers. So each of these health checkers performs a periodic check of our application, and based on this check, they report the resource as healthy or unhealthy.

      If more than 18 percent of the health checkers report as healthy, then the health check overall is healthy, otherwise it's reported as unhealthy, and in most cases, records which are unhealthy are not returned in response to queries. Now, you're going to see throughout this section of the course and the wider course itself how health checks can be used to influence how DNS responds to queries and how applications can react to component failure. So Route 53 is an essential design and operational tool that you can use to influence how resolution requests occur and how they're routed through to your various different application components, and so understanding health checks is essential to be able to design Route 53 infrastructure, integrate this with your applications, and then manage it day to day as an operational engineer. So it's really important that you understand this topic end to end, no matter which stream of the AWS certifications that you're currently studying for.

      Now, that's everything that I wanted to cover in this video. Go ahead and complete the video, and when you're ready, I'll look forward to you joining me in the next.

    1. 1. remplacer l’abattement en faveur des contribuables ayantdes enfants majeurs mariés ou chargés de famille rattachés àleur foyer fiscal par l’attribution des mêmes parts de quotientfamilial que pour les jeunes célibataires (recommandationréitérée–ministère chargé du budget et des comptes publics) ;2. dès leur majorité, adresser un courrier d’information auxjeunes les incitant à activer leur espace numérique personnel(ministère chargé du budget et des comptes publics) ;3. harmoniser les modalités de déclaration des revenusdes jeunes faisant l’objet d’exonérations (ministèrechargé du budget et des comptes publics) ;4. promouvoir une sensibilisation à l’impôt afin de familiariserles jeunes, y compris avant leur majorité fiscale, aux grandsprincipes sur lesquels repose le système fiscal français(ministère chargé du budget et des comptes publics).185L’ENTRÉE DES JEUNES DANS L’IMPôT SUR LE REVENU
    2. La Cour formule les recommandations suivantes :1. prévoir des créneaux d’accompagnement à destinationdes jeunes, notamment les plus éloignés de la pratiquesportive, sur les équipements sportifs en accès libre (Agencenationale du sport, communes et intercommunalités) ;2. développer les partenariats entre les collectivités locales etles acteurs privés pour élargir l’accès, physique et financier,des jeunes à une plus grande variété d’équipementssportifs (communes et intercommunalités) ;3. accroître le taux de pratique des élèves en EPS enassurant un suivi statistique des inaptitudes et enajustant les séances en fonction des capacités des élèvesconcernés (ministère de l’éducation nationale) ;4. cibler davantage le Pass’sport sur les jeunes lesplus éloignés de la pratique sportive (ministère dessports, de la jeunesse et de la vie associative).33L’ACCÈS DES JEUNES AU SPORT
    3. synthèse analyse les principaux thèmes et idées clés issus des sources fournies, qui abordent divers aspects de la situation des jeunes en France (15-25 ans), notamment en matière de sport, de santé mentale, d'addictions, de citoyenneté, d'impôts et de justice pénale.

      1. Activité physique et sportive

      Manque de données et de ciblage: Il n'existe pas d'étude nationale ou locale sur l'occupation effective et les profils des utilisateurs des installations sportives extérieures, en particulier pour les 15-25 ans.

      Ce manque de données limite la capacité des acteurs publics à évaluer la performance de leurs dépenses et à cibler efficacement les jeunes.

      Difficulté d'association des jeunes: Les communes rencontrent des difficultés pour impliquer les jeunes en amont des projets sportifs.

      Les besoins sont souvent définis par les associations, qui ne représentent qu'une partie de cette tranche d'âge.

      Inaptitudes en EPS: Le volume des inaptitudes en éducation physique et sportive pourrait révéler une dégradation de l'état de santé global des élèves ou traduire des freins socio-culturels ou religieux. Il n'existe pas de suivi académique ou national des absences en EPS.

      Un arrêté de 1989 prévoit l'adaptation de la pratique en cas d'inaptitude partielle, soulignant la nécessité d'un suivi statistique et pédagogique, ainsi qu'une sensibilisation des médecins.

      Hétérogénéité des politiques sportives: Malgré des efforts récents, les politiques sportives peinent à cibler efficacement des publics hétérogènes.

      Structure du tissu associatif sportif: Une majorité des clubs sportifs sont de proximité (< 100 licenciés) et représentent une part significative des licenciés (66%), mais leur poids économique est plus faible (31%) comparé aux clubs intermédiaires et élite.

      2. Santé mentale et Maisons des Adolescents (MDA)

      Rôle essentiel des MDA: Les MDA jouent un rôle d'écoute, d'évaluation et d'orientation pour les jeunes en difficulté. L'accompagnement individuel permet de les diriger vers les solutions les plus adaptées.

      Des témoignages soulignent l'impact positif des MDA : "Depuis la première fois que je suis venue ici, tout a changé et en bien, les personnes qui m’ont suivie m’ont beaucoup aidée et montré les démarches à suivre pour mon cas." (une femme de 18 ans).

      Pluridisciplinarité et orientation sanitaire: Les MDA regroupent des professionnels de différentes disciplines (médecins, psychologues, infirmiers, éducateurs spécialisés), ce qui leur donne une orientation principalement sanitaire, complétée par une dimension socio-éducative.

      Principaux sujets évoqués par les jeunes en MDA: Le mal-être, l'estime de soi, l'anxiété sont les sujets les plus fréquemment abordés (72%), suivis des relations familiales (21%) et de la scolarité (10%).

      Manque de connaissance des MDA par les jeunes: Une part importante des jeunes ne sait pas ce qu'est une MDA : "À la question « savez-vous ce qu’est une MDA ? », 37 % « non pas du tout »."

      Accessibilité géographique inégale: L'implantation des MDA dans les grandes villes garantit l'accès à de nombreux jeunes, mais elles sont moins accessibles en zones rurales.

      Des initiatives "d'aller-vers" et des équipes mobiles se développent pour réduire les zones non couvertes.

      "Il faudrait qu’il y ait des MDA dans plus de villes car pas facile de faire 30 minutes de route quand on habite en campagne et qu’il n’y a pas de bus. Ou un bus itinérant" (parent d’une fille de 13 ans).

      Coordination avec d'autres structures: Une meilleure articulation et complémentarité entre les MDA et les

      Points d'Accueil et d'Écoute Jeunes (PAEJ) sont nécessaires pour une meilleure lisibilité pour le public et les partenaires.

      Financements en hausse mais hétérogènes: Les MDA sont principalement financées par les Agences Régionales de Santé (ARS), les départements et, marginalement, le bloc communal et les régions.

      Malgré une augmentation des financements suite aux Assises de la santé mentale et de la psychiatrie en 2021, l'hétérogénéité des modalités de pilotage et la souplesse du cahier des charges ne garantissent pas une harmonisation des ressources ni une offre proportionnée aux besoins des territoires.

      Nécessité d'évaluer l'impact des MDA: L'ANMDA est favorable à une évaluation scientifique de l'impact des MDA pour valoriser leurs résultats et identifier des axes d'amélioration.

      Augmentation des troubles psychiques: La pandémie et des facteurs à plus long terme (anxiété liée aux écrans, écoanxiété, violences) ont entraîné une augmentation des troubles psychiques chez les jeunes, avec une hausse notable des prescriptions de psychotropes, surtout chez les jeunes filles.

      Renforcement du dispositif "Mon soutien psy":

      L'assurance maladie contribue au renforcement du dispositif "Mon soutien psy" en augmentant le nombre de séances prises en charge et en facilitant l'accès direct aux psychologues.

      3. Addictions chez les jeunes

      Consommation en baisse chez les mineurs, préoccupante chez les majeurs: Une baisse de la consommation d'alcool et de cannabis est observée chez les mineurs depuis 2010, mais la consommation d'alcool et de drogues illicites chez les 15-24 ans en France reste supérieure à la moyenne de l'Union européenne.

      Estimation du nombre de jeunes souffrant d'addictions: Plusieurs méthodes d'estimation donnent des chiffres variables, soulignant la complexité de cette évaluation.

      L'OFDT estime qu'un pourcentage significatif des jeunes de 17 ans présente un risque élevé d'usage problématique ou de dépendance au cannabis.

      Risques liés à la consommation de cannabis: L'usage précoce et régulier de cannabis est un facteur de risque de troubles psychiques et socio-comportementaux.

      Sous-dimensionnement des dispositifs spécifiques:

      Les Consultations Jeunes Consommateurs (CJC), dispositif spécifique aux jeunes, semblent sous-dimensionnées malgré leur utilité et pertinence reconnues par les ARS. "Enfin le seul dispositif spécifique aux jeunes - celui des « consultations jeunes consommateurs » (CJC), aujourd’hui au nombre de 260 (réparties en 540 points d’accueil) - paraît sous-dimensionné." Une évaluation nationale des CJC est nécessaire pour envisager leur développement.

      Manque de coordination et de données financières: Le financement des activités hospitalières liées aux addictions chez les jeunes manque de lisibilité, et il est difficile de calculer les coûts d'hospitalisation spécifiques.

      Il manque également un état des lieux national agrégé de l'offre médico-sociale destinée aux jeunes en état de dépendance.

      Stratégie interministérielle sans objectifs chiffrés clairs:

      La stratégie interministérielle de mobilisation contre les conduites addictives manque d'objectifs de santé publique clairs, notamment en termes de diminution de la consommation d'alcool et de drogues chez les jeunes.

      Importance de la prévention et de l'orientation: Les structures existantes devraient davantage jouer leur rôle de prévention et d'orientation, avec le soutien des ARS.

      Exemples internationaux de prévention: Les exemples du Danemark (programme MOVE) montrent l'efficacité d'une mobilisation de tous les acteurs autour d'un programme de prévention ambitieux.

      Débats autour de la légalisation du cannabis: La légalisation ou la dépénalisation du cannabis dans d'autres pays a souvent entraîné une hausse de sa consommation, avec des conséquences potentielles pour la lutte contre les addictions.

      4. Journée Défense et Citoyenneté (JDC)

      Évolution du contexte et des enjeux: Le contexte international actuel et la montée en puissance du Service National Universel (SNU) renouvellent les enjeux de la JDC, qui mérite d'être redéfinie. "Or, les contextes sociaux, nationaux et internationaux ont évolué depuis l'instauration de cette journée... ont renouvelé les enjeux de la JDC, qui mérite donc d'être redéfinie."

      Objectifs multiples et tensions: La JDC est à la fois un temps fort obligatoire du parcours citoyen, un outil de maintien du lien entre l'armée et la jeunesse, un moyen de rappeler le devoir de défense, et potentiellement un outil de recrutement pour les armées.

      La volonté du ministère des armées de "remilitariser" la JDC en l'orientant davantage vers le recrutement et le recensement des compétences s'écarte potentiellement de l'équilibre initial entre Défense et Citoyenneté.

      Recrutement : objectif de plus en plus assumé: Bien que le recrutement ne soit pas un objectif explicite du code du service national pour la JDC, les armées le considèrent indispensable pour atteindre leurs objectifs d'engagement. Une étude a établi une corrélation entre la participation à la JDC et le recrutement dans l'armée de terre.

      Adaptation et expérimentation de la JDC: Des évolutions sont en cours, avec une "JDC adaptée" et un projet de "JDC Nouvelle Génération" qui pourraient transformer profondément le contenu et les objectifs du dispositif.

      JDC en ligne pour les Français de l'étranger: La mise en place d'une organisation et la mobilisation des moyens nécessaires au déploiement de la JDC en ligne pour les jeunes Français résidant à l'étranger est une recommandation.

      Bilan mitigé du test d'illettrisme: Le test d'illettrisme réalisé lors de la JDC ne répond pas pleinement aux objectifs du ministère des armées et empiète sur le temps disponible pour les autres contenus.

      Sa suppression pourrait être envisagée sous réserve de modifications législatives.

      5. Les jeunes et l'impôt

      Entrée progressive dans l'impôt: L'entrée des jeunes dans l'impôt est marquée par des disparités de revenus et dépend de plusieurs facteurs (études, charge de famille, âge).

      Le rattachement au foyer fiscal des parents ou l'imposition distincte constituent une spécificité française.

      Dispositifs atténuant l'impôt: Plusieurs dispositifs (exonérations, déductions, crédits d'impôt) atténuent l'impôt dû par les jeunes et leurs familles, notamment en lien avec les études, l'apprentissage, les stages et certaines formes de volontariat.

      Accès aux informations fiscales: La Direction Générale des Finances Publiques (DGFiP) facilite l'accès des jeunes à leur espace particulier sur impots.gouv.fr, notamment par l'envoi d'un courrier dès l'âge de 20 ans.

      Imposition des jeunes mariés: Des règles spécifiques s'appliquent à l'imposition des jeunes mariés ou pacsés qui peuvent être rattachés au foyer fiscal de l'un ou l'autre de leurs parents sous forme d'abattement.

      6. Les jeunes et la justice pénale

      Rupture de la réponse pénale à la majorité: La réponse pénale face aux jeunes de 15 à 25 ans n'est pas homogène, marquée par une rupture lors du passage à la majorité. La justice des mineurs privilégie l'éducatif et l'individualisation, tandis que les jeunes majeurs relèvent du droit commun avec des peines de prison plus fréquentes. "Face aux jeunes de 15 à 25 ans, la réponse pénale n’est pas homogène... la réponse marque une rupture, les « jeunes majeurs » basculant dans le droit commun des procédures et des conditions d’exécution des peines."

      Évolution de la délinquance des mineurs: Si le nombre de mineurs condamnés pour crimes a diminué, la direction de la protection judiciaire de la jeunesse fait état d'une tendance à la hausse du nombre et des durées d'incarcération pour des faits d'une extrême violence.

      Atténuation de la responsabilité pénale des mineurs: Le code de la justice pénale des mineurs prévoit l'atténuation de la responsabilité des mineurs en fonction de leur âge et de leur discernement.

      Alternatives aux poursuites pour les mineurs: La réponse pénale privilégie davantage les alternatives aux poursuites pour les mineurs que pour les jeunes majeurs.

      Stabilité du taux de récidive: Le taux de jeunes de 15 à 25 ans condamnés en état de récidive ou de réitération légales reste stable autour de 45%, soulignant que la politique à l'égard des jeunes délinquants n'atteint pas pleinement ses objectifs de prévention de la récidive.

      Difficultés d'insertion des jeunes sortant de prison: Des études mettent en lumière les difficultés de santé mentale et d'insertion rencontrées par les jeunes sortant de prison.

      Nécessité de renforcer l'accompagnement et la coordination: L'action des services du ministère de la justice doit être articulée avec celle des autres acteurs (éducation, collectivités territoriales, aide sociale à l'enfance) et l'accompagnement des parents doit être renforcé. "Le principe de responsabilité parentale est inscrit dans le droit positif... C’est d’ailleurs en vertu de ce principe que l’aide sociale à l’enfance et la protection judiciaire de la jeunesse interviennent dans le milieu familial pour conforter, en premier lieu, le rôle des parents."

      Conseil des droits et devoirs des familles (CDDF): Ce dispositif de prévention de la délinquance, visant à impliquer les parents, a vu son instauration obligatoire dans les grandes communes supprimée en 2019.

      7. Éducation Artistique et Culturelle (EAC)

      Importance de l'accès à l'EAC: L'éducation artistique et culturelle est reconnue comme essentielle pour le développement des jeunes.

      Périmètre de l'EAC: Le périmètre de l'EAC s'étend au-delà des arts et lettres pour inclure la culture scientifique, le numérique et les médias.

      Organisation territoriale complexe: La gouvernance territoriale de l'EAC, basée sur des comités de pilotage régionaux et locaux, est mise en œuvre de manière diverse selon les territoires.

      8. Préoccupations des jeunes

      Inégalités sociales et environnement: Les préoccupations majeures des jeunes portent sur les inégalités sociales et les questions environnementales.

      Enjeux de sécurité: Les enjeux de sécurité, de liberté, de propriété et de résistance à l'oppression restent importants.

      En conclusion, ces sources mettent en évidence la complexité des enjeux liés à la jeunesse en France.

      Des efforts sont déployés dans de nombreux domaines, mais des défis persistent en termes de connaissance des publics, de ciblage des politiques, de coordination des acteurs, d'évaluation des dispositifs et d'adaptation aux évolutions sociétales.

      Une approche globale et concertée est nécessaire pour répondre efficacement aux besoins et aux aspirations des jeunes.

    1. Reviewer #2 (Public Review):

      Summary:

      Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

      Strengths:

      Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

      Weaknesses from the original round of review:

      The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding.

      In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

      Altogether, the major issues of this study remain unresolved:

      (1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

      (2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

      [Editors' note: The resubmitted paper was minimally revised, and many of the initial concerns remain unresolved.]

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

      Learn more at Review Commons


      Reply to the reviewers

      Manuscript number: RC-2025-02860

      Corresponding author(s): Duncan, Sproul

      [The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

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      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

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      If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      We thank the reviewers for recognizing that our work contributes 'both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development' and their insightful suggestions to improve the manuscript. We note that the reviewers suggest that the data are 'comprehensive', 'well-controlled', 'rigorously done' and 'diligently analysed'.

      Our planned revisions focus on further elucidating the broader implications of our findings for partially methylated domain formation in cancer, the effects of the methylation changes we observe on gene expression and the potential mechanisms underpinning the formation of the hypermethylated domains we observe.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

      We have reproduced the reviewer's comments in their entirety and highlighted them in blue italics.

      February 21, 2025*RE: Review Commons Refereed Preprint #RC-2025-02860 *

      *Kafetzopoulos *

      DNMT1 loss leads to hypermethylation of a subset of late replicating domains by DNMT3A

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

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

      The DNA methylation landscape is frequently altered in cancers, which may contribute to genome misregulation and cancer cell behavior. One phenomenon is the emergence of "partially methylated domains (PMDs)": intermediately methylated regions of the genome that are generally heterochromatic and late replicating. The prevailing explanation is that the DNA methyltransferase, DNMT1, is not able to maintain DNAme levels at late replicating sites in proliferating cancer cells. This could result in genome instability. In this study, Kafetzopoulos and colleagues interrogated this possibility using a common laboratory colorectal cancer cell line (HCT116). Additionally, they utilized a DNMT1 mutant line that they refer to as a knockout, even though, more accurately, it is a hypomorphic truncation. They performed several genomic assays, such as whole genome bisulfite sequencing, ChIP and repli-seq, in order to assess the effect of reduced DNMT1 activity. While expectedly, global DNAme levels are decreased, they discovered a subset of PMDs gain DNA methylation, which they term hyperPMDs. There seems to be no impact on DNA replication timing, but the authors did go on to show that the de novo DNA methyltransferase, DNMT3Α, is likely responsible for this counterintuitive increase in DNAme levels.

      *Reviewer #1 (Significance (Required)): *

      Overall, I found the data well-presented and diligently analyzed, as we have come to expect from the Sproul group. However, I am somewhat at a loss to understand both the rationale for the experimental set-up and the meaning of the results. The HCT116 cell line is already transformed but was treated as though it was a wild-type control. I was more curious to see how the PMD chromatin state and replication compare to a healthy cell.

      We focused on the comparison between WT and DNMT1 KO cells as we wanted to understand the role of DNMT1 in maintaining the organisation of the cancer methylome. We agree that, strictly, this could differ from its role in normal cells. However, we are unaware of a suitable cell line to test the consequences of DNMT1 KO in normal colon cells and testing this in vivo would be beyond the time-scale of a manuscript revision.

      To further understand the relevance of our findings in the context of carcinogenesis, we propose to analyse data derived from normal and cancerous colon tissue in the revised manuscript. Preliminary analysis shows that HCT116 PMDs are hypomethylated in a colorectal tumour but not in the normal colon (revision plan figure 1). This suggests that HCT116 cells are a model that can be used to understand PMD formation in tumours and we will extend this analysis in the revised manuscript. We will also add discussion of the caveat that DNMT1 may function differently in normal tissues and cancer cells.

      Note, revision plan figure 1 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 1. HCT116 PMDs are hypomethylated in colorectal tumours. Heatmaps and pileup plots of HCT116, normal colon and colorectal tumour DNA methylation levels for HCT116 PMDs (n=546 domains) and HMDs (n=558 domains). DNA methylation levels are mean % mCpG. PMDs and HMDs are aligned and scaled to the start and end points of each domain and ranked based on their mean methylation levels in HCT116 cells. Colon and tumour data re-analysed from a previous publication (Berman et al 2011, PMID: 22120008).

      Moreover, the link between late replication and PMDs would indicate that a DNMT1 gain-of- function line would potentially be more interesting: could more increased DNMT activity rescue the PMDs, and how would this impact the chromatin and replication states? Perhaps this is not trivial to create; I do not know if simply overexpressing DNMT1 and/or UHRF1 could act as a gain-of-function.

      We agree with the reviewer that a DNMT1 overexpression or a gain-of-function mutation cell line would be interesting to analyse and potentially informative as to the mechanism of PMD formation. However, as the reviewer notes, this is a complex experiment that could require the overexpression of partners such as UHRF1 or generation of an unknown gain-of-function mutation. In addition, the full dissection of the implications of this separate experimental strategy would entail the repetition of the majority of our experiments in DNMT1 KO cells. Instead, in the revised manuscript, we will focus on a related experiment suggested by reviewer 2 and ask whether re-expression of DNMT1 rescues DNA methylation patterns DNMT1 KO cells.

      Nevertheless, the appearance of hyperPMDs was a curious finding worth publishing. However, it is unclear what the biological relevance is. There is no effect on replication timing, and no assessment on cell behavior (eg, proliferation assays).* In other words, is DNMT3A performing some kind of compensatory action, or is it just a curiosity? Below in the significance section, I have highlighted some additional specific points *

      PMDs are important to study because cancer-associated hypomethylation is believed to drive carcinogenesis through genomic instability (Eden et al 2003, PMID: 12702868). However, the mechanisms underpinning their formation remain unclear. At present the predominant hypothesis is that PMDs emerge in heterochromatin because their late replication timing leaves insufficient time for re-methylation following DNA replication (Zhou et al 2018, PMID: 29610480 and Petryk et al 2021, PMID: 33300031). We believe that our observations of hypermethylated PMDs in DNMT1 KO cells provides important evidence contrary to this hypothesis because they disconnect domain-level methylation patterns from the replication timing program. Our work instead suggests that the localization of de novo DNMTs plays a key role in the formation of PMDs by protecting euchromatin from hypomethylation.

      To further explore this hypothesis, we propose to analyze data derived from tumours in our revised manuscript to understand the degree to which our findings are reflected in vivo. As shown above, our preliminary analysis suggests that HCT116 cell PMDs are also hypomethylated in a colorectal tumour but not the normal colon (revision plan figure 1). We will also analyze how the changes in methylome affect gene expression using our RNA-seq data.

      - Why were DNMT3A and 3B transgenes used for ChIP instead of endogenous proteins? I know the authors cited work justifying this strategy, but this still merits explanation. Also, the expression level of transgenes compared to the endogenes was not shown (neither protein nor RNA level).

      DNMT3A and B transgenes were used because antibodies against the endogenous proteins are not suitable for ChIP. Furthermore, performing these experiments using endogenously tagged proteins, required generating 3 knock-in tagged lines (we have already generated HCT116 cells with tagged DNMT3B, Masalmeh et al 2021, PMID: 33514701).

      We have previously shown that our constructs do indeed result in overexpression of DNMT3B compared to endogenous protein in this system (Masalmeh et al 2021, PMID: 33514701). However, our previous results also demonstrate that overexpressed DNMT3B recapitulates the localization of the endogenously tagged protein to the genome (Taglini et al 2024, PMID: 38291337). Others have similarly demonstrated that ectopically expressed DNMT3A and DNMT3B can be used to understand their localization on the genome (Baubec et al 2015, PMID: 25607372 and Weinberg et al 2019, PMID: 31485078).

      To address this point, we propose to add further justification of our approach and discussion of this potential limitation to a revised version of the manuscript.

      - The DNMT3A binding profile appears as though it is on the edges of the PMDs and fairly depleted within (Fig 4A,D). Could the authors comment on this?

      This is an interesting point. We note that although mean DNMT3A signal is indeed higher at the edges of hypermethylated PMDs than inside these domains, its levels are both above background and the levels observed in HCT116 cells. As suggested by reviewer 3, this could be consistent with H3K36me2 and DNMT3A spreading in from the boundaries of hypermethylated PMDs in DNMT1 KO cells. We propose to add discussion of this possibility to the revised version of the manuscript.

      - A more compelling experiment would be to assess the loss of DNMT3A genetically. How would this affect PMD DNA methylation? Maybe in this case there would be an effect on replication timing. Could a KO or KD (eg, siRNA) strategy be employed to assess this on top of either the HCT116 or DNMT1 KO.

      As the reviewer suggests, functional experiments aimed at understanding the role of DNMT3A in our system are likely to be informative. We therefore propose to include such experiments in a revised version of the manuscript.

      - What is the major H3K36me2 methylatransferase in these cells? Could an Nsd1 KO or KD strategy be used, for example, to show that indeed H3K36 methylation is required for HyperPMDs? This would complement the DNMT3A experiment above.

      H3K36 methylation is thought to be deposited in the mammalian genome by at least 8 different methyltransferase enzymes, NSD1, NSD2, NSD3, ASH1L, SETD2, SETMAR, SMYD2 and SETD3 (Wagner and Carpenter 2023, PMID: 22266761). To understand which of these might be responsible for the deposition of H3K36me2 in hypermethylated PMDs, we have examined their expression in HCT116 and DNMT1 KO cells using our RNA-seq data. This suggests that 5 of these enzymes are highly expressed in HCT116 cells and their expression levels are similar in DNMT1 KO cellsrevision plan figure 2). The other 3 putative methyltransferases have lower expression levels and, although SMYD2 is significantly upregulated in DNMT1 KO cells, its expression remains low (revision plan figure 2). It is currently unclear whether SMYD2 is a bona fide H3K36 methyltransferase (Wagner and Carpenter 2023, PMID: 22266761). We also note that in a recent study, cells lacking NSD1, NSD2, NSD3, ASH1L and SETD2 had no detectable H3K36 methylation, although expression levels of SMYD2 were not reported (Shipman et al, 2024. PMID: 39390582). Based on this analysis, it is therefore unclear which enzyme(s) might be responsible for H3K36me2 deposition in hypermethylated PMDs and delineation of this enzyme would require multiple perturbation and sequencing experiments. We therefore suggest that assessing the consequences of knocking out H3K36me2 methyltransferase activity on hypermethylated PMDs is beyond the scope of a manuscript revision. We propose to include discussion of the expression of the different H3K36me2 depositing enzymes in the revised manuscript.

      Note, revision plan figure 2 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 2. HCT116 cells express multiple H3K36 methyltransferases. Barplot of mean expression levels for putative mammalian H3K36 methyltransferases in HCT116 and DNMT1 KO cells. Expression levels are counts per million (CPM) derived from RNA-seq. Mean expression levels are derived from 9 and 4 independent cultures of HCT116 and DNMT1 KO cells respectively.

      - Based on Figure 2C, it seems that a general predictive pattern of hyperPMDs is H3K9me3-enriched and H3K27me3-depleted. Is this an accurate interpretation? Given the authors' expertise in the relationship between DNMT3A and polycomb, could they perhaps give an explanation for this phenomenon?

      The reviewer is correct. In HCT116 cells, those PMDs that become hypermethylated in DNMT1 KO cells are marked by H3K9me3 and are H3K27me3-depleted (except at their boundaries). DNMT3A is recruited to polycomb-marked regions associated with H3K27me3 through interaction of its N-terminal region with H2AK119ub. However, this mark is depleted from hypermethylated-PMDs in DNMT1 KO cells (current manuscript Figure S5D) meaning that this pathway of recruitment is unlikely to explain DNMT3A's localisation to these regions in DNMT1 KO cells. This is discussed in the current manuscript:

      We and others have reported that DNMT3A is also recruited to the polycomb-associated H2AK119ub mark through its N-terminal region (Chen et al, 2024; Gretarsson et al, 2024; Gu et al, 2022; Wapenaar et al, 2024; Weinberg et al, 2021). However, we do not observe the polycomb-associated H3K27me3 mark, which is generally tightly correlated with H2AK119ub (Ku et al, 2008), at hypermethylated PMDs suggesting that H2AK119ub does not play a role in the recruitment of DNMT3A to these regions.

      Furthermore, DNMT3A's localisation is predominantly driven by its PWWP-dependent H3K36me2 recruitment pathway unless its PWWP domain is mutated (Heyn et al 2019, PMID: 30478443, Sendžikaitė et al 2019, PMID: 31015495, Kibe et al 2021, PMID: 34048432 and Weinberg et al, 2021, PMID: 33986537). Our observations of DNMT3A at hypermethylated PMDs marked by H3K36me2 is therefore consistent with previous findings. We propose to discuss this point in the revised manuscript.

      - This is a minor point, but calling the DNMT1 mutant a "KO" seemed a bit misleading, as it is a truncation mutant. Perhaps there is a more accurate way to describe this line.

      We propose to amend the manuscript to reflect this point as suggested by the reviewer. To ensure our responses are consistent with the reviewer comments we continue to refer to this line as DNMT1 KO cells in our revision plan.

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

      *In this study, Kafetzopoulos et al. investigated the role of DNMT1-mediated methylation maintenance in cancer partially methylated domains (PMDs) using DNMT1 knockout HCT116 colorectal cancer cells. They used a range of sequencing-based approaches, including whole-genome bisulfite sequencing (WGBS), chromatin immunoprecipitation sequencing (ChIP-seq), and replication timing sequencing (Repli-seq), to define the dynamics of DNA methylation loss and gain in PMDs during DNA synthesis. Interestingly, they demonstrate that specific PMDs marked by H3K9me3 undergo a gain of DNA methylation in DNMT1-deficient HCT116 cells. This increase in methylation is associated with the loss of H3K9me3, an enrichment of H3K36me2, and the recruitment of DNMT3A. These findings suggest that de novo methyltransferase activity plays a critical role in determining which genomic regions become PMDs in cancer. *

      *The authors use a comprehensive and well-controlled set of sequencing-based techniques. While the sequencing depth for DNA methylation is somewhat limited, the inclusion of multiple biological replicates strengthens the reliability of the data. The study effectively integrates multiple layers of epigenomic information, providing a nuanced view of PMD regulation in the context of DNMT1 loss. *

      *Overall, this paper provides valuable insights into the epigenetic regulation of PMDs in cancer, and its conclusions are well supported by the data. It significantly advances our understanding of how DNMT1 loss reshapes the epigenome and highlights the interplay between de novo and maintenance methylation mechanisms in cancers. *

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

      *Reviewer #2 (Significance (Required)): *

      General assessment

      -The main strength of the study lies in the clear presentation of the data, which follows a cohesive and well-defined storyline.

      *-The authors demonstrate that both hypomethylated and hypermethylated domains occur at the late replication stage. They further investigate the dynamics of histone modifications and DNA methylation, focusing on the acquisition and loss of these marks, particularly in relation to DNMT3A and DNMT3B. *

      Limitation

      -Although the study is compelling, its primary limitation is the correlative nature of most of the data. While the high-level representations (e.g., tracks, heat maps) are convincing, the study would have been more informative if it had explored the impact of these changes on a specific set of genes or regions critical to cancer initiation and progression. For example, in the DNMT1 knockout model, how does the loss of H3K9me3, the gain of H3K36me2, and the recruitment of DNMT3A in hypermethylated PMDs affect the expression of key genes involved in colorectal cancer?

      To understand how the remodeling of DNA methylation and chromatin structure in DNMT1 KO cells affects gene expression, we propose to include an analysis of our RNA-seq data in the revised manuscript. We will also cross reference these results and our ChIP-seq with lists of colorectal cancer genes.

      Additional experiments that could provide deeper insights

      -Cross-validation in other cancer cell lines would have enable to define if these signatures are observed beyond HCT116.

      As the reviewer suggests, we propose to undertake analyses of additional samples in the revised manuscript to understand how our findings relate to domain-level methylation patterns beyond HCT116 cells. As noted above in response to reviewer 1, our preliminary analysis suggests our findings are relevant for primary colorectal tumours (revision plan figure 1).

      -Are the observed signatures permanent, or could they be reversed by reinstating the full activity of DNMT1? Since DNMT1 might be dysregulated but never completely deleted.

      To address this suggestion, we propose to include the results of a DNMT1 rescue experiment in the revised manuscript.

      -Use knockdown and overexpression experiments to track the dynamics and occurrence of these molecular events over time, providing insight into the progression and reversibility of epigenetic changes.

      This is an interesting suggestion. As the reviewer suggests, we propose to analyse data derived from time-course experiments to understand the dynamics of changes in different genomic compartments following perturbation of DNMT1.

      Advances

      -The study provides new insights into the establishment of PMD types in colorectal cancer cell lines.

      -These findings contribute both conceptually and mechanistically to our understanding of how DNA methylation patterns are regulated during cancer development.

      Audience:

      -This study will appeal to a broad audience, from researchers primarily focused on epigenetics and cancer biology to those interested in the mechanistic underpinnings of DNA methylation and its role in cancer progression. It will also be relevant to those exploring therapeutic strategies targeting epigenetic regulators in cancer.

      We thank the reviewer for their kind comments on our manuscript.

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

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

      Summary:*Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells. *

      General comments:The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C- D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

      The reviewer makes an interesting point about the potential for H3K27me3 to act as a boundary preventing H3K36me2 spread into PMDs. Multiple studies have shown that H3K36me2 restricts H3K27me3 deposition in the genome (Streubel et al 2018, PMID: 29606589, Shirane et al 2020, PMID: 32929285 and Farhangdoost et al 2021, PMID: 3362635). The structural nature of this inhibitory effect has also been resolved, demonstrating that the PRC2 catalytic subunit, EZH2 directly binds H3K36 and this is inhibited when the residue is methylated (Jani et al 2019, PMID: 30967505, Finogenova et al 2020, PMID: 33211010 and Cookis et al 2025, PMID: 39774834). The effect of H3K27me3 on H3K36me2 is less well characterised. However, previous work has suggested that inhibiting EZH2 leads to elevated H3K36me2 being established on newly replicated chromatin (Alabert et al 2020, PMID: 31995760). Expression of the EZH2-inhibiting oncohistone H3.3K27M has also been reported to lead to increased H3K36me2 dependent on NSD1/2 in diffuse intrinsic pontine gliomas (DIPG) (Stafford et al 2018, PMID: 30402543 and Yu et al 2021, PMID: 34261657). However, this increase was not reported by an independent study of H3.3K27M DIPG cells (Harutyunyan et al 2020, PMID: 33207202) and the molecular basis of the effect of H3K27me3 on H3K36me2 remains unclear.

      As the reviewer suggests, we propose to explore the relationship between H3K27me3 and H3K36me2 further in a revised manuscript along with the including further discussion of previous findings in this area.

      Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

      The relationship between H3K36me2 and H3K9me3 is far less explored than that of H3K27me3 and H3K36me2. Interestingly, we note that a recent study reported that depletion of H3K36me2 results in H3K9me3 re-distribution suggesting that H3K9me3 is restricted by H3K36me2 (Padilla et al 2024, DOI: 10.1101/2024.08.10.607446, also cited in the original manuscript).

      To understand this relationship further, we therefore propose to explore the relationship between H3K9me3 and H3K36me2 in our datasets as part of revised manuscript along with including additional discussion of relevant experimental findings.

      In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

      In accordance with the reviewer's suggestion, we propose to re-organise the revised manuscript to make it easier to follow.

      Specific Comments/Questions:

      • An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context**
* As suggested by the reviewer, we will amend the manuscript to include an expanded discussion of the DNMT1 truncation present in the cell line.

      • Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?**
*

      While differences between cancer cell line and tumour methylation patterns have previously been noted (for example Anne Rogers et al 2018, PMID: 30559935), we have previously demonstrated that HCT116 cells recapitulate CpG island methylation patterns observed in colorectal tumours (Masalmeh et al 2021, PMID: 33514701). As stated above in response to reviewer 1, we have now examined the methylation status of HCT116 PMDs in a colorectal tumour. This analysis shows that HCT116 PMDs have reduced methylation levels in a colorectal tumour but not in the normal colon (revision plan figure 1). We propose to extend this analysis of colorectal tumour samples and add them to the revised manuscript to address this point.

      Regarding the expression of DNMTs in colorectal tumours, DNMT1 is ubiquitously expressed to our knowledge. DNMT3B is reported to be overexpressed in 15-20% of cases of colorectal cancer, often as a result of amplification (Nosho et al 2009, PMID: 19470733, Ibrahim et al 2011, PMID: PMID: 21068132, Zhang et al 2018, PMID: 30468428 and Mackenzie et al 2020, PMID: 32058953). DNMT3A expression in colorectal tumours is less studied but one report suggests upregulation in at least some tumours (Robertson et al 1999, PMID: 10325416 and Zhang et al 2018, PMID: 30468428). We propose to add additional discussion of DNMT expression in colorectal cancer to the revised manuscript to clarify the degree to which our results reflect methylation regulation in primary colorectal tumours.

      • Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.**
*

      As part of our work towards revising the manuscript, we have undertaken blots of DNMT3A in our cell lines. This shows that DNMT3A levels in DNMT1 KO cells are similar to those in HCT116 cells which (revision plan figure 3). We propose to include this in the revised manuscript alongside a similar analysis of DNMT3B. We will also include an analysis of T7-DNMT3A and T7-DNMT3B levels to understand whether they are expressed to similar levels in HCT116 and DNMT1 KO cells.

      Note, revision plan figure 3 was included with the full submission but cannot be uploaded in this format.

      Revision plan figure 3. DNMT3A protein levels are similar in HCT116 and DNMT1 KO cells. Left, representative DNMT3A Western blot. Right, bar plot quantifying relative DNMT3A levels. The bar height indicates the mean levels observed in protein extracts from 3 independent cell cultures. Individual points indicate the level of each replicate.

      • Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time?*

      The reviewer makes an interesting point with regard to a potential alteration of DNMT3A residence at hypermethylated PMDs. Given that ChIP-seq signal is affected by residence time (Schmiedeberg et al 2009, PMID: 19247482), it is possible that our findings could reflect this rather than increased DNMT3A localisation. We propose to add discussion of this point as a limitation of the current study to the manuscript.

      It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).*
*

      As the reviewer suggests, we will include the data on HMDs to the main Figure 4 and include enrichments at all PMDs in the supplementary figures.

      • It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.*

      We had shown a single locus for consistency and to not overcomplicate figures which already contain multiple panels. As the reviewer suggests, we will add additional loci in the supplementary figures of our revised manuscript. We had also included the chromosome co-ordinates in the figures. In the revised version we will ensure that the precise co-ordinates are included in the legends.

      • The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.**
*

      We thank the reviewer for this point and we propose to examine the quantification of the ChIP-seq without normalizing to input to ensure that uneven input signal does not substantially contribute to our results.

      • Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.*

      We will clarify the explanation of n numbers in the revised manuscript.

      *Reviewer #3 (Significance (Required)): *

      This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

      We thank the reviewer for their insightful comments and believe that our proposed revisions will further clarify the points they raise.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      We have not yet incorporated revisions into the manuscript.

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      As stated in our responses to the reviewer comments above, we plan to address all comments. However, we suggest that two experiments proposed by the reviewers are beyond the scope of a manuscript revision and we will instead address these comments in the following manner:

      Analysis of a DNMT1 gain-of-function line (Reviewer 1). As suggested by the reviewer such a line is non-trivial to generate. It would also require extensive profiling of this new line to fully understand its implications for our findings. We therefore believe it is outwith the scope of a manuscript revision. Instead, we propose to address this comment by undertaking the related experiment suggested by Reviewer 2 and perform a DNMT1 rescue experiment in the DNMT1 KO line. Analysis of H3K36me2 methyltransferase knockout cells (Reviewer 1). Our preliminary analysis suggests that HCT116 cells express multiple H3K36 methyltransferases and that their expression does not vary greatly in DNMT1 KO cels (revision plan figure 2). This means that it is unclear which enzyme(s) might be responsible for depositing H3K36me2 in hypermethylated PMDs. Delineation of this would require the generation and analysis of multiple knockouts and we suggest it is therefore outwith the scope of a manuscript revision. To address this point we will instead include discussion of the spectrum of H3K36 methyltransferases expressed in our cells in the revised manuscript as detailed in the specific response above.

    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Cancer is linked to the acquisition of an atypical DNA methylation landscape, with broad domains of partial DNA methylation (termed PMDs). This study investigates PMDs in a colorectal cancer cell line and evaluates the contribution of DNMT1 in maintaining PMDs, using a DNMT1 KO line. The authors find that PMDs preferentially lose DNA methylation upon loss of DNMT1, but they find a number of domains that paradoxically gain DNA methylation (hyperPMDs). They attribute this gain of methylation to the action of DNMT3A through the accumulation of H3K36me2 and loss of heterochromatin mark H3K9me3. Together this work sheds light on the dynamic mechanisms regulating the atypical DNA methylation landscape in colorectal cancer cells.

      General comments:

      The introduction is informative and well written. Additionally, the work is rigorously done and analyses are clear. However, the conclusions and summary figure largely focus on the relationship between PMDs with H3K9me3 and H3K36me2, but I think the role for H3K27me3 should be revisited based on the results presented. H3K9me3 is present at PMDs and hyperPMDs, but H3K27me3 level appears to be a much more defining feature of whether they lose or gain methylation upon loss of DNMT1 (Figure 2, Figure S2C-D). There is a reported interplay between PRC2 and DNMT3A activity at DNA methylation valleys in other cell contexts (e.g., mouse embryogenesis, hematopoietic cells), so couldn't H3K27me3 be performing a 'boundary' function at PMDs and when sufficiently low, permits spread of H3K36me2 in the absence of DNMT1? I think it is worth further exploring the H3K27me3 data.

      Additionally, a key point that is illustrated in the summary figure, is the localization of H3K36me2 at HMDs and its mutual exclusivity with H3K9me3 (a mark typically associated with high DNA methylation). However, because the H3K36me2 is introduced quite late in the analysis, I feel that a rigorous evaluation of its enrichment and anti-correlation with H3K9me3 at highly methylated domains (HMDs) is missing.

      In general, I also found that I was jumping between figures a lot and needed to look at the supplements to gain the full picture. It may be beneficial to re-organize the figures.

      Specific Comments/Questions:

      1. An expanded explanation of the truncated DNMT1 in the DNMT1 KO cells would be helpful for context.
      2. Does the DNMT expression in HCT116 cells reflect the levels seen in primary colorectal cancers? Hence, do you think these cultured cells reflect aspects of DNA methylation dynamics that would be seen in tumors?
      3. Although DNMT3A/B mRNA levels are similar between DNMT1 KO and HCT116 cells, is the protein abundance altered? I think there would be value in showing a Western blot analysis, as the loss of DNMT1 protein may alter the stability of the de novo DNMTs. Is a similar level of expression of the ectopic T7-DNMT3A and T7-DNMT3B achieved in HCT116 and DNMT1 KO cells? A western blot showing this would also be valuable.
      4. Do you think that the increase in DNMT3A over HyperPMD compared to H3K9me3-marked PMDs is related to an increase in protein bound at these domains or an altered residence time? It would also be valuable to move the plot showing levels of DNMT3A/3B at HMDs, from the S4C/D to the main Figure 4, for reference. It would also be interesting to see the enrichment of DNMT3A/B at all PMDs (not just H3K9me3-marked PMDs).
      5. It appears that the same genomic locus is used multiple times across figures Fig 1A, Fig 2B, Fig 3A, Fig 4A, Fig 5B to illustrate the trends reported from the global analyses. While this has value in showing the dynamics across datasets at this region, I think it is important to illustrate that these trends can be observed elsewhere. Please add or replace some plots with additional loci. Furthermore, please add the genomic region coordinates to the figure or figure legend.
      6. The ChIP-seq data is quantified as IP/input. This quantitation can be prone to introducing artefacts into analyses if the input coverage is substantially uneven over AT-rich regions or CpG islands, or if the sequencing depth is insufficient. I would encourage the authors to check that the trends observed are still present if quantified without correcting against the inputs. If using IP/input, in the supplementary figures, I think it would be valuable to show the uncorrected quantitation of inputs across PMDs, to demonstrate that there is even coverage and this isn't contributing to any of the changes observed.
      7. Generally, the n numbers for different groups of probes can be confusing and increased clarity would be helpful.

      Significance

      This study adds to the accumulating body of evidence that DNMT3A recruitment is mediated primarily through H3K36me2 across cell contexts, shedding light on the interplay between histone modifications and de novo DNA methylation. Understanding these mechanisms is important to appreciate the role for DNMT3A in establishing DNA methylation in development and disease contexts. It does remain unclear why, upon loss of DNMT1 in colorectal cancer cells, some PMDs accumulate H3K36me2 and consequently DNA methylation, while others do not. Further study into the chromatin dynamics will be valuable in understanding determinants of the DNA methylation landscape in cancer.

    1. Reviewer #2 (Public review):

      In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting this hindbrain region.

      In line with previous work published by the PI, the topic is of clear scientific relevance, and the data presented in this manuscript are both novel and compelling. Additionally, the manuscript is well-structured, and the conclusions are robust and supported by the data. Overall, this study significantly enhances our understanding of the DVC and sheds light on key differences between rats and mice.

      I applaud the authors for the depth of their analysis. However, I have a few major concerns, comments, and suggestions that should be addressed.

      (1) If I understand the methodology correctly, mice were fasted overnight and then re-fed for 2 hours before being sacrificed (lines 91-92), which occurred 4 hours after the onset of the light phase (line 111). This means that the re-fed animals had access and consequently consumed food when they typically would not. While I completely recognize that every timepoint has its limitations, the strong influence of the circadian rhythm on the DVC gene expression (highlighted by the work published by Lukasz Chrobok), and the fact that timing of food/eating is a potent Zeitgeber, might have an impact on the analysis and should be mentioned as a potential limitation in the discussion (along with citing Dr Chrobok's work). Could this (i.e., eating during a time when the animals are not "primed by their own circadian clock to eat" potentially explain why the meal-related changes in gene expression were relatively small?

      (2) In the Materials and Methods section, LiCl is mentioned as one of the treatment conditions; however, very little corresponding data are presented or discussed. Please include these results and elaborate on the rationale for selecting LiCl over other anorectic compounds.

      (3) The number of animals used differs significantly between species, which the authors acknowledge as a limitation in the discussion. Since the authors took advantage of previously published mouse data sets (Ludwig and Dowsett data sets), I wonder if the authors could compare/integrate any rat data set currently available in rats as well to partially address the sample size disparity.

      (4) Dividing cells in AP vs NTS vs DMX clusters and analyzing potential species differences would significantly enhance the quality of the manuscript, given the partially diverse functions of these regions. This could be done by leveraging existing published datasets that employed spatial transcriptomics or more classical methodologies (e.g., PMID: 39171288, PMID: 39629676, PMID: 38092916). I would be interested to hear the authors' perspective on the feasibility of such an analysis.

      (5) Given the manuscript's focus on feeding and metabolism, I believe a more detailed description and comparison of the transcription profile of known receptors, neurotransmitters, and neuropeptides involved in food intake and energy homeostasis between mice and rats would add value. Adding a curated list of key genes related to feeding regulation would be particularly informative.

    2. Reviewer #3 (Public review):

      Summary:

      This manuscript from Cecilia H et al provides a compelling resource for single nuclei RNA sequencing data with an emphasis on facilitating the integration of future data sets across mouse and rat data sets.

      Strengths:

      There are also several interesting findings that are highlighted, even though without a functional assay the importance remains unclear. However, the manuscript properly addresses where conclusions are speculative.

      As with other snRNA seq datasets the manuscript demonstrates convincingly an increased level of complexity, while other neuronal populations like Cck and Th neurons were reproduced. Several recent findings from other groups are well addressed and put into a new context, e.g., DMV expression of AgRP (and Hcrt) was found to result from non-coding sequences, co-localization of Cck/Th was identified in a small subset. These statements are informative.

      The integration of rat data into the mouse data sets is excellent, and the comparison of cellular groups is very detailed, with interesting differences between mouse and rat data.<br /> All data sets are easily accessible and usable on open platforms, this will be an impactful resource for other researchers.

      Weaknesses:

      The data analysis seems incomplete. The title indicates the integration of mouse and rat data into a unified rodent dataset. But the discrepancy of animal numbers (30 mice vs. 2 rats) does not fit well with that focus.

      On the other hand, the mouse group is further separated into different treatments to study genetic changes that are associated with distinct energy states of fed/fasting/refeeding responses. Yet, this aspect is not addressed in depth.

      While the authors find transcriptional changes in all neuronal and non-neuronal cell types, which is interesting, the verification of known transcriptional changes (e.g., cFos) is unaddressed. cFos is a common gene upregulated with refeeding that was surprisingly not investigated, even though this should be a strong maker of proper meal-induced neuronal activation in the DMV. This is a missed opportunity either to verify the data set or to highlight important limitations if that had been attempted without success.

      Additional considerations:

      (1) The focus on transmitter classification is highlighted, but surprisingly, the well-accepted distinction of GABAergic neurons by Slc32a1 was not used, instead, Gad1 and Gad2 were used as GABAergic markers. While this may be proper for the DMV, given numerous findings that Gad1/2 are not proper markers for GABAergic neurons and often co-expressed in glutamatergic populations, this confound should have been addressed to make a case if and why they would be proper markers in the DMV.

      (2) Figure S3 for anatomical localization of clusters is excellent, but several of the cluster gene names do not have a good signal in the DMV. Specifically, the mixed neurons that do not seem to have clear marker genes. What top markers (top 10?) were used to identify these anatomical locations? At least some examples should be shown for anatomical areas to support Figure S3.

      (3) Page 15, lines 410-411: "...could not find clusters sharing all markers with our neuronal classes...". Are the authors trying to say that the DMV has more diverse neurons than other brain sites? It seems not too unusual that the hypothalamus is different from the brainstem. Maybe this could be stated more clearly, and the importance of this could be clarified.

      (4) The finding of GIRK1 astrocytes is interesting, but the emphasis that this means these astrocytes are highly/more excitable is confusing. This was not experimentally addressed and should be put into context that astrocyte activation is very different from neuronal activation. This should be better clarified in the results and discussion.

      (5) The Pdgfra IHC as verification is great, but images are not very convincing in distinguishing the 2 (mouse) or 3 (rat) classes of cells. Why not compare Pdgfra and HuC/D co-localization by IHC and snRNAseq data (using the genes for HuC/D) in the mouse and in the rat? That would also clarify how specific HuC/D is for DMV neurons, or if it may also be expressed in non-neuronal populations.

    1. 银原子价电子 ​l=0(无轨道角动量)

      这个说法——“内部轨道的电子不贡献角量子数”——需要非常小心地理解,因为它可能引起误解。更准确和完整的解释如下:

      (当前时间:2025年4月14日 星期一 下午 2:06:39,地点:新加坡)

      1. 每个电子都有角量子数:

        • 绝对不是说内部轨道的电子没有角量子数。 原子中的每一个电子,无论它处于内层轨道还是外层(价)轨道,都必须由一组四个量子数来描述其状态:主量子数 n角量子数 l(轨道角动量量子数)、磁量子数 m<0xE2><0x82><0x99>,以及自旋磁量子数 m<0xE2><0x82><0x9B>(自旋量子数 s 对所有电子都是 1/2)。
        • 因此,即使是处于最内层 1s 轨道的电子,它也具有 l=0m<0xE2><0x82><0x99>=0 的轨道角动量量子数,以及 s=1/2m<0xE2><0x82><0x9B>=±1/2 的自旋量子数。一个处于 2p 轨道的内层电子则具有 l=1
      2. 关键在于对“总角动量”的贡献:

        • 用户的问题可能源于讨论原子的总角动量(Total Angular Momentum)时的一个重要规则:一个完全填满的电子壳层(Shell)或亚层(Subshell)对原子的总轨道角动量 (L) 和总自旋角动量 (S) 的净贡献都为零。
        • 原因(泡利原理的应用):
          • 轨道角动量 (L): 在一个完全填满的亚层中(例如,p⁶, d¹⁰, f¹⁴),对于每一个磁量子数 m<0xE2><0x82><0x99>,都有电子占据。由于 m<0xE2><0x82><0x99> 的取值从 -l+l 对称分布,并且每个 m<0xE2><0x82><0x99> 值对应的轨道都被两个自旋相反的电子填满,这些电子的轨道角动量在 z 轴上的投影 m<0xE2><0x82><0x99>ħ 会成对抵消(例如,m<0xE2><0x82><0x99>=+1 和 m<0xE2><0x82><0x99>=-1 的贡献)。更严格的量子力学计算表明,对于一个闭合(完全填满)的亚层或壳层,其总轨道角动量量子数 L 必定为 0
          • 自旋角动量 (S): 在一个完全填满的亚层或壳层中,对于每个轨道(由 n, l, m<0xE2><0x82><0x99> 定义),根据泡利原理,必须包含一个自旋向上(m<0xE2><0x82><0x9B>=+1/2)和一个自旋向下(m<0xE2><0x82><0x9B>=-1/2)的电子。因此,所有电子的自旋角动量在 z 轴上的投影 m<0xE2><0x82><0x9B>ħ 的总和必定为零。同样,可以证明其总自旋角动量量子数 S 也必定为 0
        • 总角动量 (J): 由于 L=0 且 S=0,根据角动量耦合规则,闭合壳层的总角动量量子数 J(J = L + S)也必定为 0。
      3. 对原子性质的影响:

        • 因为所有完全填满的内层电子壳层/亚层对原子的总轨道角动量 L 和总自旋角动量 S 的净贡献都是零,所以原子的总角动量 J 以及与之相关的性质(如原子的总磁矩)完全由未填满的价电子层(Valence Shells)中的电子决定。
        • 这就是为什么在讨论原子光谱、化学成键、原子磁矩(例如在斯特恩-盖拉赫实验中测量的)等许多问题时,我们通常只需要关注价电子的角动量状态。内层的“电子核芯”(electron core)虽然包含很多电子,每个电子也都有自己的角动量,但它们作为一个整体,其总角动量为零,在这些低能物理现象中通常表现为一个没有净角动量和净磁矩的“背景”。

      例子:银(Ag)原子 [Kr] 4d¹⁰ 5s¹ * [Kr] 代表氪的电子核芯,这是一个闭合壳层结构,其 L=0, S=0。 * 4d¹⁰ 是一个完全填满的 d 亚层,其 L=0, S=0。 * 所以,银原子的总角动量完全由那个孤零零的 5s¹ 价电子决定。这个电子处于 s 轨道,所以其轨道角动量 l=0 (L=0)。它的自旋角动量 s=1/2 (S=1/2)。因此,银原子基态的总角动量 J = L + S = 0 + 1/2 = 1/2。这与实验观测一致。

      总结:

      说“内部轨道的电子不贡献角量子数”是不准确的。每个内部电子都具有自己的轨道角动量量子数 l 和自旋量子数 s。然而,由于泡利不相容原理,完全填满的内部电子壳层或亚层,其所有电子的轨道角动量和自旋角动量在矢量求和后,净的总轨道角动量 L 和净的总自旋角动量 S 都恰好为零。因此,这些闭合的内层结构对整个原子的总角动量 J 没有贡献,原子的总角动量及其相关性质主要由未填满的价电子层决定。

    1. Knowledge constructionFacilitatingConferencing processs —_ >3 3 Information exchange 5a Facilitating tasks >Vv and supporting use ©Searching, of learning materials 2personalizing software &eenOo2 Online socialization :=Familiarizing and providing bridges oS ; between cultural, social and &pending and learning environments ©receiving messages1 Access and motivationWelcoming andSettingupsystemencouragingand accessing| | E-moderating| | Technical supportFigure 2.1 Model of teaching and learning online through online networkingIndividual access and the induction of participants into online learning areessential prerequisites for online conference participation (stage 1, at the base ofthe flights of steps). Stage 2 involves individual participants establishing theironline identities and then finding others with whom to interact. At stage 3,participants engage in mutual exchange of information. Up to and includingstage 3,a form of co-operation occurs whereby each person supports the otherparticipants’ goals. At stage 4, course-related group discussions develop and theinteraction becomes more collaborative. At stage 5, participants look for morebenefits from the system to help them achieve personal goals and reflect on thelearning processes.Each stage requires participants to master certain technical skills (shown inthe bottom left of each step). Each stage calls for different e-moderating skills(shown on the right top of each step). The ‘interactivity bar’ running along theright of the flight of steps suggests the intensity of interactivity that you can

      Como estudante de ensino a distância, muitas vezes não tive contacto com o passo 4 do grafico da figura 2.1

    2. Individual access and the induction of participants into online learning areessential prerequisites for online conference participation (stage 1, at the base ofthe flights of steps). Stage 2 involves individual participants establishing theironline identities and then finding others with whom to interact. At stage 3,participants engage in mutual exchange of information. Up to and includingstage 3,a form of co-operation occurs whereby each person supports the otherparticipants’ goals. At stage 4, course-related group discussions develop and theinteraction becomes more collaborative. At stage 5, participants look for morebenefits from the system to help them achieve personal goals and reflect on thelearning processes.

      Este documento apresenta um Modelo Pedagógico Virtual proposto por Gilly Salmon. Segundo o que percebi, da leitura deste documento e também do Capítulo 1 do E-Book de Base para a Formação, trata-se de um modelo composto por cinco etapas que orientam a atuação do professor/e-moderador no trabalho com os alunos, com o objetivo de criar uma comunidade virtual de aprendizagem que favoreça o ensino colaborativo e promova a autonomia dos estudantes. De acordo com Salmon, para que a aprendizagem online seja bem-sucedida, os alunos necessitam de um apoio contínuo ao longo deste processo estruturado, que conduz os participantes, de forma gradual, a uma maior autonomia na sua aprendizagem.

      As cinco etapas propostas são:

      1. Acesso e Motivação: Esta etapa inicial é fundamental para garantir que todos os participantes se sintam confortáveis e capacitados para utilizar a plataforma, além de despertar o seu interesse pelo conteúdo a ser abordado. O papel do professor/e-moderador é assegurar que todos se familiarizem com a plataforma e se sintam acolhidos, devendo também disponibilizar de forma clara todas as informações relativas à unidade curricular.

      2. Socialização Online: Esta etapa estabelece a base para a criação de um ambiente social e emocional propício à aprendizagem colaborativa. Fomenta-se, assim, o desenvolvimento do sentimento de pertença ao grupo. As e-atividades devem promover a interação entre os participantes, bem como o fortalecimento das relações interpessoais e da confiança mútua.

      3. Troca de Informação: Esta etapa marca o início da interação mais direta com o conteúdo e entre os participantes, com o apoio do e-moderador. O e-moderador deve orientar e apoiar o grupo, facilitando a construção de uma aprendizagem colaborativa.

      4. Construção de Conhecimento: Nesta etapa, os participantes começam a assumir maior responsabilidade pela sua própria aprendizagem, utilizando a comunicação assíncrona para debater e construir conhecimento. As e-atividades devem incentivar a troca de ideias e a reflexão crítica. O objetivo desta fase é promover uma colaboração efetiva e a produção coletiva de conhecimento.

      5. Desenvolvimento: Nesta última etapa, os participantes assumem a responsabilidade pela sua própria aprendizagem e pela do grupo ao qual pertencem. Os alunos devem desenvolver trabalhos que promovam a reflexão e a extração de conclusões sobre o que aprenderam, bem como a aplicação prática dos conhecimentos adquiridos nos seus contextos.

      Este é um modelo que apresenta uma estrutura lógica e progressiva para a implementação eficaz do ensino online, alinhada com os princípios da aprendizagem colaborativa e da autonomia do estudante. Acredito não estar errado ao afirmar que este é o modelo seguido nesta formação. (Marco Garapa)

    1. AbstractReef-building corals are integral ecosystem engineers in tropical coral reefs worldwide but are increasingly threatened by climate change and rising ocean temperatures. Consequently, there is an urgency to identify genetic, epigenetic, and environmental factors, and how they interact, for species acclimatization and adaptation. The availability of genomic resources is essential for understanding the biology of these organisms and informing future research needs for management and and conservation. The highly diverse coral genus Acropora boasts the largest number of high-quality coral genomes, but these remain limited to a few geographic regions and highly studied species. Here we present the assembly and annotation of the genome and DNA methylome of Acropora pulchra from Mo’orea, French Polynesia. The genome assembly was created from a combination of long-read PacBio HiFi data, from which DNA methylation data were also called and quantified, and additional Illumina RNASeq data for ab initio gene predictions. The work presented here resulted in the most complete Acropora genome to date, with a BUSCO completeness of 96.7% metazoan genes. The assembly size is 518 Mbp, with 174 scaffolds, and a scaffold N50 of 17 Mbp. Structural and functional annotation resulted in the prediction of a total of 40,518 protein-coding genes, and 16.74% of the genome in repeats. DNA methylation in the CpG context was 14.6% and predominantly found in flanking and gene body regions (61.7%). This reference assembly of the A. pulchra genome and DNA methylome will provide the capacity for further mechanistic studies of a common coastal coral in French Polynesia of great relevance for restoration and improve our capacity for comparative genomics in Acropora and cnidarians more broadly.

      This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.153). These reviews (including a protocol review) are as follows.

      Reviewer 1. Yanshuo Liang

      The manuscript by Conn et al. detail the high-quality genome assembly of Acropora pulchra, a Acropora of ecological and evolutionary significance, and also analyzes its genome-wide DNA methylation characteristics. These data complement the genetic resources of the Acropora genome. This manuscript is well written and represents a valuable contribution to the field. I have some comments below for the authors to address but look forward to seeing this research published. Q1: In the first sentence of the second paragraph of the Context: This is the first study to utilize PacBio long-read HiFi sequencing to generate a high quality genome with high BUSCO completeness, in tandem with its DNA methylome for scleractinian corals. Language such as "new", "first", "unprecedented", etc, should be avoided because it often leads to unproductive controversy. As far as I know, the genome you assembled is not the first stony coral to be sequenced using PacBio long-read HiFi sequencing. Back in 2024, He et al. assembled Pocillopora verrucosa (Scleractinia) to the chromosome level using PacBio HiFi long-read sequencing and Hi-C technology. Here I would suggest please rephrase. Reference: He CP, Han TY, Huang WL, et al. Deciphering omics atlases to aid stony corals in response to global change, 11 March 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4037544/v1]. Q2: In this sentence: “On 23 October 2022, sperm samples were collected from the spawning of A.pulchra and preserved in Zymo DNA/RNA shield.” Please “A.pulchra” to “A. pulchra”. Q3: Please change all “k-mer” into “k-mer” in the manuscript. Q4: Please change “Long-Tandem Repeats” to “Long Terminal Repeats” Q5: In this sentence: “Funannotate train uses Trinity [18] and PASA [19] for ab initio predictions. Funannotate predict was then run to assign gene models using AUGUSTUS [20], GeneMark [21], and Evidence Modeler [19] to estimate final gene models.” Please write versions of these software. Q6: [20] Later references do not correspond well in the manuscript, please check!

      Reference 2. Jason Selwyn

      Is the language of sufficient quality? Yes. There are some minor grammatical issues throughout that warrent a closer reading to correct. E.g. Abstract: "...urgency to identify how genetic, epigenetic, and environmental...", "...management and and conservation...". Context: "...we aim to provide..." etc. Are all data available and do they match the descriptions in the paper? Yes. The link to the OSF repository in the PDF did not work. However, the link to the OSF repository from the github did work. Is the data acquisition clear, complete and methodologically sound? No. It isn't mentioned in the manuscript where the RNAseq data used to annotate the genome is from, nor any quality filtering steps that may have been applied to the RNA data prior to its use for annotation. Is there sufficient detail in the methods and data-processing steps to allow reproduction? Yes. Excluding the above comment about the RNA data. Additional Comments: This is a well assembled, and annotated genome that will contribute to the growing database of Acropora genomes. The manuscript could do with a simple pass to identify and correct some relatively minor grammatical issues and inconsistencies (Table 1 includes a thousands comma separator in some instances and not others) and needs to include details about the source of the RNA data used to train the ab initio gene predictors. There also appears to be a problem with the citation numbering after 20.

      **Reviewer 3. Benjamin Young ** Are all data available and do they match the descriptions in the paper? Yes. Raw reads, metadata, and genome assembly are publicly available and have a NCBI project number in which they are all linked. Is the data acquisition clear, complete and methodologically sound? Yes. Collection of sperm samples, HMW DNA extraction, and SMRT Bell Library prep are written clearly. I have asked for a few clarifications on wording in this section in the attached edited pdf document. Is there sufficient detail in the methods and data-processing steps to allow reproduction? Yes. I think the pipeline used for de-novo genome generation (including raw read cleaning and assembly), repeat masking, and gene prediction and annotation is of high quality and best practices. With the inclusion of the GitHub and all analyses scripts, it is possible to reproduce the assembly generated. Is there sufficient data validation and statistical analyses of data quality? Yes. This is not super relevant for a genome assembly paper so I have no additional comments here. Is the validation suitable for this type of data? Yes. The authors use tools such as GenomeScope2 and BUSCO for validation of their data. It would be nice to see the tool they used to identify N50 and L50 (maybe Quast) included in the methods. Additionally, I would like to see a Merqury analysis of the HifiAsm primary and alternate assemblies to show that duplicate purging was successful. Additional Comments: I would first like to commend the authors for a well assembled genome resource for a coral species that will be greatly beneficial to the wider coral and scientific community. I have provided a PDF with comments throughout for the authors to address. The majority of these are easy fixes, including things such as sentence structure, inconsistent capitalisation of subheadings, additional references for methods, clarification of statements, and other suggestions. I do have a few larger requests for this to be published, and these are the reasons for selecting the major revision option as there may need to be figure updates, and quick additional analyses to be run. 1. Can you please correct the verbiage around BUSCO analysis throughout the manuscript. It is often stated "BUSCO completeness of xx%". BUSCO doesn't directly measure completeness, rather completeness of single copy orthologs against a specific database. I have left comments throughout on potential rewording for these instances. Please also specify the exact database you used (i.e. odb10_metazoa). Finally, can you please be more specific when stating BUSCO results, specifically when you use 96.9% this is single copy and duplicated complete BUSCOS. I have left comments in the pdf again for this. 2. In the results for Genome Assembly section can you please include results (i.e. length, N50, L50, number contigs/scaffolds) for the primary assembly and the scaffolded assembly. 3. I think it would be not much work and provide additional information to show successful duplicate purging to run a Merqury analysis on the primary and alternative assemblies from HiFiAsm. 4. Can you include some additional information in the "Structural and Functional Annotation section". Specifically, can you provide information on the results from the funannoatate predict step, and then how funannotate update improved this (if at all). 5. Please double check the methods section for funannotate. From reading the funannoatate documentation I think there may be some confusion on what each step (train, predict, update, annotate) is doing. I have provided comments in the pdf to help clarify, and have also linked the funnannotate documentation. 6. On NCBI I see that an additional Acropora pulchra genome has just been made available (29th Jan 2025), with this to the chromosome level (https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_965118205.1/). I think it would be prudent to include this assemblies statistics in your Table 1, and also run a BUSCO analysis on this other assembly to compare with your one. While they got to chromosome level, you do have markedly less contigs. I do not think this is necessary for this manuscript, but future work you could look to use their chromosome assembly to get your scaffolded assembly to chromosome level. Again, I want to say this is a wonderful resource for the coral and wider scientific community, and the pipeline for de-novo assembly and annotation is best practices in my opinion. Annotated additional file: https://gigabyte-review.rivervalleytechnologies.comdownload-api-file?ZmlsZV9wYXRoPXVwbG9hZHMvZ3gvRFIvNTk0L2Nvbm5ldGFsMjAyNV9yZXZpZXdjb21tZW50cy5wZGY=

      Re-review:

      The authors have addressed all my comments and queries, and included nearly all recommendations. Thank you ! A few quick notes to fix before publication -
      

      "The input created Funannotate train uses Trinity v.2.15.2 [22] and PASA v.2.5.3 [23] for transcript assembly prior to ab initio predictions". This sentence reads weird, reword before publishing. I think maybe just remove "created Funannotate train" and then it reads correctly. Or "Funnannotate trains uses .....". - "PFAM v.37.0 [28], CAZyme [29], UniProtKB v[30] and GO [31]." Missing a few version numbers, and UniProt just has a v. - "The mitochondrial genome was successfully assembled and circularized using MitoHifi v3.2.2 The final assembled A. pulchra mitogenome is". Just missing a period i think before "The final assembly". Great job and a very useful resource for the coral community !!

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      Reply to the reviewers

      We thank the reviewers for their feedback on our paper. We have taken all their comments into account in revising the manuscript. We provide a point-by-point response to their comments, below.

      Reviewer #1

      Major comments:

      The manuscript is clearly written with a level of detail that allows others to reproduce the imaging and cell-tracking pipeline. Of the 22 movies recorded one was used for cell tracking. One movie seems sufficient for the second part of the manuscript, as this manuscript presents a proof-of-principle pipeline for an imaging experiment followed by cell tracking and molecular characterisation of the cells by HCR. In addition, cell tracking in a 5-10 day time-lapse movie is an enormous time commitment.

      My only major comment is regarding "Suppl_data_5_spineless_tracking". The image file does not load. It looks like the wrong file is linked to the mastodon dataset. The "Current BDV dataset path" is set to "Beryl_data_files/BLB mosaic cut movie-02.xml", but this file does not exist in the folder. Please link it to the correct file.

      We have corrected the file path in the updated version of Suppl. Data 5.

      Minor comments:

      The authors state that their imaging settings aim to reduce photo damage. Do they see cell death in the regenerating legs? Is the cell death induced by the light exposure or can they tell if the same cells die between the movies? That is, do they observe cell death in the same phases of regeneration and/or in the same regions of the regenerating legs?

      Yes, we observe cell death during Parhyale leg regeneration. We have added the following sentence to explain this in the revised manuscript: "During the course of regeneration some cells undergo apoptosis (reported in Alwes et al., 2016). Using the H2B-mRFPruby marker, apoptotic cells appear as bright pyknotic nuclei that break up and become engulfed by circulating phagocytes (see bright specks in Figure 2F)."

      We now also document apoptosis in regenerated legs that have not been subjected to live imaging in a new supplementary figure (Suppl. Figure 3), and we refer to these observations as follows: "While some cell death might be caused by photodamage, apoptosis can also be observed in similar numbers in regenerating legs that have not been subjected to live imaging (Suppl. Figure 3)."

      Based on 22 movies, the authors divide the regeneration process into three phases and they describe that the timing of leg regeneration varies between individuals. Are the phases proportionally the same length between regenerating legs or do the authors find differences between fast/slow regenerating legs? If there is a difference in the proportions, why might this be?

      Both early and late phases contribute to variation in the speed of regeneration, but there is no clear relationship between the relative duration of each phase and the speed of regeneration. We now present graphs supporting these points in a new supplementary figure (Suppl. Figure 2).

      To clarify this point, we have added the following sentence in the manuscript: "We find that the overall speed of leg regeneration is determined largely by variation in the speed of the early (wound closure) phase of regeneration, and to a lesser extent by variation in later phases when leg morphogenesis takes place (Suppl. Figure 2 A,B). There is no clear relationship between the relative duration of each phase and the speed of regeneration (Suppl. Figure 2 A',B')."

      Based on their initial cell tracing experiment, could the authors elaborate more on what kind of biological information can be extracted from the cell lineages, apart from determining which is the progenitor of a cell? What does it tell us about the cell population in the tissue? Is there indication of multi- or pluripotent stem cells? What does it say about the type of regeneration that is taking place in terms of epimorphosis and morphallaxis, the old concepts of regeneration?

      In the first paragraph of Future Directions we describe briefly the kind of biological information that could be gained by applying our live imaging approach with appropriate cell-type markers (see below). We do not comment further, as we do not currently have this information at hand. Regarding the concepts of epimorphosis and morphallaxis, as we explain in Alwes et al. 2016, these terms describe two extreme conditions that do not capture what we observe during Parhyale leg regeneration. Our current work does not bring new insights on this topic.

      Page 5. The authors mention the possibility of identifying the cell ID based on transcriptomic profiling data. Can they suggest how many and which cell types they expect to find in the last stage based on their transcriptomic data?

      We have added this sentence: "Using single-nucleus transcriptional profiling, we have identified approximately 15 transcriptionally-distinct cell types in adult Parhyale legs (Almazán et al., 2022), including epidermis, muscle, neurons, hemocytes, and a number of still unidentified cell types."

      Page 6. Correction: "..molecular and other makers.." should be "..molecular and other markers.."

      Corrected

      Page 8. The HCR in situ protocol probably has another important advantage over the conventional in situ protocol, which is not mentioned in this study. The hybridisation step in HCR is performed at a lower temperature (37˚C) than in conventional in situ hybridisation (65˚C, Rehm et al., 2009). In other organisms, a high hybridisation temperature affects the overall tissue morphology and cell location (tissue shrinkage). A lower hybridisation temperature has less impact on the tissue and makes manual cell alignment between the live imaging movie and the fixed HCR in situ stained specimen easier and more reliable. If this is also the case in Parhyale, the authors must mention it.

      This may be correct, but all our specimens were treated at 37˚C, so we cannot assess whether hybridisation temperature affects morphological preservation in our specimens.

      Page 9. The authors should include more information on the spineless study. What been is spineless? What do the cell lineages tell about the spineless progenitors, apart from them being spread in the tissue at the time of amputation? Do spineless progenitors proliferate during regeneration? Do any spineless expressing cells share a common progenitor cell?

      We now point out that spineless encodes a transcription factor. We provide a summary of the lineages generating spineless-expressing cells in Suppl. Figure 6, and we explain that "These epidermal progenitors undergo 0, 1 or 2 cell divisions, and generate mostly spineless-expressing cells (Suppl. Figure 5)."

      Page 10. Regarding the imaging temperature, the Materials and Methods state "... a temperature control chamber set to 26 or 27˚C..."; however, in Suppl. Data 1, 26˚C and 29˚C are indicated as imaging temperatures. Which is correct?

      We corrected the Methods by adding "with the exception of dataset li51, imaged at 29{degree sign}C"

      Page 10. Regarding the imaging step size, the Materials and Methods state "...step size of 1-2.46 µm..."; however, Suppl. Data 1 indicate a step size between 1.24 - 2.48 µm. Which is correct?

      We corrected the Methods.

      Page 11. Correct "...as the highest resolution data..." to "...at the highest resolution data..."

      The original text is correct ("standardised to the same dimensions as the highest resolution data").

      Page 11. Indicate which supplementary data set is referred to: "Using Mastodon, we generated ground truth annotations on the original image dataset, consisting of 278 cell tracks, including 13,888 spots and 13,610 links across 55 time points (see Supplementary Data)."

      Corrected

      p. 15. Indicate which supplementary data set is referred to: "In this study we used HCR probes for the Parhyale orthologues of futsch (MSTRG.441), nompA (MSTRG.6903) and spineless (MSTRG.197), ordered from Molecular Instruments (20 oligonucleotides per probe set). The transcript sequences targeted by each probe set are given in the Supplementary Data."

      Corrected

      Figure 3. Suggestion to the overview schematics: The authors might consider adding "molting" as the end point of the red bar (representing differentiation).

      The time of molting is not known in the majority of these datasets, because the specimens were fixed and stained prior to molting. We added the relevant information in the figure legend: "Datasets li-13 and li-16 were recorded until the molt; the other recordings were stopped before molting."

      Figure 4B': Please indicate that the nuclei signal is DAPI.

      Corrected

      Supplementary figure 1A. Word is missing in the figure legend: ...the image also shows weak...

      Corrected

      Supplementary Figure 2: Please indicate the autofluorescence in the granular cells. Does it correspond to the yellow cells?

      Corrected

      Video legend for video 1 and 2. Please correct "H2B-mREFruby" to "H2B-mRFPruby".

      Corrected

      Reviewer #2

      Major comments:

      MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

      This paper describes a technical advance. While previous work (Alwes et al. 2016) established some key elements of our live imaging approach, we were not at that time able to record the entire time course of leg regeneration (the longest recordings were 3.5 days long). Here we present a method for imaging the entire course of leg regeneration (up to 10 days of imaging), optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining in cuticularised adult legs (an important technical breakthrough in this experimental system), which we combine with live imaging to determine the fate of tracked cells. We have revised the abstract and introduction of the paper to point out these novelties, in relation to our previous publications.

      In the abstract we explain: "Building on previous work that allowed us to image different parts of the process of leg regeneration in the crustacean Parhyale hawaiensis, we present here a method for live imaging that captures the entire process of leg regeneration, spanning up to 10 days, at cellular resolution. Our method includes (1) mounting and long-term live imaging of regenerating legs under conditions that yield high spatial and temporal resolution but minimise photodamage, (2) fixing and in situ staining of the regenerated legs that were imaged, to identify cell fates, and (3) computer-assisted cell tracking to determine the cell lineages and progenitors of identified cells. The method is optimised to limit light exposure while maximising tracking efficiency."

      The introduction includes the following text: "Our first systematic study using this approach presented continuous live imaging over periods of 2-3 days, capturing key events of leg regeneration such as wound closure, cell proliferation and morphogenesis of regenerating legs with single-cell resolution (Alwes et al., 2016). Here, we extend this work by developing a method for imaging the entire course of leg regeneration, optimised to reduce photodamage and to improve cell tracking. We also develop a method of in situ staining of gene expression in cuticularised adult legs, which we combine with live imaging to determine the fate of tracked cells."

      MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

      The primary goal of this work was to establish a robust method for continuous long-term live imaging of regeneration, but we do appreciate that a more quantitative analysis would add value to the data we are presenting. We tried to address this request in three steps:

      First, we examined whether clear temporal patterns in cell division, cell movements or other cellular features can be observed in an accurately tracked dataset (li13-t4, tracked in Sugawara et al. 2022). To test this we used the feature extraction functions now available on the Mastodon platform (see link). We could discern a meaningful temporal pattern for cell divisions (see below); the other features showed no interpretable pattern of variation.

      Second, we asked whether we could use automated cell tracking to analyse the patterns of cell division in all our datasets. Using an Elephant deep learning model trained on the tracks of the li13-t4 dataset, we performed automated cell tracking in the same dataset, and compared the pattern of cell divisions from the automated cell track predictions with those coming from manually validated cell tracks. We observed that the automated tracks gave very imprecise results, with a high background of false positives obscuring the real temporal pattern (see images below, with validated data on the left, automated tracking on the right). These results show that the automated cell tracking is not accurate enough to provide a meaningful picture on the pattern of cell divisions.

      Third, we tried to improve the accuracy of detection of dividing cells by additional training of Elephant models on each dataset (to lower the rate of false positives), followed by manual proofreading. Given how labour intensive this is, we could only apply this approach to 4 additional datasets. The results of this analysis are presented in Figure 4.

      MC 3. The authors describe the challenges faced by their described approach: Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

      Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

      In the revised manuscript we have added data on the tracking performance of Elephant in relation to imaging depth in Suppl. Figure 3. These data confirm our original statement (which was based on manual tracking) that nuclei are more challenging to track in deeper layers.

      We point to these new results in two parts of the paper, as follows: "A smaller proportion of cells are trackable in the deeper layers (see Suppl. Figure 3)", and "Our results, summarised in Table 1A, show that the detection of nuclei can be enhanced by doubling the z resolution at the expense of xy resolution and image quality. This improvement is particularly evident in the deeper layers of the imaging stacks, which are usually the most challenging to track (Suppl. Figure 3)."

      MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

      We think that similar trade-offs will apply to all our datasets because tracking performance is constrained by the same features, which are intrinsic to our system; e.g. by the crowding of nuclei in relation to axial resolution, or the speed of mitosis in relation to the temporal resolution of imaging. We therefore do not see a clear rationale for repeating this analysis. On a practical level, our existing image datasets could not be subsampled to generate the various conditions tested in Table 1, so proving this point experimentally would require generating new recordings, and tracking these to generate ground truth data. This would require months of additional work.

      A second, related question is whether Elephant would perform equally well in detecting and tracking nuclei across different datasets. This point has been addressed in the Sugawara et al. 2022 paper, where the performance of Elephant was tested on diverse fluorescence datasets.

      Reviewer #3

      Major comments:

      The authors should clearly specify what are the key technical improvements compared to their previous studies (Alwes et al. 2016, Elife; Konstantinides & Averof 2014, Science). There, the approaches for mounting, imaging, and cell tracking are already introduced, and the imaging is reported to run for up to 7 days in some cases.

      In Konstantinides and Averof (2014) we did not present any live imaging at cellular resolution. In Alwes et al. (2016) we described key elements of our live imaging approach, but we were never able to record the entire time course of leg regeneration. The longest recordings in that work were 3.5 days long.

      We have revised the abstract and introduction to clarify the novelty of this work, in relation to our previous publications. Please see our response to comment MC1 of reviewer 2.

      While the authors mention testing the effect of imaging parameters (such as scanning speed and line averaging) on the imaging/tracking outcome, very little or no information is provided on how this was done beyond the parameters that they finally arrived to.

      Scan speed and averaging parameters were determined by measuring contrast and signal-to-noise ratios in images captured over a range of settings. We have now added these data in Supplementary Figure 1.

      The authors claim that, using the acquired live imaging data across entire regeneration time course, they are now able to confirm and extend their description of leg regeneration. However, many claims about the order and timing of various cellular events during regeneration are supported only by references to individual snapshots in figures or supplementary movies. Presenting a more quantitative description of cellular processes during regeneration from the acquired data would significantly enhance the manuscript and showcase the usefulness of the improved workflow.

      The events we describe can be easily observed in the maximum projections, available in Suppl. Data 2. Regarding the quantitative analysis, please see our response to comment MC2 of reviewer 2.

      Table 1 summarizes the performance of cell tracking using simulated datasets of different quality. However only averages and/or maxima are given for the different metrics, which makes it difficult to evaluate the associated conclusions. In some cases, only 1 or 2 test runs were performed.

      The metrics extracted from each of the three replicates, per dataset, are now included in Suppl. Data 4.

      We consistently used 3 replicates to measure tracking performance with each of the datasets. The "replicates" column label in Table 1 referred to the number of scans that were averaged to generate the image, not to the replicates used for estimating the tracking performance. To avoid confusion, we changed that label to "averaging".

      OPTIONAL: An imaging approach that allows using the current mounting strategy but could help with some of the tradeoffs is using a spinning-disk confocal microscope instead of a laser scanning one. If the authors have such a system available, it could be interesting to compare it with their current scanning confocal setup.

      Preliminary experiments that we carried out several years ago on a spinning disk confocal (with a 20x objective and the CSU-W1 spinning disk) were not very encouraging, and we therefore did not pursue this approach further. The main problem was bad image quality in deeper tissue layers.

      Minor comments:

      The presented imaging protocol was optimized for one laser wavelength only (561 nm) - this should be mentioned when discussing the technical limitations since animals tend to react differently to different wavelengths. Same settings might thus not be applicable for imaging a different fluorescent protein.

      In the second paragraph of the Results section, we explain that we perform the imaging at long wavelengths in order to minimise photodamage. It should be clear to the readers that changing the excitation wavelength will have an impact for long-term live imaging.

      For transferability, it would be useful if the intensity of laser illumination was measured and given in the Methods, instead of just a relative intensity setting from the imaging software. Similarly,more details of the imaging system should be provided where appropriate (e.g., detector specifications).

      We have now measured the intensity of the laser illumination and added this information in the Methods: "Laser power was typically set to 0.3% to 0.8%, which yields 0.51 to 1.37 µW at 561 nm (measured with a ThorLabs Microscope Slide Power Sensor, #S170C)."

      Regarding the imaging system and the detector, we provide all the information that is available to us on the microscope's technical sheets.

      The versions of analysis scripts associated with the manuscript should be uploaded to an online repository that permanently preserves the respective version.

      The scripts are now available on gitbub and online repositories. The relevant links are included in the revised manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The authors describe a workflow for preparing and imaging leg regeneration in the marine crustacean Paryhale hawaiensis. The method relies on a heatshock-inducible fluorescent histone reporter and captures the regenerating limbs of a mid-sized adult. The authors expand on the prior work that established this transgenic line (Wolff et al. 2018), determined optimal spatial and temporal sampling rates (Alwes et al. 2016), and established the use of an incremental deep learning framework to perform robust automated cell tracking (Sugawara et al. 2022), by jointly optimizing the heatshock induction, imaging parameters, tracking implementation, and integrating in situ end-point analysis to capture the entirety of the regeneration process over an incredibly long window (up to 10 days).

      Major comments

      MC 1. Given that most of the technical advances necessary to achieve the work described in this manuscript have been published previously, it would be helpful for the authors to more clearly identify the primary novelty of this manuscript. The abstract and introduction to the manuscript focus heavily on the technical details of imaging and analysis optimization and some additional summary of the implications of these advances should be included here to aid the reader.

      MC 2. The description of the regeneration time course is nicely detailed but also very qualitative. A major advantage of continuous recording and automated cell tracking in the manner presented in this manuscript would be to enable deeper quantitative characterization of cellular and tissue dynamics during regeneration. Rather than providing movies and manually annotated timelines, some characterization of the dynamics of the regeneration process (the heterogeneity in this is very very interesting, but not analyzed at all) and correlating them against cellular behaviors would dramatically increase the impact of the work and leverage the advances presented here. For example, do migration rates differ between replicates? Division rates? Division synchrony? Migration orientation? This seems to be an incredibly rich dataset that would be fascinating to explore in greater detail, which seems to me to be the primary advance presented in this manuscript. I can appreciate that the authors may want to segregate some biological findings from the method, but I believe some nominal effort highlighting the quantitative nature of what this method enables would strengthen the impact of the paper and be useful for the reader. Selecting a small number of simple metrics (eg. Division frequency, average cell migration speed) and plotting them alongside the qualitative phases of the regeneration timeline that have already been generated would be a fairly modest investment of effort using tools that already exist in the Mastodon interface, I would roughly estimate on the order of an hour or two per dataset. I believe that this effort would be well worth it and better highlight a major strength of the approach.

      MC 3. The authors describe the challenges faced by their described approach:

      Using this mode of semi-automated and manual cell tracking, we find that most cells in the upper slices of our image stacks (top 30 microns) can be tracked with a high degree of confidence. A smaller proportion of cell lineages are trackable in the deeper layers.

      Given that the authors quantify this in Table 1, it would aid the reader to provide metrics in the manuscript text at this point. Furthermore, the metrics provided in Table 1 appear to be for overall performance, but the text describes that performance appears to be heavily depth dependent. Segregating the performance metrics further, for example providing DET, TRA, precision and recall for superficial layers only and for the overall dataset, would help support these arguments and better highlight performance a potential adopter of the method might expect.

      MC 4. Performance characterization in Table 1 appears to derive from a single dataset that is then subsampled and processed in different ways to assess the impact of these changes on cell tracking and detection performance. While this is a suitable strategy for this type of optimization it leaves open the question of performance consistency across datasets. I fully recognize that this type of quantification can be onerous and time consuming, but some attempt to assess performance variability across datasets would be valuable. Manual curation over a short time window over a random sampling of the acquired data would be sufficient to assess this.

      Significance

      As a microscopist and practitioner of large-scale timelapse image acquisition and analysis, my general assessment is that the integration of such complex and data intensive experiments is non-trivial. The study's primary strengths include: 1. Novel capabilities for continuous recording and analysis of limb regeneration in a crustacean model where previous approaches were limited to piecemeal analyses. 2. The assessment of variability in the regeneration timecourse enabled by this approach. And 3. The integration of in situ endpoint analysis enabling retrospective analysis of cell lineage and terminal fate. The study's primary limitation is the lack of quantitative analysis of the resulting datasets, what this reviewer feels is one of the most promising capabilities afforded by this approach. The primary advances described in this manuscript are twofold. First, incremental optimization of imaging and image analysis approaches enabling continuous long-term imaging and robust cell tracking. Second, the potential for the integrated assessment of cellular scale behaviors and tissue level events during regeneration alongside analysis of cell fate endpoints that can be aligned to the time lapse data with cellular precision. This work will be of interest to a somewhat specialized audience, especially given the methods-intensive focus of the manuscript, particularly to microscopists, researchers interested in the biology of regeneration who may be interested in using the method, and developmental or cell biologists working with Paryhale who might benefit from adopting the long-term imaging protocols for other questions. Aligning with my expertise, my review focuses principally on the data analysis and tracking performance characterization aspects of the manuscript.

    1. Briefing Document : "L'École dans et avec la nature"

      Sources : Excerpts de la conférence "L'École dans et avec la nature" dans le cadre des "Mercredis des Savoirs",

      avec les interventions de

      • Christine Boutevin,
      • Céline Serva (CEMEA),
      • Corine Martel (IEN),
      • Sylvain Vagnon (Faculté d'éducation), et
      • Laurent Gautier (Association nationale des CEMEA).

      Date de la conférence : Non spécifiée précisément, mais présentée comme la deuxième conférence des "Mercredis des Savoirs".

      Thème central : L'importance et les enjeux de l'éducation dans et avec la nature, abordée sous un angle historique, théorique, pratique et prospectif.

      Intervenants et leurs rôles :

      • Christine Boutevin : Chargée de mission pour les "Mercredis des Savoirs", elle introduit la conférence et souligne l'intérêt actuel et structurel du thème.
      • Céline Serva (CMEA) : Présente le déroulement de la soirée et met en avant le partenariat avec les CMEA (Centres d'entraînement aux méthodes d'éducation active). Elle souligne l'objectif de retracer l'histoire de l'école du dehors, de faire des liens avec la formation citoyenne et d'expliciter les enjeux de l'éducation avec la nature pour la compréhension du monde.
      • Corine Martel (IEN) : IEN chargée de mission "Science et éducation à l'environnement et au développement durable", elle présente la mise en contexte de l'école dehors à travers des initiatives concrètes, son lien avec les programmes scolaires et les enjeux actuels (biodiversité, réchauffement climatique, inégalités).
      • Sylvain Vagnon (Faculté d'éducation) : Professeur des universités en Sciences de l'éducation, il aborde le sujet sous un angle socio-historique, retraçant l'histoire de l'école dehors, ses fondements pédagogiques (éducation nouvelle) et soulève des défis et critiques.
      • Laurent Gautier (Association nationale des CMEA) : Responsable du secteur école aux CMEA, il élargit la perspective à l'éducation dehors au sens large (périscolaire, rue, ville), met en avant la complémentarité des acteurs éducatifs et le lien avec les classes de découverte, et présente des ressources des CMEA.
      • Principaux Thèmes et Idées Clés :

      1. Mise en perspective historique de l'école dehors (Sylvain Vagnon) :

      • Les écoles de plein air (années 20-30) : Nées dans un contexte hygiéniste et sanitaire pour purifier l'air et lutter contre les maladies infantiles. L'école de Suresnes est citée comme prototype.
      • Citation : "c'est ces écoles et je vous ai mis évidemment celle de Suren qui est une école extrêmement importante hein parce que justement elle sera un prototype hein de ces écoles de plein air".
      • Les initiatives espagnoles (années 30) : Une vision de l'école dehors en lien avec l'environnement local, comme l'école de la mer à Barcelone, visant à connecter les enfants à leur environnement et aux activités de leurs parents.
      • L'éducation nouvelle (fin 19e - début 20e) : Mouvement pédagogique fondamental avec des figures comme Montessori, Decroly, Freinet, qui prônent une éducation active, concrète et en phase avec l'environnement de l'enfant.
      • Citation d'Adolphe Ferrière : "l'enfant aime la nature on le parca dans les salles closes". Cette citation illustre la critique de l'éducation traditionnelle par l'éducation nouvelle.
      • La classe promenade : Utilisée notamment par Freinet, elle permet de sortir de la salle de classe pour observer le monde et la nature, puis de revenir en classe pour travailler sur ces observations.
      • Citation de Freinet : "La classe promenade fut pour moi la planche de salut". Il décrit l'importance de ces sorties pour l'apprentissage direct et sensoriel.
      • Concepts clés de l'éducation nouvelle liés à l'école dehors : Éducation intégrale (tête, mains, cœur), prise en compte des besoins et intérêts de l'enfant, autonomie, singularité, pensée de l'individu comme être social, révolution copernicienne (enfant au centre), relation équilibrée avec les êtres vivants.

      2. Défis et critiques de l'école dehors (Sylvain Vagnon) :

      Risque d'une nature idéalisée ou mythifiée.

      Écueil de pratiques simplement transférées de l'intérieur vers l'extérieur sans adaptation pédagogique. Intention louable mais nécessité de pérennisation et de clarification pédagogique.

      Critique d'une "fantaisie petite bourgeoise" et risque de boboïsation de l'éducation par l'école dehors, soulevant la question de l'égalité d'accès. Nécessité de penser l'école dehors de la maternelle à l'université.

      3. Mise en contexte actuelle et initiatives concrètes (Corine Martel) :

      • Liens avec les enjeux actuels : Préservation de la biodiversité, réchauffement climatique, et nécessité d'une vision systémique dès le plus jeune âge.
      • Repenser le futur : Face au pessimisme des jeunes, l'éducation dehors peut offrir des perspectives et un pouvoir d'action.
      • Objectifs de Développement Durable (ODD) : Cadre pour penser l'éducation future (qualité pour tous, lutte contre les inégalités, biodiversité, climat).
      • Parcours de l'élève (maternelle à université) : Nécessité d'égalité et d'équité d'accès aux ressources, quelle que soit la localisation géographique.
      • Égalité filles-garçons et inclusion : Exemples concrets d'activités (jardinage) permettant de travailler les compétences de manière transversale et d'inclure les élèves à besoins particuliers (handicap moteur, troubles de l'attention, TSA).
      • Dispositifs institutionnels :Aires Terrestres Éducatives (ATE), Marines Éducatives (AME), Fluviales Éducatives (AFE) : Projets concrets de gestion de sites naturels par les élèves, en lien avec les collectivités et l'OFB. Objectif de 720 ATE d'ici 2030 dans l'académie.
      • Labilisation des établissements E3D (Établissement en Démarche de Développement Durable) : Formation des éco-délégués (cycle 3 à lycée), basée sur les 20 mesures pour la transition écologique à l'école.
      • Instances de participation : Conseils de vie collégienne/lycéenne (CVC/CVL), CESCE (Comité d'éducation à la santé, à la citoyenneté et à l'environnement).
      • Parcours santé : Activité physique quotidienne (APQ), lutte contre l'obésité, renforcement du système immunitaire, développement des compétences psychosociales (coopération, entraide, respect du vivant).
      • Apprentissages disciplinaires concrets : L'école dehors permet d'expérimenter, d'analyser et de comprendre de manière active les notions (ex: érosion des sols, cycle de l'eau).
      • Pédagogie de projet : L'exemple du jardin scolaire comme lieu d'apprentissage transversal (maths, français, sciences, coopération, alimentation, littérature de jeunesse).
      • Exemples concrets d'activités à différents niveaux : Classe dehors en maternelle, étude du paysage en élémentaire (sol, géologie, histoire), énergies renouvelables au collège, formation continue des professeurs.

      4. Éduquer dehors au sens large et complémentarité des acteurs (Laurent Gautier) :

      • Éduquer dehors vs. École dans la nature : Le "dehors" ne se limite pas à la nature (cour d'école, rue, ville).
      • Villes à hauteur d'enfants : Nécessité de repenser les espaces urbains pour qu'ils soient aussi des lieux éducatifs pour les enfants.
      • Prise de risque : Élément important de l'éducation, à intégrer dans un cadre sécurisé.
      • Terrains d'aventure : Espaces éducatifs extérieurs (parfois en milieu urbain) où les enfants peuvent expérimenter, construire et prendre des risques.
      • Complémentarité des acteurs éducatifs : Enseignants, ATSEM, AED, animateurs périscolaires, parents. Importance de changer la nature des rapports lors des activités extérieures.
      • Continuité éducative : De la crèche (crèches du dehors) à l'université.
      • Lien entre École dehors et Classes de découverte : Non-opposition, mais complémentarité et possibilité de passer de l'une à l'autre.
      • Ressources CEMEA : Revue "Éduquer dehors ou éduquer au dehors", dossier numérique gratuit sur Yakaméia (activités, jeux, vidéos, témoignages), podcasts "Éduquer dehors ça s'accompagne".
      • Rencontres internationales de la classe dehors : Prochaines rencontres à Marseille (14-17 mai), lieu d'échange et de mutualisation pour tous les acteurs.

      5. Éléments Soulevés lors des Questions/Réponses :

      • Partenariat avec le monde de l'éducation à l'environnement (CPIE) : Importance de créer des réseaux territoriaux pour avancer ensemble (éducation nationale, recherche, collectivités, associations). De nombreuses collaborations et démarches participatives existent déjà.
      • Formation des enseignants et des acteurs de terrain : Mise en place de formations académiques et de circonscription, développement de ressources (Magistère Canopé, repères de progression Eduscol), verdissement progressif des programmes. Nécessité de penser des séquences d'apprentissage sur le long terme, en équipe.
      • Volonté politique : Un projet éducatif est lié à un projet de société. Une ambition et une volonté politique sont nécessaires pour soutenir et développer l'éducation dehors.
      • Gestion de l'imprévu : Savoir saisir les opportunités d'apprentissage offertes par les événements inattendus lors des sorties.
      • Conception d'outils pour l'école dehors : Des ouvrages et des outils pratiques existent déjà, mais la réflexion continue.
      • Engagement des lycées : Les éco-délégués, les maisons des lycéens sont des acteurs importants. Mise en place d'initiatives concrètes (sentiers de la biodiversité). Valorisation à travers le prix de l'action éco-délégué. Formation spécifique des éco-délégués. Les 20 mesures gouvernementales pour la transition écologique visent une labellisation E3D à 100% d'ici 2030.

      Conclusion :

      • La conférence "L'École dans et avec la nature" a mis en lumière la richesse et la profondeur de ce concept éducatif.

      Ancré dans une histoire pédagogique riche, répondant aux enjeux environnementaux et sociétaux actuels, l'école dehors se présente comme une approche pertinente et nécessaire.

      Elle nécessite cependant une réflexion pédagogique approfondie, une formation adéquate des acteurs, une volonté politique forte et un développement de partenariats solides pour garantir son égalité d'accès et sa pérennisation au sein du système éducatif.

      L'élan actuel, renforcé par des initiatives concrètes et des réseaux d'acteurs engagés, ouvre des perspectives prometteuses pour l'avenir de l'éducation.

    1. Note de Briefing : "Comment faire école à celles et ceux qui n'y sont pas ?" - Présentation d'Antoine Gentil Date : 24 mai 2024 (basé sur la mention de la date de sortie de l'essai)

      Source : Transcription d'une conférence de Christine Boutevin et Antoine Gentil à la faculté d'éducation, introduisant la problématique de la rescolarisation des jeunes décrocheurs et présentant le dispositif "Starter" et l'ouvrage "Classe réparatoire : un chemin pour se réconcilier avec l'école".

      Public Cible : Acteurs et futurs acteurs de l'éducation, professionnels intéressés par la problématique du décrochage scolaire et les approches alternatives.

      Thèmes Principaux :

      • La question centrale : Comment faire école à celles et ceux qui n'y sont plus ? Cette question est le fil conducteur de la conférence et interroge les pratiques éducatives traditionnelles face aux jeunes en rupture avec le système scolaire.

      • Nécessité de se décaler d'une "école de la performance" vers une "école de la reconnaissance des individualités" et du respect des droits de l'enfant. Antoine Gentil souligne la pression de la performance et propose une approche centrée sur la reconnaissance des singularités et le respect de la Convention Internationale des Droits de l'Enfant.

      Il pose la question : "comment se décaler d'une d'une école de la performance qui envahit les discours de façon relativement obsessionnelle [...] pour penser une école de la reconnaissance des individualités ?"

      • Importance d'une approche systémique, territoriale et multipartenariale de la scolarité et de la prévention. L'intégration de la scolarité dans un réseau de partenaires et la prise en compte de l'enfant dans son écosystème familial et social sont présentées comme essentielles.

      "Comment inscrire la scolarité dans un système de prévention intégré aux institutions territorialisées multipartenariales ?"

      • Considération des vulnérabilités psychosociales, en particulier des enfants relevant de la protection de l'enfance.

      La conférence met en lumière les spécificités des enfants victimes d'abandon, de violence, d'errance, etc., et la nécessité d'adapter les propositions scolaires à leurs besoins.

      "Comment proposer une scolarité qui va tenir compte des vulnérabilités psychosociales en considérant tout particulièrement les enfants relevants de la protection de l'enfance ?"

      Présentation du dispositif "Starter" comme une "classe réparatoire" expérimentale à Grenoble.

      Antoine Gentil, enseignant spécialisé, décrit le projet Starter qui accueille des adolescents de 14-15 ans pour un parcours de rescolarisation et d'orientation d'une année. Le dispositif se caractérise par :

      • Un repérage des vulnérabilités privilégiant les jeunes de la protection de l'enfance et en situation de polyexclusion.
      • La construction de coréférences partenariales en amont et pendant le projet.
      • Un espace de recherche et de formation.

      L'engagement professionnel et associatif d'Antoine Gentil.

      Son rôle de coordonnateur de Starter et de délégué général de l'association "Par le Verbe Parer" illustre un engagement double pour l'innovation et la prévention.

      L'association "Parer" est présentée comme "un laboratoire d'idées entre professionnels qui va modéliser des actions en prévention par la mise en œuvre d'alliance éducative ou on pourrait dire aussi de tissage de de maillages territoriaux autour des situations d'enfants et d'adolescents éprouvés."

      • Principes philosophiques sous-tendant l'approche :Penser l'expérience depuis l'expérience pour l'expérience.

      • Considérer l'enfant comme un individu capacitaire et vulnérable, et non comme un simple élève ou usager.

      • Adopter une approche écosystémique et anthropologique de l'action éducative.

      • Reconnaître l'acte éducatif comme un "art de l'immanence" se jouant dans l'instant présent.

      • Souligner l'importance de la part sensible de l'acte éducatif.

      • Affirmer l'indissociabilité des enjeux d'orientation et d'individuation, d'agentivité et d'émancipation.

      "Les enjeux d'orientation sont indissociables des enjeux d'individuation ça veut dire se construire en tant qu'individu [...] rendre indissociable l'orientation de l'agentivité le fait d'être acteur de sa propre vie et d'émancipation le fait de pouvoir se sortir des déterminismes".

      • Plaider pour une logique d'"essage" plutôt que de "duplication" des projets expérimentaux, en tenant compte des spécificités territoriales.

      • Réflexion sur le décrochage scolaire : Distinguer les définitions statistiques et sociologiques, et identifier trois pôles de facteurs en jeu : difficultés d'apprentissage, angoisse envahissante, et vulnérabilités psychosociales.

      L'accent est mis sur la sur-représentation des enfants de la protection de l'enfance dans les chiffres du décrochage scolaire.

      "Les enfants de la protection de l'enfance sont massivement concernés par le décrochage scolaire [...] 13 % des enfants pris en charge par l'aide sociale à l'enfance valident le diplôme national du brevet."

      Fonctionnement du dispositif Starter :

      • Phase d'information et d'investigation en amont (1 à 2 ans).
      • Mise en place d'un coréférencement partenarial.
      • Pratique d'entretiens pour mettre l'enfant et la famille en projection et construire un dossier de candidature.
      • Commission académique statuant sur les candidatures.
      • Entretien initial marquant l'entrée dans le parcours.
      • Rythme scolaire alternant 4 semaines de classe et 2 semaines de stage.

      Éléments clés de l'approche éducative à Starter :Reconnaissance des vulnérabilités psychosociales : Être attentif aux signaux de mal-être, placer les termes "rencontrer" et "reconnaître" au centre du métier, pratiquer la "clinique éducative" (analyse de l'expérience subjective de la rencontre).

      • Relation éducative : Organisée autour des "quatre L et quatre A" (loi, limites, lieux, langage ; attention, affection, acceptation, accompagnement), importance de la pratique de l'entretien, de l'accueil au seuil de l'établissement, de l'hospitalité, de l'empathie méthodologique, de l'humour (avec prudence), du conflit éducatif et de l'autorité éducative ("une autorité qui va de soi [...] mais explicite").

      Principe fondamental : "Tu es digne d'intérêt, tu es irremplaçable." * Alliances éducatives : Travail en réseau avec les familles et les professionnels (éducateurs PJJ, assistants sociaux, équipes de soin, etc.), basé sur l'identification, la reconnaissance, l'information, le partage d'analyses, la définition d'objets de dialogue et de projets communs, et la mise de sens sur l'intervention de chaque partenaire. * Approches pédagogiques : Privilégier les pédagogies de l'explicite (compréhension des stratégies d'apprentissage, réflexivité sur les peurs liées à l'apprentissage, questionnement de la normativité) et l'expression de soi, notamment à travers l'atelier d'écriture quotidien. * Accompagnement à l'orientation : Viser l'"s'orienter" plutôt que l'"orienter", aider à la compréhension des logiques du système, autoriser le rêve, proposer des stages comme expériences initiatiques encadrées, et articuler l'orientation avec la médiation culturelle et les projets à vocation citoyenne.

      Points Saillants et Citations Clés :

      • Sur la nécessité d'un changement de perspective : "comment se décaler d'une d'une école de la performance [...] pour penser une école de la reconnaissance des individualités ?"
      • Sur l'importance de la prise en compte de l'écosystème : "comment considérer l'enfant et sa famille dans son écosystème ?"
      • Sur la définition de Starter : "[une] classe réparatoire".
      • Sur la sur-représentation des enfants de l'ASE dans le décrochage : "13 % des enfants pris en charge par l'aide sociale à l'enfance valident le diplôme national du brevet."
      • Sur le principe fondamental de la relation éducative à Starter : "Tu es digne d'intérêt, tu es irremplaçable."
      • Sur l'importance de l'alliance éducative : "agir en alliance éducative" (référence au rapport interministériel).
      • Sur la visée de l'orientation : "Est-ce qu'on oriente ou est-ce qu'on s'oriente ?"

      Prolongements et Perspectives :

      • Lecture de l'essai "Classe réparatoire : un chemin pour se réconcilier avec l'école" d'Antoine Gentil (Presses Universitaires de Grenoble, 2024).
      • Consultation des ressources documentaires au CRD de la faculté d'éducation.
      • Visionnage du film documentaire "Un bon début" de Chabi Agnas Molia et participation aux séances-débats.
      • Exploration du site internet de l'association "Par le Verbe Parer" et de la page dédiée à Starter sur le site du lycée Guinemmer de Grenoble.
      • Réflexion sur les possibilités d'essaimage des approches développées à Starter, en tenant compte des spécificités territoriales.

      En conclusion, la conférence d'Antoine Gentil met en lumière une approche éducative profondément humaine et systémique pour répondre à la complexité du décrochage scolaire, en particulier chez les jeunes les plus vulnérables.

      Le dispositif Starter, ancré dans des principes philosophiques forts et une pratique de la relation éducative attentive, offre un modèle inspirant pour repenser l'école et sa capacité à "faire école à celles et ceux qui n'y sont plus."

      L'insistance sur le travail en alliance et la nécessité de considérer l'enfant dans sa globalité constituent des pistes essentielles pour les acteurs de l'éducation souhaitant agir face à cette problématique cruciale.

      Glossaire des termes clés

      • Classe réparatoire : Terme utilisé par Antoine Gentil pour désigner un dispositif spécifique (comme Starter) visant à réconcilier les jeunes en rupture avec l'école.
      • Décrochage scolaire : Processus complexe et plurifactoriel de désaffiliation progressive d'un jeune vis-à-vis des apprentissages et de l'institution scolaire. Peut être défini statistiquement (sortie sans diplôme) ou sociologiquement (processus).
      • Vulnérabilités psychosociales : Ensemble des facteurs sociaux, affectifs et psychologiques qui fragilisent un individu et peuvent impacter négativement son parcours, notamment scolaire.
      • Protection de l'enfance : Terme générique désignant les dispositifs et les actions visant à assurer la sécurité et le bien-être des enfants en danger ou en risque de l'être, incluant l'aide sociale à l'enfance et la protection judiciaire de la jeunesse.
      • Poli/multiexclusion : Situation d'accumulation de difficultés et d'exclusions dans différents domaines de la vie (social, familial, scolaire, etc.).
      • Coréférence partenariale : Collaboration structurée entre différents professionnels (éducatifs, sociaux, de santé, etc.) autour d'un même enfant ou adolescent afin d'assurer une prise en charge globale et cohérente.
      • Clinique éducative : Approche qui met l'accent sur l'expérience subjective de la rencontre éducative, l'analyse des situations individuelles et la prise en compte de l'histoire et du vécu de l'enfant.
      • Acte éducatif (art de l'immanence) : Conception de l'enseignement et de l'éducation comme se déroulant principalement dans l'instant présent, nécessitant adaptation et réactivité face aux circonstances.
      • Alliances éducatives : Collaboration et partenariat entre l'école, la famille et les autres acteurs du territoire pour soutenir le parcours éducatif des enfants et des adolescents.
      • Empathie méthodologique : Capacité à se mettre à la place de l'enfant ou de l'adolescent pour comprendre sa situation, ses besoins et ses difficultés.
    1. 量子电路的几何化

      好的,我们来详细探讨一下“量子电路的几何化”这一前沿概念,特别是“边界的量子计算过程(如量子门操作)可能对应体时空的几何演化”这个想法。这涉及到量子信息、量子计算、全息原理(特别是AdS/CFT对偶)和量子引力等领域的交叉。

      (当前时间:2025年4月13日 星期日 中午 12:43:05,地点:新加坡)

      1. 背景:量子电路与全息对偶

      • 量子电路(Quantum Circuits): 在量子计算中,量子电路是描述量子计算过程的标准模型。它由一系列作用在量子比特(qubits)上的量子门(Quantum Gates)组成。量子门是幺正(unitary)操作,它们演化量子系统的状态,改变其叠加态和纠缠(Entanglement)。运行一个量子电路的过程,可以看作是在希尔伯特空间中对量子态进行一系列精确的幺正变换,通常会增加状态的复杂度(Complexity)和纠缠度。
      • AdS/CFT 对偶(全息对偶): 如前所述,它建立了 D 维 AdS 引力“体”(Bulk)与 (D-1) 维 CFT“边界”(Boundary)之间的等价关系。体内的引力动力学和时空几何与边界 CFT 的状态和演化一一对应。关键联系包括:
        • 边界状态 ↔ 体几何: 边界 CFT 的特定状态(如真空态、热态)对应于体内特定的时空几何(如空 AdS、AdS 黑洞)。
        • 边界纠缠 ↔ 体几何: 边界区域的纠缠熵与体内特定曲面的面积(Ryu-Takayanagi/HRT 公式)相关。

      2. “量子电路几何化”的核心思想

      这个概念的核心思想是:在全息对偶的框架下,边界 CFT 上执行的量子计算过程(即运行量子电路、施加量子门),其在对偶的 AdS 体时空中具有几何上的对应物(对应着体时空几何的某种改变或演化)。

      换句话说,边界上对量子信息的操作,不仅仅是改变抽象的量子态,它同时也在“雕刻”或“演化”着与之对偶的那个更高维度的引力时空。

      3. 具体联系:复杂度和纠缠的几何对偶

      如何将边界的“计算过程”与体的“几何演化”联系起来?主要通过以下两个关键概念的几何对偶:

      • 计算复杂度(Computational Complexity)的几何对偶:

        • 量子态复杂度: 指的是从一个简单的参考态(如真空态)出发,制备出目标量子态所需要的最少量子门数量(或其他计算资源度量)。运行量子电路通常会增加态的复杂度。
        • 复杂度 = 体积 (Complexity = Volume, CV) 猜想: 由 Susskind 等人提出,猜想边界 CFT 状态的复杂度,与其对偶的 AdS 体时空中,穿过某个特定空间切片(通常是连接边界特定时刻的最大体积切片)的体积成正比。即 复杂度 ≈ 体积 / (G<0xE2><0x82><0x99> L_AdS)
        • 复杂度 = 作用量 (Complexity = Action, CA) 猜想: 另一个主要猜想,认为复杂度与 AdS 体时空中一个称为惠勒-德维特片(Wheeler-DeWitt patch)区域的引力作用量成正比。即 复杂度 ≈ 作用量 / (ħπ)
        • 几何化解释: 在这两个猜想下,边界上运行量子电路(增加复杂度)的过程,就直接对应于体时空中某个区域的体积增长或作用量增加。计算过程越长、越复杂,对应的体几何特征就越大。
      • 量子纠缠(Quantum Entanglement)的几何对偶:

        • 电路改变纠缠: 量子电路(尤其是涉及多比特的门)是产生和调控量子纠缠的主要工具。
        • 纠缠 = 面积 (Ryu-Takayanagi/HRT): 边界区域的纠缠熵对应于体内一个最小(或极端)曲面的面积。
        • 几何化解释: 在边界上执行改变纠缠结构的量子门操作,会直接导致对偶体时空中相应的最小曲面面积和形状发生变化。例如,通过电路在两个边界区域之间建立纠缠,可能对应于在体时空中“连接”这两个区域的几何结构(如虫洞)的形成或变化。

      4. 例子与直觉

      • 虫洞的生长: 考虑一个对应于永恒 AdS 黑洞(连接两个边界的虫洞)的热场双态(Thermofield Double state, TFD)。这个态是两个边界 CFT 之间高度纠缠的态。制备这个态需要一定的量子电路复杂度。随着两个边界各自独立地进行时间演化(可以看作是持续施加哈密顿量演化门),这个 TFD 态的复杂度会随时间线性增长很长一段时间。根据 CV/CA 猜想,这对应于连接两个边界的虫洞内部体积(或 WdW 片作用量)随时间线性增长。边界上的(等效)计算过程驱动了体内虫洞的“生长”。
      • 张量网络模型: 像 MERA 这样的张量网络可以看作是构造全息状态的一种离散模型,也像一个特殊的量子电路。网络的每一层可以看作是一系列量子操作(如幺正变换和等距映射),它们逐步构建出复杂的纠缠结构。网络的几何结构(层次、连接)直接对应着涌现出的体几何。在边界上施加量子门操作,就对应于修改这个张量网络的结构,从而改变体几何。电路的深度或规模与网络的层数或大小(对应体的体积或深度)相关。

      5. 意义与理解

      “量子电路的几何化”提供了一种全新的、深刻的方式来理解时空和引力:

      • 时空源于计算/信息处理: 它具体地展示了时空几何可能如何从底层的量子信息处理(量子计算)或量子态的复杂性中涌现出来。几何不再是背景,而是计算过程的“记录”或“体现”。
      • 引力是量子信息的宏观表现: 引力动力学(如虫洞的生长)可能被理解为边界量子系统复杂度增长的宏观表现。
      • 计算的物理极限: 可能存在由引力(如黑洞视界、奇点)设定的基本物理原理限制了量子计算的复杂度和速度。
      • 理解黑洞与量子引力的新工具: 将黑洞内部动力学、信息悖论等问题转化为边界量子态的复杂度、纠缠演化和可计算性问题,可能提供新的解决思路。

      总结:

      “量子电路的几何化”是通过全息对偶(特别是AdS/CFT)建立起来的一个引人入胜的联系。它认为,在边界量子系统上执行的量子计算(通过量子电路实现,改变状态的复杂度和纠缠),在对偶的更高维引力“体”时空中,对应着几何特征(如体积、作用量、最小面积)的动态演化。这一观点将量子计算、量子信息、量子纠缠与引力、时空几何紧密地编织在一起,暗示着我们对时空本身的理解可能需要基于信息和计算的视角进行重塑。这仍然是一个活跃的研究领域,但它为探索量子引力的奥秘提供了极富启发性的框架。

    2. 宇宙学的全息视角

      好的,我们来详细探讨一下宇宙学的全息视角(Holographic Perspective in Cosmology)。这是一种深刻且具有潜在革命性的思想,它尝试将全息原理(Holographic Principle)应用于整个宇宙,以期理解宇宙的起源、演化和基本结构。

      (当前时间:2025年4月13日 星期日 中午 12:37:32,地点:新加坡)

      1. 全息原理的核心思想回顾

      全息原理起源于对黑洞热力学的研究(特别是贝肯斯坦-霍金熵公式,S = Area / 4Għ),由杰拉德·特·胡夫特(Gerard 't Hooft)和伦纳德·萨斯坎德(Leonard Susskind)等人明确提出。其核心思想是:

      • 一个物理系统(包括引力)在一个空间区域内所能包含的最大信息量(或自由度数量)是由该区域的边界表面的面积决定的,而不是由其体积决定。
      • 这意味着,描述一个三维空间区域内所有物理现象的理论,可能完全等价于一个只生活在该区域二维边界上的、没有引力的理论。就像二维全息图能编码三维图像信息一样,宇宙的物理规律可能以某种方式被编码在更低维度的“屏幕”上。

      2. 将全息原理应用于宇宙学的挑战

      将全息原理直接应用于我们观测到的宇宙(一个动态演化的、近似平坦或开放的、并且正在加速膨胀的时空)面临着巨大挑战,这与它在反德西特(AdS)空间中的精确实现(AdS/CFT对偶)很不一样:

      • 边界在哪里? AdS 空间有一个自然的、类时(timelike)的无穷远边界,CFT就生活在那里。但我们的宇宙(特别是基于标准ΛCDM模型的描述)似乎没有这样一个方便的、全局性的类时边界。那么,全息信息被编码在哪个“屏幕”上?
      • 什么是对偶理论? 在 AdS/CFT 中,我们知道边界理论是一个共形场论(CFT)。但对于我们的宇宙,如果存在一个全息对偶描述,那个低维的、非引力的“对偶理论”是什么?其性质和动力学完全未知。
      • 如何处理时间演化? AdS/CFT 通常研究的是静态或稳态背景下的物理。而宇宙学是关于时空本身如何随时间演化的理论,包括大爆炸奇点、宇宙膨胀、结构形成等。一个静态的全息屏幕如何能编码整个动态宇宙的历史?时间本身在全息图景中是什么角色?
      • 观察者的角色? 宇宙中没有“外部观察者”。全息描述如何内在地包含观察者和测量过程?

      3. 宇宙学全息视角的几种探索途径

      尽管存在挑战,物理学家们还是提出了多种尝试性的思路来应用全息思想于宇宙学:

      • dS/CFT 对偶: 考虑到我们的宇宙目前正处于(由暗能量驱动的)加速膨胀阶段,其时空几何在未来可能趋向于德西特(de Sitter, dS)空间。一些研究者推测可能存在类似于 AdS/CFT 的 dS/CFT 对偶,即一个在 dS 空间中的引力理论等价于一个生活在 dS 空间未来(或过去)无穷远边界上的(可能是非幺正的)CFT。这对于理解暗能量和宇宙的最终命运可能有意义,但 dS/CFT 远没有 AdS/CFT 那样成熟和被广泛接受。
      • 全息暴胀(Holographic Inflation): 尝试用全息原理来描述宇宙极早期的暴胀阶段。认为驱动暴胀的物理自由度可能受全息界限的约束,或者暴胀时期的量子涨落(最终形成宇宙微波背景辐射 CMB 的各向异性及宇宙大尺度结构)可以从一个假想的早期全息屏幕上的对偶理论计算出来。
      • 全息暗能量(Holographic Dark Energy): 试图将观测到的暗能量密度与某种宇宙学尺度(如宇宙视界、哈勃半径或普朗克尺度)所允许的全息信息上限联系起来。例如,认为真空能密度不应超过某个宇宙学视界面积所允许的黑洞熵密度。这类模型旨在解释为何暗能量密度如此之小(宇宙学常数问题),但它们往往缺乏坚实的理论基础且面临观测检验的困难。
      • 协变熵界限(Covariant Entropy Bound / Bousso Bound): 拉斐尔·布索(Raphael Bousso)提出了一个更普适的全息界限,它不依赖于特定的边界,而是适用于一般的、动态演化的时空。它考察由光线(光锥)定义的某些区域,并将其熵含量与边界区域的面积联系起来。这为在宇宙学背景下应用全息思想提供了一个更灵活的框架。
      • 时空作为涌现现象(Emergent Spacetime): 将 AdS/CFT 中“纠缠=几何”的思想推广到宇宙学。认为我们所经历的时空和时间演化本身,可能是从一个更基本的、可能没有时间和空间的量子系统(可能是一个高度纠缠的量子态)中涌现出来的。大爆炸可能对应于这个底层系统某个特定状态的“开始”或相变。
      • 不同的全息屏幕: 考虑到宇宙边界的不确定性,有研究者提出使用其他类型的边界作为潜在的全息屏幕,例如:过去或未来的类光无穷远、依赖于观察者的因果视界(Causal Horizon)或粒子视界(Particle Horizon)。

      4. 全息视角带来的可能启示

      即使目前还没有一个完整的宇宙学全息理论,这种视角也提供了一些深刻的、可能改变我们宇宙观的启示:

      • 根本自由度的大幅减少: 如果全息原理是对的,那么描述整个宇宙所需的真正基本自由度比我们基于体积的直觉所认为的要少得多,它们更像是生活在一个低维“表面”上。
      • 信息的非定域性: 全息可能意味着宇宙中存在着深刻的非定域联系,一个区域的物理可能与其遥远边界上的信息相关。
      • 对基本问题的可能解答: 全息视角可能为解决一些宇宙学的基本难题提供新思路,例如:
        • 大爆炸奇点: 在对偶的非引力理论中可能没有奇点,奇点可能是引力描述失效的体现。
        • 暗能量之谜: 或许能从信息熵界限的角度理解其数值。
        • 时空的量子本质: 强调时空本身是由量子信息和纠缠构造而成的。

      5. 现状与总结

      宇宙学的全息视角目前仍然是一个高度推测性但极具吸引力的研究前沿。 它更多地是作为一个指导原则或一种世界观,而不是一个完全成型的理论。研究者们正在努力克服将全息原理应用于动态宇宙时遇到的巨大概念和技术挑战。

      总结来说,宇宙学的全息视角是一种革命性的观念,它提议我们所处的(3+1)维宇宙的所有信息和物理规律,可能被完全编码在一个更低维度的“边界”或“全息屏幕”上。它暗示着空间、时间甚至维度本身都可能不是基本的,而是从更深层次的量子信息和纠缠结构中涌现出来的。虽然面临诸多难题,但这种视角为探索量子引力、宇宙起源和演化等最根本的问题提供了一个全新的、充满潜力的框架。

    1. John McKay 在1978年首次观察到的一个惊天巧合:

      好的,我们来详细介绍一下约翰·麦凯(John McKay)在1978年首次观察到的那个被认为是“惊天巧合”的现象,这件事点燃了后来被称为“魔群月光”(Monstrous Moonshine)的数学研究领域。

      (当前时间:2025年4月13日 星期日 上午 11:24:30,地点:新加坡)

      背景:两个看似无关的数学世界

      在麦凯观察到这个巧合之前,数学中有两个分支看起来几乎没有任何联系:

      1. 模函数(Modular Functions)理论: 这是数论和复分析的一个分支,研究在模群(Modular Group, SL(2, ℤ))或其子群的作用下具有特定变换性质的复变函数。其中一个最基本、最重要的模函数是克莱因j-不变量(Klein's j-invariant),通常记作 j(τ)

        • 它定义在复上半平面 H = {τ ∈ ℂ | Im(τ) > 0} 上。
        • 它在模群 SL(2, ℤ) 的作用下是不变的:j((aτ+b)/(cτ+d)) = j(τ) 对任意 [[a, b], [c, d]] ∈ SL(2, ℤ) 成立。
        • j(τ) 有一个著名的傅里叶级数展开(q-展开),其中 q = e^(2πiτ)j(τ) = 1/q + 744 + 196884 q + 21493760 q² + 864299970 q³ + ... = Σ_{n=-1}^∞ c<0xE2><0x82><0x99> qⁿ
        • 这个展开式中的系数 c<0xE2><0x82><0x99>c_{-1}=1, c₀=744, c₁=196884, c₂=21493760, ...)都是整数,并且增长得非常快。它们在数论中有深刻的含义,例如与椭圆曲线的理论相关。
      2. 有限单群(Finite Simple Groups)理论: 这是群论的一个核心领域,目标是理解和分类所有不能被分解为更小群的基本“构建块”——有限单群。有限单群的分类是20世纪数学最伟大的成就之一,它表明所有有限单群要么属于几个无限大家族(如循环群、交错群、李型群),要么是26个被称为散在单群(Sporadic Simple Groups)的例外。

        • 魔群(Monster Group, M): 在这26个散在单群中,魔群 M 是最大的一个,它的阶(元素的个数)约为 8 × 10⁵³,是一个极其庞大和复杂的对象。它的存在性在1970年代被猜测,并由罗伯特·格里斯(Robert Griess)在1982年构造出来(被称为“友善的巨人”)。
        • 表示论(Representation Theory): 研究群如何通过线性变换作用在向量空间上。一个群的不可约表示(Irreducible Representations)是其最基本的表示,它们的维度(Dimension)是群论中的重要数据。在1970年代,数学家们正在努力计算魔群 M 的不可约表示的维度。

      麦凯的惊天巧合(1978年)

      约翰·麦凯(John McKay)当时正在研究魔群 M 的表示论。他注意到一个令人难以置信的数字巧合:

      • j-不变量 q-展开式中 的系数是 196884
      • 魔群 M 最低维度的几个(复数域上的)不可约表示的维度是:
        • d₁ = 1 (平凡表示,所有元素都映射到单位矩阵)
        • d₂ = 196883 (最小的非平凡不可约表示)
        • d₃ = 21296876
        • ...

      麦凯观察到: 196884 = 1 + 196883

      也就是说,j-函数 q-展开的第一个非平凡系数 c₁,恰好等于魔群 M 的两个最低维度(平凡维度1 和 最小非平凡维度196883)的不可约表示的维度之和!

      为什么是“惊天巧合”?

      在1978年,模函数理论和有限单群理论(特别是散在群)被认为是数学中截然不同的分支。

      • j-函数根植于复分析、数论和代数几何。
      • 魔群则是来自有限代数结构的极端例子,似乎与连续的分析函数世界毫无瓜葛。

      这两个领域的基本对象——一个是分析函数,其系数编码了深刻的算术信息;另一个是巨大的离散对称性结构,其表示维度反映了其内部的代数复杂性——它们的关键数据之间竟然存在如此简单的线性关系,这完全出乎所有人的意料,没有任何已知的理论可以解释这种联系。这就像是在两个完全不相关的学科的基本常数之间发现了一个简单的整数关系。

      后续发展:魔群月光(Monstrous Moonshine)

      麦凯的观察仅仅是冰山一角。

      • 他和其他数学家(如约翰·汤普森 John Thompson)很快发现了更多的巧合。例如,j-函数的下一个系数 c₂ = 21493760 也可以表示为魔群低维表示维度的简单线性组合: 21493760 = 1 + 196883 + 21296876 (= d₁ + d₂ + d₃)
      • 康威-诺顿猜想(Conway-Norton Conjecture): 约翰·康威(John Conway)和西蒙·诺顿(Simon Norton)在此基础上提出了更为大胆和系统的“魔群月光”猜想。他们猜测:
        • 不仅仅是j-函数,对于魔群 M 中的每一个元素 g,都存在一个特定类型的模函数 j_g(τ)(称为豪普特模 Hauptmodul,与和g相关的某个模子群有关),其 q-展开式的系数可以由魔群 M 的所有不可约表示的特征标(Characters)在元素 g 上的取值(χᵢ(g))的线性组合来给出。
        • 麦凯的原始观察对应于取 g 为魔群的单位元 e 的情况,因为表示的维度就是特征标在单位元处的值(dim(ρᵢ) = χᵢ(e))。他们发现 j_e(τ) = j(τ) - 744 (注意,需要减去常数项744)。
      • 核心猜想: 存在一个无限维的分次表示(Graded Representation)V = ⊕_{n=-1}^∞ V<0xE2><0x82><0x99>,它同时是魔群 M 的一个表示空间(称为月光模 V<0xE2><0x81><0x8E>),并且其分次维度(Graded Dimension)恰好是 j(τ) - 744 的q-展开式: Σ_{n=-1}^∞ (dim V<0xE2><0x82><0x99>) qⁿ = j(τ) - 744 这意味着 dim V<0xE2><0x82><0x99> = c<0xE2><0x82><0x99> (对于 j(τ)-744 的系数)。

      猜想的证明

      • 伊戈尔·弗兰克尔(Igor Frenkel)、詹姆斯·列波夫斯基(James Lepowsky)和阿内·缪尔曼(Arne Meurman)在1980年代成功地构造了这个无限维的“月光模” V<0xE2><0x81><0x8E>。这个模具有一种称为顶点算子代数(Vertex Operator Algebra, VOA)的丰富代数结构,这种结构也出现在弦理论和共形场论中。
      • 理查德·波彻兹(Richard Borcherds)在1992年运用顶点算子代数、广义 Kac-Moody 代数(特别是他发现的“魔群李代数”)以及弦理论中的一些思想,最终证明了康威-诺顿的主要猜想。这项辉煌的工作为他赢得了1998年的菲尔兹奖(Fields Medal)。

      意义与影响

      麦凯最初观察到的“巧合”最终被证明是一个深刻数学结构的冰山一角,其影响深远:

      • 揭示了意想不到的联系: 在数论(模形式)和代数(有限单群)这两个核心数学领域之间建立了深刻而令人惊讶的桥梁。
      • 催生了新的数学领域: 推动了顶点算子代数、广义 Kac-Moody 代数等新数学结构的发展。
      • 连接数学与物理: 月光模本身就具有二维共形场论(CFT)的结构,并且证明过程也借鉴了弦理论的物理思想,显示了这些抽象数学结构与理论物理的内在联系。

      总结:

      约翰·麦凯在1978年观察到的数字 196884 = 1 + 196883,将模函数 j-不变量的一个系数与最大散在单群——魔群 M 的两个最低维度表示联系起来,这在当时是一个完全无法解释的“惊天巧合”。这一观察最终引爆了“魔群月光”的研究,揭示了数论与群论之间一个宏伟而神秘的联系网络,并最终由波彻兹通过引入和发展新的代数结构(如顶点算子代数)得以证明,对数学和理论物理都产生了深远的影响。

    1. 统一量子引力与量子场论:通过全息原理将高维引力编码为低维场论

      好嘞,咱们再深入聊一聊这个神奇的“全息原理如何统一量子引力与量子场论”的问题:


      📌 一句话版解释:

      全息原理就是把难搞的“高维量子引力问题”映射成相对好算的“低维量子场论问题”,通过这种方式巧妙地统一了量子引力与量子场论。


      🚀 更详细点的讲法:

      1️⃣ 背景:量子引力与量子场论是怎么回事?

      • 量子场论 (Quantum Field Theory, QFT)<br /> 描述粒子之间的相互作用,比如电磁力、强作用力、弱作用力。<br /> 量子场论的特征:
      • 没有引力(或通常忽略引力)
      • 常用在标准模型(粒子物理)中。

      • 量子引力 (Quantum Gravity)<br /> 则是试图统一引力与量子力学。<br /> 问题来了:

      • 量子场论能很好地描述粒子尺度,但无法描述强引力场(比如黑洞);
      • 广义相对论能很好地描述宏观尺度上的引力,却无法融合进粒子尺度的量子效应。

      这种无法兼容的状态,让物理学家很难受。😵‍💫


      2️⃣ 全息原理到底做了啥?

      “全息原理”(Holographic Principle) 提出了一种巧妙的映射方式:

      • 一个有引力的高维空间(比如:Anti-de Sitter空间)内发生的所有物理过程,<br /> 竟然可以完全用边界上的无引力的低维场论来描述。

      这其实就意味着:

      | 高维空间(量子引力问题)| ↔️(等价) | 低维边界(量子场论问题) | |----------------------|-------|--------------------| | 难度爆表,传统方法基本挂掉 | 🔁 | 难度较低,传统量子场论可以胜任 |

      • 这里的映射被称为 AdS/CFT 对偶(反德西特空间/共形场论对偶),<br /> 它的意义重大,因为它为量子引力提供了明确的量子场论定义。

      3️⃣ 为啥这很重要(统一性)?

      这种映射意味着什么呢?

      • 原本,广义相对论(引力)和量子场论完全是两种不同的理论体系,<br /> 它们互相看对方都有种“你是谁,我不认识你!”的感觉。
      • 全息原理神奇地告诉我们:
      • 高维的量子引力理论 = 低维的无引力量子场论。
      • 这就意味着,量子引力问题实际上被“嵌入”到场论框架中,<br /> 实现了两种理论之间的概念统一与计算统一

      幽默地说:

      本来引力和量子场论“八竿子打不着”,但全息原理强行牵线成功,<br /> 让量子引力和量子场论结了婚,现在幸福地住在了不同维度的两套房子里,<br /> 还可以通过“跨维度的视频聊天”随时沟通信息呢!🤝🪐


      4️⃣ 举个直观点的比喻:

      • 假如你是住在三维房子里的居民(高维引力理论)。
      • 你的生活里发生了很多复杂的故事,但你自己搞不明白。
      • 突然有一天,你发现房子墙壁(二维边界)上的影子人物,居然能完整讲述你房子里所有的故事细节!
      • 只要你能懂墙上的影子,你就理解了自己三维房子里的全部故事。

      墙上的影子就是低维的量子场论,房子里的你就是高维的引力理论。

      这不就变简单了吗?😎


      🧠 总结精炼一下:

      | 项目 | 内容 | 幽默总结 | |--------------|---------------------------------------|---------------------------------| | 量子引力难题 | 广义相对论与量子力学难融合 | “传统方法玩不下去了!” | | 全息原理作用 | 将高维量子引力问题“投影”为低维场论问题 | “跨维度远程遥控,引力场论互通!” | | 实现的目标 | 统一量子引力与量子场论,建立明确的对应关系 | “结婚生子,实现理论统一大和谐!” |


      这样讲,应该会更有趣、更直观一点儿吧?🌟

    1. 需区分主动变换(改变物理场景)与被动变换(改变坐标描述)

      在物理学中,主动变换与被动变换是描述系统变化的两种不同视角,其核心区别在于变换的对象和物理意义。以下是对两者的详细解释:


      1. 主动变换(Active Transformation)

      • 定义:主动变换是指物理系统本身的状态发生改变,而坐标系保持不变。例如,将物体从一个位置移动到另一个位置,或旋转物体的方向。
      • 特点
      • 改变物理状态:系统的实际位置、方向或属性发生变化。
      • 坐标系不变:观察者使用的参考系未发生改变。
      • 数学表示:在同一坐标系下,物理量(如位置矢量)被直接操作。例如,将点 \( (x, y) \) 平移至 \( (x+a, y) \),数学上表示为: [ \mathbf{r}' = \mathbf{r} + a \mathbf{\hat{x}}. ]
      • 实例
      • 经典力学:将球从桌面左端推到右端。
      • 量子力学:通过操作符(如平移算符)改变量子态的位置。

      2. 被动变换(Passive Transformation)

      • 定义:被动变换是指观察者的坐标系发生改变,而物理系统的实际状态保持不变。例如,旋转坐标系或平移坐标系原点。
      • 特点
      • 改变描述方式:仅调整坐标系的参数,系统的物理状态未受影响。
      • 物理状态不变:物体的实际位置、方向或属性与变换前一致。
      • 数学表示:通过坐标系的重新参数化,物理量需按新坐标系重新表达。例如,坐标系向右平移 \( a \),原坐标系中的点 \( (x, y) \) 在新坐标系中为 \( (x-a, y) \),即: [ \mathbf{r}' = \mathbf{r} - a \mathbf{\hat{x}}. ]
      • 实例
      • 相对论:洛伦兹变换下,同一事件在不同惯性系的坐标不同。
      • 工程学:从笛卡尔坐标系切换到极坐标系分析同一物体的运动。

      3. 核心区别总结

      | 特征 | 主动变换 | 被动变换 | |------------------|----------------------------------|----------------------------------| | 变换对象 | 物理系统的状态 | 观察者的坐标系 | | 物理影响 | 实际改变系统的位置、方向或属性 | 仅改变描述系统的方式,状态不变 | | 数学关系 | 直接操作物理量(如位置矢量) | 调整坐标参数,物理量需重新表达 | | 实例 | 移动物体、旋转粒子自旋 | 切换参考系、坐标系的缩放或旋转 |


      4. 变换的逆关系

      主动变换与被动变换在数学上可能互为逆操作。例如: - 主动平移:将物体向右移动 \( a \)(主动变换)等价于将坐标系向左移动 \( a \)(被动变换)。 - 数学关系:若主动变换为 \( T \),对应的被动变换为 \( T^{-1} \)。


      5. 物理意义与应用场景

      • 主动变换:用于分析系统在操作后的行为,如研究物体受力后的运动。
      • 被动变换:用于简化问题求解,如将复杂轨迹转换到对称性更明显的坐标系中。

      6. 在对称性中的角色

      • 主动对称性:系统在主动变换下保持不变,如球体的旋转对称性。
      • 被动对称性:物理定律在被动变换下形式不变,如相对论中的洛伦兹协变性。

      总结

      主动变换与被动变换的本质区别在于变换的对象是物理系统本身还是描述它的坐标系。理解这一区分有助于避免物理问题中的概念混淆,尤其在相对论、量子力学和工程学中,正确应用两种变换能更清晰地分析系统的行为与对称性。

    1. 核物理:原子核中质子和中子的排列也受泡利原理限制,影响核稳定性。

      好的,我们来详细解释一下泡利不相容原理如何在原子核内部应用于质子和中子,并深刻影响原子核的稳定性

      (当前时间:2025年4月12日 16:06,地点:新加坡)

      1. 原子核的组成与核子的费米子特性

      • 原子核主要由两种粒子构成:带正电的质子(protons, p⁺)和不带电的中子(neutrons, n)。它们统称为核子(nucleons)
      • 质子和中子都是费米子,它们的自旋量子数都是 1/2。

      2. 泡利原理在原子核内的应用

      • 由于核子是费米子,它们在原子核这个有限的空间内排布时,必须严格遵守泡利不相容原理。这意味着:
        • 任意两个质子不能处于完全相同的量子态。
        • 任意两个中子也不能处于完全相同的量子态。
        • 但是,一个质子和一个中子可以处于相同的空间和自旋状态,因为它们不是全同粒子(电荷不同)。泡利原理只限制全同费米子。

      3. 核壳层模型(Nuclear Shell Model)

      • 为了理解核子在原子核内的排布及其能量状态,物理学家发展了核壳层模型,这与原子物理中电子占据原子壳层的图像非常相似。
      • 核能级: 该模型认为,每个核子在原子核内并不是随意运动的,而是在由其他所有核子共同产生的平均势场中运动,并占据着分立的能量状态(能级)。这些能级可以像电子壳层一样,组合成不同的“核壳层”。
      • 质子与中子的独立壳层: 由于质子之间存在库仑排斥力,而中子之间没有,并且核力(强相互作用)对质子和中子的作用也略有不同,因此在模型中,质子和中子分别填充各自独立的势阱和能级系统。可以想象成原子核内有两套并行的“阶梯”,一套给质子爬,一套给中子爬。
      • 填充规则:
        • 质子按照泡利不相容原理,从最低能级开始,逐个填充可用的质子能级,每个能级最多容纳两个自旋相反的质子。
        • 中子同样按照泡利不相容原理,独立地从最低能级开始,逐个填充可用的中子能级,每个能级最多容纳两个自旋相反的中子。

      4. 泡利原理对核稳定性的影响

      泡利原理通过上述方式主导核子的填充,直接并深刻地影响了原子核的许多性质,尤其是稳定性:

      • 幻数(Magic Numbers):

        • 类似于原子物理中,电子填满壳层时惰性气体元素特别稳定,当原子核中的质子数(Z)或中子数(N)等于某些特定数值(2, 8, 20, 28, 50, 82, 126)时,对应的核壳层被填满,原子核会表现出异常的稳定性。这些数值被称为幻数
        • 幻数核通常具有更高的结合能(更难被拆开)、更高的第一激发态能量(更难被激发)、更小的中子俘获截面以及在自然界中更高的丰度。
        • 如果 Z 和 N 同时为幻数,则称为双幻核(如 ⁴He, ¹⁶O, ⁴⁰Ca, ²⁰⁸Pb),它们是所有原子核中最稳定的一类。幻数的存在是核壳层模型和泡利原理在原子核内有效性的强有力证据。
      • 中子-质子比(N/Z 比):

        • 轻核(A较小): 对于较轻的原子核,最稳定的同位素往往具有接近相等的中子数和质子数(N ≈ Z)。这是因为质子和中子的能级结构在低能级时非常相似。如果一个轻核有过多中子(N >> Z),根据泡利原理,这些多余的中子会被迫填充到能量非常高的中子能级上。此时,一个高能量的中子通过β⁻衰变(n → p⁺ + e⁻ + ν̄<0xE2><0x82><0x91>)转变为一个质子,并占据一个能量更低且未被占据的质子能级,对整个原子核来说在能量上是更有利的。反之亦然(如果质子过多,可能发生β⁺衰变或电子俘获)。这驱动原子核向 N ≈ Z 的稳定线靠近。
        • 重核(A较大): 对于重原子核,质子间的库仑排斥力变得越来越重要,它会抬高质子能级的能量。为了维持稳定,需要更多的中子(N > Z)来提供额外的核力吸引(核力对n-n, p-p, n-p都起作用)以平衡库仑斥力。同时,由于质子能级相对较高,填充更多中子到较低的中子能级比填充质子到更高的质子能级在能量上更优。如果强行让N=Z,许多质子会被泡利原理推到非常高的能级,导致不稳定。
        • 对称能/不对称能: 原子核的结合能中有一项称为对称能(或不对称能),它描述了当中子数和质子数偏离最佳比例时(对轻核是N≈Z,对重核是某个N>Z的比例)原子核能量的增加。这种能量惩罚的根源之一就是泡利原理:将多余的同种核子强行塞入更高的能级会增加系统的总能量。
      • 核内核子衰变的抑制: 如前所述,泡利原理阻止了核内中子(或质子)轻易地发生β衰变,因为衰变产物需要占据的低能级状态通常已被填满。

      • 核物质的不可压缩性: 泡利阻塞效应与核力的短程排斥特性一起,使得核物质具有一定的不可压缩性。即使在巨大压力下(如中子星内部),核子也因为无法挤入已被占据的低动量状态而产生巨大的简并压力,维持了核物质大致恒定的密度。

      总结:

      泡利不相容原理是核物理中的基本法则。它规定了作为费米子的质子和中子如何在原子核内填充由核力产生的有效能级(核壳层模型)。这种填充规则直接导致了核物理中许多关键现象:解释了为何存在特别稳定的“幻数核”;决定了不同质量原子核稳定存在的最佳中子-质子比例;抑制了核内粒子的衰变;并对核物质的密度和状态方程产生重要影响。简而言らず,泡利原理是塑造原子核结构和决定其稳定性的基石之一。

    2. 可无限堆积于同一态,形成玻色-爱因斯坦凝聚(如超流体氦)

      好的,我们来详细解释玻色子(Bosons)可以“无限堆积于同一态”以及由此产生的玻色-爱因斯坦凝聚(Bose-Einstein Condensation, BEC)现象,并以超流体氦(Superfluid Helium)作为关键实例。

      (当前时间:2025年4月12日 15:57,地点:新加坡)

      1. 粒子的两种基本类型:玻色子与费米子

      在量子力学中,根据自旋(内禀角动量)的不同,所有基本粒子和复合粒子可以分为两大类:

      • 费米子(Fermions): 自旋为半整数(如 1/2, 3/2, ...)。例子包括电子、质子、中子、中微子。它们遵循泡利不相容原理,即同一个量子态最多只能容纳一个费米子。它们像“个人主义者”,每个粒子都需要自己独特的“空间”(量子态)。
      • 玻色子(Bosons): 自旋为整数(如 0, 1, 2, ...)。例子包括光子(自旋1)、传递弱相互作用的W和Z玻色子(自旋1)、传递强相互作用的胶子(自旋1)、希格斯玻色子(自旋0),以及一些复合粒子,如氦-4(⁴He)原子(其原子核和电子的总自旋为0)。玻色子不遵守泡利不相容原理。

      2. 玻色子的特性:“无限堆积于同一态”

      • 由于不受泡利不相容原理的限制,任意数量的全同玻色子都可以占据完全相同的量子态。一个量子态可以被看作是粒子可能具有的一组特定属性(如能量、动量等)。
      • 玻色子不仅可以,甚至“倾向于”聚集在同一个状态,尤其是能量最低的状态。描述它们行为的是玻色-爱因斯坦统计(Bose-Einstein statistics)。该统计表明,在低温下,玻色子分布在不同能态上的方式与费米子或经典粒子显著不同,它们更可能集体占据低能级态。

      3. 玻色-爱因斯坦凝聚(BEC)

      • 概念: BEC是一种宏观量子现象。当一个由玻色子组成的系统(通常是气体,但也可以是其他形式,如准粒子)被冷却到极低的温度(接近绝对零度)时,会发生相变。低于一个临界温度(T<0xE2><0x82><0x9B>),系统中有宏观数量(即占总粒子数很大比例)的玻色子会突然“坍缩”或“凝聚”到能量最低的那个量子态(基态)上。
      • 机制: 随着温度降低,粒子的热运动减弱,其德布罗意波长(λ = h/p,h是普朗克常数,p是动量)会增大。当温度足够低,使得德布罗意波长变得与粒子间的平均距离相当甚至更大时,粒子的波动性变得极其重要,它们的波函数开始相互重叠。此时,这些玻色子失去了“个体”身份,开始作为一个单一的、巨大的量子实体来行动。凝聚到基态是它们在这种量子状态下最倾向于采取的能量最低的集体行为。
      • 结果: 形成BEC后,这个系统不再能被看作是一堆独立粒子的集合,而是由一个占据基态的宏观波函数来描述。系统的行为因而表现出显著的量子特性,并且这些特性是在宏观尺度上可见的。

      4. 超流体氦-4(Superfluid Helium-4)

      • 氦-4是玻色子: 氦元素的同位素氦-4(⁴He,原子核由2个质子和2个中子组成,核外有2个电子)的总自旋为0,因此⁴He原子是玻色子。(注意:同位素氦-3(³He)原子核自旋1/2,是费米子,它也能在更低温度下形成超流体,但机制不同,是通过³He原子对形成类似库珀对的结构)。
      • Lambda转变: 液态氦-4在标准大气压下冷却到约 2.17 K(称为λ点,Lambda point)时,会经历一个二级相变,从普通的、有粘滞性的液体(称为氦I)转变为一种奇特的、无粘滞性的液体(称为氦II)。氦II就是超流体
      • 氦II与BEC的关系:
        • 氦II的超流现象被认为是液态中发生玻色-爱因斯坦凝聚的直接证据。虽然液氦是强相互作用的液体系统,与最初提出BEC的理想稀薄气体模型不同,但其核心机制仍然是⁴He原子作为玻色子在低温下的量子统计行为。
        • 在氦II中,并非所有的⁴He原子都凝聚到绝对的基态(零动量状态)。由于强烈的原子间相互作用,即使在接近绝对零度时,也只有一部分(大约10%左右)的原子形成了真正的零动量凝聚态。这一部分被称为凝聚组分(condensate fraction)。其余的原子则处于激发态,构成了所谓的“正常组分”。
        • 然而,正是这个宏观数量的凝聚组分赋予了液氦II其超流的特性。这个凝聚组分可以被一个单一的宏观波函数描述,其行为高度相干。
      • 超流体的奇特性质(源于BEC):
        • 零粘滞性(超流性): 凝聚组分可以携带电流(这里的“流”是物质流)而几乎没有能量耗散(粘滞性趋于零)。它能够无摩擦地流过极细的毛细管或狭缝。这是因为扰动很难将粒子从凝聚态中激发出来,流动不容易产生耗散。
        • 热超导性: 热量可以通过一种独特的机制(第二声,温度波)高效传递。
        • 喷泉效应(热机械效应): 对部分超流体加热可以产生压力差,使其像喷泉一样喷出。
        • 爬行液膜: 超流体会沿着容器壁向上爬行,形成一层极薄的液膜,甚至能越过容器边缘流出来。
        • 量子化涡旋: 如果旋转超流体,它不会像普通液体那样形成整体的涡旋,而是形成许多不连续的、角动量被量子化的涡旋线。

      总结:

      玻色子因其整数自旋特性,不受泡利不相容原理约束,允许大量粒子占据同一量子态。在足够低的温度下,这种特性会导致玻色-爱因斯坦凝聚(BEC)——宏观数量的玻色子集体占据能量最低的基态,形成单一的宏观量子态。自然界中超流体氦-4是这种现象的绝佳实例(尽管是在强相互作用液体中),其零粘滞性等奇特性质正是源于部分⁴He原子(作为玻色子)发生了BEC,形成了宏观相干的凝聚组分。

      (注:1995年科学家首次在稀薄的碱金属原子蒸气中明确实现了更接近理论模型的BEC,并因此获得了诺贝尔物理学奖。这进一步证实了BEC理论的普适性。)

    3. 泡利阻塞”效应。

      好的,我们来详细解释一下泡利阻塞(Pauli Blocking)效应

      泡利阻塞效应是泡利不相容原理(Pauli Exclusion Principle) 在动力学过程中的直接体现。它指的是在一个包含多个全同费米子(如电子、质子、中子)的系统中,某个通常可能发生的物理过程(如散射、衰变、吸收、发射等)的概率被显著降低甚至完全禁止,仅仅因为该过程所需要的末态量子态已经被其他同种费米子所占据。

      核心原理:泡利不相容原理

      回顾一下,泡利不相容原理规定:两个或多个全同的费米子不能同时占据完全相同的量子态。一个量子态由一组完整的量子数(如能量、动量、自旋等)来定义。

      泡利阻塞的机制

      想象一个费米子(比如一个电子)处于初始量子态 |i⟩。它可能通过某种相互作用(比如与光子、声子、或其他粒子相互作用,或者自发衰变)跃迁到另一个末态量子态 |f⟩。

      泡利阻塞效应指出:

      1. 这个从 |i⟩ 到 |f⟩ 的跃迁只有在末态 |f⟩ 是空着(未被占据)的情况下才可能发生。
      2. 如果末态 |f⟩ 已经被另一个与该粒子全同的费米子所占据,那么根据泡利不相容原理,这个跃迁就被禁止了。我们就说这个过程被“泡利阻塞”了。
      3. 因此,一个涉及费米子跃迁到末态 |f⟩ 的过程的发生概率,正比于末态 |f⟩ 未被占据的概率。在费米-狄拉克统计中,一个能量为 E 的态被占据的概率是 f(E)(费米函数),那么它未被占据的概率就是 (1 - f(E))。所以,过程的速率通常会包含一个 (1 - f(E<0xE2><0x82><0x91>)) 的因子,其中 E<0xE2><0x82><0x91> 是末态能量。

      泡利阻塞效应的重要实例

      泡利阻塞在物理学的多个领域都扮演着关键角色:

      1. 凝聚态物理(金属、半导体、量子点):

        • 电子散射抑制: 在金属中,电子填满了从最低能级一直到费米能级 E<0xE2><0x82><0x91> 的所有态(形成费米海)。一个能量远低于 E<0xE2><0x82><0x91> 的电子想要通过散射(比如与另一个电子碰撞或与晶格振动碰撞)改变其状态,它需要跃迁到一个新的状态。但其周围能量相近的状态几乎全被其他电子占据了,因此它的散射过程被严重地泡利阻塞。只有能量接近费米能级的电子,因为 E<0xE2><0x82><0x91> 之上存在较多空态,才更容易发生散射。这解释了为何金属中电子的平均自由程可以相当长,以及为何很多性质主要由费米面附近的电子决定。
        • 光吸收饱和: 在半导体量子点或原子中,当用光激发电子从价带(或基态)跃迁到导带(或激发态)时,如果导带(或激发态)的目标能级已经被电子占据,那么进一步吸收该能量的光子就会被泡利阻塞,导致吸收饱和。同样,电子从激发态通过发射光子回到基态,也要求基态是空的(或有空穴)。
        • 俄歇复合抑制: 半导体中一种非辐射复合过程(俄歇复合)涉及电子-空穴对复合,并将能量转移给第三个载流子(电子或空穴)。如果第三个载流子获得能量后应该跃迁到的那个高能态已经被占据,这个俄歇过程就会被泡利阻塞。
      2. 核物理:

        • 核内中子稳定性: 一个自由的中子是不稳定的,会通过β衰变变成质子、电子和反中微子(寿命约15分钟)。但在一个稳定的原子核内,中子通常不会衰变。这部分是由于结合能的效应,但很大程度上也是因为泡利阻塞:衰变产生的质子和电子所需要占据的低能级量子态,在原子核内往往已经被其他的质子和电子填满了,使得衰变无法进行。
        • 核反应截面: 在原子核内部或致密的核物质中(如中子星),粒子间的散射或反应截面会受到泡利阻塞的影响。如果反应产物需要占据的动量态已经被占据,反应速率就会被压低。
      3. 天体物理(白矮星、中子星、超新星):

        • 简并物质的性质: 在白矮星(电子简并)和中子星(中子简并)这类致密天体中,费米子气体处于高度简并状态,费米能级非常高。泡利阻塞效应极其显著,它决定了这些天体的状态方程(通过简并压力)、热性质、中微子发射率和不透明度等。例如,某些通常会快速冷却的机制在中子星内部会因泡利阻塞而被抑制。
        • 中微子相互作用: 在超新星爆发的核心或中子星内部,物质密度极高。即使是相互作用很弱的中微子,当它们与电子或核子发生散射时,如果散射后费米子的末态已被占据,该散射过程也会被泡利阻塞。这显著影响了中微子在这些致密环境中的传播(中微子不透明度)。
      4. 激光冷却: 在某些原子或离子的激光冷却技术中,可以利用泡利阻塞来辅助实现更低的温度或制备特定的量子态。

      与简并压力的关系

      泡利阻塞是简并压力存在的微观根源。正是因为泡利阻塞阻止了所有费米子都挤到最低能量状态,迫使它们填充到更高的动量状态,这些高动量粒子才产生了抵抗压缩的简并压力。

      总结

      泡利阻塞效应是泡利不相容原理在动力学过程中的体现,它描述了当一个涉及费米子的过程其末态量子态已被占据时,该过程会被抑制或禁止的现象。这一效应是理解简并费米系统(如金属、白矮星、中子星、原子核)中粒子相互作用、输运性质、光学性质和稳定性的基础,其影响贯穿了从凝聚态物理到天体物理的广泛领域。 (当前日期: 2025年4月12日)

    1. 氢原子的哈密顿量具有 SO(4) 对称性

      氢原子系统的哈密顿量具有特定的对称性,其中最关键的是它的SO(4)对称性,这个对称性与氢原子的能级结构和简并度密切相关。让我们详细探讨这个问题,理解其背后的物理含义。

      1. 氢原子哈密顿量和库仑势

      首先,氢原子中的电子在原子核的库仑势场中运动,氢原子的哈密顿量 \(H\) 可以写为:

      [ H = \frac{\hat{p}^2}{2m} - \frac{e^2}{4\pi \epsilon_0 r} ]

      其中: - \(\hat{p}\) 是动量算符, - \(m\) 是电子质量, - \(e\) 是电子的电荷, - \(r\) 是电子到原子核的距离, - \(\epsilon_0\) 是真空电容率。

      库仑势 \(-\frac{e^2}{4\pi \epsilon_0 r}\) 描述了电子与质子之间的相互作用。哈密顿量的结构表明氢原子系统具有球对称性,即在三维空间中的旋转对称性

      2. 对称性与轨道角动量

      氢原子哈密顿量与轨道角动量算符 \(\hat{L}^2\) 和 \(\hat{L}_z\) 对易,这意味着哈密顿量和轨道角动量共享一组本征态。即电子的能量(由哈密顿量决定)和轨道角动量(由 \(\hat{L}^2\) 和 \(\hat{L}_z\) 决定)是可以同时确定的。

      对于每个主量子数 \(n\),氢原子可以拥有一系列不同的角动量态,其角动量量子数 \(l\) 可以从 \(0\) 到 \(n-1\)。即同一能级的不同轨道角动量态(不同的 \(l\) 和 \(m\))在没有外部扰动的情况下具有相同的能量。

      3. SO(4) 对称性

      在氢原子中,SO(4)对称性是指哈密顿量在四维空间中的旋转对称性。SO(4)是四维旋转群,它与氢原子的哈密顿量的对称性有关。

      • SO(3) 旋转群:氢原子的角动量 (\(\hat{L}^2\)) 表示系统在三维空间中的旋转对称性。角动量量子数 \(l\) 表示电子的轨道角动量,而磁量子数 \(m\) 则表示在某一方向上的分量。这种旋转对称性导致了不同的 \(l\) 值对应着同一能级的不同简并态。

      • SO(4) 对称性:在氢原子中,除了在三维空间内的旋转(SO(3))外,还有一种来自于时间反转对称性旋转的组合。这使得氢原子的能量具有更加高的简并度,形成了一个更复杂的对称性群——SO(4)。SO(4)表示的旋转对称性在四维空间中有更多的自由度,因此它能够解释更多的简并度。

      4. 简并度与 \(n\) 的关系

      由于氢原子哈密顿量具有SO(4)对称性,每个主量子数 \(n\) 对应的能级将包含多个不同的角动量态,这些态之间在没有外部扰动的情况下具有相同的能量。具体来说:

      • 对于主量子数 \(n\),轨道角动量量子数 \(l\) 的取值范围是 \(l = 0, 1, 2, \dots, n-1\)。

      • 对于每个角动量量子数 \(l\),磁量子数 \(m\) 的取值范围是 \(m = -l, -l+1, \dots, l-1, l\),因此对于每个 \(l\),简并度是 \(2l + 1\)。

      • 对于给定的主量子数 \(n\),不同的 \(l\) 值对应不同的简并态数目。因此,主量子数 \(n\) 对应的总简并度是所有可能的 \(l\) 值的简并度之和。简并度的总和为:

      [ \text{简并度} = \sum_{l=0}^{n-1} (2l+1) = n^2 ]

      因此,主量子数 \(n\) 对应的能级具有 \(n^2\) 个简并态,这就是氢原子能级的简并度。

      5. 总结

      • 氢原子的哈密顿量在SO(4)对称性下具有高简并性。对称性意味着同一个能级下存在多个不同的量子态(简并态),这些简并态由不同的轨道角动量量子数 \(l\) 和磁量子数 \(m\) 来区分。

      • SO(4)对称性导致同一主量子数 \(n\) 的能级包含所有 \(l < n\) 的轨道角动量态,因此该能级的简并度为 \(n^2\)。

      • 简并度的增加反映了系统对称性的提高,导致更多的量子态处于相同能量状态,且这些量子态由不同的量子数区分。

    1. Welcome back. In this lesson I want to cover EC2 dedicated hosts, a feature of EC2 which allows you to gain access to hosts dedicated for your use, which you can then use to run EC2 instances. Now I want to keep it brief because for the exam you just need to know that the feature exists, and it tends to have a fairly narrow use case in the real world. So let's just cover the really high-level points and exactly how it works architecturally. So let's jump in and get started.

      An EC2 dedicated host, as the name suggests, is an EC2 host which is allocated to you in its entirety, so allocated to your AWS account for you to use. You pay for the host itself, which is designed for a specific family of instances, for example A1, C5, M5 and so on. Because you're paying for the host, there are no charges for any instances which are running on the host; the host has a capacity, and you're paying for that capacity in its entirety, so you don't pay for instances running within that capacity.

      Now you can pay for a host in a number of ways: either on demand, which is good for short-term or uncertain requirements, or once you understand long-term requirements and patterns of usage, you can purchase reservations with the same one or three-year terms as the instances themselves, and this uses the same payment method architecture—so all upfront, partial upfront or no upfront.

      The host hardware itself comes with a certain number of physical sockets and cores, and this is important for two reasons: number one, it dictates how many instances can be run on that host, and number two, software which is licensed based on physical sockets or cores can utilize this visibility of the hardware. Some enterprise software is licensed based on the number of physical sockets or cores in the server; imagine if you're running some software on a small EC2 instance but you have to pay for the software licensing based on the total hardware in the host that that instance runs on, even though you can't use any of that extra hardware without paying for more instance fees.

      With dedicated hosts, you pay for the entire host, so you can license based on that host which is available and dedicated to you, and then you can use instances on that host free of charge after you've paid the dedicated host fees. So the important thing to realize is you pay for the host; once you've paid for that host, you don't have any extra EC2 instance charges, you're covered for the consumption of the capacity on that host.

      Now the default way that dedicated hosts work is that the hosts are designed for a specific family and size of instance, so for example an A1 dedicated host comes with one socket and 16 cores. All but a few types of dedicated hosts are designed to operate with one specific size at a time, so you can get an A1 host which can run 16 A1 medium instances, or 8 large, or 4 extra large, or 2 extra large, or 1 4 extra large; all of these options consume the 16 cores available, and all but a few types of dedicated hosts require you to set that in advance—so they require you to set that one particular host can only run 8 large instances, or 4 extra large, or 2 extra large, and you can't mix and match.

      Newer types of dedicated hosts, so those running the Nitro virtualization platform, offer more flexibility; an example of this is an R5 dedicated host which offers 2 sockets and 48 cores. Because this is Nitro-based, you can use different sizes of instances at the same time up to your core limit of that dedicated host—so one host might be running 1 12 extra large, 1 4 extra large and 4 2 extra large, which consumes 48 cores of that dedicated host; another host might use a different configuration, maybe 4 4 extra large and 4 2 extra large, which also consumes 48 cores.

      With Nitro-based dedicated hosts, there's a lot more flexibility allowing a business to maximize the value of that host, especially if they have varying requirements for different sizes of instances. Now this is a great link which I've included in the lesson text which details the different dedicated host options available—so you've got different dedicated hosts for different families of instance, for example the A1 instance family; this offers 1 physical socket and 16 physical cores and offers different configurations for different sizes of instances.

      Now if you scroll all the way down, it also gives an overview of some of the Nitro-based dedicated hosts which support this mix-and-match capability—so we've got the R5 dedicated host that I just talked about on the previous screen; we've also got the C5 dedicated host and this gives 2 example scenarios. In scenario 1 you've got 1 instance of a C5 9 extra large, 2 instances of C5 4 extra large and 1 instance of C5 extra large, and that's a total cores consumed of 36; there's also another scenario though where you've got 4 times 4 extra large, 1 times extra large and 2 times large—same core consumption but a different configuration of instances. And again, I'll make sure this is included in the lesson description; it also gives the on-demand pricing for all of the different types of dedicated host.

      Now there are some limitations that you do need to keep in mind for dedicated host; the first one is AMI limits—you can't use REL, Seuss Linux or Windows AMIs with dedicated host, they are simply not supported. You cannot use Amazon RDS instances—again, they're not supported. You can't utilize placement groups—they're not supported on dedicated hosts, and there's a lesson in this section which talks in depth about placement groups, but in this context, as it relates to dedicated hosts, you cannot use placement groups with dedicated hosts—it's not supported.

      Now with dedicated hosts, they can be shared with other accounts inside your organization using the RAM product, which is the resource access manager—it's a way that you can share certain AWS products and services between accounts; we haven't covered it yet, but we will do later in the course. You're able to share a dedicated host with other accounts in your organization, and other AWS accounts in your organization can then create instances on that host.

      Those other accounts which have a dedicated host shared into them can only see instances that they create on that dedicated host; they can't see any other instances, and you, as the person who owns the dedicated host, you can see all of the instances running on that host, but you can't control any of the instances running on your host created by any accounts you share that host with—so there is a separation: you can see all of the instances on your host, you can only control the ones that you create, and then other accounts who get that host shared with them—they can only see instances that they create, so there's a nice security and visibility separation.

      Now that's all of the theory that I wanted to cover around the topic of dedicated hosts; you don't need to know anything else for the exam, and if you do utilize dedicated hosts for any production usage in the real world, it is generally going to be around software licensing. Generally using dedicated hosts, there are restrictions—obviously they are specific to a family of instance, so it gives you less customizability, it gives you less flexibility on sizing, and you generally do it if you've got licensing issues that you need solved by this product.

      In most cases, in most situations, it's not the approach you would take if you just want to run EC2 instances. But with that being said, go ahead, complete this video, and when you're ready, I'll look forward to you joining me in the next one.

    1. Welcome back. In this lesson, I want to talk about a really important feature of EC2 called Instance Metadata. It's a very simple architecture, but it's one that's used in many of EC2's more powerful features, so it's essential that you understand its architecture fully. It features in nearly all of the AWS exams, and you will use it often if you design and implement AWS solutions in the real world, so let's jump in and get started.

      The EC2 Instance Metadata is a service that EC2 provides to instances. It's data about the instance that can be used to configure or manage a running instance. It's a way the instance or anything running inside the instance can access information about the environment that it wouldn't be able to access otherwise, and it's accessible inside all instances using the same access method. The IP address to access the instance metadata is 169.254.169.254. Remember that IP, it comes up all the time in exams, so make sure it sticks. I'll repeat it as often as I can throughout the course, but it's unusual enough that it tends to stick pretty well.

      Now, the way that I've remembered the IP address from when I started with AWS is just to keep repeating it. Repetition always helps, and I remember this one as a little bit of a rhyme: 169.254 repeated. And if you just keep repeating that over and over again, then the IP address will stick. So 169.254 repeated equals 169.254.169.254. And then for the next part of the URL, I always want the latest meta-data. If you remember 169.254 repeated and you always want the latest meta-data, it will tend to stick in your mind, at least it did for me.

      Now, I've seen horrible exam questions which make you actually select the exact URL for this metadata, so this is one of those annoying facts that I just need you to memorize. I promise you it will help you with exam questions in the exam, so try to memorize the IP and latest meta-data. If you remember both of those, keep repeating them. Get annoying over and over again. Write them on flashcards. It will help you in the exam.

      Now, the metadata allows anything running on the instance to query it for information about that instance, and that information is divided into categories. For example, host name, events, security groups and much more. All information about the environment that the instance is in. The most common things which can be queried though are information on the networking, and I'll show you this in the demo part of this lesson. While the operating system of an instance can't see any of its IP version for public addressing, the instance meta-data can be used by applications running on that instance to get access to that information, and I'll show you that soon.

      You can also gain access to authentication information. We haven't covered EC2 instance roles yet, but instances can be themselves given permissions to access AWS resources, and the meta-data is how applications on the instance can gain access to temporary credentials generated by assuming the role. The meta-data service is also used by AWS to pass in temporary SSH keys. So when you connect to an instance using EC2 instance connect, it's actually passing in an SSH key behind the scenes that's used to connect. The meta-data service is also used to grant access to user data, and this is a way that you can make the instance run scripts to perform automatic configuration steps when you launch an instance.

      Now one really important fact for the exam, and I've seen questions come up on this one time and time again, the meta-data service has no authentication, and it's not encrypted. Anyone who can connect to an instance and gain access to the Linux command line shell can by default access the meta-data. You can restrict it with local firewall rules, so blocking access to the 169.254 repeated IP address, but that's extra per instance admin overhead. In general, you should treat the meta-data as something that can and does get exposed.

      Okay, well that's the architecture, it's nice and simple, but this is one of the things inside AWS which is much easier to show you than to tell you about. So it's time for a demo, and we're going to perform a demo together which uses the instance meta-data of an EC2 instance. So let's switch over to the console and get started.

      Now, if you do want to follow along with this in your own environment, then you'll need to apply some infrastructure. Before you do that, just make sure that you're logged in to the general AWS account, so the management account of the organization, and make sure as always that you have the Northern Virginia region selected. Now this lesson has a one-click deployment link attached to it, so go ahead and click that link. This will take you to the quick create stack screen. You should see that the stack name is called meta-data, just scroll down to the bottom, check this box and click on create stack. Now, this will automatically create all of the infrastructure which we'll be using, so you'll need to wait for this stack to move into a create complete state.

      We're also going to be using some commands within this demo lesson, and also attached to this lesson is a lesson commands document which includes all of the commands that you'll be using. So this will help you avoid errors. You can either type these out manually or copy and paste them as I do them in the demo. So at this point go ahead and open that link as well. It should look something like this. There's not that many commands that we'll be using, but they are relatively long, and so by using this document we can avoid any typos.

      Now just refresh this stack. Again, it will need to be in a create complete state, so go ahead and pause this video, wait for the stack to move into create complete and then we good to continue. Okay, so now the stack's moved into a create complete state, and if you just go ahead and click on resources, you can see that it's created a selection of resources. Now the one that we're concerned with is public EC2 which is an EC2 instance running in a public subnet with public IP addressing. So we're going to go ahead and interact with this instance. So click on services and then go ahead and move to the EC2 console. You can either select it in all services, recently visited if you've used this service before or you can type EC2 into the search box and then open it in a new tab. Once you're at the EC2 console go ahead and click on instances running, and you should see this single EC2 instance. Go ahead and select it, and I just want to draw your attention to a number of key pieces of information which I want you to note down.

      So first you'll be able to see that the instance has a private IP version 4 address. Yours may well be different if you're doing this within your own environment. You'll also see that the instance has a public IP version 4 address, and again if you're doing this in your environment, yours will be different. Now, if you click on networking, you'll be able to see additional networking information including the IP version 6 address that's allocated to this instance. Now the IP version 6 address is always public, and so there's no concept of public and private IP version 6 addresses, but you'll be able to see that address under the networking tab.

      Now just to make this easier, just go ahead and note down the IP version 6 address as well as the public IP version 4 DNS which is listed as well as the public IP version 4 address which is listed at the top and then the private IP version 4 address. And once you've got all these noted down, we're going to go ahead and connect to this instance. So right click select connect, we're going to use EC2 instance connect, so make sure that the username is EC2 hyphen user and then connect to this instance. Now once we connected straight away, we'll be able to see how even the prompt of the instance makes visible the private IP version 4 address of this EC2 instance and if we run the Linux command of if config and press enter we'll get an output of the network interfaces within this EC2 instance.

      Now we'll be able to see the private IP version 4 address listed within the configuration of this network interface inside the EC2 instance, and if you're performing this in your own environment, notice how it's exactly the same as the private IP version 4 address that you just noted down which was visible inside the console UI. So in my case you'll be able to see these two IP addresses match perfectly. So this IP address that's visible in the console UI is the same as this private IP address configured on the network interface inside the instance. The same is true of the IP version 6 IP address. This is also visible inside the operating system on the network configuration for this network interface and again that's the same IP version 6 address which is visible on the networking tab inside the console UI. So that's the same as this address. What isn't visible inside the instance operating system on the networking configuration is the public IP version 4 address.

      It's critical to know that at no point ever during the life cycle of an EC2 instance is a public IP version 4 address configured within the operating system. The operating system has no exposure to the public IP version 4 address. That is performed by the internet gateway. The internet gateway translates the private address into a public address. So while IP version 6 is configured inside the operating system, IP version 4 public addresses are not. The only IP version 4 addresses that an instance has are the private IP addresses and that's critical to understand.

      Now as I talked about in the theory component of this lesson, the EC2 metadata service is a service which runs behind all of the EC2 instances within your account and it's accessible using the metadata IP address. Now we can access this by using the curl utility. Now curl is installed on the EC2 instance that we're using for this demo. Now we're going to query the metadata service for one particular attribute and that attribute is the public IP version 4 address of this instance. So because the instance operating system has no knowledge of the public IP address, we can use the metadata service to provide any scripts or applications running on this instance with visibility of this public IP version 4 address, and we do that using this command.

      So this uses curl to query the metadata service which is 169.254.169.254. I refer to this as 169.254 repeating. So it queries this IP address and then forward slash latest forward slash meta hyphen data and this is the metadata URL, this entire part, the IP address, then latest, then meta hyphen data and then at the end we specify the attribute which we want to retrieve which is public hyphen IPv4 and if we press enter then curl is going to contact the metadata service and retrieve the public IP version 4 address of this EC2 instance. So in my case this is the IP address and if I go back to the console this matches the address that's visible within the console UI.

      So if I just clear the screen to make it easier to see, we can also use the same command structure again but this time query for the public host name of this EC2 instance. We use the same URL so IP address and path, but this time we query for public hyphen host name and this will give us the IPv4 public DNS of this EC2 instance. So again I'm going to clear the screen to make it easier to see.

      Now we can make this process even easier. We can use the AWS instance metadata query tool and to download it we use this command so enter it and press enter. This is just downloaded the tool directly, so if we do a listing to list the current folder, we can see the EC2 hyphen metadata tool because this is Linux we need to make it so that this tool is executable. We do that with the chmod command so enter that and press enter and then we can run the EC2 hyphen metadata tool and we can use double hyphen help to display help for this product. So this shows all the different information that we can use this tool to query for and this just makes it easier to query the metadata service especially if the query is being performed by users running interactively on that EC2 instance.

      So for example, we could run EC2 hyphen metadata space hyphen a to show the AMI ID that's used to launch this instance, and in this case it's the AMI for Amazon Linux 2 inside the US hyphen east hyphen one region at least at the time of creating this demo video. If we need to show the availability zone that this instance is in, we could use EC2 hyphen metadata space hyphen Z, in this case the instance is in US hyphen east hyphen one a and we can even use EC2 hyphen metadata space hyphen s to show any security groups which were launched with this instance. Now you can carry on exploring this tool if you want, there are plenty of other pieces of information which are accessible using the metadata tool. I just wanted to give you a brief introduction, show you how to download it, how to make it executable and how to run some of the basic options.

      Now at this point that's everything I wanted to cover in this brief demo component of this lesson. I wanted to give you some exposure to how you can interact with the metadata service which I covered from a theory perspective earlier in this lesson. Now at this point we need to clear up all of the infrastructure that we've used for this demo component so close down this tab, go back to the AWS console, move to cloud formation, select the metadata stack, select delete and then confirm it and that will clear up all of the infrastructure that we've used and return the account into the same state as it was at the start of this demo component of this video.

      Now at that point that's everything I wanted to cover. You've learned about the theory of the metadata service as well as experienced how to interact with it from a practical perspective. So go ahead and complete this video and when you're ready I'll look forward to you joining me.

    1. 有生于无的哲学

      您提到的"有生于无"的哲学思想确实与自发对称性破缺中的一些概念有相似之处。这种比喻表达了一个从无到有从潜在到显现的过程,即某些物理现象(如质量、相互作用等)并不是通过外部强加的,而是自然自发地从“无”中显现出来,类似于哲学中的"无"转变为“有”

      1. 哲学中的“有生于无”

      在哲学中,“有生于无”常常用来描述某种存在(或现象)从原本看似“无”的状态中产生,或者从潜力中显现出来。这个概念可以在多个哲学体系中找到相似的表述:

      • 中国哲学中的“无极生太极”:这代表了宇宙的起源,从(虚无)中诞生了万象和宇宙的各种“有”,即“有生于无”。

      • 古希腊哲学中的“混沌”:在古希腊神话中,宇宙最初的状态是“混沌”或“无”,而从混沌中诞生了宇宙的秩序与生命。

      • 现代哲学中的“存在的显现”:一些现代哲学家,尤其是存在主义现象学的思想家,探讨了从“潜在的存在”到“显现的存在”的过程,类似于从无到有的生成。

      2. 物理学中的“有生于无”

      在物理学中,自发对称性破缺的过程可以看作是“有生于无”的一种体现。具体来说,尽管系统最初可能处于一个对称的状态,当系统发生自发对称性破缺时,新的物理现象(如粒子质量和相互作用的结构)就会从“无”中显现出来。这个“无”可以理解为:

      • 最初的对称状态:系统在没有外部干扰的情况下保持对称性,粒子没有质量。
      • 对称性破缺的过程:希格斯场通过非零真空期望值(VEV)自发地破坏了原有的对称性,导致了粒子质量的生成和相互作用的区分。

      这里的“无”指的是系统在对称状态下的某种潜力或“未显现的状态”,而则是指从对称性破缺后显现出来的粒子质量、相互作用和新的物理现象。

      3. 类比和哲学的关联

      自发对称性破缺的机制与"有生于无"的哲学思想之间确实有一定的类比关系:

      • 潜力到现实的转变:在物理学中,希格斯场的非零VEV(真空期望值)使得粒子从“潜在的质量”状态转变为“实际的质量”。这类似于哲学中从(潜在的可能性)到(显现的存在)的一种转化。

      • 对称性破缺与本质的生成:在标准模型中,对称性破缺导致了粒子质量的产生。这个过程从原始对称性中引出了新的物理特性,这与哲学中的无中生有、从“无序”中创造“有序”相似。

      • 从“无”到“有”的必要性:在物理学中,虽然原始的对称性状态下粒子没有质量,但这一过程并非偶然,而是由于系统的稳定性需求,选择了一个特定的方向,破坏了对称性。就像哲学中,从“无”中生成“有”是宇宙和生命自然发展的必然过程一样。

      4. 总结

      “有生于无”的哲学概念与自发对称性破缺的物理机制在某种意义上是相似的。二者都描述了一种从潜力到现实从对称到不对称从“无”到“有”的自然转变。在物理学中,粒子质量的产生和相互作用的形成是自发对称性破缺这一过程的自然结果,它不仅符合物理定律,也符合自然界中从潜在到现实的转变。

      因此,可以将自发对称性破缺看作是物理学中的一种“有生于无”的现象,反映了物理系统在特定条件下从对称状态选择了一个稳定的状态,并通过这一过程产生了粒子质量和相互作用等物理现象。

    2. 现象学的必然性

      "现象学的必然性"这个概念可以从物理学的视角理解为一些核心物理现象的必然性内在联系。具体来说,质量、对称性破缺、粒子相互作用等现象不仅仅是偶然发生的,它们的根源根植于真空态的结构。也就是说,这些现象的出现并非外部强加或偶然的,而是系统的自然结果,它们从系统的基础结构(如希格斯场的真空期望值)中自然而然地产生。

      让我们深入探讨这个概念,尤其是真空态的结构与这些核心现象之间的关系。

      1. 真空态的结构与物理现象的联系

      在量子场论和粒子物理学中,真空态是描述粒子场的最低能量状态(或基态)。通常来说,真空态并不是完全“空”的,而是充满了量子涨落和各种场的背景结构。真空的结构决定了粒子物理的许多核心现象,比如粒子质量的生成相互作用的强度,以及对称性破缺

      真空态与质量

      在标准模型中,质量的来源是希格斯场非零真空期望值(VEV)。希格斯场的VEV通过与其他粒子的相互作用为粒子赋予质量,这个过程正是自发对称性破缺的结果。

      • 质量与希格斯场的关系:希格斯场的非零真空期望值自发地破坏了原始的对称性(SU(2) × U(1)对称性),并通过与粒子的耦合为粒子(如夸克、轻子、W/Z玻色子)提供质量。希格斯场的真空期望值决定了粒子质量的大小,因此,粒子质量的形成与真空的结构密切相关。

      真空态与对称性破缺

      在标准模型中,对称性破缺的核心机制是希格斯场的非零VEV。原本的SU(2) × U(1)对称性在希格斯场选择了一个非零的真空期望值后被破坏。

      • 对称性破缺与真空的选择:希格斯场的真空期望值选择了一个特定的方向,从而自发地破坏了原有的对称性。这一过程不仅导致了粒子质量的生成,还导致了弱相互作用和电磁相互作用的区分。这表明,对称性破缺是由真空的结构(希格斯场的VEV)自发产生的,而非外部强加。

      真空态与粒子相互作用

      粒子之间的相互作用也直接与真空态的结构相关。在标准模型中,粒子与希格斯场的相互作用决定了它们的质量。而粒子之间的相互作用强度(例如电弱相互作用的强度)是由系统的真空态和希格斯场的耦合常数决定的。

      • 粒子相互作用与真空结构:粒子之间的相互作用不仅取决于它们的相互作用强度(如电弱耦合常数),还受到真空背景结构的影响。希格斯场与粒子的相互作用强度直接决定了粒子之间的相互作用类型和强度。

      2. 现象学的必然性

      当我们说这些核心现象的“必然性”时,我们指的是这些现象不是偶然的,而是源于系统的真空结构。这意味着:

      • 质量对称性破缺粒子相互作用的结构等现象是由真空态的选择自然地决定的,而这些现象不是外部强加的或人为设定的。
      • 例如,粒子质量的产生是由希格斯场的真空期望值引起的,这个过程是通过希格斯场与其他粒子的相互作用自然发生的,而这种机制的出现并非偶然,而是系统最小化能量和选择稳定态的自然结果。

      因此,现象学的必然性表示了这些物理现象背后的自然法则。它们不是偶然发生的,而是系统内部的自发行为,源自于系统的最底层结构(如真空态的选择)和自发对称性破缺等过程。

      3. 物理现象的“根源”在真空态结构

      您提到的真空态的结构决定了物理现象的根源,正是因为真空不是完全空的,它本身蕴含着各种量子场的背景结构,决定了这些核心现象的发生。例如:

      • 质量的来源:希格斯场通过其非零的真空期望值自发地破坏了电弱对称性,产生了W/Z玻色子和物质粒子的质量。
      • 相互作用结构:真空的背景选择了电弱相互作用的具体形式,并通过希格斯机制为粒子提供了质量。这个过程决定了粒子之间的相互作用强度。

      4. 总结

      • 现象学的必然性意味着粒子物理中一些核心现象(如粒子质量、对称性破缺、粒子相互作用)并非偶然或外部干预的结果,而是根植于真空态的结构,它们自然地从系统的基础结构中涌现出来。
      • 真空态的选择(如希格斯场的非零VEV)决定了系统的对称性和粒子的质量等重要特性,所有这些现象的发生都是物理规则内在逻辑的自然结果。
    1. Welcome back, and in this lesson, I'm going to be discussing EBS snapshots, which provide a few really useful features for a solutions architect. First, they're an efficient way to back up EBS volumes to S3, and by doing this, you protect the data on those volumes against availability zone issues or local storage system failure in that availability zone, and they can also be used to migrate the data that's on EBS volumes between availability zones using S3 as an intermediary. So let's step through the architecture first through this lesson, and then in the next lesson, which will be a demo, we'll get you into the AWS console for some practical experience.

      Snapshots are essentially backups of EBS volumes which are stored on S3, and EBS volumes are availability zone resilient, which means that they're vulnerable to any issues which impact an entire availability zone. Because snapshots are stored on S3, the data that snapshots store becomes region resilient, and so we're improving the resiliency level of our EBS volumes by taking a snapshot and storing it into S3. Now, snapshots are incremental in nature, and that means a few very important things. It means that the first snapshot to be taken of a volume is a full copy of all of the data on that volume. Now, I'm stressing the word "data" because a snapshot just copies the data used. So, if you use 10 GB of a 40 GB volume, then that initial snapshot is 10 GB, not the full 40 GB. The first snapshot, because it's a full one, can take some time depending on the size of the data. It's copying all of the data from a volume onto S3. Now, your EBS performance won't be impacted during this initial snapshot, but it just takes time to copy in the background. Future snapshots are fully incremental; they only store the difference between the previous snapshot and the state of the volume when the snapshot is taken, and because of that, they consume much less space and they're also significantly quicker to perform.

      Now, you might be concerned at this point hearing the word "incremental." If you've got any existing backup system or backup software experience, it was always a risk that if you lost an incremental backup, then no further backups between that point and when you next took the full backup would work, so there was a massive risk of losing an incremental backup. You don't have to worry about that with EBS. It's smart enough so that if you do delete an incremental snapshot, it makes sure that the data is moved so that all of the snapshots after that point still function, so each snapshot, even though it is incremental, can be thought of as self-sufficient.

      Now, when you create an EBS volume, you have a few choices. You can create a blank volume, or you can create a volume that's based on a snapshot. So, snapshots offer a great way to clone a volume. Because S3 is a regional service, the volume you create from a snapshot can be in a different availability zone from the original, which means snapshots can be used to move EBS volumes between availability zones. But also, snapshots can be copied between AWS regions, so you can use snapshots for global DR processes or as a migration tool to migrate the data on volumes between regions. Snapshots are really flexible.

      Visually, this is how snapshot architecture looks. So here we've got two AWS regions, US East 1 and AP Southeast 2. We have a volume in availability zone A in US East 1, and that's connected to an EC2 instance in the same availability zone. Now, snapshots can be taken of this volume and stored in S3. And the first snapshot is a full copy, so it stores all of the data that's used on the source volume. The second one is incremental, so this only stores the changes since the last snapshot. So, at the point that you create the second snapshot, only the changes between the original snapshot and now are stored in this incremental, and these are linked, so the incremental references the initial snapshot for any data that isn't changed. Now, the snapshot can be used to create a volume in the same availability zone, it can be used to create a volume in another availability zone in the same region, and that volume could then be attached to another EC2 instance, or the snapshot could be copied to another AWS region and used to create another volume in that region. So, that's the architecture, that's how snapshots work, and there's nothing overly complex about it, but I did want to cover a few final important points before we finish up.

      As a solutions architect, there are some nuances of snapshot and volume performance that you need to be aware of. These can impact projects that you design and deploy significantly, and this does come up in the exam. Now, first, when you create a new EBS volume without using a snapshot, the performance is available immediately. There's no need to do any form of initialization process. But if you restore a volume from a snapshot, it does the restore lazily. What this means is that if you restore a volume right now, then starting right now, over time it will transfer the data from the snapshot on S3 to the new volume in the background, and this process takes some time. If you attempt to read data which hasn't been restored yet, it will immediately pull it from S3, but that achieves lower levels of performance than reading from EBS directly. So, you have a number of choices. You can force a read of every block of the volume, and this is done in the operating system using tools such as DD on Linux. And this reads every block one by one on the new EBS volume, and it forces EBS to pull all the snapshot data from S3 into that volume, and this is generally something that you would do immediately when you restore the volume before moving that volume into production usage. It just ensures perfect performance as soon as your customers start using that data.

      Now, historically that was the only way to force this rapid initialization of the volume, but now there's a feature called Fast Snapshot Restore or FSR. This is an option that you can set on a snapshot which makes it instantly restore. You can create 50 of these fast snapshot restores per region, and when you enable it on a snapshot, you pick the snapshot specifically and the availability zones that you want to be able to do instant restores to. Each combination of that snapshot and an AZ is classed as one fast snapshot restore set, and you can have 50 of those per region. So, one snapshot configured to restore to four availability zones in a region represents four out of that 50 limit of FSRs per region, so keep that in mind.

      Now, FSR actually costs extra. Keep this in mind. It can get expensive, especially if you have lots of different snapshots. You can always achieve the same end result by forcing a read of every block manually using DD or another tool in the operating system. But if you really don't want to go through the admin overhead, then you've got the option of using FSR. Now, I haven't talked about EBS volume encryption yet. That's coming up in a lesson soon within this section, but encryption also influences snapshots. But don't worry, I'll be covering all of that end to end when I talk about volume encryption.

      Now, snapshots are billed using a gigabyte month metric. So, a 10 GB snapshot stored for one month represents 10 GB month. A 20 GB snapshot stored for half a month represents the same 10 GB month, and that's how you're billed. There's a certain cost for every gigabyte month that you use for snapshot storage. Now, just to stress this, this is an awesome feature specifically from a cost aspect, is that this is used data, not allocated data. You might have a volume which is 40 GB in size, but if you only use 10 GB of that, then the first full snapshot is only 10 GB. EBS doesn't charge for unused areas in volumes when performing snapshots. You're charged for the full allocated size of an EBS volume, but that's because it's allocated. For snapshots, you only bill for the data that's used on the volumes, and because snapshots are incremental, you can perform them really regularly. Only the data that's changed is stored, so doing a snapshot every five minutes won't necessarily cost more than doing one per hour.

      Now, on the right, this is visually how snapshots look. On the left, we have a 10 GB volume using 10 GB of data, so it's 100% consumed. The first snapshot, logically, will consume 10 GB of space on S3 because it's a full snapshot and it consumes whatever data is used on the volume. In the middle column, we're changing 4 GB of data out of that original 10 GB, so the bit in yellow at the bottom. The next snap references the unchanged 6 GB of data and only stores the changed 4 GB. So, the second snap only bills for 4 GB of data, the changed data. On the right, we've got 2 GB of data that's added to that volume, so the volume is now 12 GB. The next snapshot references the original 6 GB of data, so that's not stored in this snapshot. It also references the previous snapshots for 4 GB of changed data, that's also not stored in this new snapshot. The new snapshot simply adds the new 2 GB of data, so this snapshot only bills for 2 GB. At each stage, a new snapshot is only storing data inside itself, which is new or changed, and it's referencing previous snapshots for anything which isn't changed. That's why they're all incremental, and that's why you only bill each time you do a snapshot for the changed data.

      Okay, that's enough theory for now, time for a demonstration. So, in the next demo lesson, we're going to experiment with EBS volumes and snapshots and just experience practically how we can interact with them. It's going to be a simple demo, but I always find that by doing things, you retain the theory that you've learned, and this has been a lot of theory. So go ahead, complete this video, and when you're ready, we can start the demo lesson.

    1. follows the rules, is easy to read, and attracts the reader.
      1. Follow the rules. What are the rules? Who standardizes them? Why is following the rules important?
      2. Easy to read ; who defines easy to read? What becomes too convoluted or difficult to read?
      3. Attracts the reader : What attracts readers to certain documents? Are there different strategies that provoke for specific writing? Where does this idea originate?
    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment

      Examination of (a)periodic brain activity has gained particular interest in the last few years in the neuroscience fields relating to cognition, disorders, and brain states. Using large EEG/MEG datasets from younger and older adults, the current study provides compelling evidence that age-related differences in aperiodic EEG/MEG signals can be driven by cardiac rather than brain activity. Their findings have important implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac signals is essential.

      We want to thank the editors for their assessment of our work and highlighting its importance for the understanding of aperiodic neural activity. Additionally, we want to thank the three present and four former reviewers (at a different journal) whose comments and ideas were critical in shaping this manuscript to its current form. We hope that this paper opens up many more questions that will guide us - as a field - to an improved understanding of how “cortical” and “cardiac” changes in aperiodic activity are linked and want to invite readers to engage with our work through eLife’s comment function.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The present study addresses whether physiological signals influence aperiodic brain activity with a focus on age-related changes. The authors report age effects on aperiodic cardiac activity derived from ECG in low and high-frequency ranges in roughly 2300 participants from four different sites. Slopes of the ECGs were associated with common heart variability measures, which, according to the authors, shows that ECG, even at higher frequencies, conveys meaningful information. Using temporal response functions on concurrent ECG and M/EEG time series, the authors demonstrate that cardiac activity is instantaneously reflected in neural recordings, even after applying ICA analysis to remove cardiac activity. This was more strongly the case for EEG than MEG data. Finally, spectral parameterization was done in large-scale resting-state MEG and ECG data in individuals between 18 and 88 years, and age effects were tested. A steepening of spectral slopes with age was observed particularly for ECG and, to a lesser extent, in cleaned MEG data in most frequency ranges and sensors investigated. The authors conclude that commonly observed age effects on neural aperiodic activity can mainly be explained by cardiac activity.

      Strengths:

      Compared to previous investigations, the authors demonstrate the effects of aging on the spectral slope in the currently largest MEG dataset with equal age distribution available. Their efforts of replicating observed effects in another large MEG dataset and considering potential confounding by ocular activity, head movements, or preprocessing methods are commendable and valuable to the community. This study also employs a wide range of fitting ranges and two commonly used algorithms for spectral parameterization of neural and cardiac activity, hence providing a comprehensive overview of the impact of methodological choices. Based on their findings, the authors give recommendations for the separation of physiological and neural sources of aperiodic activity.

      Weaknesses:

      While the aim of the study is well-motivated and analyses rigorously conducted, the overall structure of the manuscript, as it stands now, is partially misleading. Some of the described results are not well-embedded and lack discussion.

      We want to thank the reviewer for their comments focussed on improving the overall structure of the manuscript. We agree with their suggestions that some results could be more clearly contextualized and restructured the manuscript accordingly.

      Reviewer #2 (Public review):

      I previously reviewed this important and timely manuscript at a previous journal where, after two rounds of review, I recommended publication. Because eLife practices an open reviewing format, I will recapitulate some of my previous comments here, for the scientific record.

      In that previous review, I revealed my identity to help reassure the authors that I was doing my best to remain unbiased because I work in this area and some of the authors' results directly impact my prior research. I was genuinely excited to see the earlier preprint version of this paper when it first appeared. I get a lot of joy out of trying to - collectively, as a field - really understand the nature of our data, and I continue to commend the authors here for pushing at the sources of aperiodic activity!

      In their manuscript, Schmidt and colleagues provide a very compelling, convincing, thorough, and measured set of analyses. Previously I recommended that the push even further, and they added the current Figure 5 analysis of event-related changes in the ECG during working memory. In my opinion this result practically warrants a separate paper its own!

      The literature analysis is very clever, and expanded upon from any other prior version I've seen.

      In my previous review, the broadest, most high-level comment I wanted to make was that authors are correct. We (in my lab) have tried to be measured in our approach to talking about aperiodic analyses - including adopting measuring ECG when possible now - because there are so many sources of aperiodic activity: neural, ECG, respiration, skin conductance, muscle activity, electrode impedances, room noise, electronics noise, etc. The authors discuss this all very clearly, and I commend them on that. We, as a field, should move more toward a model where we can account for all of those sources of noise together. (This was less of an action item, and more of an inclusion of a comment for the record.)

      I also very much appreciate the authors' excellent commentary regarding the physiological effects that pharmacological challenges such as propofol and ketamine also have on non-neural (autonomic) functions such as ECG. Previously I also asked them to discuss the possibility that, while their manuscript focuses on aperiodic activity, it is possible that the wealth of literature regarding age-related changes in "oscillatory" activity might be driven partly by age-related changes in neural (or non-neural, ECG-related) changes in aperiodic activity. They have included a nice discussion on this, and I'm excited about the possibilities for cognitive neuroscience as we move more in this direction.

      Finally, I previously asked for recommendations on how to proceed. The authors convinced me that we should care about how the ECG might impact our field potential measures, but how do I, as a relative novice, proceed. They now include three strong recommendations at the end of their manuscript that I find to be very helpful.

      As was obvious from previous review, I consider this to be an important and impactful cautionary report, that is incredibly well supported by multiple thorough analyses. The authors have done an excellent job responding to all my previous comments and concerns and, in my estimation, those of the previous reviewers as well.

      We want to thank the reviewer for agreeing to review our manuscript again and for recapitulating on their previous comments and the progress the manuscript has made over the course of the last ~2 years. The reviewer's comments have been essential in shaping the manuscript into its current form. Their feedback has made the review process truly feel like a collaborative effort, focused on strengthening the manuscript and refining its conclusions and resulting recommendations.

      Reviewer #3 (Public review):

      Summary:

      Schmidt et al., aimed to provide an extremely comprehensive demonstration of the influence cardiac electromagnetic fields have on the relationship between age and the aperiodic slope measured from electroencephalographic (EEG) and magnetoencephalographic (MEG) data.

      Strengths:

      Schmidt et al., used a multiverse approach to show that the cardiac influence on this relationship is considerable, by testing a wide range of different analysis parameters (including extensive testing of different frequency ranges assessed to determine the aperiodic fit), algorithms (including different artifact reduction approaches and different aperiodic fitting algorithms), and multiple large datasets to provide conclusions that are robust to the vast majority of potential experimental variations.

      The study showed that across these different analytical variations, the cardiac contribution to aperiodic activity measured using EEG and MEG is considerable, and likely influences the relationship between aperiodic activity and age to a greater extent than the influence of neural activity.

      Their findings have significant implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac fields is essential.

      We want to thank the reviewer for their thorough engagement with our work and the resultant substantive amount of great ideas both mentioned in the section of Weaknesses and Authors Recommendations below. Their suggestions have sparked many ideas in us on how to move forward in better separating peripheral- from neuro-physiological signals that are likely to greatly influence our future attempts to better extract both cardiac and muscle activity from M/EEG recordings. So we want to thank them for their input, time and effort!

      Weaknesses:

      Figure 4I: The regressions explained here seem to contain a very large number of potential predictors. Based on the way it is currently written, I'm assuming it includes all sensors for both the ECG component and ECG rejected conditions?

      I'm not sure about the logic of taking a complete signal, decomposing it with ICA to separate out the ECG and non-ECG signals, then including these latent contributions to the full signal back into the same regression model. It seems that there could be some circularity or redundancy in doing so. Can the authors provide a justification for why this is a valid approach?

      After observing significant effects both in the MEG<sub>ECG component</sub> and MEG<sub>ECG rejected</sub> conditions in similar frequency bands we wanted to understand whether or not these age-related changes are statistically independent. To test this we added both variables as predictors in a regression model (thereby accounting for the influence of the other in relation to age). The regression models we performed were therefore actually not very complex. They were built using only two predictors, namely the data (in a specific frequency range) averaged over channels on which we noticed significant effects in the ECG rejected and ECG components data respectively (Wilkinson notation: age ~ 1 + ECG rejected + ECG components). This was also described in the results section stating that: “To see if MEG<sub>ECG rejected</sub> and MEG<sub>ECG component</sub> explain unique variance in aging at frequency ranges where we noticed shared effects, we averaged the spectral slope across significant channels and calculated a multiple regression model with MEG<sub>ECG component</sub> and MEG<sub>ECG rejected</sub> as predictors for age (to statistically control for the effect of MEG<sub>ECG component</sub>s and MEG<sub>ECG rejected</sub> on age). This analysis was performed to understand whether the observed shared age-related effects (MEG<sub>ECG rejected</sub> and MEG<sub>ECG component</sub>) are in(dependent).”  

      We hope this explanation solves the previous misunderstanding.

      I'm not sure whether there is good evidence or rationale to support the statement in the discussion that the presence of the ECG signal in reference electrodes makes it more difficult to isolate independent ECG components. The ICA algorithm will still function to detect common voltage shifts from the ECG as statistically independent from other voltage shifts, even if they're spread across all electrodes due to the referencing montage. I would suggest there are other reasons why the ICA might lead to imperfect separation of the ECG component (assumption of the same number of source components as sensors, non-Gaussian assumption, assumption of independence of source activities).

      The inclusion of only 32 channels in the EEG data might also have reduced the performance of ICA, increasing the chances of imperfect component separation and the mixing of cardiac artifacts into the neural components, whereas the higher number of sensors in the MEG data would enable better component separation. This could explain the difference between EEG and MEG in the ability to clean the ECG artifact (and perhaps higher-density EEG recordings would not show the same issue).

      The reviewer is making a good argument suggesting that our initial assumption that the presence of cardiac activity on the reference electrode influences the performance of the ICA may be wrong. After rereading and rethinking upon the matter we think that the reviewer is correct and that their assumptions for why the ECG signal was not so easily separable from our EEG recordings are more plausible and better grounded in the literature than our initial suggestion. We therefore now highlight their view as a main reason for why the ECG rejection was more challenging in EEG data. However, we also note that understanding the exact reason probably ends up being an empirical question that demands further research stating that:

      “Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources. ”

      In addition to the inability to effectively clean the ECG artifact from EEG data, ICA and other component subtraction methods have also all been shown to distort neural activity in periods that aren't affected by the artifact due to the ubiquitous issue of imperfect component separation (https://doi.org/10.1101/2024.06.06.597688). As such, component subtraction-based (as well as regression-based) removal of the cardiac artifact might also distort the neural contributions to the aperiodic signal, so even methods to adequately address the cardiac artifact might not solve the problem explained in the study. This poses an additional potential confound to the "M/EEG without ECG" conditions.

      The reviewer is correct in stating that, if an “artifactual” signal is not always present but appears and disappears (like e.g. eye-blinks) neural activity may be distorted in periods where the “artifactual” signal is absent. However, while this plausibly presents a problem for ocular activity, there is no obvious reason to believe that this applies to cardiac activity. While the ECG signal is non-stationary in nature, it is remarkably more stable than eye-movements in the healthy populations we analyzed (especially at rest). Therefore, the presence of the cardiac “artifact” was consistently present across the entirety of the MEG recordings we visually inspected.

      Literature Analysis, Page 23: was there a method applied to address studies that report reducing artifacts in general, but are not specific to a single type of artifact? For example, there are automated methods for cleaning EEG data that use ICLabel (a machine learning algorithm) to delete "artifact" components. Within these studies, the cardiac artifact will not be mentioned specifically, but is included under "artifacts".

      The literature analysis was largely performed automatically and solely focussed on ECG related activity as described in the methods section under Literature Analysis, if no ECG related terms were used in the context of artifact rejection a study was flagged as not having removed cardiac activity. This could have been indeed better highlighted by us and we apologize for the oversight on our behalf. We now additionally link to these details stating that:

      “However, an analysis of openly accessible M/EEG articles (N<sub>Articles</sub>=279; see Methods - Literature Analysis for further details) that investigate aperiodic activity revealed that only 17.1% of EEG studies explicitly mention that cardiac activity was removed and only 16.5% measure ECG (45.9% of MEG studies removed cardiac activity and 31.1% of MEG studies mention that ECG was measured; see Figure 1EF).”

      The reviewer makes a fair point that there is some uncertainty here and our results probably present a lower bound of ECG handling in M/EEG research as, when I manually rechecked the studies that were not initially flagged in studies it was often solely mentioned that “artifacts” were rejected. However, this information seemed too ambiguous to assume that cardiac activity was in fact accounted for. However, again this could have been mentioned more clearly in writing and we apologize for this oversight. Now this is included as part of the methods section Literature Analysis stating that:

      “All valid word contexts were then manually inspected by scanning the respective word context to ensure that the removal of “artifacts” was related specifically to cardiac and not e.g. ocular activity or the rejection of artifacts in general (without specifying which “artifactual” source was rejected in which case the manuscript was marked as invalid). This means that the results of our literature analysis likely present a lower bound for the rejection of cardiac activity in the M/EEG literature investigating aperiodic activity.”

      Statistical inferences, page 23: as far as I can tell, no methods to control for multiple comparisons were implemented. Many of the statistical comparisons were not independent (or even overlapped with similar analyses in the full analysis space to a large extent), so I wouldn't expect strong multiple comparison controls. But addressing this point to some extent would be useful (or clarifying how it has already been addressed if I've missed something).

      In the present study we tried to minimize the risk of type 1 errors by several means, such as A) weakly informative priors, B) robust regression models and C) by specifying a region of practical equivalence (ROPE, see Methods Statistical Inference for further Information) to define meaningful effects.

      Weakly informative priors can lower the risk of type 1 errors arising from multiple testing by shrinking parameter estimates towards zero (see e.g. Lemoine, 2019). Robust regression models use a Student T distribution to describe the distribution of the data. This distribution features heavier tails, meaning it allocates more probability to extreme values, which in turn minimizes the influence of outliers. The ROPE criterion ensures that only effects exceeding a negligible size are considered meaningful, representing a strict and conservative approach to interpreting our findings (see Kruschke 2018, Cohen, 1988).

      Furthermore, and more generally we do not selectively report “significant” effects in the situations in which multiple analyses were conducted on the same family of data (e.g. Figure 2 & 4). Instead we provide joint inference across several plausible analysis options (akin to a specification curve analysis, Simonsohn, Simmons & Nelson 2020) to provide other researchers with an overview of how different analysis choices impact the association between cardiac and neural aperiodic activity.

      Lemoine, N. P. (2019). Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses. Oikos, 128(7), 912-928.

      Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208-1214.

      Methods:

      Applying ICA components from 1Hz high pass filtered data back to the 0.1Hz filtered data leads to worse artifact cleaning performance, as the contribution of the artifact in the 0.1Hz to 1Hz frequency band is not addressed (see Bailey, N. W., Hill, A. T., Biabani, M., Murphy, O. W., Rogasch, N. C., McQueen, B., ... & Fitzgerald, P. B. (2023). RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials. Clinical Neurophysiology, result reported in the supplementary materials). This might explain some of the lower frequency slope results (which include a lower frequency limit <1Hz) in the EEG data - the EEG cleaning method is just not addressing the cardiac artifact in that frequency range (although it certainly wouldn't explain all of the results).

      We want to thank the reviewer for suggesting this interesting paper, showing that lower high-pass filters may be preferable to the more commonly used >1Hz high-pass filters for detection of ICA components that largely contain peripheral physiological activity. However, the results presented by Bailey et al. contradict the more commonly reported findings by other researchers that >1Hz high-pass filter is actually preferable (e.g. Winkler et al. 2015; Dimingen, 2020 or Klug & Gramann, 2021) and recommendations in widely used packages for M/EEG analysis (e.g. https://mne.tools/1.8/generated/mne.preprocessing.ICA.html). Yet, the fact that there seems to be a discrepancy suggests that further research is needed to better understand which type of high-pass filtering is preferable in which situation. Furthermore, it is notable that all the findings for high-pass filtering in ICA component detection and removal that we are aware of relate to ocular activity. Given that ocular and cardiac activity have very different temporal and spectral patterns it is probably worth further investigating whether the classic 1Hz high-pass filter is really also the best option for the detection and removal of cardiac activity. However, in our opinion this requires a dedicated investigation on its own..

      We therefore highlight this now in our manuscript stating that:

      “Additionally, it is worth noting that the effectiveness of an ICA crucially depends on the quality of the extracted components(63,64) and even widely suggested settings e.g. high-pass filtering at 1Hz before fitting an ICA may not be universally applicable (see supplementary material of (64)).

      Winkler, S. Debener, K. -R. Müller and M. Tangermann, "On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 2015, pp. 4101-4105, doi: 10.1109/EMBC.2015.7319296.

      Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage, 207, 116117.

      Klug, M., & Gramann, K. (2021). Identifying key factors for improving ICA‐based decomposition of EEG data in mobile and stationary experiments. European Journal of Neuroscience, 54(12), 8406-8420.

      It looks like no methods were implemented to address muscle artifacts. These can affect the slope of EEG activity at higher frequencies. Perhaps the Riemannian Potato addressed these artifacts, but I suspect it wouldn't eliminate all muscle activity. As such, I would be concerned that remaining muscle artifacts affected some of the results, particularly those that included high frequency ranges in the aperiodic estimate. Perhaps if muscle activity were left in the EEG data, it could have disrupted the ability to detect a relationship between age and 1/f slope in a way that didn't disrupt the same relationship in the cardiac data (although I suspect it wouldn't reverse the overall conclusions given the number of converging results including in lower frequency bands). Is there a quick validity analysis the authors can implement to confirm muscle artifacts haven't negatively affected their results?

      I note that an analysis of head movement in the MEG is provided on page 32, but it would be more robust to show that removing ICA components reflecting muscle doesn't change the results. The results/conclusions of the following study might be useful for objectively detecting probable muscle artifact components: Fitzgibbon, S. P., DeLosAngeles, D., Lewis, T. W., Powers, D. M. W., Grummett, T. S., Whitham, E. M., ... & Pope, K. J. (2016). Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clinical neurophysiology, 127(3), 1781-1793.

      We thank the reviewer for their suggestion. Muscle activity can indeed be a potential concern, for the estimation of the spectral slope. This is precisely why we used head movements (as also noted by the reviewer) as a proxy for muscle activity. We also agree with the reviewer that this is not a perfect estimate. Additionally, also the riemannian potato would probably only capture epochs that contain transient, but not persistent patterns of muscle activity.

      The paper recommended by the reviewer contains a clever approach of using the steepness of the spectral slope (or lack thereof) as an indicator whether or not an independent component (IC) is driven by muscle activity. In order to determine an optimal threshold Fitzgibbon et al. compared paralyzed to temporarily non paralyzed subjects. They determined an expected “EMG-free” threshold for their spectral slope on paralyzed subjects and used this as a benchmark to detect IC’s that were contaminated by muscle activity in non paralyzed subjects.

      This is a great idea, but unfortunately would go way beyond what we are able to sensibly estimate with our data for the following reasons. The authors estimated their optimal threshold on paralyzed subjects for EEG data and show that this is a feasible threshold to be applied across different recordings. So for EEG data it might be feasible, at least as a first shot, to use their threshold on our data. However, we are measuring MEG and as alluded to in our discussion section under “Differences in aperiodic activity between magnetic and electric field recordings” the spectral slope differs greatly between MEG and EEG recordings for non-trivial reasons. Furthermore, the spectral slope even seems to also differ across different MEG devices. We noticed this when we initially tried to pool the data recorded in Salzburg with the Cambridge dataset. This means we would need to do a complete validation of this procedure for the MEG data recorded in Cambridge and in Salzburg, which is not feasible considering that we A) don’t have direct access to one of the recording sites and B) would even if we had access face substantial hurdles to get ethical approval for the experiment performed by Fitzgibbon et al..

      However, we think the approach brought forward by Fitzgibbon and colleagues is a clever way to remove muscle activity from EEG recordings, whenever EMG was not directly recorded. We therefore suggested in the Discussion section that ideally also EMG should be recorded stating that:

      “It is worth noting that, apart from cardiac activity, muscle activity can also be captured in (non-)invasive recordings and may drastically influence measures of the spectral slope(72). To ensure that persistent muscle activity does not bias our results we used changes in head movement velocity as a control analysis (see Supplementary Figure S9). However, it should be noted that this is only a proxy for the presence of persistent muscle activity. Ideally, studies investigating aperiodic activity should also be complemented by measurements of EMG. Whenever such measurements are not available creative approaches that use the steepness of the spectral slope (or the lack thereof) as an indicator to detect whether or not e.g. an independent component is driven by muscle activity are promising(72,73). However, these approaches may require further validation to determine how well myographic aperiodic thresholds are transferable across the wide variety of different M/EEG devices.”

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) As outlined above, I recommend rephrasing the last section of the introduction to briefly summarize/introduce all main analysis steps undertaken in the study and why these were done (for example, it is only mentioned that the Cam-CAN dataset was used to study the impact of cardiac on MEG activity although the author used a variety of different datasets). Similarly, I am missing an overview of all main findings in the context of the study goals in the discussion. I believe clarifying the structure of the paper would not only provide a red thread to the reader but also highlight the efforts/strength of the study as described above.

      This is a good call! As suggested by the reviewer we now try to give a clearer overview of what was investigated why. We do that both at the end of the introduction stating that: “Using the publicly available Cam-CAN dataset(28,29), we find that the aperiodic signal measured using M/EEG originates from multiple physiological sources. In particular, significant portions of age-related changes in aperiodic activity –normally attributed to neural processes– can be better explained by cardiac activity. This observation holds across a wide range of processing options and control analyses (see Supplementary S1), and was replicable on a separate MEG dataset. However, the extent to which cardiac activity accounts for age-related changes in aperiodic activity varies with the investigated frequency range and recording site. Importantly, in some frequency ranges and sensor locations, age-related changes in neural aperiodic activity still prevail. But does the influence of cardiac activity on the aperiodic spectrum extend beyond age? In a preliminary analysis, we demonstrate that working memory load modulates the aperiodic spectrum of “pure” ECG recordings. The direction of this working memory effect mirrors previous findings on EEG data(5) suggesting that the impact of cardiac activity goes well beyond aging. In sum, our results highlight the complexity of aperiodic activity while cautioning against interpreting it as solely “neural“ without considering physiological influences.”

      and at the beginning of the discussion section:

      “Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources (see Figure 1EF). Additionally, it is worth noting that the effectiveness of an ICA crucially depends on the quality of the extracted components(63,64) and even widely suggested settings e.g. high-pass filtering at 1Hz before fitting an ICA may not be universally applicable (see supplementary material of (64)). “

      (2) I found it interesting that the spectral slopes of ECG activity at higher frequency ranges (> 10 Hz) seem mostly related to HRV measures such as fractal and time domain indices and less so with frequency-domain indices. Do the authors have an explanation for why this is the case? Also, the analysis of the HRV measures and their association with aperiodic ECG activity is not explained in any of the method sections.

      We apologize for the oversight in not mentioning the HRV analysis in more detail in our methods section. We added a subsection to the Methods section entitled ECG Processing - Heart rate variability analysis to further describe the HRV analyses.

      “ECG Processing - Heart rate variability analysis

      Heart rate variability (HRV) was computed using the NeuroKit2 toolbox, a high level tool for the analysis of physiological signals. First, the raw electrocardiogram (ECG) data were preprocessed, by highpass filtering the signal at 0.5Hz using an infinite impulse response (IIR) butterworth filter(order=5) and by smoothing the signal with a moving average kernel with the width of one period of 50Hz to remove the powerline noise (default settings of neurokit.ecg.ecg_clean). Afterwards, QRS complexes were detected based on the steepness of the absolute gradient of the ECG signal. Subsequently, R-Peaks were detected as local maxima in the QRS complexes (default settings of neurokit.ecg.ecg_peaks; see (98) for a validation of the algorithm). From the cleaned R-R intervals, 90 HRV indices were derived, encompassing time-domain, frequency-domain, and non-linear measures. Time-domain indices included standard metrics such as the mean and standard deviation of the normalized R-R intervals , the root mean square of successive differences, and other statistical descriptors of interbeat interval variability. Frequency-domain analyses were performed using power spectral density estimation, yielding for instance low frequency (0.04-0.15Hz) and high frequency (0.15-0.4Hz) power components. Additionally, non-linear dynamics were characterized through measures such as sample entropy, detrended fluctuation analysis and various Poincaré plot descriptors. All these measures were then related to the slopes of the low frequency (0.25 – 20 Hz) and high frequency (10 – 145 Hz) aperiodic spectrum of the raw ECG.”

      With regards to association of the ECG’s spectral slopes at high frequencies and frequency domain indices of heart rate variability. Common frequency domain indices of heart rate variability fall in the range of 0.01-.4Hz. Which probably explains why we didn’t notice any association at higher frequency ranges (>10Hz).

      This is also stated in the related part of the results section:

      “In the higher frequency ranges (10 - 145 Hz) spectral slopes were most consistently related to fractal and time domain indices of heart rate variability, but not so much to frequency-domain indices assessing spectral power in frequency ranges < 0.4 Hz.”

      (3) Related to the previous point - what is being reflected in the ECG at higher frequency ranges, with regard to biological mechanisms? Results are being mentioned, but not further discussed. However, this point seems crucial because the age effects across the four datasets differ between low and high-frequency slope limits (Figure 2C).

      This is a great question that definitely also requires further attention and investigation in general (see also Tereshchenko & Josephson, 2015). We investigated the change of the slope across frequency ranges that are typically captured in common ECG setups for adults (0.05 - 150Hz, Tereshchenko & Josephson, 2015; Kusayama, Wong, Liu et al. 2020). While most of the physiological significant spectral information of an ECG recording rests between 1-50Hz (Clifford & Azuaje, 2006), meaningful information can be extracted at much higher frequencies. For instance, ventricular late potentials have a broader frequency band (~40-250Hz) that falls straight in our spectral analysis window. However, that’s not all, as further meaningful information can be extracted at even higher frequencies (>100Hz). Yet, the exact physiological mechanisms underlying so-called high-frequency QRS remain unclear (HF-QRS; see Tereshchenko & Josephson, 2015; Qiu et al. 2024 for a review discussing possible mechanisms). Yet, at the same time the HF-QRS seems to be highly informative for the early detection of myocardial ischemia and other cardiac abnormalities that may not yet be evident in the standard frequency range (Schlegel et al. 2004; Qiu et al. 2024). All optimism aside, it is also worth noting that ECG recordings at higher frequencies can capture skeletal muscle activity with an overlapping frequency range up to 400Hz (Kusayama, Wong, Liu et al. 2020). We highlight all of this now when introducing this analysis in the results sections as outstanding research question stating that:

      “However, substantially less is known about aperiodic activity above 0.4Hz in the ECG. Yet, common ECG setups for adults capture activity at a broad bandwidth of 0.05 - 150Hz(33,34).

      Importantly, a lot of the physiological meaningful spectral information rests between 1-50Hz(35), similarly to M/EEG recordings. Furthermore, meaningful information can be extracted at much higher frequencies. For instance, ventricular late potentials have a broader frequency band (~40-250Hz(35)). However, that’s not all, as further meaningful information can be extracted at even higher frequencies (>100Hz). For instance, the so-called high-frequency QRS seems to be highly informative for the early detection of myocardial ischemia and other cardiac abnormalities that may not yet be evident in the standard frequency range(36,37). Yet, the exact physiological mechanisms underlying the high-frequency QRS remain unclear (see (37) for a review discussing possible mechanisms). ”

      Tereshchenko, L. G., & Josephson, M. E. (2015). Frequency content and characteristics of ventricular conduction. Journal of electrocardiology, 48(6), 933-937.

      Kusayama, T., Wong, J., Liu, X. et al. Simultaneous noninvasive recording of electrocardiogram and skin sympathetic nerve activity (neuECG). Nat Protoc 15, 1853–1877 (2020). https://doi.org/10.1038/s41596-020-0316-6

      Clifford, G. D., & Azuaje, F. (2006). Advanced methods and tools for ECG data analysis (Vol. 10). P. McSharry (Ed.). Boston: Artech house.

      Qiu, S., Liu, T., Zhan, Z., Li, X., Liu, X., Xin, X., ... & Xiu, J. (2024). Revisiting the diagnostic and prognostic significance of high-frequency QRS analysis in cardiovascular diseases: a comprehensive review. Postgraduate Medical Journal, qgae064.

      Schlegel, T. T., Kulecz, W. B., DePalma, J. L., Feiveson, A. H., Wilson, J. S., Rahman, M. A., & Bungo, M. W. (2004, March). Real-time 12-lead high-frequency QRS electrocardiography for enhanced detection of myocardial ischemia and coronary artery disease. In Mayo Clinic Proceedings (Vol. 79, No. 3, pp. 339-350). Elsevier.

      (4) Page 10: At first glance, it is not quite clear what is meant by "processing option" in the text. Please clarify.

      Thank you for catching this! Upon re-reading this is indeed a bit oblivious. We now swapped “processing options” with “slope fits” to make it clearer that we are talking about the percentage of effects based on the different slope fits.

      (5) The authors mention previous findings on age effects on neural 1/f activity (References Nr 5,8,27,39) that seem contrary to their own findings such as e.g., the mostly steepening of the slopes with age. Also, the authors discuss thoroughly why spectral slopes derived from MEG signals may differ from EEG signals. I encourage the authors to have a closer look at these studies and elaborate a bit more on why these studies differ in their conclusions on the age effects. For example, Tröndle et al. (2022, Ref. 39) investigated neural activity in children and young adults, hence, focused on brain maturation, whereas the CamCAN set only considers the adult lifespan. In a similar vein, others report age effects on 1/f activity in much smaller samples as reported here (e.g., Voytek et al., 2015).

      I believe taking these points into account by briefly discussing them, would strengthen the authors' claims and provide a more fine-grained perspective on aging effects on 1/f.

      The reviewer is making a very important point. As age-related differences in (neuro-)physiological activity are not necessarily strictly comparable and entirely linear across different age-cohorts (e.g. age-related changes in alpha center frequency). We therefore, added the suggested discussion points to the discussion section.

      “Differences in electric and magnetic field recordings aside, aperiodic activity may not change strictly linearly as we are ageing and studies looking at younger age groups (e.g. <22; (44) may capture different aspects of aging (e.g. brain maturation), than those looking at older subjects (>18 years; our sample). A recent report even shows some first evidence of an interesting putatively non-linear relationship with age in the sensorimotor cortex for resting recordings(59)”

      (6) The analysis of the working memory paradigm as described in the outlook-section of the discussion comes as a bit of a surprise as it has not been introduced before. If the authors want to convey with this study that, in general, aperiodic neural activity could be influenced by aperiodic cardiac activity, I recommend introducing this analysis and the results earlier in the manuscript than only in the discussion to strengthen their message.

      The reviewer is correct. This analysis really comes a bit out of the blue. However, this was also exactly the intention for placing this analysis in the discussion. As the reviewer correctly noted, the aim was to suggest “that, in general, aperiodic neural activity could be influenced by aperiodic cardiac activity”. We placed this outlook directly after the discussion of “(neuro-)physiological origins of aperiodic activity”, where we highlight the potential challenges of interpreting drug induced changes to M/EEG recordings. So the aim was to get the reader to think about whether age is the only feature affected by cardiac activity and then directly present some evidence that this might go beyond age.

      However, we have been rethinking this approach based on the reviewers comments and moved that paragraph to the end of the results section accordingly and introduce it already at the end of the introduction stating that:

      “But does the influence of cardiac activity on the aperiodic spectrum extend beyond age? In a preliminary analysis, we demonstrate that working memory load modulates the aperiodic spectrum of “pure” ECG recordings. The direction of this working memory effect mirrors previous findings on EEG data(5) suggesting that the impact of cardiac activity goes well beyond aging.”

      (7) The font in Figure 2 is a bit hard to read (especially in D). I recommend increasing the font sizes where necessary for better readability.

      We agree with the Reviewer and increased the font sizes accordingly.

      (8) Text in the discussion: Figure 3B on page 10 => shouldn't it be Figure 4?

      Thank you for catching this oversight. We have now corrected this mistake.

      (9) In the third section on page 10, the Figure labels seem to be confused. For example, Figure 4 E is supposed to show "steepening effects", which should be Figure 4B I believe.

      Please check the figure labels in this section to avoid confusion.

      Thank you for catching this oversight. We have now corrected this mistake.

      (10) Figure Legend 4 I), please check the figure labels in the text

      Thank you for catching this oversight. We have now corrected this mistake.

      Reviewer #3 (Recommendations for the authors):

      I have a number of suggestions for improving the manuscript, which I have divided by section in the following:

      ABSTRACT:

      I would suggest re-writing the first sentences to make it easier to read for non-expert readers: "The power of electrophysiologically measured cortical activity decays with an approximately 1/fX function. The slope of this decay (i.e. the spectral exponent, X) is modulated..."

      Thank you for the suggestion. We adjusted the sentence as suggested to make it easier for less technical readers to understand that “X” refers to the exponent.

      Including the age range that was studied in the abstract could be informative.

      Done as suggested.

      As an optional recommendation, I think it would increase the impact of the article if the authors note in the abstract that the current most commonly applied cardiac artifact reduction approaches don't resolve the issue for EEG data, likely due to an imperfect ability to separate the cardiac artifact from the neural activity with independent component analysis. This would highlight to the reader that they can't just expect to address these concerns by cleaning their data with typical cleaning methods.

      I think it would also be useful to convey in the abstract just how comprehensive the included analyses were (in terms of artifact reduction methods tested, different aperiodic algorithms and frequency ranges, and both MEG and EEG). Doing so would let the reader know just how robust the conclusions are likely to be.

      This is a brilliant idea! As suggested we added a sentence highlighting that simply performing an ICA may not be sufficient to separate cardiac contributions to M/EEG recordings and refer to the comprehensiveness of the performed analyses.

      INTRODUCTION:

      I would suggest re-writing the following sentence for readability: "In the past, aperiodic neural activity, other than periodic neural activity (local peaks that rise above the "power-law" distribution), was often treated as noise and simply removed from the signal"

      To something like: "In the past, aperiodic neural activity was often treated as noise and simply removed from the signal e.g. via pre-whitening, so that analyses could focus on periodic neural activity (local peaks that rise above the "power-law" distribution, which are typically thought to reflect neural oscillations).

      We are happy to follow that suggestion.

      Page 3: please provide the number of articles that were included in the examination of the percentage that remove cardiac activity, and note whether the included articles could be considered a comprehensive or nearly comprehensive list, or just a representative sample.

      We stated the exact number of articles in the methods section under Literature Analysis. However, we added it to the Introduction on page 3 as suggested by the reviewer. The selection of articles was done automatically, dependent on a list of pre-specified terms and exclusively focussed on articles that had terms related to aperiodic activity in their title (see Literature Analysis). Therefore, I would personally be hesitant in calling it a comprehensive or nearly comprehensive list of the general M/EEG literature as the analysis of aperiodic activity is still relatively niche compared to the more commonly investigated evoked potentials or oscillations. I think whether or not a reader perceives our analysis as comprehensive should be up to them to decide and does not reflect something I want to impose on them. This is exacerbated by the fact that the analysis of neural aperiodic activity has rapidly gained traction over the last years (see Figure 1D orange) and the literature analysis was performed almost 2 years ago and therefore, in my eyes, only represents a glimpse in the rapidly evolving field related to the analysis of aperiodic activity.

      Figure 1E-F: It's not completely clear that the "Cleaning Methods" part of the figure indicates just methods to clean the cardiac artifact (rather than any artifact). It also seems that ~40% of EEG studies do not apply any cleaning methods even from within the studies that do clean the cardiac artifact (if I've read the details correctly). This seems unlikely. Perhaps there should be a bar for "other methods", or "unspecified"? Having said that, I'm quite familiar with the EEG artifact reduction literature, and I would be very surprised if ~40% of studies cleaned the cardiac artifact using a different method to the methods listed in the bar graph, so I'm wondering if I've misunderstood the figure, or whether the data capture is incomplete / inaccurate (even though the conclusion that ICA is the most common method is almost certainly accurate).

      The cleaning is indeed only focussed on cardiac activity specifically. This was however also mentioned in the caption of Figure 1: “We were further interested in determining which artifact rejection approaches were most commonly used to remove cardiac activity, such as independent component analysis (ICA(22)), singular value decomposition (SVD(23)), signal space separation (SSS(24)), signal space projections (SSP(25)) and denoising source separation (DSS(26)).” and in the methods section under Literature Analysis. However, we adjusted figure 1EF to make it more obvious that the described cleaning methods were only related to the ECG. Aside from using blind source separation techniques such as ICA a good amount of studies mentioned that they cleaned their data based on visual inspection (which was not further considered). Furthermore, it has to be noted that only studies were marked as having separated cardiac from neural activity, when this was mentioned explicitly.

      RESULTS:

      Page 6: I would delete the "from a neurophysiological perspective" clause, which makes the sentence more difficult to read and isn't so accurate (frequencies 13-25Hz would probably more commonly be considered mid-range rather than low or high). Additionally, both frequency ranges include 15Hz, but the next sentence states that the ranges were selected to avoid the knee at 15Hz, which seems to be a contradiction. Could the authors explain in more detail how the split addresses the 15Hz knee?

      We removed the “from a neurophysiological perspective” clause as suggested. With regards to the “knee” at ~15Hz I would like to defer the reviewer to Supplementary Figure S1. The Knee Frequency varies substantially across subjects so splitting the data at only 1 exact Frequency did not seem appropriate. Additionally, we found only spurious significant age-related variations in Knee Frequency (i.e. only one out of the 4 datasets; not shown).

      Furthermore, we wanted to better connect our findings to our MEG results in Figure 4 and also give the readers a holistic overview of how different frequency ranges in the aperiodic ECG would be affected by age. So to fulfill all of these objectives we decided to fit slopes with respective upper/lower bounds around a range of 5Hz above and below the average 15Hz Knee Frequency across datasets.

      The later parts of this same paragraph refer to a vast amount of different frequency ranges, but only the "low" and "high" frequency ranges were previously mentioned. Perhaps the explanation could be expanded to note that multiple lower and upper bounds were tested within each of these low and high frequency windows?

      This is a good catch we adjusted the sentence as suggested. We now write: “.. slopes were fitted individually to each subject's power spectrum in several lower (0.25 – 20 Hz) and higher (10-145 Hz) frequency ranges.”

      The following two sentences seem to contradict each other: "Overall, spectral slopes in lower frequency ranges were more consistently related to heart rate variability indices(> 39.4% percent of all investigated indices)" and: "In the lower frequency range (0.25 - 20Hz), spectral slopes were consistently related to most measures of heart rate variability; i.e. significant effects were detected in all 4 datasets (see Figure 2D)." (39.4% is not "most").

      The reviewer is correct in stating that 39.4% is not most. However, the 39.4% is the lowest bound and only refers to 1 dataset. In the other 3 datasets the percentage of effects was above 64% which can be categorized as “most” i.e. above 50%. We agree that this was a bit ambiguous in the sentence so we added the other percentages as well as a reference to Figure 2D to make this point clearer.

      Figure 2D: it isn't clear what the percentages in the semi-circles reflect, nor why some semi-circles are more full circles while others are only quarter circles.

      The percentages in the semi-circles reflect the amount of effects (marked in red) and null effects (marked in green) per dataset, when viewed as average across the different measures of HRV. Sometimes less effects were found for some frequency ranges resulting in quarters instead of semi circles.

      Page 8: I think the authors could make it more clear that one of the conditions they were testing was the ECG component of the EEG data (extracted by ICA then projected back into the scalp space for the temporal response function analysis).

      As suggested by the reviewer we adjusted our wording and replaced the arguably a bit ambiguous “... projected back separately” with “... projected back into the sensor space”. We thank the reviewer for this recommendation, as it does indeed make it easier to understand the procedure.

      “After pre-processing (see Methods) the data was split in three conditions using an ICA(22). Independent components that were correlated (at r > 0.4; see Methods: MEG/EEG Processing - pre-processing) with the ECG electrode were either not removed from the data (Figure 3ABCD - blue), removed from the data (Figure 2ABCD - orange) or projected back into the sensor space (Figure 3ABCD - green).”

      Figure 4A: standardized beta coefficients for the relationship between age and spectral slope could be noted to provide improved clarity (if I'm correct in assuming that is what they reflect).

      This was indeed shown in Figure 4A and noted in the color bar as “average beta (standardized)”. We do not specifically highlight this in the text, because the exact coefficients would depend on both on the analyzed frequency range and the selected electrodes.

      Figure 4I: The regressions explained at this point seems to contain a very large number of potential predictors, as I'm assuming it includes all sensors for both the ECG component and ECG rejected conditions? (if that is not the case, it could be explained in greater detail). I'm also not sure about the logic of taking a complete signal, decomposing it with ICA to separate out the ECG and non-ECG signals, then including them back into the same regression model. It seems that there could be some circularity or redundancy in doing so. However, I'm not confident that this is an issue, so would appreciate the authors explaining why it this is a valid approach (if that is the case).

      After observing significant effects both in the MEG<sub>ECG component</sub> and MEG<sub>ECG rejected</sub> conditions in similar frequency bands we wanted to understand whether or not these age-related changes are statistically independent. To test this we added both variables as predictors in a regression model (thereby accounting for the influence of the other in relation to age). The regression models we performed were therefore actually not very complex. They were built using only two predictors, namely the data (in a specific frequency range) averaged over channels on which we noticed significant effects in the ECG rejected and ECG components data respectively (Wilkinson notation: age ~ 1 + ECG rejected + ECG components). This was also described in the results section stating that: “To see if MEG<sub>ECG rejected</sub> and MEG<sub>ECG component</sub> explain unique variance in aging at frequency ranges where we noticed shared effects, we averaged the spectral slope across significant channels and calculated a multiple regression model with MEG<sub>ECG component</sub> and MEG<sub>ECG rejected</sub> as predictors for age (to statistically control for the effect of MEG<sub>ECG component</sub>s and MEG<sub>ECG rejected</sub> on age). This analysis was performed to understand whether the observed shared age-related effects (MEG<sub>ECG rejected</sub> and MEG<sub>ECG component</sub>) are in(dependent).”  

      We hope this explanation solves the previous misunderstanding.

      The explanation of results for relationships between spectral slopes and aging reported in Figure 4 refers to clusters of effects, but the statistical inference methods section doesn't explain how these clusters were determined.

      The wording of “cluster” was used to describe a “category” of effects e.g. null effects. We changed the wording from “cluster” to “category” to make this clearer stating now that: “This analysis, which is depicted in Figure 4, shows that over a broad amount of individual fitting ranges and sensors, aging resulted in a steepening of spectral slopes across conditions (see Figure 4E) with “steepening effects” observed in 25% of the processing options in MEG<sub>ECG not rejected</sub> , 0.5% in MEG<sub>ECG rejected</sub>, and 60% for MEG<sub>ECG components</sub>. The second largest category of effects were “null effects” in 13% of the options for MEG<sub>ECG not rejected</sub> , 30% in MEG<sub>ECG rejected</sub>, and 7% for MEG<sub>ECG components</sub>. ”

      Page 12: can the authors clarify whether these age related steepenings of the spectral slope in the MEG are when the data include the ECG contribution, or when the data exclude the ECG? (clarifying this seems critical to the message the authors are presenting).

      We apologize for not making this clearer. We now write: “This analysis also indicates that a vast majority of observed effects irrespective of condition (ECG components, ECG not rejected, ECG rejected) show a steepening of the spectral slope with age across sensors and frequency ranges.”

      Page 13: I think it would be useful to describe how much variance was explained by the MEG-ECG rejected vs MEG-ECG component conditions for a range of these analyses, so the reader also has an understanding of how much aperiodic neural activity might be influenced by age (vs if the effects are really driven mostly by changes in the ECG).

      With regards to the explained variance I think that the very important question of how strong age influences changes in aperiodic activity is a topic better suited for a meta analysis. As the effect sizes seems to vary largely depending on the sample e.g. for EEG in the literature results were reported at r=-0.08 (Cesnaite et al. 2023), r=-0.26 (Cellier et al. 2021), r=-0.24/r=-0.28/r=-0.35 (Hill et al. 2022) and r=0.5/r=0.7 (Voytek et al. 2015). I would defer the reader/reviewer to the standardized beta coefficients as a measure of effect size in the current study that is depicted in Figure 4A.

      Cellier, D., Riddle, J., Petersen, I., & Hwang, K. (2021). The development of theta and alpha neural oscillations from ages 3 to 24 years. Developmental cognitive neuroscience, 50, 100969.

      Cesnaite, E., Steinfath, P., Idaji, M. J., Stephani, T., Kumral, D., Haufe, S., ... & Nikulin, V. V. (2023). Alterations in rhythmic and non‐rhythmic resting‐state EEG activity and their link to cognition in older age. NeuroImage, 268, 119810.

      Hill, A. T., Clark, G. M., Bigelow, F. J., Lum, J. A., & Enticott, P. G. (2022). Periodic and aperiodic neural activity displays age-dependent changes across early-to-middle childhood. Developmental Cognitive Neuroscience, 54, 101076.

      Voytek, B., Kramer, M. A., Case, J., Lepage, K. Q., Tempesta, Z. R., Knight, R. T., & Gazzaley, A. (2015). Age-related changes in 1/f neural electrophysiological noise. Journal of Neuroscience, 35(38), 13257-13265.

      Also, if there are specific M/EEG sensors where the 1/f activity does relate strongly to age, it would be worth noting these, so future research could explore those sensors in more detail.

      I think it is difficult to make a clear claim about this for MEG data, as the exact location or type of the sensor may differ across manufacturers. Such a statement could be easier made for source projected data or in case EEG electrodes were available, where the location would be normed eg. according to the 10-20 system.

      DISCUSSION:

      Page 15: Please change the wording of the following sentence, as the way it is currently worded seems to suggest that the authors of the current manuscript have demonstrated this point (which I think is not the case): "The authors demonstrate that EEG typically integrates activity over larger volumes than MEG, resulting in differently shaped spectra across both recording methods."

      Apologies for the oversight! The reviewer is correct we in fact did not show this, but the authors of the cited manuscript. We correct the sentence as suggested stating now that:

      “Bénar et al. demonstrate that EEG typically integrates activity over larger volumes than MEG, resulting in differently shaped spectra across both recording methods.”

      Page 16: The authors mention the results can be sensitive to the application of SSS to clean the MEG data, but not ICA. I think it would be sensitive to the application of either SSS or ICA?

      This is correct and actually also supported by Figure S7, as differences in ICA thresholds affect also the detection of age-related effects. We therefore adjusted the related sentences stating now that:

      “ In case of the MEG signal this may include the application of Signal-Space-Separation algorithms (SSS(24,55)), different thresholds for ICA component detection (see Figure S7), high and low pass filtering, choices during spectral density estimation (window length/type etc.), different parametrization algorithms (e.g. IRASA vs FOOOF) and selection of frequency ranges for the aperiodic slope estimation.”

      It would be worth clarifying that the linked mastoid re-reference alone has been proposed to cancel out the ECG signal, rather than that a linked-mastoid re-reference improves the performance of the ICA separation (which could be inferred by the explanation as it's currently written).

      This is correct and we adjusted the sentence accordingly! Stating now that:

      “ Previous work(12,56) has shown that a linked mastoid reference alone was particularly effective in reducing the impact of ECG related activity on aperiodic activity measured using EEG. “

      The issue of the number of EEG channels could probably just be noted as a potential limitation, as could the issue of neural activity being mixed into the ECG component (although this does pose a potential confound to the M/EEG without ECG condition, I suspect it wouldn't be critical).

      This is indeed a very fair point as a higher amount of electrodes would probably make it easier to better isolate ECG components in the EEG, which may be the reason why the separation did not work so well in our case. However, this is ultimately an empirical question so we highlighted it in the discussion section stating that: “Difficulties in removing ECG related components from EEG signals via ICA might be attributable to various reasons such as the number of available sensors or assumptions related to the non-gaussianity of the underlying sources. Further understanding of this matter is highly important given that ICA is the most widely used procedure to separate neural from peripheral physiological sources. ”

      OUTLOOK:

      Page 19: Although there has been a recent trend to control for 1/f activity when examining oscillatory power, recent research suggests that this should only be implemented in specific circumstances, otherwise the correction causes more of a confound than the issue does. It might be worth considering this point with regards to the final recommendation in the Outlook section: Brake, N., Duc, F., Rokos, A., Arseneau, F., Shahiri, S., Khadra, A., & Plourde, G. (2024). A neurophysiological basis for aperiodic EEG and the background spectral trend. Nature Communications, 15(1), 1514.

      We want to thank the reviewer for recommending this very interesting paper! The authors of said paper present compelling evidence showing that, while peak detection above an aperiodic trend using methods like FOOOF or IRASA is a prerequisite to determine the presence of oscillatory activity, it’s not necessarily straightforward to determine which detrending approach should be applied to determine the actual power of an oscillation. Furthermore, the authors suggest that wrongfully detrending may cause larger errors than not detrending at all. We therefore added a sentence stating that: “However, whether or not periodic activity (after detection) should be detrended using approaches like FOOOF or IRASA still remains disputed, as incorrectly detrending the data may cause larger errors than not detrending at all(75).”

      RECOMMENDATIONS:

      Page 20: "measure and account for" seems like it's missing a word, can this be re-written so the meaning is more clear?

      Done as suggested. The sentence now states: “To better disentangle physiological and neural sources of aperiodic activity, we propose the following steps to (1) measure and (2) account for physiological influences.”

      I would re-phrase "doing an ICA" to "reducing cardiac artifacts using ICA" (this wording could be changed in other places also).

      I do not like to describe cardiac or ocular activity as artifactual per se. This is also why I used hyphens whenever I mention the word “artifact” in association with the ECG or EOG. However, I do understand that the wording of “doing an ICA” is a bit sloppy. We therefore reworded it accordingly throughout the manuscript to e.g. “separating cardiac from neural sources using an ICA” and “separating physiological from neural sources using an ICA”.

      I would additionally note that even if components are identified as unambiguously cardiac, it is still likely that neural activity is mixed in, and so either subtracting or leaving the component will both be an issue (https://doi.org/10.1101/2024.06.06.597688). As such, even perfect identification of whether components are cardiac or not would still mean the issue remains (and this issue is also consistent across a considerable range of component based methods). Furthermore, current methods including wavelet transforms on the ICA component still do not provide good separation of the artifact and neural activity.

      This is definitely a fair point and we also highlight this in our recommendations under 3 stating that:

      “However, separating physiological from neural sources using an ICA is no guarantee that peripheral physiological activity is fully removed from the cortical signal. Even more sophisticated ICA based methods that e.g. apply wavelet transforms on the ICA components may still not provide a good separation of peripheral physiological and neural activity76,77. This turns the process of deciding whether or not an ICA component is e.g. either reflective of cardiac or neural activity into a challenging problem. For instance, when we only extract cardiac components using relatively high detection thresholds (e.g. r > 0.8), we might end up misclassifying residual cardiac activity as neural. In turn, we can’t always be sure that using lower thresholds won’t result in misinterpreting parts of the neural effects as cardiac. Both ways of analyzing the data can potentially result in misconceptions.”

      Castellanos, N. P., & Makarov, V. A. (2006). Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. Journal of neuroscience methods, 158(2), 300-312.

      Bailey, N. W., Hill, A. T., Godfrey, K., Perera, M. P. N., Rogasch, N. C., Fitzgibbon, B. M., & Fitzgerald, P. B. (2024). EEG is better when cleaning effectively targets artifacts. bioRxiv, 2024-06.

      METHODS:

      Pre-processing, page 24: I assume the symmetric setting of fastica was used (rather than the deflation setting), but this should be specified.

      Indeed the reviewer is correct, we used the standard setting of fastICA implemented in MNE python, which is calling the FastICA implementation in sklearn that is per default using the “parallel” or symmetric algorithm to compute an ICA. We added this information to the text accordingly, stating that:

      “For extracting physiological “artifacts” from the data, 50 independent components were calculated using the fastica algorithm(22) (implemented in MNE-Python version 1.2; with the parallel/symmetric setting; note: 50 components were selected for MEG for computational reasons for the analysis of EEG data no threshold was applied).”

      Temporal response functions, page 26: can the authors please clarify whether the TRF is computed against the ECG signal for each electrode or sensory independently, or if all electrodes/sensors are included in the analysis concurrently? I'm assuming it was computed for each electrode and sensory separately, since the TRF was computed in both the forward and backwards direction (perhaps the meaning of forwards and backwards could be explained in more detail also - i.e. using the ECG to predict the EEG signal, or using the EEG signal to predict the ECG signal?).

      A TRF can also be conceptualized as a multiple regression model over time lags. This means that we used all channels to compute the forward and backward models. In the case of the forward model we predicted the signal of the M/EEG channels in a multivariate regression model using the ECG electrode as predictor. In case of the backward model we predicted the ECG electrode based on the signal of all M/EEG channels. The forward model was used to depict the time window at which the ECG signal was encoded in the M/EEG recording, which appears at 0 time lags indicating volume conduction. The backward model was used to see how much information of the ECG was decodable by taking the information of all channels.

      We tried to further clarify this approach in the methods section stating that:

      “We calculated the same model in the forward direction (encoding model; i.e. predicting M/EEG data in a multivariate model from the ECG signal) and backward direction (decoding model; i.e. predicting the ECG signal using all M/EEG channels as predictors).”

      Page 27: the ECG data was fit using a knee, but it seems the EEG and MEG data was not.

      Does this different pose any potential confound to the conclusions drawn? (having said this, Figure S4 suggests perhaps a knee was tested in the M/EEG data, which should perhaps be explained in the text also).

      This was indeed tested in a previous review round to ensure that our results are not dependent on the presence/absence of a knee in the data. We therefore added figure S4, but forgot to actually add a description in the text. We are sorry for this oversight and added a paragraph to S1 accordingly:

      “Using FOOOF(5), we also investigated the impact of different slope fitting options (fixed vs. knee model fits) on the aperiodic age relationship (see Supplementary Figure S4). The results that we obtained from these analyses using FOOOF offer converging evidence with our main analysis using IRASA.”

      Page 32: my understanding of the result reported here is that cleaning with ICA provided better sensitivity to the effects of age on 1/f activity than cleaning with SSS. Is this accurate? I think this could also be reported in the main manuscript, as it will be useful to researchers considering how to clean their M/EEG data prior to analyzing 1/f activity.

      The reviewer is correct in stating that we overall detected slightly more “significant” effects, when not additionally cleaning the data using SSS. However, I am a bit wary of recommending omitting the use of SSS maxfilter solely based on this information. It can very well be that the higher quantity of effects (when not employing SSS maxfilter) stems from other physiological sources (e.g. muscle activity) that are correlated with age and removed when applying SSS maxfiltering. I think that just conditioning the decision of whether or not maxfilter is applied based on the amount or size of effects may not be the best idea. Instead I think that the applicability of maxfilter for research questions related to aperiodic activity should be the topic of additional methodological research. We therefore now write in Text S1:

      “Considering that we detected less and weaker aperiodic effects when using SSS maxfilter is it advisable to omit maxfilter, when analyzing aperiodic signals? We don’t think that we can make such a judgment based on our current results. This is because it's unclear whether or not the reduction of effects stems from an additional removal of peripheral information (e.g. muscle activity; that may be correlated with aging) or is induced by the SSS maxfiltering procedure itself. As the use of maxfilter in detecting changes of aperiodic activity was not subject of analysis that we are aware of, we suggest that this should be the topic of additional methodological research.”

      Page 39, Figure S6 and Figure S8: Perhaps the caption could also briefly explain the difference between maxfilter set to false vs true? I might have missed it, but I didn't gain an understanding of what varying maxfilter would mean.

      Figure S6 shows the effect of ageing on the spectral slope averaged across all channels. The maxfilter set to false in AB) means that no maxfiltering using SSS was performed vs. in CD) where the data was additionally processed using the SSS maxfilter algorithm. We now describe this more clearly by writing in the caption:

      “Supplementary Figure S6: Age-related changes in aperiodic brain activity are most prominent on explained by cardiac components irrespective of maxfiltering the data using signal space separation (SSS) or not AC) Age was used to predict the spectral slope (fitted at 0.1-145Hz) averaged across sensors at rest in three different conditions (ECG components not rejected [blue], ECG components rejected [orange], ECG components only [green].”

    1. 中性流相互作用 (Neutral Current Interaction)

      好的,我们来详细解释一下中性流相互作用 (Neutral Current Interactions)。中性流相互作用是粒子物理标准模型中描述的两种弱相互作用类型之一,另一种是带电流相互作用。中性流相互作用的媒介粒子是电中性的 Z 玻色子 (Z boson)

      1. 引言

      弱相互作用是自然界四种基本相互作用之一(另外三种是强相互作用、电磁相互作用和引力)。它负责某些类型的放射性衰变,例如 β 衰变,并且在太阳内部的核聚变过程中也起着关键作用。弱相互作用由三种媒介粒子传递:带正电的 W<sup>+</sup> 玻色子,带负电的 W<sup>-</sup> 玻色子,以及电中性的 Z 玻色子。

      2. 弱相互作用

      弱相互作用与其他基本相互作用相比,强度非常弱,且作用范围极短。它能够改变夸克和轻子的种类(味),并且是唯一能够改变夸克味的相互作用。

      3. 带电流相互作用 (Charged Current Interactions)

      在详细介绍中性流相互作用之前,我们先简要回顾一下带电流相互作用。带电流相互作用通过交换 W<sup>+</sup> 或 W<sup>-</sup> 玻色子发生。这类相互作用的特点是会改变参与相互作用的费米子的电荷和味。例如:

      • β<sup>-</sup> 衰变: 一个中子(由两个下夸克和一个上夸克组成)通过发射一个 W<sup>-</sup> 玻色子衰变成一个质子(由两个上夸克和一个下夸克组成),同时产生一个电子和一个反电子中微子。在这个过程中,一个下夸克的味变成了上夸克,并且发射了带电的 W<sup>-</sup> 玻色子。
      • μ子衰变: 一个负μ子通过发射一个 W<sup>-</sup> 玻色子衰变成一个电子、一个反电子中微子和一个μ中微子。

      4. 中性流相互作用的细节

      中性流相互作用与带电流相互作用的主要区别在于其媒介粒子和相互作用的结果:

      • 媒介粒子: 中性流相互作用由电中性的 Z 玻色子 (Z<sup>0</sup>) 传递。Z 玻色子的质量约为 91.2 GeV/c²,比质子重约 97 倍。
      • 不改变电荷: 与带电流相互作用不同,中性流相互作用不会改变参与相互作用的粒子的电荷。一个带电粒子在通过中性流相互作用后仍然保持其电荷。
      • 不改变味: 中性流相互作用也不会改变参与相互作用的夸克或轻子的味。例如,一个电子在通过中性流相互作用后仍然是电子,不会变成中微子或其他轻子。同样,一个上夸克也不会变成下夸克。
      • 与所有标准模型费米子相互作用: Z 玻色子可以与标准模型中的所有夸克和轻子(包括中微子)发生相互作用。它也可以与 W 玻色子和希格斯玻色子相互作用。

      5. 中性流相互作用的例子

      以下是一些中性流相互作用的例子:

      • 中微子散射 (Neutrino Scattering): 中微子可以通过交换 Z 玻色子与带电轻子(如电子)或夸克发生散射。例如,一个电子中微子可以与一个电子交换 Z 玻色子并发生散射,而不会改变它们的种类。这是中性流相互作用的第一个实验证据。
      • 电子-质子散射 (Electron-Proton Scattering): 高能电子在散射过程中,除了通过交换光子(电磁相互作用)外,也可以通过交换 Z 玻色子与质子内部的夸克发生相互作用。在足够高的能量下,Z 玻色子的贡献变得显著。
      • Z 玻色子衰变 (Z Boson Decay): Z 玻色子本身是不稳定的,它会通过中性流相互作用衰变成一对费米子(夸克-反夸克对或轻子-反轻子对)。例如,Z 玻色子可以衰变成一对电子-正电子、一对μ子-反μ子、一对τ子-反τ子,或者各种夸克-反夸克对(例如,上夸克-反上夸克,下夸克-反下夸克等)。

      6. 实验发现

      中性流相互作用的存在于 1973 年在欧洲核子研究中心 (CERN) 的 Gargamelle 气泡室实验中首次被观测到。实验中观察到了中微子与原子核发生相互作用,但没有产生带电轻子(如电子或μ子),这表明相互作用是通过一个电中性的媒介粒子发生的,即 Z 玻色子。这一发现是弱电统一理论的巨大胜利,证实了电磁力和弱力在更高能量下是统一的。

      7. 中性流相互作用的重要性

      中性流相互作用在粒子物理学中具有重要的意义:

      • 证实弱电统一理论: 它的发现是格拉肖-温伯格-萨拉姆 (Glashow-Weinberg-Salam) 弱电统一理论的关键证据,该理论成功地将电磁相互作用和弱相互作用统一起来。
      • Z 玻色子的性质: 研究中性流相互作用可以帮助物理学家精确测量 Z 玻色子的性质,例如其质量、宽度以及与不同粒子的耦合强度。
      • 标准模型的精确检验: 中性流过程为检验标准模型的精确性提供了重要的途径。通过精确测量各种中性流相互作用的截面和分布,可以寻找与标准模型预言的偏差,从而寻找超出标准模型的新物理的迹象。
      • 中微子物理学: 中性流相互作用对于理解中微子的相互作用和性质至关重要。

      8. 理论框架

      在中性流相互作用的理论描述中,Z 玻色子是弱电规范群 SU(2)<sub>L</sub> × U(1)<sub>Y</sub> 中的一个规范玻色子。Z 玻色子是 SU(2)<sub>L</sub> 的第三个生成元 W<sup>3</sup> 和 U(1)<sub>Y</sub> 的生成元 B<sup>0</sup> 的混合态,通过所谓的希格斯机制获得质量。

      9. 与电磁相互作用的比较

      中性流相互作用(通过 Z 玻色子交换)与电磁相互作用(通过光子交换)有一些相似之处,但也有关键的区别:

      • 相似性: 两者都是通过交换中性玻色子发生的。
      • 区别: 光子是无质量的,导致电磁相互作用的作用范围是无限的;而 Z 玻色子是有质量的,导致中性流相互作用的作用范围非常短。此外,光子只与带电粒子相互作用,而 Z 玻色子与所有标准模型费米子都相互作用。

      值得注意的是,在弱电统一理论中,电磁相互作用和弱相互作用被统一描述,光子和 Z 玻色子是同一个基本理论的不同表现。

      10. 结论

      中性流相互作用是弱相互作用的一种基本形式,由电中性的 Z 玻色子传递。它与带电流相互作用的主要区别在于不改变参与相互作用的粒子的电荷和味。中性流相互作用的发现是粒子物理学发展史上的重要里程碑,有力地支持了弱电统一理论,并为我们研究基本粒子的性质和寻找新物理提供了重要的工具。

    1. Reviewer #2 (Public review):

      Summary:

      In this work the authors show that dopaminergic neurons (DANs) from the DL1 cluster in Drosophila larvae are required for the formation of aversive memories. DL1 DANs complement pPAM cluster neurons which are required for the formation of attractive memories. This shows the compartmentalized network organization of how an insect learning center (the mushroom body) encodes memory by integrating olfactory stimuli with aversive or attractive teaching signals. Interestingly, the authors found that the 4 main dopaminergic DL1 neurons act partially redundant, and that single cell ablation did not result in aversive memory defects. However, ablation or silencing of a specific DL1 subset (DAN-f1,g1) resulted in reduced salt aversion learning, which was specific to salt but no other aversive teaching stimuli tested. Importantly, activation of these DANs using an optogenetic approach was also sufficient to induce aversive learning in the presence of high salt. Together with the functional imaging of salt and fructose responses of the individual DANs and the implemented connectome analysis of sensory (and other) inputs to DL1/pPAM DANs this represents a very comprehensive study linking the structural, functional and behavioral role of DL1 DANs. This provides fundamental insight into the function of a simple yet efficiently organized learning center which displays highly conserved features of integrating teaching signals with other sensory cues via dopaminergic signaling.

      Strengths:

      This is a very careful, precise and meticulous study identifying the main larval DANs involved in aversive learning using high salt as a teaching signal. This is highly interesting because it allows to define the cellular substrates and pathways of aversive learning down to the single cell level in a system without much redundancy. It therefore sets the basis to conduct even more sophisticated experiments and together with the neat connectome analysis opens the possibility to unravel different sensory processing pathways within the DL1 cluster and integration with the higher order circuit elements (Kenyon cells and MBONs). The authors' claims are well substantiated by the data and balanced, putting their data in the appropriate context. The authors also implemented neat pathway analyses using the larval connectome data to its full advantage, thus providing network pathways that contribute towards explaining the obtained results.

      Weaknesses:

      Previous comments were fully addressed by the authors.

    2. Reviewer #3 (Public review):

      The study of Weber et al. provides a thorough investigation of the roles of four individual dopamine neurons for aversive associative learning in the Drosophila larva. They focus on the neurons of the DL-1 cluster which already have been shown to signal aversive teaching signals. But the authors go beyond the previous publications and test whether each of these dopamine neurons responds to salt or sugar, is necessary for learning about salt, bitter, or sugar, and is sufficient to induce a memory when optogenetically activated. In addition, previously published connectomic data is used to analyze the synaptic input to each of these dopamine neurons. The authors conclude that the aversive teaching signal induced by salt is distributed across the four DL-1 dopamine neurons, with two of them, DAN-f1 and DAN-g1, being particularly important. Overall, the experiments are well designed and performed, support the authors' conclusions, and deepen our understanding of the dopaminergic punishment system.

      Strengths:

      (1) This study provides, at least to my knowledge, the first in vivo imaging of larval dopamine neurons in response to tastants. Although the selection of tastants is limited, the results close an important gap in our understanding of the function of these neurons.<br /> (2) The authors performed a large number of experiments to probe for the necessity of each individual dopamine neuron, as well as combinations of neurons, for associative learning. This includes two different training regimen (1 or 3 trials), three different tastants (salt, quinine and fructose) and two different effectors, one ablating the neuron, the other one acutely silencing it. This thorough work is highly commendable, and the results prove that it was worth it. The authors find that only one neuron, DAN-g1, is partially necessary for salt learning when acutely silenced, whereas a combination of two neurons, DAN-f1 and DAN-g1, are necessary for salt learning when either being ablated or silenced.<br /> (3) In addition, the authors probe whether any of the DL-1 neurons is sufficient for inducing an aversive memory. They found this to be the case for two of the neurons, largely confirming previous results obtained by a different learning paradigm, parameters and effector.<br /> (4) This study also takes into account connectomic data to analyze the sensory input that each of the dopamine neurons receives. This analysis provides a welcome addition to previous studies and helps to gain a more complete understanding. The authors find large differences in inputs that each neuron receives, and little overlap in input that the dopamine neurons of the "aversive" DL-1 cluster and the "appetitive" pPAM cluster seem to receive.<br /> (5) Finally, the authors try to link all the gathered information in order to describe an updated working model of how aversive teaching signals are carried by dopamine neurons to the larva's memory center. This includes important comparisons both between two different aversive stimuli (salt and nociception) and between the larval and adult stages.

    3. Author response:

      The following is the authors’ response to the original reviews

      Public reviews:

      Reviewer #1 (Public Review):

      Summary:

      In this paper, Weber et al. investigate the role of 4 dopaminergic neurons of the Drosophila larva in mediating the association between an aversive high-salt stimulus and a neutral odor. The 4 DANs belong to the DL1 cluster and innervate non-overlapping compartments of the mushroom body, distinct from those involved in appetitive associative learning. Using specific driver lines, they show that activation of the DAN-g1 is sufficient to mimic an aversive memory and it is also necessary to form a high-salt memory of full strength, although optogenetic silencing of this neuron only partially affects the performance index. The authors use calcium imaging to show that the DAN-g1 is not the only one that responds to salt. DAN-c1 and d1 also respond to salt, but they seem to play no role in the assays tested. DAN-f1, which does not respond to salt, is able to lead to the formation of memory (if optogenetically activated), but it is not necessary for the salt-odor memory formation in normal conditions. However, silencing of DAN-f1 together with DAN-g1, enhances the memory deficit of DAN-g1.

      Strengths:

      The paper therefore reveals that also in the Drosophila larva as in the adult, rewards and punishments are processed by exclusive sets of DANs and that a complex interaction between a subset of DANs mediates salt-odor association.

      Overall, the manuscript contributes valuable results that are useful for understanding the organization and function of the dopaminergic system. The behavioral role of the specific DANs is accessed using specific driver lines which allow for testing of their function individually and in pairs. Moreover, the authors perform calcium imaging to test whether DANs are activated by salt, a prerequisite for inducing a negative association with it. Proper genetic controls are carried across the manuscript.

      Weaknesses:

      The authors use two different approaches to silence dopaminergic neurons: optogenetics and induction of apoptosis. The results are not always consistent, and the authors could improve the presentation and interpretation of the data. Specifically, optogenetics seems a better approach than apoptosis, which can affect the overall development of the system, but apoptosis experiments are used to set the grounds of the paper.

      The physiological data would suggest the role of a certain subset of DANs in salt-odor association, but a different partially overlapping set seems to be necessary. This should be better discussed and integrated into the author's conclusion. The EM data analysis reveals a non-trivial organization of sensory inputs into DANs and it is hard to extrapolate a link to the functional data presented in the paper.

      We would like to thank reviewer 1 for the positive evaluation of our work and for the critical suggestions for improvement. In the new version of the manuscript, we have centralized the optogenetic results and moved some of the ablation experiments to the Supplement. We also discuss in detail the experimental differences in the results. In addition, we have softened our interpretation of the specificity of memory for salt. As a result, we now emphasize more the general role of DANs for aversive learning in the larva. These changes are now also summarized and explained more simply and clearly in the Discussion, along with a revised discussion of the EM data.

      Reviewer #2 (Public Review):

      Summary:

      In this work, the authors show that dopaminergic neurons (DANs) from the DL1 cluster in Drosophila larvae are required for the formation of aversive memories. DL1 DANs complement pPAM cluster neurons which are required for the formation of attractive memories. This shows the compartmentalized network organization of how an insect learning center (the mushroom body) encodes memory by integrating olfactory stimuli with aversive or attractive teaching signals. Interestingly, the authors found that the 4 main dopaminergic DL1 neurons act redundantly, and that single-cell ablation did not result in aversive memory defects. However, ablation or silencing of a specific DL1 subset (DAN-f1,g1) resulted in reduced salt aversion learning, which was specific to salt but no other aversive teaching stimuli were tested. Importantly, activation of these DANs using an optogenetic approach was also sufficient to induce aversive learning in the presence of high salt. Together with the functional imaging of salt and fructose responses of the individual DANs and the implemented connectome analysis of sensory (and other) inputs to DL1/pPAM DANs, this represents a very comprehensive study linking the structural, functional, and behavioral role of DL1 DANs. This provides fundamental insight into the function of a simple yet efficiently organized learning center which displays highly conserved features of integrating teaching signals with other sensory cues via dopaminergic signaling.

      Strengths:

      This is a very careful, precise, and meticulous study identifying the main larval DANs involved in aversive learning using high salt as a teaching signal. This is highly interesting because it allows us to define the cellular substrates and pathways of aversive learning down to the single-cell level in a system without much redundancy. It therefore sets the basis to conduct even more sophisticated experiments and together with the neat connectome analysis opens the possibility of unraveling different sensory processing pathways within the DL1 cluster and integration with the higher-order circuit elements (Kenyon cells and MBONs). The authors' claims are well substantiated by the data and clearly discussed in the appropriate context. The authors also implement neat pathway analyses using the larval connectome data to its full advantage, thus providing network pathways that contribute towards explaining the obtained results.

      Weaknesses:

      While there is certainly room for further analysis in the future, the study is very complete as it stands. Suggestions for clarification are minor in nature.

      We would like to thank reviewer 2 for the positive evaluation of our work. In fact, follow-up work is already underway to further analyze the role of the individual DL1 DANs. We have addressed the constructive and detailed suggestions for improvement in our point-by-point responses in the “Recommendations for the authors” section.

      Reviewer #3 (Public Review):

      The study of Weber et al. provides a thorough investigation of the roles of four individual dopamine neurons for aversive associative learning in the Drosophila larva. They focus on the neurons of the DL-1 cluster which already have been shown to signal aversive teaching signals. However, the authors go far beyond the previous publications and test whether each of these dopamine neurons responds to salt or sugar, is necessary for learning about salt, bitter, or sugar, and is sufficient to induce a memory when optogenetically activated. In addition, previously published connectomic data is used to analyze the synaptic input to each of these dopamine neurons. The authors conclude that the aversive teaching signal induced by salt is distributed across the four DL-1 dopamine neurons, with two of them, DAN-f1 and DAN-g1, being particularly important. Overall, the experiments are well designed and performed, support the authors' conclusions, and deepen our understanding of the dopaminergic punishment system.

      Strengths:

      (1) This study provides, at least to my knowledge, the first in vivo imaging of larval dopamine neurons in response to tastants. Although the selection of tastants is limited, the results close an important gap in our understanding of the function of these neurons.

      (2) The authors performed a large number of experiments to probe for the necessity of each individual dopamine neuron, as well as combinations of neurons, for associative learning. This includes two different training regimens (1 or 3 trials), three different tastants (salt, quinine, and fructose) and two different effectors, one ablating the neuron, the other one acutely silencing it. This thorough work is highly commendable, and the results prove that it was worth it. The authors find that only one neuron, DAN-g1, is partially necessary for salt learning when acutely silenced, whereas a combination of two neurons, DAN-f1 and DAN-g1, are necessary for salt learning when either being ablated or silenced.

      (3) In addition, the authors probe whether any of the DL-1 neurons is sufficient for inducing an aversive memory. They found this to be the case for three of the neurons, largely confirming previous results obtained by a different learning paradigm, parameters, and effector.

      (4) This study also takes into account connectomic data to analyze the sensory input that each of the dopamine neurons receives. This analysis provides a welcome addition to previous studies and helps to gain a more complete understanding. The authors find large differences in inputs that each neuron receives, and little overlap in input that the dopamine neurons of the "aversive" DL-1 cluster and the "appetitive" pPAM cluster seem to receive.

      (5) Finally, the authors try to link all the gathered information in order to describe an updated working model of how aversive teaching signals are carried by dopamine neurons to the larva's memory center. This includes important comparisons both between two different aversive stimuli (salt and nociception) and between the larval and adult stages.

      Weaknesses:

      (1) The authors repeatedly claim that they found/proved salt-specific memories. I think this is problematic to some extent.

      (1a) With respect to the necessity of the DL-1 neurons for aversive memories, the authors' notion of salt-specificity relies on a significant reduction in salt memory after ablating DAN-f1 and g1, and the lack of such a reduction in quinine memory. However, Fig. 5K shows a quite suspicious trend of an impaired quinine memory which might have been significant with a higher sample size. I therefore think it is not fully clear yet whether DAN-f1 and DAN-g1 are really specifically necessary for salt learning, and the conclusions should be phrased carefully.

      (1b) With respect to the results of the optogenetic activation of DL-1 neurons, the authors conclude that specific salt memories were established because the aversive memories were observed in the presence of salt. However, this does not prove that the established memory is specific to salt - it could be an unspecific aversive memory that potentially could be observed in the presence of any other aversive stimuli. In the case of DAN-f1, the authors show that the neuron does not even get activated by salt, but is inhibited by sugar. Why should activation of such a neuron establish a specific salt memory? At the current state, the authors clearly showed that optogenetic activation of the neurons does induce aversive memories - the "content" of those memories, however, remains unknown.

      (2) In many figures (e.g. figures 4, 5, 6, supplementary figures S2, S3, S5), the same behavioural data of the effector control is plotted in several sub-figures. Were these experiments done in parallel? If not, the data should not be presented together with results not gathered in parallel. If yes, this should be clearly stated in the figure legends.

      We would also like to thank reviewer 3 for his positive assessment of our work. As already mentioned by reviewer 1, we understand the criticism that the salt specificity for which the individual DANs are coded is not fully always supported by the results of the work. We have therefore rewritten the relevant passages, which are also cited by the reviewer. We have also included the second point of criticism and incorporated it into our manuscript. As the control groups were always measured in parallel with the experimental animals, we can also present the data together in a sub-figure. We clearly state this now in the revised figure legends.

      Summary of recommendations to authors:

      Overall, the study is commendable for its systematic approach and solid methodology. Several weaknesses were identified, prompting the need for careful revisions of the manuscript:

      We thank the reviewers for the careful revision of our manuscript. In the subsequent sections, we aim to address their concerns as thoroughly as possible. A comprehensive one-to-one listing can be found below.

      (1) The authors should reconsider their assertion of uncovering a salt-specific memory, as the evidence does not conclusively demonstrate the exclusive necessity of DAN-f1 and DAN-g1 for salt learning. In particular, the optogenetic activation of DAN-f1 leads to plasticity but this might not be salt-specific. The precise nature of the memory content remains elusive, warranting a nuanced rephrasing of the conclusions.

      We only partially agree – optogenetic activation of DANs does not really allow to comment on its salt-specificity, true. However, we used high-salt concentrations during test. Over the years, the Gerber lab nicely demonstrated in several papers that larvae recall an aversive odor-salt memory only if salt is present during test (Gerber and Hendel, 2006; Niewalda et al 2008; Schleyer et al. 2011; Schleyer et al. 2015). The used US has to be present during test. Even at the same concentration other aversive stimuli (e.g. bitter quinine) are not able to allow the larvae to recall this particular type of memory. So, if the optogenetic activation of DAN-f1 establishes a memory that can be recalled on salt, we argue that it has to encode aspects of the salt information. On the other hand, only for DAN-g1 we see the necessity for salt learning. And – although (based on the current literature) very unlikely, we cannot fully exclude that the activation of DAN-f1 establishes a yet unknown type of memory that can be also recalled on a salt plate. Therefore, we partially agree and accordingly have rephrased the entire manuscript to avoid an over-interpretation of our data. Throughout the manuscript we avoid now to use the term salt-specific memory but rather describe the type of memory as aversive memory.

      (2) A thorough examination or discussion about the potential influence of blue light aversion on behavioral observations is necessary to ensure a balanced interpretation of the findings.

      To address this point every single behavioral experiment that uses optogenetic blue light activation runs with appropriate and mandatory controls. For blue light activation experiments, two genetic controls are used that either get the same blue light treatment (effector control, w1118>UAS-ChR2XXL) or no blue light treatment (dark control, XY-split-Gal4>UAS-ChR2XXL). For blue light inactivation experiments one group is added that has exactly the same genotype but did not receive food containing retinal. These experiments show that blue light exposure itself does not induce an aversive nor positive memory and blue light exposure does not impair the establishment of odor-high salt memory. In addition, we used the latest established transgenes available. ChR2<sup>XXL</sup> is very sensitive to blue light. Only 220 lux (60 µW/cm<sup>²</sup>) were necessary to obtain stable results. In our hands – short term exposure for up to 5 minutes with such low intensities does not induce a blue light aversion. Following the advice of the reviewer, we also address this concern by adding several sentences into the related results and methods sections.

      (3) The authors should address the limitations associated with the use of rpr/hid for neuronal ablations, such as the effects of potential developmental compensation.

      We agree with this concern. It is well possible that the ablation experiments induce compensatory effects during larval development. Such an effect may be the reason for differences in phenotypes when comparing hid,rpr ablation with optogenetic inhibition. This is now part of the discussion. In addition, we evaluated if the ablation worked in our experiments. So far controls were missing that show that the expression of hid,rpr really leads to the ablation of DANs. We now added these experiments and clearly show anatomically that the DANs are ablated (related to figure 4-figure supplement 6).

      (4) While the connectome analysis offers valuable insights into the observed functions of specific DANs in relation to their extrinsic (sensory) and intrinsic (state) inputs, integrating this data more cohesively within the manuscript through careful rewriting would enhance the coherence of the study.

      We understand this concern. Therefore, the new version of our manuscript is now intensifying the inclusion of the EM data in our interpretation of the results. Throughout the entire manuscript we have now rewritten the related parts. We have also completely revised the corresponding section in the results chapter.

      (5) More generally, the authors are encouraged to discuss internal discrepancies in the results of their functional manipulation experiments.

      Thank you for this suggestion. We do of course understand that we have not given the different results enough space in the discussion. We have now changed this and have been happy to comprehensively address the concern. 

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Here are some suggestions for clarification and improvement of the manuscript:

      (1) The authors should discuss why the silencing experiment with TH-GAL4 (Fig. 1) does not abolish memory formation (I assume that the PI should go to zero). Does it mean that other non-TH neurons are involved in salt-odor memory formation? Are there other lines that completely abolish this type of learning?

      Thank you very much for highlighting this crucial point. Indeed, the functional intervention does not completely eliminate the memory. There could be several reasons, or a combination thereof, for this outcome. For instance, it's plausible that the UAS-GtACR2 effector doesn't entirely suppress the activity of dopaminergic neurons. Additionally, the memory may comprise different types, not all of which are linked to dopamine function. It's also noteworthy that TH-Gal4 doesn't encompass all dopaminergic neurons – even a neuron from the DL1 cluster is absent (as previously reported in Selcho et al., 2009). Considering we're utilizing high salt concentrations in this experiment, it's conceivable that non gustatory-driven memories are formed based solely on the systemic effects of salt (e.g., increased osmotic pressure). These possibilities are now acknowledged in the text.

      (2) The Rpr experiments in Fig. 4 do not lead to any phenotype and there is a general assumption that the system compensates during development. However, there is no demonstration that Rpr worked or that development compensated for that. What do we learn from these data? Would it make sense to move it to supplement to make the story more compact? In addition: the conclusion at L 236 "DL1.... Are not individually necessary" is later disproved by optogenetic silencing. Similarly, optogenetic silencing of f1+g1 is affecting 1X and 3X learning, but not when using Rpr. Moreover, Rpr wdid not give any phenotype in other data in the supplementary material. I'm not sure how valid these results are.

      We acknowledge this concern and have actively deliberated various options for restructuring the presented ablation data. Ultimately, we reached a consensus that relocating Figure 4 to the supplement is warranted. Furthermore, corresponding adjustments have been made in the text. This decision amplifies the significance of the optogenetic results. In addition, we also addressed the other part of the concern. We examined the efficacy of hid and rpr in our experiments. Indeed, we successfully ablated specific DANs, as illustrated in the new anatomical data presented in Figure 4- figure supplement 6, which strengthens the interpretation of the hid,rpr experiments.

      (3) In most figures that show data for 1X and 3X training, there is no difference between these two conditions (I would suggest moving one set as a supplement). When a difference appears (Fig.5A-D) the implications are not discussed properly. Is it known that some circuits are necessary for the 1X but not for the 3X protocol? Is that a reasonable finding? I would expect the opposite, but I might lack of knowledge here. However, the optogenetic silencing of the same neurons in Figure 7 shows the same phenotype for 1X and 3X. Again, the validity of the Rpr experiments seems debatable.

      Different training protocols lead to different memory phases (STM and STM+ARM). We have shown that in the past in Widmann et al. 2016. Therefore, we are convinced that it makes sense to keep both data sets in the main manuscript. However, we agree that this was not properly introduced and discussed and therefore made the respective changes in the manuscript.

      (4) In Figure 3, it is unclear what the responses were tested against. Since they are so small and noisy there would be a need for a control. Moreover, in some cases, it looks like the DF/F is normalized to the wrong value: e.g. in DAN-c1 100mM, the activity in 0-10s is always above zero, and in pPAM with fructose is always below zero. This might not have any consequence on the results but should be adjusted.

      Thank you very much for your criticism, which we greatly appreciate. We have carefully re-examined the data and found that there was a mistake for the normalization of the values. We made the necessary adjustments to the evaluation, as per your suggestions. The updated figures, figure legends, and results have been incorporated into the new version of the manuscript. As noted by the reviewer, these corrections have not altered the interpretation of the data or the primary responses of the various DANs.

      (5) In the abstract: "Optogenetic activation of DAN-f1 and DAN-g1 alone suffices to substitute for salt punishment... Each DAN encodes a different aspect of salt punishment". These sentences might be misleading and an overstatement: only DAN-g1 shows a clear role, while the function of the other DANs in the context of salt-odor learning remains obscure.

      We have refined the respective part of the abstract accordingly. Consequently, we have reworded the related section, aiming to avoid any exaggeration.

      (6) The physiology is done in L1 larvae but behavior is tested in L3 larvae. There could be a change in this time that could explain the salt responses in c1 and d1 but no role in salt-odor learning?

      While we cannot dismiss the possibility of a developmental change from L1 to L3, a comparison of the anatomical data of the DL1 DANs from electron microscopy (EM) and light microscopy (LM) data indicates that their overall morphology remains consistent. However, it's important to note that this observation does not analyse the physiological aspects of these cells. Consequently, we have incorporated this concern into the discussion of the revised version of the manuscript.

      (7) The introduction needs some editing starting at L 129, as it ends with a discussion of a previously published EM data analysis. I would rather suggest stating which questions are addressed in this paper and which methods will be used and perhaps a hint on the results obtained.

      We understand the concern. We have added a concise paragraph to the conclusion of the introduction, highlighting the biological question, technical details, and a short hint on the acquired findings.

      (8) It is clear to me that the presentation of salt during the test is necessary for recall, however in L 166 I don't understand the explanation: how is the memory used in a beneficial way in the test? The salt is present everywhere and the odor cue is actually useless to escape it.

      Extensive research, exemplified by studies such as Schleyer et al. (2015) published in Elife, clearly demonstrates that the recall of odor-high salt memory occurs exclusively when tested on a high salt plate. Even when tested on a bitter quinine plate, the aversive memory is not recalled. This phenomenon is attributed to the triggering of motivation to recall the memory by the omnipresent abundance of the unconditioned stimulus (US) during the test, which in our case is high salt. Furthermore, the concentration of the stimulus plays a crucial role (Schleyer et al. 2011). The odor cue indicates where the situation could potentially be improved; however, if high salt is absent, this motivational drive diminishes as there is no memory present to enhance the already favorable situation. Additionally, the motivation to evade the omnipresent and unpleasant high salt stimulus persists throughout the entire 5-minute test period.

      (9) L288: the fact that f1 shows a phenotype in this experiment does not mean that it encodes a salt signal, indeed it does not respond to salt. It perhaps induces a plasticity that can be recalled by salt, but not necessarily linked to salt. The synergy between f1 and g1 in the salt assay was postulated based on exp with Rpr, but the validity of these experiments is dubious. I'm not sure there is sufficient evidence from Figures 6 and 7 to support a synergistic action between f1 and g1.

      It is true that DAN-f1 alone is not necessary for mediating a high salt teaching signal based on ablation, optogenetic inhibition and even physiology. However, optogenetic activation alone shows a memory tested on a salt plate. Given the logic explained above that is accepted by several publications, we would like to keep the statement. Especially as the joined activation with DAN-g1 gives rise to significant higher or lower values after joined optogenetic activation or inactivation (Figure 5E and F, Figure 6E and F in the new version). Nevertheless, we have modified the sentence. In the text we describe these effects now as “these results may suggest that DAN-f1 and DAN-g1 encode aspects of the natural aversive high salt teaching signal under the conditions that we tested”. We think that this is an appropriate and three-fold restricted statement. Therefore, we would like to keep it in this restricted version. However, we are happy to reconsider this if the reviewer thinks it is critical. 

      (10) I find the EM analysis hard to read. First of all, because of the two different graphical representations used in Fig. 8, wouldn't one be sufficient to make the point? Secondly, I could not grasp a take-home-message: what do we learn from the EM data? Do they explain any of the results? It seems to me that they don't provide an explanation of why some DL1 neurons respond to salt and others don't.

      We understand that the EM analysis is hard to read and have now carefully rewritten this part of the manuscript. See also general concern 4 above. The main take home message is not to explain why some DL1 neurons respond to salt and other do not. This cannot be resolved due to the missing information on the salt perceiving receptor cells. Unfortunately, we miss the peripheral nervous system in the EM - the first layer of salt information processing. However, our analysis shows clearly that the 4 DANs have their own identity based on their connectivity. None of them is the same – but to a certain extent similarities exist. This nicely reflects the physiological and behavioral results. We have now clarified that in the result to ease the understanding for the readership. In addition, we also clearly state that we don’t address the point why some DL1 neurons respond to salt and why others don’t respond.

      (11) Do the manipulations (activation and silencing) affect odor preference in the presence of salt? Did the authors test that the two odors do not drive different behaviors on the salty plate? Or did they only test the odor preference on plain agarose? Can we exclude a role for the DAN in driving multisensory-driven innate behavior?

      Innate odor preferences are not changed by the presence of salt or even other tastants (this work but see also Schleyer et al 2015, Figure 3, Elife). Even the naïve choice between two odors is the same if tested in the presence of different tastants (Schleyer et al 2015, Figure 3, Elife). This shows – at least for the tested stimuli and conditions – that are similar to the ones that we use – that there is no multisensory-driven innate odor-taste behavior. Therefore – at least to our knowledge - experiments as the ones suggested by the reviewer were never done in larval odor-taste learning studies. Therefore, we suggest that DAN activation has no effect on innate larval behavior. However, we are happy to reconsider this if the reviewer thinks it is critical. 

      (12) L 280: the authors generalize the conclusion to all DL1-DANs, but it does not apply to c1 and d1.

      Thanks for this comment. We deleted that sentence as suggested and thus do not anymore generalize the conclusion to all DL-DANs.

      (13) L345: I do not see the described differences in Fig. 8F, presynaptic sites of both types seem to appear in rather broad regions: could the author try to clarify this?

      We understand that the anatomical description of the data is often hard to read. Especially to readers that are not used to these kind of figures. We have therefore modified the text to ease the understanding and clarify the difference in the labeled brain regions for the broad readership.

      (14) L373: the conclusion on c1 is unsupported by data: this neuron responds to both salt and fructose (Figure 3 ) while the conclusion is purely based on EM data analysis.

      The sentence is not a conclusion but a speculation and we also list the cell's response to positive and negative gustatory stimuli. Therefore, we do not understand exactly what the reviewer means here. However, we have tried to address the criticism and have revised the sentences.

      (15) L385: the data on d1 seem to be inconsistent with Eschbach 2020, but the authors do not discuss if this is due to the differential vs absolute training, or perhaps the presence of the US during the test (which does not seem to be there in Eschbach, 2020) - is the training protocol really responsible for this inconsistency? For f1 the data seem to be consistent across these studies. The authors should clarify how the exp in Fig 6 differs from Eschbach, 2020 and how one could interpret the differences.

      True. This concern is correct. We now discuss the difference in more detail. Eschbach et al. used Cs-Crimson as a genetic tool, a one odor paradigm with 3 training cycles, and no gustatory cues in their approach. These differences are now discussed in the new version of the manuscript.

      (16) L460-475 A long part of this paragraph discusses the similarities between c1 and d1 and corresponding PPL1 neurons in the adult fly. However, c1 and d1 do not really show any phenotype in this paper, I'm not sure what we learn from this discussion and how much this paper can contribute to it. I would have wished for a discussion of how one could possibly reconcile the observed inconsistencies.

      Based on the comments of the different reviewers several paragraphs in the discussion were modified. We agree that the part on the larval-adult comparison is quite long. Thus we have shortened it as suggested by the reviewer.

      Minor corrections:

      L28 "resultant association" maybe resulting instead.

      L55 "animals derive benefit": remove derive.

      L78 "composing 12,000 neurons": composed of.

      L79 what is stable in a "stable behavioral assay"?

      L104: 2 times cluste.

      L122: "DL1 DANs are involved" in what?

      Fig. 1 please check subpanels labels, D repeats.

      L 362: "But how do individual neurons contribute to the teaching signal of the complete cluster?" I don't understand the question.

      L364 I did not hear before about the "labeled line hypothesis" in this context - could the author clarify?

      L368: edit "combinatorically".

      L390: "current suppression" maybe acute suppression.

      L 400 I'm not sure what is meant by "judicious functional configuration" and "redundancy". The functions of these cells are not redundant, and no straightforward prediction of their function can be done from their physiological response to salt.

      Thanks a lot for your in detail review of our manuscript. We welcome your well-taken concerns and have made the requested changes for all points that you have raised.

      Reviewer #2 (Recommendations For The Authors):

      (1) In Figure 1 the reconstruction of pPAM and DL1 DANs shows the compartmentalized innervation of the larval MB. However, the images are a bit low in color contrast to appreciate the innervation well. In particular in panel B, it is hard to identify the innervated MB body structure. A schematic model of the larval MB and DAN innervation domains like in Fig. 2A would help to clarify the innervation pattern to the non-specialist.

      We understand this concern and have changed figure 1 as suggested by the reviewer. A schematic model of the MB and DANs is now presented already in figure 1 as well as the according supplemental figure.

      (2) Blue light itself can be aversive for larvae and thus interfere with the aversive learning paradigm. Does the given Illuminance (220 lux) used in these experiments affect the behavior and learning outcome?

      Yes, in former times high intensities of blue light were necessary to trigger the first generation optogenetic tools. The high intensity blue light itself was able to establish an aversive memory (e.g. Rohwedder et al. 2016). Usage of the second generation optogenetic tools allowed us to strongly reduce the applied light intensity. Now we use 220 lux (equal to 60 µW/cm<sup>2</sup>). Please note that all Gal4 and UAS controls in the manuscript are nonsignificant different from zero. The mild blue light stimulation therefore does not serve as a teaching signal and has neither an aversive nor an appetitive effect. Furthermore, we use this mild light intensity for several other behavioral paradigms (locomotion, feeding, naïve preferences) and have never seen an effect on the behavior.

      (3) Fig.2: Except for MB054B-Gal4 only the MB expression pattern is shown for other lines. Is there any additional expression in other cells of the brain? In the legend in line 761, the reporter does not show endogenous expression, rather it is a fluorescent reporter signal labeling the mushroom body.

      The lines were initially identified by a screen on larval MB neurons done together with Jim Truman, Marta Zlatic and Bertram Gerber. Here full brain scans were always analyzed. These images can be seen in Eschbach et al. 2020, extended figure 1. Neither in their evaluation nor in our anatomical evaluation (using a different protocol) additional expression in brain cells was detectable. We also modified the figure legend as suggested.

      (4) Fig.3: Precise n numbers per experiment should be stated in the figure legend.

      True, we now present n numbers per experiment whenever necessary.

      (5) Fig.4: Have the authors confirmed complete ablation of the targeted neuron using rpr/hid? Ablations can be highly incomplete depending on the onset and strength of Gal4 expression, leaving some functionality intact. While the ablation experiments are largely in line with the acute silencing of single DANs during high salt learning performed later on (Fig.7), there is potentially an interesting aspect of developmental compensation hidden in this data. Not a major point, but potentially interesting to check.

      We agree with this criticism. We have not tested if the expression of hid,rpr in DL1 DANs does really ablate them. Therefore we did an additional experiment to show that. The new data is now present as a supplemental figure (Figure 4- figure supplement 6). The result shows that expression of hid,rpr ablates also DL1 DANs similar to earlier experiments where we used the same effectors to ablate serotoniergic neurons (Huser et al., 2012, figure 5).

      (6) The performance index in Fig. 4 and 5 sometimes seems lower and the variability is higher than in some of the other experiments shown. Is this due to the high intrinsic variability of these particular experiments, or the background effects of the rpr/hid or splitGal4 lines?

      The general variability of these experiments is within the expected and known borders. In these kind of experiments there is always some variation due to several external factors (e.g. experimental time over the year). Therefore it is always important to measure controls and experimental animals at the same time. Of course that’s what we did and we only compare directly results of individual datasets. But not between different datasets. This is further hampered given that the experiments of Figure 4 (now Figure 4- figure supplement 1) and Figure 5 (now Figure 4) differ in several parameters from other learning experiments presented later in the text. Optogenetic activation uses blue light stimulation instead of “real world” high salt. Most often direct activation of specific DANs in the brain is more stable than the external high salt stimulation. Also optogenetic inactivation uses blue light stimulation and also retinal supplemented food. Both factors can affect the measurement. We thus want to argue that it is for each experiment most often the particular parameters that affect the variability of the results rather than background effects of the rpr/hid and split-Gal4 lines.

      (7) Fig.7: This is a neat experiment showing the effects of acute silencing of individual DL1 DANs. As silencing DAN-f1/g1 does not result in complete suppression of aversive learning, it would be highly interesting to test (or speculate about) additive or modulatory effects by the other DANs. Dan-c-1/d-1 also responds to high salt but does not show function on its own in these assays. I am aware that this is currently genetically not feasible. It would however be a nice future experiment.

      True, we were intensively screening for DL1 cluster specific driver lines that cover all 4 DL1 neurons or other combinations than the ones we tested. Unfortunately, we did not succeed in identifying them. Nevertheless, we will further screen new genetic resources (e.g. Meissner et al., 2024, bioRxiv) to expand our approach in future experiments. Please also see our comment on concern 1 of reviewer 1 for further technical limitations and biological questions that can also potentially explain the absence of complete suppression of high salt learning and memory. Some of these limitations are now also mentioned and discussed in the new version of the manuscript.

      (8) The discussion is excellent. I would just amend that it is likely that larval DAN-c1, which has high interconnectivity within the larval CNS, is likely integrating state-dependent network changes, similar to the role of some DANs in innate and state-dependent preference behavior. This might contribute to modulating learned behavior depending on the present (acute) and previous environmental conditions.

      Thanks a lot for bringing this up. We rewrote this part and added a discussion on recent work on DAN-c1 function in larvae as well as results on DAN function in innate and state-dependent preference behavior.

      (9) Citation in line 1115 missing access information: "Schnitzer M, Huang C, Luo J, Je Woo S, Roitman L, et al. 2023. Dopamine signals integrate innate and learned valences to regulate memory dynamics. Research Square".

      Unfortunately this escaped our notice. The paper is now published in Nature: Huang, C., Luo, J., Woo, S.J. et al. Dopamine-mediated interactions between short- and long-term memory dynamics. Nature 634, 1141–1149 (2024). https://doi.org/10.1038/s41586-024-07819-w. We have now changed the citation. The new citation includes the missing access information.

      Reviewer #3 (Recommendations For The Authors):

      Regarding my issue about salt specificity in the public review, I want to make clear that I do not suggest additional experiments, but to be very careful in phrasing the conclusions, in particular whenever referring to the experiments with optogenetic activation. This includes presenting these experiments as "(salt) substitution" experiments - inferring that the optogenetic activation would substitute for a natural salt punishment. As important and interesting as the experiments are, they simply do not allow such an interpretation at this point.

      Results, line 140ff: When presenting the results regarding TH-Gal4 crossed to ChR2-XXL, please cite Schroll et al. 2006 who demonstrated the same results for the first time.

      Thanks for mentioning this. We now cite Schroll et al. 2006 here in the text of the manuscript.

      Figure 3: The subfigure labels (ABC) are missing.

      Unfortunately this escaped our notice. Thanks a lot – we have now corrected this mistake.

      Figure 5: For I and L, it reads "salt replaced with fru", but the sketch on the left shows salt in the test. I assume that fructose was not actually present in the test, and therefore the figure can be misleading. I suggest separate sketches. Also, I and L are not mentioned in the figure legend.

      True, this is rather confusing. Based on the well taken concern we have changed the figure by adding a new and correct scheme for sugar reward learning that does not symbolize fructose during test.

      Figure S1: The experimental sketches for E,F and G,H seem to be mixed up.

      We thank the reviewer for bringing this up. In the new version we corrected this mistake.

      Figure S5: There are three sub-figures labelled with B. Please correct.

      Again, thanks a lot. We made the suggested correction in Figure S5.

      Discussion, line 353ff: this and the following sentences can be read as if the authors have discovered the DL-1 neurons as aversive teaching mediators in this study. However, Eschbach et al. 2020 already demonstrated very similar results regarding the optogenetic activation of single DL-1 DANs. I suggest to rephrase and cite Eschbach et al. 2020 at this point.

      That is correct. Our focus was on the gustatory pathway. The original discovery was made by Eschbach et al. We have now corrected this in the discussion and clarified our contribution. It was never our intention to hide this work, as the laboratory was also involved. Nevertheless, this is an annoying omission on our side.

      Line 385-387: this sentence is only correct with respect to Eschbach et al. 2020. Weiglein et al. 2021 used ChR2-XXL as an effector, but another training regimen.

      We understand this criticism. Therefore, we changed the sentence as suggested by the reviewer. See also our response on concern 15 of reviewer 1.

      Line 389ff: I do not understand this sentence. What is meant by persistent and current suppression of activity? If this refers to the behavioural experiments, it is misleading as in the hid, reaper experiments neurons are ablated and not suppressed in activity.

      We made the requested changes in the text. It is true that the ablation of a neuron throughout larval life is different from constantly blocking the output of a persisting neuron.

      Methods, line 615 ff: the performance index is said to be calculated as the difference between the two preferences, but the equation shows the average of the preferences.

      Thanks a lot. We are sorry for the confusion. We have carefully rewritten this part of the methods section to avoid any misunderstanding.

      When discussing the organization of the DL1 cluster, on several occasions I have the impression the authors use the terms "redundant" and "combinatorial" synonymously. I suggest to be more careful here. Redundancy implies that each DAN in principle can "do the job", whereas combinatorial coding implies that only a combination of DANs together can "do the job". If "the job" is establishing an aversive salt memory, the authors' results point to redundancy: no experimental manipulation totally abolished salt learning, implying that the non-manipulated neurons in each experiment sufficed to establish a memory; and several DANs, when individually activated, can establish an aversive memory, implying that each of them indeed can "do the job".

      Based on this concern we have rewritten the discussion as suggested to be more precise when talking about redundancy or combinatorial coding of the aversive teaching signal. Basically, we have removed all the combinatorial terms and replaced them by the term “redundancy”.

      The authors mix parametric and non-parametric statistical tests across the experiments dependent on whether the distribution of the data is normal or not. It would help readers if the authors would clearly state for which data which tests were used.

      We understand the criticism and now have added an additional supplemental file that includes all the information on the statistical tests applied and the distribution of the data.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      This study experimentally examined diet-microbe-host interactions through a complex systems framework, centered on dietary oxalate. Multiple, independent molecular, animal, and in vitro experimental models were introduced into this research. The authors found that microbiome composition influenced multiple oxalate-microbe-host interfaces. Oxalobacter formigenes were only effective against a poor oxalate-degrading microbiota background and give critical new insights into why clinical intervention trials with this species exhibit variable outcomes. Data suggest that, while heterogeneity in the microbiome impacts multiple diet-host-microbe interfaces, metabolic redundancy among diverse microorganisms in specific diet-microbe axes is a critical variable that may impact the efficacy of bacteriotherapies, which can help guide patient and probiotic selection criteria in probiotic clinical trials.

      Thank you. The main message of this research, is that through complex modelling, we believe we have identified the critical variable (metabolic redundancy) that is responsible for the efficacy of probiotics designed to reduce oxalate levels, thus allowing for improved patient selection in clinical trials. We also believe that this process and the critical features identified can be translated to other critical microbial functions such as short chain fatty acid synthesis, secondary bile acid synthesis, and others.

      Strengths:

      The paper has made significant progress in both the depth and breadth of scientific research by systematically comparing multiple experimental methods across multiple dimensions. Particularly through in-depth analysis from the enzymatic perspective, it has not only successfully identified several key strains and redundant genes, which is of great significance for understanding the functions of enzymes, the characteristics of strains, and the mechanisms of genes in microbial communities, but also provided a valuable reference for subsequent experimental design and theoretical research.

      More importantly, the establishment of a novel research approach to probiotics and gut microbiota in this paper represents a major contribution to the current research field. The proposal of this new approach not only breaks through the limitations of traditional research but also offers new perspectives and strategies for the screening, optimization of probiotics, and the regulation of gut microbiota balance. This holds potential significant value for improving human health and the prevention and treatment of related diseases.

      Thank you for the comments. We believe that the approach taken here, which contrasts with conventional reductionist techniques, will be critical for translating gut microbiome research into actionable therapeutic approaches.

      Weaknesses:

      While the study has excellently examined the overall changes in microbial community structure and the functions of individual bacteria, it lacks a focused investigation on the metabolic cross-feeding relationships between oxalate-degrading bacteria and related microorganisms, failing to provide a foundational microbial community or model for future research. Although this paper conducts a detailed study on oxalate metabolism, it would be beneficial to visually present the enrichment of different microbial community structures in metabolic pathways using graphical models.

      Thank you for this critique.  In the current study, we broadly examined the response of the gut microbiota to dietary oxalate. Based on initial shotgun metagenomic results, we focused in on specific taxa and metabolic functions.  Through metagenomic and multiple culture-based studies, we quickly honed in on redundancy in oxalate-degrading function as a key feature for oxalate homeostasis. We believe that the defined microbial community we used for microbial transplants (particularly the taxonomic cohort) provides a strong, minimal community to explore oxalate homeostasis further. In fact, we are using this consortium in multiple follow-up studies to fully understand the cross-feeding that may occur among these microorganisms, as you suggest.  We note that figure 3 shows the change of species and metabolic pathways with oxalate exposure.   

      Furthermore, the authors have done a commendable job in studying the roles of key bacteria. If the interactions and effects of upstream and downstream metabolically related bacteria could be integrated, it would provide readers with even more meaningful information. By illustrating how these bacteria interact within the metabolic network, readers can gain a deeper understanding of the complex ecological and functional relationships within microbial communities. Such an integrated approach would not only enhance the scientific value of the study but also facilitate future research in this area.

      Thank you. We note that based on the collective data obtained in this study, that redundancy in the oxalate degradation is the critical feature that maintains oxalate homeostasis. However, we are interested potential metabolic interactions between microbes in our defined community and are currently investigating these interactions through extensive investigations.   

      Reviewer #2 (Public review):

      Summary:

      Using the well-studied oxalate-microbiome-host system, the authors propose a novel conceptual and experimental framework for developing targeted bacteriotherapies using a three-phase pre-clinical workflow. The third phase is based on a 'complex system theoretical approach' in which multi-omics technologies are combined in independent in vivo and in vitro models to successfully identify the most pertinent variables that influence specific phenotypes in diet-host-microbe systems. The innovation relies on the third phase since phase I and phase II are the dominant approaches everyone in the microbiome field uses.

      Thank you. As you note, the proposed phases I and II are the predominant approaches used. In fact, many clinical trials have been conducted to try and reduce urine oxalate in patients, based solely on mechanistic studies with Oxalobacter formigenes.  As noted in our manuscript, only 43% of those studies results in the intended outcome, necessitating the approach we took in the current study. Our results suggest that the reason for the high rate of failure, despite well established mechanisms, is due to insufficient patient selection that focused only on the presence or absence of O. formigenes, which is a species that exhibits very low prevalence and abundance in the human gut microbiota, normally.

      Strengths:

      The authors used a multidisciplinary approach which included:

      (1) fecal transplant of two distinct microbial communities into Swiss-Webster mice (SWM) to characterize the host response (hepatic response-transcriptomics) and microbial activity (untargeted metabolomics of the stool samples) to different oxalate concentrations;

      (2) longitudinal analysis of the N. albigulia gut microbiome composition in response to varying concentrations of oxalate by shotgun metagenomics, with deep bioinformatic analyses of the genomes assembled; and

      (3) development of synthetic microbial communities around oxalate metabolisms and evaluation of these communities' activity in oxalate degradation in vivo.

      Thank you for these comments.  In the complex modelling approach, we focused on complete microbiota from host species known to have high and low capacities for oxalate tolerance, combined with targeting specific metabolic functions vs. specific taxa that may include unknown functions important for oxalate metabolism.  Further, we examined the influence of our target communities on oxalate metabolism through multiple in vitro and in vivo studies.

      Weaknesses:

      However, I have concerns about the frame the authors tried to provide for a 'complex system theoretical approach' and how the data are interpreted within this frame. Several of the conclusions the authors provide do not seem to have sufficient data to support them.

      Thank you.  We have tried to address these concerns by adding an exhaustive figure that broadly represents our complex modelling approach that includes potential complex system-based hypotheses, how they were tested, and the host-microbiome-oxalate interactions found in our study.

      Recommendations for the authors:  

      Reviewer #2 (Recommendations for the authors):

      Major Concerns

      (1) The authors argue about the importance of bringing 'Complex System Theory' to the microbiome field systematically and consistently. However, the authors fail to introduce this theory throughout the entire manuscript. For example, the authors tried to describe key elements and their nomenclature, such as nodes and fractal layers, in the first part of the result section. But the description is wordy and not precise. It would be more useful if the authors connected the model description with a visual representation, such as a figure. Unfortunately, these elements are not emphasizing and carried across the results section and are not mentioned in the discussion section.

      We have now added a figure (Figure 7) that details this process extensively and ties each of our findings to the complex system model and nomenclature.  We have also reiterated how our results fit in the complex system model in the discussion.

      In addition, there is no straightforward approach to integrating multi-omics datasets to identify the variables that are determinants of the system. For example, Figure 1 focuses on the impact of the host, hepatic activity, to oxalate exposure on fecal transplants into Swiss Webster mice; Figure 2 focuses on the effects of oxalate exposure on stool metabolic activity, not only microbial metabolic activity, on fecal transplants into Swiss Webster mice; and Figure 3 focuses on microbiome responses to different oxalate concentration in Neotoma albigula. There is no "model" to really integrate the host, the microbiome activity, and the microbiome composition information. And, unfortunately, the data generated between experiments cannot directly integrate; see major concern # 2.

      Thank you.  We have made more clear the experimental approach and how it applied to understanding the critical factors that maintain oxalate homeostasis.  Specifically, Figure 1 established that the effect of oxalate on the host was dependent on the microbiota, rather than host genetics.  Figure 2 established the effect of oxalate on the gut microbiota was again dependent on the whole gut microbiota and that these oxalate-microbe effects also influenced oxalate-host effects through a direct multi-omic data integration.  Once we established that the oxalate effects on host and microbiota were dependent on the whole microbiota composition, Figure 3 then sought to figure out how oxalate impacted the gut microbiota, using our model of high oxalate tolerance (N. albigula). With the finding in Figure 3 that there were multiple genes attributed to the degradation of oxalate, or acetogenic, methanogenic, and sulfate reducing pathways, Figure 4 and relevant supplemental figures sought to quantify the redundancy of these pathways.  After establishing a very high degree of redundancy, we sought to use a culturomic approach to determine what environmental factors impacted oxalate metabolism and to evaluate oxalate metabolism using our defined, hypothesized communities of microorganisms.  Finally, figure 6 sought to validate our metagenomic, metabolomic, and culturomic results from multiple animal and in vitro models using targeted microbial transplants in mice.  While we did have some direct multi-omic data integration (Figures 2 and 3), the process employed here sought to systematically determine which factors were most important for the oxalate-microbiota-host relationship, and then to use those results to design the subsequent experiments.  We have added this description to the discussion, which helps to contextualize the complex system modelling approach we took here.

      Finally, the authors did not provide a novel variable that successfully influences oxalate degradation in the oxalate-microbiome-host system. The authors argue that "both resource availability and community composition impact oxalate metabolism," which we currently inferred by the failure of the clinical tries and do not provide a clear intervention strategy to develop functional bacteriotherapy. The identification of composition as an important variable that was predictable without any multi-omics approach was highlighted by the development of synthetic microbial communities. Synthetic microbial communities are critical to characterizing complex microbiomes. Still, the authors did not explain how this strategy can be used in their theoretical framework (that is their goal), and these communities are not well introduced across the manuscript; see major concern # 4.

      As stated, it is clear from the failed clinical trials that we do not fully understand what microbial features dictate oxalate homeostasis.  We have specifically identified, through fecal transplant studies, that microbial composition is critical for oxalate homeostasis and that diverse oxalate-degrading bacteria exist.  However, ours is the first study that explicitly shows that it is this diversity that controls oxalate homeostasis.  This is specifically ascertained through the targeted microbial transplants in mice whereby O. formigenes was given alone or with different combinations of other microorganisms.  In other words, we were able to replicate both successful and failed studies by manipulating which specific species were introduced into animals.  This is unprecedented in the literature.

      (2) The authors provide several conclusions that are not completely supported by the data available. For example:

      (a) Lines 236-239: "Within the framework of complex systems, results show microbe-host cooperation whereby oxalate effectively processed within the SW-NALB gut microbiota reduced overall liver activity, indicative of a beneficial impact." - The authors did not provide data related to oxalate levels of oxalate processing for this dataset.

      While we did not specifically quantify oxalate degradation for this specific study, as cited in the text when describing this Swiss-Webster, Neotoma albigula system, we have previously published multiple animal studies explicitly showing that the N. albigula animals were highly effective oxalate degraders, which is transferable to Swiss-Webster mice through fecal transplants. Since the gut microbiota’s impact on oxalate has been welll established through experiments by our group, the purpose of these specific experiments were to look the other way and examine the effect of oxalate on the gut microbiota of these two animal models.  In the referenced text, we again cited our studies showing that the SW-NALB system effectively degrades oxalate.

      (b) Lines 239-243: "Data also suggest that both the gut microbiota and the immune system are involved in oxalate remediation (redundancy), such that if oxalate cannot be neutralized in the gut microbiota or liver, then the molecule will be processed through host immune response mechanisms (fractality), in this case indicated through an overall increase in hepatic activity and specifically in mitochondrial activity." - The authors did not provide any evidence related to the immune system and oxalate metabolism.

      We corrected that statement as follows: “…in this case indicated through an overall increase in inflammatory cytokines with oxalate exposure combined with an ineffective oxalate-degrading microbiota (Figures S6a,b; S9a,b).”  In other words, if the liver and gut microbiota can’t eliminate a toxin, then the immune system must deal with it through inflammatory pathways.  Oxalate is a well established, pro-inflammatory compound.  Our data show that this is dependent on the gut microbiota.

      (c) Lines 250-252: "Following the diet trial, colon stool was collected post-necropsy and processed for untargeted metabolomics, which is a measure of total microbial metabolic output." - Although most metabolites in stool samples are indeed microbial, there are also host metabolites. So, it is not technically correct to relate the metabolomic analysis of stool samples to only microbial metabolic analysis. In addition, the authors discussed compounds such as alkaloids and cholesterol as microbial metabolites, which these compounds are more related to the diet and host correspondingly.

      We have corrected this to state: “total metabolites present in stool from the diet, microbial activity, and host activity”

      (d) Lines 270-273. "Specifically, the SW-NALB mice exhibit hallmarks of homeostatic feedback with oxalate exposure to maintain a consistent metabolic output, defined by the relatively small, net negative, microbial metabolite-hepatic gene network compared to the large, net positive, network of SW-SW mice." - How do the authors define oxalate homeostasis? In addition, do the authors imply feedback between the liver and the microbiome in which the microbiome responds to a liver response related to oxalate levels? Or could the observation in Figure 1 be explained just by microbial consumption of oxalate that would reduce the impact of oxalate that arrives at the liver?

      Oxalate homeostasis is defined in that sentence: “relatively small, net negative, microbial metabolite-hepatic gene network compared to the large, net positive, network of SW-SW mice” – in other words, for SW-NALB mice, oxalate did not produce a considerable change to either microbial or hepatic metabolic activity.  We did not really test the liver impact on gut microbiota and can’t speak to that.  We believe, based on Figure 2 data, that it is not just the degradation of oxalate that explains the lack of change in hepatic activity in SW-NALB mice, rather that the oxalate-induced shift in the gut microbiota metabolic activity broadly altered hepatic activity, as inferred from Figure 2 c.  We made this more clear in the results: “suggests that the oxalate-induced change in microbial metabolism is responsible for the change in hepatic activity”.

      (e) Lines 297-301: "The oxalate-dependent metagenomic divergence of the NALB gut microbiota (Figure 3), combined with the lack of change in the microbial metabolomic profile with oxalate exposure (Figure 2), suggest that oxalate stimulates taxonomically diverse, but metabolically redundant microorganisms, in support of maintaining homeostasis." - The authors cannot conclude anything related between taxonomic changes and microbial activity since the taxonomic data presented is for microbial enrichment in N. albigulia, and the "microbial activity data" is from the fecal transplantation experiment in SWM. These are two completely different systems with two completely different experimental designs.

      We have shown very similar results in that oxalate induces the taxonomic divergence for the NALB gut microbiota, in multiple previous studies.  The experiment in which a minimal, positive increase in microbial metabolites, was saw with oxalate was based on the SW-NALB model whereby Swiss-Webster mice have an NALB microbiota.  We show throughout the manuscript, that the impact of oxalate is very microbiota dependent and supports our claim.  However, the claim is hypothesis generating – that metabolic redundancy is important for oxalate homeostasis.  We modified our statement to make all of this more clear.   

      Related to microbial composition, the authors did not show data validating the efficiency of the fecal transplantations (allograft or xenograft) in the SWM after antibiotic treatment. They also did not show evidence of microbial composition dynamics in response to oxalate exposure.

      Again, the efficacy of fecal transplants, used in the way they were here, has been shown in multiple past studies of our group.  In past studies, we have extensively characterized the microbiota from fecal transplants and which taxa were associated with oxalate levels.  Therefore, that topic was not the focus of the current study, instead focusing on the oxalate impact on gut microbiota activity.  Our past studies, referenced multiple times through the current manuscript, were used in large part to help determine which microbes to include in our taxonomic cohort, as described in the manuscript.

      (f) Lines 301-303: "Given that data came from the same hosts sampled longitudinally, these data also reflect a microbiota that is adaptive to oxalate exposure, which is another important characteristic of complex systems." - In their dataset, what is the evidence that the microbiota of N. albigulia is adapted to oxalate exposure? Is the increase in genomes with pathways related to oxalate metabolism related to an increase of oxalate in the diet? If so, does the microbiota exposure with a higher oxalate concentration decrease the systemic level of oxalate? In neither of the experiments related to Figures 1 to 3, the authors showed a correlation of systemic oxalate levels with microbial composition, hepatic host response, or stool metabolism.

      Figure 3 explicitly shows the longitudinal impact of increasing levels of oxalate showing an increase in oxalate degrading genes (Figure 3d). The specific samples selected for analysis here come from a previous study in which we explicitly quantified changes to the gut microbiota composition and both stool and urine oxalate for every time point listed in figure 3a.  This information is explicitly stated in the methods coupled with the fact that “neither fecal nor urinary oxalate levels increased significantly.”  Again, the effect of the gut microbiota on oxalate in these model systems have been extensively studied by our group and provide the foundation for the current study to look at the effect of oxalate on the gut microbiota and host.

      Considering my last two points, the authors do not present substantial evidence to support their hypothesis that oxalate stimulates taxonomically diverse, metabolically redundant communities.

      As stated above, that oxalate stimulates taxonomically diverse taxa was ascertained through multiple past studies, as well as the current study (Figure 3e).  The metabolically redundant part is ascertained both through untargeted metabolomics (Figure 2a,b) and shotgun metagenomics (Figure 3c,d).  Further evidence for the metabolic redundancy with oxalate comes from our culturomic approach, which showed that 14.58% of isolates could grow on oxalate as a carbon and energy source, in addition to the high proportion of isolates that could grow on other carbon and energy sources, at least much more than can be ascribed to a single species  (Figure 5c).  We made this more clear in the discussion.

      (g) Lines 330-335. "Additionally, the broad diversity of species that contain oxalate-related genes suggests that the distribution of metabolic genes is somewhat independent of the distribution of microbial species, which suggests that microbial genes exist in an autonomous fractal layer, to some degree. This hypothesis is supported by studies which show a high degree of horizontal gene transfer within the gut microbiota as a means of adaptation." - This conclusion is highly speculative, especially since the author did not do any analysis to directly evaluate a relationship between the oxalate metabolic pathways and the microbial species where these pathways are present.

      Figure 3c,d,e explicitly shows the metabolic pathways and species enriched by oxalate exposure.  Figure 4d, generated using the same data from Figure 3, explicitly shows the taxa that harbor oxalate-degrading genes.   

      (h) Lines 364-366. "Collectively, data show that both resource availability and community composition impacts oxalate metabolism, which helps to define the adaptive nature of the NALB gut microbiota." - The authors indeed showed evidence that community composition impacts oxalate metabolism. However, the authors did not show any evidence to directly evaluate the resource availability to impact oxalate metabolism.

      This is explicitly shown through in vitro community-based and single species assays varying multiple different carbon and energy sources to quantify changes to oxalate degradation (chosen based on shotgun metagenomic results; Figure 5a,b).

      (3) Lines 321-325. "Acetogenic genes were also present in 97.18% of genomes, dominated by acetate kinase and formate-tetrahydrofolate ligase (Figure S3A323C). Methanogenic genes were present in 100% of genomes, dominated by phosphoserine phosphatase, atpdependent 6-phosphofructokinase, and phosphate acetyltransferase (Figure S4A-C)." - The authors spent much time analyzing the adjacent pathways related to oxalate and oxalaterelated products of oxalate metabolism. However, my understanding is that the genes used to analyze these pathways (formate metabolism, acetogenesis, methanogenesis), such as the ones named above, are not unique/specific for those pathways but participate in other "housekeeping" pathways. What is the relevance of these analyses when those genes are not unique/specific to the function/pathways that the authors describe? If I infer correctly, these bioinformatic analyses aim to evaluate the hypothesis of whether oxalate metabolism could be a social/cooperation metabolism and whether other species could participate in the metabolism of oxalate subproducts. However, these analyses did not explicitly evaluate this hypothesis.

      The reviewer is correct in that we aimed to evaluate the potential that oxalate metabolism could benefit from metabolic cooperation.  The specific genes chosen for this analysis were those explicitly listed in the target metabolic pathways in KEGG, as described.  However, while the analyses do show the strong potential that the CO2 and formate produced from oxalate degradation could be used in these other pathways, as intended, the genes can be used in other metabolic pathways.  We did, however, explicitly test the hypothesis that formate, produced from oxalate degradation, could be utilized by the gut microbiota.  While the targeted transplants with the taxonomic cohort did not clearly show the use of formate in this way, those from the metabolic cohort did (Figures 6d and S8d).  This question is still in ongoing investigations in our group.  

      We have made it more clear that our genome analyses provide the potential for metabolic redundancy rather than definitive proof for metabolic redundancy, which was evaluated more extensively in other experiments from this study.

      (a) Lines 481-484. "Collectively, data offer strong support for the hypothesis that metabolic redundancy among diverse taxa, is the primary driver of oxalate homeostasis, rather than metabolic cooperation in which the by-products of oxalate degradation are used in downstream pathways such as acetogenesis, methanogenesis, and sulfate reduction." - Although the authors recognize that their data about the metabolic cooperation hypothesis is inconclusive, they never tested the hypothesis related to metabolic cooperation, as mentioned above. This is highly speculative.

      As stated above, the targeted microbial transplants to animals and in vitro studies (Figure 5e,f) did explicitly test the cooperation hypothesis, but it the results did not support it and instead pointed much more strongly to metabolic redundancy.    

      (4) Lines 355-359. "Cohorts, defined in the STAR methods, were used to delineate hypotheses that either carbon and energy substrates are sufficient to explain known effects of the oxalate-degrading microbial network or that additional aspects of taxa commonly stimulated by dietary oxalate are required to explain past results (taxa defined through previous meta-analysis of studies)." - The definition of the metabolic cohorts and the taxonomic cohorts should not be hidden in the material and methods section. It should be explicit and clearly explained in the main text. Related, the table presented in Figure 5D is exceptionally confusing and does not help to understand and differentiate between the metabolic and the taxonomic cohorts. The authors need to explicitly identify the synthetic communities used in each cohort and each group by their members and their characteristics in supplementary tables.

      In the sentences before those referenced, we state: “Culturomic data recapitulates molecular data to show a considerable amount of redundancy surrounding oxalate metabolism (Fig. 5C). Isolates generated from this assay were used for subsequent study (metabolic cohort; Figure 5D). Additionally, a second cohort was defined and commercially purchased based both on known metabolic functions and the proportion of studies that saw an increase in their taxonomic population with oxalate consumption (Fig. 5D; taxonomic cohort). Where possible, isolates from human sources were obtained.”  Figure 5d explicitly shows the specific species used in each cohort along with the groups they were in for transplant studies, the explicit metabolic pathways we were targeting, along with the % of studies that these species were associated with oxalate metabolism.  All of this information is both in the main text of the results and in the figure legends.  It is not hidden in the methods, but the methods do reiterate what was also placed in the results.   

      In Figures 5 and 6, the authors used the following groups with the corresponding nomenclature: 'Group 1, No_bact; Group 2, Ox; Group 3, Ox_form; Group 4, All; Group 5, No_ox'. Although the information related to these groups is present in the material and method section in lines 1139-1143, the authors also need to explicitly explain the groups and their nomenclature in the main text.

      Since this information is explicitly and succinctly given in the referenced figures, I believe that adding the same information in the text would be too redundant.

      Related to the development of the synthetic communities. How did the authors prepare the synthetic communities or 'cohort' for the in vitro experiments? 

      We added more information for the preparation of microbes and execution of the in vitro assays, as needed.  

      Also, it is unclear in the material and method section how the metabolic profile of each isolated was evaluated (Figure 5C). Related to the bacteria isolated from the culturomic assays, including Figure 5C and metabolic cohort, the authors indeed reported the isolation methodology in lines 1262-1275. However, there is no information about the sequencing of these isolates. The authors should present these isolates as a list (supplementary table) with their names, taxonomy, metabolic profile, and Genome ID if these genomes were submitted to NCBI.

      We added additional information for how metabolic cohort isolates were chosen and how they were taxonomically identified.  The taxonomy and substrate utilization of isolates are in Figure 5D.  We did not sequence the genomes of metabolic cohort bacteria.  However, the ATCC isolates, which comprise the taxonomic cohort, are publicly available.

      The author presented the 248 metagenomics assembles in Figure S1 in a circular chart in context with other genomes. However, the metagenomic assembles should be presented in a table form, with their name, taxonomy, coverage, completeness, and Genome ID, if these genomes were submitted to NCBI.

      The information for the genomes submitted to the NCBI is provided in the data availability statement.  However, we added a table (Table S9) that includes the requested information.   

      (5) Lines 371-3374: "To delineate hypotheses of metabolic redundancy or cooperation for mitigating the negative effects of oxalate on the gut microbiota and host, two independent diet trials were conducted with analogous microbial communities derived from the metabolic and taxonomic cohorts". 

      Lines 494-496: "we and others have found that oxalate can differentially exhibit positive or negative effects on microbial growth and metabolism dependent on the species and environment present" - What is the evidence that oxalate has a negative effect on the gut microbiota? The authors clearly showed the negative effect of oxalate on the host. Although there are reports in the literature of oxalate consumers with a negative effect on the microbiome, such as Lactobacilli and Bifidobacteria, there is no evidence in this manuscript about a negative effect of oxalate on the microbiome, and there is not an experimental design to evaluate it.

      These data are presented in Figure 2A and B.  As stated, oxalate led to a net reduction in total microbial metabolites produced of 34 metabolites, with a significant shift in overall metabolome, indicative of metabolic inhibition.  This is in comparison to the net gain of 9 metabolites, with no significant shift overall,  in the mice with the NALB microbiota.  The positive and negative effects of oxalate on the whole gut microbiota here are bolstered by previous studies on the effect of oxalate on pure cultures as discussed and cited on line 623624.

      (6) Related to the last section, it is hard to really compare the results of the taxonomic cohort versus the metabolic cohort when the data of one cohort is in the main figure and the other in a supplementary figure. In addition, all the comparisons between the two cohorts seem to be qualitative. For any comparisons, the authors need to do a statistical comparison between the groups of the two cohorts.

      The comparison of the two sets of data are indeed qualitative.  This is because these mouse models were run in separate experiments to test separate hypotheses (whether utilization of specific substrates is enough to improve oxalate metabolism or if specific taxa previously responsive to dietary oxalate was better, which is stated in the manuscript).  Given that these experimental models were tested separately, it would not be statistically valid to do a direct statistical comparison, even though the experimental procedures were the same and the only difference were the transplanted bacteria.  The separation of the experiments into a main and supplemental figure was done out of necessity given the very large amount of data and many experimental mouse models that were run in this study overall.   

      Minor Comments.

      (1) The authors should define 'antinutrients'. This term is not a familiar concept and could create confusion.

      This is defined in line 104 “molecules produced in plants to deter herbivory, disrupt homeostasis by targeting the function of the microbiome, host, or both”

      (2) The authors should explicitly describe the N. albigulia, aka White-throated woodrat system, as early as possible in the result section.

      We added some statements about the Swiss webster and N. albigula gut microbiota as poor and effective oxalate degraders in the second section of the results.

      (3) SW-SW mice exhibited an oxalate-dependent alteration of 219 hepatic genes, with a net increase in activity. In comparison, the SW-NALB mice exhibited an oxalate-dependent alteration of 21 genes with a net decrease in activity. However, the visual representation of the PCoA in Figure 1B showed that the most different samples are the SW-NALB 0% and 1.5%. Could you please explain this difference?

      In Figure 1b, the SW-NALB data are represented by the blue and black data points, which directly overlap with each other.  The SW-SW data are the orange and purple data points, which exhibit very little overlap.  

      (4) Is Table S7 the same as Table S6? If not, there is a missing supplementary table.

      These tables are different.  We ensured that both are present.

      (5) How did the authors test bacterial growth in in vivo studies (Figure 5B)?

      We added a statement to the culturomic section of the methods – we used media with or without oxalate and quantified colony-forming units.

      (6) A section of 16S rRNA metagenomics in the material and method section is not used across the main manuscript.

      These data are presented in figures S7 and S10, as stated in the results.  We added statements in the results to clarify that these figures show the 16S sequencing data.

      (7) Lines 506-511: "Collectively, data from the current and previous studies on the effect of oxalate exposure on the gut microbiota support the hypothesis that the gut microbiota serves as an adaptive organ in which specific, metabolically redundant microbes respond to and eliminate dietary components, for the benefit of themselves, but which can residually protect or harm host health depending on the dietary molecules and gut microbiota composition." - What is the benefit to bacteria in eliminating oxalate? This is highly speculative to this system.

      The benefit to bacteria is stated earlier in that paragraph – “In the current (Figs. 2B, 5B) and previous studies(33,34,64,65), we and others have found that oxalate can differentially exhibit positive or negative effects on microbial growth and metabolism dependent on the species and environment present.”

      (8) Lines 504 -506: "Importantly, the near-universal presence of formate metabolism genes suggest that formate may be an even greater source of ecological pressure (Figures S2-S5)."

      - Formate is primarily produced by fermentative anaerobic bacteria, such as Bacteroides, Clostridia, and certain species of Escherichia coli, since formate would be present in anaerobic communities independently of oxalate. How is formate an even greater source of ecological pressure?

      We added a statement about the toxicity of formate to both bacteria and mammalian hosts.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public Review):

      Summary

      In this study, the authors build upon previous research that utilized non-invasive EEG and MEG by analyzing intracranial human ECoG data with high spatial resolution. They employed a receptive field mapping task to infer the retinotopic organization of the human visual system. The results present compelling evidence that the spatial distribution of human alpha oscillations is highly specific and functionally relevant, as it provides information about the position of a stimulus within the visual field.

      Using state-of-the-art modeling approaches, the authors not only strengthen the existing evidence for the spatial specificity of the human dominant rhythm but also provide new quantification of its functional utility, specifically in terms of the size of the receptive field relative to the one estimated based on broad band activity.

      We thank the reviewer for their positive summary.

      Weakness 1.1

      The present manuscript currently omits the complementary view that the retinotopic map of the visual system might be related to eye movement control. Previous research in non-human primates using microelectrode stimulation has clearly shown that neuronal circuits in the visual system possess motor properties (e.g. Schiller and Styker 1972, Schiller and Tehovnik 2001). More recent work utilizing Utah arrays, receptive field mapping, and electrical stimulation further supports this perspective, demonstrating that the retinotopic map functions as a motor map. In other words, neurons within a specific area responding to a particular stimulus location also trigger eye movements towards that location when electrically stimulated (e.g. Chen et al. 2020).

      Similarly, recent studies in humans have established a link between the retinotopic variation of human alpha oscillations and eye movements (e.g., Quax et al. 2019, Popov et al. 2021, Celli et al. 2022, Liu et al. 2023, Popov et al. 2023). Therefore, it would be valuable to discuss and acknowledge this complementary perspective on the functional relevance of the presented evidence in the discussion section.

      The reviewer notes that we do not discuss the oculomotor system and alpha oscillations. We agree that the literature relating eye movements and alpha oscillations are relevant.

      At the Reviewer’s suggestion, we added a paragraph on this topic to the first section of the Discussion (section 3.1, “Other studies have proposed … “).

      Reviewer #2 (Public Review):

      Summary:

      In this work, Yuasa et al. aimed to study the spatial resolution of modulations in alpha frequency oscillations (~10Hz) within the human occipital lobe. Specifically, the authors examined the receptive field (RF) tuning properties of alpha oscillations, using retinotopic mapping and invasive electroencephalogram (iEEG) recordings. The authors employ established approaches for population RF mapping, together with a careful approach to isolating and dissociating overlapping, but distinct, activities in the frequency domain. Whereby, the authors dissociate genuine changes in alpha oscillation amplitude from other superimposed changes occurring over a broadband range of the power spectrum. Together, the authors used this approach to test how spatially tuned estimated RFs were when based on alpha range activity, vs. broadband activities (focused on 70-180Hz). Consistent with a large body of work, the authors report clear evidence of spatially precise RFs based on changes in alpha range activity. However, the size of these RFs were far larger than those reliably estimated using broadband range activity at the same recording site. Overall, the work reflects a rigorous approach to a previously examined question, for which improved characterization leads to improved consistency in findings and some advance of prior work.

      We thank the reviewer for the summary.

      Strengths:

      Overall, the authors take a careful and well-motivated approach to data analyses. The authors successfully test a clear question with a rigorous approach and provide strong supportive findings. Firstly, well-established methods are used for modeling population RFs. Secondly, the authors employ contemporary methods for dissociating unique changes in alpha power from superimposed and concomitant broadband frequency range changes. This is an important confound in estimating changes in alpha power not employed in prior studies. The authors show this approach produces more consistent and robust findings than standard band-filtering approaches. As noted below, this approach may also account for more subtle differences when compared to prior work studying similar effects.

      We thank the reviewer for the positive comments.

      Weaknesses:

      Weakness 2.1 Theoretical framing:

      The authors frame their study as testing between two alternative views on the organization, and putative functions, of occipital alpha oscillations: i) alpha oscillation amplitude reflects broad shifts in arousal state, with large spatial coherence and uniformity across cortex; ii) alpha oscillation amplitude reflects more specific perceptual processes and can be modulated at local spatial scales. However, in the introduction this framing seems mostly focused on comparing some of the first observations of alpha with more contemporary observations. Therefore, I read their introduction to more reflect the progress in studying alpha oscillations from Berger's initial observations to the present. I am not aware of a modern alternative in the literature that posits alpha to lack spatially specific modulations. I also note this framing isn't particularly returned to in the discussion.

      This was helpful feedback. We have rewritten nearly the entire Introduction to frame the study differently. The emphasis is now on the fact that several intracranial studies of spatial tuning of alpha (in both human and macaque) tend to show increases in alpha due to visual stimulation, in contrast to a century of MEG/EEG studies, from Berger to the present, showing decreases. We believe that the discrepancy is due to an interaction between measurement type and brain signals. Specifically, intracranial measurements sum decreases in alpha oscillations and increases in broadband power on the same trials, and both signals can be large. In contrast, extracranial measures are less sensitive to the broadband signals and mostly just measure the alpha oscillation. Our study reconciles this discrepancy by removing the baseline broadband power increases, thereby isolating the alpha oscillation, and showing that with iEEG spatial analyses, the alpha oscillation decreases with visual stimulation, consistent with EEG and MEG results.

      Weakness 2.2 A second important variable here is the spatial scale of measurement.

      It follows that EEG based studies will capture changes in alpha activity up to the limits of spatial resolution of the method (i.e. limited in ability to map RFs). This methodological distinction isn't as clearly mentioned in the introduction, but is part of the author's motivation. Finally, as noted below, there are several studies in the literature specifically addressing the authors question, but they are not discussed in the introduction.

      The new Introduction now explicitly contrasts EEG/MEG with intracranial studies and refers to the studies below.

      Weakness 2.3 Prior studies:

      There are important findings in the literature preceding the author's work that are not sufficiently highlighted or cited. In general terms, the spatio-temporal properties of the EEG/iEEG spectrum are well known (i.e. that changes in high frequency activity are more focal than changes in lower frequencies). Therefore, the observations of spatially larger RFs for alpha activities is highly predicted. Specifically, prior work has examined the impact of using different frequency ranges to estimate RF properties, for example ECoG studies in the macaque by Takura et al. NeuroImage (2016) [PubMed: 26363347], as well as prior ECoG work by the author's team of collaborators (Harvey et al., NeuroImage (2013) [PubMed: 23085107]), as well as more recent findings from other groups (Luo et al., (2022) BioRxiv: https://doi.org/10.1101/2022.08.28.505627). Also, a related literature exists for invasively examining RF mapping in the time-voltage domain, which provides some insight into the author's findings (as this signal will be dominated by low-frequency effects). The authors should provide a more modern framing of our current understanding of the spatial organization of the EEG/iEEG spectrum, including prior studies examining these properties within the context of visual cortex and RF mapping. Finally, I do note that the author's approach to these questions do reflect an important test of prior findings, via an improved approach to RF characterization and iEEG frequency isolation, which suggests some important differences with prior work.

      Thank you for these references and suggestions. Some of the references were already included, and the others have been added.

      There is one issue where we disagree with the Reviewer, namely that “the observations of spatially larger RFs for alpha activities is highly predicted”. We agree that alpha oscillations and other low frequency rhythms tend to be less focal than high frequency responses, but there are also low frequency non-rhythmic signals, and these can be spatially focal. We show this by demonstrating that pRFs solved using low frequency responses outside the alpha band (both below and above the alpha frequency) are small, similar to high frequency broadband pRFs, but differing from the large pRFs associated with alpha oscillations. Hence we believe the degree to which signals are focal is more related to the degree of rhythmicity than to the temporal frequency per se. While some of these results were already in the supplement, we now address the issue more directly in the main text in a new section called, “2.5 The difference in pRF size is not due to a difference in temporal frequency.”

      We incorporated additional references into the Introduction, added a new section on low frequency broadband responses to the Results (section 2.5), and expanded the Discussion (section 3.2) to address these new references.

      Weakness 2.4 Statistical testing:

      The authors employ many important controls in their processing of data. However, for many results there is only a qualitative description or summary metric. It appears very little statistical testing was performed to establish reported differences. Related to this point, the iEEG data is highly nested, with multiple electrodes (observations) coming from each subject, how was this nesting addressed to avoid bias?

      We reviewed the primary claims made in the manuscript and for each claim, we specify the supporting analyses and, where appropriate, how we address the issue of nesting. Although some of these analyses were already in the manuscript, many of them are new, including all of the analyses concerning nesting. We believe that putting this information in one place will be useful to the reader, and we now include this text as a new section in supplement, Graphical and statistical support for primary claims.

      Reviewer #2 (Recommendations For The Authors):

      Recommendation 2.1:

      Data presentation: In several places, the authors discuss important features of cortical responses as measured with iEEG that need to be carefully considered. This is totally appropriate and a strength of the author's work, however, I feel the reader would benefit from more depiction of the time-domain responses, to help better understand the authors frequency domain approach. For example, Figure 1 would benefit from showing some form of voltage trace (ERP) and spectrogram, not just the power spectra. In addition, part (a) of Figure 1 could convey some basic information about the timing of the experimental paradigm.

      We changed panel A of Figure 1 to include the timing of the experimental paradigm, and we added panels C and D to show the electrode time series before and after regression out of the ERP.

      Recommendation 2.2

      Update introduction to include references to prior EEG/iEEG work on spatial distribution across frequency spectrum, and importantly, prior work mapping RFs with different frequencies.

      We have addressed this issue and re-written our introduction. Please refer to our response in Public Review for further details.

      Recommendation 2.3

      Figure 3 has several panels and should be labeled to make it easier to follow.The dashed line in lower power spectra isn't defined in a legend and is missing from the upper panel - please clarify.

      We updated Figure 3 and reordered the panels to clarify how we computed the summary metrics in broadband and alpha for each stimulus location (i.e., the “ratio” values plotted in panel B). We also simplified the plot of the alpha power spectrum. It now shows a dashed line representing a baseline-corrected response to the mapping stimulus, which is defined in the legend and explained in the caption.

      Recommendation 2.4

      Power spectra are always shown without error shading, but they are mean estimates.

      We added error shading to Figures 1, 2 and 3.

      Recommendation 2.5

      The authors deal with voltage transients in response to visual stimulation, by subtracting out the trail averaged mean (commonly performed). However, the efficacy of this approach depends on signal quality and so some form of depiction for this processing step is needed.

      We added a depiction of the processing steps for regressing out the averaged responses in Figure 1 in an example electrode (panels C and D). We also show in the supplement the effect of regressing out the ERP on all the electrode pRFs. We have added Supplementary Figure 1-2.

      Recommendation 2.6

      I have a similar request for the authors latency correction of their data, where they identified a timing error and re-aligned the data without ground truth. Again, this is appropriate, but some depiction of the success of this correction is very critical for confirming the integrity of the data.

      We now report more detail on the latency correction, and also point out that any small error in the estimate would not affect our conclusions (4.6 ECoG data analysis | Data epoching). The correction was important for a prior paper on temporal dynamics (Groen et al, 2022), which used data from the same participants and estimated the latency of responses. In this paper, our analyses are in the spectral domain (and discard phase), so small temporal shifts are not critical. We now also link to the public code associated with that paper, which implemented the adjustment and quantified the uncertainty in the latency adjustment.

      More details on latency adjustment provided in section 4.6.

      Recommendation 2.7

      In many places the authors report their data shows a 'summary' value, please clarify if this means averaging or summation over a range.

      For both broadband and alpha, we derive one summary value (a scalar) for trial for each stimulus. For broadband, the summary metric is the ratio of power during a given trial and power during blanks, where power in a trial is the geometric mean of the power at each frequency within the defined band). This is equation 3 in the methods, which is now referred to the first time that summary metrics are mentioned in the results.  For alpha, the summary metric is the height of the Gaussian from our model-based approach. This is in equations 1 and 2, and is also now referred to the first time summary metrics are mentioned in the results.

      We added explanation of the summary metrics in the figure captions and results where they are first used, and also referred to the equations in the methods where they are defined.

      Recommendation 2.8

      The authors conclude: "we have discovered that spectral power changes in the alpha range reflect both suppression of alpha oscillations and elevation of broadband power." It might not have been the intention, but 'discovered' seems overstated.

      We agree and changed this sentence.

      Recommendation 2.9

      Supp Fig 9 is a great effort by the authors to convey their findings to the reader, it should be a main figure.

      We are glad you found Supplementary Figure 9 valuable. We moved this figure to the main text.

      Reviewer #3 (Public Review):

      Summary:

      This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

      We thank the reviewer for the summary.

      Strengths:

      The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

      We thank the reviewer for the positive comments.

      Weaknesses:

      I have three major concerns:

      Weakness 3.1. Low N / no single subject results/statistics:

      The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

      We have added substantial changes to deal with subject specific effects, including new results and new figures.

      • Figure 4 now shows variance explained by the alpha pRF broken down by each participant for electrodes in V1 to V3. We also now show a similar figure for dorsolateral electrodes in Supplementary Figure 4-2.

      • Figure 5, which shows results from individual electrodes in V1 to V3, now includes color coding of electrodes by participant to make it clear how the electrodes group with participant. Similarly, for dorsolateral electrodes, we show electrodes grouped by participant in Supplementary Figure 5-1. Same for Supplementary Figure 6-2.

      • Supplementary Figure 7-2 now shows the benefits of our model-based approach for estimating alpha broken down by individual participants.

      • We also now include a new section in the supplement that summarizes for every major claim, what the supporting data are and how we addressed the issue of nesting electrodes by participant, section Graphical and statistical support for primary claims.

      Weakness 3.2. Separation between V1-V3 and dorsolateral electrodes:

      Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

      Electrode assignment was probabilistic to reflect uncertainty in the mapping between location and retinotopic map. The probabilistic assignment is handled in two ways.

      (1) For visualizing results of single electrodes, we simply go with the maximum probability, so no electrode is visualized for both groups of data. For example, Figure 5a (V1-V3) and supplementary Figure 5-1a (dorsolateral electrodes) have no electrodes in common: no electrode is in both plots.

      (2) For quantitative summaries, we sample the electrodes probabilistically (for example Figures 4, 5c). So, if for example, an electrode has a 20% chance of being in V1 to V3, and 30% chance of being in dorsolateral maps, and a 50% chance of being in neither, the data from that electrode is used in only 20% of V1-V3 calculations and 30% of dorsolateral calculations. In 50% of calculations, it is not used at all. This process ensures that an electrode with uncertain assignment makes no more contribution to the results than an electrode with certain assignment. An electrode with a low probability of being in, say, V1-V3, makes little contribution to any reported results about V1-V3. This procedure is essentially a weighted mean, which the reviewer suggests in the recommendations. Thus, we believe there is not a problem of “double counting”.

      The alternative would have been to use maximum probability for all calculations. However, we think that doing so would be misleading, since it would not take into account uncertainty of assignment, and would thus overstate differences in results between the maps.

      We now clarify in the Results that for probabilistic calculations, the contribution of an electrode is limited by the likelihood of assignment (Section 2.3). We also now explain in the methods why we think probabilistic sampling is important.

      Weakness 3.3. Alpha pRFs are larger than broadband pRFs:

      First, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility.

      We addressed this question in a new paragraph in Discussion section 3.1 (“What is the function of the large alpha pRFs?”, paragraph beginning… “Another possible interpretation is that the poorer model fit in the alpha pRF is due to lower signal-to-noise”). This paragraph both refers to prior work on the relationship between noise and pRF size and to our own control analyses (Supplementary Figure 5-2).

      Weakness 3.4 Statistics

      Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

      We agree that it is important for the reader to be able to link specific results and analyses to specific claims. We are not convinced that null hypothesis statistical testing is always the best approach. This is a topic of active debate in the scientific community.

      We added a new section that concisely states each major claim and explicitly annotates the supporting evidence. (Section 4.7). Please also refer to our responses to Reviewer #2 regarding statistical testing (Reviewer weakness 2.4 “Statistical testing”)

      Weakness 3.5 Summary

      While I judge these issues as crucial, I can also appreciate the considerable effort and thoughtfulness that went into this study. I think that addressing these concerns will substantially raise the confidence of the readership in the study's findings, which are potentially important and interesting.

      We again thank the reviewer for the positive comments.

      Reviewer #3 (Recommendations For The Authors):

      Suggestions for how to address the three major concerns:

      Suggestion 3.1.

      I am very well aware that it's very hard to have n=30 in a visual cortex ECOG study. That's fine. Best practice would be to have a linear mixed effects model with patients as a random effect. However, for some figures with just 3-4 patients (Figure 4,5) the sample size might be too small even for that. At the very minimum, I would expect to show in figures/describe in text all results per patient (perhaps one can do statistics within each patient, and show for each patient that the effect is significant). Even in primate studies with just two subjects it is expected to show that the results replicate for subject A and B. It is necessary to show that your results don't depend on a single unrepresentative subject. And if they do, at least be transparent about it.

      We have addressed this thoroughly. Please see response to Weakness 3.1 (“Low N / no single subject results/statistics”).

      Suggestion 3.2.

      I just don't get it. I would simply assign an electrode to V1-V3 or dorsolateral cortex based on which area has the highest probability. It doesn't make sense to me that an electrode that has 60% of being in dorsolateral cortex and only 10% to be in V1-V3 would be assigned as both V1-V3 and dorsolateral. Also, what's the rationale to include such electrode in the analysis for let's say V1-V3 (we have weak evidence to believe it's there)? I would either assign electrodes based on the highest probability, or alternatively do a weighted mean based on the probability of each electrode belonging to each region group (e.g. electrode with 40% to be in V1-V3, will get twice the weight as an electrode who has 20% to be in V1-V3) but this is more complicated.

      We have addressed this issue. Please refer to our response in Public Review (“Weakness 3.2 Separation between V1-V3 and dorsolateral”) for details.

      Suggestion 3.3.

      First, to exclude the possibility that alpha pRF are larger simply because they have a worse fit to the neural data, I would show if there is a correlation between the goodnessof-fit and pRF size (for alpha and broadband signals, separately). No [negative] correlation between goodness-of-fit and pRF size would be a good sign. I would also compare alpha & broadband receptive field size when controlling for the goodness-of-fit (selecting electrodes with similar goodness-of-fit for both signals). If the results replicate this way it would be convincing.

      Second, there are no statistical tests in section 2.4, possibly also in others. Even if you employ bootstrap / Monte-Carlo resampling methods you can extract a p-value.

      We have addressed this issue. Please refer to our response in Public Review Point 3.3 (“Alpha pRFs are larger than broadband pRFs”) for further details.

      Suggestion 3.4.

      Also, I don't understand the resampling procedure described in lines 652-660: "17.7 electrodes were assigned to V1-V3, 23.2 to dorsolateral, and 53 to either " - but 17.7 + 23.2 doesn't add up to 53. It also seems as if you assign visual areas differently in this resampling procedure than in the real data - "and randomly assigned each electrode to a visual area according to the Wang full probability distributions". If you assign in your actual data 27 electrodes to both visual areas, the same should be done in the resampling procedure (I would expect exactly 35 V1-V3 and 45 dorsolateral electrodes in every resampling, just the pRFs will be shuffled across electrodes).

      We apologize for the confusion.

      We fixed the sentence above, clarified the caption to Table 2, and also explained the overall strategy of probabilistic resampling better. See response to Public Review point 3.2 for details.

      Suggestion 3.5.

      These are rather technical comments but I believe they are crucial points to address in order to support your claims. I genuinely think your results are potentially interesting and important but these issues need to be first addressed in a revision. I also think your study may carry implications beyond just the visual domain, as alpha suppression is observed for different sensory modalities and cortical regions. Might be useful to discuss this in the discussion section.

      Agree. We added a paragraph on this point to the Discussion (very end of 3.2).

    1. Reviewer #2 (Public review):

      In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin (Nlgn) family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Nlgn1-3 specifically from astrocytes in mice, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. To further extend this finding, the authors additionally analyzed human neurons co-cultured with mouse glia lacking expression of Nlgn1-4. No difference in excitatory synaptic transmission was observed between neurons cultured in the present of wildtype vs. Nlgn1-4 conditional knockout glia. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

      Overall, this is a strong study that addresses a fundamental and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, Nlgn1-3 are efficiently deleted from astrocytes in vivo, and that this deletion does not lead to major alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes. While the co-culture experiments are somewhat more difficult to interpret due to lack of a control for the effect of wildtype mouse astrocytes on human neurons, they are also consistent with the notion that deletion of Nlgn1-4 from astrocytes has no consequences for the function of excitatory synapses. Together, the data from this study provide compelling and important evidence that, whatever the role of astrocytic Neuroligins may be, they do not contribute substantially to synapse formation or function under the conditions investigated.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public Review):

      Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

      We thank the reviewer for the balanced and informative summary.

      Reviewer #2 (Public Review):

      In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Neuroligins 1, 2 and 3 specifically from astrocytes, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

      Overall, this is a strong study that addresses an important and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, no alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes, are observed.

      We are also grateful for this reviewer’s constructive comments.

      One caveat to this study is that the authors do not directly provide evidence that their Tamoxifen-inducible conditional deletion paradigm does indeed result in efficient deletion of all three Neuroligins from astrocytes. Using a Cre-dependent tdTomato reporter line, they show that tdTomato expression is efficiently induced by the current paradigm, and they refer to a prior study showing efficient deletion of Neuroligins from neurons using the same conditional Nlgn1-3 mouse lines but a different Cre driver strategy. However, neither of these approaches directly provide evidence that all three Neuroligins are indeed deleted from astrocytes in the current study. In contrast, Stogsdill et al. employed FACS and qPCR to directly quantify the loss of Nlgn2 mRNA from astrocytes. This leaves the current Golf et al. study somewhat vulnerable to the criticism, however unlikely, that their lack of synaptic effects may be a consequence of incomplete Neuroligin deletion, rather than a true lack of effect of astrocytic Neuroligins.

      The concern is valid. In the original submission of this paper, we did not establish that the Cre recombinase we used actually deleted neuroligins in astrocytes. We have now addressed this issue in the revised paper with new experiments as described below.

      However, the reviewer’s impression that the Stogsdill et al. paper confirmed full deletion of Nlgn2 is a misunderstanding of the data in that paper. The reviewer is correct that Stogsdill et al. performed FACS to test the efficacy of the GLAST-Cre mediated deletion of Nlgn2-flox mice, followed by qRT-PCR comparing heterozygous with homozygous mutant mice. With their approach, no wild-type control could be used, as these would lack reporter expression. However, this experiment does NOT allow conclusions about the degree of recombination, both overall recombination (i.e. recombination in all astrocytes regardless of TdT+) and recombination in TdT+ astrocytes because it doesn’t quantify recombination. To quantify the degree of recombination, the paper would have had to perform genomic PCR measurements.  

      The problem with the data on the degree of recombination in the Stogsdill et al. (2017) paper, as we understand them, is two-fold.

      First, the GLAST-Cre line only targets ~40-70% of astrocytes, at least as evidenced by highly sensitive Cre-reporter mice in a variety of studies using this Cre line. The 40-70% variation is likely due to differences in the reporter mice and the tamoxifen injection schedule used. In comparison, we are targeting most astrocytes using the Aldh1l1-CreERT2 mice. Moreover, GLAST-Cre mice exhibit neuronal off-targeting, consistent with at least some of the remaining Nlgn2 qRT-PCR signal in the FACS-sorted cells. As we describe next, this signal also likely comes from astrocytes where recombination was incomplete This is the reason why we, like everyone else, are now using the Aldh1l1-Cre line that has been shown to be more efficient both in terms of the overall targeting of astrocytes (i.e. nearly complete) and the level of recombination observed in reporter(+) astrocytes.

      Second, Stogsdill et al. detected a significant decrease in the Nlgn2 qRT-PCR signal in the FACS-sorted homozygous Nlgn2 KO cells compared to the heterozygous Nlgn2 KO cells but the Nlgn2 qRT-PCR signal was still quite large. The data is presented as normalized to the HET condition. As a result, we don’t know the true level of gene deletion (i.e. compared to TdT- astrocytes). For example, based on the Stogsdill et al. data the HET manipulation could have induced only a 20% reduction in Nlgn2 mRNA levels in TdT(+) astrocytes, in which case the KO would have produced a 40% reduction in Nlgn2 mRNA in TdT(+) astrocytes. Moreover, it is possible based on our own experience with the GLAST-Cre line, that the reporter may also not turn on in some astrocytes where other alleles have been independently recombined – just as some astrocytes that are Td(+) would still be wild-type or heterozygous for Nlgn2. Thus, it is impossible to calculate the actual percentage of recombination from these data, even in TdT(+) cells, absent of PCR of genomic DNA from isolated cells. Alternatively, comparison of mRNA levels using primers sensitive to floxed sequences in wild-type controls versus cKO mice would have also yielded a much better idea of the recombination efficiency.

      In summary, it is unclear whether the Nlgn2 deletion in the Stogsdill et al. paper was substantial or marginal – it is simply impossible to tell.

      Reviewer #3 (Public Review):

      This study investigates the roles of astrocytes in the regulation of synapse development and astrocyte morphology using conditional KO mice carrying mutations of three neuroligins1-3 in astrocytes with the deletion starting at two different time points (P1 and P10/11). The authors use morphological, electrophysiological, and cell-biological approaches and find that there are no differences in synapse formation and astrocyte cytoarchitecture in the mutant hippocampus and visual cortex. These results differ from the previous results (Stogsdill et al., 2017), although the authors make several discussion points on how the differences could have been induced. This study provides important information on how astrocytes and neurons interact with each other to coordinate neural development and function. The experiments were well-designed, and the data are of high quality.

      We also thank this reviewer for helpful comments!

      Recommendations for the authors:

      This project was meant to rigorously test the intriguing overall question whether neuroligins, which are abundantly expressed in astrocytes, regulate synapse formation as astrocytic synapse organizers. The goal of the paper was NOT to confirm or dispute the conclusion by Stogsdill et al. (Nature 2017) that Nlgn2 expressed in astrocytes is essential for excitatory synapse formation and that astrocytic Nlgn1-3 are required for proper astrocyte morphogenesis. Instead, the project was meant to address the much broader question whether the abundant expression of any neuroligin, not just Nlgn2, in astrocytes is essential for neuronal excitatory or inhibitory synapse formation and/or for the astrocyte cytoarchitecture. We felt that this was an important question independent of the Stogsdill et al. paper. We analyzed in our experiments young adult mice, a timepoint that was chosen deliberately to avoid the possibility of observing a possible developmental delay rather than a fundamental function that extends beyond development.

      We do recognize that the conclusion by Stogsdill et al. (2017) that Nlgn2 expression in astrocytes is essential for excitatory synapse formation was very exciting to the field but contradicted a large literature demonstrating that Nlgn2 protein is exclusively localized to inhibitory synapses and absent from excitatory synapses (to name just a few papers, see Graf et al., Cell 2004; Varoqueaux et al., Eur. J. Cell Biol. 2004; Patrizi et al., PNAS 2008;  Hoon et al., J. Neurosci. 2009). In addition, the conclusion of Stogsdill et al. that astrocytic Nlgn2 specifically drove excitatory synapse formation was at odds with previous findings documenting that the constitutive deletion of Nlgn2 in all cells, including astrocytes, has no effect on excitatory synapse numbers (again, to name a few papers, see Varoqueaux et al., Neuron 2006; Blundell et al., Genes Brain Behav. 2008; Poulopoulos et al., Neuron 2009; Gibson et al., J. Neurosci. 2009). These contradictions conferred further urgency to our project, but please note that this project was primarily driven by our curiosity about the function of astrocytic neuroligins, not by a fruitless desire to test the validity of one particular Nature paper.

      The general goal of our paper notwithstanding, few papers from our lab have received as much attention and as many negative comments on social media as this paper when it was published as a preprint. Because we take these criticisms seriously, we have over the last year performed extensive additional experiments to ensure that our findings are well founded. We feel that, on balance, our data are incompatible with the notion that astrocytic neuroligins play a fundamental role in excitatory synapse formation but are consistent with other prior findings obtained with neuroligin KO mice. In the new data we added to the paper, we not only characterized the Cre-mediated deletion of neuroligins in depth, but also employed an independent second system -human neurons cultured on mouse glia- to further validate our conclusions as described below. Although we believe that our results are incompatible with the notion that astrocytic neuroligins fundamentally regulate excitatory or inhibitory synapse formation, we also conclude with regret that we still don’t know what astrocytic neuroligins actually do. Thus, the function of astrocytic neuroligins, as there surely must be one, remains a mystery.

      Finally, there are many possible explanations for the discrepancies between our conclusions and those of Stogsdill et al. as described in our paper. Most of these explanations are technical and may explain why not only our, but also the results of many other previous studies from multiple labs, are inconsistent with the conclusions by Stogsdill et al. (2017), as discussed in detail in the revised paper.

      Reviewer #1 (Recommendations For The Authors):

      The paper is very clear and well written. I have only one comment and that is to increase the sizes of Figs 2, 4 and 6 so that the imaging panels can be seen more clearly. Also, although I know the n numbers are provided in the figure legends, the authors may help the reader by providing them in the results when key data and findings are reported.

      We agree and have followed the reviewer’s suggestions as best as we could.

      Reviewer #2 (Recommendations For The Authors):

      (1) Given the strength and importance of the claims that the authors make, I would highly recommend adding some quantitative evidence regarding the efficacy of deletion in astrocytes, e.g. using the same strategy as in Stogsdill et al. As unlikely as it may be that Neuroligin deletion is in fact incomplete, this possibility cannot be excluded unless directly measured. To avoid future discussions on this subject, it seems that the onus is on the authors to provide this information.

      We concur that this is an important point and have devoted a year-long effort to address it. Note, however, that the strategy employed by Stogsdill et al. does not actually allow conclusions about their recombination efficiency. As described above, it only allows the conclusion that some recombination took place. The Stogsdill et al. Nature paper (2017) is a bit confusing on this point. This approach is thus not appropriate to address the question raised by the reviewer.

      We have performed two experiments to address the issue raised by the reviewer.

      First, we used a viral (i.e. AAV2/5) approach to express Rpl22 with a triple HA-tag, also known as Ribotag, which allows us to purify ribosome-bound mRNA from targeted cells for downstream gene expression analysis. The novel construct is driven by the GfaABC1D promoter and includes two additional features which make it particularly useful. First, upstream of Ribotag is a membrane-targeted, Lck-mVenus followed by a self-cleaving P2A sequence. This allows easy visualization of targeted astrocytes. Second, we have incorporated a cassette of four copies of six miRNA targeting sequences (4x6T) for mIR-124 as was recently published (Gleichman et al., 2023) to eliminate off-target expression in neurons. Based on qPCR analysis, the updated construct allowed >95% de-enrichment of neuronal mRNA and slightly improved observed recombination rates (~10% per gene) relative to an earlier version without 4x6T. Mice that were injected with tamoxifen at P1, similar to other experiments in the paper, were then stereotactically injected at ~P35-40 within the dorsal hippocampus with AAV2/5-GfaABC1D-Lck-mVenus-P2A-Rpl22-HA-4x6T. Approximately 3 weeks later, acute slices were prepared, visualized for fluorescence, and both CA1 and nearby cortex that was partially targeted were isolated for downstream ribosome affinity purification with HA antibodies. Total RNA was saved as input. qPCR was performed using assays that are sensitive to the exons that are floxed in the Nlgn123 cKO mice, so that our quantifications are not confounded by potential differences in non-sense mediated decay. Our control data reveals a striking enrichment of an astrocyte marker gene (e.g. aquaporin-4) and de-enrichment of genes for other cell types. In the CA1, we observed robust loss of Nlgn3 (~96%), Nlgn2 (~86%), and Nlgn1 (65%) gene expression. Similarly, in the cortex, we observed a similarly robust loss of Nlgn3 (93%), Nlgn2 (83%), and Nlgn1 (72%) expression. Given that our targeting of astrocytes based on Ai14 Cre-reporter mice was ~90-99%, these reductions are striking and definitive. The existence of some residual transcript reflects the presence of a small population of astrocytes heterozygous for Nlgn2 and Nlgn3. In contrast, Nlgn1 appears more difficult to recombine and it is likely that some astrocytes are either heterozygous or homozygous knockout cells. Although it is thus possible that Nlgn1 could provide some compensation in our experiments, it is worth noting that Stogsdill et al. found that only Nlgn2 and Nlgn3 knockdown with shRNAs resulted in impaired astrocyte morphology by P21. Moreover, they found that Nlgn2 cKO in astrocytes with PALE of a Cre-containing pDNA impaired astrocyte morphology in a gene-dosage dependent manner and suppressed excitatory synapse formation at P21. Thus, our inability to delete all of Nlgn1 doesn’t readily explain contradictions between our findings and theirs.

      Second, in an independent approach we have cultured glia from mouse quadruple conditional Nlgn1234 KO mice and infected the glia with lentiviruses expressing inactive (DCre, control) or active Cre-recombinase. We confirmed complete recombination by PCR. We then cultured human neurons forming excitatory synapses on the glia expressing or lacking neuroligins and measured the frequency and amplitude of mEPSCs as a proxy for synapse numbers and synaptic function. As shown in the new Figure 9, we detected no significant changes in mEPSCs, demonstrating in this independent system that the glial neuroligins do not detectably influence excitatory synapse formation.

      (2) Along the same lines, the authors should be careful not to overstate their findings in this direction. For example, the figure caption for Figure 2 reads 'Nlgn1-3 are efficiently and selectively deleted in astrocytes by crossing triple Nlgn1-3 conditional KO mice with Adh1l1-CreERT2 driver mice and inducing Cre-activity with tamoxifen early during postnatal development'. This is not technically correct and should be modified to reflect that the authors are not in fact assessing deletion of Nlgn1-3, but only expression of a tdTomato reporter.

      We agree – this is essentially the same criticism as comment #1.

      (3) In general, the animal numbers used for the experiments are rather low. With an n = 4 for most experiments, only large abnormalities would be detected anyway, while smaller alterations would not reach statistical significance due to the inherent biological and technical variance. For the most part, this is not a concern, since there really is no difference between WTs and Nlgn1-3 cKOs. However, trends are observed in some cases, and it is conceivable that these would become significant changes with larger n's, e.g. Figure 3H (Vglut2); Figure 4E (VGlut2 S.P., D.G.); Figure 6D (Vglut2). Increasing the numbers to n = 6 here would greatly strengthen the claims that no differences are observed.

      We concur that small differences would not have been detected in our experiments but feel that given the very large phenotypes of the neuroligin deletions in neurons and of the phenotypes reported by Stogsdill et al. (2017), which also did not employ a large number of animals, a very small phenotype in astrocytes would not have been very informative.

      Minor points:

      (1) Please state the exact genetic background for the mouse lines used.

      Our lab generally uses hybrid CD1/Bl6 mice to avoid artifacts produced by inbred genetic mutations in so-called ‘pure’ lines, especially Bl6 mice. This standard protocol was followed in the present study. Thus, the mice are on a mixed CD1/Bl6 hybrid background.

      Reviewer #3 (Recommendations For The Authors):

      (1) Figure 4 demonstrates that neuroligin 1-3 deletions restricted to astrocytes do not affect the number of excitatory and inhibitory synapses in layer IV of the primary visual cortex. This conclusion could be further strengthened if the authors could provide electrophysiological evidence such as mE/IPSCs.

      We agree but have chosen a different avenue to further test our conclusions because slice electrophysiological experiments are time-consuming, labor intensive, and difficult to quantitate, especially in cortex.

      Specifically, we have co-cultured human neurons with astrocytes that either contain or lack neuroligins (new Fig. 9). With this experimental design, we have total control over ALL neuroligins in astrocytes. Electrophysiological recordings then demonstrated that the complete deletion of all glial neuroligins has no effect on mEPSC frequencies and amplitudes. Although clearly much more needs to be done, the new results confirm in an independent system that glial neuroligins have no effect on synapse formation in the neurons, even though neurons depend on astrocytes for synaptogenic factors as Ben Barres brilliantly showed a decade ago. However, it is important to note that dissociated glia in culture, while synaptogenic, are reactive and may not faithfully recapitulate all roles of astrocytes in synaptogenesis.

      (2) It would help readers if the images showing the punctate double marker stainings of excitatory/inhibitory synapses are presented in merged colors (i.e., yellow colors for red and green puncta colors).

      We have tried to improve the visualization of the rather voluminous studies we performed and illustrate in the figures as best as we could.

      (3) The resolutions of the images in the figures are not good, although I guess it is because the images are for review processes.

      We apologize and would like to assure the reviewer that we are supplying high-resolution images to the journal.

      (4) Typos in lines 82 and 274.

      We have corrected these errors.

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

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      Reply to the reviewers

      Manuscript number: RC- 2025-02880

      Corresponding author(s): Monica, Gotta

      1. General Statements [optional]

      We thank the reviewers for their useful comments that will improve our manuscript and make it clearer. We agree with Reviewer 1 that SDS-22 has more general functions in cellular processes by maintaining GSP-1/-2 levels, rather than only regulating cell polarity. We have now modified our conclusion in the text (all changes are highlighted in yellow) and we hope that it is now more clear and better explained. Below we address the reviewer’s comments one by one and indicate how we have or will address the comments in the final version. We expect the revisions to take 2-3 months.

      2. Description of the planned revisions

      Major comments

      Reviewer 1

      (1) Overall, the evidence supporting the core finding that SDS-22 is required for normal GSP-1/2 levels is strong and well documented. The experiments were performed well and controls, statistics, replicates were appropriate. Our only slight reservation was whether the effect of sds-22(RNAi) on stability may be overstated due to the use of GFP fusions to GSP-1/2 for this analysis. The authors note these alleles are hypomorphic, potentially raising the possibility that GFP tags destabilise the proteins and make them more prone to degradation. Ideally this would be repeated with an untagged allele via Western (e.g. Peel et al 2017 for relevant antibodies).

      We thank the reviewer for the general comments. To address this important point on the protein levels we have requested GSP-1 and GSP-2 antibodies reported in Peel et al and Tzur et al (Peel et al, 2017; Tzur et al, 2012). The published GSP-1 antibody has been used in western blot, and the GSP-2 antibody has been used in both immunostaining and Western blot analysis. Despite our efforts, we were not able to detect GSP-2 neither on western blots nor on immunostainings with the aliquot we have received. On the opposite, GSP-1 antibodies worked well on western blot. We have already measured the GSP-1 levels in SDS-22 depleted embryos (N=2, see below) and we observed reduced levels, confirming our initial result. However, as the reviewer rightly pointed out, the levels are reduced by 20% (rather than about 50% as in the GFP strain), suggesting that indeed the GFP fusion does contribute to the instability. We will measure GSP-1 levels in at least an additional sds-22(RNAi) experiment and in sds-22(E153A) embryos.

      Left, Western Blot of embryonic extracts from N2 in ctrl(RNAi) and sds-22(RNAi) embryos. Tubulin is used as a loading control. Right, Fold change of GSP-1 normalized to Tubulin levels. N = 2.

      Since we could not detect endogenous GSP-2 with the antibodies we have received, we will generate an OLLAS-tagged GSP-2 strain. OLLAS is a commonly used tag consisting of 14 amino acids (Park et al, 2008), with an additional 4 amino acids as a linker. The tag is much smaller than mNeonGreen, which consists of approximately 270 amino acids. We will then measure the GSP-2 levels using the ollas antibody in sds-22(RNAi) embryos. We will also cross this strain with sds-22(E153A) and measure OLLAS::GSP-2 levels in this mutant. If this strain is not embryonic lethal, as in the case of the mNG::gsp-2; sds-22(E153A) (Fig EV6A), it will also suggest that ollas::gsp-2 does not behave as hypomorph.

      These data will complement the data shown in Fig 6.

      (2) The role for SDS-22 in polarity is rather weak. Both the SDS-22 depletion phenotypes and the ability of SDS-22 depletion to suppress pkc-3(ts) polarity phenotypes are modest (and weaker in than GSP-2 depletion). For example, the images in Figure 1B appear striking, but from Movie S1 it is clear that this isn't a full rescue as PAR-2 is initially uniformly enriched on the cortex (rather than mostly cytoplasmic) and it is never fully cleared. In the movie, the clearance at the point of pronuclear meeting is very modest. Quantitation might be helpful here (i.e. as in Figure 3G). As the authors state, it seems that SDS-22 does not have a specific role in polarity beyond the general effect on GSP-1/2 levels. This does not undermine the core message of the paper, but we would recommend downplaying the conclusions with respect to contributing to polarity establishment. For example "...suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos" implies a regulatory role for SDS-22 in polarity, but we would interpret it as simply helping reduce aberrant degradation of GSP-1/2 and this impacts a variety of cellular processes including polarity.

      We agree with the reviewer that the rescue of pkc-3ts polarity defects by SDS-22 depletion is not as strong as GSP-2 depletion, and as suggested, we have re-quantified the phenotype, as we did in Fig 3G, as shown below in Fig 1C.

      This has replaced Fig.1 in the manuscript.

      Accordingly, we have clarified this in the text in several locations. We have added “partial” rescue in many places and modified conclusions in the results and discussion. The changes are all highlighted and the major ones are also below:

      From Result Line 119-121, page 5:

      “In contrast, depletion of SDS-22 resulted in PAR-2 localization being restricted to the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

      To: Result Line 122-125, page 5

      “In contrast, depletion of SDS-22 resulted in PAR-2 localization being enriched in the posterior cortex in 87.5% of the one-cell stage embryos (Fig 1B,C) and PAR-2 was localized to the P1 blastomere after the first cell-division (Movie EV1).”

      • *

      From Result Line 172-175, page 7:

      “Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2 in polarity establishment.”

      To: Result Line 178-181, page 7

      “Our data show that depletion of SDS-22 results in a smaller PAR-2 domain, partially suppresses the polarity defects of a pkc-3 temperature sensitive strain and the aberrant PAR-2 localization observed in the PAR-2(L165V) mutant strain. As SDS-22 is a conserved PP1 regulator, our data suggest that SDS-22 positively regulates GSP-2.”

      From Result Line 256-257, page 10:

      “suggesting that the interaction of SDS-22 with the PP1 phosphatases is important for polarity establishment.”

      To: Result Line 264-265, page 10

      “suggesting that the interaction of SDS-22 with the PP1 phosphatases contributes to polarity establishment”

      • *

      From Result Line 311-313, page 12:

      To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases.

      To: Result Line 319-322, page 12

      To conclude, while our genetic data on PAR-2 cortical localization suggest that SDS-22 is not required to fully activate GSP-1 and/or GSP-2, depletion or mutation of SDS-22 results in a reduced activity of the phosphatases, as shown by phospho-histone H3 (Ser10) levels. This suggests that SDS-22 plays a general role in regulating GSP-1 and GSP-2, which is not specific to cell polarity.

      From Result Line 391-392, page 15:

      In summary, our results show that SDS-22 maintains the levels of GSP-1 and GSP-2 by protecting them

      392 from proteasome mediated degradation.

      To: Result Line 402-403, page 15

      In summary, these data show that SDS-22 is important to maintain the levels of GSP-1 and GSP-2 by protecting them from proteasome mediated degradation.

      We have also rephrased our conclusion according to Reviewer 1’s suggestion.

      From Introduction Line 95-101, Page 4:

      Here we show that SDS-22 depletion rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C), similarly to the depletion of GSP-2. Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish SDS-22 as a regulator of PAR polarity establishment in the C. elegans one-cell embryo and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024).

      To: Introduction Line 96-101, Page 4

      *Here we show that SDS-22 depletion partially rescues the polarity defects caused by reduced PAR-2 phosphorylation in the pkc-3(ne4246) mutant at the semi-restrictive temperature (24°C). Depletion of SDS-22 results in lower GSP-1 and GSP-2 protein levels which can be rescued by depleting proteasomal subunits. These results establish that SDS-22 contributes to cell polarity by regulating GSP-1/-2 levels and are consistent with and complement the recent data in mammalian cells showing that SDS22 is important to control the stability of the PP1 phosphatase (Cao et al., 2024). *

      From Discussion Line 417-420, page 17:

      Depletion of SDS-22, or mutation of its E153 residue (E153A) important for SDS-22-PP1 interaction resulted in reduced GSP-1/-2 protein levels, decreased dephosphorylation of a PP1 substrate, and a smaller PAR-2 domain, suggesting that SDS-22 regulates GSP-1/-2 activity to control the loading of PAR-2 to the posterior cortex in one-cell stage C. elegans embryos.

      To: Discussion Line 426-429, page 17

      *Here we find that a conserved PP1 regulator, SDS-22, when depleted, results in a smaller PAR-2 domain and can partially rescue the polarity defects of a pkc-3(ne4246) mutant. We demonstrate that SDS-22 contributes to the activity of GSP-1/-2 by protecting them from proteasomal degradation and maintaining their protein levels. *

      Add new discussion to Discussion Line 429-432, page 17:

      Taken together, our data suggest that the role of SDS-22 in polarity is indirect via the regulation of GSP-1/-2 levels. In support of this, SDS-22 depletion results in broader GSP-1/-2 dependent phenotypes such as increased Phospho-H3 (Ser10) (Fig 5) and centriole duplication defects in later-stage embryos (Peel et al., 2017).

      • *

      (3) Specificity of SDS-22 effects on polarity. SDS-22 (or GSP-1/2) depletion is likely to have effects on many pathways. We wondered whether some of the polarity phenotypes may not be specifically due to changes in the PAR-2 phosphorylation cycle as implied.

      One candidate is the actomyosin cortex. It was noticeable that control and sds-22 embryos were different: In Movies S1, S2, and S3 control embryos show either stronger or more persistent cortical ruffling or pseudocleavage furrows. This is also visible in Figure 3A. Is it possible that disruption of SDS-22 reduces cortical flows (time, intensity or duration) and could this explain the small reduction in anterior PAR-2 spreading and thus the slightly smaller domain size measured in Figures 1B and 3A.

      We have noticed that SDS-22 depletion results in less ruffling and reduced pseudocleavage furrows. To properly address this question we should have a condition in which we can rescue the cortical flow reduction in the SDS-22 depletion and measure the PAR-2 domain. Since we do not know how SDS-22 reduces the flows, we could not come up with a clean experiment to address this issue and are happy to have suggestions.

      We believe that the most rigorous way to address this issue, as reviewer 1 points out, is to clearly address this limitation in the text. We have now added this in the discussion:

      Discussion Line 463-466, page 18:

      Consistent with GSP-2 reduced levels, SDS-22 depleted or E153A mutant embryos also have a smaller PAR-2 domain. However, since these embryos also show reduced cortical ruffling (Movie EV1,2) and are smaller (Fig EV2C) we cannot exclude that these two phenotypes also contribute to the smaller size of the PAR-2 domain.

      • *

      A potentially related issue could be embryo size. sds-22 embryos generally seem to be smaller than wild-type (e.g. Figure 1B(left), 4A(left column), and particularly EV3). Is this consistently true? Could cell size effects change the ability of embryos to clear anterior PAR-2 domains as described in EV3? Klinkert et al (2018, biorXiv) note that reducing the size of air-1(RNAi) embryos reduces the frequency of bipolar PAR-2 domains.

      Quantification of perimeter of embryos at pronuclear meeting in live zygotes. Sample size (n) is indicated in the graph, each dot represents a single embryo and mean is shown. N = 5. The P value was determined using two-tailed unpaired Student’s t test.

      We quantified the perimeter of the embryos and as seen by quantification, there is a weak but significant decrease of size in the absence of SDS-22, and in SDS-22(E153A) mutant, as shown above. We have now added the data of the RNAi in the supplementary information and mentioned it in the results.

      Results Line 129, page 5:

      SDS-22 depleted embryos also displayed a smaller size (Fig EV2C).

      Klinkert et al reported that reducing the size of air-1(RNAi) embryos by depletion of ANI-2, a homolog of the actomyosin scaffold protein anillin, reduces the frequency of bipolar PAR-2 domains (Klinkert et al, 2018). In the image shown in the paper on bioRxiv, the PAR-2 domain appears small but there are no quantifications and these data have been removed from the published paper.

      From published data, a smaller embryo size does not appear to correlate with smaller PAR-2 domain. Chartier et al show that depletion of ANI-2 reduces embryo size without changing the relative anterior PAR-6 domain (Chartier et al, 2011), thereby suggesting that the posterior PAR-2 domain should not change either. In addition, Hubatsch et al reported that small embryos depleted of ima-3 tend to have larger PAR-2 domains, whereas larger embryos depleted of C27D9.1 exhibit smaller PAR-2 domains (Hubatsch et al, 2019), which is the opposite of what we see. We do not believe that the smaller PAR-2 domain is the important message of our paper. Our main question was whether PAR-2 was cortical or not and since GSP-2 had a smaller domain, we decided to quantify the PAR-2 domain length in the different RNAi conditions and mutants. Since RNAi of C27D9.1 which makes embryos bigger, results in a small PAR-2 domain, again we do not know how to experimentally address this question, unless the reviewer has a suggestion. As for the point above, we will clearly highlight this limitation in the discussion (see our reply to the previous point, now it is in Discussion Line 463-466, page 18).

      We would stress that these comments relate to interpreting the polarity phenotypes and do not undermine the core finding that SDS-22 stabilises GSP-1/2.

      We thank the reviewer and we hope that by performing the experiments mentioned above and by changing the text, their comments are properly addressed.

      Reviewer 2

      Major comment: Consistent with the model that PP1 activity is reduced in the absence of SDS-22, the authors show that a surrogate PP1 target (phospho-histone H3) becomes hyper-phosphorylated. To strengthen the study, the authors could consider performing an OPTIONAL experiment (see below) of assaying the phosphorylation status of PAR-2 itself, as this is proposed to be the target of both PKC-3 and PP1, and represent the mechanism of PAR-2 polarization.

      We thank the reviewer for this comment and also for pointing out that there is technical difficulty in the proposed experiment.

      We have already attempted to address this point without success in Calvi et al (Calvi et al, 2022), using western blot analysis (see below). For this we used the GFP::PAR-2 strain and used a GFP antibody (shown below in the left panel), as none of the anti-PAR-2 antibodies (neither the ones produced by us nor the ones produced by other laboratories) were working on western blot. We observed several bands of GFP::PAR-2 but were not able to determine if these represented phosphorylated forms or to compare the ratio of phosphorylated to unphosphorylated PAR-2. We did use λ-PPase in the embryonic extracts but we did not always observe a clear difference. We show three experiments below.

      Left, __Western blots of gfp::par-2 embryonic extract in the presence or absence of λ-PPase (+/- PhosSTOP) and probed with anti-GFP and anti-Tubulin antibodies. Right,__ Representative images of fixed embryos with indicated genotypes at one-, two- and four-cell stages. DNA (DAPI) is gay. Scale bars, 5 μm. Anterior is to the left and posterior to the right.

      One possible explanation is that the role of GSP-1/-2 in PAR-2 dephosphorylation is specific to the very early embryos. As shown in the right panel above, despite PAR-2(RAFA) remaining cytoplasmic in one- and two-cell embryos due to lack of binding to GSP-1/-2, it can localize to internal cortices in four-cell stage embryos, similarly to the control and suggesting that in later embryos other mechanisms are intervening. One limitation of our Western Blot is that it is not possible to isolate only early embryos, which are a minority in a mixed population of embryos. This may mask difference of phosphorylation status of PAR-2 in the early stages.

      For the revision, we plan to blot PAR-2 using GFP antibody in gfp::par-2 embryo lysates, with both control and sds-22(RNAi) treatment. We will also compare the GFP::PAR-2 bands between gfp::par-2 and gfp::par-2; sds-22(E153A) mutant samples. We are not very hopeful and our failures with gsp-1/2 RNAi (unpublished) are why we did not try with SDS-22 but it is definitely worth giving it a go and we will.

      As for Hao et al (Hao et al, 2006) the result was quite clear. In this paper however, the authors used a transgene strain of PAR-2. We have never tried to use a transgene (the proteins are usually overexpressed) but we can deplete SDS-22 in a PAR-2 transgene as well and see if a difference is observed.



      Reviewer 3

      Major comments: major issues affecting the conclusions

      Overall, the authors' conclusions are supported by their data. The data and methods are presented clearly, with appropriate replicates and statistics. Here I propose two experiments to strengthen the link between some of their data and their claims. These experiments could take a month or two to complete.

      Experiment 1

      It would be helpful if the authors could show that blocking the proteasome in the zygote restores GSP-1/-2 levels in the absence of SDS-22 or even better in the SDS-22(E153A) mutant. This would provide more direct evidence to support their claim that SDS-22 regulates polarity by protecting PP1 from proteasomal degradation. While they are currently conducting this experiment in the germline, they cannot assess polarity there. However, in the zygote, they would be able to examine the PAR-2 domain (polarity). To do this, the authors could permeabilise the embryos and apply a proteasome inhibitor.

      This would be a straightforward experiment if we were using culture cells. One problem with the set up is that much of the protein of the one-cell embryo is inherited from the egg and the reduction in SDS-22 depletion or mutant happens already in the germline (Fig 6-7). Even if the proteasome is inhibited in embryos, the whole division process only takes 20 minutes and we wonder whether the timing will be sufficient to inhibit the proteasome, produce more protein and rescue the phenotype. We will try, as only this will tell us.

      One alternative approach would be to apply the proteasome inhibitor to adult worms in liquid culture for several hours before dissection. This would aim to inhibit degradation in the germline, therefore allowing us to test whether GSP-1/-2 levels are restored in the embryos with SDS-22 disruption. However, proteasome inhibition in the germline impairs oogenesis (Shimada et al, 2006), suggesting that we might incur in the same problem (unless we succeed in timing the inhibition).

      One additional experiment that we will try is to deplete other proteasomal subunits that result in a lower level or proteasomal activity reduction. As reported by Fernando et al (Fernando et al, 2022), depletion of RPN-9, -10, or -12 impairs proteasomal activity, but worms remain fertile.

      Quantification of mNG::GSP-2 and GFP::GSP-1fluorescence intensity in rpn-12, rpn-9, and rpn-10(RNAi) normalized to ctrl(RNAi). Mean is shown and error bars indicate SD. Dots in graphs represent individual embryo measurements and sample size (n) is indicated inside the bars in the graph. N = 1.

      So far, our data suggest that the GSP-1/-2 levels are weakly but significantly increased in the embryos (16.8% for GSP-2 and 12.5% for GSP-1) following RPN-12 depletion (see above). We will co-deplete RPN-12 and SDS-22 to assess if the protein levels of GSP-1/-2 are rescued. We will also deplete RPN-12 in gfp::gsp-1; sds-22(E153A) strains to test if GSP-1 levels are rescued. We cannot measure GSP-2 levels in mNG::GSP-2; sds-22(E153A) because they are embryonic lethal (see details below in the reply to minor comments of Reviewer 3).

      Left, Representative midsection images of gfp::gsp-1 and gfp::gsp-1;sds-22(E153A) embryos in ctrl(RNAi) and rpn-12(RNAi).__ Right, __Quantification of GFP::GSP-1 intensity levels. N = 1.

      Our preliminary data showed that similar to germlines (Fig 7G-I), RPN-12 depletion in gfp::gsp-1; sds-22(E153A) rescued the reduction of GSP-1 levels in embryos (shown above). We will perform two additional experiments to quantify GSP-1 levels.

      We will also test if the smaller PAR-2 domain in sds-22(E153A) mutant is rescued by RPN-12 depletion. With these experiments, we aim to answer if proteasome inhibition rescues the reduced levels of GSP-1/-2 and thereby rescues the reduced PAR-2 domain when SDS-22 is depleted or mutated.

      Experiment 2

      The posterior localization of PAR-2 after co-RNAi of GSP-1 and SDS-22 contrasts with the absence of PAR-2 at the cortex when both GSP-1 and GSP-2 are depleted. This difference may be due to the partial reduction of GSP-2 levels when SDS-22 is depleted, compared to the more substantial reduction of GSP-2 upon GSP-2 RNAi. Have the authors considered combining full depletion of GSP-1 with partial depletion of GSP-2 to see if PAR-2 remains present and localized to the posterior? This experiment could help clarify the discrepancy between the phenotypes and further support the role of SDS-22 in regulating GSP-2 protein levels. Additionally, by titrating PP1, the authors may be able to determine the minimum amount of PP1 needed to establish the PAR-2 domain.

      We will try this experiment but, assuming we find a condition in which we can fully deplete GSP-1 and only half of GSP-2, one problem is that it is impossible to control the levels of both GSP-1 and 2 and measure the PAR-2 domain in the same embryos (which would be the most rigorous way to perform the experiment so that we know the amount of depletion and correlate with the PAR-2 domain length). The only thing we can do is the same depletion time in the 3 different strains (the mNG::gsp-2, the gfp::gsp-1 and the gfp::par-2) and assume that the depletion will work the same in the three different strains.

      • *

      Minor comments

      Reviewer 1

      Minor Points

      • The link between lethality and polarity of the zygote is not always obvious and whether they are connected (or not) could probably be made clearer. Indeed, the source of lethality is unclear, particularly given that loss of SDS-22 on its own strongly impacts lethality with minimal effects on polarity (at least in the zygote).

      In many cases, we have reported embryonic lethality as information, not with a precise scope to correlate the lethality with the phenotype. We apologize for the lack of clarity. We know that embryonic lethality is normally associated with severe polarity defects. As example, in the par-2(RAFA) mutant and in the pkc-3ts mutant at temperatures around 24-25°C cortical polarity is lost, embryos divide symmetrically and synchronously and die (Calvi et al., 2022; Rodriguez et al, 2017) and many more references for the PAR mutants (Kemphues et al, 1988; Kirby et al, 1990; Morton et al, 1992). We and others have also shown that depletion of GSP-2 can rescue the lethality of pkc-3(ts) but only at a semipermissive temperature when there is still residual PKC-3 activity (Calvi et al., 2022; Fievet et al, 2013). As our aim was to identify the regulator of GSP-2, we tested the potential regulators by RNAi in the pkc-3(ts), with the assumptions that a regulator, similar to GSP-2, would rescue the pkc-3(ts) polarity defects and lethality. As it turns out, SDS-22 is not a canonical regulator of GSP-2. The partial rescue of the polarity defects is most likely the result of the fact that SDS-22 lowers the level of GSP-2. However, SDS-22 is probably involved in many other functions that involve GSP-1 and GSP-2 (as shown for example:(Beacham et al, 2022; Peel et al., 2017)) and it is embryonic lethal. We do not know, however, whether the embryonic lethality is the results of the sum of the various functions of SDS-22 or it is due to a specific function.

      To clarify it better, we have now explained the connection between polarity and lethality in the text,

      From Result Line 111-114, page 5:

      We first asked whether depletion of any of these three regulators suppress the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (in which PKC-3 is partially active, temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated), similar to depletion of the catalytic subunit GSP-2.

      To Results Line 111-117, page 5:

      *When the temperature sensitive mutant pkc-3(ne4246) is grown at semi-permissive temperature, the residual PKC-3 activity is not sufficient to exclude PAR-2 from the anterior cortex. These embryos cannot establish polarity and die. Depletion of the catalytic subunit GSP-2 in this strain suppresses PAR-2 mislocalization and the resulting polarity defects, thereby rescuing embryonic lethality. We first asked whether depletion of any of these three identified regulators suppresses the embryonic lethality of pkc-3(ne4246); gfp::par-2 embryos at the semi-permissive temperature of 24°C (temperature used in all experiments with the pkc-3(ne4246) mutant, unless otherwise stated) , similar to depletion of GSP-2. *

      From Result Line 241-242, page 10:

      We next asked whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

      To Results Line 223-224, page 9:

      Because of this, we decided to test whether sds-22(E153A) was able to rescue the lethality and the polarity defects of pkc-3(ne4246) embryos.

      • Formally, the conclusion that reduced GSP-1/2 in SDS-22 depletion conditions is due to increased proteasomal degradation is not shown directly as there is no data on rates just steady-state levels. We agree that the genetic data is strongly suggestive of this model and it is consistent with work of other labs. Thus this is the most likely scenario, but could in principle reflect reduced expression that is balanced by reduced degradation.

      We agree with the reviewer. To address this point, we will perform RT-PCR analysis to measure the gene expression levels of gsp-1 and gsp-2 from control, SDS-22 depletion and sds-22(E153A) embryos.

      • It is interesting that sds-22(E153A) caused a stronger decrease in oocyte GSP-1 levels than sds-22(RNAi) (Fig 7). The authors may want to comment on this result.

      As we performed depletion of SDS-22 by RNAi feeding from L4 stage, we might not see strong reduction of GSP-1 in oocytes compared to that in sds-22(E153A) mutant, which carries an endogenous mutation of SDS-22 throughout the life cycle.

      Left, Representative images of gfp::gsp-1 germlines in ctrl(RNAi) and sds-22(RNAi), comparing to gfp::gsp-1; sds-22(E153A); ctrl(RNAi). __Right, __Quantification of GFP::GSP-1 intensity levels in the cytoplasm and nucleus of -1 and -2 oocytes. N = 1.

      To address this point we have performed an experiment where we have depleted SDS-22 starting from L1s. As shown above, RNAi feeding of SDS-22 from L1 stage showed a similar reduction of GSP-1 (16.1% in the cytoplasm; 24.6% in the nucleus) as in gfp::gsp-1; sds-22(E153A), which was stronger comparing to feeding from L4 (8.8% in the cytoplasm; 17.4% in the nucleus, Fig 7D-E). This supports our hypothesis that the difference shown in Fig 7D-I might result from a relative short RNAi depletion of SDS-22 from L4 stage comparing to endogenous SDS-22(E153A) mutation. This experiment was done only once and will be repeated. If confirmed, we will add a sentence in the text. As RNAi feeding of SDS-22 from L1 stage impairs the formation of germlines, we will keep the protocol using SDS-22 RNAi feeding in L4 worms for other experiments in this study.

      • "At polarity establishment, the PP1 phosphatases GSP-1/-2 dephosphorylate PAR-2 allowing its cortical posterior accumulation." This statement, possibly inadvertently, implies temporal regulation, which has not been shown.

      We have changed the sentence, as suggested by the reviewer:

      To Introduction Line 59-60, page 3:

      The PP1 phosphatases GSP-1/-2 dephosphorylate PAR 2 allowing its cortical posterior accumulation and embryo polarization.

      • It would be ideal if the authors could explicitly state how they define pronuclear meeting. For example in Figure 1B, the embryos look like they are a few minutes past pronuclear meeting (e.g. compared to Figure 3), but maybe the pronuclei tend to meet more centrally in these conditions? Given that PAR-2 clearance is changing in time in some of these cases (based on looking at the movies), staging needs to be very accurate to get the best comparisons.

      We apologize for the lack of clarity. Pronuclear meeting is defined when the two pronuclei first contact each other.

      As noted by Reviewer 1, it is true that the pronuclei in pkc-3ts mutant tend to meet more centrally compared to control embryos. The same finding was also observed on PKC-3 inhibition (through depletion, mutation or inhibitor treatment) by Rodriguez et al (Rodriguez et al., 2017). In addition, Kirby et al reported that mutations in the anterior PAR complex lead to the mislocalization of the pronuclei, causing them to meet more in the center (Kirby et al., 1990). We now specify this in the Material and Methods.

      Add in Material and Methods Line 633-635, page 22:

      *The stage of pronuclear meeting is defined when the two pronuclei first contact each other. In pkc-3(ne4246) embryos, the two pronuclei exhibited a tendency to meet more centrally compared to controls (Fig 1B, Movie EV1), as shown in (Kirby et al, 1990; Rodriguez et al, 2017). *

      As Reviewer 1 mentioned, accurate staging is crucial, as PAR-2 clearance can vary over time. The measurements were done in the first frame where pronuclei touch each other. However, in Fig. 1B we had shown one pkc-3ts; sds-22(RNAi) embryo one frame (10 seconds) later. We have now corrected this (see the updated Figure 1B).

      • In the interests of data-availability, upon publication the authors would deposit the raw mass spec data underlying Figure EV1.

      The reviewer is right, this was forgotten. We have now added as supplementary material the Dataset EV1 and EV2.

      Reviewer 3

      Minor comments: important issues that can confidently be addressed

      In the introduction (line 83), it's unclear what reconciles the contradictory data. I also have difficulty understanding this point in the discussion (line 435).

      We apologize for the lack of clarity and have now modified the text:

      From Introduction Line 82-84, page 4:

      This underscores the complex roles of SDS22 in regulating PP1 function and reconciling the contradictory data obtained in vivo and in vitro (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

      To Introduction Line 81-85, page 4:

      These two recent findings suggest that while SDS-22 is required for the biogenesis of PP1 holoenzymes, its removal is essential to have an active PP1. This dual role of SDS-22 explains how SDS22 behaves as an inhibitor in biochemical assays in vitro but as an activator in vivo (Cao et al., 2024; Cao et al, 2022; Kueck et al., 2024; Lesage et al, 2007).

      From Discussion Line 435-436, page 17:

      These data reconcile the contradictory in vivo and in vitro observations.

      To Discussion Line 447-451, page 17:

      Given that SDS-22 both stabilizes PP1 levels and inhibits its activity, this dual role clarifies the apparent contradiction: while SDS-22 is essential for PP1 activity in vivo (because it is essential for the biogenesis/stability), it inhibits PP1 activity in vitro (as it needs to be removed to have an active PP1), while in vivo it is removed by p97/Valosin resulting in active PP1.

      Additionally, in the results section (line 389), it's not clear why the gonads cannot be studied in the strain with dead embryos. Are the gonads also altered in a way that prevents their observation?

      We explained this in the material and methods part (Line 583-584, 588-592), page 21.

      To clarify it better in the main text, we have now modified

      Results Line 377-378, page 15:

      Since depletion of these subunits results in worms with very little to no progeny (Fernando et al., 2022)

      Results Line 396-401, page 15:

      *Since we use the embryonic lethality phenotype of the mNG::gsp-2; sds-22(E153A) strain to recognize the homozygote sds-22(E153A), this precluded the possibility to analyze the germlines of homozygote mNG::gsp-2; sds-22(E153A) worms depleted of RNP-6.1 or RPN-7, as these worms do not have progenies (Fernando et al., 2022) and we therefore cannot distinguish the sds-22(E153A) homozygote from the sds-22(E153A) heterozygote (see material and methods for details). *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • *

      We have re-quantified the data in Fig 1B and displayed as in Fig 1C.

      We have double checked our data and corrected Fig 3G.

      We have modified the text to address many of the comments of the reviewer about clarity and rigor.

      We have added supplementary information Fig EV2C and Dataset EV1 and EV2.

      Other experiments performed are still preliminary and only shown in this revision letter.

      4. Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *

      We believe with the reply, the text changes and the experiments that we have proposed and started, we will address all comments of the reiewers.

      • *

      References

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      Calvi I, Schwager F, Gotta M (2022) PP1 phosphatases control PAR-2 localization and polarity establishment in C. elegans embryos. J Cell Biol 221

      Chartier NT, Salazar Ospina DP, Benkemoun L, Mayer M, Grill SW, Maddox AS, Labbe JC (2011) PAR-4/LKB1 mobilizes nonmuscle myosin through anillin to regulate C. elegans embryonic polarization and cytokinesis. Curr Biol 21: 259-269

      Fernando LM, Quesada-Candela C, Murray M, Ugoaru C, Yanowitz JL, Allen AK (2022) Proteasomal subunit depletions differentially affect germline integrity in C. elegans. Front Cell Dev Biol 10: 901320

      Fievet BT, Rodriguez J, Naganathan S, Lee C, Zeiser E, Ishidate T, Shirayama M, Grill S, Ahringer J (2013) Systematic genetic interaction screens uncover cell polarity regulators and functional redundancy. Nat Cell Biol 15: 103-112

      Hao Y, Boyd L, Seydoux G (2006) Stabilization of cell polarity by the C. elegans RING protein PAR-2. Dev Cell 10: 199-208

      Hubatsch L, Peglion F, Reich JD, Rodrigues NT, Hirani N, Illukkumbura R, Goehring NW (2019) A cell size threshold limits cell polarity and asymmetric division potential. Nat Phys 15: 1075-1085

      Kemphues KJ, Priess JR, Morton DG, Cheng NS (1988) Identification of genes required for cytoplasmic localization in early C. elegans embryos. Cell 52: 311-320

      Kirby C, Kusch M, Kemphues K (1990) Mutations in the par genes of Caenorhabditis elegans affect cytoplasmic reorganization during the first cell cycle. Dev Biol 142: 203-215

      Klinkert K, Levernier N, Gross P, Gentili C, von Tobel L, Pierron M, Busso C, Herrman S, Grill SW, Kruse K et al (2018) Aurora A depletion reveals centrosome-independent polarization mechanism in C.elegans. bioRxiv: 388918

      Morton DG, Roos JM, Kemphues KJ (1992) par-4, a gene required for cytoplasmic localization and determination of specific cell types in Caenorhabditis elegans embryogenesis. Genetics 130: 771-790

      Park SH, Cheong C, Idoyaga J, Kim JY, Choi JH, Do Y, Lee H, Jo JH, Oh YS, Im W et al (2008) Generation and application of new rat monoclonal antibodies against synthetic FLAG and OLLAS tags for improved immunodetection. J Immunol Methods 331: 27-38

      Peel N, Iyer J, Naik A, Dougherty MP, Decker M, O'Connell KF (2017) Protein Phosphatase 1 Down Regulates ZYG-1 Levels to Limit Centriole Duplication. PLoS Genet 13: e1006543

      Rodriguez J, Peglion F, Martin J, Hubatsch L, Reich J, Hirani N, Gubieda AG, Roffey J, Fernandes AR, St Johnston D et al (2017) aPKC Cycles between Functionally Distinct PAR Protein Assemblies to Drive Cell Polarity. Dev Cell 42: 400-415 e409

      Shimada M, Kanematsu K, Tanaka K, Yokosawa H, Kawahara H (2006) Proteasomal ubiquitin receptor RPN-10 controls sex determination in Caenorhabditis elegans. Mol Biol Cell 17: 5356-5371

      Tzur YB, Egydio de Carvalho C, Nadarajan S, Van Bostelen I, Gu Y, Chu DS, Cheeseman IM, Colaiacovo MP (2012) LAB-1 targets PP1 and restricts Aurora B kinase upon entrance into meiosis to promote sister chromatid cohesion. PLoS Biol 10: e1001378

    1. Author response:

      The following is the authors’ response to the original reviews

      We thank the reviewers for their thoughtful feedback. We have made substantial revisions to the manuscript to address each of their comments, as we detail below. We want to highlight one major change in particular that addresses a concern raised by both reviewers: the role of the drift rate in our models. Motivated by their astute comments, we went back through our models and realized that we had made a particular assumption that deserved more scrutiny. We previously assumed that the process of encoding the observations made correct use of the objective, generative correlation, but then the process of calculating the weight of evidence used a mis-scaled, subjective version of the correlation. These assumptions led us to scale the drift rate in the model by a term that quantified how the standard deviation of the observation distribution was affected by the objective correlation (encoding), but to scale the bound height by the subjective estimate of the correlation (evidence weighing). However, we realized that encoding may also depend on the subjective correlation experienced by the participant. We have now tested several alternative models and found that the best-fitting model assumes that a single, subjective estimate of the correlation governs both encoding and evidence weighing. An important consequence of updating our models in this way is that we can now account for the behavioral data without needing the additional correlation-dependent drift terms (which, as reviewer #2 pointed out, were difficult to explain).

      We also note that we changed the title slightly, replacing “weighting” with “weighing” for consistency with our usage throughout the manuscript.

      Please see below for more details about this important point and our responses to the reviewers’ specific concerns. 

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The behavioral strategies underlying decisions based on perceptual evidence are often studied in the lab with stimuli whose elements provide independent pieces of decision-related evidence that can thus be equally weighted to form a decision. In more natural scenarios, in contrast, the information provided by these pieces is often correlated, which impacts how they should be weighted. Tardiff, Kang & Gold set out to study decisions based on correlated evidence and compare the observed behavior of human decision-makers to normative decision strategies. To do so, they presented participants with visual sequences of pairs of localized cues whose location was either uncorrelated, or positively or negatively correlated, and whose mean location across a sequence determined the correct choice. Importantly, they adjusted this mean location such that, when correctly weighted, each pair of cues was equally informative, irrespective of how correlated it was. Thus, if participants follow the normative decision strategy, their choices and reaction times should not be impacted by these correlations. While Tardiff and colleagues found no impact of correlations on choices, they did find them to impact reaction times, suggesting that participants deviated from the normative decision strategy. To assess the degree of this deviation, Tardiff et al. adjusted drift-diffusion models (DDMs) for decision-making to process correlated decision evidence. Fitting these models to the behavior of individual participants revealed that participants considered correlations when weighing evidence, but did so with a slight underestimation of the magnitude of this correlation. This finding made Tardiff et al. conclude that participants followed a close-to-normative decision strategy that adequately took into account correlated evidence.

      Strengths:

      The authors adjust a previously used experimental design to include correlated evidence in a simple, yet powerful way. The way it does so is easy to understand and intuitive, such that participants don't need extensive training to perform the task. Limited training makes it more likely that the observed behavior is natural and reflective of everyday decision-making. Furthermore, the design allowed the authors to make the amount of decision-related evidence equal across different correlation magnitudes, which makes it easy to assess whether participants correctly take account of these correlations when weighing evidence: if they do, their behavior should not be impacted by the correlation magnitude.

      The relative simplicity with which correlated evidence is introduced also allowed the authors to fall back to the well-established DDM for perceptual decisions, which has few parameters, is known to implement the normative decision strategy in certain circumstances, and enjoys a great deal of empirical support. The authors show how correlations ought to impact these parameters, and which changes in parameters one would expect to see if participants misestimate these correlations or ignore them altogether (i.e., estimate correlations to be zero). This allowed them to assess the degree to which participants took into account correlations on the full continuum from perfect evidence weighting to complete ignorance. With this, they could show that participants in fact performed rational evidence weighting if one assumed that they slightly underestimated the correlation magnitude.

      Weaknesses:

      The experiment varies the correlation magnitude across trials such that participants need to estimate this magnitude within individual trials. This has several consequences:

      (1) Given that correlation magnitudes are estimated from limited data, the (subjective) estimates might be biased towards their average. This implies that, while the amount of evidence provided by each 'sample' is objectively independent of the correlation magnitude, it might subjectively depend on the correlation magnitude. As a result, the normative strategy might differ across correlation magnitudes, unlike what is suggested in the paper. In fact, it might be the case that the observed correlation magnitude underestimates corresponds to the normative strategy.

      We thank the reviewer for raising this interesting point, which we now address directly with new analyses including model fits (pp. 15–24). These analyses show that the participants were computing correlation-dependent weights of evidence from observation distributions that reflected suboptimal misestimates of correlation magnitudes. This strategy is normative in the sense that it is the best that they can do, given the encoding suboptimality. However, as we note in the manuscript, we do not know the source of the encoding suboptimality (pp. 23–24). We thus do not know if there might be a strategy they could have used to make the encoding more optimal.

      (2) The authors link the normative decision strategy to putting a bound on the log-likelihood ratio (logLR), as implemented by the two decision boundaries in DDMs. However, as the authors also highlight in their discussion, the 'particle location' in DDMs ceases to correspond to the logLR as soon as the strength of evidence varies across trials and isn't known by the decision maker before the start of each trial. In fact, in the used experiment, the strength of evidence is modulated in two ways:

      (i) by the (uncorrected) distance of the cue location mean from the decision boundary (what the authors call the evidence strength) and

      (ii) by the correlation magnitude. Both vary pseudo-randomly across trials, and are unknown to the decision-maker at the start of each trial. As previous work has shown (e.g. Kiani & Shadlen (2009), Drugowitsch et al. (2012)), the normative strategy then requires averaging over different evidence strength magnitudes while forming one's belief. This averaging causes the 'particle location' to deviate from the logLR. This deviation makes it unclear if the DDM used in the paper indeed implements the normative strategy, or is even a good approximation to it.

      We appreciate this subtle, but important, point. We now clarify that the DDM we use includes degrees of freedom that are consistent with normative decision processes that rely on the imperfect knowledge that participants have about the generative process on each trial, specifically: 1) a single drift-rate parameter that is fit to data across different values of the mean of the generative distribution, which is based on the standard assumption for these kinds of task conditions in which stimulus strength is varied randomly from trial-to-trial and thus prevents the use of exact logLR (which would require stimulus strength-specific scale factors; Gold and Shadlen, 2001); 2) the use of a collapsing bound, which in certain cases (including our task) is thought to support a stimulus strength-dependent calibration of the decision variable to optimize decisions (Drugowitsch et al, 2012); and 3) free parameters (one per correlation) to account for subjective estimates of the correlation, which affected the encoding of the observations that are otherwise weighed in a normative manner in the best-fitting model.

      Also, to clarify our terminology, we define the objective evidence strength as the expected logLR in a given condition, which for our task is dependent on both the distance of the mean from the decision boundary and the correlation (p. 7). 

      Given that participants observe 5 evidence samples per second and on average require multiple seconds to form their decisions, it might be that they are able to form a fairly precise estimate of the correlation magnitude within individual trials. However, whether this is indeed the case is not clear from the paper.

      These points are now addressed directly in Results (pp. 23–24) and Figure 7 supplemental figures 1–3. Specifically, we show that, as the reviewer correctly surmised above, empirical correlations computed on each trial tended to be biased towards zero (Fig 7–figure supplement 1). However, two other analyses were not consistent with the idea that participants’ decisions were based on trial-by-trial estimates of the empirical correlations: 1) those with the shortest RTs did not have the most-biased estimates (Fig 7–figure supplement 2), and 2) there was no systematic relationship between objective and subjective fit correlations across participants (Fig 7–figure supplement 3).

      Furthermore, the authors capture any underestimation of the correlation magnitude by an adjustment to the DDM bound parameter. They justify this adjustment by asking how this bound parameter needs to be set to achieve correlation-independent psychometric curves (as observed in their experiments) even if participants use a 'wrong' correlation magnitude to process the provided evidence. Curiously, however, the drift rate, which is the second critical DDM parameter, is not adjusted in the same way. If participants use the 'wrong' correlation magnitude, then wouldn't this lead to a mis-weighting of the evidence that would also impact the drift rate? The current model does not account for this, such that the provided estimates of the mis-estimated correlation magnitudes might be biased.

      We appreciate this valuable comment, and we agree that we previously neglected the potential impact of correlation misestimates on evidence strength. As we now clarify, the correlation enters these models in two ways: 1) via its effect on how the observations are encoded, which involves scaling both the drift and the bound; and 2) via its effect on evidence weighing, which involves scaling only the bound (pp. 15–18). We previously assumed that only the second form of scaling might involve a subjective (mis-)estimate of the correlation. We now examine several models that also include the possibility of either or both forms using subjective correlation estimates. We show that a model that assumes that the same subjective estimate drives both encoding and weighing (the “full-rho-hat” model) best accounts for the data. This model provides better fits (after accounting for differences in numbers of parameters) than models with: 1) no correlation-dependent adjustments (“base” model), 2) separate drift parameters for each correlation condition (“drift” model), 3) optimal (correlation-dependent) encoding but suboptimal weighing (“bound-rho-hat” model, which was our previous formulation), 4) suboptimal encoding and weighing (“scaled-rho-hat” model), and 5) optimal encoding but suboptimal weighing and separate correlation-dependent adjustments to the drift rate (“boundrho-hat plus drift” model). We have substantially revised Figures 5–7 and the associated text to address these points.

      Lastly, the paper makes it hard to assess how much better the participants' choices would be if they used the correct correlation magnitudes rather than underestimates thereof. This is important to know, as it only makes sense to strictly follow the normative strategy if it comes with a significant performance gain.

      We now include new analyses in Fig. 7 that demonstrate how much participants' choices and RT deviate from: 1) an ideal observer using the objective correlations, and 2) an observer who failed to adjust for the fit subjective correlation when weighing the evidence (i.e., using the subjective correlation for encoding but a correlation of zero for weighing). We now indicate that participants’ performance was quite close to that predicted by the ideal observer (using the true, objective correlation) for many conditions. Thus, we agree that they might not have had the impetus to optimize the decision process further, assuming it were possible under these task conditions.

      Reviewer #2 (Public review):

      Summary:

      This study by Tardiff, Kang & Gold seeks to: i) develop a normative account of how observers should adapt their decision-making across environments with different levels of correlation between successive pairs of observations, and ii) assess whether human decisions in such environments are consistent with this normative model.

      The authors first demonstrate that, in the range of environments under consideration here, an observer with full knowledge of the generative statistics should take both the magnitude and sign of the underlying correlation into account when assigning weight in their decisions to new observations: stronger negative correlations should translate into stronger weighting (due to the greater information furnished by an anticorrelated generative source), while stronger positive correlations should translate into weaker weighting (due to the greater redundancy of information provided by a positively correlated generative source). The authors then report an empirical study in which human participants performed a perceptual decision-making task requiring accumulation of information provided by pairs of perceptual samples, under different levels of pairwise correlation. They describe a nuanced pattern of results with effects of correlation being largely restricted to response times and not choice accuracy, which could partly be captured through fits of their normative model (in this implementation, an extension of the well-known drift-diffusion model) to the participants' behaviour while allowing for misestimation of the underlying correlations.

      Strengths:

      As the authors point out in their very well-written paper, appropriate weighting of information gathered in correlated environments has important consequences for real-world decisionmaking. Yet, while this function has been well studied for 'high-level' (e.g. economic) decisions, how we account for correlations when making simple perceptual decisions on well-controlled behavioural tasks has not been investigated. As such, this study addresses an important and timely question that will be of broad interest to psychologists and neuroscientists. The computational approach to arrive at normative principles for evidence weighting across environments with different levels of correlation is very elegant, makes strong connections with prior work in different decision-making contexts, and should serve as a valuable reference point for future studies in this domain. The empirical study is well designed and executed, and the modelling approach applied to these data showcases a deep understanding of relationships between different parameters of the drift-diffusion model and its application to this setting. Another strength of the study is that it is preregistered.

      Weaknesses:

      In my view, the major weaknesses of the study center on the narrow focus and subsequent interpretation of the modelling applied to the empirical data. I elaborate on each below:

      Modelling interpretation: the authors' preference for fitting and interpreting the observed behavioural effects primarily in terms of raising or lowering the decision bound is not well motivated and will potentially be confusing for readers, for several reasons. First, the entire study is conceived, in the Introduction and first part of the Results at least, as an investigation of appropriate adjustments of evidence weighting in the face of varying correlations. The authors do describe how changes in the scaling of the evidence in the drift-diffusion model are mathematically equivalent to changes in the decision bound - but this comes amidst a lengthy treatment of the interaction between different parameters of the model and aspects of the current task which I must admit to finding challenging to follow, and the motivation behind shifting the focus to bound adjustments remained quite opaque. 

      We appreciate this valuable feedback. We have revised the text in several places to make these important points more clearly. For example, in the Introduction we now clarify that “The weight of evidence is computed as a scaled version of each observation (the scaling can be applied to the observations or to the bound, which are mathematically equivalent; Green and Swets, 1966) to form the logLR” (p. 3). We also provide more details and intuition in the Results section for how and why we implemented the DDM the way we did. In particular, we now emphasize that the correlation enters these models in two ways: 1) via its effect on encoding the observations, which scales both the drift and the bound; and 2) via its effect on evidence weighing, which scales only the bound (pp. 15–18).

      Second, and more seriously, bound adjustments of the form modelled here do not seem to be a viable candidate for producing behavioural effects of varying correlations on this task. As the authors state toward the end of the Introduction, the decision bound is typically conceived of as being "predefined" - that is, set before a trial begins, at a level that should strike an appropriate balance between producing fast and accurate decisions. There is an abundance of evidence now that bounds can change over the course of a trial - but typically these changes are considered to be consistently applied in response to learned, predictable constraints imposed by a particular task (e.g. response deadlines, varying evidence strengths). In the present case, however, the critical consideration is that the correlation conditions were randomly interleaved across trials and were not signaled to participants in advance of each trial - and as such, what correlation the participant would encounter on an upcoming trial could not be predicted. It is unclear, then, how participants are meant to have implemented the bound adjustments prescribed by the model fits. At best, participants needed to form estimates of the correlation strength/direction (only possible by observing several pairs of samples in sequence) as each trial unfolded, and they might have dynamically adjusted their bounds (e.g. collapsing at a different rate across correlation conditions) in the process. But this is very different from the modelling approach that was taken. In general, then, I view the emphasis on bound adjustment as the candidate mechanism for producing the observed behavioural effects to be unjustified (see also next point).

      We again appreciate this valuable feedback and have made a number of revisions to try to clarify these points. In addition to addressing the equivalence of scaling the evidence and the bound in the Introduction, we have added the following section to Results (Results, p.18):

      “Note that scaling the bound in these formulations follows conventions of the DDM, as detailed above, to facilitate interpretation of the parameters. These formulations also raise an apparent contradiction: the “predefined” bound is scaled by subjective estimates of the correlation, but the correlation was randomized from trial to trial and thus could not be known in advance. However, scaling the bound in these ways is mathematically equivalent to using a fixed bound on each trial and scaling the observations to approximate logLR (see Methods). This equivalence implies that in the brain, effectively scaling a “predefined” bound could occur when assigning a weight of evidence to the observations as they are presented.”

      We also note in Methods (pp. 40–41):

      “In the DDM, this scaling of the evidence is equivalent to assuming that the decision variable accumulates momentary evidence of the form (x1 + x2) and then dividing the bound height by the appropriate scale factor. An alternative approach would be to scale both the signal and noise components of the DDM by the scale factor. However, scaling the bound is both simpler and maintains the conventional interpretation of the DDM parameters in which the bound reflects the decision-related components of the evidence accumulation process, and the drift rate represents sensory-related components.”

      We believe we provide strong evidence that participants adjust their evidence weighing to account for the correlations (see response below), but we remain agnostic as to how exactly this weighing is implemented in the brain.

      Modelling focus: Related to the previous point, it is stated that participants' choice and RT patterns across correlation conditions were qualitatively consistent with bound adjustments (p.20), but evidence for this claim is limited. Bound adjustments imply effects on both accuracy and RTs, but the data here show either only effects on RTs, or RT effects mixed with accuracy trends that are in the opposite direction to what would be expected from bound adjustment (i.e. slower RT with a trend toward diminished accuracy in the strong negative correlation condition; Figure 3b). Allowing both drift rate and bound to vary with correlation conditions allowed the model to provide a better account of the data in the strong correlation conditions - but from what I can tell this is not consistent with the authors' preregistered hypotheses, and they rely on a posthoc explanation that is necessarily speculative and cannot presently be tested (that the diminished drift rates for higher negative correlations are due to imperfect mapping between subjective evidence strength and the experimenter-controlled adjustment to objective evidence strengths to account for effects of correlations). In my opinion, there are other candidate explanations for the observed effects that could be tested but lie outside of the relatively narrow focus of the current modelling efforts. Both explanations arise from aspects of the task, which are not mutually exclusive. The first is that an interesting aspect of this task, which contrasts with most common 'univariate' perceptual decision-making tasks, is that participants need to integrate two pieces of information at a time, which may or may not require an additional computational step (e.g. averaging of two spatial locations before adding a single quantum of evidence to the building decision variable). There is abundant evidence that such intermediate computations on the evidence can give rise to certain forms of bias in the way that evidence is accumulated (e.g. 'selective integration' as outlined in Usher et al., 2019, Current Directions in Psychological Science; Luyckx et al., 2020, Cerebral Cortex) which may affect RTs and/or accuracy on the current task. The second candidate explanation is that participants in the current study were only given 200 ms to process and accumulate each pair of evidence samples, which may create a processing bottleneck causing certain pairs or individual samples to be missed (and which, assuming fixed decision bounds, would presumably selectively affect RT and not accuracy). If I were to speculate, I would say that both factors could be exacerbated in the negative correlation conditions, where pairs of samples will on average be more 'conflicting' (i.e. further apart) and, speculatively, more challenging to process in the limited time available here to participants. Such possibilities could be tested through, for example, an interrogation paradigm version of the current task which would allow the impact of individual pairs of evidence samples to be more straightforwardly assessed; and by assessing the impact of varying inter-sample intervals on the behavioural effects reported presently.

      We thank the reviewer for this thoughtful and valuable feedback. We have thoroughly updated the modeling section to include new analysis and clearer descriptions and interpretations of our findings (including Figs. 5–7 and additional references to the Usher, Luyckx, and other studies that identified decision suboptimalities). The comment about “an additional computational step” in converting the observations to evidence was particularly useful, in that it made us realize that we were making what we now consider to be a faulty assumption in our version of the DDM. Specifically, we assumed that subjective misestimates of the correlation affected how observations were converted to evidence (logLR) to form the decision (implemented as a scaling of the bound height), but we neglected to consider how suboptimalities in encoding the observations could also lead to misestimates of the correlation. We have retained the previous best-fitting models in the text, for comparison (the “bound-rho-hat” and “bound-rho-hat + drift” models). In addition, we now include a “full-rho-hat” model that assumes that misestimates of rho affect both the encoding of the observations, which affects the drift rate and bound height, and the weighing of the evidence, which affects only the bound height. This was the best-fitting model for most participants (after accounting for different numbers of parameters associated with the different models we tested). Note that the full-rho-hat model predicts the lack of correlation-dependent choice effects and the substantial correlation-dependent RT effects that we observed, without requiring any additional adjustments to the drift rate (as we resorted to previously).

      In summary, we believe that we now have a much more parsimonious account of our data, in terms of a model in which subjective estimates of the correlation are alone able to account for our patterns of choice and RT data. We fully agree that more work is needed to better understand the source of these misestimates but also think those questions are outside the scope of the present study.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      A few minor comments:

      (1) Evidence can be correlated in multiple ways. It could be correlated within individual pieces of evidence in a sequence, or across elements in that sequence (e.g., across time). This distinction is important, as it determines how evidence ought to be accumulated across time. In particular, if evidence is correlated across time, simply summing it up might be the wrong thing to do. Thus, it would be beneficial to make this distinction in the Introduction, and to mention that this paper is only concerned with the first type of correlation.

      We now clarify this point in the Introduction (p. 5–6).

      (2) It is unclear without reading the Methods how the blue dashed line in Figure 4c is generated. To my understanding, it is a prediction of the naive DDM model. Is this correct?

      We now specify the models used to make the predictions shown in Fig. 4c (which now includes an additional model that uses unscaled observations as evidence).

      (3) In Methods, given the importance of the distribution of x1 + x2, it would be useful to write it out explicitly, e.g., x1 + x2 ~ N(2 mu_g, ..), specifying its mean and its variance.

      Excellent suggestion, added to p. 38.

      (4) From Methods and the caption of Figure 6 - Supplement 1 it becomes clear that the fitted DDM features a bound that collapses over time. I think that this should also be mentioned in the main text, as it is a not-too-unimportant feature of the model.

      Excellent suggestion, added to p. 15, with reference to Fig. 6-supplement 1 on p. 20.

      (5) The functional form of the bound is 2 (B - tb t). To my understanding, the effective B changes as a function of the correlation magnitude. Does tb as well? If not, wouldn't it be better if it does, to ensure that 2 (B - tb t) = 0 independent of the correlation magnitude?

      In our initial modeling, we also considered whether the correlation-dependent adjustment, which is a function of both correlation sign and magnitude, should be applied to the initial bound or to the instantaneous bound (i.e., after collapse, affecting tb as well). In a pilot analysis of data from 22 participants in the 0.6 correlation-magnitude group, we found that this choice had a negligible effect on the goodness-of-fit (deltaAIC = -0.9, protected exceedance probability = 0.63, in favor of the instantaneous bound scaling). We therefore used the instantaneous bound version in the analyses reported in the manuscript but doubt this choice was critical based on these results. We have clarified our implementation of the bound in Methods (p. 43–44).

      Reviewer #2 (Recommendations for the authors):

      In addition to the points raised above, I have some minor suggestions/open questions that arose from my reading of the manuscript:

      (1) Are the predictions outlined in the paper specific to cases where the two sources are symmetric around zero? If distributions are allowed to be asymmetric then one can imagine cases (i.e. when distribution means are sufficiently offset from one another) where positive correlations can increase evidence strength and negative correlations decrease evidence strength. There's absolutely still value and much elegance in what the authors are showing with this work, but if my intuition is correct, it should ideally be acknowledged that the predictions are restricted to a specific set of generative circumstances.

      We agree that there are a lot of ways to manipulate correlations and their effect on the weight of evidence. At the end of the Discussion, we emphasize that our results apply to this particular form of correlation (p. 32).

      (2) Isn't Figure 4C misleading in the sense that it collapses across the asymmetry in the effect of negative vs positive correlations on RT, which is clearly there in the data and which simply adjusting the correlation-dependent scale factor will not reproduce?

      We agree that this analysis does not address any asymmetries in suboptimal estimates of positive versus negative correlations. We believe that those effects are much better addressed using the model fitting, which we present later in the Results section. We have now simplified the analyses in Fig. 4c, reporting the difference in RT between positive and negative correlation conditions instead of a linear regression.

      (3) I found the transition on p.17 of the Results section from the scaling of drift rate by correlation to scaling of bound height to be quite abrupt and unclear. I suspect that many readers coming from a typical DDM modelling background will be operating under the assumption that drift rate and bound height are independent, and I think more could be done here to explain why scaling one parameter by correlation in the present case is in fact directly equivalent to scaling the other.

      Thank you for the very useful feedback, we have substantially revised this text to make these points more clearly.

      (4) P.3, typo: Alan *Turing*

      That’s embarrassing. Fixed.

      (5) P.27, typo: "participants adopt a *fixed* bound"

      Fixed.

    1. Reviewer #2 (Public review):

      Summary:

      The authors aimed to elucidate the role of Ctla-4 in maintaining intestinal immune homeostasis by using a novel Ctla-4-deficient zebrafish model. This study addresses the challenge of linking CTLA-4 to inflammatory bowel disease (IBD) due to the early lethality of CTLA-4 knockout mice. Four lines of evidence were shown to show that Ctla-4-deficient zebrafish exhibited hallmarks of IBD in mammals: 1) impaired epithelial integrity and infiltration of inflammatory cells; 2) enrichment of inflammation-related pathways and the imbalance between pro- and anti-inflammatory cytokines; 3) abnormal composition of immune cell populations; and 4) reduced diversity and altered microbiota composition. By employing various molecular and cellular analyses, the authors established ctla-4-deficient zebrafish as a convincing model of human IBD.

      Strengths:

      The characterization of the mutant phenotype is very thorough, from anatomical to histological and molecular levels. The finding effectively established ctla-4 mutants as a novel zebrafish model for investigating human IBD. Evidence from the histopathological and transcriptome analysis was very strong and supports a severe interruption of immune system homeostasis in the zebrafish intestine. Additional characterization using sCtla-4-Ig further probed the molecular mechanism of the inflammatory response, and provided a potential treatment plan for targeting Ctla-4 in IBD models.

      Weaknesses:

      To probe the molecular mechanism of Ctla-4, the authors used a spectrum of antibodies that target Ctla-4 or its receptors. The phenotype assayed was lymphocyte proliferation, while it was the composition rather than number of immune cells that was observed to be different in the scRNASeq assay. Although sCtla-4 has an effect of alleviating the IBD-like phenotypes, I found this explanation a bit oversimplified.

      Comments on revised version:

      The authors have sufficiently addressed all my concerns and I don't have further suggestions.

    2. Reviewer #3 (Public review):

      Summary:

      Current study on the mutant zebrafish for IBD modeling is worth trying. The author provided lots of evidence, including histopathological observation, gut microflora, as well as intestinal tissue or mucosa cells' transcriptomic data. The multi-omic study has demonstrated the enteritis pathology at multi levels in zebrafish model.

      Strengths:

      The important immune checkpoint of Treg cells were knockout in zebrafish, and the enteritis were found then. It could be a substitution of mouse knockout model to investigate the molecular mechanism of gut disease.

      Weaknesses:

      (1) In Fig. 2I, as to the purple glycogen signals stained by PAS was ignored for the quantitative statistics. The purple stained area could be calculated by ImageJ.<br /> (2) Those characters in Fig. 3G are too small to recognize. It is suggested to adjusted this picture or just put it in the supplementation, with bigger size.<br /> (3) The tissue seems damaged for IgG ctrl in Fig. 8B. It is suggested to find another slice to present here.<br /> (4) Line 667 & 743: "16S rRNA sequencing" should be "16S rRNA gene sequencing". Please check this point throughout the text.

    1. Reviewer #1 (Public review):

      This work presents data from three species (mice, rats, and humans) performing an evidence accumulation task, that has been designed to be as similar as possible between species (and is based on a solid foundation of previous work on decision-making). The tasks are well-designed, and the analyses are solid and clearly presented - showing that there are differences in the overall parameters of the decision-making process between the species. This is valuable to neuroscientists who aim to translate behavioral and neuroscientific findings from rodents to humans and offers a word of caution for the field in readily claiming that behavioral strategies and computations are representative of all mammals. The dataset would be of great interest to the community and may be a source of further modelling of across-species behavior, but unfortunately, neither data or code are currently shared.

      A few other questions remain, that make the conclusions of the paper a bit hard to assess:

      (1) The main weakness is that the authors claim that all species rely on evidence accumulation as a strategy, but this is not tested against other models (see e.g. Stine et al. https://elifesciences.org/articles/55365): the fact that the DDM fits rather well does not mean that this is the strategy that each species was carrying out.

      (2) In all main analyses, it is unclear what the effect is of the generative flash rate and how this has been calibrated between species. Only in Figure 6C do we see basic psychometric functions, but these should presumably also feature as a crucial variable dominating the accuracy and RTs (chronometric functions) across species. The very easy trials are useful to constrain the basic sensorimotor differences that may account for RT variability, e.g. perhaps the small body of mice requires them to move a relatively longer distance to trigger the response.

      (3) The GLM-HMM results (that mice are not engaged in all trials) are very important, but they imply that mouse DDM fits may well be more similar to rats and humans if done only on engaged trials. Could it be that the main species differences are driven by different engagement state occupations?

      (4) It would be very helpful if the authors could present a comprehensive overview (perhaps a table) of the factors that may be relevant for explaining the observed species differences. This may include contextual/experimental variables (age range (adolescent humans vs. mice/rats, see https://www.jax.org/news-and-insights/jax-blog/2017/november/when-are-mice-considered-old; reward source, etc) and also outcomes (e.g. training time required to learn the task, # trials per session and in total).

    2. Reviewer #3 (Public review):

      Summary:

      This study directly compares decision-making strategies between three species, humans, rats, and mice. Based on a new and common behavioral task that is largely shared across species, specific features of evidence accumulation could be quantified and compared between species. The authors argue their work provides a framework to study decision-making across species, which can be studied by the same decision models. The authors report specific features of decision-making strategies, such as humans having a larger decision threshold leading to more accurate responses, and rodents deciding under time pressure.

      Strengths:

      The behavioral task is set up in similar, comparable ways across species, allowing for employing the same decision models and directly comparing specific features of decision behavior. This approach is compelling since it is otherwise challenging to compare behavior between species. Data analysis is solid and does not only quantify features of classic drift-diffusion models, but also additional commonly applied behavior models or features such as win-stay/lose-shift strategies, reward-maximization behavior, and slow, latent changes in behavior strategies. This approach reveals some interesting species differences, which are a starting point to investigate species-specific decision strategies more deeply and could inform a broad set of past and future behavior studies commonly used in cognitive and neuroscience.

      Weaknesses:

      (1) The choice of the stimulus difficulty is unclear, as choosing a single, specific evidence strength (80:20) could limit model fitting performance and interpretation of psychometric curves. This could also limit conclusions about species differences since the perceptual sensitivity seems quite different between species. Thus, the 80:20 lies at different uncertainty levels for the different species, which are known to influence behavioral strategies. This might be addressed by exploiting the distribution of actually delivered flashes, but it remained unclear to me to what degree this is the case. Previous perceptual discrimination studies typically sample multiple evidence levels to differentiate the source of variability in choice behavior.

      (2) The authors argue that their task is novel and that their task provides a framework to investigate perceptual decision-making. However, very similar, and potentially more powerful, perceptual decision-making tasks (e.g., using several evidence strength levels) have been used in humans, non-human primates, rats, mice, and other species. In some instances, analogous behavioral tasks, including studies using the same sensory stimulus, have been used across multiple species. While these may have been published in different papers, they have been conducted in some instances by the same lab and using the same analyses. Further, much of this work is not referenced here. This limits the impact of this work.

      (3) The employed drift-diffusion model has many parameters, which are not discussed in detail. Results in Supplementary Figures 3-5 are not explained or discussed, including the interpretation that model recovery tests fail to recover some of the parameters (eg, Figures S3E, G). This makes the interpretation of such models more difficult.

      (4) The results regarding potential reward-maximization strategies are compelling and connect perceptual and normative decision models. The results are however limited by the different inter-trial intervals and trial initiation times between species, which are shown in Figure S6. It's unclear to me how to interpret, for example, how the long trial initiation times in rats relate to a putative reward-maximizing strategy. This compares to the very low trial initiation times (ie, very 'efficient') of humans, even though they are 'too accurate' in terms of their sampling time. Reward-maximizing strategies seem difficult with such different trial times and in the absence of experimental manipulation.

    1. Reviewer #3 (Public review):

      The manuscript is focused on local bulbar mechanisms to solve the flexibility-stability dilemma in contrast to long-range interactions documented in other systems (hippocampus-cortex). The network performance is assessed in a perceptual learning task: the network is presented with alternating, similar artificial stimuli (defined as enrichment) and the authors assess its ability to discriminate between these stimuli by comparing the mitral cell representations quantified by Fisher discriminant analysis. The authors use enhancement in discriminability between stimuli as a function of the degree of specificity of connectivity in the network to quantify the formation of an odor-specific network structure which as such has memory - they quantify memory as the specificity of that connectivity.

      The focus on neurogenesis, excitability, and synaptic connectivity of abGCs is topical, and the authors systematically built their model, clearly stating their assumptions and setting up the questions and answers. In my opinion, the combination of latent dendritic representations, excitability, and apoptosis in an age-dependent manner is interesting and as the authors point out leads to experimentally testable hypotheses. I have however several concerns with the novelty of the work, the lack of referencing of previous work on granule cells-mitral cell interactions more generally, and the biological plausibility of the model that, in my opinion, should be further addressed to better contextualize the model.

      (1) The authors find that a network with age-dependent synaptic plasticity outperforms one with constant age-independent plasticity and that having more GC per se is not sufficient to explain this effect. In addition, having an initial higher excitability of GCs leads to increased performance. To what degree the increased excitability of abGCs is conceptually necessarily independent of them having higher synaptic plasticity rates / fast synapses?

      (2) The authors do not mention previous theoretical work on the specificity of mitral to granule cell interactions from several groups (Koulakov & Rinberg - Neuron, 2011; Gilra & Bhalla, PLoSOne, 2015; Grabska-Bawinska...Mainen, Pouget, Latham, Nat. Neurosci. 2017; Tootoonian, Schaefer, Latham, PLoS Comput. Biol., 2022), nor work on the relevance of top-down feedback from the olfactory cortex on the abGC during odor discrimination tasks (Wu & Komiyama, Sci. Adv. 2020), or of top-down regulation from the olfactory cortex on regulating the activity of the mitral/tufted cells in task engaged mice (Lindeman et al., PLoS Comput. Biol., 2024), or in naïve mice that encounter odorants (in the absence of specific context; Boyd, et al., Cell Rep, 2015; Otazu et al., Neuron 2015, Chae et al., Neuron, 2022). In particular, the presence of rich top-down control of granule cell activity (including of abGCs) puts into question the plausibility of one of the opening statements of the authors with respect to relying solely on local circuit mechanisms to solve the flexibility-stability dilemma. I think the discussion of this work is important in order to put into context the idea of specific interactions between the abGCs and the mitral cells.

      (3) To what the degree of specific connectivity reflects a specific stimulus configuration, and is a good proxy for determining the stimulus discriminability and memory capacity in terms of temporal activity patterns (difference in latency/phase with respect to the respiration cycle, etc.) which may account to a substantial fraction of ability to discriminate between stimuli? The authors mention in the discussion that this is, indeed, an upper bound and specific connectivity is necessary for different temporal activity patterns, but a further expansion on this topic would help in understanding the limitations of the model.

      (4) Reward or reward prediction error signals are not considered in the model. They however are ubiquitous in nature and likely to be encountered and shape the connectivity and activity patterns of the abGC-mitral cell network. Including a discussion of how the model may be adjusted to incorporate reward/error signals would strengthen the manuscript.

      Specific Comments

      (1) Lines 84-86; 507-509; Eq(3): Sensory input is defined by a basal parameter of MCs spontaneous activity (Sspontaneus) and the odor stimuli input (Siodor) but is not clear from the main text or methods how sensory inputs (glomerular patterns) were modeled.

      (2) Lines 118-122: The used perceptual learning task explanation is done only in the context of the discriminability of similar artificial stimuli using the Fisher discriminant and "Memory" metric. A detailed description of the logic of the perceptual learning task methods and objective, taking into account Comment 1, would help to better understand the model.

      (3) Rapid re-learning of forgotten odor pair is enabled by sensory-dependent dendritic elaboration of neurons that initially encoded the odors and the observed re-learning would occur even if neurogenesis was blocked following the first enrichment and even though the initial learning did require neurogenesis. When this would ever occur in nature? The re-learning of an odor period? Why is this highlighted in the study?

    1. Welcome back and in the next two lessons I'll be stepping you through two types of VPC endpoint. Now in this lesson I'll be talking about gateway endpoints and in the next I'll be covering interface endpoints. Now they're both used in roughly the same way, they provide the same functionality but they're used for different AWS services and the way that they achieve this functionality from a technical point is radically different. So let's get started and in this lesson I want to cover gateway endpoints.

      So at a high level gateway endpoints they provide private access to supported services and at the time of creating this lesson the services that work with gateway endpoints are S3 and DynamoDB. So what I mean when I say private access in the context of this lesson, I mean that they allow a private only resource inside of VPC or any resource inside a private only VPC to access S3 and DynamoDB. Remember that both of these are public services.

      Normally when you want to access AWS public services from within a VPC you need infrastructure and configuration. Normally this is an internet gateway that you need to create and attach to the VPC and then for the resources inside that VPC you need to grant them either a public IP version 4 address and IP version 6 address or you need to implement one or more NAT gateways which allow instances with private IP addresses to access these public services. So these services exist outside of the VPC and so normally public IP addressing is required and a gateway endpoint allows you to provide access to these services without implementing that public infrastructure.

      Now the way that this works is that you create a gateway endpoint and these are created per service per region. So let's use an example of S3 in the US East 1 or Northern Virginia region. So you create this gateway 4S3 in US East 1 and you associate it with one or more subnets in a particular VPC. Now a gateway endpoint doesn't actually go into VPC subnets. What happens is that when you allocate the gateway endpoint to particular subnets something called a prefix list is added to the route tables for those subnets and this prefix list uses the gateway endpoint as a target.

      Now a prefix list is just like what you would find on a normal route but it's an object, it's a logical entity which represents these services. So it represents S3 or DynamoDB. Imagine this is a list of IP addresses that those services use but where the list is kept updated by AWS. So this prefix list is added to the route table. The prefix list is used as the destination and the target is the gateway endpoint. And this means in this example that any traffic destined for S3 as it exits these subnets it goes via the gateway endpoint rather than the internet gateway.

      Now it is important for the exam to remember that a gateway endpoint does not go into a particular subnet or an availability zone, it's highly available across all availability zones in a region by default. Like an internet gateway it's associated with a VPC but with a gateway endpoint you just set which subnets are going to be used with it and it automatically configures this route on the route tables for those subnets with this prefix list. So it's just something that's configured on your behalf by AWS.

      A gateway endpoint is a VPC gateway object, it is highly available, it operates across all availability zones in that VPC, it does not go into a particular subnet. So remember that for the exam because that is different than interface endpoints which we'll be covering next. Now when you're implementing gateway endpoints you can configure endpoint policies and an endpoint policy allows you to control what things can be connected to by that gateway endpoint. So we can apply an endpoint policy to our gateway endpoint and only allow it to connect to a particular subset of S3 buckets.

      And this is great if you run a private only high security VPC and you want to grant resources inside that VPC access to certain S3 buckets but not the entire S3 service so you can use an endpoint policy to restrict it to particular S3 buckets. Now gateway endpoints can only be used to access services in the same region. So you can't for example access an S3 bucket which is located in the AP Southeast 2 region from a gateway endpoint in the US East 1 region, it's in the same region only.

      So in summary gateway endpoints support two main use cases. First you might have a private VPC and you want to allow that private VPC to access public resources in this case S3 or DynamoDB. Maybe you have software or application updates stored in S3 and want to allow a super secure VPC to be able to access them without allowing other public access or access to other S3 buckets. Now the second type of architecture that gateway endpoints can help support is the idea of private only S3 buckets.

      Gateway endpoints can help prevent leaky buckets. S3 buckets as you know by now can be locked down by creating a bucket policy and applying it to that S3 bucket. So you could configure a bucket policy to only accept operations coming from a specific gateway endpoint. And because S3 is private by default for anything else the implicit deny would apply. So if you allow operations only from a specific gateway endpoint you implicitly deny everything else. And that means that the S3 bucket is a private only bucket.

      One limitation of gateway endpoints that you should be aware of the exam is that they're only accessible from inside that specific VPC. There are logical gateway objects and you can only access logical gateways created inside of VPC from that VPC.

      So before we finish up with this theory lesson let's quickly look at the architecture visually because it will probably help you understand exactly how all of the components fit together. Without using gateway endpoints this is the type of architecture that you've been using so far in the course. Two availability zones each with two subnets one public and green on the right and one private in blue on the left. Resources in the public subnets on the right can be given public IP version 4 addresses and so access public space resources using those addresses through the VPC router via the internet gateway into the public space and then through to the public resource S3 in this example.

      Now private instances can't do this they still go via the VPC router but they need to use a NAT gateway which provides them with a NATed public IP version 4 address to use and then this public address that's owned by the NAT gateway is used via the internet gateway and finally through to the public resource again S3. The problem with this architecture is that the resources have public internet access either directly for public resources or via the NAT gateway for private only EC2 instances.

      If you want instances inside the VPC to be able to access S3 but not the public internet then it's problematic. If you work in a heavily regulated industry and you need to create VPCs which are private only with no internet connectivity then that is almost impossible to do without using gateway endpoints.

      Using gateway endpoints we can change this architecture. Architecturally to use gateway endpoints we create one inside of VPC and when creating it we associate it with one or more subnets and this means that a prefix list is added to the route table for that subnet. This means that any traffic which leaves the private instances inside those subnets now has a route to the public service so it will go via the gateway endpoint and they won't need public addresses to talk to that service. Imagine the gateway endpoint is being inside your VPC but having a tunnel to the public service and that way data can flow from private services inside the VPC through the gateway endpoint to the public service without needing any public addressing.

      Note how this VPC has no internet gateway and no NAT gateway. The private instance has no access to anything else outside the VPC only S3 and that's only because we've created the gateway endpoint. We could even go one step further using a bucket policy on the S3 bucket and denying any access which doesn't come via the gateway endpoint.

      Now a couple of important things to remember for the exam gateway endpoints are highly available by design. You don't need to worry about AZ placement just like internet gateways that's all handled for you by the VPC service. For the exam just know that gateway endpoints are not accessible outside of the VPC that they're associated with and in terms of access control endpoint policies can be used on gateway endpoints to control what the endpoint can be used to access.

      So if you did want to allow access to one or two S3 buckets only rather than the entire service then that's something which can be controlled by using an endpoint policy on the gateway endpoint.

      Now that's everything that I wanted to cover in this lesson about the theory and architecture of gateway endpoints. In the next lesson we're going to be covering interface endpoints which offer similar functionality to gateway endpoints but and this is critical they're implemented in a very different way from an architecture perspective and that difference really does matter for the exam. And if you intend to use these products in real world production implementations. But at this point thanks for watching we finished everything that I wanted to cover so go ahead finish up this video and when you're ready I look forward to you joining me in the next lesson.

    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment

      This study presents valuable findings related to seasonal brain size plasticity in the Eurasian common shrew (Sorex araneus), which is an excellent model system for these studies. The evidence supporting the authors' claims is convincing. However, the authors should be careful when applying the term adaptive to the gene expression changes they observe; it would be challenging to demonstrate the differential fitness effects of these gene expression changes. The work will be of interest to biologists working on neuroscience, plasticity, and evolution.

      We appreciate the reviewers’ suggestions and comments. For the phylogenetic ANOVA we used (EVE), which tests for a separate RNA expression optimum specific to the shrew lineage consistent with expectations for adaptive evolution of gene expression. But, as you noted, while this analysis highlights many candidate genes evolving in a manner consistent with positive selection, further functional validation is required to confirm if and how these genes contribute to Dehnel’s phenomenon. In the discussion, we now emphasize that inferred adaptive expression of these genes is putative and outline that future studies are needed to test the function of proposed adaptations. For example, cell line validations of BCL2L1 on apoptosis is a case study that tests the function of a putatively adaptive change in gene expression, and it illuminates this limitation. We also have refined our discussion to focus more on pathway-level analyses rather than on individual genes, and have addressed other issues presented, including clarity of methods and using sex as a covariate in our analyses.

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      In this paper, Thomas et al. set out to study seasonal brain gene expression changes in the Eurasian common shrew. This mammalian species is unusual in that it does not hibernate or migrate but instead stays active all winter while shrinking and then regrowing its brain and other organs. The authors previously examined gene expression changes in two brain regions and the liver. Here, they added data from the hypothalamus, a brain region involved in the regulation of metabolism and homeostasis. The specific goals were to identify genes and gene groups that change expression with the seasons and to identify genes with unusual expression compared to other mammalian species. The reason for this second goal is that genes that change with the season could be due to plastic gene regulation, where the organism simply reacts to environmental change using processes available to all mammals. Such changes are not necessarily indicative of adaptation in the shrew. However, if the same genes are also expression outliers compared to other species that do not show this overwintering strategy, it is more likely that they reflect adaptive changes that contribute to the shrew's unique traits.

      The authors succeeded in implementing their experimental design and identified significant genes in each of their specific goals. There was an overlap between these gene lists. The authors provide extensive discussion of the genes they found.

      The scope of this paper is quite narrow, as it adds gene expression data for only one additional tissue compared to the authors' previous work in a 2023 preprint. The two papers even use the same animals, which had been collected for that earlier work. As a consequence, the current paper is limited in the results it can present. This is somewhat compensated by an expansive interpretation of the results in the discussion section, but I felt that much of this was too speculative. More importantly, there are several limitations to the design, making it hard to draw stronger conclusions from the data. The main contribution of this work lies in the generated data and the formulation of hypotheses to be tested by future work.

      Thank you for your interest in our manuscript and for your insights. We addressed your comments below: we now highlight the limitations of our study design in the discussion and emphasize that, while a second optimum of gene expression in shrews is consistent with adaptive evolution, we recognize that not all sources of variation in gene expression can be fully accounted for. We highlight the putative nature of these results in our revisions, especially in our new limitations section (lines 541-555).

      Strengths:

      The unique biological model system under study is fascinating. The data were collected in a technically sound manner, and the analyses were done well. The paper is overall very clear, well-written, and easy to follow. It does a thorough job of exploring patterns and enrichments in the various gene sets that are identified.

      I specifically applaud the authors for doing a functional follow-up experiment on one of the differentially expressed genes (BCL2L1), even if the results did not support the hypothesis. It is important to report experiments like this and it is terrific to see it done here.

      We are glad to hear that you found our manuscript fascinating and clearly written. While we hoped to see an effect of BCL2L1 on apoptosis as proposed, we agree that reporting null results is valuable when validating evolutionary inferences.

      Weaknesses:

      While the paper successfully identifies differentially expressed seasonal genes, the real question is (as explained by the authors) whether these are evolved adaptations in the shrews or whether they reflect plastic changes that also exist in other species. This question was the motivation for the inter-species analyses in the paper, but in my view, these cannot rigorously address this question. Presumably, the data from the other species were not collected in comparable environments as those experienced by the shrews studied here. Instead, they likely (it is not specified, and might not be knowable for the public data) reflect baseline gene expression. To see why this is problematic, consider this analogy: if we were to compare gene expression in the immune system of an individual undergoing an acute infection to other, uninfected individuals, we would see many, strong expression differences. However, it would not be appropriate to claim that the infected individual has unique features - the relevant physiological changes are simply not triggered in the other individuals. The same applies here: it is hard to draw conclusions from seasonal expression data in the shrews to non-seasonal data in the other species, as shrew outlier genes might still reflect physiological changes that weren't active in the other species.

      There is no solution for this design flaw given the public data available to the authors except for creating matched data in the other species, which is of course not feasible. The authors should acknowledge and discuss this shortcoming in the paper.

      Thank you for taking the time to provide such insightful feedback. As you noted, whiles shrews experience seasonal size changes, their environments may differ from the other species used in this experiment, leading to increased or decreased expression of certain genes and reducing our ability accurately detect selection across the phylogeny. Although we sought to control for as many sources of variation as possible, such as using only post-pubescent, wild, or non-domesticated individuals when feasible, we recognize that not all sources of variation can be fully accounted for within a practical experiment. We agree that these sources of variation can introduce both false positives and negatives into our results, and we have now highlighted this limitation within our discussion (lines 538-552).

      Related to the point above: in the section "Evolutionary Divergence in Expression" it is not clear which of the shrew samples were used. Was it all of them, or only those from winter, fall, etc? One might expect different results depending on this. E.g., there could be fewer genes with inferred adaptive change when using only summer samples. The authors should specify which samples were included in these analyses, and, if all samples were used, conduct a robustness analysis to see which of their detected genes survive the exclusion of certain time points.

      Thank you for this attention to detail. We used spring adults for this analysis. This decision was made as only used post pubescent individuals for all species in the analysis, and this was the only season where adult shrews were going through Dehnel’s phenomenon. We have now clarified this in both the methods and results (line 247 and line 667)

      In the same section, were there also genes with lower shrew expression? None are mentioned in the text, so did the authors not test for this direction, or did they test and there were no significant hits?

      We did test for decreased shrew expression compared to the rest of the species, but there were no significant genes with significant decreases. We hypothesize that there are two potential reasons for this results; 1) If a gene were to be selected for decreased expression, selection for constitutive expression of the gene across all species may be weak, and thus found in other lineages as well, or 2) decreased or no expression may relax selection on the coding regions, and thus these genes are not pulled out as we identify 1:1 orthologs. This is consistent with results provided from the original methods manuscript. Thank you for pointing out that we did not discuss this information in the text, and we now include it in our results (lines 250-251).

      The Discussion is too long and detailed, given that it can ultimately only speculate about what the various expression changes might mean. Many of the specific points made (e.g. about the blood-brain-barrier being more permissive to sensing metabolic state, about cross-organ communication, the paragraphs on single, specific genes) are a stretch based on the available data. Illustrating this point, the one follow-up experiment the authors did (on BCL2L1) did not give the expected result. I really applaud the authors for having done this experiment, which goes beyond typical studies in this space. At the same time, its result highlights the dangers of reading too much into differential expression analyses.

      We agree with your point, while our extensive discussion is useful for testing future hypotheses, ultimately some of the discussion may be too speculative for our readers. To amend this, we have reduced some portions of our discussion and focused more on pathways than individual genes, including removing mechanisms related to HRH2, FAM57B, GPR3, and GABAergic neurons. We hope that this highlights to the reader the speculative nature of many of our results.

      There is no test of whether the five genes observed in both analyses (seasonal change and inter-species) exceed the number expected by chance. When two gene sets are drawn at random, some overlap is expected randomly. The expected overlap can be computed by repeated draws of pairs of random sets of the same size as seen in real data and by noting the overlap between the random pairs. If this random distribution often includes sets of five genes, this weakens the conclusions that can be drawn from the genes observed in the real data.

      Thank you for highlighting this approach, it is greatly needed. After running this test, we found that observed overlapping genes were more than the expected overlap, yet not significant. We now show this in our methods (lines 277-278) and results (lines 719-720).

      Reviewer #2 (Public review):

      Summary:

      Shrews go through winter by shrinking their brain and most organs, then regrow them in the spring. The gene expression changes underlying this unusual brain size plasticity were unknown. Here, the authors looked for potential adaptations underlying this trait by looking at differential expression in the hypothalamus. They found enrichments for DE in genes related to the blood-brain barrier and calcium signaling, as well as used comparative data to look at gene expression differences that are unique in shrews. This study leverages a fascinating organismal trait to understand plasticity and what might be driving it at the level of gene expression. This manuscript also lays the groundwork for further developing this interesting system.

      We are glad you found our manuscript interesting and thank and thank you for your feedback. We hope that we have addressed all of your concerns as described below.

      Strengths:

      One strength is that the authors used OU models to look for adaptation in gene expression. The authors also added cell culture work to bolster their findings.

      Weaknesses:

      I think that there should be a bit more of an introduction to Dehnel's phenomenon, given how much it is used throughout.

      Thank you for this insight. With a lengthy introduction and discussion, we agree that the importance of Dehnel’s phenomenon may have been overshadowed. We have shortened both sections and emphasized the background on Dehnel’s phenomenon in the first two paragraphs of the introduction, allowing this extraordinary seasonal size plasticity to stand out.

      Reviewer #3 (Public review):

      Summary:

      In their study, the authors combine developmental and comparative transcriptomics to identify candidate genes with plastic, canalized, or lineage-specific (i.e., divergent) expression patterns associated with an unusual overwintering phenomenon (Dehnel's phenomenon - seasonal size plasticity) in the Eurasian shrew. Their focus is on the shrinkage and regrowth of the hypothalamus, a brain region that undergoes significant seasonal size changes in shrews and plays a key role in regulating metabolic homeostasis. Through combined transcriptomic analysis, they identify genes showing derived (lineage-specific), plastic (seasonally regulated), and canalized (both lineage-specific and plastic) expression patterns. The authors hypothesize that genes involved in pathways such as the blood-brain barrier, metabolic state sensing, and ion-dependent signaling will be enriched among those with notable transcriptomic patterns. They complement their transcriptomic findings with a cell culture-based functional assessment of a candidate gene believed to reduce apoptosis.

      Strengths:

      The study's rationale and its integration of developmental and comparative transcriptomics are well-articulated and represent an advancement in the field. The transcriptome, known for its dynamic and plastic nature, is also influenced by evolutionary history. The authors effectively demonstrate how multiple signals-evolutionary, constitutive, and plastic-can be extracted, quantified, and interpreted. The chosen phenotype and study system are particularly compelling, as it not only exemplifies an extreme case of Dehnel's phenotype, but the metabolic requirements of the shrew suggest that genes regulating metabolic homeostasis are under strong selection.

      Weaknesses:

      (1) In a number of places (described in detail below), the motivation for the experimental, analytical, or visualization approach is unclear and may obscure or prevent discoveries.

      Thank you for finding our research and manuscript compelling, as well as the valuable feedback that will drastically improve our manuscript. We hope that we have alleviated your concerns below by following your instructions below.

      (2) Temporal Expression - Figure 1 and Supplemental Figure 2 and associated text:

      - It is unclear whether quantitative criteria were used to distinguish "developmental shift" clusters from "season shift" clusters. A visual inspection of Supplemental Figure 2 suggests that some clusters (e.g., clusters 2, 8, and to a lesser extent 12) show seasonal variation, not just developmental differences between stages 1 and 2. While clustering helps to visualize expression patterns, it may not be the most appropriate filter in this case, particularly since all "season shift" clusters are later combined in KEGG pathway and GO analyses (Figure 1B).

      - The authors do not indicate whether they perform cluster-specific GO or KEGG pathway enrichment analyses. The current analysis picks up relevant pathways for hypothalamic control of homeostasis, which is a useful validation, but this approach might not fully address the study's key hypotheses.

      Thank you for this valuable feedback. We did not want to include clusters we deemed to be related to development, as this should not be attributed to changes associated with Dehnel’s phenomenon. We did this through qualitative, visual inspection, which we realize can differ between parties (i.e., clusters 2, 8, and 12 appeared to be seasonal). Qualitatively, we were looking for extreme divergence between Stage 1 and Stage 5 individuals, as expression was related to season and not development, then the average of these stages within cluster should be relatively similar. We have now quantified this as large differences in z-score (abs(summer juvenile-summer adult)>1.25) without meaningful interseason variations determined by a second local maximum (abs(autumn-winter)<0.5 and abs(winter-summer)<0.5)), and added it both our methods (lines 699-702) and results (line 192).

      Regarding the combination of clusters for pathway enrichment compared to individual pathways, we agree that combining clusters may be more informative for overall homeostasis, compared to individual clusters which may inform us on processes directly related to Dehnel’s phenomenon. Initially, we were tentative to conduct this analysis, as clusters contain small gene sets, reducing the ability to detect pathway enrichments. We have now included this analysis, which is reported in our methods (lines 703-704), results (lines 203-204)., and new supplemental table.

      (3) Differential expression between shrinkage (stage 2) and regrowth (stage 4) and cell culture targets

      - The rationale for selecting BCL2L1 for cell culture experiments should be clarified. While it is part of the apoptosis pathway, several other apoptosis-related genes were identified in the differential gene expression (DGE) analysis, some showing stronger differential expression or shrew-specific branch shifts. Why was BCL2L1 prioritized over these other candidates?

      We agree that our rationale for validating BCL2L1 function in neural cell lines was not clearly explained in the manuscript. We selected BCL2L1 because it is the furthest downstream gene in the apoptotic pathway, thus making it the most directly involved gene in programmed cell death, whereas upstream genes could influence additional genes or alternative processes. We have clarified this choice in the revised methods section (lines 748-750).

      - The authors mention maintaining (or at least attempting to maintain) a 1:1 sex ratio for the comparative analysis, but it is unclear if this was also done for the S. araneus analysis. If not, why? If so, was sex included as a covariate (e.g., a random effect) in the differential expression analysis? Sex-specific expression elevates with group variation and could impact the discovery of differentially expressed genes.

      Regarding the use of sex as a covariate, we acknowledge the concerns raised. In our evolutionary analyses, we maintained a balanced sex ratio within species when possible. EVE models handle the effect of sex on gene expression as intraspecific variation. In shrews, however, we used males exclusively, as females were only found among juvenile individuals. Including those juvenile females would have introduced age effects, with perhaps a larger effect on our results. For the seasonal data, we have now included sex as a covariate in differential expression analyses. However, our design is imbalanced in relation to sex, which we have now discussed in our methods (lines 713-714) and discussion limitations (lines 544-548).

      (4) Discussion: The term "adaptive" is used frequently and liberally throughout the discussion. The interpretation of seasonal changes in gene expression as indicators of adaptive evolution should be done cautiously as such changes do not necessarily imply causal or adaptive associations.

      Thank you for this insight. We have reviewed our discussion and clarified that adaptations are putative (i.e. lines 146, 285, and 332), and highlighted this in our limitations section.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) I would recommend always spelling out "Dehnel's phenomenon" or even replacing this term (after crediting the DP term) with the more informative "seasonal size plasticity". Every time I saw "DP", I had to remind myself what this referred to. If the authors choose not to do so, please use the acronym consistently (e.g. line 186 has it spelled out).

      We have replaced the acronym DP with either the full term or the more informative “seasonal size plasticity” throughout the text.

      (2) Line 202: "DEG" has not been defined. Simply add to the line before.

      Thank you for this attention to detail. We have added this to the line above (210).

      (3) Please add a reference for the "AnAge" tool that was used to determine if samples were pubescent.

      Thank you for identifying this oversight. We have now cited the proper paper in line 634.

      (4) In the BCL2L1 section in the results, add a callout to Figure 2D.

      We have now added a callout to Figure 2D within the results (line 234).

      Reviewer #2 (Recommendations for the authors):

      (1) Line 122: is associated? These adaptations?

      Thank you for identifying that we were missing the words “associated with” here. We have fixed this in the revision.

      (2) The first paragraph of the Results should be moved to the methods, except maybe the number of orthologs.

      Thank you for this insight. We have removed this portion from the results section.

      (3) Why a Bonferroni correction on line 188? That seems too strict.

      We agree the Bonferroni correction is strict. Results when using other less strict methods for controlling false discovery rate are also not significant after correction. These corrections can be found within the data, however, we only report on the Bonferroni correction.

      (4) Line 427: "is a novel candidate gene for several neurological disorders" needs some references. I see them a couple of sentences later, but that's quite a sentence with no references at the end.

      We have added the proper citations for this sentence (line 524).

      Reviewer #3 (Recommendations for the authors):

      (1) Temporal Expression - Figure 1 and Supplemental Figure 2 and associated text Line176-193:

      - The authors report the total number of genes meeting inclusion criteria (>0.5-fold change between any two stages and 2 samples >10 normalized reads), but it would be more informative to also provide the number of genes within each temporal cluster. This would offer a clearer understanding of how gene expression patterns are distributed over time.

      Unfortunately, this information is difficult to depict on our figure and would use too much space in the text. We have thus added a description of the range of genes in a new supplemental table depicting this information.

      - It is unclear whether quantitative criteria were used to distinguish "developmental shift" clusters from "season shift" clusters. A visual inspection of Supplemental Figure 2 suggests that some clusters (e.g., clusters 2, 8, and to a lesser extent 12) show seasonal variation, not just developmental differences between stages 1 and 2. While clustering helps to visualize expression patterns, it may not be the most appropriate filter in this case, particularly since all "season shift" clusters are later combined in KEGG pathway and GO analyses (Fig. 1B). Using a differential gene expression criterion might be more suitable. For example, do excluded genes show significant log-fold differences between late-stage comparisons?

      As previously mentioned, we have now quantified seasonal shifts as large differences in z-score (abs(summer juveniles-summer adults)>1.25) without meaningful interseason variations determined by a second local maximum (abs(autumn-winter)<0.5 and abs(winter-summer)<0.5)), and added it to our methods (lines 699-702).  We then follow this up with differential expression analyses as described in Figure 2.

      - Did the authors perform cluster-specific GO or KEGG pathway enrichment analyses instead of focusing on the combined set of genes across the season shift clusters? While I understand that the small number of genes in each cluster may be limiting, if pathways emerge from cluster-specific analysis, they could provide more detailed insights into the functional significance of these temporal expression patterns. The current analysis picks up relevant pathways for hypothalamic control of homeostasis, which is a useful validation, but this approach might not fully address the study's key hypotheses. Additionally, no corrections for multiple hypothesis testing were applied, as noted in the results. A more refined gene set (e.g., using differential expression criteria, described above) could be more appropriate for these analyses.

      We have now included cluster-specific KEGG enrichments as previously described.

      (2) Differential expression between shrinkage (stage 2) and regrowth (stage 4) and cell culture targets - Figure 2 and lines195-227:

      - The rationale for selecting BCL2L1 for cell culture experiments should be clarified. While it is part of the apoptosis pathway, several other apoptosis-related genes were identified in the differential gene expression (DGE) analysis, some showing stronger differential expression or shrew-specific branch shifts. Why was BCL2L1 prioritized over these other candidates?

      We have now included the reasoning for further validation of BCL2L1 as described above.

      - The relevance of the "higher degree" differentially expressed genes needs more explanation. Although this group of genes is highlighted in the results, they are not featured in any subsequent analyses, leaving their importance unclear.

      Thank you for this insight. We have removed this from the methods as it is not relevant to subsequent analyses or conclusions.

      - The authors mention maintaining (or at least attempting to maintain) a 1:1 sex ratio for the comparative analysis (Line 525), but it is unclear if this was also done for the S. araneus analysis. If so, was sex included as a covariate (e.g., a random effect) in the differential expression analysis?

      We have now incorporated information on sex as described above.

      (3) Discussion:

      The term "adaptive" is used frequently and liberally throughout the discussion, but the authors should be cautious in interpreting seasonal changes in gene expression as indicators of adaptive evolution. Such changes do not necessarily imply causal or adaptive associations, and this distinction should be clearly stated when discussing the results.

      Thank you for this feedback and we agree with your conclusion, while a second expression optimum in the shrew lineage is indicative of adaptive expression, we cannot fully determine whether these are caused by genetic or environmental factors, despite careful attention to experimental design. We have highlighted this as a limitation in the discussion.

      (4) Minor Editorial Comment:

      Line 105: "... maintenance of an energy budgets..." delete "an"

      We have removed this grammatical error.

    1. 质子自旋危机

      好的,我们来详细介绍一下质子自旋危机 (Proton Spin Crisis)。质子自旋危机是指在 20 世纪 80 年代末,实验结果出人意料地表明,质子内部夸克的自旋只占质子总自旋(1/2 ħ)的一小部分。这一发现挑战了早期对质子结构的简单夸克模型,并促使物理学家们更深入地理解质子的内部构成以及胶子和轨道角动量所扮演的角色。

      1. 引言

      质子是构成原子核的基本粒子之一,它是一种费米子,具有 1/2 ħ 的自旋。在粒子物理学的早期发展中,人们提出了一个简单的模型来描述质子的内部结构,即质子由三个价夸克组成:两个上夸克 (u) 和一个下夸克 (d)。这个模型在解释质子的许多性质方面取得了成功。然而,一项关键实验却揭示了一个令人费解的结果,引发了所谓的“质子自旋危机”。

      2. 质子的简单夸克模型

      根据简单的夸克模型,质子的自旋应该主要来源于其三个价夸克的自旋。夸克是自旋为 1/2 的费米子。为了解释质子的总自旋为 1/2,人们认为两个上夸克的自旋方向相同(例如都向上),而下夸克的自旋方向相反(向下),或者反过来。这样,三个夸克自旋的矢量和就可以给出质子的总自旋 1/2。

      3. 欧洲μ子合作组织 (EMC) 实验

      引发质子自旋危机的关键实验是由欧洲μ子合作组织 (European Muon Collaboration, EMC) 在 1980 年代末进行的。该实验利用高能偏振μ子束轰击偏振质子靶。通过测量散射μ子的自旋不对称性,物理学家们可以提取关于质子内部自旋结构的信息,特别是不同组分(包括夸克)对质子自旋的贡献。这种实验属于深度非弹性散射 (Deep Inelastic Scattering, DIS) 的范畴。

      4. 令人惊讶的结果

      EMC 实验的结果非常令人惊讶:夸克的自旋只占质子总自旋的大约 30%(在一些分析中甚至更低)。这个数值远低于简单夸克模型的预测,后者认为夸克的自旋应该贡献质子的绝大部分自旋。这个差异被称为“质子自旋危机”。

      5. 胶子的作用

      质子除了包含三个价夸克外,还包含大量的虚夸克-反夸克对(被称为海夸克)以及传递强相互作用的胶子。胶子是自旋为 1 的玻色子,它们也可能对质子的总自旋做出贡献。胶子可以携带本征自旋 (1 ħ),并且也可以通过它们的轨道角动量来贡献。EMC 实验的结果暗示胶子可能在质子的自旋结构中扮演着重要的角色。

      6. 夸克和胶子的轨道角动量

      除了本征自旋外,质子内部的夸克和胶子在运动时也可能具有轨道角动量。质子的总自旋是其所有组成粒子(夸克和胶子)的自旋和轨道角动量的总和。

      7. 自旋求和规则

      质子的自旋 (1/2) 可以用一个自旋求和规则来表示,它将质子的自旋分解为不同组分的贡献:

      1/2 = 1/2 ΔΣ + ΔG + L<sub>q</sub> + L<sub>g</sub>

      其中: * ΔΣ 是夸克自旋对质子自旋的总贡献。EMC 实验主要探测了这个量,发现它远小于预期。 * ΔG 是胶子自旋对质子自旋的贡献。 * L<sub>q</sub> 是夸克的轨道角动量对质子自旋的贡献。 * L<sub>g</sub> 是胶子的轨道角动量对质子自旋的贡献。

      因子 1/2 出现在 ΔΣ 前面是因为夸克是自旋 1/2 的粒子。

      8. 后续实验和改进

      EMC 实验的结果引起了物理学界的广泛关注,并促使了后续一系列更精确的实验来进一步研究质子的自旋结构。这些实验包括自旋μ子合作组织 (Spin Muon Collaboration, SMC)、SLAC 的 E154 和 E155 实验、DESY 的 HERMES 实验以及 CERN 的 COMPASS 实验等。这些实验使用了不同的探测粒子(电子、正电子)并扩展了实验的运动学范围,进一步证实并完善了 EMC 实验的结果。

      9. 目前的理解

      基于这些实验和大量的理论研究,目前对质子自旋结构的理解如下:

      • 夸克的自旋贡献: 夸克的自旋只贡献了质子总自旋的大约 30%。
      • 胶子的自旋贡献: 胶子被认为对质子的自旋有显著贡献,估计可能达到 50% 左右。然而,胶子自旋的直接测量非常困难,这仍然是一个活跃的研究领域。
      • 轨道角动量的贡献: 夸克和胶子的轨道角动量也被认为对质子的总自旋有贡献,但其具体数值尚不完全清楚。

      因此,质子的自旋并非仅仅来源于三个价夸克的自旋,胶子以及夸克和胶子的轨道角动量都扮演着重要的角色。

      10. 实验挑战

      精确测量胶子的自旋以及夸克和胶子的轨道角动量面临着巨大的实验挑战。胶子通常通过涉及极化质子-质子碰撞的过程来探测,在这些过程中胶子起着重要的作用。轨道角动量的直接测量则更加困难。

      11. 理论研究

      理论物理学家们也在积极努力,利用微扰量子色动力学 (perturbative QCD)、格点量子色动力学 (lattice QCD) 以及各种强子结构模型来理解质子的自旋结构。这些理论研究旨在提供一个更完整的质子内部夸克、胶子和轨道角动量相互作用的图景。

      12. 质子自旋危机的意义

      “质子自旋危机”这个名称反映了最初发现时对简单夸克模型的冲击。它的重要性在于:

      • 揭示了简单模型的局限性: 它表明早期对质子结构的简单夸克模型是不完整的。
      • 强调了胶子和轨道角动量的作用: 它突出了胶子以及夸克和胶子的轨道角动量在质子结构中的重要性。
      • 推动了大量的研究工作: 它激发了大量的实验和理论研究,旨在更全面地理解核子的自旋结构。

      13. 结论

      质子自旋危机是粒子物理学中一个重要的转折点。最初的令人惊讶的发现,即夸克只贡献了质子自旋的一小部分,引发了广泛的研究,这些研究至今仍在进行,并为我们提供了关于核子内部夸克和胶子动力学更为丰富的图景。虽然“危机”这个词可能有些夸张,但它确实标志着我们对质子结构的理解从一个简单的模型走向了一个更为复杂和精细的层次。

    1. Reviewer #1 (Public review):

      Summary

      In this manuscript, De La Forest Divonne et al. build a repertory of hemocytes from adult Pacific oysters combining scRNAseq data with cytologic and biochemical analyses. Three categories of hemocytes were described previously in this species (i.e. blast, hyalinocyte and granulocytes). Based on scRNAseq data, the authors identified 7 hemocyte clusters presenting distinct transcriptional signatures. Using Kegg pathway enrichment and RBGOA, the authors determined the main molecular features of the clusters. In parallel, using cytologic markers, the authors classified 7 populations of hemocytes (i.e. ML, H, BBL, ABL, SGC, BGC, and VC) presenting distinct sizes, nucleus sizes, acidophilic/basophilic, presence of pseudopods, cytoplasm/nucleus ratio and presence of granules. Then, the authors compared the phenotypic features with potential transcriptional signatures seen in the scRNAseq. The hemocytes were separated in a density gradient to enrich for specific subpopulations. The cell composition of each cell fraction was determined using cytologic markers and the cell fractions were analysed by quantitative PCR targeting major cluster markers (two per cluster). With this approach, the authors could assign cluster 7 to VC, cluster 2 to H, and cluster 3 to SGC. The other clusters did not show a clear association with this experimental approach. Using phagocytic assays, ROS, and copper monitoring, the authors showed that ML and SGC are phagocytic, ML produces ROS, and SGC and BGC accumulate copper. Then with the density gradient/qPCR approach, the authors identified the populations expressing anti-microbial peptides (ABL, BBL, and H). At last, the authors used Monocle to predict differentiation trajectories for each subgroup of hemocytes using cluster 4 as the progenitor subpopulation.

      The manuscript provides a comprehensive characterisation of the diversity of circulating immune cells found in Pacific oysters.

      Strengths

      The combination of scRNAseq, cytologic markers and gradient based hemocyte sorting offers an integrative view of the immune cell diversity.<br /> Hemocytes represent a very plastic cell population that has key roles in homeostatic and challenged conditions. Grasping the molecular features of these cells at the single-cell level will help understand their biology.<br /> This type of study may help elucidate the diversification of immune cells in comparative studies and evolutionary immunology.

      Weaknesses

      Several figures show inconsistency leading to erroneous conclusions and some conclusions are poorly supported. Moreover, the manuscript remains highly descriptive with limited comparison with the available literature.

      Comments on revisions:

      The authors replied to most comments.

    1. 威尔逊RG

      好的,我们来详细介绍一下威尔逊重整化群 (Wilsonian Renormalization Group, Wilsonian RG)。威尔逊重整化群是一种理解量子场论和统计力学中尺度依赖性的强大方法,它通过系统地去除高能量(或短距离)的自由度来研究理论在不同能量尺度下的行为。与传统上侧重于消除无穷大的重整化方法不同,威尔逊 RG 更强调理论的有效性和尺度演化。

      1. 引言

      威尔逊 RG 由物理学家肯尼斯·威尔逊在 20 世纪 70 年代初发展起来,它提供了一个更直观和物理的视角来看待重整化。其核心思想是,当我们研究一个物理系统在某个特定的能量尺度时,我们并不需要知道远高于这个能量尺度的物理规律的全部细节。威尔逊 RG 提供了一种系统的方法来“积分掉”这些高能量的自由度,从而得到在较低能量尺度上描述物理现象的有效理论。

      2. 尺度依赖性问题

      正如我们之前讨论过的,物理现象通常依赖于我们探测它们的能量尺度。威尔逊 RG 的目标是直接理解这种尺度依赖性。例如,当我们观察一个材料的磁性时,在原子尺度上的微观相互作用与宏观尺度上的磁化强度之间存在着复杂的联系。威尔逊 RG 提供了一个桥梁,连接了不同尺度上的物理描述。

      3. 积分掉自由度的思想

      威尔逊 RG 的核心在于逐步去除高能量(短波长)的自由度。想象一个定义在某个高能截止 Λ₀ 的量子场论。这个截止可以理解为我们理论有效的最高能量尺度,或者我们对物理规律了解的最高能量尺度。当我们想要研究更低的能量尺度时,我们可以通过以下步骤进行:

      • 识别高能模式: 将理论中的自由度按照能量(或动量)进行划分。我们首先关注那些能量接近截止 Λ₀ 的高能模式。
      • 积分掉高能模式: 通过路径积分等方法,将这些高能模式从理论中“积分掉”。这个过程会产生一个新的有效理论,它只包含能量低于某个新截止 Λ₀/b(其中 b > 1)的自由度。
      • 重标度 (Rescaling): 为了能够与原始理论进行比较,并将截止恢复到原来的值(或者某个参考值),我们需要对剩余的低能模式的动量和场进行重标度。这个重标度过程会引入与尺度因子 b 相关的因子。

      4. 动量空间图景

      威尔逊 RG 通常在动量空间中进行描述,这使得能量尺度的概念更加清晰。

      • 初始理论与截止: 我们从一个定义在动量空间中,动量大小 |k| < Λ₀ 的理论开始。Λ₀ 是紫外截止。
      • 切片动量壳: 我们考虑一个薄的动量壳,其动量大小满足 Λ₀/b < |k| < Λ₀。这些是我们要积分掉的高动量模式。
      • 积分掉高动量模式: 对这些高动量模式进行积分,得到一个只包含 |k| < Λ₀/b 的低动量模式的有效理论。这个有效理论的拉格朗日量会包含一些新的项,这些项是由高动量模式的贡献产生的。
      • 重标度: 对动量和场进行重标度,使得新的截止回到 Λ₀。例如,我们可以将动量 k' = bk,以及相应地重标度场变量。

      5. 重整化群变换

      积分掉高动量模式并进行重标度的组合构成了一次威尔逊 RG 变换。通过重复应用这个变换,我们可以研究理论在不同能量尺度下的行为,并观察理论的参数(例如耦合常数)是如何演化的。这一系列的变换构成了一个在所有可能理论的空间(即所有可能的拉格朗日量的空间)中的“流”。

      6. 不动点 (Fixed Points)

      在 RG 流中,不动点是指那些在 RG 变换下保持不变的理论。当一个理论的参数在 RG 变换后不发生变化时,我们就说它位于一个不动点。不动点对应于具有尺度不变性的理论。在临界现象的研究中,不动点扮演着核心的角色。

      7. 相关、无关和边缘算符 (在威尔逊 RG 视角下)

      在靠近不动点的区域,我们可以根据拉格朗日量中的算符在 RG 流下的行为对其进行分类:

      • 相关算符 (Relevant Operators): 它们的耦合常数在能量尺度降低时增大。这意味着它们在低能物理中变得更加重要。
      • 无关算符 (Irrelevant Operators): 它们的耦合常数在能量尺度降低时减小。这意味着它们在高能物理中可能重要,但在低能下变得不重要。
      • 边缘算符 (Marginal Operators): 它们的耦合常数随能量尺度的变化非常缓慢(通常是对数形式)。边缘算符可以进一步分为边缘相关和边缘无关,取决于其耦合常数是缓慢增大还是减小。

      8. 与可重整化性的联系

      威尔逊 RG 提供了一个理解可重整化性的不同视角。一个可重整化的理论是指,当我们将截止 Λ₀ 推向无穷大时,其 RG 流会趋向于某个不动点。无关算符虽然可能出现在初始理论中,但在流向低能的过程中会逐渐消失,因此不会影响不动点附近的低能物理。不可重整化的理论的 RG 流则会远离简单的不动点,这通常意味着我们需要无限多个参数来描述低能物理。

      9. 普适性 (Universality)

      威尔逊 RG 自然地解释了普适性现象。普适性是指具有非常不同的微观细节的物理系统,在临界点附近会表现出相同的宏观行为(例如相同的临界指数)。这是因为这些不同的微观理论在 RG 流下会流向同一个不动点,而临界行为是由不动点的性质决定的。

      10. 威尔逊 RG 的应用

      威尔逊 RG 在物理学的多个领域都有重要的应用:

      • 临界现象和相变: 威尔逊 RG 是研究二级相变和计算临界指数的强大工具。不动点通常对应于临界点。
      • 构建有效场理论: 威尔逊 RG 提供了一种系统的方法,通过积分掉高能量自由度来推导有效场理论。
      • 理解耦合常数的尺度依赖性: RG 流描述了耦合常数如何随着能量尺度的变化而变化,这与耦合常数的“跑动”概念一致。

      11. 威尔逊方法的优点

      与传统的重整化方法相比,威尔逊 RG 具有以下优点:

      • 物理直觉: 它提供了一个更直接的物理图像,解释了尺度依赖性。
      • 处理不可重整化理论: 它可以用于理解和处理不可重整化的理论,将它们视为在一定能量尺度下有效的理论。
      • 解释普适性: 它自然地解释了不同微观系统在临界点附近表现出相同宏观行为的现象。

      12. 结论

      威尔逊重整化群是量子场论和统计力学中一个基础性的框架,它提供了一种强大且直观的方式来理解物理理论如何随着能量尺度的变化而演化。通过系统地积分掉高能量自由度,威尔逊 RG 揭示了不动点、相关/无关算符等关键概念,并在临界现象、有效场理论构建以及理解耦合常数的尺度依赖性等方面有着广泛的应用。它为我们理解不同尺度上的物理现象之间的联系提供了一个深刻的视角。

    1. Author response:

      Reviewer #1:

      Strengths:

      (1) Using a fairly generic ecological model, the method can identify the change in the relative importance of different ecological forces (distribution of interspecies interactions, demographic noise, and immigration) in different sample groups. The authors focus on the case of the human gut microbiota, showing that the data are consistent with a higher influence of species interactions (relative to demographic noise and immigration) in a disease microbiota state than in healthy ones. (2) The method is novel, original, and it improves the state-of-the-art methodology for the inference of ecologically relevant parameters. The analysis provides solid evidence for the conclusions. 

      Weaknesses:

      In the way it is written, this work might be mostly read by physicists. We believe that, with some rewriting, the authors could better highlight the ecological implications of the results and make the method more accessible to a broader audience.

      We thank the reviewer for their positive and constructive feedback. We particularly appreciate the recognition of the novelty and robustness of our method, as well as the insight that it sheds light on the shifting ecological forces between healthy and diseased microbiomes. In response to the concern about the manuscript’s accessibility, we aim to revise key sections – including the Introduction, Results, and Discussion – to more clearly articulate the ecological relevance of our theoretical findings. We would like to emphasize that our approach offers a novel perspective for analyzing individual species' abundances, as well as for understanding interaction patterns and stability at the community level. By placing our results within a broader context accessible to readers from diverse backgrounds, we aim for the revised version to appeal to a wider audience, including ecologists and microbiome scientists, while preserving the rigor of our underlying statistical physics framework.

      Reviewer #2:

      Strengths:

      A well-written article, relatively easy to follow and transparent despite the high degree of technicality of the underlying theory. The authors provide a powerful inferring procedure, which bypasses the issue of having only compositional data. 

      Weaknesses:

      (1) This sentence in the introduction seems key to me: "Focusing on single species properties as species abundance distribution (SAD), it fails to characterise altered states of microbiome." Yet it is not explained what is meant by 'fail', and thus what the proposed approach 'solves'. (2) Lack of validation, following arbitrary modelling choices made (symmetry of interactions, weak-interaction limit, uniform carrying capacity). Inconsistent interpretation of instability. Here, instability is associated with the transition to the marginal phase, which becomes chaotic when interaction symmetry is broken. But as the authors acknowledge, the weak interaction limit does not reproduce fat-tailed abundance distributions found in data. On the other hand, strong interaction regimes, where chaos prevails, tend to do so (Mallmin et al, PNAS 2024). Thus, the nature of the instability towards which unhealthy microbiomes approach is unclear. (3) Three technical points about the methodology and interpretation. a) How can order parameters ℎ and 𝑞0 can be inferred, if in the compositional data they are fixed by definition? b) How is it possible that weaker interaction variance is associated with an approach to instability, when the opposite is usually true? c) Having an idea of what the empirical data compares to the theoretical fits would be valuable. Implications: As the authors say, this is a proof of concept. They point at limits and ways to go forward, in particular pointing at ways in which species abundance distributions could be better reproduced by the predicted dynamical models. One implication that is missing, in my opinion, is the interpretability of the results, and what this work achieves that was missing from other approaches (see weaknesses section above): what do we learn from the fact that changes in microbial interactions characterise healthy from unhealthy microbiota? For instance, what does this mean for medical research?

      We greatly appreciate the reviewer’s thoughtful analysis highlighting both the strengths and areas of ambiguity in our work.

      (1) To clarify the sentence on the limitations of species abundance distributions (SADs), we aim to explain in the revised version that while SADs summarize the relative abundance of individual species, they fail to capture the species-species correlations that we have shown (Seppi et al., Biomolecules 2023) to be more susceptible to the healthy state of the host. Our method thus focused on the interaction statistics among species, providing insights into underlying dynamics and stability of the microbiomes and their differences between healthy and unhealthy hosts.

      (2) Regarding model assumptions, we acknowledge that the weak interaction regime and symmetry hypotheses simplify the analysis and may not capture all empirical richness, such as fat-tailed distributions of species abundance. However, we interpret instability not as a path to chaos per se, but as a transition toward a multi-attractor phase, where each microbiome reaches a different fixed point. This is consistent with prior empirical findings invoking the “Anna Karenina principle”, where healthy microbiomes resemble one another, but disease states tend to deviate from this picture (see Pasqualini et al., PLOS Comp. Bio. 2024). We consider our framework as a starting point and agree that further extensions incorporating strong interaction regimes (as suggested by Mallmin et al., PNAS 2024) or relaxing other model assumptions could reveal even richer dynamical patterns. The computational pipeline we present can be, in fact, easily generalizable to include different population dynamics models.

      On the technical questions: (a) While compositional data constrain relative abundances, we can still estimate diversity-dependent parameters (h and q0) using alpha-diversity statistics across samples, which show meaningful variation; (b) The counter-intuitive instability that the reviewer pointed out arises from the interplay between demographic stochasticity and quenched disorder. It is the combined contribution of these two factors in phase space – not either one alone – that drives the transition. For clarity, see Figure 1 in Altieri et al., Phys. Rev. Lett. 2021; (c) We plan to include plots that compare empirical data to theoretical model fits. This will help visualize how well the model captures observed microbial community properties demographic noise (𝑇), healthy communities are more stable (i.e., distantσ from the and how even with larger species interaction heterogeneity (σ) and larger critical line), as measured, by the replicon eigenvalue. Finally, regarding interpretability and implications: by showing that ecological interaction networks – not just species identities – differ between healthy and unhealthy states, our work suggests a conceptual shift. This could inform medical strategies aimed at restoring community-level stability rather than targeting individual microbes. In the revised Discussion section, we will elaborate on this point to better highlight its practical implications and outline potential directions for future research.

      Reviewer #3:

      Strengths:

      The modeling efforts of this study primarily rely on a disordered form of the generalized Lotka-Volterra (gLV) model. This model can be appropriate for investigating certain systems, and the authors are clear about when and how more mechanistic models (i.e., consumer-resource) can lead to gLV. Phenomenological models such as this have been found to be highly useful for investigating the ecology of microbiomes, so this modeling choice seems justified, and the limitations are laid out. 

      Weaknesses:

      The authors use metagenomic data of diseased and healthy patients that were first processed in Pasqualini et al. (2024). The use of metagenomic data leads me to a question regarding the role of sampling effort (i.e., read counts) in shaping model parameters such as h. This parameter is equal to the average of 1/# species across samples because the data are compositional in nature. My understanding is that it was calculated using total abundances (i.e., read counts). The number of observed species is strongly influenced by sampling effort, so it would be useful if the number of reads were plotted against the number of species for healthy and diseased subjects. However, the role of sampling effort can depend on the type of data, and my instinct about the role that sampling effort plays in species detection is primarily based on 16S data. The dependency between these two variables may be less severe for the authors' metagenomic pipeline. This potential discrepancy raises a broader issue regarding the investigation of microbial macroecological patterns and the inference of ecological parameters. Often microbial macroecology researchers rely on 16S rRNA amplicon data because that type of data is abundant and comparatively low-cost. Some in microbiology and bioinformatics are increasingly pushing researchers to choose metagenomics over 16S. Sometimes this choice is valid (discovery of new MAGs, investigate allele frequency changes within species, etc.), sometimes it is driven by the false equivalence "more data = better". The outcome, though, is that we have a body of more-or-less established microbial macroecological patterns which rest on 16S data and are now slowly incorporating results from metagenomics. To my knowledge, there has not been a systematic evaluation of the macroecological patterns that do and do not vary by one's choice in 16S vs. metagenomics. Several of the authors in this manuscript have previously compared the MAD shape for 16S and metagenomic datasets in Pasqualini et al., but moving forward, a more comprehensive study seems necessary.

      We thank the reviewer for this insightful and nuanced comment, which particularly highlights the broader methodological context of our data sources. Indeed, metagenomic sequencing introduces different biases with respect to 16S data. First, we would like to emphasize that we estimated the order parameters from the data by using relative abundances. Second, while the concern regarding the influence of sequencing depth and species diversity on the estimation of the order parameters is valid, we refer to a previous publication by some of the authors (Pasqualini et al., 2024; see Figure 4, panels g and h). There, we pointed out that the observed outcome is weakly influenced by sequencing depth in our dataset, while the main impact on the order parameters estimate comes from the species diversity of the two groups. In the same publication, we showed that other well-known patterns (species abundance distribution, mean abundance distribution) are also observed. Also, to mitigate the effect of the number of samples and sequencing depth, we estimated the order parameters by a bootstrap procedure (90% of samples for healthy and diseased groups, 5000 resamples), which resulted in the error bars in Figure 2.

      We also fully agree with the broader call for a systematic comparison of macroecological patterns derived from 16S and metagenomic data. While some of us have already begun exploring this direction (e.g., Pasqualini et al., 2024), the reviewer’s comment highlights its significance and motivates us to pursue a more comprehensive, integrative analysis across data types. While we found qualitative agreement of these patterns with previous publications (e.g., Grilli, Nature Comm. 2020), we will acknowledge this as an important future direction in the Discussion section.

      References

      (1) Seppi, M., Pasqualini, J., Facchin, S., Savarino, E.V. and Suweis, S., 2023. Emergent functional organization of gut microbiomes in health and diseases. Biomolecules, 14(1), p.5.

      (2) Pasqualini, J., Facchin, S., Rinaldo, A., Maritan, A., Savarino, E. and Suweis, S., 2024. Emergent ecological patterns and modelling of gut microbiomes in health and in disease. PLOS Computational Biology, 20(9), p.e1012482.

      (3) Mallmin, E., Traulsen, A. and De Monte, S., 2024. Chaotic turnover of rare and abundant species in a strongly interacting model community. Proceedings of the National Academy of Sciences, 121(11), p.e2312822121.

      (4) Altieri, A., Roy, F., Cammarota, C., & Biroli, G. (2021). Properties of equilibria and glassy phases of the random Lotka-Volterra model with demographic noise. Physical Review Letters, 126(25), 258301.

      (5) Grilli, J. (2020). Macroecological laws describe variation and diversity in microbial communities. Nature communications, 11(1), 4743.

    1. Reviewer #1 (Public review):

      The manuscript by Liao et al investigates the mechanisms that induce ephrin expression in spinal cord lateral motor column (LMC) neurons to facilitate axon guidance into the dorsal and ventral limb. The authors show that Sp1 and its co-activators p300 and CBP are required to induce ephrin expression to modulate the responsiveness of motor neurons to external ephrin cues. The study is well done and convincingly demonstrates the role of Sp1 in motor neuron axon guidance.

      Further discussion and clarification of some results would further improve the study.

      (1) The mechanism that the authors propose (Figure 7) and is also supported by their data is that Sp1 induces ephrinA5 in LMCm and ephrinB2 in LMCl to attenuate inappropriate responses to external ephrins in the limb. Therefore, deletion of Sp1 should result in mistargeting of LMCl and LMCm axons, as shown in the mouse data, but no overt changes in the number of axons in the ventral and dorsal limb. From the mouse backfills, it seems that an equal number of LMCm/LMCl project into the wrong side of the limb. However, the chick data show an increase of axons projecting into the ventral limb in the Sp1 knockout. Is this also true in the mouse? The authors state that medial and lateral LMC neurons differ in their reliance on Sp1 function but that is not supported by the mouse backfill data (27% vs 32% motor neurons mistargeted). Also, the model presented in Figure 7 does not explain how Sp1 overexpression leads to axon guidance defects.

      (2) The authors do not directly show changes in ephrin expression in motor neurons, either in chick or mouse, after Sp1 knockout, which is the basis of their model. The experiment in Figure 4G seems to be Sp1 overexpression rather than knockdown (as mentioned in the results) and NSC-34 cells may not be relevant to motor neurons in vivo. NSC-34 experiments are also not described in the methods.

      (3) There is no information about how the RNA-sequencing experiment was done (which neurons were isolated, how, at what age, how many replicates, etc) so it is hard to interpret the resulting data.

      (4) It is unclear why the authors chose to use a Syn1-cre driver rather than a motor neuron restricted cre driver. Since this is a broad neuronal cre driver, the behavioral defects shown in Figure 7 may not be solely due to Sp1 deletion in motor neurons. Are there other relevant neuronal populations that express Sp1 that are targeted by this cre-mediated deletion?

    2. Reviewer #2 (Public review):

      Summary:

      This study shows that transcription factor Sp1 is required for correct ventral vs. dorsal targeting of limb-innervating LMC motor neurons using mouse and chick as model systems. In a wild-type embryo, lateral LMC axons specifically target dorsal muscles while medial LMC axons target ventral muscles. The authors convincingly show that this specificity is lost when Sp1 is knocked down or knocked out - axons of both lateral and medial LMC motor neurons project to both dorsal and ventral muscles in mutant conditions. The authors then conduct RNA-seq and ChIP experiments to show that Sp1 loss of function disrupts Ephrin-Epha receptor signaling pathway genes. These molecules are known to provide attractive or repulsive cues to guide LMC axons to their targets. The authors show that attraction/repulsion properties of medial and lateral LMC axons to specific Ephrin/Epha molecules are in fact disrupted in Sp1 mutants using ex vivo explant studies. Finally, the authors show that behaviors like coordinated movement and grip strength are also affected in Sp1 mutant mice. This study convincingly shows that Sp1 is important for correct circuit wiring of LMC neurons, and moves the field forward by elucidating a new level of transcriptional regulation required in this process. However, the claims made by the authors that the mode of Sp1-mediated regulation is through cis-attenuation of Epha activity is not well supported. These and additional strengths and weaknesses in approach and in data interpretation are discussed below.

      Strengths:

      (1) The study convincingly shows that wildtype levels of Sp1 are necessary for LMC axon targeting specificity. The combination of the following approaches is a strength:<br /> a) Both loss of function and gain of function experiments are performed for Sp1 and show complementary effects on the axon targeting phenotype.<br /> b) Retrograde labeling of LMC neurons from dorsal and ventral muscles shows that Sp1 mutants clearly lose the specificity of LMC axon targeting.<br /> c) The authors also use explant experiments to show that both loss of Sp1 and gain of Sp1 show clear changes in attraction and repulsion to specific ephrin and epha receptor molecules.<br /> d) The Sp1 loss and gain of function experiments are well controlled to show that the changes in axon wiring observed are not due to cell death, cell fate switches, or due to unequal numbers of medial and lateral LMC neurons being labeled in the experiments.

      (2) It is also convincing that Sp1 requires cofactors p300 and CBP for its function. In the absence of these cofactors, the gain of function phenotypes of Sp1 are subdued.

      Weaknesses:

      (1) The robustness of RNAseq and ChIP experiments is difficult to judge as methods are not described. For example, it is unclear if RNAseq is performed on purified motor neurons or on whole spinal cords. This is an important consideration as Sp1 is a broadly expressed protein.

      (2) The authors state that expression of Ephrin A5 and Ephrin B2 is reduced based on RNAseq data, however, it is not shown that this reduction occurs specifically in LMC neurons.

      (3) The authors show Sp1 ChIP peaks at Ephrin B2 promoter, but nothing is mentioned about peaks at Eprin A5 or other types of signaling molecules like Sema7a, which are also differentially expressed in Sp1 mutants. There is also no mention of the correlation between changes in gene expression seen in RNAseq data and the binding profile of Sp1 seen in ChIP data, which could help establish the robustness of these datasets.

      (4) The authors conclude that Sp1 functions by activating Ephrin A5 in medial LMC and Ephrin B2 in lateral LMC. The argument, as I understand it, is that this activation leads to cis attenuation of their respective Epha receptors and therefore targeting the correct muscle. Though none of the data presented go against this hypothesis, this hypothesis is also not fully supported. Specifically:<br /> a) It would be important to know that modulation of Sp1 expression leads to changes in EphrinA5 and B2 in LMC lateral/medial neurons.<br /> b) It would also be important to show that none of the other changes caused by Sp1 are responsible for axon mistargeting by performing rescue experiments with Ephrin A5 and Ephrin B2.<br /> c) To make the most convincing case, experiments showing increased or decreased cis-binding of Ephrin molecules with Epha receptors would be necessary. This study would still be compelling without this last experiment, but the language in the abstract would need to be modulated.

      (5) All behavior experiments are done in a pan-neuronal knockout of Sp1. As Sp1 is broadly expressed in neurons, a statement describing whether and why the authors think the phenotypes arise from Sp1's function in LMC motor neurons would be helpful. Experimentally, rescue experiments in which Sp1 is restored in LMC neurons or motor neurons would also make this claim more convincing.

    3. Author response:

      Reviewer 1:

      (1) Clarification of axon mistargeting patterns and model interpretation

      We will clarify the apparent discrepancy between chick and mouse axon mistargeting data. Specifically, we will expand the explanation in the main text and Figure 7 legend and/or revise the model in Figure 7 to better reflect observed phenotypes and clarify how Sp1 overexpression contributes to mistargeting.

      (2) Evidence for Sp1-dependent ephrin expression

      We agree that demonstrating ephrin expression changes in motor neurons is essential. We will: • Conduct in situ hybridization and/or immunostaining for ephrins in control and Sp1 mutant spinal cords from both chick and mouse embryos.

      Clarify and expand the methodological details of the NSC-34 cell experiments shown in Figure 4G.

      (3) RNA-seq experiment details

      We will revise the Methods section to provide additional experimental details.

      (4) Use of Syn1-cre

      We acknowledge concerns about the broad expression of Syn1-cre. To address this:

      We will clarify our rationale for using Syn1-cre and describe its expression pattern in the spinal cord.

      We are evaluating the feasibility of additional experiments using a motor neuron-specific Cre driver to confirm cell-type specificity.

      We will include a new paragraph in the Discussion addressing potential contributions from other neuronal populations.

      Reviewer 2:

      (1) & (2) Clarification and localization of RNA-seq data

      We will expand the Methods section to provide greater detail on the RNA-seq approach. In addition, we will validate ephrin downregulation in LMC neurons using in situ hybridization and/or immunostaining.

      (3) Integration of ChIP and RNA-seq data We will:

      Report additional ChIP peaks for ephrinA5 and other differentially expressed genes such as Sema7a.

      Add a summary figure that integrates ChIP and RNA-seq results to strengthen the link between Sp1 binding and transcriptional regulation.

      (4) Clarification of the cis-attenuation model

      We recognize that our data do not yet directly demonstrate Sp1’s role in cis-attenuation. To address this:

      We will revise the abstract and main text to frame Sp1's role in cis-attenuation as a hypothesis. • We are exploring the feasibility of ephrinA5 and B2 rescue experiments in Sp1-deficient embryos to test specificity.

      (5) Behavioral phenotypes and cell-type specificity

      We will clarify that behavioral phenotypes may result from combined effects across neuron populations due to Syn1-cre expression. To address this:

      We are planning rescue experiments with Sp1 expression in chick embryos to test for rescue of axon misrouting.

      We will include a new paragraph in the Discussion to highlight this limitation and discuss alternative interpretations.

      Reviewer 3:

      We appreciate your positive evaluation and support for the rigor of our study.

      In response to your suggestions:

      We are revising the manuscript to improve clarity and flow, particularly the transitions between datasets.

      We will update Figure 7 and the associated text to more clearly convey the working model and avoid overinterpretation.

      We thank all reviewers for their constructive feedback and are committed to addressing each point thoroughly. All revisions will be clearly marked in the resubmitted manuscript.

    1. 等级问题 (Hierarchy Problem)

      好的,我们来详细介绍一下粒子物理学中的等级问题 (Hierarchy Problem)。等级问题是指在基本粒子物理学中,基本相互作用的强度之间存在着巨大且无法解释的差异,特别是引力与其它三种基本力(强力、弱力和电磁力)之间的巨大差距。这个问题也体现在基本粒子的质量尺度上,最显著的是普朗克尺度和电弱尺度之间的巨大差异。

      1. 引言

      等级问题最核心的体现是引力相对于其他基本力的极度微弱。在基本粒子的典型能量尺度上,引力的强度比弱力弱大约 10³⁴ 倍。这种巨大的差异让物理学家感到困惑,因为在理论上,我们期望所有基本力的强度在非常高的能量尺度下(例如普朗克尺度)能够统一起来。

      2. 普朗克尺度 (Planck Scale)

      普朗克尺度大约在 10¹⁹ GeV(吉电子伏特),是理论物理中一个非常重要的能量尺度。在这个尺度下,量子引力效应预计会变得非常显著,我们现有的经典引力理论(广义相对论)将不再适用。普朗克尺度是由三个基本常数构建出来的:引力常数 (G)、普朗克常数 (ħ) 和光速 (c)。

      3. 电弱尺度 (Electroweak Scale)

      电弱尺度大约在 100 GeV 左右,是与弱相互作用以及 W 和 Z 玻色子的质量相关的能量尺度。希格斯玻色子的质量(约 125 GeV)也位于这个尺度附近。标准模型成功地描述了电磁力和弱力在电弱尺度上的统一。

      4. 等级 (The Hierarchy)

      等级问题指的就是普朗克尺度(~10¹⁹ GeV)和电弱尺度(~10² GeV)之间存在的巨大差距,约为 10¹⁷ 个数量级。这意味着,与描述其他基本力的理论相比,描述引力的理论在能量尺度上存在着巨大的“鸿沟”。

      5. 希格斯玻色子与量子修正

      等级问题最尖锐的体现与希格斯玻色子的质量有关。希格斯玻色子是标准模型中赋予其他基本粒子质量的粒子,其质量约为 125 GeV,处于电弱尺度。

      在量子场论中,粒子的质量会受到量子修正的影响,这些修正来源于虚粒子在循环图中的贡献。对于希格斯玻色子来说,它与标准模型中的所有粒子都相互作用,包括那些可能存在于非常高能量尺度上的粒子,理论上甚至可以高达普朗克尺度。

      这些量子修正通常会使得希格斯玻色子的质量趋向于理论中最高的能量尺度(例如普朗克尺度),因为这些修正中存在二次发散。这意味着,如果没有某种机制来抑制这些修正,希格斯玻色子的质量应该非常巨大,远高于我们观测到的 125 GeV。

      6. 精细调节问题 (Fine-Tuning Problem)

      为了使希格斯玻色子的质量保持在观测到的 125 GeV 左右,原始的“裸质量”和巨大的量子修正之间必须存在极其精确的抵消。这种抵消的精度需要达到大约 1 比 10³⁴,这被认为是非常不自然的,需要对理论的参数进行极其精细的“调节”才能实现。这就是等级问题的核心——为什么希格斯玻色子的质量如此之小,远低于普朗克尺度,而量子效应却倾向于将其推高到普朗克尺度?

      7. 为什么这是一个问题?

      物理学家认为这种精细调节是有问题的,主要基于以下几点:

      • 自然性 (Naturalness): 在物理学中,我们通常期望理论的参数具有“自然的”数值,而不是需要极其精确的调整才能与观测结果相符。如此精细的调节似乎暗示着我们的理论可能并不完备,或者存在着我们尚未理解的更深层次的原理。
      • 稳定性 (Stability): 如此精确的抵消需要维持,即使我们考虑更高阶的量子修正。这使得理论显得非常不稳定。

      8. 解决等级问题的可能方案

      物理学家提出了许多理论来试图解决等级问题,以下是一些主要的方案:

      • 超对称 (Supersymmetry, SUSY): 超对称理论引入了费米子和玻色子之间的对称性。对于标准模型中的每一个粒子,都存在一个具有相反自旋统计的超对称伙伴。超对称性可以导致标准模型粒子和它们的超对称伙伴的量子修正相互抵消,将二次发散减弱为对数发散,这是一种更温和的发散。
      • 复合希格斯模型 (Composite Higgs Models): 这类模型认为希格斯玻色子不是一个基本粒子,而是由更基本的组成部分构成的复合粒子。这种机制可以自然地降低产生希格斯质量的能量尺度。
      • 额外维度 (Extra Dimensions): 一些理论提出存在我们无法直接观测到的额外的空间维度。这些额外维度可以降低有效普朗克尺度,使其更接近电弱尺度,从而减小或消除等级差异。例如,大额外维度模型和弯曲额外维度模型(如 Randall-Sundrum 模型)。
      • 人为原理 (Anthropic Principle): 这是一种更具争议的观点,认为宇宙的基本常数和参数被精细调节,以允许生命的存在。这并非一个物理机制,而是一种基于观测偏差的论证。

      9. 实验检验

      这些解决等级问题的方案通常会预言新的粒子或现象,可以通过实验来检验,主要的实验场所是粒子加速器,例如大型强子对撞机 (LHC)。例如,超对称理论预言了超对称粒子的存在,复合希格斯模型预言了希格斯玻色子性质的偏差,而额外维度模型可能导致新粒子的产生或引力在特定能量尺度上的改变。

      10. 目前的状况

      到目前为止,LHC 尚未发现明确的超出标准模型的新粒子,例如预期质量范围内的超对称粒子。这导致一些物理学家重新评估这些理论,并继续寻找新的解决等级问题的思路。

      11. 结论

      等级问题是粒子物理学中最重要和最令人困惑的未解问题之一。希格斯玻色子质量的精细调节问题突显了电弱尺度和普朗克尺度之间巨大的、看似不自然的差异。物理学家们提出了各种各样的理论来试图解决这个问题,而实验的进展将最终帮助我们揭示自然界的真实面貌。解决等级问题可能会引领我们走向超越标准模型的新物理。

    1. The use of a DAG-like structure to solve consensus has been introduced in previous works,especially in asynchronous networks. Hashgraph [ 4 ] builds an unstructured DAG, with eachblock containing two references to previous blocks, and on top of the DAG, the miners runan inefficient binary agreement protocol. This leads to expected exponential time complexity

      What kind of complexity is meant here?

      I guess voting, as witnesses of r+1 vote on each witness of r. But that's n^2, quadratic, and can be computed incrementally..

      Latency is somewhat logarithmical as f(nodes) https://hyp.is/1q-pjhaiEfC4ggdV4IIKxA/arxiv.org/pdf/1912.05895

    1. Reviewer #1 (Public review):

      In this study, Li et al et al. investigated the role of miR-283 in regulating cardiac aging and its potential contribution to age-related bradyarrhythmia. Using Drosophila as a model, the authors demonstrated that systemic overexpression or knockdown of miR-283 induced age-associated bradycardia. Notably, the study found that miR-283 knockdown in ventral-lateral neurons (LNvs), rather than in the heart, was sufficient to induce bradyarrhythmia, an effect the authors linked to the upregulation of miR-283 expression in both the brain and heart. The study also explored the beneficial impact of exercise on cardiac aging, showing that endurance training mitigated bradyarrhythmia, correlating with reduced miR-283 accumulation in the brain and myocardium.

      The conclusions of this paper are mostly well supported by data; however, some concerns arise from the unexpected finding that bradyarrhythmia was triggered by miR-283 knockdown in LNvs rather than in the heart, suggesting a non-cell-autonomous mechanism. A more precise mechanistic explanation linking miR-283 dysregulation in LNvs to cardiac dysfunction would strengthen the study's conclusions. While the authors propose cwo as a potential target of miR-283, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Additionally, it remains unclear whether reduced miR-283 levels in LNvs lead to accelerated aging rather than a cardiac-specific effect. Likewise, the potential influence of miR-283 on the circadian clock and its broader impact on aging warrant further investigation.

      Major Comments:

      (1) A significant concern arises from the unexpected outcome observed in miR-283 knockdown in LNvs, which suggests a non-cell-autonomous mechanism. Elucidating the mechanisms by which miR-283 deficiency leads to the observed phenotypes would provide a more comprehensive understanding of the study's implications.

      (2) The authors propose cwo as a potential target of miR-283; however, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Similarly, direct evidence demonstrating that cwo is a bona fide target of miR-283 in LNvs should be provided.

      (3) It remains unclear whether miR-283 knockdown in LNvs results in accelerated aging rather than a cardiac-specific effect. This hypothesis is supported by observations that pdf>miR-283SP animals exhibit systemic premature senescence (elevated SA-β-gal activity in both the heart and brain), cardiac dysfunction, impaired climbing ability, and reduced lifespan.

      (4) The finding that reduced miR-283 levels in LNvs lead to accelerated aging raises an important, yet unexplored, question: does miR-283 influence the circadian clock, thereby broadly affecting aging?

      Two aspects of this question should be addressed:<br /> (a) Is the circadian rhythm disrupted in miR-283 knockdown experiments?<br /> (b) Do circadian rhythm defects impact aging?

      (5) The authors state that miR-283 knockdown in LNvs led to bradyarrhythmia, which was mainly caused by miR-283 upregulation in the whole brain and heart. However, it is unclear which experiments support this conclusion. Could the authors clarify this point?

      (6) Given that miR-283 expression varies with age, could the upregulation of miR-283 in both the brain and heart be a consequence of accelerated aging rather than a specific effect of miR-283 knockdown in LNvs?

      (7) While the beneficial effects of exercise on cardiac function appear clear, the claim that this effect is mediated through miR-283 function in LNvs seems premature. The data suggest that exercise-induced improvement occurs in both wild-type and miR-283-SP animals, raising the possibility that exercise acts through a miR-283-independent mechanism.

    1. Reviewer #1 (Public review):

      Summary:

      In this manuscript, Rosero and Bai examined how the well-known thermosensory neuron in C. elegans, AFD, regulates context-dependent locomotory behavior based on the tactile experience. Here they show that AFD uses discrete cGMP signalling molecules and independent of its dendritic sensory endings regulates this locomotory behavior. The authors also show here that AFD's connection to one of the hub interneurons, AIB, through gap junction/electrical synapses, is necessary and sufficient for the regulation of this context-dependent locomotion modulation.

      Strengths:

      This is an interesting paper showcasing how a sensory neuron in C. elegans can employ a distinct set of molecular strategies and different physical parts to regulate a completely distinct set of behaviors, which were not been shown to be regulated by AFD before. The experiments were well performed and the results are clear. However, there are some questions about the mechanism of this regulation. This reviewer thinks that the authors should address these concerns before the final published version of this manuscript.

      Weaknesses:

      (1) The authors argued about the role of prior exposure to different physical contexts which might be responsible for the difference in their locomotory behaviour. However, the worms in the binary chamber (with both non-uniformly sized and spaced pillars) experienced both sets of pillars for one hour prior to the assay and they were also free to move between two sets of environments during the assay. So, this is not completely a switch between two different types of tactile barriers (or not completely restricted to prior experience), but rather a difference between experiencing a more complex environment vs a simple uniform environment. They should rephrase their findings. To strictly argue about the prior experience, the authors need to somehow restrict the worms from entering the uniform assay zone during the 1hr training period.

      (2) The authors here argued that the sensory endings of AFD are not required for this novel role of AFD in context-dependent locomotion modulation. However, gcy-18 has been shown to be exclusively localized to the ciliated sensory endings of AFD and even misexpression of GCY-18 in other sensory neurons also leads to localizations in sensory endings (Nguyen et. al., 2014 and Takeishi et. al., 2016). They should check whether gcy-18 or tax-2 gets mislocalized in kcc-3 or tax-1 mutants.

      (3) MEC-10 was shown to be required for physical space preference through its action in FLP and not the TRNs (PMID: 28349862). Since FLP is involved in harsh touch sensation while TRNs are involved in gentle touch sensation, which are the neuron types responsible for tactile sensation in the assay arena? Does mec-10 rescue in TRNs rescue the phenotype in the current paper?

      (4) The authors mention that the most direct link between TRNs and AFD is through AIB, but as far as I understand, there are no reports to suggest synapses between TRNs and AIB. However, FLP and AIB are connected through both chemical and electrical synapses, which would make more sense as per their mec-10 data. (the authors mentioned about the FLP-AIB-AFD circuit in their discussion but talked about TRNs as the sensory modality). mec-10 rescue experiment in TRNs would clarify this ambiguity.

      (5) Do inx-7 or inx-10 rescue in AFD and AIB using cell-specific promoters rescue the behaviour?

      (6) How Guanylyl cyclase gcy-18 function is related to the electrical synapse activity between AFD and AIB? Is AFD downstream or upstream of AIB in this context?

    2. Reviewer #3 (Public review):

      Summary:

      Rosero and Bai report an unconventional role of AFD neurons in mediating tactile-dependent locomotion modulation, independent of their well-established thermosensory function. They partially elucidate the signaling mechanisms underlying this AFD-dependent behavioral modulation. The regulation does not require the sensory dendritic endings of AFD but rather the AFD neurons themselves. This process involves a distinct set of cGMP signaling proteins and CNG channel subunits separate from those involved in thermosensation or thermotaxis. Furthermore, the authors demonstrate that AIB interneurons connect AFD to mechanosensory circuits through electrical synapses. They conclude that, beyond its primary function in thermosensation, AFD contributes to context-dependent neuroplasticity and behavioral modulation via broader circuit connectivity.

      While the discovery of multifunctionality in AFD is not entirely unexpected, given the limited number of neurons in C. elegans (302 in total), the molecular and cellular mechanisms underlying this AFD-dependent behavioral modulation, as revealed in this study, provide valuable insights into the field.

      Strengths:

      (1) The authors uncover a novel role of AFD neurons in mediating tactile-dependent locomotion modulation, distinct from their well-established thermosensory function.

      (2) They provide partial insights into the signaling mechanisms underlying this AFD-dependent behavioral modulation.

      (3) The neural behavior assays utilizing two types of microfluidic chambers (uniform and binary chambers) are innovative and well-designed.

      (4) By comparing AFD's role in locomotion modulation to its thermosensory function throughout the study, the authors present strong evidence supporting these as two independent functions of AFD.

      (5) The finding that AFD contributes to context-dependent behavioral modulation is significant, further reinforcing the growing evidence that individual neurons can serve multiple functions through broader circuit connectivity.

      Weaknesses:

      (1) Limited Behavioral Assays: The study relies solely on neural behavior assays conducted using two types of microfluidic chambers (uniform and binary chambers) to assess context-dependent locomotion modulation. No additional behavioral assays were performed. To strengthen the conclusions, the authors should validate their findings using an independent method, at the very least by testing AFD-ablated animals and gcy-18 mutants with a second behavioral approach.

      (2) Clarity in Behavioral Assay Methodology: The methodology for conducting the behavioral assays is unclear. It appears that worms were free to move between the exploration and assay zones, with no control over the duration each worm spent in either zone. This lack of regulation may introduce variability in tactile experience across individuals, potentially affecting the reproducibility and quantitativeness of the method. The authors should clarify whether and how they accounted for this variability.

      (3) Potential Developmental and Behavioral Confounds in Mutant Analysis: Several neuronal mutant strains were used in this study, yet the effects of these mutations on development and general behavior (e.g., movement ability) were not discussed. Although young adult worms were used for behavioral assays, were they at similar biological ages? To rule out confounding factors, locomotion assays assessing movement ability should be conducted (see reference PMID 25561524).

      (4) Definition and Baseline Measurements for Locomotion Categories: The finding that tax-4 and kcc-3 contribute to basal locomotion but not to context-dependent locomotion modulation is intriguing. The authors argue that distinct mechanisms regulate these two processes; however, the study does not clearly define the concepts of "basal locomotion" and "context-dependent locomotion," nor does it provide baseline measurements. A clear definition and baseline data are needed to support this conclusion.

    1. Reviewer #1 (Public review):

      Summary:

      In this manuscript, Campbell et al. assess how intracranial theta-burst stimulation (TBS) applied to the basolateral amygdala in 23 epilepsy patients affects neuronal spiking in the medial temporal lobe and prefrontal cortex during a visual recognition memory task.

      Strengths:

      This is an incredibly rare dataset; collecting single-unit spiking data from behaving humans during active intracranial stimulation is a Herculaean task, with immense potential for translational studies of how stimulation may be applied to modulate biological mechanisms of memory. The authors utilize careful, high-quality methodology throughout (e.g. task design, spike recording and sorting, statistical analysis), providing high confidence in the validity of their findings.

      Weaknesses:

      (1) This is an exploratory study that doesn't explore quite enough. Critically, the authors make a point of mentioning that neuronal firing properties vary across cell types, but only use baseline firing rate as a proxy metric for cell type. This leaves several important explorations on the table, not limited to the following:<br /> a) Do waveform shape features, which can also be informative of cell type, predict the effect of stimulation?<br /> b) Is the autocorrelation of spike timing, which can be informative about temporal dynamics, altered by stimulation? This is especially interesting if theta-burst stimulation either entrains theta-rhythmic spiking or is more modulatory of endogenously theta-modulated units.<br /> c) The authors reference the relevance of spike-field synchrony (30-55 Hz) in animal work, but ignore it here. Does spike-field synchrony (comparing the image presentation to post-stimulation) change in this frequency range? This does not seem beyond the scope of investigation here.<br /> d) How does multi-unit activity respond to stimulation? At this somewhat low count of neurons (total n=156 included) it would be valuable to provide input on multi-unit responses to stimulation as well.<br /> e) Several intracranial studies have implicated proximity to white matter in determining the effects of stimulation on LFPs; do the authors see an effect of white matter proximity here?

      (2) It is a little confusing to interpret stimulation-induced modulation of neuronal spiking in the absence of stimulation-induced change in behavior. How do the authors findings tell us anything about the neural mechanisms of stimulation-modulated memory if memory isn't altered? In line with point #1, I would suggest a deeper dive into behavior (e.g. reaction time? Or focus on individual sessions that do change in Figure 4A?) to make a stronger statement connecting the neural results to behavioral relevance.

      (3) It is not clear to me why the assessment of firing rates after image onset and after stim offset is limited to one second - this choice should be more theoretically justified, particularly for regions that spike as sparsely as these.

      (4) This work coincides with another example of human intracranial stimulation investigating the effect on firing rates (doi: https://doi.org/10.1101/2024.11.28.625915). Given how incredibly rare this type of work is, I think the authors should discuss how their work converges with this work (or doesn't).

      (5) What information does the pseudo-population analysis add? It's not totally clear to me.

    1. Reviewer #1 (Public review):

      This is an interesting manuscript aimed at improving the transcriptome characterization of 52 C. elegans neuron classes. Previous single-cell RNA seq studies already uncovered transcriptomes for these, but the data are incomplete, with a bias against genes with lower expression levels. Here, the authors use cell-specific reporter combinations to FACS purify neurons and bulk RNA sequencing to obtain better sequencing depth. This reveals more rare transcripts, as well as non-coding RNAs, pseudogenes, etc. The authors develop computational approaches to combine the bulk and scRNA transcriptome results to obtain more definitive gene lists for the neurons examined.

      To ultimately understand features of any cell, from morphology to function, an understanding of the full complement of the genes it expresses is a pre-requisite. This paper gets us a step closer to this goal, assembling a current "definitive list" of genes for a large proportion of C. elegans neurons. The computational approaches used to generate the list are based on reasonable assumptions, the data appear to have been treated appropriately statistically, and the conclusions are generally warranted. I have a few issues that the authors may choose to address:

      (1) As part of getting rid of cross-contamination in the bulk data, the authors model the scRNA data, extrapolate it to the bulk data and subtract out "contaminant" cell types. One wonders, however, given that low expressed genes are not represented in the scRNA data, whether the assignment of a gene to one or another cell type can really be made definitive. Indeed, it's possible that a gene is expressed at low levels in one cell, and high levels in another, and would therefore be considered a contaminant. The result would be to throw out genes that actually are expressed in a given cell type. The definitive list would therefore be a conservative estimate, and not necessarily the correct estimate.

      (2) It would be quite useful to have tested some genes with lower expression levels using in vivo gene-fusion reporters to assess whether the expression assignments hold up as predicted. i.e. provide another avenue of experimentation, non-computational, to confirm that the decontamination algorithm works.

      (3) In many cases, each cell class would be composed of at least 2 if not more neurons. Is it possible that differences between members of a single class would be missed by applying the cleanup algorithms? Such transcripts would be represented only in a fraction of the cells isolated by scRNAseq, and might then be considered not real.

      (4) I didn't quite catch whether the precise staging of animals was matched between the bulk and scRNAseq datasets. Importantly, there are many genes whose expression is highly stage-specific or age-specific so even slight temporal differences might yield different sets of gene expression.

      (5) To what extent does FACS sorting affect gene expression? Can the authors provide some controls?

    2. Reviewer #3 (Public review):

      The manuscript by Barrett et al. "Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons" presents a comprehensive approach to integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to refine transcriptomic profiles of individual C. elegans neurons. The study addresses the limitations of scRNA-seq, such as the under-detection of lowly expressed and non-polyadenylated transcripts, by leveraging the sensitivity of bulk RNA-seq. The authors deploy a computational method, LittleBites, to remove non-neuronal contamination in bulk RNA-seq, that aims to enhance specificity while preserving the sensitivity advantage of bulk sequencing. Using this approach, the authors identify lowly expressed genes and non-coding RNAs (ncRNAs), many of which were previously undetected in scRNA-seq data.

      Overall, the study provides high-resolution gene expression data for 53 neuron classes, covering a wide range of functional modalities and neurotransmitter usage. The integrated dataset and computational tools are made publicly available, enabling community-driven testing of the robustness and reproducibility of the study. Nevertheless, while the study represents a relevant contribution to the field, certain aspects of the work require further refinement to ensure the robustness and rigor necessary for peer-reviewed publication. Below, I outline the areas where improvements are needed to strengthen the overall impact and reliability of the findings.

      (1) The study relies on thresholding to determine whether a gene is expressed or not. While this is a common practice, the choice of threshold is not thoroughly justified. In particular, the choice of two uniform cutoffs across protein-encoding RNAs and of one distinct threshold for non-coding RNAs is somewhat arbitrary and has several limitations. This reviewer recommends the authors attempt to use adaptive threshold-methods that define gene expression thresholds on a per-gene basis. Some of these methods include GiniClust2, Brennecke's variance modeling, HVG in Seurat, BASiCS, and/or MAST Hurdle model for dropout correction.

      (2) Most importantly, the study lacks independent experimental validation (e.g., qPCR, smFISH, or in situ hybridization) to confirm the expression of newly detected lowly expressed genes and non-coding RNAs. This is particularly important for validating novel neuronal non-coding RNAs, which are primarily inferred from computational approaches.

      (3) The novel biology is somewhat limited. One potential area of exploration would be to look at cell-type specific alternative splicing events.

      (4) The integration method disproportionately benefits neuron types with limited representation in scRNA-seq, meaning well-sampled neuron types may not show significant improvement. The authors should quantify the impact of this bias on the final dataset.

      (5) The authors employ a logit transformation to model single-cell proportions into count space, but they need to clarify its assumptions and potential pitfalls (e.g., how it handles rare cell types).

      (6) The LittleBites approach is highly dependent on the accuracy of existing single-cell references. If the scRNA-seq dataset is incomplete or contains classification biases, this could propagate errors into the bulk RNA-seq data. The authors may want to discuss potential limitations and sensitivity to errors in the single-cell dataset, and it is critical to define minimum quality parameters (e.g. via modeling) for the scRNAseq dataset used as reference.

      (7) Also very important, the LittleBites method could benefit from a more intuitive explanation and schematic to improve accessibility for non-computational readers. A supplementary step-by-step breakdown of the subtraction process would be useful.

      (8) In the same vein, the ROC curves and AUROC comparisons should have clearer annotations to make results more interpretable for readers unfamiliar with these metrics.

      (9) Finally, after the correlation-based decontamination of the 4,440 'unexpressed' genes, how many were ultimately discarded as non-neuronal?<br /> a) Among these non-neuronal genes, how many were actually known neuronal genes or components of neuronal pathways (e.g., genes involved in serotonin synthesis, synaptic function, or axon guidance)?<br /> b) Conversely, among the "unexpressed" genes classified as neuronal, how many were likely not neuron-specific (e.g., housekeeping genes) or even clearly non-neuronal (e.g., myosin or other muscle-specific markers)?

      (10) To increase transparency and allow readers to probe false positives and false negatives, I suggest the inclusion of:<br /> a) The full list of all 4,440 'unexpressed' genes and their classification at each refinement step. In that list flag the subsets of genes potentially misclassified, including:<br /> - Neuronal genes wrongly discarded as non-neuronal.<br /> - Non-neuronal genes wrongly retained as neuronal.<br /> b) Add a certainty or likelihood ranking that quantifies confidence in each classification decision, helping readers validate neuronal vs. non-neuronal RNA assignments.<br /> This addition would enhance transparency, reproducibility, and community engagement, ensuring that key neuronal genes are not erroneously discarded while minimizing false positives from contaminant-derived transcripts.

    1. Reviewer #1 (Public review):

      Summary:

      The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine mapping.

      Strengths:

      The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

      Weaknesses:

      (1) Although the authors employ state-of-the-art analytical approaches for the identification of SVs, the overall accuracy remains suboptimal, as indicated by an F1 score of 74.0%, particularly in tandem repeat regions. To enhance accuracy, it would be beneficial to explore alternative SV detection methods or develop novel approaches. Given the value of the reference panel and the fact that improved SV accuracy would lead to more precise SV imputation and GWAS results, investing effort in methodological refinement is highly encouraged.

      (2) From the Methods section, it appears that the authors employed Beagle for both the "leave-one-out" imputation and the UK Biobank imputation. It would be better to explicitly clarify this in the Results section and provide a detailed description of the corresponding procedures and parameters in the Methods section for both analyses, as this represents a key aspect of the study. Additionally, Beagle is not specifically designed for SV imputation, the imputation quality of SVs is generally lower than that of SNVs. Exploring strategies to improve SV imputation, such as developing a novel method with reference panel data, may enhance performance. It is also important to assess how this reduced imputation quality may influence GWAS results. For instance, it would be useful to examine whether associated SVs exhibit higher imputation quality and whether SVs with lower quality are less likely to achieve significant association signals. In addition, the lower imputation quality observed for INV, DUP, and BND variants (Figure 3) may be due to their greater lengths (Figure 2). It is better to investigate the relationship between SV length and imputation quality.

      (3) All examples presented in the manuscript focus on SVs that overlap with genes. It may also be valuable to investigate SVs that do not overlap with genes but intersect with enhancer regions. SVs can contribute to disease by altering regulatory elements, such as enhancers, which play a crucial role in gene expression. Including such analyses would further demonstrate the utility of SV-GWAS and provide deeper insights into the functional impact of SVs.

      (4) The data availability link currently provides only a VCF file ("sniffles2_joint_sv_calls.vcf.gz") containing the identified SVs. It would be beneficial for the authors to make all raw sequencing data (FASTQ files) and key processed datasets (such as alignment results and merged SV and SNV files) available. Providing these resources would enable other researchers to develop improved SV detection and imputation methods or conduct further genetic analyses. Furthermore, establishing a dedicated website for data access, along with a genome browser for SV visualization, could significantly enhance the impact and accessibility of the study. Additionally, all code, particularly the SV imputation pipeline accompanied by a detailed tutorial, should be deposited in a public repository such as GitHub. This would support researchers in imputing SVs and conducting SV-GWAS on their own datasets.

    2. Reviewer #3 (Public review):

      Summary:

      This study successfully identified genetic loci associated with various traits by generating large-scale long-read sequencing data from a diverse set of samples. This study is significant because it not only produces large-scale long-read genome sequencing data but also demonstrates its application in actual genetics research. Given its potential utility in various fields, this study is expected to make a valuable contribution to the academic community and to this journal. However, there are several critical aspects that could be improved. Below are specific comments for consideration.

      Strengths:

      Producing high-quality, large-scale variant datasets and imputation datasets

      Weaknesses:

      (1) Data availability

      Currently, it appears that only the Genomic Lens SV Panel is available on the webpage described in the Data Availability section. It is unclear whether the authors intend to release the raw sequencing data. Since the study utilized samples from the 1000 Genomes Project, there should be no restriction on making the data publicly accessible. Given this, would the authors consider making the raw sequencing reads publicly available? If so, NCBI SRA or EBI ENA would be the most appropriate repositories for data deposition. I strongly encourage the authors to consider public data release.

      Additionally, accessing the Genomic Lens SV Panel data does not seem straightforward. The manuscript should provide a more detailed description of how researchers can access and utilize these data. In my opinion, the best approach would be to upload the variant data (VCF files) to a public database such as the European Variation Archive (EVA) hosted by EBI.

      I strongly request that the authors publicly deposit the variant data. At a minimum:

      a) The joint genotype data for all 888 samples from the 1000 Genomes Project must be publicly available.<br /> b) For the UK Biobank samples, at least allele frequency data should be disclosed.

      Since eLife has a well-established data-sharing policy, compliance with these guidelines is essential for publication in this journal.

      (2) Long-read sequencing data quality

      While the manuscript presents N50 read length and mean or median read base quality for each sample in a table, it would be highly beneficial to visualize these data in figures as well. A violin plot or similar visualization summarizing these distributions would significantly improve data presentation.

      Notably, the base quality of ONT long-read sequencing data appears lower than expected. This may be attributed to the use of pore version 9.4.1, but the unexpectedly low base quality still warrants attention. It would be helpful to include a small figure within Figure 2 to illustrate this point. A visual representation of read length distribution and base quality distribution would strengthen the manuscript.

      (3) Variant detection precision, recall, and F1 score

      This study focuses on insertions and deletions (indels) {greater than or equal to}50 bp, but it remains unclear how well variants <50 bp are detected. I am particularly interested in the precision, recall, and F1 score for variants between 5-49 bp.

      While ONT base quality is relatively low, single-base variants are challenging to analyze, but variants {greater than or equal to}5 bp should still be detectable as their read accuracy is still approximately 90%, making analysis feasible. Given that Sniffles supports the detection of variants as small as 1 bp, I strongly encourage the authors to conduct an additional analysis.

      A simple two-category classification (e.g., 5-49 bp and {greater than or equal to}50 bp) should suffice. Additionally, a comparative analysis with HiFi and short-read sequencing data would be highly valuable. If possible, I strongly recommend that all detected variants {greater than or equal to}5 bp be made publicly available as VCF files.

      (4) Assembly-based methods

      Given the low read accuracy and low sequencing depth in this dataset, it is understandable that genome assembly is challenging. However, the latest high-quality human genome datasets-such as those produced by the Human Pangenome Reference Consortium (HPRC)-demonstrate that assembly-based approaches provide significant advantages, particularly for resolving complex and long structural variants.

      Since HPRC data also utilize 1000 Genomes Project samples, it would be highly informative to compare the accuracy of ONT sequencing in this study with HPRC's assembly-based genome data. The recent publication on 47 HPRC samples provides a valuable reference for such a comparison. Given its relevance, the authors should consider providing a comparative analysis with HPRC data.

      References:

      (1) A draft human pangenome reference<br /> https://www.nature.com/articles/s41586-023-05896-x

      (2) The Human Pangenome Project: a global resource to map genomic diversity<br /> https://www.nature.com/articles/s41586-022-04601-8

      (3) A pangenome reference of 36 Chinese populations<br /> https://www.nature.com/articles/s41586-023-06173-7

      (4) Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits<br /> https://www.nature.com/articles/s41588-021-00865-4

      (5) Increased mutation and gene conversion within human segmental duplications<br /> https://www.nature.com/articles/s41586-023-05895-y

      (6) Structural polymorphism and diversity of human segmental duplications<br /> https://www.nature.com/articles/s41588-024-02051-8

      (7) Highly accurate Korean draft genomes reveal structural variation highlighting human telomere evolution<br /> https://academic.oup.com/nar/article/53/1/gkae1294/7945385

  2. inst-fs-iad-prod.inscloudgate.net inst-fs-iad-prod.inscloudgate.net
    1. By 2013, 46 percent of the county's population spoke a language other than English at home. 2 Latino immi-grants today make up more than a third of che population

      This really shows how much Orange County has changed over the years. It’s no longer just the stereotype of a wealthy, white suburb—it’s way more diverse now. The fact that almost half the people speak a different language at home says a lot about the shift. It makes you think about how places evolve and how important it is to recognize those changes.

    2. I think this sentence just really shows the wealth disparity between people of different races especially between white people and people of color (particularly blacks and Hispanics as they have been marginalized in this country for hundreds of years). It is so interesting that 2 cities that are so close together have such different per capita incomes. It is clearly not an issue of difference in location or industry, but rather because of race. I think there are many systems in place that prevent some minorities from being able to make the same amount of money as their white counterparts, a large one being access to resources such as education.

    1. 图中5种顶点

      这张图展示了各种费曼图顶点(vertices)在不同相互作用中的表现。费曼图是粒子物理学中用来描述粒子之间相互作用的工具。图中的每个部分对应不同类型的基本相互作用,以下是对图中各个部分的详细解释:

      1. 强相互作用顶点(Strong Vertices)

      • 强相互作用胶子(gluon)传递,作用于夸克胶子之间。在图中,强相互作用顶点通常表现为夸克与胶子的交互。
      • 这些顶点代表了夸克之间的强相互作用,并且涉及量子色动力学(QCD)。胶子是无质量的,与夸克通过色荷相互作用。

      2. 弱相互作用顶点(Weak Vertices)

      • 弱相互作用涉及W玻色子Z玻色子,它们是传递弱相互作用的媒介粒子。
      • 在图中,弱相互作用顶点通常包括电子夸克以及W/Z玻色子的交互。
      • 弱相互作用负责某些粒子的衰变过程,如β衰变,并且它在左手性粒子中表现得尤为显著。

      3. 电磁相互作用顶点(Electromagnetic Vertex)

      • 电磁相互作用通过光子(photon)进行,它影响带电粒子(如电子、夸克等)。
      • 在图中,电磁相互作用顶点通常是光子与粒子的耦合,表现为费曼图中的γ线。这个相互作用是通过电荷传递的,作用于所有带电粒子。

      4. 电弱相互作用顶点(Electroweak Vertices)

      • 电弱相互作用电磁力弱相互作用结合在一起,媒介粒子为WZ玻色子,并且通过它们与粒子进行相互作用。
      • 图中表示了电子夸克与这些玻色子的相互作用,表现为多个粒子和玻色子之间的耦合。这一部分展示了标准模型电弱统一理论的核心。

      5. 希格斯相互作用顶点(Higgs Vertices)

      • 希格斯机制是标准模型的一个关键部分,它涉及到希格斯玻色子与其他粒子的相互作用,解释了粒子质量的来源。
      • 在图中,希格斯相互作用顶点展示了希格斯玻色子与其他基本粒子(如电子、夸克等)相互作用的方式。这些相互作用通过希格斯场传递,使粒子获得质量。

      总结

      这张图通过不同类型的费曼图顶点展示了粒子之间相互作用的基本方式,涵盖了强相互作用弱相互作用电磁相互作用电弱相互作用以及希格斯机制。每个顶点代表了不同类型的相互作用,在粒子物理学的标准模型中具有重要意义。图中的线条和符号帮助理解不同粒子间的相互作用如何影响它们的性质和行为。

    2. 幽默费曼图

      上图呈现了一系列学术互动的Feynman图,借用物理学中的Feynman图来幽默地描绘不同学术场合中的互动。每一行展示了学术环境中的不同情景,用类比的方式和物理学中的量子粒子交互来描述这些学术情境。下面是图中各部分的详细解释:

      第一行:学生和教授的互动

      1. Student avoids Professor
      2. 描述了学生躲避教授的情况。图中学生的轨迹是一个远离教授的线,暗示学生在尽量避开教授的注意。这个场景可能是指学生回避与教授交流,可能因为工作不够进展或不想面对教授的要求。

      3. Professor Ignores Grad Student

      4. 描述了教授忽视研究生的情况。图中教授的线条与学生的线条方向相反,暗示教授不对学生做出任何反应或忽略了学生的存在。这个场景幽默地反映了教授忙于其他事务或未对学生的工作给予反馈。

      第二行:学生和教授之间的互动

      1. Mutual Avoidance
      2. 描述学生和教授都在互相避免。图中的两条线交叉,指示两者彼此都有回避的态度,这通常发生在学生和教授都没有主动联系或交流时,可能是因为某种不便或尴尬的情境。

      3. "Advising"

      4. 描述教授的指导。在这个场景中,学生和教授的线条交错,表示学生希望得到指导,但教授看似有些困惑或不明确如何提供帮助。这里以虚拟导师困惑的学生为幽默描述,暗示教授提供的建议可能不够明确或学生感觉教授给出的指导不够有效。

      第三行:学术互动的其他情境

      1. Quirk Exchange
      2. 描述学生与教授之间的“怪癖交换”。图中的相互交错的波动线条代表教授和学生在不同的理解和工作方式上的互动,幽默地反映了学生和教授之间的沟通不顺畅或者彼此的思想差异。

      3. Postdoc Propagation

      4. 描述了博士后与教授之间的关系。图中显示博士后经历了从第一职位到第二、第三职位的变化,暗示博士后在学术界的职业发展过程可能不是一帆风顺的,可能多次经历职位转换或者不稳定状态。这里“教授”和“博士后”的线条表示两者间的互动,表明博士后在求职过程中可能会依赖教授的推荐。

      第四行:学术世界的挑战

      1. Graduation Annihilation
      2. 这部分描述了学位论文提交和审查过程,表现出学生和学术委员会成员之间的互动。图中学生的“工作”被审查和评审过程所“消耗”,这反映了学生在完成学位要求时面对的高压力和严苛审查。

      3. Productivity Decay

      4. 描述学术生涯中的生产力衰退,图中反映了“研究生”与“全球研究”之间的互动,暗示学术工作中的“生产力”在长时间内逐渐衰退,或许因为长时间的研究和焦虑使得工作逐步减少。这里幽默地提到“你听过PHD漫画吗?”,可能在暗示学术环境中的压力和讽刺。

      总结

      这幅图通过运用量子物理学中的Feynman图风格,将学术界中的常见情境进行了幽默的描绘。每个情境对应着学术生涯中的一个典型阶段,反映了学生、教授、博士后等学术成员之间的复杂互动。Feynman图用来表现这些学术“相互作用”不仅为物理学家所熟知,同时也给这些学术困境增添了一些幽默的色彩。

    1. 强CP问题

      强CP问题是粒子物理中的一个深刻且未解的难题,涉及到量子色动力学(QCD)中的CP对称性破缺。这个问题之所以重要,是因为它直接关系到我们如何理解强相互作用的行为,尤其是对称性破缺以及物理常数的精确度。

      1. CP对称性破缺与强相互作用

      • CP对称性是粒子物理中的一种对称性,它包括C(电荷共轭)对称性P(空间反射)对称性
      • C对称性:物理过程在粒子和反粒子之间对称。
      • P对称性:物理过程在空间反射(镜像对称)下是对称的。

      • CP破缺指的是某些物理过程在粒子和反粒子之间以及空间反射下并不对称。我们知道,弱相互作用已经展示了显著的CP破缺(如K介子衰变B介子衰变),但是我们尚未观察到在强相互作用中的CP破缺。

      2. 强相互作用中的CP破缺问题

      强CP问题的核心在于,强相互作用理论(量子色动力学,QCD)中有一个参数,叫做θ参数θ-QCD),它表示强相互作用中潜在的CP破缺。如果这个θ参数是非零的,那么我们就应该在强相互作用中观察到明显的CP破缺

      3. θ参数与CP破缺

      在QCD中,理论上强相互作用的拉格朗日量中包含一个与CP破缺相关的项。这个项涉及到一个被称为θ参数的量,它影响强相互作用的行为:

      [ \mathcal{L}{\text{QCD}} = \mathcal{L}\text{QCD} + \theta \frac{g_s^2}{32\pi^2} G_{\mu\nu}^a \tilde{G}^{a \mu\nu} ]

      其中: - \( G_{\mu\nu}^a \) 是胶子场强张量,描述强相互作用中的胶子场。 - \( \tilde{G}^{a \mu\nu} \) 是这个场强张量的双重反演。 - \( \theta \) 是一个常数,表示强相互作用中的潜在CP破缺源。

      如果θ参数是非零的,这意味着强相互作用中存在CP破缺,从而产生一系列实验上可以检测到的效应。例如,应该在中子和其反粒子的行为中看到CP破缺的迹象。

      4. 为什么强CP问题是一个谜?

      尽管理论上QCD中存在θ参数,实验上我们从未观察到强相互作用中的CP破缺。事实上,实验的精度已经到达了极高的水平,且结果显示强相互作用几乎是CP对称的。例如,实验测量表明中子电偶极矩的上限非常小(大约 \(10^{-26} \, e \cdot \text{cm}\))。根据标准QCD理论,如果θ参数非零,那么中子应该拥有显著的电偶极矩,而我们观察到的中子电偶极矩极小,远小于标准模型的预测值。

      这个差异被称为强CP问题,即:为什么θ参数非常小,甚至接近零? 这是一个尚未得到合理解释的问题。

      5. 强CP问题的解决尝试

      科学家提出了几种可能的方式来解决强CP问题:

      (1) 色召唤(Peccei-Quinn)机制和轴子

      色召唤机制是解决强CP问题的一个重要理论。由PecceiQuinn在1977年提出,色召唤机制假设了一个新的自旋0的粒子,即轴子(Axion),它被引入来解决强CP问题。

      • 轴子是一个轻的无质量粒子,它的存在可以通过改变θ参数的效应,迫使它的值在宇宙演化的过程中自然地趋近于零,从而避免了强相互作用中显著的CP破缺效应。
      • 轴子被预测为一个与暗物质相关的粒子,并且成为暗物质候选粒子之一。通过寻找轴子的存在(如轴子质量和交互作用),物理学家有望找到解决强CP问题的线索。

      (2) θ参数的极小值

      另一种解决方案是假设θ参数本身非常小,甚至为零。某些对称性破缺机制(例如通过超对称性)可能会导致θ参数在自然界中本身为零或者极小。然而,这个假设并没有为我们提供任何具体的机制,仍然没有直接的理论支持。

      (3) 超对称性与额外维度

      超对称性(SUSY)额外维度理论也可能提供解决强CP问题的途径。这些理论可能引入新的粒子和相互作用,导致θ参数变得非常小或通过其他机制自然趋近于零。

      6. 实验探测

      由于强CP问题的解决与轴子的存在密切相关,因此一些实验正致力于探测轴子及其相关效应: - 轴子探测实验:如CASPEr实验等,正在探测与轴子相关的信号。 - 中子电偶极矩实验:通过精确测量中子和其他重子的电偶极矩,实验可以进一步约束θ参数的范围,进而帮助理解强CP问题。

      7. 总结

      强CP问题是粒子物理中的一个深刻未解难题,涉及到量子色动力学(QCD)中的CP对称性破缺。标准模型中,QCD理论中含有一个θ参数,它本应引入强相互作用中的CP破缺效应,但实验上我们没有观察到这一效应,导致强CP问题的出现。解决这一问题的一个可能途径是引入轴子,它可能解释为什么θ参数如此小。尽管有一些理论框架(如色召唤机制)提出了解决方案,强CP问题仍然是粒子物理中的一个悬而未解的难题。

    2. 重子不对称性问题

      重子不对称性(Baryon Asymmetry)是指在宇宙中物质反物质之间的不对称性,尤其是在重子(即由三个夸克组成的粒子,如质子和中子)和反重子(由三个反夸克组成的粒子)之间的差异。这个问题是粒子物理和宇宙学中的一个关键未解之谜,因为我们观察到宇宙几乎完全由物质组成,而几乎没有反物质。

      1. 重子不对称性问题的核心

      • 物质与反物质的对称性:根据物理学中的CPT对称性(电荷共轭对称性、空间反射对称性和时间反演对称性),在粒子物理的标准模型中,物质和反物质应该是完全对称的。也就是说,粒子与反粒子在基本属性上(如质量、寿命、反应方式等)应该是完全对等的。

      • 为什么物质占主导:但是我们观测到,宇宙中几乎没有反物质,几乎所有的物质都由重子(如质子、中子等)组成,反物质(反重子)几乎完全缺失。这就引出了一个问题:为什么宇宙中物质和反物质的比例如此悬殊? 这种不对称性被称为重子不对称性

      2. 重子不对称性的产生

      要解释宇宙中物质和反物质的不对称性,物理学家提出了几个条件,称为重子生成条件(Baryogenesis)。这些条件包括:

      • C(电荷共轭)对称性破缺:物质和反物质在相互作用时应该有不同的行为。即反粒子和粒子之间的相互作用应该有所不同,导致一个方向上的偏差,造成物质多于反物质。

      • CP(电荷和宇称)对称性破缺:CP对称性破缺意味着粒子和反粒子之间不完全对称,导致了偏向物质的生成。这种破缺是在强相互作用、弱相互作用甚至超对称性中都会出现的现象。例如,弱相互作用(涉及W和Z玻色子)就表现出显著的CP破缺。

      • 热平衡打破:在宇宙的早期,温度极高,粒子和反粒子应当在碰撞中互相湮灭,保持物质和反物质的平衡。然而,随着宇宙的膨胀和降温,某些特定的物理过程使得物质相对于反物质的生成速率更高,导致了物质多于反物质的最终结果。

      3. 重子不对称性的实验和理论

      • 实验观察:我们目前观测到的宇宙中,几乎没有反物质存在。这意味着,在宇宙大爆炸后不久的某个时刻,物质和反物质之间的对称性被打破,导致了更多的物质产生。现有的实验和天文学观测(如宇宙微波背景辐射)间接支持了这一观点,但我们还无法完全解释为什么如何重子不对称性在早期宇宙中形成。

      • 理论模型

      • 大统一理论(GUT):大统一理论预言,在非常高的能量尺度下(接近\( 10^{15} \ \text{GeV} \)的能标),电弱相互作用和强相互作用可能会统一。这个统一能标下的物理过程可能导致重子不对称性的产生。例如,某些GUT模型中的粒子衰变可以直接生成更多的物质而非反物质。
      • 超对称性(SUSY):超对称性也提供了一些可能的机制来解释重子不对称性。通过引入超对称粒子,某些超对称模型可能会解释物质和反物质之间的不对称性。

      4. 实验研究与前景

      目前,许多粒子物理实验和天文观测都在寻找解释重子不对称性的方法:

      • 粒子加速器实验:例如,大型强子对撞机(LHC)等实验通过研究高能碰撞,寻找与重子生成相关的物理过程,尤其是CP对称性破缺和弱相互作用中的不对称性。

      • CP破缺测量:科学家已经在一些粒子的衰变过程(例如B介子衰变)中观察到CP破缺现象,这为重子不对称性的形成提供了线索。

      • 宇宙微波背景辐射:天文学家通过观测宇宙微波背景辐射(CMB)研究宇宙的早期状态,从而间接探讨物质和反物质之间的差异。

      5. 总结

      重子不对称性是粒子物理和宇宙学中的重要问题,指的是宇宙中物质和反物质之间的差异。这个问题涉及到CPT对称性破缺CP对称性破缺等深层次的物理现象,至今仍然是未解的难题。理论模型(如大统一理论和超对称性)尝试解释这一现象,而实验研究则持续在寻找可能的证据和机制,以最终解答这一宇宙级难题。

    1. 下图解释

      这张图展示了与中微子振荡相关的PMNS矩阵(Pontecorvo-Maki-Nakagawa-Sakata矩阵),并详细说明了中微子味和质量本征态之间的转化关系。下面是对图中内容的逐项解释:

      1. 中微子振荡(Neutrino Oscillations)

      • 中微子振荡指的是中微子在传播过程中,可以在不同的味态之间转化。这种现象在中微子实验中得到了直接观察,证明了中微子有质量并且存在味的混合。
      • 该过程由PMNS矩阵描述,这个矩阵描述了中微子味态质量本征态之间的混合关系。

      2. PMNS矩阵(Pontecorvo-Maki-Nakagawa-Sakata矩阵)

      • 该矩阵描述了三个中微子味态(电子中微子 \( \nu_e \)、缪子中微子 \( \nu_\mu \)、τ中微子 \( \nu_\tau \))与三个中微子质量本征态(\( \nu_1 \)、\( \nu_2 \)、\( \nu_3 \))之间的关系。

      • 其中的元素表示了中微子味中微子质量态之间的混合强度。矩阵中的复数相位混合角(例如 \( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \))决定了这些转化的强度和相位。

      3. 中微子质量本征态与味本征态

      • 质量本征态(\( \nu_1, \nu_2, \nu_3 \))指的是中微子以它们的质量状态(即不同质量的中微子)存在的方式。
      • 味本征态(\( \nu_e, \nu_\mu, \nu_\tau \))是我们通常测量到的中微子类型。
      • 由于中微子的质量,质量本征态和味本征态是不同的,中微子可以在它们之间发生振荡(即味的转化)。

      4. PMNS矩阵的结构

      图中给出了PMNS矩阵的形式,矩阵的元素表示了味态和质量态之间的混合系数。每个元素都涉及一个混合角(例如 \( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \))和一个复相位(例如 CP相位)。

      • 矩阵元素:矩阵中每个元素(例如 \( V_{e1}, V_{e2}, V_{e3} \))表示了中微子味与质量本征态之间的关系。
      • \( c_{ij} = \cos\theta_{ij} \)\( s_{ij} = \sin\theta_{ij} \),这些表示了不同味的混合角。

      • CP相位:图中提到的CP相位(由\( e^{-i\delta} \)表示)是描述CP对称性破缺的关键。它影响了中微子振荡中的混合方式,并且与宇宙中物质和反物质的非对称性相关。

      • Majorana相位:在某些Majorana粒子衰变中会出现Majorana相位,这一相位与中微子的CP对称性破缺有关。该相位在测量中微子质量和宇宙物质/反物质不对称性时是非常重要的。

      5. 图中描述的实验现象

      • 大气中微子振荡(Atmospheric):涉及到中微子通过大气中的长距离传播发生的振荡。该过程涉及到\( \theta_{23} \),即缪子中微子与其他中微子味之间的混合。
      • 太阳中微子振荡(Solar):在太阳中产生的中微子会发生振荡,通过\( \theta_{12} \)的角度与其他中微子味转化。
      • CP相位:图中提到的CP相位影响了中微子的振荡模式,尤其是在反物质和物质之间的对称性破缺方面。

      6. PMNS矩阵的参数

      图中提到PMNS矩阵有6个参数: - 3个混合角(\( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \)),它们决定了中微子之间的转化几率。 - 2个主要的CP相位,影响了中微子味的混合模式。 - 2个Majorana相位,这些相位出现在Majorana粒子衰变中,通常在\( \beta\beta \)衰变实验中才会出现。

      7. 总结

      这张图总结了中微子味混合的基础内容,介绍了PMNS矩阵的数学结构及其物理意义,特别是与中微子振荡、CP对称性破缺和Majorana相位相关的内容。通过理解这些混合角和相位,我们能够更好地理解中微子如何在不同的味之间转化,以及这种转化如何与中微子的质量和宇宙的物质-反物质不对称性相关。

    1. 下图解释

      这张图展示了与中微子振荡相关的PMNS矩阵(Pontecorvo-Maki-Nakagawa-Sakata矩阵),并详细说明了中微子味和质量本征态之间的转化关系。下面是对图中内容的逐项解释:

      1. 中微子振荡(Neutrino Oscillations)

      • 中微子振荡指的是中微子在传播过程中,可以在不同的味态之间转化。这种现象在中微子实验中得到了直接观察,证明了中微子有质量并且存在味的混合。
      • 该过程由PMNS矩阵描述,这个矩阵描述了中微子味态质量本征态之间的混合关系。

      2. PMNS矩阵(Pontecorvo-Maki-Nakagawa-Sakata矩阵)

      • 该矩阵描述了三个中微子味态(电子中微子 \( \nu_e \)、缪子中微子 \( \nu_\mu \)、τ中微子 \( \nu_\tau \))与三个中微子质量本征态(\( \nu_1 \)、\( \nu_2 \)、\( \nu_3 \))之间的关系。

      • 其中的元素表示了中微子味中微子质量态之间的混合强度。矩阵中的复数相位混合角(例如 \( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \))决定了这些转化的强度和相位。

      3. 中微子质量本征态与味本征态

      • 质量本征态(\( \nu_1, \nu_2, \nu_3 \))指的是中微子以它们的质量状态(即不同质量的中微子)存在的方式。
      • 味本征态(\( \nu_e, \nu_\mu, \nu_\tau \))是我们通常测量到的中微子类型。
      • 由于中微子的质量,质量本征态和味本征态是不同的,中微子可以在它们之间发生振荡(即味的转化)。

      4. PMNS矩阵的结构

      图中给出了PMNS矩阵的形式,矩阵的元素表示了味态和质量态之间的混合系数。每个元素都涉及一个混合角(例如 \( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \))和一个复相位(例如 CP相位)。

      • 矩阵元素:矩阵中每个元素(例如 \( V_{e1}, V_{e2}, V_{e3} \))表示了中微子味与质量本征态之间的关系。
      • \( c_{ij} = \cos\theta_{ij} \)\( s_{ij} = \sin\theta_{ij} \),这些表示了不同味的混合角。

      • CP相位:图中提到的CP相位(由\( e^{-i\delta} \)表示)是描述CP对称性破缺的关键。它影响了中微子振荡中的混合方式,并且与宇宙中物质和反物质的非对称性相关。

      • Majorana相位:在某些Majorana粒子衰变中会出现Majorana相位,这一相位与中微子的CP对称性破缺有关。该相位在测量中微子质量和宇宙物质/反物质不对称性时是非常重要的。

      5. 图中描述的实验现象

      • 大气中微子振荡(Atmospheric):涉及到中微子通过大气中的长距离传播发生的振荡。该过程涉及到\( \theta_{23} \),即缪子中微子与其他中微子味之间的混合。
      • 太阳中微子振荡(Solar):在太阳中产生的中微子会发生振荡,通过\( \theta_{12} \)的角度与其他中微子味转化。
      • CP相位:图中提到的CP相位影响了中微子的振荡模式,尤其是在反物质和物质之间的对称性破缺方面。

      6. PMNS矩阵的参数

      图中提到PMNS矩阵有6个参数: - 3个混合角(\( \theta_{12} \), \( \theta_{23} \), \( \theta_{13} \)),它们决定了中微子之间的转化几率。 - 2个主要的CP相位,影响了中微子味的混合模式。 - 2个Majorana相位,这些相位出现在Majorana粒子衰变中,通常在\( \beta\beta \)衰变实验中才会出现。

      7. 总结

      这张图总结了中微子味混合的基础内容,介绍了PMNS矩阵的数学结构及其物理意义,特别是与中微子振荡、CP对称性破缺和Majorana相位相关的内容。通过理解这些混合角和相位,我们能够更好地理解中微子如何在不同的味之间转化,以及这种转化如何与中微子的质量和宇宙的物质-反物质不对称性相关。

    1. Author response:

      (This author response relates to the first round of peer review by Biophysics Colab. Reviews and responses to both rounds of review are available here: https://sciety.org/articles/activity/10.1101/2023.10.23.563601.)

      General Assessment:

      Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, molecular modelling, and cryogenic electron microscopy (cryo-EM). Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be a landmark study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. Although the mechanism of LPC activation of Panx channels remains unresolved, this study provides an excellent foundation for future studies and importantly provides clinical relevance.

      We thank the reviewers for their time and effort in reviewing our manuscript. Based on their valuable comments and suggestions, we have made substantial revisions. The updated manuscript now includes two new experiments supporting that lysophospholipid-triggered channel activation promotes the release of signaling molecules critical for immune response and demonstrates that this novel class of agonist activates the inflammasome in human macrophages through endogenously expressed Panx1. To better highlight the significance of our findings, we have excluded the cryo-EM panel from this manuscript. We believe these changes address the main concerns raised by the reviewers and enhance the overall clarity and impact of our findings. Below, we provide a point-by-point response to each of the reviewers’ comments.

      Recommendations:

      (1) The authors present a tremendous amount of data using different approaches, cells and assays along with a written presentation that is quite abbreviated, which may make comprehension challenging for some readers. We would encourage the authors to expand the written presentation to more fully describe the experiments that were done and how the data were analysed so that the 2 key conclusions can be more fully appreciated by readers. A lot of data is also presented in supplemental figures that could be brought into the main figures and more thoroughly presented and discussed.

      We appreciate and agree with the reviewers’ observation. Our initial manuscript may have been challenging to follow due to our use of both wild-type and GS-tagged versions of Panx1 from human and frog origins, combined with different fluorescence techniques across cell types. In this revision, we used only human wild-type Panx1 expressed in HEK293S GnTI- cells, except for activity-guided fractionation experiments, where we used GS-tagged Panx1 expressed in HEK293 cells (Fig. 1). For functional reconstitution studies, we employed YO-PRO-1 uptake assays, as optimizing the Venus-based assay was challenging. We have clarified these exceptions in the main text. We think these adjustments simplify the narrative and ensure an appropriate balance between main and supplemental figures.

      (2) It would also be useful to present data on the ion selectivity of Panx channels activated by LPC. How does this compare to data obtained when the channel is activated by depolarization? If the two stimuli activate related open states then the ion selectivity may be quite similar, but perhaps not if the two stimuli activate different open states. The authors earlier work in eLife shows interesting shifts in reversal potentials (Vrev) when substituting external chloride with gluconate but not when substituting external sodium with N-methyl-D-glucamine, and these changed with mutations within the external pore of Panx channels. Related measurements comparing channels activated by LPC with membrane depolarization would be valuable for assessing whether similar or distinct open states are activated by LPC and voltage. It would be ideal to make Vrev measurements using a fixed step depolarization to open the channel and then various steps to more negative voltages to measure tail currents in pinpointing Vrev (a so called instantaneous IV).

      We fully agree with the reviewer on the importance of ion selectivity experiments. However, comparing the properties of LPC-activated channels with those activated by membrane depolarization presented technical challenges, as LPC appears to stimulate Panx1 in synergy with voltage. Prolonged LPC exposure destabilizes patches, complicating G-V curve acquisition and kinetic analyses. While such experiments could provide mechanistic insights, we think they are beyond the scope of current study.

      (3) Data is presented for expression of Panx channels in different cell types (HEK vs HEKS GnTI-) and different constructs (Panx1 vs Panx1-GS vs other engineered constructs). The authors have tried to be clear about what was done in each experiment, but it can be challenging for the reader to keep everything straight. The labelling in Fig 1E helps a lot, and we encourage the authors to use that approach systematically throughout. It would also help to clearly identify the cell type and channel construct whenever showing traces, like those in Fig 1D. Doing this systematically throughout all the figures would also make it clear where a control is missing. For example, if labelling for the type of cell was included in Fig 1D it would be immediately clear that a GnTI- vector alone control for WT Panx1 is missing as the vector control shown is for HEK cells and formally that is only a control for Panx2 and 3. Can the authors explain why PLC activates Panx1 overexpressed in HEK293 GnTl- cells but not in HEK293 cells? Is this purely a function of expression levels? If so, it would be good to provide that supporting information.

      As mentioned above, we believe our revised version is more straightforward to digest. We have improved labeling and provided explanations where necessary to clarify the manuscript. While Panx1 expression levels are indeed higher in GnTI- than in HEK293 cells, we are uncertain whether the absence of detectable currents in HEK293 cells is solely due to expression levels. Some post-translational modifications that inhibit Panx1, such as lysine acetylation, may also impact activity. Future studies are needed to explore these mechanisms further.

      (4) The mVenus quenching experiments are somewhat confusing in the way data are presented. In Fig 2B the y axis is labelled fluorescence (%) but when the channel is closed at time = 0 the value of fluorescence is 0 rather than 100 %, and as the channel opens when LPC is added the values grow towards 100 instead of towards 0 as iodide permeates and quenches. It would be helpful if these types of data could be presented more intuitively. Also, how was the initial rate calculated that is plotted in Fig 2C? It would be helpful to show how this is done in a figure panel somewhere. Why was the initial rate expressed as a percent maximum, what is the maximum and why are the values so low? Why is the effect of CBX so weak in these quenching experiments with Panx1 compared to other assays? This assay is used in a lot of experiments so anything that could be done to bolster confidence is what it reports on would be valuable to readers. Bringing in as many control experiments that have been done, including any that are already published, would be helpful.

      We modified the Y-axis in Figure 2 to “Quench (%)” for clarity. The data reflects fluorescence reduction over time, starting from LPC addition, normalized to the maximal decrease observed after Triton-X100 addition (3 minutes), enabling consistent quenching value comparisons. Although the quenching value appears small, normalization against complete cell solubilization provides reproducible comparisons. We do not fully understand why CBX effects vary in Venus quenching experiments, but we speculate that its steroid-like pentacyclic structure may influence the lysophospholipid agonistic effects. As noted in prior studies (DOI: 10.1085/jgp.201511505; DOI: 10.7554/eLife.54670), CBX likely acts as an allosteric modulator rather than a simple pore blocker, potentially contributing to these variations.

      (5) Could provide more information to help rationalize how Yo-Pro-1, which has a charge of +2, can permeate what are thought to be anion favouring Panx channels? We appreciate that the biophysical properties of Panx channel remain mysterious, but it would help to hear how a bit more about the authors thinking. It might also help to cite other papers that have measured Yo-Pro-1 uptake through Panx channels. Was the Strep-tagged construct of Panx1 expressed in GnTI- cells and shown to be functional using electrophysiology?

      Our recent study suggest that the electrostatic landscape along the permeation pathway may influence its ion selectivity (DOI: 10.1101/2024.06.13.598903). However, we have not yet fully elucidated how Panx1 permeates both anions and cations. Based on our findings, ion selectivity may vary with activation stimulus intensity and duration. Cation permeation through Panx1 is often demonstrated with YO-PRO-1, which measures uptake over minutes, unlike electrophysiological measurements conducted over milliseconds to seconds. We referenced two representative studies employing YO-PRO-1 to assess Panx1 activity. Whole-cell current measurements from a similar construct with an intracellular loop insertion indicate that our STREP-tagged construct likely retains functional capacity.

      (6) In Fig 5 panel C, data is presented as the ratio of LPC induced current at -60 mV to that measured at +110 mV in the absence of LPC. What is the rationale for analysing the data this way? It would be helpful to also plot the two values separately for all of the constructs presented so the reader can see whether any of the mutants disproportionately alter LPC induced current relative to depolarization activated current. Also, for all currents shown in the figures, the authors should include a dashed coloured line at zero current, both for the LPC activated currents and the voltage steps.

      We used the ratio of LPC-induced current to the current measured at +110 mV to determine whether any of the mutants disproportionately affect LPC-induced current relative to depolarization-activated current. Since the mutants that did not respond to LPC also exhibited smaller voltage-stimulated currents than those that did respond, we reasoned that using this ratio would better capture the information the reviewer is suggesting to gauge. Showing the zero current level may be helpful if the goal was to compare basal currents, which in our experience vary significantly from patch to patch. However, since we are comparing LPC- and voltage-induced currents within the same patch, we believe that including basal current measurements would not add useful information to our study.

      Given that new experiments included to further highlight the significance of the discovery of Panx1 agonists, we opted to separate structure-based mechanistic studies from this manuscript and removed this experiment along with the docking and cryo-EM studies.

      (7) The fragmented NTD density shown in Fig S8 panel A may resemble either lipid density or the average density of both NTD and lipid. For example, Class7 and Class8 in Fig.S8 panel D displayed split densities, which may resemble a phosphate head group and two tails of lipid. A protomer mask may not be the ideal approach to separate different classes of NTD because as shown in Fig S8 panel D, most high-resolution features are located on TM1-4, suggesting that the classification was focused on TM1-4. A more suitable approach would involve using a smaller mask including NTD, TM1, and the neighbouring TM2 region to separate different NTD classes.

      We agree with the reviewer and attempted 3D classification using multiple smaller masks including the suggested region. However, the maps remained poorly defined, and we were unable to confidently assign the NTD.

      (8) The authors don’t discuss whether the LPC-bound structures display changes in the external part of the pore, which is the anion-selective filter and the narrower part of the pore. If there are no conformational changes there, then the present structures cannot explain permeability to large molecules like ATP. In this context, a plot for the pore dimension will be helpful to see differences along the pore between their different structures. It would also be clearer if the authors overlaid maps of protomers to illustrate differences at the NTD and the "selectivity filter."

      Both maps show that the narrowest constriction, formed by W74, has a diameter of approximately 9 Å. Previous steered molecular dynamics simulations suggest that ATP can permeate through such a constriction, implying an ion selection mechanism distinct from a simple steric barrier.

      (9) The time between the addition of LPC to the nanodisc-reconstituted protein and grid preparation is not mentioned. Dynamic diffusion of LPC could result in equal probabilities for the bound and unbound forms. This raises the possibility of finding the Primed state in the LPC-bound state as well. Additionally, can the authors rationalize how LPC might reach the pore region when the channel is in the closed state before the application of LPC?

      We appreciate the reviewer’s insight. We incubated LPC and nanodisc-reconstituted protein for 30 minutes, speculating that LPC approaches the pore similarly to other lipids in prior structures. In separate studies, we are optimizing conditions to capture more defined conformations.

      (10) In the cryo-EM map of the “resting” state (EMDB-21150), a part of the density was interpreted as NTD flipped to the intracellular side. This density, however, is poorly defined, and not connected to the S1 helix, raising concerns about whether this density corresponds to the NTD as seen in the “resting” state structure (PDB-ID: 6VD7). In addition, some residues in the C-terminus (after K333 in frog PANX1) are missing from the atomic model. Some of these residues are predicted by AlphaFold2 to form a short alpha helix and are shown to form a short alpha helix in some published PANX1 structures. Interestingly, in both the AF2 model and 6WBF, this short alpha helix is located approximately in the weak density that the authors suggest represents the “flipped” NTD. We encourage the authors to be cautious in interpreting this part as the “flipped” NTD without further validation or justification.

      We agree that the density corresponding the extended NTD into the cytoplasm is relatively weak. In our recent study, we compared two Panx1 structures with or without the mentioned C-terminal helix and found evidence suggesting the likelihood of NTD extension (DOI: 10.1101/2024.06.13.598903). Nevertheless, to prevent potential confusion, we have removed the cryo-EM panel from this manuscript.

      (11) Since the authors did not observe densities of bound PLC in the cryo-EM map, it is important to acknowledge in the text the inherent limitations of using docking and mutagenesis methods to locate where PLC binds.

      Thank you for the suggestion. We have removed this section to avoid potential confusion.

      Optional suggestions:

      (1) The authors used MeOH to extract mouse liver for reversed-phase chromatography. Was the study designed to focus on hydrophobic compounds that likely bind to the TMD? Panx1 has both ECD and ICD with substantial sizes that could interact with water soluble compounds? Also, the use of whole-cell recordings to screen fractions would not likely identify polar compounds that interact with the cytoplasmic part of the TMD? It would be useful for the authors to comment on these aspects of their screen and provide their rationale for fractionating liver rather than other tissues.

      We have added a rationale in line 90, stating: “The soluble fractions were excluded from this study, as the most polar fraction induced strong channel activities in the absence of exogenously expressed pannexins.” Additionally, we have included a figure to support this rationale (Fig. S1A).

      (2) The authors show that LPCs reversibly increase inward currents at a holding voltage of -60 mV (not always specified in legends) in cells expressing Panx1 and 2, and then show families of currents activated by depolarizing voltage steps in the absence of LPC without asking what happens when you depolarize the membrane after LPC activation? If LPCs can be applied for long enough without disrupting recordings, it would be valuable to obtain both I-V relations and G-V relations before and after LPC activation of Panx channels. Does LPC disproportionately increase current at some voltages compared to others? Is the outward rectification reduced by LPC? Does Vrev remain unchanged (see point above)? Its hard to predict what would be observed, but almost any outcome from these experiments would suggest additional experiments to explore the extent to which the open states activated by LPC and depolarization are similar or distinct.

      Unfortunately, in our hands, the prolonged application of lysolipids at concentrations necessary to achieve significant currents tends to destabilize the patch. This makes it challenging to obtain G-V curves or perform the previously mentioned kinetic analyses. We believe this destabilization may be due to lysolipids’ surfactant-like qualities, which can disrupt the giga seal. Additionally, prolonged exposure seems to cause channel desensitization, which could be another confounding factor.

      (3) From the results presented, the authors cannot rule out that mutagenesis-induced insensitivity of Panx channels to LPCs results from allosteric perturbations in the channels rather than direct binding/gating by LPCs. In Fig 5 panel A-C, the authors introduced double mutants on TM1 and TM2 to interfere with LPC binding, however, the double mutants may also disrupt the interaction network formed within NTD, TM1, and TM2. This disruption could potentially rearrange the conformation of NTD, favouring the resting closed state. Three double Asn mutants, which abolished LPC induced current, also exhibited lower currents through voltage activation in Fig 5S, raising the possibility the mutant channels fail to activate in response to LPC due to an increased energy barrier. One way to gain further insight would be to mutate residues in NTD that interact with those substituted by the three double Asn mutants and to measuring currents from both voltage activation and LPC activation. Such results might help to elucidate whether the three double Asn mutants interfere with LPC binding. It would also be important to show that the voltage-activated currents in Fig. S5 are sensitive to CBX?

      Thank you for the comment, with which we agree. Our initial intention was to use the mutagenesis studies to experimentally support the docking study. Due to uncertainties associated with the presented cryo-EM maps, we have decided to remove this study from the current manuscript. We will consider the proposed experiments in a future study.

      (4) Could the authors elaborate on how LPC opens Panx1 by altering the conformation of the NTDs in an uncoordinated manner, going from “primed” state to the “active” state. In the “primed” state, the NTDs seem to be ordered by forming interactions with the TMD, thus resulting in the largest (possible?) pore size around the NTDs. In contrast, in the “active” state, the authors suggest that the NTDs are fragmented as a result of uncoordinated rearrangement, which conceivably will lead to a reduction in pore size around NTDs (isn’t it?). It is therefore not intuitive to understand why a conformation with a smaller pore size represents an “active” state.

      We believe the uncoordinated arrangement of NTDs is dynamic, allowing for potential variations in pore size during the activated conformation. Alternatively, NTD movement may be coupled with conformational changes in TM1 and the extracellular domain, which in turn could alter the electrostatic properties of the permeation pathway. We believe a functional study exploring this mechanism would be more appropriately presented as a separate study.

      (5) Can the authors provide a positive control for these negative results presented in Fig S1B and C?

      The positive results are presented in Fig. 1D and E.

      (6) Raw images in Fig S6 and Fig S7 should contain units of measurement.

      Thank you for pointing this out.

      (7) It may be beneficial to show the superposition between primed state and activated state in both protomer and overall structure. In addition, superposition between primed state and PDB 7F8J.

      We attempted to superimpose the cryo-EM maps; however, visually highlighting the differences in figure format proved challenging. Higher-resolution maps would allow for model building, which would more effectively convey these distinctions.

      (8) Including particles number in each class in Fig S8 panel C and D would help in evaluating the quality of classification.

      Noted.

      (9) A table for cryo-EM statistics should be included.

      Thanks, noted.

      (10) n values are often provided as a range within legends but it would be better to provide individual values for each dataset. In many figures you can see most of the data points, which is great, but it would be easy to add n values to the plots themselves, perhaps in parentheses above the data points.

      While we agree that transparency is essential, adding n-values to each graph would make some figures less clear and potentially harder to interpret in this case. We believe that the dot plots, n-value range, and statistical analysis provide adequate support for our claims.

      (11) The way caspase activation of Panx channels is presented in the introduction could be viewed as dismissive or inflammatory for those who have studied that mechanism. We think the caspase activation literature is quite convincing and there is no need to be dismissive when pointing out that there are good reasons to believe that other mechanisms of activation likely exist. We encourage you to revise the introduction accordingly.

      Thank you for this comment. Although we intended to support the caspase activation mechanism in our introduction, we understand that the reviewer’s interpretation indicates a need for clarification. We hope the revised introduction removes any perception of dismissiveness.

      (12) Why is the patient data in Fig 4F normalized differently than everything else? Once the above issues with mVenus quenching data are clarified, it would be good to be systematic and use the same approach here.

      For Fig. 4F, we used a distinct normalization method to account for substantial day-to-day variation in experiments involving body fluids. Notably, we did not apply this normalization to other experimental panels due to their considerably lower day-to-day variation.

      (13) What was the rational for using the structure from ref 35 in the docking task?

      The docking task utilized the human orthologue with a flipped-up NTD. We believe that this flipped-up conformation is likely the active form that responds to lysolipids. As our functional experiments primarily use the human orthologue for biological relevance, this structure choice is consistent. Our docking data shows that LPC does not dock at this site when using a construct with the downward-flipped NTD.

      (14) Perhaps better to refer to double Asn ‘substitutions’ rather than as ‘mutations’ because that makes one think they are Asn in the wt protein.

      Done.

      (15) From Fig S1, we gather that Panx2 is much larger than Panx1 and 3. If that is the case, its worth noting that to readers somewhere.

      We have added the molecular weight of each subtype in the figure legend.

      (16) Please provide holding voltages and zero current levels in all figures presenting currents.

      We provided holding voltages. However, the zero current levels vary among the examples presented, making direct comparisons difficult. Since we are comparing currents with and without LPC, we believe that indicating zero current levels is unnecessary for this study.

      (17) While the authors successfully establish lysophospholipid-gating of Panx1 and Panx2, Panx3 appears unaffected. It may be advisable to be more specific in the title of the article.

      We are uncertain whether Panx3 is unaffected by lysophospholipids, as we have not observed activation of this subtype under any tested conditions.

    1. Reviewer #1 (Public review):

      Summary:

      This study aims to understand the malaria antigen-specific cTfh profile of children and adults living in malaria holoendemic area. PBMC samples from children and adults were unstimulated or stimulated with PfSEA-1A or PfGARP in vitro for 6h and analysed by a cTfh-focused panel. Unsupervised clustering and analysis on cTfh was performed. The main conclusions are: A) the children cohort has a more diverse (cTfh1/2/17) recall responses compared to adults (mainly cTfh17) and, B) Pf-GARP stimulates better cTfh17 responses in adults, thus a promising vaccine candidate.

      Strengths:

      This study is, in general, well-designed and with excellent data analysis. The use of unsupervised clustering is a nice attempt to understand the heterogeneity of cTfh cells.

      Weaknesses:

      The authors have provided additional data in Supplementary Figures 14-16. However, I remain concerned about whether cTfh cells are truly responding to antigen stimulation. In Supplementary Figure 15A-F, the IFNg responses appear as expected, SEB elicits the strongest response, as it stimulates bulk T cells, and the staining is promising, showing a clear distinction between IFNg+ and IFNg- populations. However, in Supplementary Figure 15I-N, the IL-21 secretion assay is concerning. The FACS plots make it difficult to distinguish IL-21+ from IL-21- cells, raising concerns about the validity of this analysis. Additionally, in panel J, the responses to PfSEA-1A or PfGARP appear even greater than those to SEB stimulation. In PBMCs, only a small percentage of T cells should be specific to a particular antigen. How can the positive control (SEB) produce a weaker response than stimulation with a specific antigen? This suggests that the IL-21 secretion assay may not have worked, making the authors' interpretation unreliable.

      I also have similar concerns about the IL-4 secretion in Sup Figure 16. First, the FACS plot shows that appear double-positive for IL-21 and IL-4, so it suggests the staining may be due to autofluorescence rather than true cytokine signals. Also in B-C the responses of SEB stimulation is generally weaker than stimulated by one antigen, further questioning the reliability of the IL-4 assay. In summary, I am not convinced that the in vitro antigen stimulation assay worked as intended. Consequently, the manuscript's claims regarding PfSEA-1A- and PfGARP-specific cTfh responses are not sufficiently supported by the presented data.

    1. Briefing Document : "Agir auprès des parents d’adolescents" - Rendez-vous de la santé

      Date: 16 mai 2024 (basé sur le contenu de la source)

      Source: Excerpts de "Rendez-vous de la santé - Agir auprès des parents d’adolescents" (transcription d'une discussion) Participants principaux: Emily Greffier (Coordinatrice en parentalité, Association Oxygène), Madame Lodé (Infirmière scolaire), autres participants (parents, professionnels).

      Introduction:

      Ce document de briefing synthétise les principaux thèmes et idées abordés lors d'un "Rendez-vous de la santé" axé sur l'accompagnement des parents d'adolescents.

      La discussion a exploré la perception des parents quant à leur sollicitation, l'approche à adopter dans l'accompagnement parental, et l'importance du travail en réseau et de la prise en compte des spécificités des quartiers prioritaires.

      Thème 1 : La sollicitation des parents aujourd'hui

      La discussion s'est ouverte sur l'affirmation : "Être parents aujourd'hui, c'est d'être sollicité à chaque instant". Un sondage rapide a révélé une majorité de participants d'accord avec cette idée.

      Points clés et arguments :

      • Sollicitation multipartenaire : Les parents sont sollicités par de nombreux acteurs : centres sociaux, éducation nationale (écoles, collèges, lycées), structures fréquentées par l'enfant (accueil de loisirs, etc.), et bien sûr, leurs propres enfants. Emily Greffier souligne que "on se rend bien compte que le parent il est sollicité par plein de partenaires".
      • Rôle du centre social : Le centre social se positionne comme un "médiateur" et un "guide" pour orienter les parents vers les ressources appropriées. Leur mission est d'identifier les besoins des parents et des partenaires pour créer du lien.
      • Travail au long terme : L'accompagnement parental n'est pas une démarche qui commence à l'adolescence, mais un travail continu dès la petite enfance. Les structures comme les multi-accueils jouent un rôle précoce dans la mise en confiance des parents.
      • Spécificités des quartiers prioritaires : Dans ces quartiers, les familles monoparentales (souvent des mères) sont particulièrement sollicitées, devant gérer seules l'éducation de leurs enfants et les nombreux rendez-vous avec les professionnels (CMPP, etc.). Cette sur-sollicitation peut entraîner un oubli de leurs propres besoins.
      • Sentiment de jugement : Les parents des quartiers prioritaires peuvent se sentir constamment jugés par les nombreux professionnels qui interviennent auprès de leurs enfants, même si ces professionnels adoptent une posture bienveillante.
      • Confiance en soi : Un manque de confiance en soi et un sentiment d'insécurité quant à leurs compétences parentales peuvent entraver la capacité des parents à proposer un cadre sécurisant à leurs enfants.
      • Contre-argument : Madame Lodé, infirmière scolaire, a exprimé un désaccord partiel, notant que certains adolescents ne sont pas forcément à l'aise avec leurs parents et se tournent davantage vers les grands-parents. Cela nuance l'idée d'une sollicitation constante des parents par leurs adolescents.

      Thème 2 : L'approche dans l'accompagnement parental : apporter des réponses ou accompagner la recherche ?

      Une deuxième affirmation a été soumise au vote : "Agir avec les parents, c'est leur apporter des réponses". Les avis étaient plus partagés.

      Points clés et arguments en faveur d'apporter des réponses (avec nuances) :

      • L'accompagnement peut orienter les parents vers des réponses qu'ils n'ont pas trouvées, les aidant ainsi. Il est "primordial des épauler les parents aussi dans leur rôle" (participation d'un parent).
      • Points clés et arguments contre une approche centrée sur l'apport de réponses :
      • Partir du cadre de référence de la famille : Il est crucial de ne pas imposer ses propres solutions et d'adopter une "démarche active d'écoute" pour comprendre les capacités et les besoins spécifiques de chaque famille (Madame Mac).
      • Accompagner plutôt qu'imposer : L'approche privilégiée est d'accompagner les parents, de les orienter, mais de laisser la place au rôle parental. Il s'agit de "soutien" et d'orientation, pas de substitution (Madame Langlois).
      • Démarche d'écoute et non directive : Les professionnels doivent écouter les familles et entendre leurs besoins sans leur donner de réponses directes. L'objectif est d'aider les parents à "cheminer" et à trouver leurs propres solutions, notamment à travers les échanges entre pairs. Emily Greffier insiste : "jamais on va leur donner de réponse, c'est-à-dire que on les aide à cheminer et à ce qu'elle trouve les réponses par elles-mêmes".
      • Valorisation des échanges entre pairs : Les lieux d'échange entre parents sont importants car ce sont les pairs qui peuvent se donner des réponses basées sur leur propre expérience. Le rôle du professionnel est de faciliter ce dialogue.
      • Co-construction et prise en compte des besoins des parents : Les thématiques des rencontres parents sont définies en fonction des besoins exprimés par les parents eux-mêmes, et non imposées par les professionnels.
      • Utilisation d'outils participatifs : Lors des rencontres, des outils ludiques et participatifs sont utilisés pour faciliter le débat et permettre à chacun de s'exprimer et de trouver sa place.
      • Évaluation des séances : Les rencontres parents font l'objet d'une évaluation par les participants (via des smileys et des questions) afin de recueillir leur opinion et leur ressenti.

      Thème 3 : Importance du travail en partenariat et de la coordination

      L'efficacité des actions de soutien à la parentalité repose sur la coordination et la mise en réseau des acteurs.

      • Points clés et exemples :Travail avec les établissements scolaires : Oxygène travaille étroitement avec les écoles primaires, maternelles et le collège (participation aux conseils d'école, accompagnement à la scolarité, lien avec les enseignants et directeurs).
      • Participation aux instances de coordination : Oxygène participe aux "participants de soutien" et aux équipes pluridisciplinaires de soutien mises en place par l'Éducation Nationale et la ville de Dieppe, permettant d'échanger des informations sur les jeunes en difficulté et de coordonner les accompagnements.
      • Lien avec la coordinatrice RARe+ : Le travail étroit avec la coordinatrice du Réseau d'Aides et de Ressources pour l'Éducation dans les quartiers prioritaires facilite la mise en lien des projets des établissements scolaires et des actions du centre social.
      • Rencontres parents multi-niveaux : Des rencontres sont organisées pour les parents de différents niveaux scolaires (de la maternelle au collège) sur des thématiques définies avec eux.
      • Objectif de cohérence et de confiance : Le travail en réseau vise à montrer aux familles une cohérence entre les différents acteurs et à renforcer la confiance des parents.

      Thème 4 : Valorisation des compétences psychosociales et de l'éducation populaire

      L'accompagnement parental intègre une dimension de développement des compétences psychosociales des parents et s'inscrit dans une logique d'éducation populaire.

      • Points clés et exemples :Ateliers sur les compétences psychosociales (CPS) : Le centre social met en place des ateliers axés sur la confiance en soi, la prise de position, la prise de décision, etc., pour les habitants.
      • Impact positif des ateliers CPS : Des témoignages montrent que ces ateliers ont permis à des mères de gagner en confiance et d'adopter une posture plus bienveillante et à l'écoute dans les groupes de parole.
      • Rendre les habitants acteurs : L'éducation populaire vise à rendre les parents acteurs de la société, en les accompagnant dans le développement de leurs compétences sociales, la gestion de leurs émotions et de leur stress.
      • Valorisation du rôle parental et du droit à l'erreur : Il est important de valoriser le rôle complexe de parent et de reconnaître le droit à l'erreur, car il n'existe pas de "notice" prédéfinie.
      • Compétences parentales et CPS : Les compétences parentales sont étroitement liées aux compétences psychosociales : gestion des émotions, communication, capacité à comprendre les informations, à demander de l'aide, etc.
      • Organisation de temps d'échange avec des professionnels : Pour faciliter la compréhension du système médico-social, des temps d'échange sont organisés avec des professionnels (par exemple, du CMPP) pour clarifier leurs missions et rendre les parents plus à l'aise pour les solliciter.

      Conclusion:

      Ce "Rendez-vous de la santé" a mis en lumière la complexité du rôle de parent aujourd'hui, notamment en termes de sollicitation.

      Il a souligné l'importance d'une approche d'accompagnement parental basée sur

      • l'écoute,
      • la co-construction et
      • la valorisation des compétences des parents

      plutôt que sur la simple transmission de réponses.

      Le travail en réseau et la prise en compte des spécificités des quartiers prioritaires apparaissent comme des éléments essentiels pour un soutien efficace à la parentalité.

      La démarche d'éducation populaire et le développement des compétences psychosociales des parents sont également des leviers importants pour renforcer leur autonomie et leur bien-être.

    1. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      This a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

      (1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

      We thank the reviewer for the comment. We have added a better clarification on this in the discussion of revised manuscript. The lipid classes that get impacted by the depletion of Wag31 vs overexpression are different. Wag31 is an adaptor protein that interacts with proteins of the ACCase complex (Meniche et al., 2014; Xu et al., 2014) that synthesize fatty acid precursors and regulate their activity (Habibi Arejan et al., 2022).

      The varied response on lipid homeostasis could be attributed to a change in the stoichiometry of these interactions of Wag31. While Wag31 depletion would prevent such interactions from occurring and might affect lipid synthesis that directly depends on Wag31-protein partner interactions, its overexpression would lead to promiscuous interactions and a change in the stoichiometry of native interactions that would ultimately modulate lipid synthesis pathways.

      (2) The pulldown assays results are interesting, but links are tentative.

      We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of FLAG-Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates but not in the control were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

      As mentioned in line 139 of the previous version of the manuscript, we agree that the interactions can either be direct or through a third partner. The fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. As mentioned above, this caveat was stated in the previous version of the manuscript.

      (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

      We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

      Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

      Reviewer #2 (Public review):

      Summary:

      Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

      Strengths:

      (1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence. (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

      Weaknesses:

      (1) MS experiments provide some evidence for novel protein-protein interactions. However, the pulldown experiments lack a valid negative control.

      We thank the reviewer for the comment. We have included two non-interactors of Wag31 i.e. MmpL4 and MmpS5 which were not identified in our interactome database as negative controls in the experiment. As shown in Figure S3, we performed His pull-down experiments with both of them independently twice, each time with a positive control (known interactor of Wag31 (Msm2092)). Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG tagged-MmpL4 or -MmpS5 or Msm2092 (revised Fig. S3c). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody (revised Fig. S3d.). The data presented confirms that the interactions validated through the pull down assay were indeed specific.

      (2) The role of the N-terminus in the protein-protein interaction has not been ruled out.

      We thank the reviewer for the comment. Wag31<sub>Msm</sub> is a 272 amino acids long protein. The Nterminal of Wag31, which houses the DivIVA-domain, comprises the first 60 amino acids. Previously, we attempted to express the N-terminal (60 aa long) and the C-terminal (212 aa long) truncated proteins in various mycobacterial shuttle vectors to perform MS/MS experiments. Despite numerous efforts, neither expressed with the N/C-terminal FLAG tag or no tag in episomal or integrative vectors due to instability of the protein. Eventually, we successfully expressed the C-terminal Wag31 with an N and Cterminal hexa-His tag. However, this expression was not sufficient or stable enough for us to perform Ni<sup>2+</sup>-affinity pull-down experiments for mass spectrometry. N-terminal of Wag31 could not be expressed in M. smegmatis even with N and C-terminal Hexa-His tags.

      To rule out the role of the N-terminal in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVA-domain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called  Wag31<sub>∆C</sub>  flanked by 6X His tags at both the termini was expressed in E. coli and mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31<sub>∆1-60</sub or Wag31<sub>∆N</sub> (in the revised manuscript) were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7e-g). Thus, we used the same set of interactors to test our hypothesis. Briefly, His-  Wag31<sub>∆C</sub>  was mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAGMmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His- Wag31<sub>∆C</sub>  couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its Cterminal. However, we can’t ignore the possibility of other interactors binding to the N-terminal of Wag31. Unfortunately, due to poor expression/instability of  Wag31<sub>∆C</sub>  in mycobacterial shuttle vectors, we are unable to perform a global interactome analysis of  Wag31<sub>∆C</sub>

      Reviewer #3 (Public review):

      Summary:

      This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

      Strengths:

      The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

      Weaknesses:

      The following major concerns should be addressed.

      • Authors use 10-N-Nonyl-acridine orange (NAO) as a marker for cardiolipin localization. However, given that NAO is known to bind to various anionic phospholipids, how do the authors know that what they are seeing is specifically visualizing cardiolipin and not a different anionic phospholipid? For example, phosphatidylinositol is another abundant anionic phospholipid in mycobacterial plasma membrane.

      We thank the reviewer for the comment. Despite its promiscuous binding to other anionic phospholipids, 10-N-Nonyl-acridine orange is widely used to stain Cardiolipin and determine its localisation in bacterial cells and mitochondria of eukaryotes (Garcia Fernandez et al., 2004; Mileykovskaya & Dowhan, 2000; Renner & Weibel, 2011). This is because it has a stronger affinity for Cardiolipin than other anionic phospholipids with the affinity constant being 2 × 10<sup>6</sup> M−<sup>1</sup> for Cardiolipin association and 7 × 10<sup>4</sup> M−<sup>1</sup> for that of phosphatidylserine and phosphatidylinositol association (Petit et al., 1992). Additionally, there is not yet another stain available for detecting Cardiolipin. Our proteinlipid binding assays suggest that Wag31 preferentially binds to Cardiolipin over other anionic phospholipids (Fig. 4b), hence it is likely that the majority of redistribution of NAO fluorescence that we observe might be contributed by Cardiolipin mislocalization due to altered Wag31 levels, with smaller degree of NAO redistribution intensity coming indirectly from other anionic phospholipids displaced from the membrane due to the loss of membrane integrity and cell shape changes due to Wag31.

      • Authors' data show that the N-terminal region of Wag31 is important for membrane tethering. The authors' data also show that the N-terminal region is important for sustaining mycobacterial morphology. However, the authors' statement in Line 256 "These results highlight the importance of tethering for sustaining mycobacterial morphology and survival" requires additional proof. It remains possible that the N-terminal region has another unknown activity, and this yet-unknown activity rather than the membrane tethering activity drives the morphological maintenance. Similarly, the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. The authors should tone down these overstatements or provide additional data to support their claims.

      We agree with the reviewer that there exists a possibility for another function of the N-terminal that may contribute to sustaining mycobacterial physiology and survival. We would revise our statements in the paper to reflect the data. Results shown suggest that the tethering activity of the Nterminal region may contribute to mycobacterial morphology and survival. However, additional functions of this region can’t be ruled out. Similarly, the maintenance of lipid homeostasis by Wag31 may be associated with its tethering activity, although other mechanisms could also contribute to this process.

      • Authors suggest that Wag31 acts as a scaffold for the IMD (Fig. 8). However, Meniche et. al. has shown that MurG as well as GlfT2, two well-characterized IMD proteins, do not colocalize with Wag31 (DivIVA) (https://doi.org/10.1073/pnas.1402158111). IMD proteins are always slightly subpolar while Wag31 is located to the tip of the cell. Therefore, the authors' biochemical data cannot be easily reconciled with microscopic observations in the literature. This raises a question regarding the validity of protein-protein interaction shown in Figure 7. Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors should interpret their data more cautiously. If authors cannot provide additional data and sufficient justifications, they should avoid proposing a confusing model like Figure 8 that contradicts published observations.

      In the literature, MurG and GlfT2 have been shown to have polar localisation (Freeman et al., 2023; Hayashi et al., 2016; Kado et al., 2023) and two groups have shown slightly sub-polar localisation of MurG (García-Heredia et al., 2021; Meniche et al., 2014). Additionally, (Freeman et al., 2023) showed SepIVA to be a spatio-temporal regulator of MurG. MS/MS analysis of Wag31 immunoprecipitation data yielded both MurG and SepIVA to be interactors of Wag31 (Fig. 3). Given Wag31 also displays polar localisation, it is likely that it associates with the polar MurG. However, since a sub-polar localisation of MurG has also been reported, it is possible that they do not interact directly and another protein mediates their interaction. Based on the above, we will modify the model proposed in Fig. 8.

      We agree that for validation of interaction, we performed pulldown experiments by mixing E. coli lysates expressing His-Wag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript and propose a model that reflects the results we obtained.

      References:

      Freeman, A. H., Tembiwa, K., Brenner, J. R., Chase, M. R., Fortune, S. M., Morita, Y. S., & Boutte, C. C. (2023). Arginine methylation sites on SepIVA help balance elongation and septation in Mycobacterium smegmatis. Mol Microbiol, 119(2), 208-223. https://doi.org/10.1111/mmi.15006

      Garcia Fernandez, M. I., Ceccarelli, D., & Muscatello, U. (2004). Use of the fluorescent dye 10-N-nonyl acridine orange in quantitative and location assays of cardiolipin: a study on different experimental models. Anal Biochem, 328(2), 174-180. https://doi.org/10.1016/j.ab.2004.01.020

      García-Heredia, A., Kado, T., Sein, C. E., Puffal, J., Osman, S. H., Judd, J., Gray, T. A., Morita, Y. S., & Siegrist, M. S. (2021). Membrane-partitioned cell wall synthesis in mycobacteria. eLife, 10. https://doi.org/10.7554/eLife.60263

      Habibi Arejan, N., Ensinck, D., Diacovich, L., Patel, P. B., Quintanilla, S. Y., Emami Saleh, A., Gramajo, H., & Boutte, C. C. (2022). Polar protein Wag31 both activates and inhibits cell wall metabolism at the poles and septum. Front Microbiol, 13, 1085918. https://doi.org/10.3389/fmicb.2022.1085918

      Hayashi, J. M., Luo, C. Y., Mayfield, J. A., Hsu, T., Fukuda, T., Walfield, A. L., Giffen, S. R., Leszyk, J. D., Baer, C. E., Bennion, O. T., Madduri, A., Shaffer, S. A., Aldridge, B. B., Sassetti, C. M., Sandler, S. J., Kinoshita, T., Moody, D. B., & Morita, Y. S. (2016). Spatially distinct and metabolically active membrane domain in mycobacteria. Proc Natl Acad Sci U S A, 113(19), 5400-5405. https://doi.org/10.1073/pnas.1525165113

      Kado, T., Akbary, Z., Motooka, D., Sparks, I. L., Melzer, E. S., Nakamura, S., Rojas, E. R., Morita, Y. S., & Siegrist, M. S. (2023). A cell wall synthase accelerates plasma membrane partitioning in mycobacteria. eLife, 12, e81924. https://doi.org/10.7554/eLife.81924

      Meniche, X., Otten, R., Siegrist, M. S., Baer, C. E., Murphy, K. C., Bertozzi, C. R., & Sassetti, C. M. (2014). Subpolar addition of new cell wall is directed by DivIVA in mycobacteria. Proc Natl Acad Sci U S A, 111(31), E32433251. https://doi.org/10.1073/pnas.1402158111

      Mileykovskaya, E., & Dowhan, W. (2000). Visualization of phospholipid domains in Escherichia coli by using the cardiolipin-specific fluorescent dye 10-N-nonyl acridine orange. J Bacteriol, 182(4), 1172-1175. https://doi.org/10.1128/JB.182.4.1172-1175.2000

      Petit, J. M., Maftah, A., Ratinaud, M. H., & Julien, R. (1992). 10N-nonyl acridine orange interacts with cardiolipin and allows the quantification of this phospholipid in isolated mitochondria. Eur J Biochem, 209(1), 267273. https://doi.org/10.1111/j.1432-1033.1992.tb17285.x

      Renner, L. D., & Weibel, D. B. (2011). Cardiolipin microdomains localize to negatively curved regions of Escherichia coli membranes. Proc Natl Acad Sci U S A, 108(15), 6264-6269. https://doi.org/10.1073/pnas.1015757108

      Schägger, H. (2006). Tricine-SDS-PAGE. Nat Protoc, 1(1), 16-22. https://doi.org/10.1038/nprot.2006.4

      Xu, W. X., Zhang, L., Mai, J. T., Peng, R. C., Yang, E. Z., Peng, C., & Wang, H. H. (2014). The Wag31 protein interacts with AccA3 and coordinates cell wall lipid permeability and lipophilic drug resistance in Mycobacterium smegmatis. Biochem Biophys Res Commun, 448(3), 255-260. https://doi.org/10.1016/j.bbrc.2014.04.116

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      (1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect in levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.

      We thank the reviewer for the comment. We have included a clarification for this in the discussion section.

      (2) The pulldown assays results are interesting, but the links are tentative.

      We thank the reviewer for the comment. The interactome of Wag31 was identified through the immunoprecipitation of Flag-tagged Wag31 complemented at an integrative locus in Wag31 mutant background to avoid overexpression artifacts. We used Msm::gfp expressing an integrative copy (at L5 locus) of FLAG-GFP as a control to subtract non-specific interactions. The experiment was performed in biological triplicates, and interactors that appeared in all replicates were selected for further analysis. Although we identified more than 100 interactors of Wag31, we analyzed only the top 25 hits, with a PSM cut-off 18 and unique peptides5. Additionally, two of Wag31's established interactors, AccD5 and Rne, were among the top five hits, thus validating our data.

      Though we agree that the interactions can either be direct or through a third partner, the fact that we obtained known interactors of Wag31 makes us believe these interactions are genuine. Moreover, for validation, we performed pulldown experiments by mixing E. coli lysates expressing HisWag31 full-length or truncated protein with M. smegmatis lysates expressing FLAG-tagged interacting proteins. The wash conditions used were quite stringent for these pull-down assays—the wash buffer contained 1% Triton X100 that eliminates all non-specific and indirect interactions. However, we agree that we cannot conclusively state that the interactions are direct without purifying the proteins and performing the experiment. We will describe this caveat in the revised manuscript.

      (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

      We thank the reviewer for the comment. In this manuscript, we are trying to convey that Wag31 is a spatiotemporal regulator of lipid metabolism. It is a peripheral protein that is hooked to the membrane via Cardiolipin and forms a scaffold at the poles, which helps localize several enzymes involved in lipid metabolism.

      Homeostasis is the process by which an organism maintains a steady-state of balance and stability in response to changes. Depletion of Wag31 not only results in delocalisation of lipids in intracellular lipid inclusions but also leads to changes in the levels of various lipid classes. Advancement in the field of spatial biology underscores the importance of native localization of various biological molecules crucial for maintaining a steady-cell of the cell. Hence, we have used the word “homeostasis” to describe both the changes observed in lipid metabolism.

      Reviewer #2 (Recommendations for the authors):

      I recommend the following experiments to strengthen the data presented:

      (1) Include a non-interacting FLAG-tagged protein as a negative control in the pull-down experiment to strengthen this data.

      We thank the reviewer for the comment. As suggested, we have included non-interacting FLAGtagged proteins as negative controls in the pulldown experiment. We chose MmpL4 and MmpS5 which were not found in the Wag31 interactome data. We performed pull-down experiments with both of them and included an interactor of Wag31 i.e. Msm2092 as a positive control. Fig. S3b revised shows E. coli lysate expressing His-Wag31 which was incubated with Msm lysates expressing either FLAG taggedMmpL4 or -MmpS5 or -Msm2092 (Fig. S3c revised). The mixed lysates were pulled down with Cobalt beads that bind to the His-tagged protein and analysed using Western blot analysis by probing with anti-FLAG antibody. The pull down experiments were performed independently twice, every time with Msm2092 as the positive control (Fig. S3d. revised).

      (2) Perform the pull-down experiments using only the Wag31 N-terminus to rule out any role that it may have in the protein-protein interactions.

      We thank the reviewer for the comment. To rule out the possibility of N-terminal of Wag31 in mediating protein-protein interactions, we cloned the N-terminal of Wag31 that comprises the DivIVAdomain in pET28b vector (Fig. 7a revised). Subsequently, the truncated protein, hereafter called Wag31<sub>∆C</sub> flanked by 6X His tags at both the termini was expressed in E. coli and subsequently mixed with Msm lysates expressing interactors of Wag31 (Fig. 7b-c revised). Earlier experiments with Wag31<sub>∆1-60</sub> or Wag31<sub>∆N</sub>  were performed with MurG, SepIVA, Msm2092 and AccA3 (Fig. 7 previous) so we used the same set of interactors to test our hypothesis. Briefly, His-Wag31<sub>∆C</sub>was mixed with Msm lysates expressing either FLAG-MurG, -SepIVA, -Msm2092 or -AccA3 and pull down experiments were performed as described previously. FLAG-MmpS5, a non-interactor of Wag31 was used as a negative control. As shown in Fig. 7d revised, His-Wag31 could bind to all the four interactors whereas His-Wag31<sub>∆C</sub> couldn’t, strengthening the conclusion that interactions of Wag31 with other proteins are mediated by its C-terminal. However, we can’t ignore the possibility of other proteins binding to the Nterminal of Wag31. Unfortunately, due to poor expression/instability of Wag31<sub>∆C</sub> in mycobacterial shuttle vectors, we couldn’t perform a global interactome analysis of Wag31<sub>∆C</sub>.

      Minor comments:

      - Please check the legend of Fig. 1g, it appears to be labelled incorrectly.

      We have checked it. It is correct. From Fig. 1g we are trying to reflect on the percentages of cells of the three strains i.e. Msm+ATc, Δwag31-ATc, and Δwag31+ATc displaying rod, round or bulged morphology.

      - For MS/MS analysis, a GFP control is mentioned but it is not indicated how this was incorporated in the data analysis. This information should be added.

      We have incorporated that in the revised methodology.

      - The information presented in Fig. 3a, e and f could be combined in one table.

      We appreciate the idea of the reviewer but we prefer a pictorial representation of the data. It allows readers to consume the information in parts, make quicker comparisons and understand trends easily.

      - Fig. 4c Wag31K20A appears smaller in size than the wild-type protein - why is this the case? Is this not a single amino acid substitution?

      Though K20A is a single amino acid substitution, it alters the mobility of Wag31 on SDS-PAGE gel. The sequence analysis of the plasmid expressing Wag31<sub>K20A</sub> doesn’t show additional mutations other than the desired K20A. The change in mobility could be due to a change in the conformation of Wag31<sub>K20A</sub> or its ability to bind to SDS or both that modify its mobility under the influence of electric field.

      - Please clarify what is contained in the first panel of fig 4e. compared to what is in the second panel.

      The first panel represents CL-Dil-Liposomes before incubation with Wag31-GFP and the second panel shows CL-Dil-Liposomes after incubation with Wag31-GFP. The third panel shows the mixture as observed in the green channel to investigate the localisation of Wag31-GFP in the liposome-protein mix. Fourth panel shows the merged of second and third.

      - The data in Fig 6d suggests higher levels of CL in the ∆wag31 compared to wild-type - how do the authors reconcile this with the MS data in Fig. 2g showing lower CL levels?

      Fig. 6d represents the distribution of CL localisation in the tested strains of mycobacteria whereas Fig. 2g shows the absolute levels of CL in various strains. We attribute greater confidence on the lipidomics data which suggests down regulation of CL species. The NAO staining and microscopy is merely for studying localization of the CL along the cell, and cannot be used to reliably quantify or equate it to CL levels. The staining using a probe such as NAO is dependent on factors such as hydrophobicity and permeability of the cell wall, which we expect to be severely altered in a Wag31 mutant. Therefore, the increased staining of NAO seen in Wag31 mutant could just be reflective of the increased uptake of the dye rather than absolute levels of CL. The specificity of staining and localization however can be expected to be unaltered.

      Reviewer #3 (Recommendations for the authors):

      Following are suggestions for improving the writing and presentation.

      • Figure 1, the meaning of the yellow arrows present in f and h should be mentioned in the figure legend.

      We have incorporated that in the revised legend. In Fig.1f, the yellow arrowhead represents the bulged pole morphology whereas in Fig. 1h, it indicates intracellular lipid inclusions.

      • Figure 7 legend refers to panels g, h, and i. However, Figure 7 only has panels a-c. The legend lacks a description of panel c.

      We have corrected the typos and the legend.

      • Figure S1, F2-R2 and F3-R3 expected sizes should be stated in the legend of the figure.

      We have updated the legends.

      • Figure S5, is this the same figure as 5e? If so, there is no need for this figure.

      We have removed Fig. S5.

      • Methods need to be written more carefully with enough details. I listed some of the concerns below.

      Detailed methodology was previously provided in the supplementary material and now we have moved it to the materials and methods in the revised manuscript.

      • Line 392, provide more details on western blotting. What is the secondary antibody? What image documentation system was used?

      We have updated the methodology.

      • Line 400, while the methods may be the same as the reference 64, authors should still provide key details such as the way samples were fixed and processed for SEM and TEM.

      We have provided a detailed description of the same in methodology in the revised version.

      • Line 437, how do authors calculate the concentration of liposome to be 10 µM? Do they possibly mean the concentration of phospholipids used to make the liposomes?

      Yes, this is the concentration of total lipids used to make liposomes. 1 μM of Wag31 or its mutants were mixed with 100 nm extruded liposomes containing 10 μm total lipid in separate Eppendorf tubes.

      • Supplemental Line 9, "turns of" should read "turns off".

      We have edited this.

      • Supplemental Line 13, define LHS and RHS.

      LHS or left hand sequence and RHS or right hand sequence refers to the upstream and downstream flanking regions of the gene of interest.

      • Supplemental Line 20, indicate the manufacturer of the microscope and type of the objective lens.

      We have added these details now.

      • Supplemental Line 31, define MeOH, or use a chemical formula like chloroform.

      MeOH is methanol. We have provided a chemical formula in the revised version.

      • Supplemental Line 53, indicate the concentration of trypsin.

      We have included that in the revised version.

      • Supplemental Line 72, g is not a unit. "30,000 g" should be "30,000x g".

      We have revised this in the manuscript.

      • Supplemental Line 114, provide more details on western blotting. What is the manufacturer of antiFLAG antibody? What is the secondary antibody? How was the antibody binding visualized? What image documentation system was used?

      We have provided these details in the revised version.

    1. Welcome back. Over the remaining lessons in this section, you're going to learn how to build a complex, multi-tier, custom VPC step by step. One of the benefits of the VPC product is that you can start off simple and layer components in piece by piece. This lesson will focus on just the VPC shell, but by the end of this section, you'll be 100% comfortable building a pretty complex private network inside AWS. So let's get started.

      Now, don't get scared off by this diagram, but this is what we're going to implement together in this section, of course. Right now, it might look complicated, but it's like building a Lego project—we'll start off simple and add more and more complexity as we go through the section. This is a multi-tier, custom VPC. If you look at the IP plan document that I linked in the last lesson, it's using the IP address at the first range of the US Region 1 for the general account, so 10.16.0.0/16, so the VPC will be configured to use that range. Inside the VPC, there'll be space for four tiers running in four availability zones for a total of 16 possible subnets.

      Now, we'll be creating all four tiers—so reserved, database, app, and web—but only three availability zones, A, B, and C. We won't be creating any subnets in the capacity reserved for the future availability zone, so that's the part at the bottom here. In addition to the VPC that we'll create in this lesson, the subnets that we'll create in the following lessons will also, as we look through the section of the course, be creating an internet gateway which will give resources in the VPC public access. We'll be creating NAT gateways which will give private instances outgoing-only access, and we'll be creating a bastion host which is one way that we can connect into the VPC.

      Now, using bastion hosts is frowned upon and isn't best security practice for getting access to AWS VPCs, but it's important that you understand how not to do something in order to appreciate good architectural design. So I'm going to step you through how to implement a bastion host in this part of the course, and as we move through later sections of the course, you'll learn more secure alternatives. Finally, later on in the section, we'll also be looking at network access control lists on knuckles, which can be used to secure the VPC, as well as data transfer costs for any data that moves in and around the VPC.

      Now, this might look intimidating, but don't worry, I'll be explaining everything every step of the way. To start with though, we're going to keep it simple and just create the VPC. Before we do create a VPC, I want to cover some essential architectural theory, so let's get started with that.

      VPCs are a regionally isolated and regionally resilient service. A VPC is created in a region and it operates from all of the AZs in that region. It allows you to create isolated networks inside AWS, so even in a single region in an account, you can have multiple isolated networks. Nothing is allowed in or out of a VPC without a piece of explicit configuration. It's a network boundary and it provides an isolated glass radius. What I mean by this is if you have a problem inside a VPC—so if one resource or a set of resources are exploited—the impact is limited to that VPC or anything that you have connected to it.

      I talked earlier in the course about the default VPC being set up by AWS using the same static structure of one subnet per availability zone using the same IP address ranges and requiring no configuration from the account administrator. Well, custom VPCs are pretty much the opposite of that. They let you create networks with almost any configuration, which can range from a simple VPC to a complex multi-tier one such as the one that we're creating in this section. Custom VPCs also support hybrid networking, which let you connect your VPC to the cloud platforms as well as on-premises networks, and we'll cover that later on in the course.

      When you create a VPC, you have the option of picking default or dedicated dependency. This controls whether the resources created inside the VPC are provisioned on shared hardware or dedicated hardware. So be really careful with this option. If you pick default, then you can choose on a per-resource basis later on when you provision resources as whether it goes on shared hardware or dedicated hardware. If you pick dedicated tenancy at a VPC level, then that's locked in—any resources that you create inside that VPC have to be on dedicated hardware. So you need to be really careful with this option because dedicated tenancy comes at a cost premium, and my rule on this is unless you really know that you require dedicated, then pick default, which is the default option.

      Now, VPC can use IP version for private and public IPs. The private side block is the main method of IP communication for the VPC. So by default, everything uses these private addresses. Public IPs are used when you want to make resources public, when you want them to communicate with the public internet or the AWS public zone, or you want to allow communication to them from the public internet. Now, VPC is allocated one mandatory private IP version for side block—this is configured when you create the VPC, which you'll see in a moment when we actually create a VPC.

      Now, this primary block has two main restrictions: it can be at its smallest a /28 prefix, meaning the entire VPC has 16 IP addresses (and some of those can't be used—more on that in the next lesson when I talk about subnet, though), and at the largest, a VPC can use a /16 prefix, which is 65,536 IDs. Now, you can add secondary IP version for side blocks after creation, but by default, at the time of creating this lesson, there's a maximum of five of those, but they can be increased by using a support ticket. But generally, when you're thinking conceptually about a VPC, just imagine that it's got a pool of private IP version 4 addresses, and optionally, it can use public addresses.

      Now, another optional configuration is that a VPC can be configured to use IP version 6 by assigning a /56 IP V6 sider to the VPC. Now, this is a feature set which is still being enjoyed, so not everything works with the same level of features as it does for IP version 4, but with the increasing worldwide usage of IP version 6, in most circumstances, you should start looking at applying an IP version 6 range as a default. An important thing about IP version 6 is that the range is either allocated by AWS—as in, you have no choice on which range to use—or you can select to use your own IP version 6 addresses, addresses which you own. You can't pick a block like you can with IP version 4—either let AWS assign it or you use addresses that you own.

      Now, IP version 6 IPs don't have the concept of private and public—the range of IP version 6 addresses that AWS uses are all publicly routable by default. But if you do use them, you still have to explicitly allow connectivity to and from the public internet. So don't worry about security concerns—it just removes an admin overhead because you don't need to worry about this distinction between public and private.

      Now, AWS VPCs also have fully featured DNS. It's provided by round 53, and inside the VPC, it's available on the base IP address of the VPC plus 2. So the VPC is 10.0.0.0, and the DNS IP will be 10.0.0.2. Now, there are two options which are critical for how DNS functions in a VPC, so I've highlighted both of them. The first is a setting called enable DNS host names, and this indicates whether instances with public IP addresses in a VPC are given public DNS host names. So if this is set to true, then instances do get public DNS host names. If it's not set to true, they don't.

      The second option is enable DNS support, and this indicates whether DNS is enabled or disabled in the VPC—so DNS resolution. If it is enabled, then instances in the VPC can use the DNS IP address, so the VPC plus 2 IP address. If this is set to false, then this is not available. Now, why I mention both of these is if you do have any questions in the exam or any real-world situations where you're having DNS issues, these two should be the first settings that you check, switched on or off as appropriate. And in the demo part of this lesson, I'll show you where to access those.

      Speaking of which, it's now time for the demo component of this lesson, and we're going to implement the framework of VPC for the Animals for Life organization together inside our AWS account. So let's go ahead and finish the theory part of this lesson right now, and then in the next lesson, the demo part will implement this VPC together.

    1. Author response:

      The following is the authors’ response to the previous reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of taste cells of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi" (in terms of human taste); these kokumi stimuli appear to enhance other canonical tastes, increasing what are essentially hedonic attributes of other stimuli. The mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model, and comes to a similar conclusion, albeit with some small differences between the two rodent species.

      Strengths:

      The data show effects of ornithine on taste/intake in laboratory rats: In two-bottle and briefer intake tests, adding ornithine results in higher intake of most, but all not all stimuli tested. Bilateral chorda tympani (CT) nerve cuts or the addition of GPRC6A antagonists decreased or eliminated these effects. Ornithine also evoked responses by itself in the CT nerve, but mainly at higher concentrations; at lower concentrations it potentiated the response to monosodium glutamate. Finally, immunocytochemistry of taste cell expression indicated that GPRC6A was expressed predominantly in the anterior tongue, and co-localized (to a small extent) with only IP3R3, indicative of expression in a subset of type II taste receptor cells.

      Weaknesses:

      As the authors are aware, it is difficult to assess a complex human taste with complex attributes, such as kokumi, in an animal model. In these experiments they attempt to uncover mechanistic insights about how ornithine potentiates other stimuli by using a variety of established experimental approaches in rats. They partially succeed by finding evidence that GPRC6A may mediate effects of ornithine when it is used at lower concentrations. In the revision they have scaled back their interpretations accordingly. A supplementary experiment measuring certain aspects of the effects of ornithine added to Miso soup in human subjects is included for the express purpose of establishing that the kokumi sensation of a complex solution is enhanced by ornithine; however, they do not use any such complex solutions in the rat studies. Moreover, the sample size of the human experiment is (still) small - it really doesn't belong in the same manuscript with the rat studies.

      Despite the reviewer’s suggestion, we would like to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, which is then followed by basic animal experiments to investigate the underlying mechanisms of kokumi in humans.

      We did not present the additive effects of ornithine on miso soup in the present rat study because our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26) already confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred to plain miso soup by mice.

      Furthermore, we believe that our sample size (n = 22) is comparable to those employed in other studies. For example, the representative kokumi studies by Ohsu et al. (Ref. #9), Ueda et al. (Ref. #10), Shibata et al. (Ref. #20), Dunkel et al. (Ref. #37), and Yang et al. (Ref. #44) used sample sizes of 20, 19, 17, 9, and 15, respectively.

      Reviewer #2 (Public review):

      Summary:

      The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

      Strengths:

      The authors provide compelling evidence that ornithine enhances the palatability of several chemical stimuli (i.e., IMP, MSG, MPG, Intralipos, sucrose, NaCl, quinine). Ornithine also increases CT nerve responses to MSG. Additionally, the authors provide evidence that the effects of ornithine are mediated by GPRC6A, a G-protein-coupled receptor family C group 6 subtype A, and that this receptor is expressed primarily in fungiform taste buds. Taken together, these results indicate that ornithine enhances the palatability of multiple taste stimuli in rats and that the enhancement is mediated, at least in part, within fungiform taste buds. This is an important finding that could stand on its own. The question of whether ornithine produces these effects by eliciting kokumi-like perceptions (see below) should be presented as speculation in the Discussion section.

      Weaknesses:

      I am still unconvinced that the measurements in rats reflect the "kokumi" taste percept described in humans. The authors conducted long-term preference tests, 10-min avidity tests and whole chorda tympani (CT) nerve recordings. None of these procedures specifically model features of "kokumi" perception in humans, which (according to the authors) include increasing "intensity of whole complex tastes (rich flavor with complex tastes), mouthfulness (spread of taste and flavor throughout the oral cavity), and persistence of taste (lingering flavor)." While it may be possible to develop behavioral assays in rats (or mice) that effectively model kokumi taste perception in humans, the authors have not made any effort to do so. As a result, I do not think that the rat data provide support for the main conclusion of the study--that "ornithine is a kokumi substance and GPRC6A is a novel kokumi receptor."

      Kokumi can be assessed in humans, as demonstrated by the enhanced kokumi perception observed when miso soup is supplemented with ornithine (Fig. S1). Currently, we do not have a method to measure the same kokumi perception in animals. However, in the two-bottle preference test, our previous companion paper (Fig. 1B in Mizuta et al. 2021, Ref. #26) confirmed that miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) was statistically significantly (P < 0.001) preferred over plain miso soup by mice.

      Of the three attributes of kokumi perception in humans, the “intensity of whole complex tastes (rich flavor with complex tastes)” was partly demonstrated in the present rat study. In contrast, “mouthfulness (the spread of taste and flavor throughout the oral cavity)” could not be directly detected in animals and had to be inferred in the Discussion. “Persistence of taste (lingering flavor)” was evident at least in the chorda tympani responses; however, because the tongue was rinsed 30 seconds after the onset of stimulation, the duration of the response was not fully recorded.

      It is well accepted in sensory physiology that the stronger the stimulus, the larger the tonic response—and consequently, the longer it takes for the response to return to baseline. For example, Kawasaki et al. (2016, Ref. #45) clearly showed that the duration of sensation increased proportionally with the concentration of MSG, lactic acid, and NaCl in human sensory tests. The essence of this explanation has been incorporated into the Discussion (p. 12).

      Why are the authors hypothesizing that the primary impacts of ornithine are on the peripheral taste system? While the CT recordings provide support for peripheral taste enhancement, they do not rule out the possibility of additional central enhancement. Indeed, based on the definition of human kokumi described above, it is likely that the effects of kokumi stimuli in humans are mediated at least in part by the central flavor system.

      We agree with the reviewer’s comment. Our CT recordings indicate that the effects of kokumi stimuli on taste enhancement occur primarily at the peripheral taste organs. The resulting sensory signals are then transmitted to the brain, where they are processed by the central gustatory and flavor systems, ultimately giving rise to kokumi attributes. This central involvement in kokumi perception is discussed on page 12. Although kokumi substances exert their effects at low concentrations—levels at which the substance itself (e.g., ornithine) does not become more favorable or (in the case of γ-Glu-Val-Gly) exhibits no distinct taste—we cannot rule out the possibility that even faint taste signals from these substances are transmitted to the brain and interact with other taste modalities.

      The authors include (in the supplemental data section) a pilot study that examined the impact of ornithine on variety of subjective measures of flavor perception in humans. The presence of this pilot study within the larger rat study does not really mice sense. While I agree with the authors that there is value in conducting parallel tests in both humans and rodents, I think that this can only be done effectively when the measurements in both species are the same. For this reason, I recommend that the human data be published in a separate article.

      Despite the reviewer’s suggestion, we intend to include the human sensory experiment. Our rationale is that we must first demonstrate that the kokumi of miso soup is enhanced by the addition of ornithine, and then follow up with basic animal experiments to investigate the potential underlying mechanisms of kokumi in humans.

      In our previous companion paper (Fig. 1B in Mizuta et al., 2021, Ref. #26), we confirmed with statistical significance (P < 0.001) that mice preferred miso soup supplemented with 3 mM L-ornithine (but not D-ornithine) over plain miso soup. However, as explained in our response to Reviewer #2’s first concern (in the Public review), it is difficult to measure two of the three kokumi attributes—aside from the “intensity of whole complex tastes (rich flavor with complex tastes)”—in animal models.

      The authors indicated on several occasions (e.g., see Abstract) that ornithine produced "synergistic" effects on the CT nerve response to chemical stimuli. "Synergy" is used to describe a situation where two stimuli produce an effect that is greater than the sum of the response to each stimulus alone (i.e., 2 + 2 = 5). As far as I can tell, the CT recordings in Fig. 3 do not reflect a synergism.

      We appreciate your comments regarding the definition of synergy. In Fig. 5 (not Fig. 3), please note the difference in the scaling of the ordinate between Fig. 5D (ornithine responses) and Fig. 5E (MSG responses). When both responses are presented on the same scale, it becomes evident that the response to 1 mM ornithine is negligibly small compared to the MSG response, which clearly indicates that the response to the mixture of MSG and 1 mM ornithine exceeds the sum of the individual responses to MSG and 1 mM ornithine. Therefore, we have described the effect as “synergistic” rather than “additive.” The same observation applies to the mice experiments in our previous companion paper (Fig. 8 in Mizuta et al. 2021, Ref. #26), where synergistic effects are similarly demonstrated by graphical representation. We have also added the following sentence to the legend of Fig. 5:

      “Note the different scaling of the ordinate in (D) and (E).”

      Reviewer #3 (Public review):

      Summary:

      In this study the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste. The researchers confirmed in rats their previous work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

      Strengths:

      The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants including: inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl; salt); citric acid (sour) and quinine hydrochloride (bitter). Robust effects of ornithine were observed in the cases of IMP, MSG, MPG and sucrose; and little or no effects were observed in the cases of sodium chloride, citric acid; quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. Inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify a role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

      At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). These alternatives are appropriately discussed and, taken together, the experimental results favor the authors' interpretation that C6A mediates the Ornithine responses. The authors provide preliminary data in Suppl. 3 for the possibility of co-expression of C6A with the CaSR.

      Weaknesses:

      The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9).

      Ornithine and umami substances interact to produce synergistic effects in both directions—ornithine enhances responses to umami substances, and vice versa. These effects may depend on the concentrations used, as described in the Discussion (pp. 9–10). Further studies are required to clarify the precise nature of this interaction.

      One issue that is not addressed, and could be usefully addressed in the Discussion, relates to the potential effects of kokumi substances on the threshold concentrations of key tastants such as glutamate. Thus, an extension of taste distribution to additional areas of the mouth (previously referred to as 'mouthfulness') and persistence of taste/flavor responses (previously referred to as 'continuity') could arise from a reduction in the threshold concentrations of umami and other substances that evoke taste responses.

      Thank you for this important suggestion. If ornithine reduces the threshold concentrations of tastants—including glutamate—and enhances their suprathreshold responses, then adding ornithine may activate additional taste cells. This effect could explain kokumi attributes such as an “extension of taste distribution” and possibly the “persistence of responses.” As shown in Fig. 2, the lowest concentrations used for each taste stimulus are near or below the thresholds, which indicates that threshold concentrations are reduced—especially for MSG and MPG. We have incorporated this possibility into the Discussion as follows (p.12):

      “Kokumi substances may reduce the threshold concentrations as well as they increase the suprathreshold responses of tastants. Once the threshold concentrations are lowered, additional taste cells in the oral cavity become activated, and this information is transmitted to the brain. As a result, the brain perceives this input as coming from a wider area of the mouth.”

      The status of one of the compounds used as an inhibitor of C6A, the gallate derivative EGCG, as a potential inhibitor of the CaSR or T1R1/T1R3 is unknown. It would have been helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response.

      Thank you for this important comment. We attempted to identify a specific inhibitor of CaSR. Although we considered using NPS-2143—a commonly used CaSR inhibitor—it is known to also inhibit GPRC6A. We agree that using a specific CaSR inhibitor would be beneficial and plan to pursue this in future studies.

      It would have been helpful to include a positive control kokumi substance in the two bottle preference experiment (e.g., one of the known gamma glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

      We agree with this comment. In retrospect, it may have been advantageous to directly compare the potencies of CaSR and GPRC6A agonists in enhancing taste preferences—and to evaluate the sensitivity of these preferences to CaSR and GPRC6A antagonists. However, we did not include γ-Glu-Val-Gly in the present study because we have already reported its supplementation effects on the ingestion of basic taste solutions in rats using the same methodology in a separate paper (Yamamoto and Mizuta, 2022, Ref. #25). The results from both studies are compared in the Discussion (p. 11).

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Major:

      I am not convinced by the Author's arguments for including the human data. I appreciate their efforts in adding a few (5) subjects and improving the description, but it still feels like it is shoehorned into this paper, and would be better published as a different manuscript.

      This human study is short, but it is complete rather than preliminary. The rationale for us to include the human data as supplementary information is shown in responses to the reviewer’s Public review.

      Minor concerns:

      Page 3 paragraph 1: Suggest "contributing to palatability".

      Thank you for this suggestion. We have rewritten the text as follows:

      “…, the brain further processes these sensations to evoke emotional responses, contributing to palatability or unpleasantness”.

      Page 4 paragraph 2: The text still assumes that "kokumi" is a meaningful descriptor for what rodents experience. Re-wording the following sentence like this could help:

      "Neuroscientific studies in mice and rats provide evidence that gluthione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi taste as experienced by humans. However, to our..."

      Or something similar.

      Thank you for this suggestion. We have rewritten the sentence according to your suggestion as follows:

      "Neuroscientific studies (23,25,30) in mice and rats provide evidence that glutathione and y-Glu-Val-Gly activate CaSRs, and modify behavioral responses to other tastants in a way that may correspond to kokumi as experienced by humans”.

      Page 7 paragraph 1 - put the concentrations of Calindol and EGCG used (in the physiology exps) in the text.

      We have added the concentrations: “300 µM calindol and 100 µM EGCG”.

      Reviewer #2 (Recommendations for the authors):

      I have included all of my recommendations in the public review section.

      Reviewer #3 (Recommendations for the authors):

      Although the definitions of 'thickness', 'mouthfulness' and 'continuity' have been revised very helpfully in the Introduction, 'mouthfulness' reappears at other points in the MS e.g., Page 4, Results, Line 3; Page 9, Line 3. It is best replaced by the new definition in these other locations too.

      We wish to clarify that our revised text stated, “…to clarify that kokumi attributes are inherently gustatory, in the present study we use the terms ‘intensity of whole complex tastes (rich flavor with complex tastes)’ instead of ‘thickness,’ ‘mouthfulness (spread of taste and flavor throughout the oral cavity)’ instead of ‘continuity,’ and ‘persistence of taste (lingering flavor)’ instead of ‘continuity.’” The term “mouthfulness” was retained in our text, though we provided a more specific explanation. In the re-revised version, we have added “(spread of taste in the oral cavity)” immediately after “mouthfulness.”

      I doubt that many scientific readers will be familliar with the term 'intragemmal nerve fibres' (Page 8, Line 4). It is used appropriately but it would be helpful to briefly define/explain it.

      We have added an explanation as follows:

      “… intragemmal nerve fibers, which are nerve processes that extend directly into the structure of the taste bud to transmit taste signals from taste cells to the brain.”

      I previously pointed out the overlap between the CaSR's amino acid (AA) and gamma-glutamyl-peptide binding site. I was surprised by the authors' response which appeared to miss the point being made. It was based on the impacts of selected mutations in the receptor's Venus FlyTrap domain (Broadhead JBC 2011) on the responses to AAs and glutathione analogs. The significantly more active analog, S-methylglutathione is of additional interest because, like glutathione itself, it is present in mammalian body fluids. My apologies to the authors for not more carefully explaining this point.

      Thank you for this comment. Both CaSR and GPRC6A are recognized as broad-spectrum amino acid sensors; however, their agonist profiles differ. Aromatic amino acids preferentially activate CaSR, whereas basic amino acids tend to activate GPRC6A. For instance, among basic amino acids, ornithine is a potent and specific activator of GPRC6A, while γ-Glu-Val-Gly in addition to amino acids is a high-potency activator of CaSR. It remains unclear how effectively ornithine activates CaSR and whether γ-glutamyl peptides also activate GPRC6A. These questions should be addressed in future studies.

    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment

      This valuable study uses consensus-independent component analysis to highlight transcriptional components (TC) in high-grade serous ovarian cancers (HGSOC). The study presents a convincing preliminary finding by identifying a TC linked to synaptic signaling that is associated with shorter overall survival in HGSOC patients, highlighting the potential role of neuronal interactions in the tumour microenvironment. This finding is corroborated by comparing spatially resolved transcriptomics in a small-scale study; a weakness is in being descriptive, non-mechanistic, and requiring experimental validation.”

      We sincerely thank the editors for their valuable and constructive feedback. We are grateful for the recognition of our findings and the importance of identifying transcriptional components in high-grade serous ovarian cancers.

      We acknowledge the editors’ observation regarding the descriptive nature of our study and its limited mechanistic depth. We agree that additional experimental validation would further strengthen our conclusions. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study. In addition, recent reviews focused on the emerging field of cancer neuroscience emphasize the early stages the field is in, specifically in terms of a mechanistic understanding of the contributions of tumor-infiltrating nerves in tumor initiation and progression (Amit et al., 2024; Hwang et al., 2024). Nonetheless, we wish to emphasize that emerging mechanistic preclinical studies have demonstrated the influence of tumour-infiltrating nerves on disease progression (Allen et al., 2018; Balood et al., 2022; Darragh et al., 2024; Globig et al., 2023; Jin et al., 2022; Restaino et al., 2023; Zahalka et al., 2017). Several of these studies include contributions from our co-authors and feature in vitro and in vivo research on head and neck squamous cell carcinoma as well as high-grade serous ovarian carcinoma samples. This study further strengthens the preclinical work by showing in patient data, the potential relevance of neuronal signaling on disease outcome.

      For instance, Restiano et al. (2023) demonstrated that substance P, released from tumour-infiltrating nociceptors, potentiates MAP kinase signaling in cancer cells, thereby driving disease progression. Crucially, this effect was shown to be reversible in vivo by blocking the substance P receptor (Restaino et al., 2023). These findings offer compelling evidence of the role of tumour innervation in cancer biology.

      Our current study in tumor samples of patients with high-grade serous ovarian cancer identifies a transcriptional component that is enriched for genes for which the protein is located in the synapse. We believe that the previously published mechanistic insights support our findings and suggest that this transcriptional component could serve as a valuable screening tool to identify innervated tumours based on bulk transcriptomes. Clinically, this information is highly relevant, as patients with innervated tumours may benefit from alternate therapeutic strategies targeting these innervations.

      Reviewer #1 (Public review)

      This manuscript explores the transcriptional landscape of high-grade serous ovarian cancer (HGSOC) using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs) associated with patient outcomes. The study analyzes 678 HGSOC transcriptomes, supplemented with 447 transcriptomes from other ovarian cancer types and noncancerous tissues. By identifying 374 TCs, the authors aim to uncover subtle transcriptional patterns that could serve as novel drug targets. Notably, a transcriptional component linked to synaptic signaling was associated with shorter overall survival (OS) in patients, suggesting a potential role for neuronal interactions in the tumour microenvironment. Given notable weaknesses like lack of validation cohort or validation using another platform (other than the 11 samples with ST), the data is considered highly descriptive and preliminary.

      Strengths:

      (1) Innovative Methodology:

      The use of c-ICA to dissect bulk transcriptomes into independent components is a novel approach that allows for the identification of subtle transcriptional patterns that may be overshadowed in traditional analyses.

      We thank the reviewer for recognizing the strengths and novelty of our study. We appreciate the positive feedback on using consensus-independent component analysis (c-ICA) to decompose bulk transcriptomes, which allowed us to detect subtle transcriptional signals often overlooked in traditional analyses.

      (2) Comprehensive Data Integration:

      The study integrates a large dataset from multiple public repositories, enhancing the robustness of the findings. The inclusion of spatially resolved transcriptomes adds a valuable dimension to the analysis.

      We thank the reviewer for recognizing the robustness of our study through comprehensive data integration. We appreciate the acknowledgment of our efforts to leverage a large, multi-source dataset, as well as the additional insights gained from spatially resolved transcriptomes. We consider this integrative approach enhances the depth of our analysis and contributes to a more nuanced understanding of the tumour microenvironment.

      (3) Clinical Relevance:

      The identification of a synaptic signaling-related TC associated with poor prognosis highlights a potential new avenue for therapeutic intervention, emphasizing the role of the tumour microenvironment in cancer progression.

      We appreciate the recognition of the clinical implications of our findings. The identification of a synaptic signaling-related transcriptional component associated with poor prognosis underscores the potential for novel therapeutic targets within the tumour microenvironment. We agree that this insight could open new avenues for intervention and further highlights the role of neuronal interactions in cancer progression.

      Weaknesses:

      (1) Mechanistic Insights:

      While the study identifies TCs associated with survival, it provides limited mechanistic insights into how these components influence cancer progression. Further experimental validation is necessary to elucidate the underlying biological processes.

      We acknowledge the point regarding the limited mechanistic insights provided in our study. We agree that further experimental validation would significantly enhance our understanding of how the biological processes captured by these transcriptional components influence cancer progression. We are planning and executing the experiments for  a future study to provide mechanistic insights into the associations found in this study.

      Our analyses were performed on publicly available bulk and spatial resolved expression profiles. To investigate the mechanistic insights in future studies, we plan to integrate spatial transcriptomic data with immunohistochemical analysis of the same tumour samples to validate our findings. Additionally, we have initiated efforts to set up in vitro co-cultures of neurons and ovarian cancer cells. These co-cultures will enable us to investigate how synaptic signaling impacts ovarian cancer cell behavior.

      (2) Generalizability:

      The findings are primarily based on transcriptomic data from HGSOC. It remains unclear how these results apply to other subtypes of ovarian cancer or different cancer types.

      To respond to this remark, we utilized survival data from Bolton et al. (2022) and TCGA to investigate associations between TC activity scores and overall survival of patients with ovarian clear cell carcinoma, the second most common subtype of epithelial ovarian cancer, and  other cancer types respectively. However, we acknowledge the limitations of TCGA survival data, as highlighted in the referenced article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8726696/). Additionally, as shown in Figure 5, we provided evidence of TC121 activity across various cancer types, suggesting broader relevance. For the results of the analyses mentioned above, please refer to our response to remark 1.3 of the recommendation section (page 4).

      (3) Innovative Methodology:

      Requires more validation using different platforms (IHC) to validate the performance of this bulk-derived data. Also, the lack of control over data quality is a concern.

      We acknowledge the value of validating our results with alternative platforms such as IHC. We are planning and executing the experiments for a future study to provide mechanistic insights into the associations found in this study.

      We implemented regarding data quality control, the following measures to ensure the reliability of our analysis:

      Bulk Transcriptional Profiles: To assess data quality, we conducted principal component analysis (PCA) on the sample Pearson product-moment correlation matrix. The first principal component (PCqc), which explains approximately 80-90% of the variance, was used to distinguish technical variability from biological signals (Bhattacharya et al., 2020). Samples with a correlation coefficient below 0.8 relative to PCqc were identified as outliers and excluded. Additionally, MD5 hash values were generated for each CEL file to identify and remove duplicate samples. Expression values were standardized to a mean of zero and a variance of one for each gene to minimize probeset- or gene-specific variability across datasets (GEO, CCLE, GDSC, and TCGA).

      Spatial Transcriptional Profiles: PCA was also applied to spatial transcriptomic data for quality control. Only samples with consistent loading factor signs for the first principal component across all individual spot profiles were retained. Samples failing this criterion were excluded from further analyses.

      (4) Clinical Application:

      Although the study suggests potential drug targets, the translation of these findings into clinical practice is not addressed. Probably given the lack of some QA/QC procedures it'll be hard to translate these results. Future studies should focus on validating these targets in clinical settings.”

      Regarding clinical applications, we acknowledge the importance of further exploring strategies targeting synaptic signaling and neurotransmitter release in the tumour microenvironment (TME). As partially discussed in the first version of the manuscript, drugs such as ifenprodil and lamotrigine—commonly used to treat neuronal disorders—can block glutamate release, thereby inhibiting subsequent synaptic signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine blocks the formation of synaptic vesicles (Reid et al., 2013; Williams et al., 2001). Previous in vitro studies with HGSOC cell lines demonstrated that ifenprodil significantly reduced cancer cell proliferation, while reserpine triggered apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). The findings highlight the potential of such approaches to disrupt synaptic neurotransmission in the TME.

      To address potential translation of our findings into clinical practice more comprehensively, we have included additional details in the manuscript:

      Section discussion, page 16, lines 338-341:

      “This interaction can be targeted with pan-TRK inhibitors such as entrectinib and larotrectinib. Both drugs are showing promising results in multiple phase II trials, including ovarian cancer and breast cancer patients. Furthermore, a TRKB-specific inhibitor was developed (ANA-12), but has not been subjected to any clinical trials in cancer so far (Ardini et al., 2016; Burris et al., 2015; Drilon et al., 2018, 2017).”

      On page 17, lines 361-374:

      “Strategies to disrupt neuronal signaling and neurotransmitter release in neurons target key elements of excitatory neurotransmission, such as calcium flux and vesicle formation. Drugs like ifenprodil and lamotrigine, commonly used to treat neuronal disorders, block glutamate release and subsequent neuronal signaling. Additionally, the vesicular monoamine transporter (VMAT) inhibitor reserpine prevents synaptic vesicle formation (Reid et al., 2013; Williams, 2001). In vitro studies with HGSOC cell lines have demonstrated that ifenprodil significantly inhibits tumour proliferation, while reserpine induces apoptosis in cancer cells (North et al., 2015; Ramamoorthy et al., 2019). These approaches hold promise for inhibiting neuronal signaling and interactions in the TME.”

      Reviewer #2 (Public review):

      Summary:

      Consensus-independent component analysis and closely related methods have previously been used to reveal components of transcriptomic data that are not captured by principal component or gene-gene coexpression analyses.

      Here, the authors asked whether applying consensus-independent component analysis (c-ICA) to published high-grade serous ovarian cancer (HGSOC) microarray-based transcriptomes would reveal subtle transcriptional patterns that are not captured by existing molecular omics classifications of HGSOC.

      Statistical associations of these (hitherto masked) transcriptional components with prognostic outcomes in HGSOC could lead to additional insights into underlying mechanisms and, coupled with corroborating evidence from spatial transcriptomics, are proposed for further investigation.

      This approach is complementary to existing transcriptomics classifications of HGSOC.

      The authors have previously applied the same approach in colorectal carcinoma (Knapen et al. (2024) Commun. Med).

      Strengths:

      (1) Overall, this study describes a solid data-driven description of c-ICA-derived transcriptional components that the authors identified in HGSOC microarray transcriptomics data, supported by detailed methods and supplementary documentation.

      We thank the reviewer for acknowledging the strength of our data-driven approach and the use of consensus-independent component analysis (c-ICA) to identify transcriptional components within HGSOC microarray data. We aimed to provide comprehensive methodological detail and supplementary documentation to support the reproducibility and robustness of our findings. We believe this approach allows for the identification of subtle transcriptional signals that might have been overlooked by traditional analysis methods.

      (2) The biological interpretation of transcriptional components is convincing based on (data-driven) permutation analysis and a suite of analyses of association with copy-number, gene sets, and prognostic outcomes.

      We appreciate the positive feedback on the biological interpretation of our transcriptional components. We are pleased that our approach, which includes data-driven permutation testing and analyses of associations with copy-number alterations, gene sets, and prognostic outcomes, was found to be convincing. These analyses were integral to enhancing our findings’ robustness and biological relevance.

      (3) The resulting annotated transcriptional components have been made available in a searchable online format.

      Thank you for this important positive remark.

      (4) For the highlighted transcriptional component which has been annotated as related to synaptic signalling, the detection of the transcriptional component among 11 published spatial transcriptomics samples from ovarian cancers appears to support this preliminary finding and requires further mechanistic follow-up.

      Thank you for acknowledging the accessibility of our annotated transcriptional components. We prioritized making these data available in a searchable online format to facilitate further research and enable the community to explore and validate our findings.

      Weaknesses:

      (1) This study has not explicitly compared the c-ICA transcriptional components to the existing reported transcriptional landscape and classifications for ovarian cancers (e.g. Smith et al Nat Comms 2023; TCGA Nature 2011; Engqvist et al Sci Rep 2020) which would enable a further assessment of the additional contribution of c-ICA - whether the cICA approach captured entirely complementary components, or whether some components are correlated with the existing reported ovarian transcriptomic classifications.

      We acknowledge the reviewer’s insightful suggestion to compare our c-ICA-derived transcriptional components with previously reported ovarian cancer classifications, such as those from Smith et al. (2023), TCGA (2011), and Engqvist et al. (2020). To address this, we incorporated analyses comparing the activity scores of our transcriptional components with these published landscapes and classifications, particularly focusing on any associations with overall survival. Additionally, we evaluated correlations between gene signatures from a subset of these studies and our identified TCs, enhancing our understanding of the unique contributions of the c-ICA approach. Please refer to our response to remark 10 for the results of these analyses.

      (2) Here, the authors primarily interpret the c-ICA transcriptional components as a deconvolution of bulk transcriptomics due to the presence of cells from tumour cells and the tumour microenvironment.

      However, c-ICA is not explicitly a deconvolution method with respect to cell types: the transcriptional components do not necessarily correspond to distinct cell types, and may reflect differential dysregulation within a cell type. This application of c-ICA for the purpose of data-driven deconvolution of cell populations is distinct from other deconvolution methods that explicitly use a prior cell signature matrix.”

      We acknowledge that c-ICA, unlike traditional deconvolution methods, is not specifically designed for cell-type deconvolution and does not rely on a predefined cell signature matrix. While we explored the transcriptional components in the context of tumour and microenvironmental interactions, we agree that these components may not correspond directly to distinct cell types but rather reflect complex patterns of dysregulation, potentially within individual cell populations.

      Our goal with c-ICA was to uncover hidden transcriptional patterns possibly influenced by cellular heterogeneity. However, we recognize these patterns may also arise from regulatory processes within a single cell type. To investigate further, we used single-cell transcriptional data (~60,000 cell-types annotated profiles from GSE158722) and projected our transcriptional components onto these profiles to obtain activity scores, allowing us to assess each TC’s behavior across diverse cellular contexts after removing the first principal component to minimize background effects. Please refer to our response to remark 2.2 in the recommendations to the authors (page 14) for the results of this analysis.

      References

      Allen JK, Armaiz-Pena GN, Nagaraja AS, Sadaoui NC, Ortiz T, Dood R, Ozcan M, Herder DM, Haemerrle M, Gharpure KM, Rupaimoole R, Previs R, Wu SY, Pradeep S, Xu X, Han HD, Zand B, Dalton HJ, Taylor M, Hu W, Bottsford-Miller J, Moreno-Smith M, Kang Y, Mangala LS, Rodriguez-Aguayo C, Sehgal V, Spaeth EL, Ram PT, Wong ST, Marini FC, Lopez-Berestein G, Cole SW, Lutgendorf SK, diBiasi M, Sood AK. 2018. Sustained adrenergic signaling promotes intratumoral innervation through BDNF induction. Cancer Res 78 (12):3233-3242.

      Ardini E, Menichincheri M, Banfi P, Bosotti R, Ponti CD, Pulci R, Ballinari D, Ciomei M, Texido G, Degrassi A, Avanzi N, Amboldi N, Saccardo MB, Casero D, Orsini P, Bandiera T, Mologni L, Anderson D, Wei G, Harris J, Vernier J-M, Li G, Felder E, Donati D, Isacchi A, Pesenti E, Magnaghi P, Galvani A. 2016. Entrectinib, a Pan–TRK, ROS1, and ALK Inhibitor with activity in multiple molecularly defined cancer Indications. Mol Cancer Ther 15:628–639.

      Balood M, Ahmadi M, Eichwald T, Ahmadi A, Majdoubi A, Roversi Karine, Roversi Katiane, Lucido CT, Restaino AC, Huang S, Ji L, Huang K-C, Semerena E, Thomas SC, Trevino AE, Merrison H, Parrin A, Doyle B, Vermeer DW, Spanos WC, Williamson CS, Seehus CR, Foster SL, Dai H, Shu CJ, Rangachari M, Thibodeau J, Rincon SVD, Drapkin R, Rafei M, Ghasemlou N, Vermeer PD, Woolf CJ, Talbot S. 2022. Nociceptor neurons affect cancer immunosurveillance. Nature 611:405–412.

      Bhattacharya A, Bense RD, Urzúa-Traslaviña CG, Vries EGE de, Vugt MATM van, Fehrmann RSN. 2020. Transcriptional effects of copy number alterations in a large set of human cancers. Nat Commun 11:715.

      Burris HA, Shaw AT, Bauer TM, Farago AF, Doebele RC, Smith S, Nanda N, Cruickshank S, Low JA, Brose MS. 2015. Abstract 4529: Pharmacokinetics (PK) of LOXO-101 during the first-in-human Phase I study in patients with advanced solid tumors: Interim update. Cancer Res 75:4529–4529.

    1. Note de synthèse : Dix conseils pour bien gérer les relations parents-enseignants (Jean-Louis Auduc)

      Source : Extraits de "https://maromme.circonscription.ac-normandie.fr/IMG/pdf/ecole-parents_-_dix_conseils_de_jl_auduc.pdf" (initialement paru dans l'Expresso du Café pédagogique, 31/01/2014).

      Ce document présente dix conseils concrets de Jean-Louis Auduc, ancien directeur d'IUFM, pour améliorer et gérer efficacement les relations entre parents et enseignants à différents niveaux de la scolarité. L'auteur analyse les dynamiques en jeu et propose des pistes d'action pour instaurer la confiance et une collaboration fructueuse au bénéfice de l'enfant.

      Thèmes principaux et idées importantes :

      1) Combattre et vaincre les peurs réciproques :

      • Peurs des parents : Jugement sur leur enfant perçu comme un jugement sur eux-mêmes, sentiment d'incompétence parentale, peur de ce qu'ils vont entendre sur leur enfant, peur des "représailles" sur leur enfant suite à leurs interventions, et surtout, la peur d'être convoqués, souvent synonyme de problèmes.
      • Citation (Mehdi Hatri) : "Peur du jugement des enseignants sur leur capacité à être de bons parents, peur face au pouvoir tant des enseignants que de l’institution école, notamment en matière de redoublement, d’orientation ou de sanctions, peur de ce qu’ils vont entendre sur leur enfant..."
      • Obstacles chez les enseignants : Ne pas accepter la critique, considérer tout désaccord comme un conflit, difficultés à prévoir des temps de rencontre, percevoir les parents uniquement comme "utiles" pour des activités ponctuelles.
      • Obstacles chez les parents : Informations parfois inexactes données par l'enfant, réticence à entrer en conflit avec l'enseignant.
      • Peurs des enseignants : Peur d'une surveillance, de l'ingérence, de devoir se justifier, manque de formation à la gestion d'entretien et à l'annonce d'informations, peur de parler en groupe, d'être déstabilisé, de ne pas maîtriser la situation ou d'être mal compris.
      • Citation (Mehdi Hatri) : "Les enseignants sont partagés entre deux attitudes contradictoires : - celle qui consiste à envisager les parents comme des partenaires potentiels - celle qui se limite à se méfier d’eux. Je pense que cette attitude paradoxale s’explique du fait qu’ils ont peur.(…) peur d’être jugé sur ses méthodes et ses compétences, peur de se faire agresser tant physiquement que verbalement, peur d’être dépourvu face à des interrogations de parents » …"
      • Conséquences d'une mauvaise communication : Messages contradictoires nuisant au développement de l'enfant, incohérence lourde de conséquences, déficit de stimulation, difficultés à comprendre les valeurs sociales.
      • Bénéfices d'une communication réussie : Attitude plus positive des parents envers l'école, meilleure compréhension des pédagogies, sentiment de valorisation et d'appartenance, meilleure transition enfant-famille/école, confiance accrue des enfants.

      2) Comment gérer l’accueil des parents d’élèves :

      • Il est erroné de considérer les parents peu présents comme "démissionnaires". Leurs contraintes horaires (travail atypique) peuvent être un facteur.
      • Les horaires des services scolaires doivent correspondre aux rythmes de vie des familles.
      • Les lieux d'accueil et d'écoute doivent répondre aux besoins des familles et être construits avec elles, en les impliquant à toutes les étapes (diagnostic, mise en œuvre, évaluation).
      • La classe peut être un premier lieu d'accueil possible.

      3) Comment construire des relations de confiance entre parents et enseignants ?

      • Ne pas seulement "convoquer" en cas de difficulté, mais "inviter" pour parler de l'enfant et construire une coopération.
      • Le dialogue ne signifie pas un consensus mou, mais une confrontation exigeante de points de vue pour dépasser les tensions.
      • Les tensions sont normales en raison des positionnements différents (intérêt général de la classe vs. intérêt particulier de l'enfant).
      • Le refus de dialogue est inquiétant. La confrontation nécessaire doit se faire dans le respect, en plaçant l'enfant au centre et en précisant les règles de l'échange.

      4) Comment aborder les différentes rencontres avec les familles ?

      • Différents types de rencontres existent (circonstancielles, obligatoires, sollicitées).
      • Conseils pour chaque rencontre : Prendre l'enfant tel qu'il est, encourager et valoriser les réussites (même minimes et extrascolaires).
      • Ce qu'il ne faut pas faire : Se laisser déborder, adopter une attitude fataliste, se justifier systématiquement, blâmer la famille, penser pouvoir tout résoudre seul (conseiller d'autres professionnels si besoin), répondre à l'agressivité par l'agressivité, être "ouvert" uniquement pour certaines choses (sorties) et "fermé" pour d'autres (apprentissage).
      • Les demandes de rencontre doivent être présentées comme des "rendez-vous".
      • Privilégier le dialogue à l'écrit "à chaud".
      • Tenir un historique des rencontres précisant : Qui a provoqué le rendez-vous, quelles informations ont été données, quelles questions restent en suspens, quelles décisions ont été prises et leur suivi, les potentialités de travail commun ou les problèmes rencontrés.

      5) Le jeune doit-il être présent lors des réunions parents-enseignants ?

      • Pas de réponse générale. La rencontre est initialement un entretien entre adultes.
      • Il peut être utile que l'enfant n'assiste pas à la première partie, mais puisse être appelé à la fin pour entendre les accords entre parents et enseignants.

      6) Comment se donner les moyens de réussir les réunions parents-enseignants ?

      • Impliquer réellement les parents pour qu'ils considèrent l'école de leur enfant comme "leur" école.
      • Pour une rencontre efficace : Préparer une liste de points à aborder, commencer par une réussite de l'enfant, envisager un tiers en cas de conflit potentiel, être précis dans le message (utiliser des fiches si besoin), écouter attentivement les inquiétudes, expliquer calmement le diagnostic, laisser l'interlocuteur présenter le sien sans l'interrompre, faire reformuler pour s'assurer de la compréhension.
      • Ne pas être défensif face à la colère, rester pondéré, ne pas rendre les parents responsables des problèmes, ne pas se précipiter sur les solutions mais chercher avec la famille l'origine des difficultés.
      • Travailler la fierté des enfants et des parents.
      • Laisser du temps pour les questions des parents en rappelant que les problèmes personnels se règlent en privé et qu'il faut éviter les désaccords devant l'enfant.
      • Méthode pour orienter vers un "spécialiste" : Rédiger une note à l'attention du spécialiste pendant l'entretien et la remettre aux parents dans une enveloppe, favorisant ainsi une consultation plus fréquente.
      • Citation : "Pour bien montrer aux parents que c’est en professionnel « généraliste » que l’enseignant adresse à un « spécialiste » les parents, il peut être utile de toujours prévoir lors d’une rencontre parents/enseignants quelques enveloppes et feuilles de papier. Pendant qu’il indique le spécialiste aux parents, l’enseignant rédige quelques lignes à destination de celui-ci pour lui indiquer pourquoi cette famille vient le voir. En fin d’entretien, il remet l’enveloppe aux parents en lui indiquant de remettre cette enveloppe lors de leur rendez-vous avec la personne indiquée. L’expérience a montré qu’une telle procédure ne délégitimait pas l’enseignant en tant que professionnel et entraînait une plus fréquente consultation du professionnel spécialiste » bien plus fréquente qu’à l’occasion d’une simple indication orale."
      • À la fin de l'entretien : Faire le point sur ce qui a été dit, s'assurer de la compréhension de la participation attendue des parents, présenter les solutions comme un constat commun, rassurer et positiver, proposer un point de suivi, choisir l'outil de correspondance adapté pour le suivi.

      7) Comment accueillir les parents non-francophones ?

      Se référer au texte intégral pour ce point.

      8) Comment permettre aux parents d’épauler, d’aider leurs enfants ?

      • Rappeler régulièrement que toutes les familles peuvent soutenir la scolarité au quotidien.
      • Rôle des parents : Organiser un cadre de travail (horaire régulier, concentration, préparation du cartable, repos), rassurer l'enfant (cohérence famille-école, respect des demandes de l'enseignant, éviter les critiques devant l'enfant), encourager et soutenir (confiance, accepter l'erreur, répondre positivement aux questions, parler avec l'enfant, rencontrer les responsables d'associations si l'enfant est accompagné).

      9) Comment annoncer une nouvelle « difficile » aux parents ?

      • Préparer l'annonce pour que les parents gardent espoir et ne se bloquent pas.
      • Travail d'équipe indispensable : Impliquer tous les enseignants et personnels spécialisés pour une vision globale de l'enfant. La décision "difficile" doit être présentée collectivement.
      • Citation : "Un travail d’équipe : annoncer une mauvaise nouvelle nécessite un travail d’équipe impliquant tous les enseignants et les personnels spécialisés intervenant dans l’établissement afin que tous les éléments concernant le jeune puissent être connus de toute l’équipe. C’est normalement à l’ensemble des membres de l’équipe de présenter une décision « difficile » à la famille."
      • Importance de la temporalité : Il n'est pas toujours efficace de commencer par la présence d'"experts".
      • Privilégier une première rencontre avec un parent ou un ancien élève ayant vécu une situation similaire peut aider les parents à accepter la réalité et à ne pas se sentir seuls. Ces personnes sont des "témoins d'espoir".
      • Citation : "Dans le cas d’une décision « difficile » et perçue comme une « mauvaise nouvelle » par les parents, il n’est pas sur que le « tiers » présent à la réunion pour aider la famille doive être un expert ou un professionnel de l’éducation nationale. Un parent d’élève, voire un ancien élève ayant vécu la même situation quelques années auparavant peut être un atout pour aider les parents à vivre la dure réalité. Le but de l’entretien d’annonce de la décision est de donner de l’espoir à la famille en lui indiquant les voies possibles pour la réussite de l’enfant. Ces personnes seront pour les parents des témoins d’espoir. Elles permettent de mieux gérer le second temps de la rencontre avec les professionnels et les « experts » et de faciliter la prise de décision par la famille ainsi mobilisée vers les voies possibles facilitant la poursuite de scolarité de l’enfant."
      • L'exemple de l'annonce de troubles autistiques chez un jeune enfant illustre l'intérêt de faire témoigner d'autres parents ayant vécu la même situation avant l'intervention des experts.

      10) Comment montrer aux parents qu’il doit exister un « jardin secret » partagé dans la classe entre les enseignants et les élèves et que tout ne peut être dit aux familles ?

      • Être totalement transparent risque de ne pas préserver l'intimité et l'espace privé de l'enfant, préjudiciable à son autonomie.
      • Il faut gérer les contradictions entre les messages scolaires et les pratiques familiales en permettant à l'élève de construire ses propres choix.
      • Tout ne peut pas être dit aux familles, surtout concernant les adolescents, qui ont besoin de confiance et d'intimité pour grandir et développer leur estime de soi.
      • Citation : "On ne peut pas tout dire à la famille concernant ce que fait un jeune dans la classe, notamment lorsqu’il s’agit d’un (e) adolescent (e). Le jeune, pour grandir, pour développer son « estime de soi » doit avoir confiance dans le monde des adultes et dans les institutions. Elle ou il a besoin de se sentir « libres » dans ses différents espaces et de pouvoir préserver son intimité."
      • Un enseignant peut légitimement refuser de répondre à des questions intrusives sur les interactions entre élèves, de la même manière qu'il ne demandera pas aux parents les détails de la vie privée de leur enfant à la maison.
      • La confidentialité partagée sur certains sujets participe à la construction du futur adulte.

      • En conclusion, ce document souligne l'importance d'une approche empathique, collaborative et respectueuse dans les relations parents-enseignants. Vaincre les peurs mutuelles, établir la confiance, communiquer clairement et tenir compte des réalités de chaque famille sont des clés essentielles pour favoriser le bien-être et la réussite des élèves. L'auteur insiste sur la nécessité de considérer les parents comme des partenaires à part entière de la communauté éducative.

    1. Reviewer #1 (Public review):

      The aim of this study was a better understanding of the reproductive life history of acoels. The acoel Hofstenia miamia, an emerging model organism, is investigated; the authors nevertheless acknowledge and address the high variability in reproductive morphology and strategies within Acoela.

      The morphology of male and female reproductive organs in these hermaphroditic worms is characterised through stereo microscopy, immunohistochemistry, histology, and fluorescent in situ hybridization. The findings confirm and better detail historical descriptions. A novelty in the field is the in situ hybridization experiments, which link already published single-cell sequencing data to the worms' morphology. An interesting finding, though not further discussed by the authors, is that the known germline markers cgnl1-2 and Piwi-1 are only localized in the ovaries and not in the testes.

      The work also clarifies the timing and order of appearance of reproductive organs during development and regeneration, as well as the changes upon de-growth. It shows an association of reproductive organ growth to whole body size, which will be surely taken into account and further explored in future acoel studies. This is also the first instance of non-anecdotal degrowth upon starvation in H. miamia (and to my knowledge in acoels, except recorded weight upon starvation in Convolutriloba retrogemma [1]).

      Egg laying through the mouth is described in H. miamia for the first time as well as the worms' behavior in egg laying, i.e. choosing the tanks' walls rather than its floor, laying eggs in clutches, and delaying egg-laying during food deprivation. Self-fertilization is also reported for the first time.

      The main strength of this study is that it expands previous knowledge on the reproductive life history traits in H. miamia and it lays the foundation for future studies on how these traits are affected by various factors, as well as for comparative studies within acoels. As highlighted above, many phenomena are addressed in a rigorous and/or quantitative way for the first time. This can be considered the start of a novel approach to reproductive studies in acoels, as the authors suggest in the conclusion. It can be also interpreted as a testimony of how an established model system can benefit the study of an understudied animal group.

      The main weakness of the work is the lack of convincing explanations on the dynamics of self-fertilization, sperm storage, and movement of oocytes from the ovaries to the central cavity and subsequently to the pharynx. These questions are also raised by the authors themselves in the discussion. Another weakness (or rather missing potential strength) is the limited focus on genes. Given the presence of the single-cell sequencing atlas and established methods for in situ hybridization and even transgenesis in H. miamia, this model provides a unique opportunity to investigate germline genes in acoels and their role in development, regeneration, and degrowth. It should also be noted that employing Transmission Electron Microscopy would have enabled a more detailed comparison with other acoels, since ultrastructural studies of reproductive organs have been published for other species (cfr e.g. [2],[3],[4]). This is especially true for a better understanding of the relation between sperm axoneme and flagellum (mentioned in the Results section), as well as of sexual conflict (mentioned in the Discussion).

      (1) Shannon, Thomas. 2007. 'Photosmoregulation: Evidence of Host Behavioral Photoregulation of an Algal Endosymbiont by the Acoel Convolutriloba Retrogemma as a Means of Non-Metabolic Osmoregulation'. Athens, Georgia: University of Georgia [Dissertation].<br /> (2) Zabotin, Ya. I., and A. I. Golubev. 2014. 'Ultrastructure of Oocytes and Female Copulatory Organs of Acoela'. Biology Bulletin 41 (9): 722-35.<br /> (3) Achatz, Johannes Georg, Matthew Hooge, Andreas Wallberg, Ulf Jondelius, and Seth Tyler. 2010. 'Systematic Revision of Acoels with 9+0 Sperm Ultrastructure (Convolutida) and the Influence of Sexual Conflict on Morphology'.<br /> (4) Petrov, Anatoly, Matthew Hooge, and Seth Tyler. 2006. 'Comparative Morphology of the Bursal Nozzles in Acoels (Acoela, Acoelomorpha)'. Journal of Morphology 267 (5): 634-48.

    1. Reviewer #1 (Public review):

      Summary:

      The authors report the role of a novel gene Aff3ir-ORF2 in flow induced atherosclerosis. They show that the gene is anti-inflammatory in nature. It inhibits the IRF5 mediated athero-progression by inhibiting the causal factor (IRF5). Furthermore, authors show a significant connection between shear stress and Aff3ir-ORF2 and its connection to IRF5 mediated athero-progression in different established mice models which further validates the ex vivo findings.

      Strengths:

      (1) Adequate number of replicates were used for this study.<br /> (2) Both in vitro and in vivo validation was done.<br /> (3) Figures are well presented<br /> (4) In vivo causality is checked with cleverly designed experiments

      Weaknesses:

      (1) Inflammatory proteins must be measured with standard methods e.g ELISA as mRNA level and protein level does not always correlate.<br /> (2) RNA seq analysis has to be done very carefully. How does the euclidean distance correlate with the differential expression of genes. Do they represent neighborhood? If they do how does this correlation affect the conclusion of the paper?<br /> (3) Volcano plot does not indicate q value of the shown genes. It is advisable to calculate q value for each of the genes which represents the FDR probability of the identified genes.<br /> (4) GO enrichment was done against Global gene set or local geneset? Authors should provide more detailed information about the analysis.<br /> (5) If the analysis was performed against global gene set. How does that connect with this specific atherosclerotic microenvironment?<br /> (6) what was the basal expression of genes and how does the DGE (differential gene expression) values differ?<br /> (7) How did IRF5 picked from GO analysis? was it within 20 most significant genes?<br /> (8) Microscopic studies should be done more carefully? There seems to be a global expression present on the vascular wall for Aff3ir-ORF2 and the expression seems to be similar like AFF3 in fig 1.

      Comments on Revision:

      The authors have adequately addressed my concerns.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors report the role of a novel gene Aff3ir-ORF2 in flow-induced atherosclerosis. They show that the gene is anti-inflammatory in nature. It inhibits the IRF5-mediated athero-progression by inhibiting the causal factor (IRF5). Furthermore, the authors show a significant connection between shear stress and Aff3ir-ORF2 and its connection to IRF5 mediated athero-progression in different established mice models which further validates the ex vivo findings.

      Strengths:

      (1) An adequate number of replicates were used for this study.

      (2) Both in vitro and in vivo validation was done.

      (3) The figures are well presented.

      (4) In vivo causality is checked with cleverly designed experiments.

      We thank you for your positive remarks.

      Weaknesses:

      (1) Inflammatory proteins must be measured with standard methods e.g ELISA as mRNA level and protein level does not always correlate.

      Thanks. We have followed your advice and performed ELISA experiments to measure the concentrations of inflammatory cytokines, including IL-6 and IL-1β. The newly acquired results have been included in Figure 2E (Line 160-163) in the revised manuscript.

      (2) RNA seq analysis has to be done very carefully. How does the euclidean distance correlate with the differential expression of genes. Do they represent the neighborhood?

      If they do how does this correlation affect the conclusion of the paper?

      We thank the reviewer for this professional comments and apologize for the confusion. The heatmap using Euclidean distance was generated based on the expression levels of all differentially expressed genes (calculated with deseq2). Since its interpretation overlaps with the volcano plot presented in Figure 4B, we have moved the heatmap to Figure S5A in the revised manuscript and provided a detailed description in the figure legend (Lines 106-108 in the supporting information). Additionally, to better illustrate the variation among all samples, we have performed PCA analysis and included the new results in Figure 4A of the revised manuscript.

      (3) The volcano plot does not indicate the q value of the shown genes. It is advisable to calculate the q value for each of the genes which represents the FDR probability of the identified genes.

      Thank you for your careful review. We apologize for the incorrect labeling.

      It was P.adj value. The label for Figure 4B has been corrected in the revised manuscript. 

      (4) GO enrichment was done against the Global gene set or a local geneset? The authors should provide more detailed information about the analysis.

      Thank you. We performed GO enrichment analysis against the global gene set. The description of the results has been updated in the revised manuscript (Lines 222–224).

      (5) If the analysis was performed against a global gene set. How does that connect with this specific atherosclerotic microenvironment?

      Thank you for your insightful comments. We have followed your advice and investigated the functional characteristics of these differentially expressed genes in the context of the atherosclerotic microenvironment. The RNA-seq differential gene list was further mapped onto the atherosclerosis-related gene dataset (PMID: 27374120), resulting in 363 overlapping genes. The 363 genes were subjected to bioinformatics enrichment analysis using Gene Ontology (GO) databases. GO analysis of these genes revealed enrichment in processes related to cell−cell adhesion and leukocyte activation involved in immune response (Figure S5B), which is highly consistent with the observed effects of AFF3ir-ORF2 on VCAM-1 expression. The newly acquired data are presented in Figure S5B and the description of the results is included in the revised manuscript (Line 227-233).

      (6) What was the basal expression of genes and how did the DGE (differential gene expression) values differ?

      Thanks for the comments. The RNA-sequencing data has been submitted to GEO datasets (GSE286206), making the basal gene expression data available to readers.

      The differential expression analysis was performed using DESeq2 (v1.4.5) (PMID: 25516281) with a criterion of 1.5-fold change and P<0.05. We has included the description in the revised manuscript in Lines 220-222 and Lines 575-576.

      (7) How was IRF5 picked from GO analysis? was it within the 20 most significant genes?

      Sorry for the confusion. IRF5 was not identified through GO analysis. To determine the upstream transcriptional regulators, we used the ChEA3 database to predict potential upstream transcription factors based on all differentially expressed genes. The top 20 transcription factors were selected based on their scores. To further explore their relationship with atherosclerosis, these top 20 transcription factors were mapped to the atherosclerosis-related gene list in the DisGeNET database. IRF5 and IRF8 were the only two overlapping genes. To clarify this process, we have included a more detailed description of the IRF prediction approach in the revised manuscript (Lines 234–239).

      (8) Microscopic studies should be done more carefully? There seems to be a global expression present on the vascular wall for Aff3ir-ORF2 and the expression seems to be similar to AFF3 in Figure 1.

      We thank the reviewer for the valuable suggestion. We have followed your advice and provided the more representative images in Figure 1F.

      Reviewer #2 (Public review):

      Summary:

      The authors recently uncovered a novel nested gene, Aff3ir, and this work sets out to study its function in endothelial cells further. Based on differences in expression correlating with areas of altered shear stress, they investigate a role for the isoform Aff3ir-ORF2 in endothelial activation and development of atherosclerosis downstream of disturbed shear stress. Using a knockout mouse model and in vivo overexpression experiments, they demonstrate a strong potential for Aff3ir-ORF2 to alleviate atherosclerosis. They find that Aff3ir-ORF2 interacts with the pro-inflammatory transcription factor IRF5 and retains it in the cytoplasm, hence preventing upregulation of inflammation-associated genes. The data expands our knowledge of IRF5 regulation which could be relevant to researchers studying various inflammatory diseases as well as adding to our understanding of atherosclerosis development.

      Strengths:

      The in vivo data is solid using immunofluorescence staining to assess AFF3ir-ORF2 expression, a knockout mouse model, overexpression and knockdown studies, and rescue experiments in combination with two atherosclerotic models to demonstrate that Aff3ir-ORF2 can lessen atherosclerotic plaque formation in ApoE<sup>-/-</sup> mice.

      We thank you for your positive remarks.

      Weaknesses:

      While the in vivo data is generally convincing, a few data panels have issues and will need addressing. Also, the knockout mouse model will need to be described, since the paper referred to in the manuscript does not actually report any knockout mouse model. Hence it is unclear how Aff3ir-ORF2 is targeted, but Figure S2B shows that targeting is partial, since about 30% expression remains at the RNA level in MEFs isolated from the knockout mice.

      We thank you for the valuable comments. 

      First, we have followed your advice and included detailed information regarding the animal construction in the revised manuscript in Line 405-415. Additionally, the genotyping results have been included in new Figure S3A.

      Second, we acknowledge your concern about the knockout efficiency of ORF2 in mice. While the PCR assay indicated approximately 30% residual expression, our Western blot analysis of aorta samples demonstrated that ORF2 protein was barely detectable in knockout mice, as shown in new Figure S3B-C. Besides, our in vivo experiments using MEF from WT and AFF3ir-ORF2<sup>-/-</sup> mice (Figure 4I) further confirmed successful knockout. 

      Third, we have included a discussion addressing the discrepancies between PCR and Western blot results. In addition to technical differences between the two methods, the nature of AFF3ir-ORF2 may also contribute to these inconsistencies. The parent gene AFF3 is located in a genetically variable region and can be excised via intron 5 to form a replicable transposon, which translocates to other chromosomes and has been linked to leukemia (PMID: 34995897, 12203795, 12743608, and 17968322). AFF3ir is located in the intron 6, thus it exists in the transposon, which may complicate the measurement of its expression. Replicable transposons can exist as extrachromosomal elements, allowing them to be inherited across generations. We have included these discussion in the revised manuscript in Line 188-196.

      While the effect on atherosclerosis is clear, the conclusion that this is the result of reduced endothelial cell activation is not supported by the data. The mouse model is described as a global knockout and the shRNA knockdowns (Figure 5) and overexpression data in Figure 2 are not cell type-specific. Only the overexpression construct in Figure 6 uses an ICAM-2 promoter construct, which drives expression in endothelial cells, though leaky expression of this promoter has been reported in the literature. Therefore, other cell types such as smooth muscle cells or macrophages could be responsible for the effects observed.

      Thank you for your critical comment. To address your concern, we have made the following three revisions:

      First, we have analyzed the expression of AFF3ir-ORF2 in the vascular wall with or without intima in WT and AFF3ir-ORF2 knockout mice. As shown in Figure 1B and Figure S1A, while the expression of AFF3ir-ORF2 was notably downregulated in the aortic intima of athero-prone regions compared to the protective region, it remained largely unchanged in the aortic wall without intima across different regions of the aorta. This suggested that AFF3ir-ORF2 might play a predominant role in endothelial cells rather than other cell types in the context of shear stress.

      Second, we have used human endothelial cells (HUVECs) to further confirm our findings. As shown in Figure 2C and Figure S2B, we found that AFF3ir-ORF2 overexpression could attenuate disturbed shear stress-induced IRF5 nuclear translocation and the expression of inflammatory genes in HUVECs, suggesting the potential anti-inflammatory effects of AFF3ir-ORF2 in endothelial cells.

      Third, we agree with the reviewer’s comment that we cannot completely exclude the potential involvement of other cell types. Hence, we have included a limitation statement in the discussion part in Lines 341-344.

      The weakest part of the manuscript is the in vitro experiment using some nonidentifiable expression differences. The data is used to hypothesise on a role for IRF5 in the effects observed with Aff3ir-ORF2 knockout.

      Thank you for the comments. To address your concerns, we have made the following two changes:

      First, we have further investigated the functional features of the differential genes from the RNA-seq in the context of atherosclerotic microenvironment. The differential gene list was mapped onto the atherosclerosis-related gene dataset (PMID: 27374120), and a total of 363 genes overlapped. These 363 genes were subjected to bioinformatics enrichment analysis using Gene Ontology (GO) databases. GO analysis showed that these genes were mainly enriched in cell−cell adhesion and leukocyte activation involved in immune response, which aligns with the expression of VCAM-1 affected by AFF3ir-ORF2. The newly acquired data are presented in Figure S5B and the description of the results has been updated in the revised manuscript (Line 227-233).

      Second, we have further verified the RNA-seq results in vitro. Several classical inflammatory factors, including ICAM-1, CCL5, and CXCL10, which mRNA levels were significantly downregulated in RNA-seq and were also identified as target genes of IRF5, were analyzed. We found that AFF3ir-ORF2 deficiency aggravated, while AFF3ir-ORF2 overexpression attenuated, the expression of ICAM-1, CCL5, and CXCL10 induced by disturbed shear stress (New Figure S5D). Besides, the regulation of ICAM-1 by AFF3ir-ORF2 was confirmed at both protein and mRNA levels in HUVECs (Figure 2C-D and Figure S2B). 

      Overall, the paper succeeds in demonstrating a link between Aff3ir-ORF2 and atherosclerosis, but the cell types involved and mechanisms remain unclear. The study also shows a functional interaction between Aff3ir-ORF2 and IRF5 in embryonic fibroblasts, but any relevance of this mechanism for atherosclerosis or any cell types involved in the development of this disease remains largely speculative.

      Thank you for all the valuable comments. The specific responses have been provided above. Briefly, we have followed your advice and further confirmed the regulation of AFF3ir-ORF2 on IRF5 in endothelial cells. Besides, the RNA-seq results have been further analyzed, and partial results have been verified in endothelial cells to support the anti-inflammatory role of AFF3ir-ORF2. We greatly appreciate the reviewer’s insightful comments, which guided our revisions and contributed to significantly improving the paper.

      Reviewer #3 (Public review):

      This study is to demonstrate the role of Aff3ir-ORF2 in the atheroprone flow-induced EC dysfunction and ensuing atherosclerosis in mouse models. Overall, the data quality and comprehensiveness are convincing. In silico, in vitro, and in vivo experiments and several atherosclerosis were well executed. To strengthen further, the authors can address human EC relevance.

      We thank you for your positive remarks and insightful comments.

      Major comments:

      (1) The tissue source in Figures 1A and 1B should be clarified, the whole aortic segments or intima? If aortic segment was used, the authors should repeat the experiments using intima, due to the focus of the current study on the endothelium.

      We thank you for the suggestion. The tissue used in Figures 1A and 1B was from aortic intima. The description has been updated for clarity in the revised manuscript on Lines 114-125. 

      (2) Why were MEFs used exclusively in the in vitro experiments? Can the authors repeat some of the critical experiments in mouse or human ECs?

      Thank you for this insightful comment. Isolation and culture of mouse primary aortic ECs were notorious technically difficult and shear stress experiment require a large number of cells. Considering MEFs exhibit responses consistent with those of ECs, which has been delicately proved (PMID: 23754392), we used MEFs in our in vitro experiments.

      However, following your valuable advice, we have now employed human ECs (HUVECs) to confirm our findings. Consistent with our results in MEFs, we found that AFF3ir-ORF2 overexpression reduced the expression of inflammatory genes induced by disturbed shear stress at both protein and mRNA levels in HUVECs (Figure 2C, Figure S2B). Notably, despite the significant anti-inflammatory effects of AFF3irORF2, the sequence of this gene is not conserved in Homo sapiens and lacks an initiation codon, which is why we did not further proceed with the loss-of-function experiments.

      (3) The authors should explain why AFF3ir-ORF2 overexpression did not affect the basal level expression of ICAM-1, VCAM-1, IL-1b, and IL-6 under ST conditions (Figure 2A-C).

      We thank you for raising this critical question. Indeed, we found that AFF3ir-ORF2 overexpression did not affect the basal level of inflammatory genes under ST conditions, while it exerted anti-inflammatory effects under OSS conditions. One underlying reason might be the relative low level of expression of inflammatory genes under ST compared to OSS conditions. Additionally, as our findings suggested, AFF3ir-ORF2 exerted its anti-inflammatory role by binding to IRF5 and inhibiting IRF5 nuclear translocation. However, as shown in Figure 4I, IRF5 might be predominantly localized in the cytoplasm rather than the nucleus under ST conditions.

      We have included the description in the revised manuscript on Lines 157-163.

      (4) Please include data from sham controls, i.e., right carotid artery in Figure 2E.

      Thank you for the suggestion. We have followed your advice and included sham controls (staining of the right carotid arteries) in Figure S2E.

      (5) Given that the merit of the study lies in the effect of different flow patterns, the legion areas in AA and TA (Figure 3B, 3C) should be separately compared.

      We have followed your valuable suggestion and included the additional statistical results in Figure 3C in the revised manuscript.

      (6) For confirmatory purposes for the variations of IRF5 and IRF8, can the authors mine available RNA-seq or even scRNA-seq data on human or mouse atherosclerosis? This approach is important and could complement the current results that are lacking EC data.

      Thank you for your valuable suggestion. In the present study, we found that disturbed flow did not alter the protein level of IRF5 but promoted its nuclear translocation. Following your advice, we analyzed the expression of IRF5 in human ECs (GSE276195) and atherosclerotic mouse arteries (GSE222583) using public databases. Consistently, IRF5 did not show significant changes in mRNA levels under these conditions (Figure S5E-F), suggesting that the regulation of IRF5 in the context of disturbed flow or atherosclerosis is primarily post-translational.

      (7) With the efficacy of using AAV-ICAM2-AFF3ir-ORF2 in atherosclerosis reduction (Figure 6), the authors are encouraged to use lung ECs isolated from the AFF3ir-ORF2/-mice to recapitulate its regulation of IRF5.

      We greatly appreciate your valuable suggestion to use lung ECs from mice. We have observed that AFF3ir-ORF2 deficiency enhanced the nuclear translocation of IRF5 induced by OSS. Noteworthy, the transcriptional levels of IRF5 were minimally affected by AFF3ir-ORF2 deficiency. Hence, to recapitulate the regulation of IRF5 with lung ECs isolated from the AFF3ir-ORF2<sup>-/-</sup> mice, it would require treating lung ECs with OSS followed by isolation of subcellular components. However, both in vitro shear stress treatment and subcellular fraction isolation require a large number of cells, and mouse lung ECs are difficult to culture and pass through several passages. Therefore, we hope the reviewer understands that these experiments were not performed. As an alternative, we have confirmed the transcriptional activity changes of IRF5 due to AFF3ir-ORF2 manipulation by analyzing the expression of its target genes indicated from RNA-seq results in both the intima of mouse aorta (Figure S5C-D) and HUVECs (Figure 2C-D and Figure S2B). Our findings show that AFF3ir-ORF2 deficiency increases, while its overexpression decreases, the expression levels of IRF5-targeted genes in endothelial cells.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      Figure 2H - As I understand it, this is MFI measurement of VCAM. Please change accordingly.

      Thanks. Corrected.

      Reviewer #2 (Recommendations for the authors):

      My major concern is the use of MEFs for all in vitro experiments. All experiments should be done in endothelial cells if the aim is to show a mechanism relevant to endothelial activation and atherosclerosis. Lines 314-316 of the conclusion are absolutely not supported by the data.

      Thank you for the insightful comment. Following your advice, we have employed human ECs (HUVECs) to confirm our findings. Consistent with the findings in MEFs, we found that AFF3ir-ORF2 decreased the expression of inflammatory genes induced by disturbed shear stress, both at protein and mRNA levels in HUVECs (Figure 2C, Figure S2B). 

      Since the in vivo experiments are not cell type-specific, it would be important to test and compare the expression of Aff3ir-ORF2 in endothelial cells as well as smooth muscle and macrophages to support any claim of cell type involvement in the effects observed.

      We thank you for the valuable suggestion. In the revised manuscript, we have followed your suggestion and analyzed the expression pattern of AFF3ir-ORF2 in different regions of the aorta with or without endothelium. We observed a marked reduction in AFF3ir-ORF2 expression in the intima of the aortic arch compared to that in the intima of the thoracic aorta (Figure 1B-C). In contrast, the expression of AFF3irORF2 in the media and adventitia was comparable between the aortic arch and thoracic aorta (Figure S1A-B). These findings provide further evidence supporting the predominant role of endothelial cells. The description has been modified accordingly in the revised manuscript on Lines 121-134.

      The results of the RNA-seq experiment should be disclosed. The experiment should be deposited on GEO or similar and a table of differentially expressed genes added to the manuscript.

      Thank you for the suggestion. We have followed your advice and submitted the RNA-sequencing data to GEO datasets (GSE286206). Besides, a table of differentially expressed genes has been included in the revised manuscript as Table S3.

      Minor comments:

      (1) Figure 1A. Missing the labels of the target.

      Thanks. Corrected. 

      (2) Figure 1D. Cell alignment in AA compared to TA suggests that the image is of the outer curvature, but Figure 1F is showing that the outer curvature is expressing more ORF2 than the inner. Why was the outer curvature chosen for this panel and is it true to conclude on that assumption that expression of ORF2 compares as TA > Outer > Inner curvature?

      We thank you for the insightful suggestion. We have followed your advice and performed en-face immunofluorescence staining of AFF3ir-ORF2 and quantification of AFF3ir-ORF2 expression in AA inner, AA outer, and TA regions. As shown in new Figure 1D-E, the results indeed indicated that expression of AFF3irORF2 compares as TA > AA outer > AA inner.

      (3) Figure 2H. Target mislabelled as ICAM-1 instead of VCAM-.

      Thanks. Corrected. 

      (4) Figure S1A. VE-cad staining and cell shape differ between control and overexpression. Is this a phenotype or are different areas of the vasculature shown, which would make it hard to interpret since Aff3ir-ORF2 levels differ in different vessel areas?

      We thank the reviewer for raising this important question. For Figure S1A, only common carotid arteries were used for the staining. The potential differences in cell shape observed might be due to variations in the procedure during immunofluorescence staining. To avoid any misinterpretation, more representative images have been provided in the revised Figure S2C.

      (5) Figure 3D-G. Images are not representative of the quantification results.

      Thank you. More representative images have been replaced in the revised Figure 3D and Figure 3F.

      (6) Line 220. Data for IRF8 are not shown in the figure to support this claim.

      Thank you for pointing this out. The expression level of IRF8 has been included in Figure S5C.

      (7) Figure 6F. AAV-AFF3ir-ORF2 panel order inverted.

      Thanks. Corrected. 

      (8) Line 401. Type "hat" instead of "h at".

      Sorry for the typo. Corrected.

      Reviewer #3 (Recommendations for the authors):

      Minor comments:

      (1)  The rationale for the following sentence (lines 126-128) is lacking: "Moreover, 126 we observed the expression of AFF3ir-ORF2 in longitudinal sections of the mouse aorta (B. 127 Li et al., 2019)".

      Thanks. The rationale for these experiments have been included in the revised manuscript on Line 127-129. 

      (2) The source of antibodies against AFF3ir-ORF1 and AFF3ir-ORF2 used in western blot and immunostaining experiments were not mentioned in the manuscript.

      Thanks. The antibody information has been included in the method part on Line 456-457, 510-511. 

      (3) The rationale and data interpretation is not clear for the following sentence (lines 220-221): "In addition, neither IRF5 nor IRF8 expression was regulated by AFF3irORF2 220 (Figure 4F)".

      Thank you for pointing this out. The expression level of IRF8 has been included in Figure S5C. The sentence has been modified accordingly on Lines 253254. 

      (4) The quality of AFF3ir-ORF2 blot in Figure 4I needs improvement.

      Thanks. More representative images have been included in Figure 4I.

      (5) It appears that AFF3ir-ORF2 was present in both cytoplasm and nucleus. Does AFF3ir-ORF2 have a nuclear entry peptide? Also, the nuclear entry of AFF3ir-ORF2 can be enhanced by an immunofluorescence staining experiment.

      Thank you for your insightful comments. Indeed, although we did not observe any significant subcellular changes in the localization of AFF3ir-ORF2 under shear stress conditions, our immunostaining results revealed that AFF3ir-ORF2 is localized in both the cytoplasm and nucleus. To explore whether AFF3ir-ORF2 contains nuclear localization signals, we utilized the NLStradamus tool (http://www.moseslab.csb.utoronto.ca/NLStradamus/) to analyze its sequence. The predication indicated that AFF3ir-ORF2 lacks a nuclear localization signal.

    1. She sat like Patience on a monument, 1017  Smiling at grief.

      This powerful image shows silent suffering. “Patience on a monument” suggests a statue — unmoving, dignified, and eternal. Smiling while grieving suggests deep emotional control or resignation. It’s Viola’s hidden pain, disguised as calm.

    2. She never told her love, 1013  But let concealment, like a worm i’ th’ bud, 1014  Feed on her damask cheek.

      Viola’s metaphor compares hidden love to a worm eating a flower from the inside. It suggests that not expressing love causes internal pain and decay, even if everything looks fine on the surface. The “damask cheek” symbolizes beauty being silently consumed.

    1. Last year in seventh grade, I was in a new kind of class. Instead of focusing on purely reading, writing, and doing tests where you sit down and write, it was interdisciplinary (we combined English, social studies, and science) and focused on community, giving freedom, and exploring new ways to prove knowledge. The first time I walked in, I was very nervous. I didn’t know what to expect. Like I said, it was all new to me. The first few days were miserable. We had to do this thing called a “walk and talk.” A walk and talk involved walking somewhere—in our case around a track— with someone you either are completely unfamiliar with or someone you don’t know that well. To this day you still couldn’t pay me to do another one, although I appreciate the desire for us all to get to know each other. We were still learning to adjust to our new environment, and suddenly we were being told that we had to go and have conversations with each other when some of the people in our class barely knew each other at that point. But this experience was a preview for what was to come. In our new class, we rarely ever just sat down and listened to someone talk. We spent most of our time doing hands-on activities; we did a lot of group work, too, and it made me actually enjoy school.

      Last year in seventh grade, I was in a new kind of class. [Annotation 1: Introduction of a new and transformative learning experience.] Instead of focusing on purely reading, writing, and doing tests where you sit down and write, it was interdisciplinary (we combined English, social studies, and science) and focused on community, giving freedom, and exploring new ways to prove knowledge. [Annotation 2: Highlights the interdisciplinary nature and focus on community, freedom, and alternative assessment methods.] The first time I walked in, I was very nervous. I didn’t know what to expect. Like I said, it was all new to me. The first few days were miserable. [Annotation 3: Initial nervousness and negative experience.] We had to do this thing called a “walk and talk.” A walk and talk involved walking somewhere—in our case around a track— with someone you either are completely unfamiliar with or someone you don’t know that well. To this day you still couldn’t pay me to do another one, although I appreciate the desire for us all to get to know each other. [Annotation 4: Description of "walk and talk" activity and mixed feelings about it.] We were still learning to adjust to our new environment, and suddenly we were being told that we had to go and have conversations with each other when some of the people in our class barely knew each other at that point. But this experience was a preview for what was to come. [Annotation 5: Connects the initial discomfort to the overall learning approach.] In our new class, we rarely ever just sat down and listened to someone talk. We spent most of our time doing hands-on activities; we did a lot of group work, too, and it made me actually enjoy school. [Annotation 6: Positive shift towards hands-on activities and group work, leading to enjoyment of school.]

    1. 时空依赖的函数

      在时空数据库的背景下,时空依赖的函数(Spatio-Temporal Functional Dependency, STFD) 是一种描述数据中时空属性之间关系的数学工具。它扩展了传统数据库中的函数依赖(Functional Dependency, FD)概念,将时间和空间维度同时纳入考虑,用于刻画数据在时空变化中的规律性。以下是对其核心概念和意义的详细解释:


      1. 时空依赖的函数的基本定义

      在传统数据库中,函数依赖 \( X \rightarrow Y \) 表示属性集 \( X \) 的值唯一确定属性集 \( Y \) 的值。而在时空数据库中,时空函数依赖需要同时考虑时间(T)空间(S)的维度。例如: - 时空函数依赖 \( X \rightarrow Y \) 可能表示:在某一时刻 \( t \) 和某一空间位置 \( s \),属性 \( X \) 的值唯一决定属性 \( Y \) 的值。 - 更严格的定义可能涉及时空键(Spatio-Temporal Key)时空码(Spatio-Temporal Code),即一组属性(包括时间、空间和其他属性)能够唯一标识一条时空记录。


      2. 时空依赖的函数的特点

      (1) 多维性

      时空依赖同时考虑时间、空间和属性的关联,例如: - 示例:在物流系统中,一辆货车的实时位置(空间坐标)和时间戳(时间)共同决定了其行驶状态(如速度、货物状态)。 - 数学表达:可能表示为 \( (T, S) \rightarrow A \),其中 \( T \) 是时间,\( S \) 是空间坐标,\( A \) 是其他属性(如温度、速度)。

      (2) 动态性

      时空数据是动态变化的,因此函数依赖可能随时间或空间的变化而变化: - 示例:某地区的气温(属性 \( A \))在不同时间(\( T \))和不同地理位置(\( S \))下可能有不同的依赖关系。例如,\( (T, S) \rightarrow A \) 表示气温由时间和空间唯一决定。

      (3) 约束冗余

      时空函数依赖用于减少数据冗余和保持数据一致性。例如: - 如果 \( (T, S) \rightarrow A \) 成立,则在任何时刻 \( t \) 和位置 \( s \),属性 \( A \) 的值必须唯一,避免重复或矛盾的数据记录。


      3. 时空依赖的函数在数据库规范化中的作用

      时空数据库的规范化(如时空一范式、二范式等)依赖于时空函数依赖的分析,以消除冗余并确保数据完整性。例如: - 时空一范式(ST-1NF):要求所有时空数据以原子形式存储,消除嵌套或重复的时空记录。 - 时空二范式(ST-2NF):在满足 ST-1NF 的基础上,确保不存在非主属性对时空键的部分依赖。 - 时空三范式(ST-3NF):进一步消除非主属性之间的传递依赖,例如 \( T \rightarrow S \),而 \( S \rightarrow A \),则 \( T \rightarrow A \) 可能形成传递依赖,需通过范式化处理。


      4. 时空依赖的函数的应用场景

      (1) 物流与交通管理

      • 示例:追踪货车的实时位置(空间)和时间,确保每辆车的行驶路径(属性)由时空键唯一确定。
      • 依赖关系:\( (T, 车辆ID) \rightarrow 路径 \),表示同一时间、同一车辆的路径唯一。

      2) 环境监测

      • 示例:大气污染传感器的监测数据(如PM2.5浓度)需记录时间、地理位置,且浓度值由时空键唯一决定: [ (T, 经度, 纬度) \rightarrow PM2.5 ]

      (3) 社交媒体与位置服务

      • 示例:用户的位置(空间)和时间戳(时间)共同决定其活动状态(如签到地点、动态内容)。

      5. 时空依赖与传统函数依赖的区别

      | 特性 | 传统函数依赖(FD) | 时空函数依赖(STFD) | |-------------------|---------------------------------------|----------------------------------------| | 维度 | 仅涉及属性间的依赖 | 同时涉及时间、空间和属性的依赖 | | 动态性 | 静态关系,不随时间变化 | 动态关系,可能随时间或空间变化 | | 冗余控制 | 通过消除属性冗余减少数据重复 | 通过时空键控制时空维度的冗余 | | 规范化目标 | 优化关系数据库的结构 | 优化时空数据的存储与查询效率 |


      6. 时空依赖的函数的挑战

      1. 复杂性:时空数据的高维性增加了依赖关系的分析难度。
      2. 动态维护:时空数据随时间变化,依赖关系可能需要动态更新。
      3. 存储与查询效率:时空函数依赖需平衡数据冗余与查询性能,例如索引设计需同时考虑时空维度。

      总结

      时空依赖的函数是时空数据库中用于描述数据在时空维度上规律性的重要工具。它通过时空键和函数依赖规则,帮助设计高效、一致的时空数据模型,减少冗余,并支持复杂时空查询(如轨迹分析、时空模式挖掘)。其核心在于将时间、空间与属性的关联纳入统一框架,为处理动态变化的地理信息、物流轨迹、环境监测等场景提供了理论基础。

    1. Author response:

      Reviewer 1: “The authors over-emphasized this study's relevance to RP disease (i.e. patients and mammals are not capable of regeneration like zebrafish).”

      It is true that humans and other mammals are not capable of regeneration.  This is why we and many other groups study zebrafish to identify mechanisms of regeneration that successfully form new rods.  That said, our previous paper on the molecular basis or retinal remodeling in this zebrafish model system (Santhanam et al., 2023; Cell Mol Life Sci. 2023;80(12):362) revealed remarkable similarities in the stress and physiological responses of rods, cones, RPE and inner retinal neurons to those in mammalian RP models.  Thus, we believe this zebrafish is an adequate model of RP and an excellent model to study rod regeneration. 

      Reviewer 1: “They under-explained this regeneration's relevance or difference to normal developmental process, which is pretty much conserved in evolution.”  and:

      Reviewer 3: “It would also benefit from integration with single-cell multiome data from developing retinas (Lyu, et al. 2023).”

      It is an excellent suggestion to compare the regenerative response we have studied in a chronic degeneration/regeneration model to the trajectory of developmental rod formation. In Lyu, et at. 2023, it was found that while retinal regeneration has similarities to retinal development, it does not precisely recapitulate the same transcription factors and processes. Any differences between this trajectory and that revealed in developmental studies would be enlightening.  We intend to do such analyses to add to a revised manuscript in the future. 

      Reviewer 2: “Perhaps the authors can consider explaining why the Prdm1a knock-down cells would have a higher Retp1 signal per cell in Fig 9B. Is this a representative picture? This appears to contradict Figure 8's conclusion, although I could tell that the number of Retp1+ cells in the ONL appears to be lower.”

      These are different experimental paradigms.  Figure 8 shows knockdown 48 hours after injection, at which time prdm1a knockdown is affecting rhodopsin expression directly.  That experiment investigated whether prdm1a knockdown affected progenitor proliferation.  Figure 9 shows a time point 6 days after injection, at which time we were asking if prdm1a knockdown affected differentiation of progenitors into rods. 

      Reviewer 2: “The authors noted "Surprisingly, the knockdown of prdm1a resulted in a significantly higher number of rhodopsin-positive cells in the INL (p=0.0293)", while it appears in Figure 9B, 9C that the difference is 2 cells vs 0 in a rightly broader field. It seems to be too strong of a statement for this effect.”

      This was a very unexpected finding.  We included statistics (Figure 9D) to support the finding, so we don’t think it is too strong a statement to make.  Speculation as to what might cause this is fascinating.  Are Muller cells producing progenitors that fail to migrate to the ONL before differentiating into rods?  The lack of BrdU labeling does not support this idea.  Do neurogenic progenitor cells in the INL differentiate towards rods via a pathway that does not require prdm1a?  Perhaps.  Perhaps there are other explanations.

      Reviewer 2: “It appears to this reviewer that the proteomic data didn't reveal much in line with the overall hypothesis or the mechanism, and it's unclear why the authors went for proteomics rather than bulk RNA-seq or ChIP-seq for a transcription factor knock-down experiment. Overall this is a minor point.”

      We agree that bulk RNA sequencing would provide a similar answer, possibly with greater sensitivity.  We chose proteomics for two reasons: 1) We wanted an independent assessment of the knockdown effects that could evaluate whether the knockdowns worked and what pathways were affected.  Since our pathway comparison is to single cell RNAseq data, bulk RNA seq did not seem to be fully independent. 2) Because we used translation-blocking antisense oligos for most knockdown experiments, we did not expect the transcript abundance of the targeted gene to be affected, although these oligos can lead to target transcript degradation.  Thus, we were not likely to be able to validate that our knockdown worked with this technique. 

      Reviewer 3: “The gene regulatory network analysis here would also benefit from the addition of matched scATAC-Seq data, …”

      This is certainly true, and the reviewer points to several studies that have made excellent use of this strategy.  Given the 1-2 year timeline to obtain and analyze such data, it is unlikely that we will be able to incorporate such data in our revised manuscript, but we hope to do so for follow-up studies.

      Reviewer 3: “The description of the time points analyzed is vague, stating only that "fish from 6 to 12 months of age were analyzed". Since photoreceptor degeneration is progressive, it is unclear how progenitor behavior changes over time, or how the gene expression profile of other cell types such as microglia, cones, or surviving rods is altered by disease progression.”

      We have shown in a previous study (Santhanam et al. Cells. 2020;9(10)) that rod degeneration and regeneration are in a steady state from at least 4 to 8 months of age, and in other experiments in the lab at least to 12 months of age.  In this age range, regeneration keeps up with the pace of degeneration, both of which are very fast.  This encompasses the cell types that we specifically study in this manuscript.  The reviewer is right that other cell types could undergo changes.  This is a separate topic of study in the lab.

    1. Reviewer #2 (Public review):

      Summary:

      In this manuscript the authors were providing a proof of concept that they can identify and mutate a cholesterol-binding site of a high-interest class B receptor, the GLP-1R, and functionally characterize the impact of this mutation on receptor behavior in the membrane and downstream signaling with the intent that similar methods can be useful to optimize small molecules that as ligands or allosteric modulators of GLP-1R can improve the therapeutic tools targeting this signaling system.

      Strengths:

      The majority of results on receptor behavior are elucidated in INS-1 cells expressing the wt or mutant GLP-1R, with one experiment translating the findings to primary mouse beta-cells. I think this paper lays a very strong foundation to characterize this mutation and does a good job discussing how complex cholesterol-receptor interactions can be (ie lower cholesterol binding to V229A GLP-1R, yet increased segregation to lipid rafts). Table 1 and Figure 9 are very beneficial to summarize the findings. The lower interaction with cholesterol and lower membrane diffusion in V229A GLP-1R resembles the reduced diffusion of wt GLP-1R with simv-induced cholesterol reductions, by presumably decreasing the cholesterol available to interact with wt GLP-1R. The effects of this mutation are not due to differences in Ex-4:recepotor affinity. I think this paper will be of interest to many physiologists who may not be familiar with many of the techniques used in this paper and the authors largely do a good job explaining the goals of using each method in the results section. While not necessary for this paper, a comparison of islet cholesterol content after this cholesterol diet vs the more typical 60% HFD used in obesity research would be beneficial for GLP-1 physiology research broadly to take these findings into consideration with model choice.

      Weaknesses:

      There are no obvious weaknesses in this manuscript and overall, I believe the authors achieved their aims and have demonstrated the importance of cholesterol interactions on GLP-1R functioning in beta-cells.

      Certainly many follow-up experiments are possible from these initial findings and of primary interest is how this mutation affects insulin homeostasis in vivo under different physiological conditions. One of the biggest pathologies in insulin homeostasis in obesity/t2d is an elevation of baseline insulin release (as modeled in Fig 1E) that renders the fold-change in glucose stimulated insulin levels lower and physiologically less effective. Future work by the authors may determine the effects of the GLP-1R V229A mutation on insulin secretion responses under diet-induced metabolic stress conditions. Furthermore, the authors may additionally investigate if V229A would have the same impact in a different cell type, especially in neurons, with implications in the regulation of satiation, gut motility, and especially nausea, which are of high translational interest.

      The comparison is drawn in the discussion between this mutation and ex4-phe1 to have biased agonism towards Gs over beta-arrestin signaling. Ex4-phe1 lowered pica behavior (a proxy for nausea) in the authors previously co-authored paper on ex4-phe1 (PMID 29686402) and drawing a parallel for this mutation or modification of cholesterol binding to potentially mitigate nausea is a novel direction.

    2. Author response:

      The following is the authors’ response to the original reviews

      Public Reviews:

      Reviewer #1 (Public review):

      Summary:

      The authors demonstrate impairments induced by a high cholesterol diet on GLP-1R dependent glucoregulation in vivo as well as an improvement after reduction in cholesterol synthesis with simvastatin in pancreatic islets. They also map sites of cholesterol high occupancy and residence time on active versus inactive GLP-1Rs using coarse-grained molecular dynamics (cgMD) simulations and screened for key residues selected from these sites and performed detailed analyses of the effects of mutating one of these residues, Val229, to alanine on GLP-1R interactions with cholesterol, plasma membrane behaviour, clustering, trafficking and signalling in pancreatic beta cells and primary islets, and describe an improved insulin secretion profile for the V229A mutant receptor.

      These are extensive and very impressive studies indeed. I am impressed with the tireless effort exerted to understand the details of molecular mechanisms involved in the effects of cholesterol for GLP-1 activation of its receptor. In general, the study is convincing, the manuscript well written and the data well presented.

      Some of the changes are small and insignificant which makes one wonder how important the observations are. For instance, in figure 2 E (which is difficult to interpret anyway because the data are presented in percent, conveniently hiding the absolute results) does not show a significant result of the cyclodextrin except for insignificant increases in basal secretion. That is not identical to impairment of GLP-1 receptor signaling!

      We assume that the reviewer refers to Figure 1E, where we show the percentage of insulin secretion in response to 11 mM glucose +/- exendin-4 stimulation in mouse islets pretreated with vehicle or MβCD loaded with 20 mM cholesterol. While we concur with the reviewer that the effect in this case is triggered by increased basal insulin secretion at 11 mM glucose, exendin-4 appears to no longer compensate for this increase by proportionally amplifying insulin responses in cholesterol-loaded islets, leading to a significantly decreased exendin-4induced insulin secretion fold increase under these circumstances, as shown in Figure 1F. We interpret these results as a defect in the GLP-1R capacity to amplify insulin secretion beyond the basal level to the same extent as in vehicle conditions. An alternative explanation is that there is a maximum level of insulin secretion in our cells, and 11 mM glucose + exendin-4 stimulation gets close to that value. With the increasing effect of cholesterol-loaded MβCD on basal secretion at 11 mM glucose, exendin-4 stimulation would then appear to work less well.

      We have performed a simple experiment to investigate this possibility: insulin secretion following stimulation with a secretagogue cocktail (20 mM glucose, 30 mM KCl, 10 µM FSK and 100 µM IBMX) in islets +/- MβCD/cholesterol loading to determine if maximal stimulation had been reached or not in our original experiment. This experiment, now included in Supplementary Figure 1C, demonstrates that insulin secretion can increase up to ~4% (from ~2%) in our islets, supporting our initial conclusion. We have also included absolute insulin concentrations as well as percentages of secretion for all the experiments included in the study in the new Supplementary File 1 to improve the completeness of the report.

      To me the most important experiment of them all is the simvastatin experiment, but the results rest on very few numbers and there is a large variation. Apparently, in a previous study using more extensive reduction in cholesterol the opposite response was detected casting doubt on the significance of the current observation. I agree with the authors that the use of cyclodextrin may have been associated with other changes in plasma membrane structure than cholesterol depletion at the GLP-1 receptor.

      We agree with the reviewer that the insulin secretion results in vehicle versus LPDS/simvastatin treated mouse islets (Figure 1H, I) are relatively variable. We have therefore performed 2 extra biological repeats of this experiment (for a total n of 7). Results now show a significant increase in exendin-4-stimulated secretion with no change in basal secretion in islets pre-incubated with LPDS/simvastatin.  

      The entire discussion regarding the importance of cholesterol would benefit tremendously from studies of GLP-1 induced insulin secretion in people with different cholesterol levels before and after treatment with cholesterol-lowering agents. I suspect that such a study would not reveal major differences.

      We agree with the reviewer that such study would be highly relevant. While this falls outside the scope of the present paper, we encourage other researchers with access to clinical data on GLP-1R agonist responses in individuals taking cholesterol lowering agents to share their results with the scientific community. We have highlighted this point in the paper discussion to emphasise the importance of more research in this area.

      Reviewer #2 (Public review):

      Summary:

      In this manuscript the authors provided a proof of concept that they can identify and mutate a cholesterol-binding site of a high-interest class B receptor, the GLP-1R, and functionally characterize the impact of this mutation on receptor behavior in the membrane and downstream signaling with the intent that similar methods can be useful to optimize small molecules that as ligands or allosteric modulators of GLP-1R can improve the therapeutic tools targeting this signaling system.

      Strengths:

      The majority of results on receptor behavior are elucidated in INS-1 cells expressing the wt or mutant GLP-1R, with one experiment translating the findings to primary mouse beta-cells. I think this paper lays a very strong foundation to characterize this mutation and does a good job discussing how complex cholesterol-receptor interactions can be (ie lower cholesterol binding to V229A GLP-1R, yet increased segregation to lipid rafts). Table 1 and Figure 9 are very beneficial to summarize the findings. The lower interaction with cholesterol and lower membrane diffusion in V229A GLP-1R resembles the reduced diffusion of wt GLP-1R with simv-induced cholesterol reductions, although by presumably decreasing the cholesterol available to interact with wt GLP-1R. This could be interesting to see if lowering cholesterol alters other behaviors of wt GLP-1R that look similar to V229A GLP-1R. I further wonder if the authors expect that increased cholesterol content of islets (with loading of MβCD saturated with cholesterol or high-cholesterol diets) would elevate baseline GLP-1R membrane diffusion, and if a more broad relationship can be drawn between GLP-1R membrane movement and downstream signaling.

      Membrane diffusion experiments are difficult to perform in intact islets as our method requires cell monolayers for RICS analysis. We however agree that it is of interest to investigate if cholesterol loading affects GLP-1R diffusion. To this end, we have performed further RICS analysis in INS-1 832/3 SNAP/FLAG-hGLP-1R cells pretreated with vehicle or MβCD loaded with 20 mM cholesterol (new Supplementary Figures 1D and 1E). Interestingly, results show significantly increased plasma membrane diffusion of exendin-4-stimulated receptors, with no change in basal diffusion, following MβCD/cholesterol loading. This behaviour differs from that of the V229A mutant receptor which shows reduced diffusion under basal conditions, a pattern that mimics that of the WT receptor under low cholesterol conditions (by pre-treatment with LPDS/simvastatin).

      Weaknesses:

      I think there are no obvious weaknesses in this manuscript and overall, I believe the authors achieved their aims and have demonstrated the importance of cholesterol interactions on GLP-1R functioning in beta-cells. I think this paper will be of interest to many physiologists who may not be familiar with many of the techniques used in this paper and the authors largely do a good job explaining the goals of using each method in the results section.

      The intent of some methods, for example the Laurdan probe studies, are better expanded in the discussion.

      We have expanded on the rationale behind the use of Laurdan to assess behaviours of lipid packed membrane nanodomains in the methods, results and discussion of the revised manuscript.

      I found it unclear what exactly was being measured to assess 'receptor activity' in Fig 7E and F.

      Figures 7E and F refer to bystander complementation assays measuring the recruitment of nanobody 37 (Nb37)-SmBiT, which binds to active Gas, to either the plasma membrane (labelled with KRAS CAAX motif-LgBiT), or to endosomes (labelled with Endofin FYVE domain-LgBiT) in response to GLP-1R stimulation with exendin-4. This assay therefore measures GLP-1R activation specifically at each of these two subcellular locations. We have included a schematic of this assay in the new Supplementary Figure 3 to clarify the aim of these experiments.

      Certainly many follow-up experiments are possible from these initial findings and of primary interest is how this mutation affects insulin homeostasis in vivo under different physiological conditions. One of the biggest pathologies in insulin homeostasis in obesity/t2d is an elevation of baseline insulin release (as modeled in Fig 1E) that renders the fold-change in glucose stimulated insulin levels lower and physiologically less effective. No difference in primary mouse islet baseline insulin secretion was seen here but I wonder if this mutation would ameliorate diet-induced baseline hyperinsulinemia.

      We concur with the reviewer that it would be interesting to determine the effects of the GLP1R V229A mutation on insulin secretion responses under diet-induced metabolic stress conditions. While performing in vivo experiments on glucoregulation in mice harbouring the V229A mutation falls outside the scope of the present study, we have included ex vivo insulin secretion experiments in islets from GLP-1R KO mice transduced with adenoviruses expressing SNAP/FLAG-hGLP-1R WT or V229A and subsequently treated with vehicle versus MβCD loaded with 20 mM cholesterol to replicate the conditions of Figure 1E in the new Supplementary Figure 4.

      I would have liked to see the actual islet cholesterol content after 5wks high-cholesterol diet measured to correlate increased cholesterol load with diminished glucose-stimulated inulin. While not necessary for this paper, a comparison of islet cholesterol content after this cholesterol diet vs the more typical 60% HFD used in obesity research would be beneficial for GLP-1 physiology research broadly to take these findings into consideration with model choice.

      We have included these data in Supplementary Figure 1A.

      Another area to further investigate is does this mutation alter ex4 interaction/affinity/time of binding to GLP-1 or are all of the described findings due to changes in behavior and function of the receptor?

      To answer this question, have performed binding affinity experiments, which show no differences, in INS-1 832/3 SNAP/FLAG-hGLP-1R WT versus V229A cells (new Supplementary Figure 2D).

      Lastly, I wonder if V229A would have the same impact in a different cell type, especially in neurons? How similar are the cholesterol profiles of beta-cells and neurons? How this mutation (and future developed small molecules) may affect satiation, gut motility, and especially nausea, are of high translational interest. The comparison is drawn in the discussion between this mutation and ex4-phe1 to have biased agonism towards Gs over beta-arrestin signaling. Ex4-phe1 lowered pica behavior (a proxy for nausea) in the authors previously co-authored paper on ex4-phe1 (PMID 29686402) and I think drawing a parallel for this mutation or modification of cholesterol binding to potentially mitigate nausea is worth highlighting.

      While experiments in neurons are outside the scope of the present study, we have added this worthy point to the discussion and hypothesise on possible effects of GLP-1R mutants with modified cholesterol interactions on central GLP-1R actions in the revised manuscript.

      Reviewer #1 (Recommendations for the authors):

      There are no line numbers

      These have now been added.

      Abstract: "Cholesterol is a plasma membrane enriched lipid" - sorry for being finicky, but shouldn't this read; "a lipid often enriched in plasma membranes"

      We have modified the abstract to state that: “Cholesterol is a lipid enriched at the plasma membrane”.

      p. 4 "Moreover, islets extracted from high cholesterol-fed mice". How do you "extract islets"?

      We have exchanged the term “extracted” by “isolated”. Islet isolation is described in the paper methods section.

      p. 4 The sentence "These effects were accompanied by decreased GLP-1R plasma membrane diffusion under vehicle conditions, measured by Raster Image Correlation Spectroscopy (RICS) in rat insulinoma INS-1 832/3 cells with endogenous GLP-1R deleted [INS-1 832/3 GLP-1R KO cells (27)] stably expressing SNAP/FLAG-tagged human GLP-1R (SNAP/FLAG-hGLP-1R), an effect that is normally triggered by agonist binding (28), as also observed here (Supplementary Figure 1C, D)" is a masterpiece of complexity. Perhaps breaking up would facilitate reading?

      This paragraph has now been modified in the revised manuscript.

      p. 5. I cannot evaluate the "coarse grain molecular dynamics" studies.

      Reviewer #2 (Recommendations for the authors):

      I view this as an excellent manuscript with very comprehensive work and clear translational relevance. I don't think any further experiments are needed for the scope outlined in this manuscript. The discussion is already long but a short postulation on how this may translate to GLP-1R-cholesterol interactions in other cell types, specifically neurons with the intent on manipulating satiation and nausea, could be worthwhile.

      This has now been added.

      The only thing for readability I would suggest is a sentence in the results mentioning why you're doing the Laurdan analysis, and what is the output for assessing 'receptor activity' in the membrane and endosomes.

      Both points have now been added.

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      The authors examine CD8 T cell selective pressure in early HCV infection using. They propose that after initial CD8-T mediated loss of virus fitness, in some participants around 3 months after infection, HCV acquires compensatory mutations and improved fitness leading to virus progression.

      Strengths:

      Throughout the paper, the authors apply well-established approaches in studies of acute to chronic HIV infection for studies of HCV infection. This lends rigor the to the authors' work.

      Weaknesses:

      (1) The Discussion could be strengthened by a direct discussion of the parallels/differences in results between HIV and HCV infections in terms of T cell selection, entropy, and fitness.

      We have added a direct discussion of the parallels/differences between HIV and HCV throughout the discussion including at lines 308 – 310 and 315 -327.

      Lines 308-310: “In fact, many parallels can be drawn between HIV infections and HCV infections in the context of emerging viral species that escape T cell immune responses.”

      Lines: 315-327: “One major difference between HCV and HIV infection is the event where patients infected with HCV have an approximately 25% chance to naturally clear the infection as opposed to just achieving viral control in HIV infections. Here, we probed the underlying mechanism, and questioned how the host immune response and HCV mutational landscape can allow the virus to escape the immune system. To understand this process, taking inspiration from HIV studies (24), a quantitative analysis of viral fitness relative to viral haplotypes was conducted using longitudinal samples to investigate whether a similar phenomenon was identified in HCV infections for our cohort for patients who progress to chronic infection. We observed a decrease in population average relative fitness in the period of <90DPI with respect to the T/F virus in chronic subjects infected with HCV. The decrease in fitness correlated positively with IFN-γ ELISPOT responses and negatively with SE indicating that CD8+ T-cell responses drove the rapid emergence of immune escape variants, which initially reduced viral fitness. This is similarly reflected in HIV infected patients where strong CD8+ T-cell responses drove quicker emergence of immune escape variants, often accompanied by compensatory mutations (24).”

      (2) In the Results, please describe the Barton model functionality and why the fitness landscape model was most applicable for studies of HCV viral diversity.

      This has been added to the introduction section rather than Results as we feel that it is more appropriate to show why it is most applicable to HCV viral diversity in the background section of the manuscript. We write at lines 77-90:

      “Barton et al.’s [23] approach to understand HIV mutational landscape resulting in immune escape had two fundamental points: 1) replicative fitness depends on the virus sequence and the requirement to consider the effect of co-occurring mutations, and 2) evolutionary dynamics (e.g. host immune pressure). Together they pave the way to predict the mutational space in which viral strains can change given the unique immune pressure exerted by individuals infected with HIV. This model fits well with the pathology of HCV infection. For instance, HIV and HCV are both RNA viruses with rapid rate of mutation. Additionally, like HIV, chronic infection is an outcome for HCV infected individuals, however, unlike HIV, there is a 25% probability that individuals infected with HCV will naturally clear the virus. Previously published studies [9] have shown that HIV also goes through a genetic bottleneck which results in the T/F virus losing dominance and replaced by a chronic subtype, identified by the immune escape mutations. The concepts in Barton’s model and its functionality to assess the fitness based on the complex interaction between viral sequence composition and host immune response is also applicable to early HCV infection.”

      (3) Recognize the caveats of the HCV mapping data presented.

      We have now recognized the caveats of the HCV mapping data at lines 354-256 “While our findings here are promising, it should be recognized that although the bioinformatics tool (iedb_tool.py) proved useful for identifying potential epitopes, there could be epitopes that are not predicted or false-positive from the output which could lead to missing real epitopes”

      (4) The authors should provide more data or cite publications to support the authors' statement that HCV-specific CD8 T cell responses decline following infection.

      We have now clarified at lines 352-353 that the decline was toward “selected epitopes that showed evidence of escape”.

      Furthermore, we have cited two publications at line 352 that support our statement.

      (5) Similarly, as the authors' measurements of HCV T and humoral responses were not exhaustive, the text describing the decline of T cells with the onset of humoral immunity needs caveats or more rigorous discussion with citations (Discussion lines 319-321).

      We have now added a caveat in the discussion at lines 357-360 which reads

      “In conclusion, this study provides initial insights into the evolutionary dynamics of HCV, showing that an early, robust CD8+ T-cell response without nAbs strongly selects against the T/F virus, enabling it to escape and establish chronic infection. However, these findings are preliminary and not exhaustive, warranting further investigation to fully understand these dynamics. “

      (6) What role does antigen drive play in these data -for both T can and antibody induction?

      It is possible that HLA-adapted mutations could limit CD8 T cell induction if the HLAs were matched between transmission pairs, as has been shown previously for HIV (https://doi.org/10.1371/journal.ppat.1008177) with some data for HCV (https://journals.asm.org/doi/10.1128/jvi.00912-06). However, we apologise as we are not entirely sure that this is what the reviewer is asking for in this instance.

      (7) Figure 3 - are the X and Y axes wrongly labelled? The Divergent ranges of population fitness do not make sense.

      Our apologies, there was an error with the plot in Figure 3 and the X and Y axis were wrongly labelled. This has now been resolved.

      (8) Figure S3 - is the green line, average virus fitness?

      This has now been clarified in Figure S3.

      (9) Use the term antibody epitopes, not B cell epitopes.

      We now use the term antibody epitopes throughout the manuscript.

      Reviewer #1 (Recommendations for the authors):

      Recommendations for improving the writing and presentation:

      (1) Introduction:

      Line 52: 'carry mutations B/T cell epitopes'. Two points

      i) These are antibody epitopes (and antibody selection) not B cell epitopes

      We have corrected this sentence at line 55 which now reads: “carry mutations within epitopes targeted by B cells and CD8+ T cells”.

      ii) To avoid confusion, add text that mutations were generated following selection in the donor.

      For HCV, it is unclear if mutations are generated following selection or have been occurring in low frequencies outside detection range. Only when selection by host immune pressure arises do the potentially low-frequency variants become dominant. However, we do acknowledge it is potentially misleading to only mention new variants replacing the transmitted/founder population. We have modified the sentence at line 52 to read:

      “At this stage either an existing variant that was occurring in low-frequency outside detection range or an existing variant with novel mutations generated following immune selection is observed in those who progress to chronic infection”

      - Lines 51-56: Human studies of escape and progression are associative, not causative as implied.

      Correct, evidence suggesting that escape and progression are currently associative. We have now corrected these lines to no longer suggest causation.

      - Line 65: Suggest you clarify your meaning of 'easier'?

      This sentence, now at line 72, has been modified to: “subtype 1b viruses have a higher probability to evade immune responses”

      (2) Results:

      - Line 147: Barton model (ref'd in Intro) is directly referred to here but not referenced.

      The reference has been added.

      - The authors should cite previous HIV literature describing associations between the rate of escape and Shannon Entropy e.g. the interaction between immunodominance, entropy, and rate of escape in acute HIV infection was described in Liu et al JCI 2013 but is not cited.

      We have now cited previous HIV research at line 147-151, adding Liu et al:

      “Additionally, the interaction between immunodominance, entropy, and escape rate in acute HIV infection has been described, where immunodominance during acute infection was the most significant factor influencing CD8+ T cell pressure, with higher immunodominance linked to faster escape (27). In contrast, lower epitope entropy slowed escape, and together, immunodominance and entropy explained half of the variability in escape timing (27).”

      - Line 319: The authors suggest that HCV-specific CD8 T cell response declines following early infection. On what are they basing this statement? The authors show their measured T cell responses decline but their approach uses selected epitopes and they are therefore unable to assess total HCV T cell response in participants (Where there is no escape, are T cell magnitudes maintained or do they still decline?). Can the authors cite other studies to support their statement?

      We have now clarified that the decline was toward “selected epitopes that showed evidence of escape”. Furthermore, we also cite two studies to support our findings.

      - Throughout the authors talk in terms of CD8 T cells but the ELISpot detects both CD4 and CD8 T cell responses. I suggest the authors be more explicit that their peptide design (9-10mers) is strongly biased to only the detection of CD8 T cells.

      To make this clearer and more explicit we have now added to the methods section at line 433-435:

      “While the ELISpot assay detects responses from both CD4 and CD8 T cells, our peptide design (9-10mers) is strongly biased toward CD8 T-cell detection. We have therefore interpreted ELISpot responses primarily in terms of CD8 T-cell activity.”

      - The points made in lines 307-321 could be more succinct

      We have now edited the discussion (lines 307 – 321) to make the points more succinct (now lines 307-323).

      Minor corrections to text, figures:

      - Figure 2: suggest making the Key bigger and more obvious.

      We have now made the key bigger and more obvious

      - Figure 3 A & D....is there an error on the X-axis...are you really reporting ELISpot data of < 1 spot/10^6? Perhaps the X and Y axes are wrongly labelled?

      Our apologies, there was an error with the plot in Figure 3 and the X and Y axis were wrongly labelled. This has now been resolved.

      - Figure 5: As this is PBMC, remove CD8 from the description of ELISpot. 

      We have now removed CD8 from the description of ELISpot in both Figure 5 and Figure S3

      Reviewer #2 (Public review):

      Summary:

      In this work, Walker and collaborators study the evolution of hepatitis C virus (HCV) in a cohort of 14 subjects with recent HCV infections. They focus in particular on the interplay between HCV and the immune system, including the accumulation of mutations in CD8+ T cell epitopes to evade immunity. Using a computational method to estimate the fitness effects of HCV mutations, they find that viral fitness declines as the virus mutates to escape T-cell responses. In long-term infections, they found that viral fitness can rebound later in infection as HCV accumulates additional mutations.

      Strengths:

      This work is especially interesting for several reasons. Individuals who developed chronic infections were followed over fairly long times and, in most cases, samples of the viral population were obtained frequently. At the same time, the authors also measured CD8+ T cell and antibody responses to infection. The analysis of HCV evolution focused not only on variation within particular CD8+ T cell epitopes but also on the surrounding proteins. Overall, this work is notable for integrating information about HCV sequence evolution, host immune responses, and computational metrics of fitness and sequence variation. The evidence presented by the authors supports the main conclusions of the paper described above.

      Weaknesses:

      One notable weakness of the present version of the manuscript is a lack of clarity in the description of the method of fitness estimation. In the previous studies of HIV and HCV cited by the authors, fitness models were derived by fitting the model (equation between lines 435 and 436) to viral sequence data collected from many different individuals. In the section "Estimating survival fitness of viral variants," it is not entirely clear if Walker and collaborators have used the same approach (i.e., fitting the model to viral sequences from many individuals), or whether they have used the sequence data from each individual to produce models that are specific to each subject. If it is the former, then the authors should describe where these sequences were obtained and the statistics of the data.

      If the fitness models were inferred based on the data from each subject, then more explanation is needed. In prior work, the use of these models to estimate fitness was justified by arguing that sequence variants common to many individuals are likely to be well-tolerated by the virus, while ones that are rare are likely to have high fitness costs. This justification is less clear for sequence variation within a single individual, where the viral population has had much less time to "explore" the sequence landscape. Nonetheless, there is precedent for this kind of analysis (see, e.g., Asti et al., PLoS Comput Biol 2016). If the authors took this approach, then this point should be discussed clearly and contrasted with the prior HIV and HCV studies.

      We thank the reviewer for pointing out the weakness in our explanation and description of the fitness model. The model has been generated using publicly released viral sequences and this has been described in a previous publication by Hart et al. 2015. T/F virus from each of the subjects chronically infected with HCV in our cohort were given to the model by Hart et al. to estimate the initial viral fitness of the T/F variant. Subsequent time points of each subject containing the subvariants of the viral population were also estimated using the same model (each subtype). For each subject, these subvariant viral fitness values were divided by the fitness value of the initial T/F virus (hence relative fitness of the earliest time points with no mutations in the epitope regions were a value of 1.000). All other fitness values are therefore relative fitness to the T/F variant.

      We have further clarified this point in the methods section “Estimating survival fitness of viral variant” to better describe how the data of the model was sourced (Lines 465-499).

      To add to the reviewer’s point, we agree that sequence variants common to many individuals are likely to be well-tolerated by the virus and this event was observed in our findings as our data suggested that immune escape variants tended to revert to variants that were closer the global consensus strain. Our previous publications have indicated that T/F viruses during transmission were variants that were “fit” for transmission between hosts, especially in cases where the donor was a chronic progressor, a single T/F is often observed. Progression to immune escape and adaptation to chronic infection in the new host has an in-between process of genetic expansion via replication followed by a bottleneck event under immune pressure where overall fitness (overall survivability including replication and exploring immune escape pathways) can change. Under this assumption we questioned whether the observation reported in HIV studies (i.e. mutation landscapes that allow HIV adaptation to host) also happens in HCV infections. Furthermore, cohort used in this study is a rare cohort where patients were tracked from uninfected, to HCV RNA+, to seroconversion and finally either clearing the virus or progression to chronic infection. Thus, it is of importance to understand the difference between clearance and chronic progression.

      Another important point for clarification is the definition of fitness. In the abstract, the authors note that multiple studies have shown that viral escape variants can have reduced fitness, "diminishing the survival of the viral strain within the host, and the capacity of the variant to survive future transmission events." It would be helpful to distinguish between this notion of fitness, which has sometimes been referred to as "intrinsic fitness," and a definition of fitness that describes the success of different viral strains within a particular individual, including the potential benefits of immune escape. In many cases, escape variants displace variants without escape mutations, showing that their ability to survive and replicate within a specific host is actually improved relative to variants without escape mutations. However, escape mutations may harm the virus's ability to replicate in other contexts. Given the major role that fitness plays in this paper, it would be helpful for readers to clearly discuss how fitness is defined and to distinguish between fitness within and between hosts (potentially also mentioning relevant concepts such as "transmission fitness," i.e., the relative ability of a particular variant to establish new infections).

      Thank you for pointing out the weakness of our definition of fitness. We have now clarified this at multiple sections of the paper: In the abstract at lines 18-21 and in the introduction at lines 64-69.

      These read:

      Lines 18-21: “However, this generic definition can be further divided into two categories where intrinsic fitness describes the viral fitness without the influence of any immune pressure and effective fitness considers both intrinsic fitness with the influence of host immune pressure.”

      Lines 64-69: “This generic definition of fitness can be further divided into intrinsic fitness (also referred to as replicative fitness), where the fitness of sequence composition of the variant is estimated without the influence of host immune pressure. On the other hand, effective fitness (from here on referred to as viral fitness) considers fundamental intrinsic fitness with host immune pressure acting as a selective force to direct mutational landscape (19)[REF], which subsequently influences future transmission events as it dictates which subvariants remain in the quasispecies.”

      One concern about the analysis is in the test of Shannon entropy as a way to quantify the rate of escape. The authors describe computing the entropy at multiple time points preceding the time when escape mutations were observed to fix in a particular epitope. Which entropy values were used to compare with the escape rate? If just the time point directly preceding the fixation of escape mutations, could escape mutations have already been present in the population at that time, increasing the entropy and thus drawing an association with the rate of escape? It would also be helpful for readers to include a definition of entropy in the methods, in addition to a reference to prior work. For example, it is not clear what is being averaged when "average SE" is described.

      We thank the reviewer to point out the ambiguity in describing average SE. This has been rectified by adding more information in the methods section (Lines 397 to 400):

      “Briefly, SE was calculated using the frequency of occurrence of SNPs based on per codon position, this was further normalized by the length of the number of codons in the sequence which made up respective protein. An average SE value was calculated for each time point in each protein region for all subjects until the fixation event.”

      To answer the reviewer’s question, we computed entropy at multiple time points preceding the observation in the escape mutation. The escape rate was calculated for the epitopes targeted by immune response. We compared the average SE based on change of each codon position and then normalised by protein length, where the region contained the epitope and the time it took to reach fixation. We observed that if the protein region had a higher rate of variation (i.e. higher average SE) then we also see a quicker emergence of an immune escape epitope. Since we took SE from the very first time point and all subsequent time points until fixation, we do not think that escape mutations already been present at the population would alter the findings of the association with rate of escape. Especially, these escape mutations were rarely observed at early time points. It is likely that due to host immune pressure that the escape variant could be observed, the SE therefore suggest the liberty of exploration in the mutation landscape. If the region was highly restrictive where any mutations would result in a failed variant, then we should observe relatively lower values of average SE. In other words, the higher variability that is allowed in the region, the greater the probability that it will find a solution to achieve immune escape.

      Reviewer #2 (Recommendations for the authors):

      In addition to the main points above, there are a few minor comments and suggestions about the presentation of the data.

      (1) It's not clear how, precisely, the model-based fitness has been calculated and normalized. It would be helpful for the authors to describe this explicitly. Especially in Figure 3, the plotted fitness values lie in dramatically different ranges, which should be explained (maybe this is just an error with the plot?).

      We have now clarified how the model-based fitness has been calculated and normalized in the method section “Estimating survival fitness of viral variants” at line 465-472.

      “The model used for estimating viral fitness has been previously described by Hart et al. (19). Briefly, the original approach used HCV subtype 1a sequences to generate the model for the NS5B protein region. To update the model for other regions (NS3 and NS2) as well as other HCV subtypes in this study, subtype 1b and subtype 3a sequences were extracted from the Los Almos National Laboratory HCV database. An intrinsic fitness model was first generated for each subtype for NS5B, NS3 and NS2 region of the HCV polyprotein. Then using, longitudinally sequenced data from patients chronically infected with HCV as well as clinically documented immune escape to describe high viral fitness variants, we generated estimates of the viral fitness for subjects chronically infected with HCV in our cohort.”

      Our apologies, there was an error with the plot in Figure 3. This has now been resolved.

      (2) In different plots, the authors show every pairwise comparison of ELISPOT values, population fitness, average SE, and rate of escape. It may be helpful to make one large matrix of plots that shows all of these pairwise comparisons at the same time. This could make it clear how all the variables are associated with one another. To be clear, this is a suggestion that the authors can consider at their discretion.

      Thank you for the suggestion to create a matrix of plots for pairwise comparisons. While this approach could indeed clarify variable associations, implementing it is outside the scope of this project. We appreciate the idea and may consider it in future studies as we continue to expand on this work.

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      Referee #3

      Evidence, reproducibility and clarity

      The study by Yasmin & colleagues tackles an important question, what is the molecular nature of specificity that arises from otherwise highly similar proteins. In this case, they focus on two proteins with epigenetic activity, DNMT3A and DNMT3B, using a functional readout of their ability to methylate DNA in a model that specifically requires DNMT3B function at a subset of the genome, i.e. DNMT3B-dependent regions. This includes characterizing the role of DNMT3B in these regions in stem cell-to-embryoid body differentiation experiments, using genomic assays to probe DNA methylation dynamics. By removing DNMT3B and ectopically expressing a variety of sophisticated mutants, the authors attempt to show the protein domains required for specificity. However, several questions remain about the strength of the data to support the claims, particularly with respect to the ectopically expressed mutant DNMT3B proteins.

      Major comments:

      1. The strength of this study is in the very nice addback strategy for probing DNMT3B specificity, where the designed mutants seem highly useful to ask critical questions. However, the stability of the mutant proteins (i.e. cellular expression levels) and question of protien levels in the nucleus are insufficient evidence for the conclusions stated in the paper. With the exception of the Dnmt3b1-KI clones (top panel fig 3B), it seems like most mutants are not expressed at wildtype levels. How much of the results are driven by differences in expression, relative to the wildtype protein? While this a technically challenging problem, there are various methods to establish roughly matched expression such as integration into a stronger locus for expression or tuning the promoter sequence for expression of a construct. Given the mutants are key for the main conclusions of the study, this seems critical to address, though would substantially increase effort required for the paper.
      2. Characterisation of the datasets supporting effects seems lacking in several instances. For example, the text states that DMNT3B null cells behave similarly to wildtype cells but supporting data (FigS2A-C) or that Dnmt3b1-KI and Dnmt3b3-KI behave normally with respect to differentiation (FigS4C), seem insufficient evidence for this, with largely summary plots supporting the statements. Similarly, several of the MBDseq datasets seem discordant, such as FigS2G or FigS4D(right panel) where the x-y axis for scatterplots are clearly not equivalent suggesting global effects on the data. The authors should also clearly demonstrate the levels of DNMT3A throughout their EB timecourse for mutant lines, where this seems especially important given their readout is DNA methylation dynamics.
      3. An optional analysis that could support the claims of the paper would be to contrast the effect sizes in their cellular model with existing datasets that profile DNA methylation dynamics in vivo, where these have been captured at early developmental levels. This would nicely show that their functional readout in relation to normal processes.

      Minor comments:

      1. Several figures require addressing, listed here:
        • Fig1B the points are not so legible when overplotted, consider reducing the size of the datapoint circles or turning into "*" representations.
        • Fig4I seems not to have a figure legend.
        • FigS2G should be represented as a square and not as a rectangle, as this visually condenses on axis relative to the other.
        • FigS3A is unclear, could more be added to the legend to describe what exactly is the schematic representing?
        • FigS3D the axis seems not aligned with the barplot positioning?
      2. The Dnmt3b-PAS-KI clone 1 does not seem to well-cluster with the 2nd and 3rd clone, could this be a clonal effect at the global level?
      3. The text states (page 7, third paragraph) that in the two differentiation models the identity of the CGIs that exhibit different dynamics largely match, though no direct comparison (i.e. delta-delta effect) is show, rather a summary plot of either is presented side-by-side. This seems insufficient evidence of the statement, and a direct comparison of the fold changes would help.
      4. The clonal effect sizes would benefit from more quantitative comparisons throughout the manuscript, broken down to raw data. For example, the statement in page 8 paragraph two that the effects on independent clones were fully consistent is show from largely a PCA plot, which seems incomplete evidence that replicates behave consistently. More transparent analysis of clonality from the raw data would be helpful for the reader.
      5. The statement in the discussion that the authors experimental system affords 'homogeneous and highly synchronised onset and progression of XCI", but it seems unclear from the data provided in the manuscript that cells exhibit differentiation in a synchronized manner. Softening this statement seems apt here.

      Significance

      The question of specificity is highly important, not just to the field of epigenetics and DNA methylation where this study is particularly relevant, but also to a broader audience. Many of our cells proteins are highly homologous but have nevertheless highly divergent activities. Molecular explanations of specificity are therefore critical to understand phenotype and how traits can be acquired through gene paralogue evolution. Here, by focusing on a particularly apt example, the similar DNMT3A/B proteins, this study offers a nice breakdown with the potential to tie back the results to locus specific activity in the genome. The strongest aspect is the comparison of sophisticated mutants in a matched experimental setting, however, the experiments do not seem sufficient to support the broad conclusions of the study. From a genomics standpoint, the experimental setup is impressive, but requires additional work to show that matched expression levels of wildtype/mutant proteins still maintains the phenotypes reported.

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      Referee #2

      Evidence, reproducibility and clarity

      DNA methylation controls gene expression and genome stability during development and in healthy adult cells. It is frequently abnormal in diseases including cancer. Therefore, there is a clear impetus for the community to better understand the mechanisms that underlie DNA methylation patterns in mammalian genomes, including how the mark is deposited on specific regions. Of particular interest are CpG islands, which correspond to the majority of promoters, and are typically devoid of methylation. However, in specific cases, including during differentiation and X chromosome inactivation, CpG islands acquire extensive DNA methylation in a de novo methylation process. There are 2 de novo DNA methyltransferases expressed in the embryo: DNMT3A and DNMT3B. They are globally similar, but only DNMT3B contributes to de novo DNA methylation during X inactivation (Gendrel 2012, from the authors' lab). The question of this paper is: why is that?

      The model system used by the authors is female mouse ES cells, which during differentiation in vitro inactivate one of their X chromosomes. They use a hybrid line to distinguish parental alleles, and a genetic trick to ensure that the same chromosome is inactivated in all cells. Figure 1 validates the system, showing that CGIs on the inactivated X acquire DNA methylation during the differentiation process into EBs, along with some autosomal CGIs (this is done by MBD-seq). Some are fast to gain methylation, some slower. A similar analysis is carried out during differentiation into NPCs.

      The authors then move on to functional experiments by knocking out DNMT3B in their system. The KO clones are extensively characterized (Fig 2 and S2). They lack DNMT3B but retain DNMT3A levels similar to WT. However, they fail to methylate the majority of CGIs during X inactivation, confirming that DNMT3B (and not DNMT3A) is the principal actor in this process.

      The next question is: which domain(s) of DNMT3B is/are involved in this function. For this, the authors rescue their KO clones with cDNAs encoding different isoforms of DNMT3B, namely 3B1 (active) and 3B3 (inactive). They found that 3B1 fully rescued proper DNA methylation and gene expression during differentiation, whereas 3B3 had no effect (Fig 3 and S4).

      Having found that 3B1 expression fully rescues the DNMT3B KO, they move on to a more precise delineation of the important domains (Fig 4). Domain-swapping experiments show that the catalytic domain itself does not contribute to the specificity of DNMT3B (see my note on this experiment below). A similar strategy is then employed to test the contribution of the PWWP and ADD domains to 3B function. I did not find this part very clear. My understanding is that the swapped rescue construct has some activity on gene bodies even before differentiation. I gather from the lower part of Panel 4F that the PAS construct mostly fails to rescue DNA methylation during differentiation, but I am a little confused by the phrasing. It would be very easy to solve this with a summary graphic.

      Lastly, they move on to an examination of the Nter part of DNMT3B, using their domain swapping/rescue approach (Figures 5 and S6). A first experiment suggests that the Nter of 3A cannot substitute for the Nter of 3B (see note below). A second set of experiments shows that the presence of residues 1-218 is necessary for DNMT3B to function, and that smaller deletions within this region also inactivate the protein (see notes below).

      The paper is very well written. The figures are clear. The experiments are well controlled and correctly interpreted, except for the points below.

      Fig 4A: it is looking like the DNMT3B1-3A-Cat rescue construct is vastly overexpressed relative to the endogenous protein. This is surprising as the DNMT3B1 rescue construct is not (Figure 3B). What is the reason for this? Is a different promoter or rescue method being used? I feel that the overexpression level weakens the conclusion that the catalytic domain of 3A can perfectly replace that of 3B.

      Fig 4G: similarly, the two different DNMT3B-PAS-KI clones show widely different levels of DNMT3B expression. Are they both used to generate the data of Fig 4H? A third rescue clone is shown in Fig S5B. What experiment(s) was it used for?

      The clarity of the section concerning DNMT3B-PAS-KI clones can be improved easily.

      Fig 5B: the DNMT3a2(N)3b-KI clones show 3B expression levels that are ~10% of endogenous. I find it hard to conclude from this that the Nter of 3A cannot replace the Nter of 3B. Unless the authors can show that a 10% level of 3B expression is enough to fully rescue the KO of 3B.

      Fig S6D: same comment for the DeltaA and DeltaB construct. I am not seeing the data for DeltaC. In the absence of expression data, the methylation data for this mutant are not interpretable.

      Fig 5B. Is it clear that the Delta 1-218 protein is nuclear? What about the Delta A-E mutants?

      I suggest that the authors add the following paper to their reference list: Wapenaar EMBO Rep 2024 PMID: 39528729

      Significance

      I feel that the target audience is somewhat specialized. In addition, the novelty of the paper is diminished by the existence of published papers, in particular: DNMT3B PWWP mutations cause hypermethylation of heterochromatin. Taglini F, ..., Sproul D. EMBO Rep. 2024 Mar;25(3):1130-1155. doi: 10.1038/s44319-024-00061-5. Epub 2024 Jan 30. PMID: 38291337

      This paper uses a different system (human cancer cells), but arrives at the same conclusion, ie the Nter of DNMT3B is necessary for de novo DNA methylation. In addition, it shows that the Nter interacts with HP1.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      In this study the authors used a previously established mESCs iXist-ChrX cell line to investigate the mechanisms underpinning developmentally regulated CGI methylation through differentiation into embryoid bodies and MBD-seq profiling. They show that their system recapitulates developmentally regulated DNA methylation at CGIs on the X chromosome and autosomes before using a knockout and rescue system to determine that this is dependent on the DNMT3B1 isoform. Through domain swap experiments, they then go on to suggest that this requires the PWWP-ADD domain and that the N-terminal region of DNMT3B1.

      Major comments:

      • Are the key conclusions convincing?
      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
      • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
      • Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
      • Are the data and the methods presented in such a way that they can be reproduced?
      • Are the experiments adequately replicated and statistical analysis adequate?

      Overall, this is an interesting study that provides insights into the role of different domains of DNMT3B in establishing DNA methylation patterns during development. However, it suffers from poor annotation and description throughout. More specific comments are:

      1. The manuscript provides some details as to the replication of experiments but fails to show the replicate data in the vast majority of cases. Instead, representative data is presented alongside global correlations for some experiments. However, global correlations can mask differences between replicates. The data from all replicates should be shown in the manuscript and clear details provided regarding the replication strategy in the methods and figure legends. For example, were the different knock-in clones generated from independent DNMT3B knockout clones? For individual experiments this would be a minor point however, collectively this is a major point. It is particularly important given the variation highlighted in point 2 below.
      2. Different DNMT3B knock-in clones show high variability in expression levels. Have the authors investigated whether the discrepancy in protein levels contributes to the differences in methylation patterns seen? A non-comprehensive list of examples is given in the minor comments section.
      3. There is variation in the level of expression of different DNMT3B constructs detected by Western blot. Could this be caused by differences in protein stability? It would be helpful to see an assessment of protein stability to determine whether this contributes to the variable expression. For example, the DNMT3B3-KI has lower levels than the DNMT3B1-KI (Figure 3B) and this could potentially contribute to the differences in DNA methylation observed.
      4. The study lacks statistical tests to support the conclusions drawn from the analysis of the sequencing data. For example, are the differences in CGI methylation between DNMT3B1-KI and DNMT3B3-KI statistically significant? For individual analyses this would be a minor comment but given the lack throughout the study, this classes as a major comment.
      5. Chromatin marks play major roles in DNMT3A and DNMT3B recruitment (Tibben & Rothbart 2024) and the N-terminal region, PWWP and ADD domains have direct or indirect chromatin reading activity. However, the manuscript does not detail the chromatin environment of the CGIs studied. This could potentially be addressed through experiments, analysis of existing data or discussion.
      6. As the authors state in their discussion, MBD-seq may only detect very dense methylation. This could potentially obscure lower levels of DNA in some conditions. Analysis of a few loci by alternative methods, such as targeted bsPCR or EM-PCR would help support key results and rule out the possibility that some of the rescue constructs are able to partially rescue DNA methylation patterns.
      7. While some expression data is shown, there is currently no investigation as to whether the different DNMT3B domain swap constructs have impact on transcriptional silencing on Xi/autosomal sites.
      8. The text relating to the section on the PWWP-ADD domain is very brief and currently unclear. Expanding this section and specifying which data are derived from ES cells vs differentiated cells would help to clarify. We also suggest that it would be clearer to move this data to the main figure and to move the results of the catalytic domain experiments, which are negative, to the supplementary.
      9. The authors suggest that PWW-ADD domain region of DNMT3B is required for developmental methylation of Xi and autosomal CGIs. However, there is no further dissection as to whether this requirement is due to the PWWP, ADD or the intervening region.
      10. Throughout the text autosomal and Xi CGIs are both analysed. The introduction highlights SMCHD1 as important in methylating CGIs on Xi but that PCGF6 complex for the autosomal targets. This suggests separate mechanisms target DNMT3B to these loci. However, based on the results presented here, these two different types of targets have similar DNMT3B1 domain requirements. It would be interesting to see discussion with regard to this point in the manuscript.

      Minor comments:

      • Specific experimental issues that are easily addressable.
      • Are prior studies referenced appropriately?
      • Are the text and figures clear and accurate?
      • Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
      • Introduction: Methylation of tumour suppressor gene CGI promoters is mentioned alongside examples of developmental CGI methylation. It would be useful to place rare event in context. Methylation of CGIs is common in cancer but very few of these correspond to tumour suppressor genes. It would also be useful to discuss how DNMT3B might be involved in these events.
      • Introduction: The authors mention the DNMT3A1's N-terminal region recruits it to H2AK119ub1. It would also be useful to discuss recent work on the N-terminal of DNMT3B which can bind HP1-alpha to mediate H3K9me3 recruitment (Taglini et al 2024). This paper is currently only cited in the discussion.
      • Throughout the manuscript DNMT3B1 is referred to as the catalytic isoform, however it is not the only catalytic isoform of DNMT3B.
      • Page 5: 'the presence of non-catalytic isoforms, notably DNMT3L and DNMT3B3'. This statement incorrectly suggests DNMT3L is a non-catalytic isoform of DNMT3B.
      • The authors refer to the protein complex as heterodimers when referring to a DNMT3B -DNMT3L or DNMT3B3-DNMT3A complex. However, the consensus of structural studies is that they form tetramers.
      • Marker sizes are not included on blots with the exception of Figure 3B.
      • Western blots are cropped closely. It would be useful if full blots were shown in the supplementary particularly given the presence of extra bands in some blots and different DNMT3B isoforms.
      • Details about the Western blot methods (eg visualisation and antibodies) are missing.
      • It would be useful if the size of different groups was annotated in plots. This is given in Figure 1D for example but not in Figure 2E.
      • The authors show data confirming DNMT3B knockout by western blot. However, they do not provide details of the strategy for generating the knockout (ie vector used, sgRNAs, screening process). Could the authors also provide additional details as to whether there is any sequencing to confirm the results on the knockout?
      • Page 9: The authors state "Since Dnmt3b-/- cells have normal levels of DNMT3A", but show no data to support this statement. This is particularly relevant as they have generated new DNMT3B knockout clones for this study so they cannot be assumed to behave similarly to previous studies.
      • Figure 1B: There is poor PCA clustering between replicates at some timepoints, particularly Day 8.
      • Figure 1G: Different colours are used for the different timepoints in this figure. We are unclear if this is deliberate.
      • Page 7: The authors state "... the two categories largely matched between the two differentiation systems (Figure S1C-G)". It is difficult to draw this interpretation from the data presented as no explicit overlap is shown.
      • Figure 2E: MBD-seq peaks for DNMT3B-independent loci in WT sample have a dip in the middle of the peak (also seen in Figure 5F). Could the authors explain why this might be and why it only appears in some experiments?
      • Clarification as to whether the DNMT3B -dependent and -independent loci are located on chromosome X or autosomes (e.g.: Figure 2D, E).
      • Figure S2C: the chromatin RNA-seq is not explained in the text or figure.
      • Figure S2E: Suggests WT is one of 5 replicates. The authors should show all replicates.
      • Figure S2H: What genes are included in the metaplot?
      • DNMT3B rescue knock-in clones. As shown in figure 2A, there are two different Dnmt3b-/- cell line clones. Could the authors clarify whether all the CRISPR KI clones are produced from the same parental Dnmt3b-/- cell line clone?
      • Figure 3B: The two clones shown for Dnmt3b3-KI have variable expression. Do the individual replicates for the Dnmt3b3-KI clones show similar methylation patterns?
      • Figure 3E: in the MBD-seq metaplots, there is a peak present at -/+ 4kb. What are these peaks and why do they appear at 4kb distance? Similar peaks are seen in other metaplots.
      • In figure 3F, the signal for both Dnmt3b1-KI and Dnmt3b3-KI at DNMT3B-independent CGIs is higher than in the KO. This suggests that these may not be DNMT3B independent but this point is unaddressed in the text.
      • Figure S3A: it is currently unclear what has been modelled in this figure, adding labels of what has been plotted along the x- and y- axis may aid in understanding.
      • Figure 4A: Dnmt3b1-3a-Cat-KI appears very highly expressed. Is the WT shown the endogenous protein? Could this higher expression be because the chimeric protein is more stable than DNMT3B1? There are also multiple bands on this blot.
      • Plots panels are inconsistently ordered, e.g.: Figure 3F is dependent then independent. 4F is independent then dependent.
      • Figure 4G: the expression level of the Dnmt3b-PAS-KI varies significantly between the clones shown. There are also two bands on the blot, both for the wt and KI. Clarify if WT is endogenous.
      • Figure 4H: The figure lacks a legend to indicate the scale of the colour density used.
      • Figure 4F,H: Could the authors clarifying what data (clones and number of replicates) are presented in the representative plots. Does the different protein levels between the clones result in any differences in DNA methylation?
      • Page 12: The authors cite Boyko et al when discussing potential differences between the ADD domains of DNMT3A and B. However, they do not cite the study of Lu et al., 2023 (https://doi.org/10.1093/nar/gkad972) which reaches a different conclusion.
      • Figure S4A: The position of the 750 residue is inconsistent across the isoforms in this schematic.
      • Figure 5A: Schematic suggests the chimeric protein is DNMT3A2(N)-3B. However, DNMT3A2 lacks the N terminal region so presumably this should be DNMT3A1(N)-3B. This applies other figure panels using this construct.
      • Figure 5B: Many lanes on this blot are unlabelled and it would be useful to clarify what these extra lanes show.
      • Figure 5C: For Dnmt3a2(N)3b-KI the levels of methylation appear to be lower than Dnmt3b-/- and it would be useful to understand why this might be the case.
      • Figure 5D: would be helpful to indicate which CGIs are DNMT3B dependent and independent.
      • Figure 5F: Dnmt3a2(N)3b-KI data not included for the autosomal peaks
      • Figure 5G, H: It is difficult to see if there are any differences between the deletions in this heatmap. For example, it appears that levels of methylation on autosomal DNMT3B-dependent loci are very similar between the KO and rescue constructs. ∆D also appears to have a lesser effect than the other deletions on the Xi CGIs. A more quantitative representation of the data would help with interpretation.
      • S5E has different colour scale to other heatmaps. Red is low and in other heatmaps red is high.
      • Figure S6C: sequence conservation is shown for primates. However as mouse Dnmt3b is used throughout the paper, including the mouse NT would be a useful comparison. This is particularly relevant given that the NT is the region that varies the most between mouse and human proteins (Molaro et al 2020 https://doi.org/10.1093/molbev/msaa044 ).
      • Figure S6D: There is variable expression levels between the clones of the different deletions. The deletion ∆C is also not shown in this figure meaning that no data is shown to support the statement that it is unstable.
      • It would be useful to clarify in the text that "deletion of residues 98-146" corresponds to ∆C. It is also unclear why MBD-seq data for this deletion was included if it is unstable.
      • Discussion, page 15: The authors propose that DNMT3B could directly bind to H3K9me3. However, a study they cite, Taglini et al., 2024 (Figure S8C, D), suggests this is not the case.
      • Discussion: When discussing regulatory element methylation on Xi. An uncited statement is included: 'This observation may help to explain a prior observation that loss of DNMT3B1 alone does not result in significant de-repression of Xi during embryogenesis'. However this model appears contradictory to the observation that DNMT1 is not required for Xi silencing, given that DNMT1 KO embryos would be expected to have very low DNA methylation (eg Sado et al 2000, https://doi.org/10.1006/dbio.2000.9823 and Sado et al 2004, https://doi.org/10.1242/dev.00995).

      Referee cross-commenting

      Having reviewed the comments of the other reviewers, we agree that they are very similar and we have no issues with them. We note that Reviewer 3 notes that considering nuclear protein levels is important in the context of this study and we agree that this is an important additional consideration that we did not consider in our review.

      Significance

      The manuscript is an interesting study on the role of DNMT3B in X inactivation and development. It will be of interest to scientists who work on these fundamental processes. In addition, given the roles of DNA methylation in gene regulation, cancer, aging and disease more generally the findings are likely to be of interest to many others.

      Our expertise is in epigenetics and its regulation in disease, with a specific focus on DNA methylation and DNMTs.

    1. Reviewer #1 (Public review):

      Summary:

      Sandkuhler et al. re-evaluated the biological functions of TANGO2 homologs in C. elegans, yeast, and zebrafish. Compared to the previously reported role of TANGO2 homologs in transporting heme, Sandkuhler et al. expressed a different opinion on the biological functions of TANGO2 homologs. With the support of some results from their tests, they conclude that 'there is insufficient evidence to support heme transport as the primary function of TANGO2', in addition to their claims on the role of TANGO2 in modulating metabolism. While the differences are reported in this study, more work is needed to elucidate the biological function of TANGO2.

      Strengths:

      (1) This work revisited a set of key experiments, including the toxic heme analog GaPP survival assay, the fluorescent ZnMP accumulation assay, and the multi-organismal investigations documented by Sun et al. in Nature 2022, which is critical for comparing the two works.

      (2) This work reported additional phenotypes for the C. elegans mutant of the TANGO2 homologs, including lawn avoidance, reduced pharyngeal pumping, smaller brood size, faster exhaustion under swimming test, and a shorter lifespan. These phenotypes are important for understanding the biological function of TANGO2 homologs, while they were missing from the report by Sun et al.

      (3) Investigating the 'reduced GaPP consumption' as a cause of increased resistance against the toxic GaPP for the TANGO2 homologs, hrg-9 hrg-10 double null mutant provides a valuable perspective for studying the biological function of TANGO2 homologs.

      (4) This work thoroughly evaluated the role of TANGO2 homologs in supporting yeast growth using multiple yeast strains and also pointed out the mitochondrial genome instability feature of the yeast strain used by Sun et al.

      Weaknesses:

      (1) A detailed comparison between this work and the work of Sun et al. on experimental protocols and reagents in the main text will be beneficial for readers to assess critically.

      (2) The GaPP used by Sun et al. (purchased from Frontier Scientific) is more effective in killing the worm than the one used in this study (purchased from Santa Cruz). Is the different outcome due to the differences in reagents? Moreover, Sun et al. examined the lethality after 3-4 days, while this work examined the lethality after 72 hours. Would the extra 24 hours make any difference in the result?

      (3) This work reported the opposite result of Sun et al. for the fluorescent ZnMP accumulation assay. However, the experimental protocols used by the two studies are massively different. Sun et al. did the ZnMP staining by incubating the L4-stage worms in an axenic mCeHR2 medium containing 40 μM ZnMP (purchased from Frontier Scientific) and 4 μM heme at 20 ℃ for 16 h, while this work placed the L4-stage worms on the OP50 E. coli seeded NGM plates treated with 40 μM ZnMP (purchased from Santa Cruz) for 16 h. The liquid axenic mCeHR2 medium is bacteria-free, heme-free, and consistent for ZnMP uptake by worms. This work has mentioned that the hrg-9 hrg-10 double null mutant has bacterial lawn avoidance and reduced pharyngeal pumping phenotypes. Therefore, the ZnMP staining protocol used in this work faces challenges in the environmental control for the wild type vs. the mutant. The authors should adopt the ZnMP staining protocol used by Sun et al. for a proper evaluation of fluorescent ZnMP accumulation.

      (4) A striking difference between the two studies is that Sun et al. emphasize the biochemical function of TANGO2 homologs in heme transporting with evidence from some biochemical tests. In contrast, this work emphasizes the physiological function of TANGO2 homologs with evidence from multiple phenotypical observations. In the discussion part, the authors should address whether these observed phenotypes in this study can be due to the loss of heme transporting activities upon eliminating TANGO2 homologs. This action can improve the merit of academic debate and collaboration.

    2. Reviewer #3 (Public review):

      In this paper, Sandkuhler et al. reassessed the role of TANGO2 as a heme chaperone proposed by Sun et al in a recently published paper (https://doi.org/10.1038/s41586-022-05347-z) by partially repeating and failing to replicate experiments therein. Overall, Sandkuhler et al. conclude that the heme-related roles of TANGO2 had been overemphasized by Sun et al. especially because the hrg9 gene does not exclusively respond to different regimens of heme synthesis/uptake but is susceptible to a greater extent to, for example, oxidative stress.

      In recent years, the discussion around the heme-related roles of TANGO2 has been tantalizing but is still far from a definitive consensus. Discrepancies between results and their interpretation are a testament to how challenging and ambitious the understanding of TANGO2 and the phenotypes associated with TANGO2 defects are. Overall, the work presented by Sandkuhler et al. in this manuscript challenges the recent developments in the field and promotes the continuous characterisation of TANGO2 in relation to heme homeostasis.

      A few comments and questions:

      (1) The authors stress - with evidence provided in this paper or indicated in the literature - that the primary role of TANGO2 and its homologues is unlikely to be related to heme trafficking, arguing that observed effects on heme transport are instead downstream consequences of aberrant cellular metabolism. But in light of a mounting body of evidence (referenced by the authors) connecting more or less directly TANGO2 to heme trafficking and mobilization, it is recommended that the authors comment on how they think TANGO2 could relate to and be essential for heme trafficking, albeit in a secondary, moonlighting capacity. This would highlight a seemingly common theme in emerging key players in intracellular heme trafficking, as it appears to be the case for GAPDH - with accumulating evidence of this glycolytic enzyme being critical for heme delivery to several downstream proteins.

      (2) The observation - using eat-2 mutants and lawn avoidance behaviour - that survival patterns can be partially explained by reduced consumption, is fascinating. It would be interesting to quantify the two relative contributions.

      (3) In the legend to Figure 1A it's a bit unclear what the differently coloured dots represent for each condition. Repeated measurements, worms, independent experiments? The authors should clarify this.

      (4) It would help if the entire fluorescence images (raw and processed) for the ZnMP treatments were provided. Fluorescence images would also benefit Figure 1B.

      (5) Increasingly, the understanding of heme-dependent roles relies on transient or indirect binding to unsuspected partners, not necessarily relying on a tight affinity and outdating the notion of heme as a static cofactor. Despite impressive recent advancements in the detection of these interactions (for example https://doi.org/10.1021/jacs.2c06104; cited by the authors), a full characterisation of the hemome is still elusive. Sandkuhler et al. deemed it possible but seem to question that heme binding to TANGO2 occurs. However, Sun et al. convincingly showed and characterised TANGO2 binding to heme. It is recommended that the authors comment on this.

    1. this saying in statistics: All models are wrong, but some are useful [d18]

      In math model, we often use formulas, models, and variables to represent various complicated stuffs in reality. For example, we can use a function to describe a population growth or use a matrix to represent a transportation network. However, we all know that all these models are used for abstract expression in reality. At here, the article mentions that 2 + 2 = 4 only works when all four units are identical, but, in reality, each unit has its own difference just like apples which have different sizes. So, we need to always have a crtical thinking and humble when we use these models and data. After reading this chapter, I have a question, in our daily lives, can we fully rely on data to make decisions or we have to leave some space for non-data decision since if all the data try to simplified reality

    2. So all data that you might find is a simplification. There are many seemingly simple questions that in some situations or for some people, have no simple answers, questions like: What country are you from? What if you were born in one country, but moved to another shortly after? What if you are from a country that no longer exists like Czechoslovakia? Or from an occupied territory? How many people live in this house? Does a college student returning home for the summer count as living in that house? How many words are in this chapter? Different programs use different rules for what counts as a “word” [d19] E.g., this page has “2 + 2 = 4”, which Microsoft Word counts as 5 words, and Google Docs counts as 3 words.

      I find this discussion interesting. Data is try's to be person specific but struggles not to be black or white, it struggles to sit in that grey zone. This is important because the real people that data is trying to represent might not fit into the data.

    1. Reviewer #1 (Public review):

      This study investigates the role of site-specific DNA methylation changes during spermatogenesis and their contribution to paternal epigenetic inheritance. Using MethylCap-seq, the authors identify a transient, site-specific loss of DNA methylation at transcription start sites (TSSs) of late spermatogenesis genes during the transition from differentiating spermatogonia (KIT+) to pachytene spermatocytes (PS). This demethylation event correlates with the gain of H3K4me3, which presets nucleosome retention sites in mouse sperm. The study proposes that selective loss of DNA methylation at a subset of promoters is required for nucleosome retention and the establishment of epigenetic states that may influence embryonic gene regulation. These findings provide complementary insights to earlier work by the Peters lab, "DNA methylation modulates nucleosome retention in sperm and H3K4 methylation deposition in early mouse embryos."

      Overall, the study presents a valuable dataset; however, additional analyses could strengthen the conclusions and provide further mechanistic insights.

      Major Comments:

      (1) Prior work should be acknowledged and used for comparative analysis. A key proposal in this study is that regions undergoing DNA methylation loss retain histones, influencing the zygote's epigenetic landscape. However, previous studies (e.g., Peters et al.) have shown that regions losing methylation in DNMT3a/b knockout (KO) mice do not necessarily retain histones, suggesting additional factors are involved. Moreover, Peters et al. demonstrated that regions of low DNA methylation in sperm render paternal alleles permissive for H3K4me3 establishment in early embryos, independent of the paternal inheritance of sperm-borne H3K4me3. Comparing these findings would refine the model presented in this study.

      (2) Figure 2A: The data suggest an increase in methylation peaks in PS cells. How does this align with the hypomethylation observed in Figure 1D? Reconciling these observations would improve clarity.

      (3) Figure 4A: The effect size of demethylation on nucleosome retention is unclear - do all demethylated promoters retain histones or only a subset? Quantifying this would clarify whether DNA methylation loss consistently predicts nucleosome retention.

      (4) Prior studies have generated bisulfite sequencing data from Tet KO sperm. Do the regions that undergo demethylation during the KIT+ to PS transition overlap with those misregulated in TET KO sperm? Integrating this comparison could provide further insight into the regulation of site-specific demethylation.

      (5) The role of SCML2 enrichment in germline stem cells and its connection to H3K27me3 deposition in later germ cells is unclear. Earlier figures show that regions undergoing DNA demethylation from KIT+ to PS include genes expressed in later-stage germ cells.

      Why is SCML2 enrichment occurring in germline stem cells (GSCs)? Why is H3K27me3 only acquired at later stages if SCML2 is already present? Is SCML2 preventing premature expression independent of K27ME?

      Showing the dynamics of H3K27me3 and SCML2 across these stages would clarify the proposed conclusions.

    2. Reviewer #2 (Public review):

      Summary:

      This study profiles the genome-wide distribution of DNA methylation using methylation capture sequencing in four stages of male germ cells: Thy1+ (undifferentiated spermatogonia), Kit+ (differentiated spermatogonia), pachytene spermatocytes, and round spermatids. These analyses revealed site-specific loss of DNA methylation in pachytene cells compared with differentiating spermatogonia. Integrated analysis using published datasets indicates that hypomethylated sites correlate with nucleosome retention sites and bivalent histone methylation sites in sperm.

      Strengths:

      The methyl-seq approach provides a comprehensive profile of DNA methylation in male germ cells. The concept that DNA hypomethylation in meiotic cells precedes histone modification and histone retention in sperm is interesting.

      Weaknesses:

      (1) In the title, the word "presets" should be changed to "precedes" or "correlates with". Preset means a causal relationship, which is not the case. This needs to be changed throughout the manuscript. For example, in the abstract, "predetermine" needs to be changed to "precede".

      (2) The statement that "Based on these results, we propose that meiosis is a process of epigenetic reprogramming that sets up embryonic gene regulation" (lines 94-95) is a speculation that in the opinion of this reviewer should be removed from the text. It is too broad and not supported by the data presented.

      (3) Figure 1B: details are missing. How many cells were analyzed/used? How many times was this experiment done [(The number of experiments (n)]? Were the changes statistically significant (Lines 109-111)?

      (4) Figure 1A and Figure 1D: These seem to be contradictory. According to Figure 1D, leptotene/zygotene spermatocytes show bright 5mC staining. However, the diagram in 1A shows delayed recovery of DNA methylation. The authors should clarify this. It appears that 5mC was high in Kit+ spermatogonia and leptotene/zygotene spermatocytes, and then decreased in pachytene spermatocytes.

      (5) L121-122: Statement: These results suggest that 5mC levels change dynamically during spermatogenesis before and after the transient reduction of DNA methylation in the premeiotic S phase. In order to make this claim about the premeiotic S phase, I suggest performing 5mC staining in premeiotic S phase cells, which can be pulse-labelled with BrdU or cite a reference if available.

    1. 正四面体的稳定子群

      正四面体的稳定子群详解


      1. 基本概念

      稳定子群(Stabilizer Subgroup)是群论中的一个概念,指群作用下保持某个特定元素(如顶点、面或边)不变的所有群元素的集合。在正四面体的对称群中,稳定子群描述了保持某一顶点位置不变的所有旋转操作。


      2. 正四面体的对称群

      正四面体的对称群包含所有保持其形状不变的旋转和反射操作: - 旋转群(Proper Symmetry Group):仅包含旋转操作,阶数为 12(即共有12种旋转对称操作)。 - 全对称群(Full Symmetry Group):包含旋转和反射操作,阶数为 24

      此处重点讨论旋转群中的稳定子群。


      3. 稳定子群的定义与结构

      针对顶点的稳定子群: - 目标元素:选择正四面体的一个顶点(例如顶点 \( A \))。 - 稳定子群:所有保持顶点 \( A \) 固定的旋转操作组成的子群。

      具体分析: - 旋转轴:通过顶点 \( A \) 和其对面中心的连线(如图1所示)。 - 旋转角度:绕该轴旋转 120°240°,以及恒等操作(0°)。 - 元素构成: - 恒等元(\( e \)):不旋转。 - 旋转 \( r \):绕轴旋转 120°。 - 旋转 \( r^2 \):绕轴旋转 240°(即 \( r \) 的平方)。

      因此,稳定子群的阶数为 3,同构于循环群 \( \mathbb{Z}_3 \)(即 \( C_3 \))。


      4. 具体例子与几何意义

      以顶点 \( A \) 为例: - 旋转轴:连接顶点 \( A \) 和对面三角形的中心点(如图2所示)。 - 旋转效果: - 旋转 120°:顶点 \( A \) 保持不动,其他三个顶点沿三角形面循环移动。 - 旋转 240°:顶点 \( A \) 保持不动,其他三个顶点沿相反方向循环移动。 - 轨道与稳定子关系: - 根据轨道-稳定子定理,顶点 \( A \) 的轨道大小为 4(正四面体有4个顶点)。 - 稳定子群阶数为 3,旋转群阶数为 12,满足 \( |\text{Orb}(A)| \cdot |\text{Stab}(A)| = 4 \cdot 3 = 12 \)。


      5. 正四面体的稳定子群分类

      正四面体共有 4 个稳定子群,每个对应一个顶点: - 每个稳定子群的阶数均为 3,且结构相同(同构于 \( C_3 \))。 - 不同稳定子群之间通过共轭关系关联,例如顶点 \( A \) 的稳定子群与顶点 \( B \) 的稳定子群可通过旋转整个正四面体相互转换。


      6. 稳定子群的几何与代数性质

      • 几何性质
      • 稳定子群的旋转轴通过顶点和对面中心,且每个轴对应一个三角形面的中心。
      • 旋转操作保持顶点 \( A \) 固定,但改变其他顶点的位置。
      • 代数性质
      • 每个稳定子群是旋转群 \( \text{SO}(3) \) 的子群,且是阿贝尔群(因循环群的交换性)。
      • 稳定子群并非旋转群的正规子群(因共轭关系下不同顶点的稳定子群不同)。

      7. 稳定子群与对称群的关系

      • 旋转群的生成
      • 正四面体的旋转群可由两个稳定子群生成,例如通过组合不同顶点的旋转操作。
      • 对称群的分解
      • 全对称群(阶24)包含旋转群(阶12)和反射操作,而稳定子群仅涉及旋转部分。

      8. 应用与意义

      • 几何学
      • 稳定子群帮助分析正四面体的局部对称性,例如确定顶点周围的旋转对称性。
      • 群论
      • 稳定子群是轨道-稳定子定理的典型应用案例,揭示群作用与子群阶数的关联。
      • 晶体学
      • 在描述晶体对称性时,稳定子群可用于分析分子或晶格的局部对称性。

      9. 总结

      正四面体的稳定子群是保持某一顶点不变的所有旋转操作的集合,其核心性质如下: - 结构:每个稳定子群同构于循环群 \( C_3 \),包含3个元素(0°、120°、240°旋转)。 - 作用:通过旋转轴连接顶点与对面中心,保持顶点固定但改变其他顶点位置。 - 群论意义:是轨道-稳定子定理的直观体现,揭示对称性与子群阶数的关系。


      附图说明

      • 图1:正四面体的旋转轴示意图(通过顶点 \( A \) 和对面中心)。
      • 图2:120°旋转后顶点 \( A \) 保持固定,其他顶点循环移动。

      通过以上分析,正四面体的稳定子群不仅是群论中的抽象概念,更是理解其对称性与几何结构的关键工具。

    1. 正规子群概念具有可比性

      环的理想(Ideal)和群的正规子群(Normal Subgroup)在代数结构中确实存在深刻的相似性,它们都是构建商结构(商群、商环)的关键工具,并且在同态基本定理中扮演核心角色。以下是它们的对比分析:


      1. 定义与核心条件

      (1) 正规子群(Normal Subgroup)

      • 定义:设 \(G\) 是群,\(H \subseteq G\) 是子群。若对任意 \(g \in G\),有 \(gH = Hg\)(即左陪集等于右陪集),则 \(H\) 是 \(G\) 的正规子群,记作 \(H \triangleleft G\)。
      • 关键条件:<br /> 对任意 \(h \in H\)、\(g \in G\),有 \(g^{-1}hg \in H\)(即子群在共轭作用下不变)。

      (2) 环的理想(Ring Ideal)

      • 定义:设 \(R\) 是环,\(I \subseteq R\) 是子环。若对任意 \(r \in R\)、\(i \in I\),有 \(ri \in I\) 且 \(ir \in I\),则 \(I\) 是 \(R\) 的理想,记作 \(I \triangleleft R\)。
      • 关键条件:<br /> 理想对环的乘法具有“吸收性”,即环中任意元素与理想元素的乘积仍在理想内。

      2. 共同点

      (1) 子结构

      • 子群与子环:<br /> 正规子群是群的子群,理想是环的子环。
      • 商结构的构建基础:<br /> 两者均允许构建商结构(商群 \(G/H\)、商环 \(R/I\)),且商结构的运算必须是良定义的。

      (2) 同态核的角色

      • 群同态:群同态的核(所有映射到单位元的元素)必然是正规子群。
      • 环同态:环同态的核(所有映射到零元的元素)必然是理想。

      (3) 同态基本定理

      • 群同态基本定理:若 \(\phi: G \to G'\) 是群同态,则 \(G/\ker\phi \cong \text{Im}\phi\)。
      • 环同态基本定理:若 \(\psi: R \to R'\) 是环同态,则 \(R/\ker\psi \cong \text{Im}\psi\)。
      • 相似性:两者均通过商结构描述同态像与核的关系。

      (4) 共轭不变性

      • 正规子群:共轭不变性(\(gHg^{-1} = H\))确保商群的运算封闭。
      • 理想:乘法吸收性(\(rI = Ir\))确保商环的乘法封闭。

      3. 差异点

      (1) 代数结构复杂性

      • :仅涉及一种运算(乘法),需满足结合律和逆元存在性。
      • :涉及两种运算(加法和乘法),需满足加法交换群、乘法结合律及分配律。
      • 结果:理想需同时处理加法和乘法的兼容性,而正规子群仅需处理单一运算。

      (2) 定义条件

      • 正规子群:要求子群在群元素的共轭下不变。
      • 理想:要求子环在环元素的乘法下被“吸收”(即乘法封闭性)。

      (3) 应用方向

      • 正规子群:用于研究群的结构、商群、群扩张等,如伽罗瓦理论中的伽罗瓦群。
      • 理想:用于环的结构分析(如素理想、极大理想)、多项式环、代数数论中的唯一分解问题。

      (4) 在加法群中的关系

      • 理想与正规子群:<br /> 环 \(R\) 的加法群是阿贝尔群,其子群均为正规子群。但理想不仅是加法群的正规子群,还需满足乘法吸收性。<br /> 因此,理想是子环 + 乘法吸收性,而正规子群仅需满足共轭不变性。

      4. 深层联系

      (1) 商结构的良定义性

      • 群的商群:若 \(H\) 不是正规子群,则左陪集与右陪集不一致,乘法运算无法定义。
      • 环的商环:若 \(I\) 不是理想,则乘法运算 \( (a+I)(b+I) = ab + I \) 可能不封闭(如 \(ab \notin I\))。
      • 核心思想:两者均通过子结构的“不变性”确保商结构运算的良定义性。

      (2) 同构定理的统一性

      • 群同构定理环同构定理的结构几乎一致(如第一、第二同构定理),均依赖于商结构的同构关系。
      • 知识库引用:如知识库[1]指出,环同态基本定理是群同态基本定理的直接推广,仅需验证乘法部分的条件。

      (3) 历史与动机

      • 正规子群:源于群论中研究子群的共轭不变性(如伽罗瓦理论中的置换群)。
      • 理想:源于代数数论中整数环的唯一分解问题(如高斯、库默尔、戴德金的工作)。
      • 共同目标:通过子结构的特殊性质,将复杂结构分解为更简单的商结构。

      5. 总结

      环的理想与群的正规子群在代数结构中扮演相似但不完全相同的角色: - 相似性:<br /> 1. 两者均是子结构,且满足某种“不变性”(共轭不变性/乘法吸收性)。<br /> 2. 用于构建商结构(商群、商环),并作为同态核的核心。<br /> 3. 在同态基本定理中体现统一性。<br /> - 差异性:<br /> 1. 环的理想需同时处理加法和乘法,而群的正规子群仅涉及单一运算。<br /> 2. 理想是子环+乘法吸收性,而正规子群是子群+共轭不变性。

      理解这两者的联系与区别,有助于把握代数结构中“子结构构建商结构”的核心思想,是深入学习群论、环论及模论的基础。

  3. social-media-ethics-automation.github.io social-media-ethics-automation.github.io
    1. Caroline Delbert. Some People Think 2+2=5, and They’re Right. Popular Mechanics, October 2023. URL: https://www.popularmechanics.com/science/math/a33547137/why-some-people-think-2-plus-2-equals-5/ (visited on 2023-11-24).

      Article talks about Kareem Carr and how he pushes the idea that 2+2 = 5 depends on many different things (in his words, axioms). It sparked controversy on Twitter but is mentioned in Popular Mechanics that he speaks some truth due to certain aspects such as chemistry and physics. 2 cups baking soda + 2 cups vinegar for example means more than 4 cups of reacted foam that produces. It's also an idea that has been around for a century where Math has been pushed beyond what is on paper when it comes to just numbers. For example, a 5 on a scale for pain could mean something different for many people. Could mean it hurts a lot or not as much.

    1. Another significant contributor to the mistreatment of wild animals in captivity is the inadequate policies and implementation of policies regarding animal treatment. APHISAnimal Plant and Health Inspection Services; a federal body under the US Department of Agriculture that sets regulatory standards for facilities, operations, health, husbandry, sanitation, and transportation of zoo animals.2 They are also in charge of enforcing the requirements of the Animal Welfare Act (AWA)3 and the Marine Mammal Protection Act (MMPA).4 has been shown to be ineffective at ensuring that facilities are complying with the regulations outlined in the AWA and the MMPAMarine Mammal Protection Act; a federal law that limits the taking or importing of marine mammals except in the case of government-approved permits. The Animal Plant and Health Inspection Services (APHIS) is in charge of issuing these permits and enforcing the MMPA’s regulations.8, allowing animals to die from the negligence of their captive facilities.131 This inefficiency is likely due to the fact that APHIS is an extremely overwhelmed government body in charge of regulating a multitude of environmental issues.132 As of 2004, APHIS employed only 104 inspectors, but was in charge of inspecting over 2,000 facilities. The organization simply does not have the resources needed to adequately monitor animal treatment.133 When animal mistreatment goes unnoticed by those who have the power to enforce regulations, this allows mistreatment to continue because the establishments responsible are not facing the necessary consequences. Other organizations that set guidelines for animal mistreatment, such as the WAZAWorld Association of Zoos and Aquariums; an international body that creates guidelines for optimal wild animal care in zoos and aquariums. WAZA cannot enforce these guidelines; zoos and aquariums voluntarily join WAZA and choose to follow its guidelines.5 and the AZA, lack the power to enforce these guidelines, and gaining membership into these organizations is voluntary, meaning no captive establishment is forced to abide by these guidelines.134, 135 Moreover, less than 10% of American zoos are accredited by the AZAAmerican Zoo and Aquarium Association; a federal body that sets voluntary standards for animal care in zoos and aquariums and accredits organizations if they meet the credentials.6, meaning over 90% of zoos face very little oversight and do not have stringent guidelines to abide by.136

      negligence

    1. 上中心列的构造

      上中心列(上中心列)的构造详解:以四元数群 \( Q_8 \) 为例


      1. 上中心列的定义

      上中心列(Upper Central Series) 是群 \( G \) 的一个子群列: [ 1 = Z_0(G) \triangleleft Z_1(G) \triangleleft Z_2(G) \triangleleft \cdots \triangleleft Z_c(G) = G, ] 其中每个 \( Z_{i+1}(G) \) 是通过以下方式构造的: [ Z_{i+1}(G) = \pi_i^{-1}\left( Z\left( \frac{G}{Z_i(G)} \right) \right), ] 这里 \( \pi_i: G \to G/Z_i(G) \) 是自然同态,而 \( Z(G/Z_i(G)) \) 是商群 \( G/Z_i(G) \) 的中心。

      幂零级(Nilpotency Class):若存在最小的 \( c \) 使得 \( Z_c(G) = G \),则称 \( c \) 为群 \( G \) 的幂零级。


      2. 四元数群 \( Q_8 \) 的上中心列构造

      四元数群 \( Q_8 \) 的阶为8,其元素为 \( { \pm 1, \pm i, \pm j, \pm k } \),满足 \( i^2 = j^2 = k^2 = ijk = -1 \)。


      步骤1:构造 \( Z_1(Q_8) \)
      • 定义:\( Z_1(Q_8) = Z(Q_8) \),即群的中心。
      • 计算
      • 四元数群的中心仅包含与所有元素交换的元素。
      • 显然,\( 1 \) 和 \( -1 \) 与所有元素交换,而 \( i, j, k \) 不与彼此交换(例如 \( ij = k \neq -k = ji \))。
      • 因此,\( Z(Q_8) = { \pm 1 } \),即 \( Z_1(Q_8) = { \pm 1 } \)。

      步骤2:构造 \( Z_2(Q_8) \)
      • 商群:\( Q_8 / Z_1(Q_8) \cong \mathbb{Z}_2 \times \mathbb{Z}_2 \)。
      • 因为 \( Q_8/Z_1(Q_8) \) 的阶为 \( 8/2 = 4 \),且其元素为 \( { Z_1, iZ_1, jZ_1, kZ_1 } \)。
      • 该商群的乘法表是阿贝尔的,因为 \( (iZ_1)(jZ_1) = ijZ_1 = kZ_1 \),而 \( (jZ_1)(iZ_1) = jiZ_1 = -kZ_1 = kZ_1 \)(因 \( -k \in kZ_1 \)),故交换性成立。
      • 商群的中心:\( Z(Q_8/Z_1(Q_8)) = Q_8/Z_1(Q_8) \),因为商群本身是阿贝尔群,其中心是自身。
      • 原像计算
      • \( Z_2(Q_8) = \pi_1^{-1}\left( Z(Q_8/Z_1(Q_8)) \right) = \pi_1^{-1}(Q_8/Z_1(Q_8)) = Q_8 \),
      • 因为自然同态 \( \pi_1 \) 的原像包含所有元素(商群的中心是整个商群)。

      步骤3:终止条件
      • 幂零级判定:由于 \( Z_2(Q_8) = Q_8 \),上中心列在第二步终止。
      • 结论:\( Q_8 \) 的幂零级为2,上中心列为: [ {1} \triangleleft { \pm 1 } \triangleleft Q_8. ]

      3. 关键性质与验证

      性质1:每个 \( Z_{i+1}(G) \) 是正规子群
      • 因为 \( Z(G/Z_i(G)) \) 是商群的中心,自然同态的原像 \( \pi_i^{-1}(Z(G/Z_i(G))) \) 是正规子群。
      性质2:商群 \( Z_{i+1}(G)/Z_i(G) \subseteq Z(G/Z_i(G)) \)
      • 这是构造的直接结果,确保每个因子群是中心的。
      验证幂零性
      • 四元数群 \( Q_8 \) 是幂零群
      • 其上中心列在有限步(2步)达到整个群,满足幂零群的定义。
      • 其导出列(导群序列)也验证这一点:\( Q_8' = { \pm 1 } \),而 \( (Q_8')' = {1} \),导长为2,与幂零级一致。

      4. 与下中心列的对比

      下中心列(Lower Central Series) 是另一类重要子群列: [ G = \gamma_1(G) \geq \gamma_2(G) \geq \gamma_3(G) \geq \cdots, ] 其中 \( \gamma_{i+1}(G) = [G, \gamma_i(G)] \)(换位子群)。

      \( Q_8 \) 的下中心列: - \( \gamma_1(Q_8) = Q_8 \), - \( \gamma_2(Q_8) = [Q_8, Q_8] = { \pm 1 } \), - \( \gamma_3(Q_8) = [{ \pm 1 }, Q_8] = {1} \), - 因此下中心列长度为2,与上中心列的幂零级一致。


      5. 总结

      上中心列的构造步骤为: 1. 初始:\( Z_0(G) = {e} \)。 2. 递推:对每个 \( i \geq 1 \),计算商群 \( G/Z_{i-1}(G) \) 的中心,取其原像得到 \( Z_i(G) \)。 3. 终止:当 \( Z_c(G) = G \) 时停止,此时 \( c \) 为幂零级。

      四元数群 \( Q_8 \) 的上中心列展示了幂零群的典型特征:通过逐层“中心化”逐步扩展子群,最终覆盖整个群,且其幂零级与下中心列的长度一致。


      如果需要进一步讨论其他群(如交换群、非幂零群)的上中心列,或更深入的理论细节,请随时提问!

    1. 四元群 ​Q8​(幂零级2)

      上中心列(上中心列)的构造详解:以四元数群 \( Q_8 \) 为例


      1. 上中心列的定义

      上中心列(Upper Central Series) 是群 \( G \) 的一个子群列: [ 1 = Z_0(G) \triangleleft Z_1(G) \triangleleft Z_2(G) \triangleleft \cdots \triangleleft Z_c(G) = G, ] 其中每个 \( Z_{i+1}(G) \) 是通过以下方式构造的: [ Z_{i+1}(G) = \pi_i^{-1}\left( Z\left( \frac{G}{Z_i(G)} \right) \right), ] 这里 \( \pi_i: G \to G/Z_i(G) \) 是自然同态,而 \( Z(G/Z_i(G)) \) 是商群 \( G/Z_i(G) \) 的中心。

      幂零级(Nilpotency Class):若存在最小的 \( c \) 使得 \( Z_c(G) = G \),则称 \( c \) 为群 \( G \) 的幂零级。


      2. 四元数群 \( Q_8 \) 的上中心列构造

      四元数群 \( Q_8 \) 的阶为8,其元素为 \( { \pm 1, \pm i, \pm j, \pm k } \),满足 \( i^2 = j^2 = k^2 = ijk = -1 \)。


      步骤1:构造 \( Z_1(Q_8) \)
      • 定义:\( Z_1(Q_8) = Z(Q_8) \),即群的中心。
      • 计算
      • 四元数群的中心仅包含与所有元素交换的元素。
      • 显然,\( 1 \) 和 \( -1 \) 与所有元素交换,而 \( i, j, k \) 不与彼此交换(例如 \( ij = k \neq -k = ji \))。
      • 因此,\( Z(Q_8) = { \pm 1 } \),即 \( Z_1(Q_8) = { \pm 1 } \)。

      步骤2:构造 \( Z_2(Q_8) \)
      • 商群:\( Q_8 / Z_1(Q_8) \cong \mathbb{Z}_2 \times \mathbb{Z}_2 \)。
      • 因为 \( Q_8/Z_1(Q_8) \) 的阶为 \( 8/2 = 4 \),且其元素为 \( { Z_1, iZ_1, jZ_1, kZ_1 } \)。
      • 该商群的乘法表是阿贝尔的,因为 \( (iZ_1)(jZ_1) = ijZ_1 = kZ_1 \),而 \( (jZ_1)(iZ_1) = jiZ_1 = -kZ_1 = kZ_1 \)(因 \( -k \in kZ_1 \)),故交换性成立。
      • 商群的中心:\( Z(Q_8/Z_1(Q_8)) = Q_8/Z_1(Q_8) \),因为商群本身是阿贝尔群,其中心是自身。
      • 原像计算
      • \( Z_2(Q_8) = \pi_1^{-1}\left( Z(Q_8/Z_1(Q_8)) \right) = \pi_1^{-1}(Q_8/Z_1(Q_8)) = Q_8 \),
      • 因为自然同态 \( \pi_1 \) 的原像包含所有元素(商群的中心是整个商群)。

      步骤3:终止条件
      • 幂零级判定:由于 \( Z_2(Q_8) = Q_8 \),上中心列在第二步终止。
      • 结论:\( Q_8 \) 的幂零级为2,上中心列为: [ {1} \triangleleft { \pm 1 } \triangleleft Q_8. ]

      3. 关键性质与验证

      性质1:每个 \( Z_{i+1}(G) \) 是正规子群
      • 因为 \( Z(G/Z_i(G)) \) 是商群的中心,自然同态的原像 \( \pi_i^{-1}(Z(G/Z_i(G))) \) 是正规子群。
      性质2:商群 \( Z_{i+1}(G)/Z_i(G) \subseteq Z(G/Z_i(G)) \)
      • 这是构造的直接结果,确保每个因子群是中心的。
      验证幂零性
      • 四元数群 \( Q_8 \) 是幂零群
      • 其上中心列在有限步(2步)达到整个群,满足幂零群的定义。
      • 其导出列(导群序列)也验证这一点:\( Q_8' = { \pm 1 } \),而 \( (Q_8')' = {1} \),导长为2,与幂零级一致。

      4. 与下中心列的对比

      下中心列(Lower Central Series) 是另一类重要子群列: [ G = \gamma_1(G) \geq \gamma_2(G) \geq \gamma_3(G) \geq \cdots, ] 其中 \( \gamma_{i+1}(G) = [G, \gamma_i(G)] \)(换位子群)。

      \( Q_8 \) 的下中心列: - \( \gamma_1(Q_8) = Q_8 \), - \( \gamma_2(Q_8) = [Q_8, Q_8] = { \pm 1 } \), - \( \gamma_3(Q_8) = [{ \pm 1 }, Q_8] = {1} \), - 因此下中心列长度为2,与上中心列的幂零级一致。


      5. 总结

      上中心列的构造步骤为: 1. 初始:\( Z_0(G) = {e} \)。 2. 递推:对每个 \( i \geq 1 \),计算商群 \( G/Z_{i-1}(G) \) 的中心,取其原像得到 \( Z_i(G) \)。 3. 终止:当 \( Z_c(G) = G \) 时停止,此时 \( c \) 为幂零级。

      四元数群 \( Q_8 \) 的上中心列展示了幂零群的典型特征:通过逐层“中心化”逐步扩展子群,最终覆盖整个群,且其幂零级与下中心列的长度一致。


      如果需要进一步讨论其他群(如交换群、非幂零群)的上中心列,或更深入的理论细节,请随时提问!

    1. 通过数论条件(模 p 同余和整除性),极大限制了Sylow子群的可能个数

      好的,我将通过一个具体的例子——阶为12的群,详细解释Sylow第三定理的应用,并逐步说明如何利用定理的两个条件(模条件和整除条件)来分析Sylow子群的个数。


      例子:阶为12的群

      设群 \( G \) 的阶为 \( |G| = 12 \),分解为 \( 12 = 2^2 \times 3 \)。<br /> 我们需要确定 \( G \) 的Sylow 2-子群和Sylow 3-子群的个数 \( n_2 \) 和 \( n_3 \),并验证它们是否满足Sylow第三定理的条件。


      1. Sylow 3-子群的个数 \( n_3 \)

      • 素数 \( p = 3 \),Sylow 3-子群的阶为 \( 3 \)。
      • 非 \( p \)-部分 \( m = 12 / 3 = 4 \)
      • Sylow第三定理条件
      • 模条件:\( n_3 \equiv 1 \mod 3 \);
      • 整除条件:\( n_3 \mid 4 \)(即 \( n_3 \) 是4的因数)。

      可能的 \( n_3 \) 值: - 4的因数为 1, 2, 4。 - 满足 \( n_3 \equiv 1 \mod 3 \) 的是 14(因为 \( 1 \mod 3 = 1 \),\( 4 \mod 3 = 1 \))。

      因此,\( n_3 \) 可能为 1 或 4


      2. Sylow 2-子群的个数 \( n_2 \)

      • 素数 \( p = 2 \),Sylow 2-子群的阶为 \( 4 \)。
      • 非 \( p \)-部分 \( m = 12 / 4 = 3 \)
      • Sylow第三定理条件
      • 模条件:\( n_2 \equiv 1 \mod 2 \)(即 \( n_2 \) 是奇数);
      • 整除条件:\( n_2 \mid 3 \)(即 \( n_2 \) 是3的因数)。

      可能的 \( n_2 \) 值: - 3的因数为 1, 3。 - 满足 \( n_2 \equiv 1 \mod 2 \) 的是 13(因为两者都是奇数)。

      因此,\( n_2 \) 可能为 1 或 3


      3. 结合其他定理进一步分析

      通过Sylow第三定理,我们只能确定 \( n_3 \) 和 \( n_2 \) 的可能值,但需结合其他信息(如群的结构或具体例子)进一步缩小范围。


      4. 具体例子验证

      情况1:假设 \( n_3 = 1 \) - Sylow 3-子群是唯一的,因此是正规子群。 - 此时 \( G \) 的结构可能为 \( \mathbb{Z}_3 \times \mathbb{Z}_4 \)(循环群)或 \( \mathbb{Z}_3 \rtimes \mathbb{Z}_4 \)(半直积,非交换)。

      情况2:假设 \( n_3 = 4 \) - 存在4个Sylow 3-子群,彼此共轭,且每个Sylow 3-子群的阶为3。 - 此时群的结构可能为 \( A_4 \)(交错群,非交换)。

      关于 \( n_2 \) 的具体值: - 若 \( n_2 = 1 \),则Sylow 2-子群是唯一的,因此是正规子群。此时群可能为 \( \mathbb{Z}_{12} \)(循环群)。 - 若 \( n_2 = 3 \),则存在3个Sylow 2-子群,此时群可能为 \( A_4 \)\( \mathbb{Z}_3 \rtimes \mathbb{Z}_4 \)


      5. 总结Sylow第三定理的作用

      通过Sylow第三定理,我们能够: 1. 限制可能的Sylow子群个数:例如,对于阶为12的群,\( n_3 \) 只能是1或4,\( n_2 \) 只能是1或3。 2. 推断群的结构: - 若 \( n_3 = 1 \) 且 \( n_2 = 1 \),群是交换群(如 \( \mathbb{Z}_{12} \))。 - 若 \( n_3 = 4 \) 且 \( n_2 = 3 \),群可能是非交换群(如 \( A_4 \))。


      关键公式与结论

      对于群 \( G \) 的阶 \( |G| = p^\alpha m \): - Sylow \( p \)-子群的个数 \( n_p \) 满足: - 模条件:\( n_p \equiv 1 \mod p \); - 整除条件:\( n_p \mid m \)。


      进一步思考

      • 若 \( n_p = 1 \):唯一的Sylow \( p \)-子群是正规子群,可用于分解群结构。
      • 若 \( n_p > 1 \):群的结构可能更复杂,需结合其他Sylow子群或群作用分析。

      通过这个例子,您可以看到Sylow第三定理如何通过模条件和整除条件,将Sylow子群的个数限制在有限范围内,从而帮助分析群的结构。如果需要更多例子或细节,请随时提问!

    2. :通过Sylow定理分析其子群结构,逐步分解为更简单的子群

      阶为 \( p^\alpha \) 的群的子群结构分析与分解


      1. 基本概念与性质

      阶为 \( p^\alpha \) 的群(\( p \) 为素数,\( \alpha \in \mathbb{N} \))称为 有限 \( p \)-群。这类群具有以下关键性质: - 中心非平凡:\( Z(G) \)(群的中心)的阶至少为 \( p \)。 - 子群的阶:所有子群的阶均为 \( p^k \)(\( 1 \leq k \leq \alpha \))。 - 极大子群:每个极大子群的指数为 \( p \),即其阶为 \( p^{\alpha-1} \)。


      2. Sylow定理在 \( p \)-群中的应用

      虽然Sylow定理主要用于分析一般群的 \( p \)-子群结构,但对 \( p \)-群本身也有以下启示: - Sylow \( p \)-子群的唯一性:群 \( G \) 本身是唯一的Sylow \( p \)-子群(因 \( |G| = p^\alpha \)),故 \( n_p = 1 \)。 - 子群的共轭性:所有子群的共轭性在 \( p \)-群中可能简化,但由于 \( G \) 是自同构的,需结合中心性质分析。


      3. 分解策略与步骤

      通过以下步骤逐步分解 \( p \)-群的结构:


      步骤1:寻找中心元素
      • 中心非平凡性:由 \( p \)-群的性质,\( Z(G) \) 的阶至少为 \( p \)。
      • 构造循环子群:取 \( z \in Z(G) \),\( z \neq e \),则 \( \langle z \rangle \) 是 \( G \) 的一个 循环子群,阶为 \( p^k \)(\( 1 \leq k \leq \alpha \))。

      步骤2:考虑商群
      • 商群的结构:取 \( N = \langle z \rangle \),则商群 \( G/N \) 的阶为 \( p^{\alpha - k} \)。
      • 递归分解:对 \( G/N \) 进行同样的分析,直到商群的阶为 \( 1 \)。

      步骤3:利用极大子群
      • 极大子群的存在性:每个 \( p \)-群有极大子群,且每个极大子群的阶为 \( p^{\alpha - 1} \)。
      • 覆盖所有子群:通过极大子群的逐层嵌套,可将 \( G \) 分解为一个链: [ {e} \triangleleft G_1 \triangleleft G_2 \triangleleft \cdots \triangleleft G_\alpha = G ] 其中 \( |G_i| = p^i \)。

      步骤4:结合交换性分析
      • 交换性与中心扩张
      • 若 \( G \) 是 交换群(即阿贝尔 \( p \)-群),则其结构由基本定理确定:\( G \cong \mathbb{Z}{p^{k_1}} \times \cdots \times \mathbb{Z}{p^{k_m}} \),其中 \( k_1 + \cdots + k_m = \alpha \)。
      • 若 \( G \) 非交换,则需通过中心扩张构造,例如: [ G/Z(G) \cong \text{非交换 } p\text{-群} ]

      4. 具体例子:低阶 \( p \)-群的分解

      以下以 \( p^2 \)-阶群为例说明分解过程:


      例1:阶为 \( p^2 \) 的群
      • 可能的结构
      • 循环群:\( \mathbb{Z}_{p^2} \)。
      • 直积群:\( \mathbb{Z}_p \times \mathbb{Z}_p \)(当 \( p = 2 \) 时称为 Klein四元群)。
      • 分解步骤
      • 中心分析:\( Z(G) = G \)(若 \( G \) 为交换群),或 \( Z(G) \) 阶为 \( p \)(若 \( G \) 非交换)。
      • 子群结构
        • 对于 \( \mathbb{Z}{p^2} \),子群为 \( {e} \)、\( \mathbb{Z}_p \) 和 \( \mathbb{Z}{p^2} \)。
        • 对于 \( \mathbb{Z}_p \times \mathbb{Z}_p \),所有子群均为 \( p \)-阶循环子群或本身。

      例2:阶为 \( p^3 \) 的群
      • 可能的结构
      • 循环群:\( \mathbb{Z}_{p^3} \)。
      • 直积群:\( \mathbb{Z}_{p^2} \times \mathbb{Z}_p \)。
      • 非交换群
        • Heisenberg群:\( H(\mathbb{Z}_p) \),其元素为上三角矩阵,对角线为1,且乘法为矩阵乘法。
        • 其他非交换 \( p \)-群(如 \( \mathbb{Z}_p \times \mathbb{Z}_p \rtimes \mathbb{Z}_p \))。
      • 分解步骤
      • 中心分析:所有 \( p^3 \)-阶群的中心至少为 \( p \)-阶。
      • 子群链: [ {e} \triangleleft \langle z \rangle \triangleleft H \triangleleft G ] 其中 \( H \) 是阶为 \( p^2 \) 的子群(可能为交换或非交换)。

      5. 一般 \( p^\alpha \)-群的分解框架

      1. 归纳法:假设所有阶为 \( p^{\beta} \)(\( \beta < \alpha \))的群结构已知,通过以下步骤分解阶为 \( p^\alpha \) 的群:
      2. 取中心元素 \( z \in Z(G) \),生成子群 \( N = \langle z \rangle \)。
      3. 商群 \( G/N \) 的阶为 \( p^{\alpha - 1} \),递归分解 \( G/N \)。
      4. 通过中心扩张或半直积构造 \( G \) 的结构。

      5. 可解性:所有 \( p \)-群均为 可解群,因为其子群链的因子均为循环群。


      6. 总结

      通过Sylow定理和 \( p \)-群的性质,分解阶为 \( p^\alpha \) 的群的步骤可归纳为: 1. 利用中心元素生成子群,逐步构建子群链。 2. 分析商群结构,递归分解为低阶 \( p \)-群。 3. 结合交换性与非交换性,确定具体结构(如循环群、直积群或非交换群)。


      7. 关键结论

      • 子群的阶:所有子群的阶均为 \( p^k \)(\( 1 \leq k \leq \alpha \))。
      • 极大子群:每个极大子群的阶为 \( p^{\alpha - 1} \),且其个数可通过Sylow定理的模条件分析。
      • 可解性与结构:\( p \)-群的可解性使其结构可通过逐步分解完全解析。

      如果需要更具体的例子或深入某个步骤,请随时提出!

    3. 若群 G 是单群,则其所有Sylow子群的个数 ​np​=1,否则存在正规子群。例:​A5​(阶60)是单群,因

      好的,我来详细解释这个问题,并结合例子说明单群的判定与Sylow子群个数的关系。


      1. 单群的定义

      单群(Simple Group) 是指没有非平凡正规子群的群,即其正规子群只能是群本身或平凡子群(仅含单位元)。<br /> 例如: - 循环群 \( \mathbb{Z}_p \)(\( p \) 为素数)是单群,因为其子群只能是自身或平凡群。 - 交错群 \( A_5 \)(阶60)是典型的非交换单群。


      2. Sylow定理与单群的关联

      根据Sylow定理的第三定理: - Sylow \( p \)-子群的个数 \( n_p \) 满足: 1. \( n_p \equiv 1 \mod p \); 2. \( n_p \) 整除群阶的非 \( p \)-部分(即 \( n_p \mid m \),其中 \( |G| = p^\alpha m \) 且 \( p \nmid m \))。

      关键结论:<br /> 如果 某个 \( n_p = 1 \),则唯一的Sylow \( p \)-子群是正规子群(因为所有Sylow \( p \)-子群共轭,唯一则必被所有共轭保持)。<br /> 若群是单群,则不能存在这样的非平凡正规子群,因此 所有 \( n_p \neq 1 \)


      3. 例:\( A_5 \) 是单群

      \( A_5 \) 的阶为60,分解为 \( 60 = 2^2 \cdot 3 \cdot 5 \)。<br /> 我们需要验证其所有Sylow子群的个数 \( n_2, n_3, n_5 \) 均不等于1:

      3.1 计算 \( n_5 \)(Sylow 5-子群的个数)

      • \( |G| = 60 \),Sylow 5-子群的阶为 \( 5 \)。
      • \( n_5 \equiv 1 \mod 5 \),且 \( n_5 \mid 12 \)(\( 60/5 = 12 \))。
      • 可能的 \( n_5 \) 是 6(\( 6 \equiv 1 \mod 5 \) 且 \( 6 \mid 12 \)),因此 \( n_5 = 6 \neq 1 \)。

      3.2 计算 \( n_3 \)(Sylow 3-子群的个数)

      • Sylow 3-子群的阶为 \( 3 \)。
      • \( n_3 \equiv 1 \mod 3 \),且 \( n_3 \mid 20 \)(\( 60/3 = 20 \))。
      • 可能的 \( n_3 \) 是 10(\( 10 \equiv 1 \mod 3 \) 且 \( 10 \mid 20 \)),因此 \( n_3 = 10 \neq 1 \)。

      3.3 计算 \( n_2 \)(Sylow 2-子群的个数)

      • Sylow 2-子群的阶为 \( 4 \)。
      • \( n_2 \equiv 1 \mod 2 \),且 \( n_2 \mid 15 \)(\( 60/4 = 15 \))。
      • 可能的 \( n_2 \) 是 15(\( 15 \equiv 1 \mod 2 \) 且 \( 15 \mid 15 \)),因此 \( n_2 = 15 \neq 1 \)。

      结论:所有 \( n_p \neq 1 \),因此 \( A_5 \) 没有非平凡Sylow子群作为正规子群,故是单群。


      4. 反向思考:若 \( n_p = 1 \),群就不是单群

      假设群 \( G \) 的某个 \( n_p = 1 \),则唯一的Sylow \( p \)-子群 \( P \triangleleft G \) 是正规子群。<br /> 若 \( P \) 是非平凡的(即 \( P \neq {e} \) 且 \( P \neq G \)),则 \( G \) 不是单群。<br /> 例外情况: - 若 \( G \) 本身是 素数阶循环群(如 \( \mathbb{Z}_p \)),则唯一的Sylow \( p \)-子群是 \( G \) 本身,此时 \( n_p = 1 \) 但群仍为单群(因无非平凡子群)。


      5. 其他例子验证

      例1:15阶群

      • \( |G| = 15 = 3 \cdot 5 \)。
      • \( n_3 \equiv 1 \mod 3 \),且 \( n_3 \mid 5 \Rightarrow n_3 = 1 \)。
      • \( n_5 \equiv 1 \mod 5 \),且 \( n_5 \mid 3 \Rightarrow n_5 = 1 \)。
      • 因此 \( G \) 有唯一的Sylow 3-子群和Sylow 5-子群,二者均为正规子群,故 \( G \) 是交换群(如 \( \mathbb{Z}_{15} \)),非单群

      例2:60阶非单群

      • 若 \( G \) 的阶为60,且 \( n_5 = 1 \),则存在正规Sylow 5-子群,故 \( G \) 不是单群。

      6. 总结

      • 单群的判定条件:<br /> 若群 \( G \) 是单群,则 所有Sylow子群的个数 \( n_p \neq 1 \),否则存在非平凡正规子群。
      • \( A_5 \) 的例子:<br /> 因 \( n_5 = 6 \neq 1 \),\( n_3 = 10 \neq 1 \),\( n_2 = 15 \neq 1 \),故 \( A_5 \) 没有非平凡正规子群,是单群。

      如果还有疑问,欢迎继续提问!

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

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      Reply to the reviewers

      Revision Plan (Response to Reviewers)

      1. General Statements [optional]

      Response: We are pleased the reviewers appreciate the power of this novel proteomics methodology that allowed us to uncover new depths on the complexity of the ribosome ubiquitination code in response to stress. We also appreciate that the reviewers think that this is a "very timely" study and "interesting to a broad audience" that can change the models of translation control currently adopted in the field. Characterizing complex cellular processes is critical to advance scientific knowledge and our work is the first of its kind using targeted proteomics methods to unveil the integrated complexity of ribosome ubiquitin signals in eukaryotic systems. We also appreciate the fairness of the comments received and below we offer a comprehensive revision plan substantially addressing the main points raised by the reviewers. According to the reviewers' suggestions, we will also expand our studies to two additional E3 ligases (Mag2 and Not4) known to ubiquitinate ribosomes, which will create an even more complete perspective of ubiquitin roles in translation regulation.

      2. Description of the planned revisions

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

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

              __Response: __We appreciate the evaluation of our work and that the power of our proteomics method established a good foundation for the study. We also understand the reviewer's concerns and we will detail below a plan to enhance quantification and increase systematic comparisons. The experiments presented here were conducted with biological replicates, but in several instances, we focused on presence and absence of bands, or their pattern (mono vs poly-ub) because of the semi-quantitative nature of immunoblots. We will revise the figures and present their quantification and statistical analyses. In additional, we did not intend to use these stressors interchangeably, but instead, to use select conditions to highlight the complexity the stress response. In particular, we followed up with H2O2 *versus* 4-NQO because both chemicals are considered sources of oxidative stress. Even though it is unfeasible to compare every single stress condition in every strain background, in the revised version, we will include additional controls to increase the cohesion of the narrative, and expand the comparison between MMS, H2O2, and 4-NQO, as suggested. Details below.
      

      To strengthen the work, the following revisions are essential:

      R1.1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.

              __Response: __As requested, we will display quantification and statistical analysis of the suggested and new immunoblots that will be conducted during the revision period.
      

      R1.3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.

              __Response: __We will follow the reviewers' suggestion and redesign the analysis to increase consistency and prioritize data under identical conditions. To increase confidence in the mRNA data analysis, we intend to perform follow up experiments and analyze protein abundance of *ARG proteins* and *CTT1 *under different conditions. The remaining data using non-parallel comparisons will be moved to supplemental material and de-emphasized in the final version of the manuscript.
      

      R1.4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

              __Response: __To ensure a better comparison across strains and conditions, we will re-run several experiments and focus on our main stress conditions. Specifically:
      
      • 3D: We plan to re-run this experiment and include MMS

      • 3E: We plan to perform the same panel of experiments in rad6D ,and display WT data as main figure.

      • 4A-B: We plan to perform translation output (HPG incorporation) experiments with MMS as suggested

      • 4C: We plan to re-run blots for p-eIF2a under MMS for improved comparison.

      Reviewer #1 (Significance (Required)):

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

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

      In this manuscript the authors use a new target proteomics approach to quantify site-specific ubiquitin modification across the ribosome before and after oxidative stress. Then they validate their findings following in particular ubiquitination of Rps20 and Rps3 and extend their analysis to different forms of oxidative stress. Finally they question the relevance of two known actors of ribosome ubiquitination, Hel2 and Rad6. It is not easy to summarize the observations because in fact the major finding is that the patterns of ribosome ubiquitination occur in a stresser and enyzme specific manner (even when considering only oxidative stress). However, the complexity revealed by this study is very relevant for the field, because it underlies that the ubiquitination code of ribosomes is not easy to interpret with regard to translation dynamics and responses to stress or players involved. It suggests that some of the models that have generally been adopted probably need to be amended or completed. I am not a proteomics expert, so I cannot comment on the validity of the new proteomics approach, of whether the methods are appropriately described to reproduce the experiments. However, for the follow up experiments, the results following Rps20 and Rps3 ubiquitination are well performed, nicely controlled and are appropriately interpreted.

      Maybe what one can regret is that the authors have limited their analysis to the study of Hel2 and Rad6, and not included other enyzmes that have already been associated with regulation of ribosome ubiquitination, to get a more complete picture. It may not take that much time to test more mutants, but of course there is the risk that rather than enable to make a working model it might make things even more complex.

              __Response: __We value the positive evaluation of our work. We also appreciate the notion that it meaningfully expands the knowledge on the complexity of the ribosome ubiquitination code, challenges the current models of translation control, and conducted well-performed, and nicely controlled experiments. To address the main concern of the reviewer, we will expand our work by studying two additional enzymes involved in ribosome ubiquitination (Mag2 and Not4) and provide a more comprehensive picture of this integrated system. Specifically, we will generate yeast strains deleted for *MAG2* and *NOT4*, and evaluate their impact in ribosome ubiquitination under our main conditions of stress. We will investigate the role of these additional E3s in translation output (HPG incorporation), and in inducing the integrated stress response via phosphorylated eIF2α and Gcn4 expression. Additional follow up experiments will be performed according to our initial results.
      

      Reviewer #2 (Significance (Required)):

      In recent years, regulation of translation elongation dynamics has emerged as a much more relevant site of control of gene expression that previously envisonned. The ribosome has emerged as a hub for control of stress responses. Therefore this study is certainly very timely and interesting for a broad audience. However, it does fall short of giving any simple picture, and maybe the only point one can question is whether it is interesting to publish a manuscript that concludes that regulation is complicated, without really being able to provide any kind of suggestive model.

      My feeling is nevertheless that it will impact how scientists in the field design their experiments and what they will conclude. It will certainly also drive new experiments and approaches, and lead to investigations on how all the different players in regulation of ribosome modification talk to each other and signal to signaling pathways.

              __Response: __We appreciate the comments and the balanced view that studies like ours will still be impactful and contribute to a number of fields in multiple and meaningful ways. With the new experiments proposed here, and used of additional mutants and strains, we intend to propose and provide a more unified model that explain this complex and dynamic relationship.
      

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

      Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

      In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

      Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

      Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

      Major Comments:

      I consider the additional experiments essential to support the claims of the paper.

      R3.1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.

              __Response: __We understand the importance of the suggested experiment. We have already requested and kindly received strains expressing these mutations, which will reduce the time required to successfully address this point. We will perform our translation and ISR assays such as the one referred by the reviewer in Figs. 4A-C and 5E, and results will determine the role of individual ribosome ubiquitination sites in translation control.
      

      R3.2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

              __Response: __As was also requested by reviewer 1 and discussed above (point R1.1), we will conduct quantification and display statistical analyses for our immunoblots. In addition, we will re-run the aforementioned experiments to improve quantification following the reviewers' request (same gel & diluted control samples).
      

      Reviewer #3 (Significance (Required)):

      • General assessment:

      Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

      • Advance:

      In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

      • Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

      • The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

      __ Response:__ We appreciate that our work will be valuable to a number of fields in protein dynamics and that our method advances the field by measuring ribosome ubiquitination relatively and stoichiometrically in response to stress.

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Response: All requested changes require experiments and data analyses, and a complete revision plan is delineated above in section #2.

      • *

      4. Description of analyses that authors prefer not to carry out

      • *

      R1.2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.

              __Response: __Although we understand the interest on the proposed result for consistency, this is the only requested experiment that we do not intend to conduct. Because of the lack of overall ubiquitination of ribosomal proteins in *rad6**D* in response to H2O2 (e.g., Silva et al., 2015, Simoes et al., 2022), we believe that this PRM experiment in unlikely to produce meaningful insight on the ubiquitination code. In this context, we expected that sites regulated by Hel2 will be the ones largely modified in rad6*D *and we followed up on them via immunoblot. Moreover, this experiment would not be time or cost-effective, and resources and efforts could be used to strengthen other important areas of the manuscript, such as including the E3's Mag2 and Not4 into our work.
      
    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      Recent studies have shown that the ubiquitination of uS3 (Rps3) is crucial for the quality control of nonfunctional rRNA, specifically in the process known as 18S noncoding RNA degradation (NRD). Additionally, the ubiquitination of uS10 (Rps20) plays a significant role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress are not yet fully understood.

      In this study, the authors developed a targeted proteomics method to quantify the dynamics of ribosome ubiquitination in response to oxidative stress, both relatively and stoichiometrically. They identified 11 ribosomal sites that exhibited increased ubiquitin modification after exposure to hydrogen peroxide (H2O2). This included two known targets: uS10 and uS3 (of Hel2), which recognize collided ribosomes and initiate the processes of 18S NRD and translation quality control (RQC). Using isotope-labeled peptides, the researchers demonstrated that these modifications are non-stoichiometric and display significant variability among different peptides.

      Furthermore, the authors explored how specific enzymes in the ubiquitin system affect these modifications and their impact on global translation regulation. They found that uS3 (Rps3) and uS10 (Rps20) were modified differently by various stressors, which in turn influenced the Integrated Stress Response (ISR). The authors suggest that different types of stressors alter the pattern of ubiquitinated ribosomes, with Rad6 and Hel2 potentially competing for specific subpopulations of ribosomes.

      Overall, this study emphasizes the complexity of the ubiquitin ribosomal code. However, further experiments are necessary to validate these findings before publication.

      Major Comments:

      I consider the additional experiments essential to support the claims of the paper.

      1. To understand the roles of ribosome ubiquitination at the specific sites, the authors must perform stressor-specific suppression of global translation, as demonstrated in Figures 4 and 5. This should include the uS10-K6R/K8R and uS3-K212R mutants.
      2. It is crucial to ensure that experiments are adequately replicated and that statistical analysis is thorough, with precise quantification. For a more accurate comparison between wild-type (WT) and Hel2 deletion mutants regarding ribosome ubiquitination, the authors should quantify the ubiquitinated ribosomes in both WT and Hel2 mutants under stress conditions. This quantification should be conducted on the same blot, using diluted control samples. Similarly, in Figures 3F and 4C, for an accurate comparison between WT and Hel2 or Rad6 deletion mutants, the authors should quantify the ubiquitinated ribosomes across these conditions. Again, this quantification should be performed on the same blot with the dilution of control samples.

      Significance

      General assessment:

      Recent studies reveal that the ubiquitination of uS3 (Rps3) is essential for the quality control of nonfunctional rRNA (18S NRD), while the ubiquitination of uS10 (Rps20) plays a crucial role in ribosome-associated quality control (RQC). However, the dynamics of ribosome ubiquitination in response to oxidative stress remain unclear.

      Advance:

      In this study, the authors developed a targeted proteomics method to quantify ribosome ubiquitination dynamics in response to oxidative stress, both relatively and stoichiometrically. By utilizing isotope-labeled peptides, they demonstrated that these modifications are non-stoichiometric and exhibit significant variability across different peptides. They identified 11 ribosomal sites that showed increased ubiquitin modification following H2O2 exposure, including two known targets of Hel2, which recognize collided ribosomes and induce translation quality control (RQC).

      Audience: This information will be of interest to a specialized audience in the fields of translation, ribosome function, quality control, ubiquitination, and proteostasis.

      The field: Translation, ribosome function, quality control, ubiquitination, and proteostasis.

    3. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

      However, the study's focus shifts in a manner that introduces several limitations. Following the rigorous PRM-based analyses, the reliance on Western blotting without replication or quantification (e.g., single-experiment data in Figs. 3-5) significantly weakens the evidence. Experimental design becomes inconsistent, with variable combinations of stressors (H₂O₂, MMS, 4-NQO) and genetic backgrounds (WT, hel2Δ, rad6Δ) that preclude systematic comparisons. For instance, Fig. 3C/E and Fig. 4 omit critical controls (e.g., MMS in Fig. 4, rad6Δ in Fig. 3E), while Fig. 5 conflates distinct variables by comparing H₂O₂-treated rad6Δ with MMS-treated hel2Δ-a design that obscures causal relationships. Furthermore, Fig. 3F highlights that 4-NQO and MMS elicit divergent responses in hel2Δ, undermining the rationale for using these stressors interchangeably. These inconsistencies culminate in a fragmented narrative; attempts to link ISR activation or ribosome stalling to RP ubiquitylation become impossible, leaving the primary takeaway as "stress responses are complex" rather than advancing mechanistic insight.

      To strengthen the work, the following revisions are essential:

      1. Repeat and quantify immunoblots: All Western blotting data require biological replicates and statistical analysis to support claims.
      2. Leverage the PRM platform: Apply the established quantitative proteomics approach to validate or extend findings in Fig. 3 (e.g., RAD6-dependent ubiquitylation), ensuring methodological consistency.
      3. Remove non-parallel comparisons: The mRNA expression analysis in Fig. 5, which compares dissimilar conditions (e.g., rad6Δ + H₂O₂ vs. hel2Δ + MMS), should be omitted or redesigned to enable direct, strain- and stressor-matched contrasts.
      4. Standardize experimental variables: Restructure the study to maintain identical genetic backgrounds and stressors across all figures, enabling systematic interrogation of enzyme- or stress-specific effects on the ubiquitin code.

      Significance

      The authors present a potentially powerful proteomics platform using parallel reaction monitoring (PRM) to quantitatively profile ribosomal protein (RP) ubiquitylation, with a focus on yeast under hydrogen peroxide (H₂O₂) stress. This approach robustly identifies both known and novel RP modifications, including basal ubiquitylation events previously undetected, and identifies Hel2-dependent mechanisms. The data support the conclusion that RPs are regulated by a multifaceted ubiquitin code, establishing a good foundation for the study.

    1. super

      Just a way to refer to anything from the superclass.

      Eg: you want to override a method from the superclass in the subclass but need the calculated value from the superclass. you access that method with a Super.methodName() inside the overriden methodName() in the subclass and do whatever to that value. Overrides Super keyword

    1. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

      Learn more at Review Commons


      Referee #1

      Evidence, reproducibility and clarity

      In this work, the authors used yeast cell as a model system to study the abovementioned question. They established a model protein system using fluorescently labeled proteins that can form both agglomerates and aggregates. Using imaging experiments, they arguably showed that agglomerates do not colocalize with the proteostasis machinery, echoing what was observed by proteomics results. The proteomics results after pull down assay to study the interactome revealed that agglomerate-size-dependent changes were dependent on the cell-wall and plasma-membrane proteins. On the other hand, as expected, the misfolded proteins (aggregates) showed heavy involvement of proteostasis network components.

      Although the experiments still lack some controls and failed to support some of the conclusions, I found this work is a nice complement of the field to emphasize the point that "aggregates" and "agglomerates" are two different states, which is often mistaken by lots of researchers in recent years, in particular with the membraneless organelles (LLPS). I support its publication after the authors may consider the following suggestions and make necessary improvement.

      Major concerns:

      My major concern was raised by the lack of evidence to support the model system's folding state in the cell. 1. In Figure 1 and 2, I found the evidence to distinguish the folded state of proteins in the cells was limited. The concept of using hybrid imaging technique to prove the folding state is not a common experiment. The description of Figure 2 was very limited. I am sure the general audience can be convinced that the model proteins were actually folded and form agglomeration. 2. In addition, for mutants formed aggregates, the authors may consider to perform fractionation or crosslinking or native page experiment to show the evidence of protein misfolding and aggregation. 3. Have the authors considered to use FRAP assay to distinguish "aggregates" and "agglomerates" states in the cell? Does each of the state display different dynamics in the cell?

      Minor concerns:

      1. In Figure 3, it is very interesting to see such patten. I wonder why some of the chaperones were not responsive to misfolded proteins but some were very addicted to proteostasis. Could you elaborate more on this point? Are they chaperone sensitive, namely selective to 60/10, 70/40 or 90 system?
      2. In Figure 6, I suggest to add GO analysis and KEGG analysis to distinguish pathways and functional mismatch between "aggregates" and "agglomerates" interactomics.
      3. The quantity control for the proteomics studies is needed, namely the reproducibility of 3 repeats?
      4. This may beyond the scope of this work. I am interested whether the authors could point out whether similar works can be done in mammalian cells. What is the model system for mammalian cell that can form "agglomerates".

      Referees cross-commenting

      I read through the other two reviewers' comments, which I found reasonable. It seems like all reviewers agreed that this work is of enough significance for the field only with several technical concerns.

      Significance

      The submitted manuscript emphasized on a very important but often misleading concept: "aggregates" and "agglomerates" are two different states of protein structures in the cell with distinct physiological roles. However, these two states are of very similar phenotype: punctate structure in the cell. While the proteostasis network has been well-established for its central role of protein quality control and coping with misfolded and aggregated proteome, the authors attempted to profile the mechanism and physiological impact of mutation-induced folded-state protein filamentation, namely a model of "agglomerates". Such overarching goal of this work clearly pointed out the novelty of this work. Clearly, this is a new angle and aspect remained to be clarified for the field.

    1. Author response:

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

      Reviewer #1 (Public review):

      Summary:

      The present study aims to associate reproduction with age-related disease as support of the antagonistic pleiotropy hypothesis of ageing, predominantly using Mendelian Randomization. The authors found evidence that early-life reproductive success is associated with advanced ageing.

      Strengths:

      Large sample size. Many analyses.

      Weaknesses:

      There are some errors in the methodology, that require revisions.

      In particular, the main conclusions drawn by the authors refer to the Mendelian Randomization analyses. However, the authors made a few errors here that need to be reconsidered:

      (1) Many of the outcomes investigated by the authors are continuous outcomes, while the authors report odds ratios. This is not correct and should be revised.

      Thank you for your observation. We have revised the manuscript to ensure that the results for continuous outcomes are appropriately reported using beta coefficients, which indicate the change in the outcome per unit increase in exposure. This will accurately reflect the nature of the analysis and provide a clearer interpretation of continuous outcomes (lines 56-109).

      (2) Some of the odds ratios (for example the one for osteoporosis) are really small, while still reaching the level of statistical significance. After some checking, I found the GWAS data used to generate these MR estimates were processed by the program BOLT-LLM. This program is a linear mixed model program, which requires the transformation of the beta estimates to be useful for dichotomous outcomes. The authors should check the manual of BOLT-LLM and recalculate the beta estimates of the SNP-outcome associations prior to the Mendelian Randomization analyses. This should be checked for all outcomes as it doesn't apply to all.

      Thank you for your detailed feedback. We have reviewed all the GWAS data used in our MR analyses and confirmed that all GWAS of continuous traits have already been processed using the BOLT-LMM, including age at menarche, age at first birth, BMI, frailty index, father's age at death, mother's age at death, DNA methylation GrimAge acceleration, age at menopause, eye age, and facial aging. Most of the dichotomous outcomes have not been processed by BOLT-LMM, including late-onset Alzheimer's disease, type 2 diabetes, chronic heart failure, essential hypertension, cirrhosis, chronic kidney disease, early onset chronic obstructive pulmonary disease, breast cancer, ovarian cancer, endometrial cancer, and cervical cancer, except osteoporosis. We have reprocessed the GWAS beta values of osteoporosis and re-conducted the MR analysis (lines 74-75; lines 366-373).

      (3) The authors should follow the MR-Strobe guidelines for presentation.

      Thank you for your suggestion to follow the MR-STROBE guidelines for the presentation of our study. We appreciate the importance of adhering to these standardized guidelines to ensure clarity and transparency in reporting Mendelian Randomization (MR) analyses. We confirm that the MR components of our research are structured and presented following the MR-STROBE checklist. In addition to the MR analyses, our study also integrates Colocalization analysis, Genetic correlation analysis, Ingenuity Pathway Analysis (IPA), and population validation to provide a more comprehensive understanding of the genetic and biological context. While these analyses are not strictly covered by MR-STROBE guidelines, they complement the MR results by offering additional validation and mechanistic insights.

      We have structured our manuscript to separate these complementary analyses from the core MR results, maintaining alignment with MR-STROBE for the MR-specific components. The additional analyses are discussed in dedicated sections to highlight their unique contributions and avoid conflating them with the MR findings.

      (4) The authors should report data in the text with a 95% confidence interval.

      Thank you for your feedback. We have added the 95% confidence intervals for the reported data within the main text to enhance clarity and provide comprehensive context (lines 56-109). Additionally, the complete analysis data, including all detailed results, can be found in Table S3.

      (5) The authors should consider correction for multiple testing

      Thank you for your comment regarding the need to consider correction for multiple testing. We agree that correcting for multiple comparisons is an important step to control for the possibility of false-positive findings, particularly in studies involving large numbers of statistical tests. In our study, we carefully considered the issue of multiple testing and adopted the following approach:

      Context of Multiple Testing: The tests we conducted were hypothesis-driven, focusing on specific relationships (e.g., genetic correlation, colocalization, and Mendelian Randomization). These analyses are based on priori hypotheses supported by existing literature or biological relevance.

      Statistical Methods: Where applicable, we applied appropriate measures to account for multiple tests. For instance, in Mendelian Randomization, sensitivity analyses serve to validate the robustness of the results.

      We believe that the methodology and corrections applied in our study appropriately address concerns about multiple testing, given the hypothesis-driven nature of our analyses and the rigorous steps taken to validate our findings. If you feel that additional corrections are required for specific parts of the analysis, we would be happy to further clarify or revise as needed.

      Reviewer #2 (Public review):

      Summary:

      The authors present an interesting paper where they test the antagonistic pleiotropy theory. Based on this theory they hypothesize that genetic variants associated with later onset of age at menarche and age at first birth have a positive causal effect on a multitude of health outcomes later in life, such as epigenetic aging and prevalence of chronic diseases. Using a mendelian randomization and colocalization approach, the authors show that SNPs associated with later age at menarche are associated with delayed aging measurements, such as slower epigenetic aging and reduced facial aging, and a lower risk of chronic diseases, such as type 2 diabetes and hypertension. Moreover, they identified 128 fertility-related SNPs that are associated with age-related outcomes and they identified BMI as a mediating factor for disease risk, discussing this finding in the context of evolutionary theory.

      Strengths:

      The major strength of this manuscript is that it addresses the antagonistic pleiotropy theory in aging. Aging theories are not frequently empirically tested although this is highly necessary. The work is therefore relevant for the aging field as well as beyond this field, as the antagonistic pleiotropy theory addresses the link between fitness (early life health and reproduction) and aging.

      Points that have to be clarified/addressed:

      (1) The antagonistic pleiotropy is an evolutionary theory pointing to the possibility that mutations that are beneficial for fitness (early life health and reproduction) may be detrimental later in life. As it concerns an evolutionary process and the authors focus on contemporary data from a single generation, more context is necessary on how this theory is accurately testable. For example, why and how much natural variation is there for fitness outcomes in humans?

      Thank you for these insightful questions. We appreciate the opportunity to clarify how we approach the testing of AP theory within a contemporary human cohort and address the evolutionary context and comparative considerations with the disposable soma theory.

      We recognize that modern human populations experience selection pressures that differ from those in the past, which may affect how well certain genetic variants reflect historical fitness benefits. Nonetheless, the genetic variation present today still offers valuable insights into potential AP mechanisms through statistical associations in contemporary cohorts. We believe that AP can indeed be explored in current populations by examining genetic links between reproductive traits and age-related health outcomes. In our study, we investigate whether certain genetic variants linked to reproductive timing—such as age at menarche and age at first birth—also correlate with late-life health risks. By identifying SNPs associated with both early-life reproductive success and adverse aging outcomes, we aim to capture the evolutionary trade-offs that AP theory suggests.

      Despite contemporary selection pressures that differ from historical conditions, there remains natural genetic variation in traits like reproductive timing and longevity in humans today. This diversity allows us to apply MR to test causal relationships between reproductive traits and aging outcomes, providing insights into potential AP mechanisms. Prior studies have demonstrated that reproductive behaviors exhibit significant heritability and have identified genetic loci associated with reproductive timing (1,2). This genetic variation facilitates causal inference in modern cohorts, despite environmental and healthcare advances that might modulate these associations (3). By leveraging genetic risk scores for reproductive timing, our study captures the necessary variability to assess potential AP effects, thus providing valuable insights into how evolutionary trade-offs may continue to influence human health outcomes.

      How do genetic risk score distributions of the exposure data look like?

      Thank you for your question. Our study is focused on Mendelian Randomization (MR) analysis, which aims to infer causal relationships between exposures and outcomes. While genetic risk scores (GRS) provide valuable insights at an individual level, they do not directly align with our study's objective, which is centered on population-level causal inference rather than individual-level genetic risk assessment. In MR, we use genetic variants as instrumental variables to determine the causal effect of an exposure on an outcome. GRS analysis typically focuses on summarizing an individual's risk based on multiple genetic variants, which is outside the scope of our current research. Therefore, we did not perform or analyze the distribution of genetic risk scores, as our primary goal was to understand broader causal relationships using established genetic instruments.

      Also, how can the authors distinguish in their data between the antagonistic pleiotropy theory and the disposable soma theory, which considers a trade-off between investment in reproduction and somatic maintenance and can be used to derive similar hypotheses? There is just a very brief mention of the disposable soma theory in lines 196-198.

      In our manuscript, we test AP theory specifically by examining genetic variants associated with reproductive timing and their association with age-related health risks in later life. MR and genetic risk scores allow us to assess these associations, directly testing the hypothesis that certain alleles enhancing reproductive success might have adverse effects on aging outcomes. This gene-centered approach aligns with AP’s premise of genetic trade-offs, enabling us to observe whether alleles associated with early-life reproductive traits correlate with increased risks of age-related diseases. Distinguishing from disposable soma theory, which would predict a general trade-off in energy allocation affecting somatic maintenance and not specific genetic effects, our data focuses on how certain alleles have differential impacts across life stages. Our findings thus support AP theory over disposable soma by highlighting the effects of specific genetic loci on both reproductive and aging phenotypes. However, future research could indeed explore the intersection of these theories, for example, by examining how resource allocation and genetic predispositions interact to influence longevity in various environmental contexts.

      (2) The antagonistic pleiotropy theory, used to derive the hypothesis, does not necessarily distinguish between male and female fitness. Would the authors expect that their results extrapolate to males as well? And can they test that?

      Emerging evidence suggests that early puberty in males is linked to adverse health outcomes, such as an increased risk of cardiovascular disease, type 2 diabetes, and hypertension in later life (4). A Mendelian randomization study also reported a genetic association between the timing of male puberty and reduced lifespan (5). These findings support the hypothesis that genetic variants associated with delayed reproductive timing in males might similarly confer health benefits or improved longevity, akin to the patterns observed in females. This would suggest that similar mechanisms of antagonistic pleiotropy could operate in males as well.

      In our study, BMI was identified as a mediator between reproductive timing and disease risk. Given that BMI is a common risk factor for age-related diseases in both males and females (6-9), it is plausible that similar mechanisms involving BMI, reproductive timing, and disease risk could exist in males. This shared mediator points to the possibility that, while reproductive timelines may differ, the pathways through which these traits influence aging outcomes may be consistent across genders.

      AP theory could potentially be tested in males, as the principles of the theory may extend to analogous reproductive traits in males, such as age at puberty and testosterone levels, which could similarly influence health outcomes later in life. However, as our current study focuses specifically on female reproductive traits, testing the AP theory in males is outside the scope of this work. We acknowledge the importance of exploring these mechanisms in males, and we hope that future research will address this by investigating male-specific reproductive traits and their relationship to aging and health outcomes.

      (3) There is no statistical analyses section providing the exact equations that are tested. Hence it's not clear how many tests were performed and if correction for multiple testing is necessary. It is also not clear what type of analyses have been done and why they have been done. For example in the section starting at line 47, Odds Ratios are presented, indicating that logistic regression analyses have been performed. As it's not clear how the outcomes are defined (genotype or phenotype, cross-sectional or longitudinal, etc.) it's also not clear why logistic regression analysis was used for the analyses.

      Thank you for your thoughtful comments regarding the statistical analyses and the clarification of methods and variables used in the study.

      Statistical Analyses Section: We have included a detailed explanation of all statistical analyses in the Methods section (lines 291–408), specifying the rationale for the choice of methods, the variables analyzed, and their relationships. Additionally, we have provided the relevant equations or statistical models used where appropriate to ensure transparency.

      Beta Values and Odds Ratios: In the Results section (starting at line 56), both Beta values and Odds Ratios are presented: Beta values were used for analyses of continuous outcomes to quantify the linear relationship between predictors and outcomes. Odds Ratios (ORs) were calculated for binary or categorical disease outcomes to describe the relative odds of an outcome given specific exposures or independent variables.

      Validation and Regression Analyses: For further validation of the MR results, we conducted analyses using the UK Biobank dataset (starting at line 162). Logistic regression analysis was then employed for disease risk assessments involving categorical outcomes (e.g., diseased or not).

      We hope that this clarifies the methods and their applicability to our study, as well as the rationale for the presentation of Beta values and Odds Ratios. If further details or refinements are required, we are happy to incorporate them.

      (4) Mendelian Randomization is an important part of the analyses done in the manuscript. It is not clear to what extent the MR assumptions are met, how the assumptions were tested, and if/what sensitivity analyses are performed; e.g. reverse MR, biological knowledge of the studied traits, etc. Can the authors explain to what extent the genetic instruments represent their targets (applicable expression/protein levels) well?

      Thank you for your insightful comments regarding the Mendelian Randomization (MR) analysis and the evaluation of its assumptions. Below, we provide additional clarification on how the MR assumptions were addressed, sensitivity analyses performed, and the representativeness of the genetic instruments (starting at line 314):

      Relevance Assumption (Genetic instruments are associated with the exposure): “We identified single nucleotide polymorphisms (SNPs) associated with exposure datasets with p < 5 × 10<sup>-8</sup> (10,11). In this case, 249 SNPs and 67 SNPs were selected as eligible instrumental variables (IVs) for exposures of age at menarche and age at first birth, respectively. All selected SNPs for every exposure would be clumped to avoid the linkage disequilibrium (r<sup>2</sup> = 0.001 and kb = 10,000).” “During the harmonization process, we aligned the alleles to the human genome reference sequence and removed incompatible SNPs. Subsequent analyses were based on the merged exposure-outcome dataset. We calculated the F statistics to quantify the strength of IVs for each exposure with a threshold of F>10 (12).”

      Independence Assumption (Genetic instruments are not associated with confounders, Genetic instruments affect the outcome only through the exposure): Then we identified whether there were potential confounders of IVs associated with the outcomes based on a database of human genotype-phenotype associations, PhenoScanner V2 (13,14) (http://www.phenoscanner.medschl.cam.ac.uk/), with a threshold of p < 1 × 10<sup>-5</sup>. IVs associated with education, smoking, alcohol, activity, and other confounders related to outcomes would be excluded.

      Sensitivity Analyses Performed: A pleiotropy test was used to check if the IVs influence the outcome through pathways other than the exposure of interest. A heterogeneity test was applied to ensure whether there is a variation in the causal effect estimates across different IVs. Significant heterogeneity test results indicate that some instruments are invalid or that the causal effect varies depending on the IVs used. MRPRESSO was applied to detect and correct potential outliers of IVs with NbDistribution = 10,000 and threshold p = 0.05. Outliers would be excluded for repeated analysis. The causal estimates were given as odds ratios (ORs) and 95% confidence intervals (CI). A leave-one-out analysis was conducted to ensure the robustness of the results by sequentially excluding each IV and confirming the direction and statistical significance of the remained remaining SNPs.

      Supplemental post-GWAS analysis: Colocalization analysis (starting at line 356), Genetic correlation analysis (starting at line 366).

      Our MR analysis adheres to the guidelines for causal inference in MR studies. By combining multiple sensitivity analyses and ensuring the quality of genetic instruments, we demonstrate that the results are robust and unlikely to be driven by confounding or pleiotropy.

      (5) It is not clear what reference genome is used and if or what imputation panel is used. It is also not clear what QC steps are applied to the genotype data in order to construct the genetic instruments of MR.

      Starting in line 314, the steps of SNPs selection were included in the Methods part. “We identified single nucleotide polymorphisms (SNPs) associated with exposure datasets with p < 5 × 10<sup>-8</sup> (10,11). In this case, 249 SNPs and 67 SNPs were selected as eligible instrumental variables (IVs) for exposures of age at menarche and age at first birth, respectively. All selected SNPs for every exposure would be clumped to avoid the linkage disequilibrium (r<sup>2</sup> = 0.001 and kb = 10,000). Then we identified whether there were potential confounders of IVs associated with the outcomes based on a database of human genotype-phenotype associations, PhenoScanner V2 (13,14) (http://www.phenoscanner.medschl.cam.ac.uk/), with a threshold of p < 1 × 10<sup>-5</sup>. IVs associated with education, smoking, alcohol, activity, and other confounders related to outcomes would be excluded. During the harmonization process, we aligned the alleles to the human genome reference sequence and removed incompatible SNPs. Subsequent analyses were based on the merged exposure-outcome dataset. We calculated the F statistics to quantify the strength of IVs for each exposure with a threshold of F>10 (12). If the effect allele frequency (EAF) was missing in the primary dataset, EAF would be collected from dsSNP (https://www.ncbi.nlm.nih.gov/snp/) based on the population to calculate the F value.” The SNP numbers of exposures for each outcome and F statistics results were listed in supplemental table S2.

      (6) A code availability statement is missing. It is understandable that data cannot always be shared, but code should be openly accessible.

      We have added it to the manuscript (starting at line 410).

      Reviewer #2 (Recommendations for the authors):

      (1) The outcomes seem to be genotypes (lines 274-288). In MR, genotypes are used as an instrument, representing an exposure, which is then associated with an outcome that is typically observed and measured at a later moment in time than the predictors. If both exposure and outcome are genotypes it is not clear how this works in terms of causality; it would rather reflect a genetic correlation. One would expect the genotypes that function as instruments for the exposure to have a functional cascade of (age-related) effects, leading to an (age-related) outcome. From line 149 the outcomes seem to be phenotypes. Can the authors please clearly explain in each section what is analyzed, how the analyses were done, and why the analyses were done that way?

      Thank you for your insightful comment. We understand the concern regarding the use of genotypes as both exposures and outcomes and the implications this has for interpreting causality versus genetic correlation. To clarify, in our study, the outcomes analyzed in the MR framework are indeed genotypes, starting from line 47. We use genotypes as instrumental variables for exposures, which are then linked to phenotypic outcomes observed at a later stage, in line with standard MR principles.

      To improve the robustness of the MR results, we validated the genetic associations in the population with phenotype data from UK Biobank (lines 162-203), and the detailed methods were listed in lines 385-408.

      (2) Overall, the English writing is good. However, some small errors slipped in. Please check the manuscript for small grammar mistakes like in sentences 10 (punctuation) and 33 (grammar).

      Thank you for your feedback. We appreciate your careful review and attention to detail. We thoroughly rechecked the manuscript for any grammatical errors, including punctuation and sentence structure, especially in sentences 11 and 35 in revised manuscript, as suggested.

      (3) There is currently no results and discussion section.

      The manuscript was submitted as Short Reports article type with a combined Results and Discussion section. We have added the section title of Discussion.

      (4) Why did the authors not include SNPs associated with age at menopausal onset? See for example: https://www.nature.com/articles/s41586-021-03779-7https://urldefense.com/v3/__https://www.nature.com/articles/s41586-021-03779-7__;!!HYjtAOY1tjP_!Kl_ZKCmWOQEnvEbl46TG0TuhlsxapwvFdAFfZJkMvz8z7XhX5VEA1cT8CVvNu8xrv9k679Kl0XTrxwSajUeiXWm04XP4$.

      Thank you for your information. Our manuscript focuses on the antagonistic pleiotropy theory, which posits that inherent trade-off in natural selection, where genes beneficial for early survival and reproduction (like menarche and childbirth) may have costly consequences later. So, we only included age at menarche and age at first childbirth as exposures in our research.

      (5) Can the authors include genetic correlations between menarche, age at first child, BMI, and preferably menopause?

      Thank you for your suggestion. We acknowledge that including genetic correlations between age at menarche, age at first childbirth, BMI, and menopause can provide valuable context to our analysis. While our current MR study sets age at menarche and age at first childbirth as exposures and menopause as the outcome, and we have already included results that account for BMI-related SNPs before and after correction, we recognize the importance of assessing genetic correlations.

      To address this, we calculated the genetic correlations between these traits to provide insight into their shared genetic architecture. This analysis helps clarify whether there is a significant genetic overlap between the two exposures and between exposure and outcome, which can inform and support the interpretation of our MR results. We appreciate your suggestion and include these calculations to enhance the robustness and comprehensiveness of our study. In the genetic correlations analysis, LDSC software was applied and the genetic correlation values for all pairwise comparisons among age at menarche, age at first birth, BMI, and age at menopause onset were calculated(15,16). The results are listed in Table S6.

      (6) Line 39-40: that is not entirely true. There is also amounting evidence that socioeconomic factors cause earlier onset of menarche through stress-related mechanisms: https://doi.org/10.1016/j.annepidem.2010.08.006https://urldefense.com/v3/__https://doi.org/10.1016/j.annepidem.2010.08.006__;!!HYjtAOY1tjP_!Kl_ZKCmWOQEnvEbl46TG0TuhlsxapwvFdAFfZJkMvz8z7XhX5VEA1cT8CVvNu8xrv9k679Kl0XTrxwSajUeiXZ4vbX0y$

      Thank you so much for your information. We changed it to “Considering reproductive events are partly regulated by genetic factors that can manifest the physiological outcome later in life”.

      (7) Why did the authors choose to work with studies derived from IEU Open GWAS? as it is often does not contain the most recent and relevant GWAS for a specific trait.

      We chose to work with studies derived from the IEU Open GWAS database after careful consideration of several sources, including the GWAS Catalog database and recently published GWAS papers. Our selection criteria focused on publicly available GWAS with large sample sizes and a higher number of SNPs to ensure robust analysis. For specific traits such as late-onset Alzheimer's disease and eye aging, we used GWAS data published in scientific articles to ensure that our research reflects the latest findings in the field.

      (1) Barban, N. et al. Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nat Genet 48, 1462-1472 (2016). https://doi.org/10.1038/ng.3698

      (2) Tropf, F. C. et al. Hidden heritability due to heterogeneity across seven populations. Nat Hum Behav 1, 757-765 (2017). https://doi.org/10.1038/s41562-017-0195-1

      (3) Stearns, S. C., Byars, S. G., Govindaraju, D. R. & Ewbank, D. Measuring selection in contemporary human populations. Nat Rev Genet 11, 611-622 (2010). https://doi.org/10.1038/nrg2831

      (4) Day, F. R., Elks, C. E., Murray, A., Ong, K. K. & Perry, J. R. Puberty timing associated with diabetes, cardiovascular disease and also diverse health outcomes in men and women: the UK Biobank study. Sci Rep 5, 11208 (2015). https://doi.org/10.1038/srep11208

      (5) Hollis, B. et al. Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan. Nat Commun 11, 1536 (2020). https://doi.org/10.1038/s41467-020-14451-5

      (6) Field, A. E. et al. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med 161, 1581-1586 (2001). https://doi.org/10.1001/archinte.161.13.1581

      (7) Singh, G. M. et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One 8, e65174 (2013). https://doi.org/10.1371/journal.pone.0065174

      (8) Kivimaki, M. et al. Obesity and risk of diseases associated with hallmarks of cellular ageing: a multicohort study. Lancet Healthy Longev 5, e454-e463 (2024). https://doi.org/10.1016/S2666-7568(24)00087-4

      (9) Kivimaki, M. et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol 10, 253-263 (2022). https://doi.org/10.1016/S2213-8587(22)00033-X

      (10) Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50, 912-919 (2018). https://doi.org/10.1038/s41588-018-0152-6

      (11) Gao, X. et al. The bidirectional causal relationships of insomnia with five major psychiatric disorders: A Mendelian randomization study. Eur Psychiatry 60, 79-85 (2019). https://doi.org/10.1016/j.eurpsy.2019.05.004

      (12) Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res 26, 2333-2355 (2017). https://doi.org/10.1177/0962280215597579

      (13) Staley, J. R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207-3209 (2016). https://doi.org/10.1093/bioinformatics/btw373

      (14) Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 35, 4851-4853 (2019). https://doi.org/10.1093/bioinformatics/btz469

      (15) Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 47, 1236-1241 (2015). https://doi.org/10.1038/ng.3406

      (16) Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47, 291-295 (2015). https://doi.org/10.1038/ng.3211

    1. Reviewer #1 (Public review):

      Summary:

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

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

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

      Strengths:

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

      Weaknesses:

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

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

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

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

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

    1. Author response:

      The following is the authors’ response to the previous reviews

      Reviewer #1: 

      (1) As discussed in review and nicely simulated by the authors, the large figure error indicated by profilometry (~10 um in some cases on average) is inconsistent with the optical performance improvements observed, suggesting that those measurements are inaccurate.

      I see no reason to include these inaccurate measurements.  

      We agree with the Referee and removed the indicated figure (old Supplementary Fig. 4) and data.

      Reviewer #3:

      (1) It would be interesting to comment on how the addition of a coverslip changes the performance of the uncorrected microendoscope compared to the use of bare grin lenses. 

      We modified the discussion section (page 18) and added a new reference (#36) to include the request of the Referee.

      (2) In Figure 6C-H, the authors can indeed show data corresponding to all detected cells, but I still think that the statistics should be calculated using the same effective FOV. 

      We modified Figure 6 legend to include the request of the Referee.

      (3) Authors could present the images in Figures 4-6 as in the original version, with a scale bar in the centre of the FOV that is different for the two types of objectives (corrected vs uncorrected). They could add a short justification for this choice, and perhaps present the other version for Figure 4 in a supplementary information sheet (with similar scale bars at the centre of the FOV for both types of objectives). It would allow readers to appreciate that the FOV still appears significantly enlarged with this other presentation.

      As requested by the Referee, we modified the text in the Result section (page 11) and added the additional version of Figure 4 as Figure 4-figure supplement 1.

    1. Author response:

      The following is the authors’ response to the original reviews

      eLife Assessment

      This study presents potentially valuable insights into the role of climbing fibers in cerebellar learning. The main claim is that climbing fiber activity is necessary for optokinetic reflex adaptation, but is dispensable for its long-term consolidation. There is evidence to support the first part of this claim, though it requires a clearer demonstration of the penetrance and selectivity of the manipulation. However, support for the latter part of the claim is incomplete owing to methodological concerns, including unclear efficacy of longer-duration climbing fiber activity suppression.

      We sincerely appreciate the thoughtful feedback provided by the reviewer regarding our study on the role of climbing fibers in cerebellar learning. Each point raised has been carefully considered, and we are committed to addressing them comprehensively. We acknowledge the importance of addressing methodological concerns, particularly regarding the efficacy of long-term suppression of CF activity, as well as ensuring clarity regarding the penetrance and selectivity of our manipulation. To this end, we have outlined plans for substantial revisions to the manuscript to adequately address these issues.

      Public Reviews:

      Reviewer #1 (Public Review):

      Summary:

      The study by Seo et al highlights knowledge gaps regarding the role of cerebellar complex spike (CS) activity during different phases of learning related to optokinetic reflex (OKR) in mice. The novelty of the approach is twofold: first, specifically perturbing the activity of climbing fibers (CFs) in the flocculus (as opposed to disrupting communication between the inferior olive (IO) and its cerebellar targets globally); and second, examining whether disruption of the CS activity during the putative "consolidation phase" following training affects OKR performance.

      The first part of the results provides adequate evidence supporting the notion that optogenetic disruption of normal CF-Purkinje neuron (PN) signaling results in the degradation of OKR performance. As no effects are seen in OKR performance in animals subjected to optogenetic irradiation during the memory consolidation or retrieval phases, the authors conclude that CF function is not essential beyond memory acquisition. However, the manuscript does not provide a sufficiently solid demonstration that their longterm activity manipulation of CF activity is effective, thus undermining the confidence of the conclusions.

      Strengths:

      The main strength of the work is the aim to examine the specific involvement of the CF activity in the flocculus during distinct phases of learning. This is a challenging goal, due to the technical challenges related to the anatomical location of the flocculus as well as the IO. These obstacles are counterbalanced by the use of a well-established and easy-to-analyse behavioral model (OKR), that can lead to fundamental insights regarding the long-term cerebellar learning process.

      Weaknesses:

      The impact of the work is diminshed by several methodological shortcomings.

      Most importantly, the key finding that prolonged optogenetic inhibition of CFs (for 30 min to 6 hours after the training period) must be complemented by the demonstration that the manipulation maintains its efficacy. In its current form, the authors only show inhibition by short-term optogenetic irradiation in the context of electrical-stimulation-evoked CSs in an ex vivo preparation. As the inhibitory effect of even the eNpHR3.0 is greatly diminished during seconds-long stimulations (especially when using the yellow laser as is done in this work (see Zhang, Chuanqiang, et al. "Optimized photo-stimulation of halorhodopsin for long-term neuronal inhibition." BMC biology 17.1 (2019): 1-17. ), we remain skeptical of the extent of inhibition during the long manipulations. In short, without a demonstration of effective inhibition throughout the putative consolidation phase (for example by showing a significant decrease in CS frequency throughout the irradiation period), the main claim of the manuscript of phase-specific involvement of CF activity in OKR learning cannot be considered to be based on evidence.

      Second, the choice of viral targeting strategy leaves gaps in the argument for CF-specific mechanisms. CaMKII promoters are not selective for the IO neurons, and even the most precise viral injections always lead to the transfection of neurons in the surrounding brainstem, many of which project to the cerebellar cortex in the form of mossy fibers (MF). Figure 1Bii shows sparsely-labelled CFs in the flocculus, but possibly also MFs. While obtaining homogenous and strong labeling in all floccular CFs might be impossible, at the very least the authors should demonstrate that their optogenetic manipulation does not affect simple spiking in PNs.

      Finally, while the paper explicitly focuses on the effects of CF-evoked complex spikes in the PNs and not, for example, on those mediated by molecular layer interneurons or via direct interaction of the CF with vestibular nuclear neurons, it would be best if these other dimensions of CF involvement in cerebellar learning were candidly discussed.

      We appreciate the reviewer’s thorough evaluation, which thoughtfully highlights the strengths and areas for improvement in our study.

      We agree with the reviewer’s recognition of the novelty of our approach, particularly in specifically perturbing climbing fiber (CF) activity in the flocculus and examining its effects across distinct phases of learning. Additionally, our use of the well-established OKR behavior paradigm provides a robust framework for investigating cerebellar learning processes, further strengthening our study.

      To address concerns regarding the efficacy of long-term optogenetic inhibition and the specificity of viral targeting, we conducted additional experiments. These include in vivo monitoring of CF activity during the irradiation period, confirming sustained inhibition of complex spikes throughout the consolidation phase. To ensure precise targeting and mitigate potential side effects, such as unintended modification of Purkinje cell (PC) simple spike activity, we demonstrated that optogenetic suppression of CF transmission did not affect simple spike firing. Furthermore, we made additional characterizations to confirm the specificity of viral targeting.

      Lastly, we recognize the importance of exploring alternative mechanisms underlying CF involvement in cerebellar learning. Accordingly, we expanded the manuscript to provide a more comprehensive discussion of these mechanisms, offering a clearer perspective on the broader implications of our findings.

      Reviewer #2 (Public Review):

      Summary:

      The authors aimed to explore the role of climbing fibers (CFs) in cerebellar learning, with a focus on optokinetic reflex (OKR) adaptation. Their goal was to understand how CF activity influences memory acquisition, memory consolidation, and memory retrieval by optogenetically suppressing CF inputs at various stages of the learning process.

      Strengths:

      The study addresses a significant question in the cerebellar field by focusing on the specific role of CFs in adaptive learning. The authors use optogenetic tools to manipulate CF activity. This provides a direct method to test the causal relationship between CF activity and learning outcomes.

      Weaknesses:

      Despite shedding light on the potential role of CFs in cerebellar learning, the study is hampered by significant methodological issues that question the validity of its conclusions. The absence of detailed evidence on the effectiveness of CF suppression and concerns over tissue damage from optogenetic stimulation weakens the argument that CFs are not essential for memory consolidation. These challenges make it difficult to confirm whether the study's objectives were fully met or if the findings conclusively support the authors' claims. The research commendably attempts to unravel the temporal involvement of CFs in learning but also underscores the difficulties in pinpointing specific neural mechanisms that underlie the phases of learning. Addressing these methodological issues, investigating other signals that might instruct consolidation, and understanding CFs' broader impact on various learning behaviors are crucial steps for future studies.

      We appreciate the reviewer’s recognition of the significance of our study in addressing the fundamental question of the role of CF in adaptive learning within the cerebellar field. The use of optogenetic tools indeed provides a direct means to investigate the causal relationship between CF activity and learning outcomes.

      To address concerns regarding the effectiveness of CF suppression during consolidation, we plan to conduct further in-vivo recordings. These will demonstrate how reliably CF transmission can be suppressed through optogenetic manipulation over an extended period.

      In response to the concern about potential tissue damage from laser stimulation, we believe that our optogenetic manipulation was not strong enough to induce significant heat-induced tissue damage in the flocculus. According to Cardin et al. (2010), light applied through an optic fiber may cause critical damage if the intensity exceeds 100 mW, which is eight times stronger than the intensity we used in our OKR experiment. Furthermore, if there had been tissue damage from chronic laser stimulation, we would expect to see impaired long-term memory reflected in abnormal gain retrieval results tested the following day. However, as shown in Figures 2 and 3, there were no significant abnormalities in consolidation percentages even after the optogenetic manipulation.

      Finally, we appreciate the reviewer’s recognition of the challenges involved in pinpointing specific neural mechanisms. We plan to expand the discussion to address these complexities and outline future research directions.

      Recommendations for the authors:

      Reviewer #1 (Recommendations For The Authors):

      Inhibitory optogenetic actuators are generally problematic, especially in time frames longer than seconds. If the authors wish to be able to inhibit activity in the flocculus-targeting CFs for a long time, maybe it would make sense to try to retrogradely transfect the IO neurons from the flocculus (using a cre-lox approach) with inhibitory DREADDs. This approach is also full of problems, so the absence or significant decrease in CS activity throughout the period of manipulation must be demonstrated.

      In addition to re-examining the strength of the evidence regarding the role of CFs in the consolidation and retrival phases, the manuscript would benefit from significant reworking of the details in the manuscript and figures. Below is a possibly incomplete list of things we would want to highlight:

      (1) While the text states the authors "... verified the potential reduction of Cs firing rate in PCs of awake mice in vivo by inhibiting CF signals", the data nor a figure are shown. This is of critical importance when judging the reliability of the following results. The data presented in panels Figure 1D-E should also be improved to be more informative, specifically, the waveforms of EPSCs should be shown in higher resolution. We are not informed about how many cells/slices/animals the results are obtained from, nor how many trials were done per condition. Finally, the in vitro data is from vermal Purkinje neurons, while the focus of the work is in the flocculus. Please provide these verifications for the flocculus.

      To verify the suppression of complex spike (Cs) activity, we conducted additional in-vivo experiments and added Figure 2, which presents recordings of Cs firing rates from Purkinje cells (PCs) during optogenetic suppression of climbing fiber (CF) activity. These data demonstrate that the suppression specifically and robustly targets Cs activity without affecting simple spike firing, as shown in Figure 2C. The results presented in Figure 2 were acquired at 40 minutes of optostimulation, consistently showing effective suppression of Cs activity throughout this period. While continuous recordings over several hours were not performed, the stability and sustained suppression observed at the 40-minute mark strongly suggest that the manipulation remains effective during the extended durations required for the behavioral tests.

      Additionally, we have improved Figure 1D by enhancing the resolution of EPSC waveforms and including more detailed information in the figure legend regarding the number of cells and animals analyzed. For the current-clamp mode data (Figures 1E and F), we clarified the experimental conditions to provide additional context. While the in vitro data were collected from vermal PCs, these experiments were intended to illustrate the fundamental properties of CF-PC transmission.

      (2) It is challenging to get a homogenous transfection of all CFs in a given region. To be able to judge the significance of the results, the readers should be provided with material allowing assessing the transfection quality. The images shown in panels Bi-ii are spatially restricted and of too low quality to make judgements. Also, it is not stated whether the images shown are from GFP or NpHR-transfected animals. These different payloads are delivered using different viral capsids (AAV1 vs. AAV9) that have significantly different transfection capacities and results from AAV9-CamKIIGFP cannot be generalized to AAV1-CamKII-NpHR. Please show the expression for the capsid used with NpHR.

      To clarify, the images in Figure Bi-ii are representative of GFP expression in animals transfected using AAV1-CamKII-EGFP. The purpose of these panels is to confirm the successful targeting of the region of interest rather than to evaluate viral tropism or capsid-specific transfection efficiency. Moreover, while the transfection characteristics of AAV1 and AAV9 may differ, the key experimental parameter of effective CF suppression was validated through in-vivo electrophysiological recordings, which robustly confirm the efficacy of NpHR expression.

      (3) Finally, please show the location of the optic fiber implant in the flocculus from post-mortem images.

      In Figure 3a of our revised manuscript, we added post-mortem histological images showing the exact location of the optic fiber implants in the flocculus. These images provided clear confirmation that the optogenetic stimulation was targeted to the correct anatomical region, ensuring that the observed effects are attributable to CF manipulation in the flocculus.

      Reviewer #2 (Recommendations For The Authors):

      (1) The efficacy of CF suppression is questionable. The histology in Figure 1 shows that only a handful of CFs are transduced in their approach. This observation casts doubt on the claimed complete suppression of CF-evoked EPSCs in every recorded PC in the same figure. This necessitates a more detailed explanation for this apparent discrepancy. Also, the absence of current-clamp recordings to measure the effect on CF-evoked complex spiking in PCs and the lack of detail regarding the timing of optogenetic actuation (continuous or pulsed) during these slice experiments are also significant omissions.

      We are providing additional in vivo electrophysiological recordings showing sustained CF suppression in awake animals (Figure 2). These recordings will directly demonstrate the extent of CFevoked complex spike (Cs) suppression.

      Moreover, we have included additional data of current-clamp recordings to measure the impact of CF suppression on Cs activity (Figures 1E and 1F). Regarding the timing of the optogenetic actuation, the stimulation was applied continuously in the slice experiments.

      (2) The authors claim that their method effectively suppresses CF activity in vivo, yet they do not present any supporting data. Given the histological evidence provided, it's questionable whether their approach truly impacts the CF population broadly, casting doubts on the efficacy of their suppression approach to identify the role of CFs during behavior. To address these concerns, further experiments and detailed quantification are essential to validate the extent and uniformity of CF suppression achieved.

      As we responded earlier, we conducted additional in-vivo experiments with continuous recordings of CF-evoked complex spike (Cs) activity during optogenetic suppression (Figure 2). These data directly demonstrate effective and sustained inhibition of CF transmission throughout the behavioral experiments. Quantification of CF suppression revealed consistent inhibition across the manipulation period, with no observable alterations in Purkinje cell simple spike firing rates, confirming that our intervention specifically targeted CF activity without off-target effects. In addition to the in-vivo data, the in-vitro data presented in Figure 1 (lines 107~116) further validate the efficacy of our optogenetic manipulation, showing consistent suppression of CF transmission without any failures. These findings collectively confirm the reliability and specificity of our suppression approach for studying CF contributions to behavior.

      (3) To optogenetically test the role of CFs in memory consolidation, the authors deliver continuous, high-power light to the flocculus (13 mW for 6 hrs). This extends well beyond typical experimental conditions. The sustained nature of the light exposure thus brings into question the consistency and reliability of CF suppression over time. Firstly, it is imperative to determine whether CF activity is suppressed throughout this extended period. Secondly, the intensity and duration of light exposure carry a significant risk of causing extensive damage to the surrounding tissue. Given these concerns, a thorough histological examination is warranted to assess the potential adverse effects on tissue integrity. Such an analysis is crucial not only for validating the experimental outcomes but also for ensuring that the observed effects are not confounded by light-induced tissue damage.

      To address whether CF activity is suppressed throughout the extended period, we included new in-vivo recordings demonstrating robust suppression of CF transmission, as evidenced by inhibited complex spikes sustained at 40 minutes of optostimulation. Regarding potential tissue damage, our optogenetic protocol used a light intensity (13 mW), which is much lower than the 75 mW threshold reported by Cardin et al. (2010) as sufficient to maintain normal neuronal activity. Moreover, critical damage typically requires intensities exceeding 100 mW for several hours (Cardin, Jessica A., et al. "Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2." Nature protocols 5.2 (2010): 247-254.). Finally, we observed no abnormalities in long-term memory consolidation or gain retrieval (Figures 3C, 4C, 4F), further supporting that our light stimulation did not induce tissue damage.

      (4) The generalizability of their findings to various learning behaviors remains uncertain. Given that the flocculus plays a role in vestibulo-ocular reflex (VOR) adaptation, which encompasses both CFdependent and CF-independent learning types (gain increase and gain decrease, respectively), this system could offer a more feasible approach for investigating hypotheses about the role of CFs in guiding distinct learning processes.

      In response to the reviewer’s comment on the generalizability of our findings to learning behaviors involving both CF-dependent and CF-independent mechanisms, we acknowledge the importance of examining these dynamics in cerebellar motor adaptation systems, such as the OKR. Although our study used an OKR task, findings from VOR studies apply here. Ke et al. (2009) demonstrated that VOR gain increases (CF-dependent) and gain decreases (CF-independent) involve distinct plasticity processes (Ke, Michael C., Cong C. Guo, and Jennifer L. Raymond. "Elimination of climbing fiber instructive signals during motor learning." Nature neuroscience 12.9 (2009): 1171-1179), suggesting that CF engagement is task-dependent, particularly for larger error signals that require CF-guided adaptation.

      Similarly, our OKR findings suggest that CF-dependent pathways are likely used for large, persistent errors, whereas CF-independent mechanisms may drive more gradual adjustments. This alignment between OKR and VOR systems supports the generalizability of CF-selective adaptation across cerebellar learning tasks. We have elaborated on this point in our revised manuscript (lines 219~237), clarifying how CF-dependent and CF-independent mechanisms can generalize across motor learning contexts in the cerebellum.

      (5) The acute effect of CF suppression on OKR eye movements warrants investigation. If OKR eye movements are altered by their method, this could complicate the interpretation of their results.

      During our experiments, we monitored ocular movements during CF optogenetic manipulation and found no aberrant effects, such as nystagmus. As shown in Figures 4G and 4H, disrupting CF signaling during gain retrieval did not alter the gain, confirming that our manipulation neither acutely affects ocular reflexes nor induces abnormal eye movement. Therefore, it leads to the conclusion that the observed effects are specific to learning and memory processes.

      (6) The authors raise the potential issue of inducing presynaptic LTD in CFs. Can they be sure that their manipulation doesn't generate a similar effect? Additional controls or techniques to accurately interpret the results are needed considering this concern.

      However, our discussion does not claim that optogenetic suppression directly induces CF-LTD. Instead, we posit that CF suppression may have mimicked the functional consequences of CFLTD, such as reduced complex spike (Cs) activity and associated calcium signaling. This, in turn, may have indirectly interfered with the induction of parallel fiber-Purkinje cell (PF-PC) LTD, thereby preventing gain enhancement during learning.

      This hypothesis is consistent with previous studies highlighting the interplay between CF and PF synaptic plasticity in cerebellar motor learning. For example, Hansel and Linden (2000) and Weber et al. (2003) discuss how changes at CF synapses can modulate Cs waveforms and calcium dynamics, which are critical for PF-PC LTD. Coesmans et al. (2004) and Han et al. (2007) further elaborate on the necessity of CF input for effective PF-PC LTD induction during learning tasks such as retinal slip correction.

      While our experiments were not designed to directly measure CF-LTD, the observed prevention of gain enhancement aligns with the hypothesis that CF suppression functionally disrupted downstream PF-PC LTD. We have clarified these points in our revised manuscript (lines 250~258) to avoid misunderstanding.

      (7) The specific timeframe for OKR consolidation remains uncertain, with evidence from numerous studies indicating that cerebellar memory consolidation unfolds over several days. Therefore, a more thorough investigation into these extended durations, supported by control experiments to validate the outcomes, would significantly strengthen the study's conclusions, and provide clearer insights into the consolidation process of OKR learning.

      Our current study specifically focused on the early phase of the post-learning period, as supported by findings from several studies: Cooke et al., (2004); Titley et al., (2007); Steinmetz et al., (2016); Seo et al., (2024)

      These studies collectively indicate that cerebellar-dependent memory consolidation—including OKR—can occur rapidly during the early consolidation phase. While the specific mechanisms examined in these studies vary (e.g., synaptic plasticity, intrinsic plasticity, or circuit-level changes), they consistently demonstrate that modifications in the cerebellum after the early consolidation period no longer influence memory storage or performance. This evidence strongly supports the relevance of our experimental focus and the timing of our interventions.

      We acknowledge the importance of investigating extended consolidation periods, which could indeed provide additional insights. However, given our current aims, the rapid consolidation dynamics observed in the early phase are most relevant to the questions addressed in this study. We have elaborated on these matter in our revised manuscript (lines 273~283).

      (8) Issues around whether the authors have control over CF activity with their optogenetic intervention raise questions of whether learning can be recovered during the training procedure if the optogenetic stimuli are halted. Specifically, if suppression is applied for three blocks (what the authors refer to as "sessions") during the training procedure and then ceases, does learning rapidly recover in the immediately following blocks?

      While we did not directly examine the restoration of learning capability within the same training session following the cessation of optogenetic inhibition, we believe several aspects of our experimental design and insights from prior studies support our interpretation.

      Our optogenetic intervention specifically targeted Purkinje cells (PCs) in the flocculus and was applied continuously during designated training sessions to modulate cerebellar activity. Notably, Medina et al. (2001) demonstrated that transient inactivation of the cerebellar cortex impairs the expression of learned responses but does not disrupt the underlying plasticity mechanisms (Medina, Javier F., Keith S. Garcia, and Michael D. Mauk. "A mechanism for savings in the cerebellum." Journal of Neuroscience 21.11 (2001): 4081-4089.). This finding suggests that cerebellar plasticity remains intact and functional even after transient perturbations.

      Therefore, it is plausible that once optogenetic inhibition is lifted, the cerebellar network regains its capacity for learning and adaptation, as the intrinsic plasticity and memory encoding processes remain preserved. While we acknowledge that direct experimental confirmation of rapid recovery in our setup was not performed, this interpretation is consistent with our experimental framework and the broader literature.

      (9) The study does not fully explore the instructive signals/mechanisms underlying the memory consolidation process. A detailed investigation into potential instructive signals for consolidation beyond CF-induced signaling, like the simple spiking of PCs, could significantly enhance the study's conclusions. Indeed, there is currently no evidence to suggest that CFs play a role in the consolidation phase anyway so testing their role seems a bit of a strawman argument.

      While our study primarily focused on characterizing CF-dependent pathways, we acknowledge that memory consolidation is likely driven by a multifaceted interplay of instructive signals beyond CF-induced mechanisms. In particular, Purkinje cell (PC) simple spiking may act as a critical signal during the consolidation phase, either complementing or functioning independently of CF input. Emerging evidence suggests that simple spiking can modulate downstream circuitry in ways that stabilize and strengthen memory traces.

      To address this, we have expanded the discussion in the revised manuscript to explore potential instructive signals for consolidation, including PC simple spiking, local circuit plasticity within the cerebellar cortex, and its interaction with the cerebellar nuclei. We propose that these mechanisms collectively contribute to the transfer and stabilization of motor memory, offering a more comprehensive framework for understanding consolidation. We have elaborated on these matter in our revised manuscript (lines 238~250).

      (10) Previous reports have highlighted the necessity of CF activity for extinction/memory maintenance (Medina et al. 2002; Kim et al. 2020). That is, the absence of CF activity is consequential for cerebellar function. These results present a potential contrast to the findings reported in this current study. This discrepancy raises important questions about the experimental conditions, methodologies, and interpretations of CF function across different studies. A thorough discussion comparing these divergent outcomes is essential, as it could elucidate the specific contexts or conditions under which CF activity influences memory processes.

      We acknowledge that previous studies (Medina et al., 2002; Kim et al., 2020) have suggested a role for climbing fiber (CF) activity in extinction. However, our study specifically focuses on the acquisition phase of motor learning and does not extend to extinction or maintenance. As such, we have revised our discussion to limit interpretations strictly to the scope of our findings and removed references to extinction.

      The discrepancies between our results and prior work may arise from differences in methodologies and behavioral paradigms. For instance, we utilized optogenetic inhibition to achieve precise temporal and spatial control of CF activity, whereas previous studies employed pharmacological or lesion methods that may have broader effects on the cerebellar circuitry. Additionally, differences in behavioral paradigms, such as the optokinetic reflex (OKR) task used in our study compared to the eye-blink conditioning tasks in prior studies, may demand distinct roles for CF signaling depending on the specific requirements for error correction and adaptation.

      This clarification is now incorporated into our revised manuscript, and the discussion has been streamlined to focus on the phase-specific role of CF activity during acquisition without extending to extinction or maintenance (lines 259~270).

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public Review):

      The article emphasizes vocal social behavior but none of the experiments involve a social element. Marmosets are recorded in isolation which could be sufficient for examining the development of vocal behavior in that particular context. However, the early-life maturation of vocal behavior is strongly influenced by social interactions with conspecifics. For example, the transition of cries and subharmonic phees which are high-entropy calls to more low-entropy mature phees is affected by social reinforcement from the parents. And this effect extends cross context where differences in these interaction patterns extend to vocal behavior when the marmosets are alone. From the chord diagrams, cries still consist of a significant proportion of call types in lesioned animals. Additionally, though it is an intriguing finding that the infants' phee calls have acoustic differences being 'blunted of variation, less diverse and more regular,' the suggestion that the social message conveyed by these infants was 'deficient, limited, and/or indiscriminate' is not but can be tested with, for example, playback experiments.

      We recognize that our definition of vocal social behavior is not within the normal realm of direct social interactions. We were particularly interested in marmoset vocalizations as a social signal, such as phees, cries and twitter, even when their family members or conspecifics are not visibly present. Generally speaking, in the laboratory, infant marmosets make few calls when in the presence of another conspecific, but when isolated they naturally make phee calls to reach out to their distantly located relatives. In this context, while we did not assess the animals interacting directly, we assessed what are normally referred to as ‘social contact calls,’ hence the term ‘social vocalizations.’ Playback recordings might provide potential evidence of antiphonal calling as a means of social interaction and might reveal the poor quality of the social message conveyed by the infant, but even here, the vocalizing marmoset would be calling to a non-visible conspecific. Thus, although our experiment lacked a direct social element, our data suggest that in the absence of a functioning ACC in early life, infant calls that convey social information, and which would elicit feedback from parents and other family members, may be compromised, and this could potentially influence how that infant develops its social interactive skills. We have now commented on the significance of social vocalizations in the introductory text (page 3) and discussion (page 15).

      The manuscript would benefit from the addition of more details to be able to better determine if the conclusions are well supported by the data. Understanding that this is very difficult data to get, the number of marmosets and some variability in the collection of the data would allow for the plotting of each individual across figures. For example, in the behavioral figures, which is the marmoset that is in the behavioral data that has a sparing of the ACC lesion in one hemisphere? Certain figures, described below in the recommendations for the authors, could also do with additional description.

      Thanks for these suggestions. We have plotted the individual animals in the relevant figures and addressed the comments and recommendations listed below.

      Reviewer #1 (Recommendations For The Authors):

      Given the number of marmosets, variability in the collected data, lesion extent, and different controls, I would like to see more plots with individuals indicated (perhaps with different symbols). More details could also be added for several plots.

      Figure 2D (new) and 2E now have plots that represent the individual animals, each represented by a different symbol.

      Figure 2A) Since lesions are bilateral, could you also show the extent of the lesions on the other side for completeness?

      Our intention was to process one hemisphere of each brain for Golgi staining to examine changes in cell morphology in the ACC and associated brain regions following the lesion. Unfortunately, the Golgi stain was unsuccessful. Consequently, we were unable to use the tissue to reconstruct the bilateral extent of the lesion. We did, however, first establish the bilateral nature of the lesion through coronal slices of the animals MRI scan before processing the intact hemisphere to confirm the bilateral extent of the lesion. The MRI scans (every 5th section) for each control and lesioned animal is compiled in a figure in the supplementary materials (Fig. S1). These scans show that the ACC-lesioned animals have bilateral lesions with one animal (ACC1) showing some sparing in one hemisphere, as we noted in the text. We have now made reference to this supplemental figure in the text (page 5).

      Figure 2B/C) In Figure 2B, control and ACC lesions are in the columns while right next to it in 2C, ACC lesion and control are in the rows. Could these figures be adjusted so that they are consistent?

      We have now adjusted these figures and updated the figure legends accordingly.

      Figure 2C) Is there quantification of the 'loss of neurons and respective increase in glial cells at the lesioned site especially at the interface between gray and white matter'? There are multiple slices for each animal.

      Thanks for suggesting this. We have now quantified these data which are presented as a new graph as Fig. 2D. These data revealed a significant loss of neurons (NeuN) in the ACC group as well as an increase in glial cells (GFAP and Iba1) relative to the controls. The figure legend and results have also been updated.

      Figure 2C) It is difficult for me to distinguish between white and purple - could you show color channels independently since images were split into separate channels for each fluorophore?

      Fig. 2C has been revised to better visualize the neurons and glia at the gray and white matter interface. We found that grayscale images for each channel offered a better contrast than separating the channels for each fluorophore.

      Figure 2C/D) I like how there are individual dots here for the individual marmosets. Since there are four in each group, could they be represented throughout with symbols (with a key indicating the pair and also the control condition)? For example, were there changes in the histology for control animals that got saline injections as opposed to those that didn't get any surgery?

      We have highlighted the individual animals with different symbols in the figures. Although some animals were twin pairs, it was not possible to have twins in all cases. Only two sets were twins. We have indicated the symbols that represent the twin pair in Fig. 2 as well as the MRI scans of the twin pairs in Fig. S1. There were no observed changes in histology for the sham animals relative to the other non-sham controls. The MRI scan for one sham CON2 shows herniated tissue in the right hemisphere which is a normal consequence of brain exposure caused by a craniotomy.

      Figure 3D-E) Here, individual data points could be informative especially given that some animals are missing data past the third week.

      To prevent cluttering the figure with too many data points, we have added the sample size for each group in the figure legend (pages 33).

      Figure 3D/F) What exactly is the period that goes into this analysis? In the text, 'Further analysis showed that the ACC lesion had minimal effects on the rate of most call types during this period'. Is this period from weeks 3 to 6 relative to the proportions in week 2? I think I also don't quite understand the chord diagram. The legend says 'the numbers around each chord diagram represents relative probability value for each call type transition' so how does that relate to the proportion of these call types? It looks like there is a wider slice for cries for ACC-lesioned animals each week. I also don't see in the week 4 chord diagram, the text description of 'elevation in the rate of 'other' calls, which comprised tsik, egg, eck, chatter and seep calls. These calls were significantly elevated in animals after the ACC lesion."

      We apologize for the confusion. Fig 3D and Fig 3F are not directly related. Fig. 3D shows the different types of emitted calls. The figure shows the averaged data per group pooled from post-surgery weeks (week 3 – week 6). It represents the proportion of individual call types relative to the total number of calls during each recording period. The only major finding here was the increased rate of ‘other’ calls comprising tsik, egg, ock, chatter and seep calls. These calls were significantly elevated in animals after the ACC lesion.

      While Fig. 3D represents the differences in the proportion of calls, the chord diagrams in Fig. 3F represents the probability of call-to-call transition obtained from a probability matrix. At postnatal week 6, marmosets with ACC lesions showed a higher likelihood of transitions between all call types, but less frequent transitions between social contact calls relative to sham controls. The chord diagrams visualize the weighted probabilities and directionality of these transitions between the different call types. Weighted probabilities were used to account for variations in call counts. The thickness of the arrows or links indicates the probability of a call transition, while the numbers surrounding each chord diagram represent the relative probability value for each specific transition. We have now reworded the text and clarified these details in the figure legend (pages 32-33).

      Figure 3E) How is the ratio on the y-axis calculated here?

      The y-axis represents the averaged value of the ratios of the number of social contact calls relative to non-social contact calls in each recording per subject per group (i.e., (x̄ (# social calls / # non-social calls). This is now included in the figure legend and the axis is updated (page 32).

      Also, cries could be considered a 'social contact call' since they are produced by infants to elicit responses from the parents. There is also the hypothesis in the literature that cries transition into phees.

      The reviewer is correct. Cries are often considered a social contact call because they elicit parental feedback. We decided to separate cry-calls from other social contact calls for two reasons. First, in our sample, we found cry behavior to be highly variable across the animals. For example, one control infant cried incessantly whereas another control infant cried less than normal. This extreme variability in animals of the same group masked the features between animals that reliably differentiated between them. Second, cry-calls elicit feedback from parents who are normally within the vicinity of the infant whereas phee calls elicit antiphonal phee calls from any distantly located conspecific. In other words, the context in which these calls are often elicited are very different.

      The use of 'syntactical' is a bit jarring to me because outside of linguistics, its use in animal communication generally refers to meaning-bearing units that can be combined into well-formed complexes such as pod-specific whale songs or predator alarm calls with concatenated syllable types in some species of monkeys. To my knowledge, individual phee syllables have not been currently shown to carry information on their own and may be better described as 'sequential' rather than 'syntactical'.

      We agree. We have made this change accordingly.

      Figure 4B) How many phee calls with differing numbers of syllables are present each week? How equal is the distribution given that later analyses go up to 5 syllables?

      The total number of phee calls with differing number of syllables ranged between 20-40 phees. This number varied between subjects, per week. The most common were 3- and 4-syllable phee calls which ranged from 7-15. Due to this variability, Fig. 4B presents the average syllable count. The axis is now updated.

      Figure 4C-E) How is the data combined here? Is there a 2nd syllable, the combined data from the 2nd syllable from phee calls of all lengths (1 - 5?). If so, are there differences based on how long the total sequence is?

      The combined data represents the specific syllable (e.g., the 1st syllable in a 2-syllable phee, in a 3-syllable phee and in a 4-syllable phee) irrespective of the length of the sequence in a sequence. No differences were observed between 2nd syllable in a 2 syllable phee and 2nd syllable in a 3 or a 4 syllable phee. We have included this detail in the figure legend (page 33-34).

      So duration is a vocal parameter that is highly dependent on physical factors such as body size and lung volume, where there differences in physical growth between the pairs of ACC-lesioned marmosets and their twins? Entropy is less closely tied to these physical factors but has previously been shown to decrease as phee calls mature, which we can also see in the negative relationship of the control animals. Do you know of experiments that show that lower entropy calls are more 'blunted'?

      Thank you for raising the important issue of physical growth factors. For twin pairs, it is not uncommon for one infant to be slightly bigger, heavier or stronger than the other presumably because one gets more access to food. With increasing age, we did not observe significant changes in bodyweight between the groups. We examined grip strength in all infants as a means of assessing how well the infant was able to access food during nursing. Poor grip strength would indicate a lower propensity to ‘hang on’ to the mother for nursing which could lead to lower weight gain and reduced physical growth. We found that both grip strength and body weight increased as the infants got older and both parameters were equivalent. We have included an additional figure to show the normal increase in both weight and grip strength to the supplemental materials (Fig. S3) and have made reference to this in the text (page 8).

      As for entropy, it’s impact on the emotional quality of vocalizations has not been systematically explored. Generally speaking, high entropy relates to high randomness and distortion in the signal. Accordingly, one view posits low-entropy phee calls represent mature sounding calls relative to noisy and immature high-entropy calls (e.g., Takahasi et al 2017). In the current study, the reduction in syllable entropy observed for both groups of animals with increasing age is consistent with this view. At the same time entropy can relate to vocal complexity; high entropy refers to complex and variable sound patterns whereas low entropy sounds are predictable, less diverse and simple vocal sequences (Kershenbaum, A. 2013. Entropy rate as a measure of animal vocal complexity. Bioacoustics, 23(3), 195–208). One possibility is that call maturity does not equate directly to emotional quality. In other words, a low-entropy mature call can also be lacking in emotion as observed in humans with ACC damage; these patients show mature speech, but they lack the variations in rhythms, patterns and intonation (i.e., prosody) that would normally convey emotional salience and meaning. Our observation of a reduction in phee syllable entropy in the ACC group in the context of being short and loud with reduced peak frequency is consistent with this view. Our use of the word ‘blunt’ was to convey how the calls exhibited by the ACC group were potentially lacking emotional meaning. Beyond this speculation, we are not aware of any papers that have examined the relationship between entropy and blunted calls directly. We have now included this speculation in the discussion (pages 12-13).

      Reviewer #2 (Public Review):

      The authors state that the integrity of white matter tracts at the injection site was impacted but do not show data.

      We have added representative micrographs of a control and ACC-lesioned animal in a new supplementary figure which shows the neurotoxin impacted the integrity of white matter tracts local to the site of the lesion (Fig. S2).

      The study only provides data up to the 6th week after birth. Given the plasticity of the cortex, it would be interesting to see if these impairments in vocal behavior persist throughout adulthood or if the lesioned marmosets will recover their social-vocal behavior compared to the control animals.

      We agree. Our original intention was to examine behavior into adulthood. Unfortunately, the COVID-19 pandemic compromised the continuation of the study. We were limited by the data that we were allowed to acquire due to imposed restrictions. Some non-vocalization data collected when the animals were young adults is currently being prepared for another paper.

      Even though this study focuses entirely on the development of social vocalizations, providing data about altered social non-vocal behaviors that accompany ACC lesions is missing. This data can provide further insights and generate new hypotheses about the exact role of ACC in social vocal development. For example, do these marmosets behave differently towards their conspecifics or family members and vice versa, and is this an alternate cause for the observed changes in social-vocal development?

      We agree. At the time however, apparatus for assessing behavior between the infant’s family and non-family members was not available. Assessing such behaviors in the animals holding room posed some difficulty since marmosets are easily distracted by other animals as well as the presence of an experimenter, amongst other things. This is an area of investigation we are currently pursuing.

      Reviewer #3 (Public Review):

      It is striking to find that the vocal repertoire of infant marmosets was not significantly affected by ACC lesions. During development, the neural circuits are still maturing and the role of different brain regions may evolve over time. While the ACC likely contributes to vocalizations across the lifespan, its relative importance may vary depending on the developmental stage. In neonates, vocalizations may be more reflexive or driven by physiological needs. At this stage, the ACC may play a role in basic socioemotional regulation but may not be as critical for vocal production. Since the animals lived for two years, further analysis might be helpful to elucidate the precise role of ACC in the vocal behavior of marmosets.

      Figure 3D. According to the Introduction "...infant ACC lesions abolish the characteristic cries that infants normally issue when separated from its mother". Are the present results in marmosets showing the opposite effect? Please discuss.

      To date, the work of Maclean (1985) is the only publication that describes the effect of early cingulate ablation on the spontaneous production of ‘separation calls’ largely construed as cries, coos and whimpers in response to maternal separation. All of this work was largely performed in rhesus macaques or squirrel monkeys. In addition to ablating the cingulate cortex, Maclean found that it was necessary to ablate the subcallosal (areas 25) and preseptal cingulate cortex (presumably referring to prelimbic area 32) to permanently eliminate the spontaneous production of separation cry calls. Our ablation of the ACC was more circumscribed to area 24 and is therefore consistent with MacLean’s earlier work that removal of ACC alone does not eliminate cry behavior. In adults, ACC ablation is insufficient at eliminating vocalization as well. We make reference to this on pages 13-14 of the discussion.

      Figure 3E and Discussion. Phees are mature contact calls and cries immature contact calls (Zhang et al, 2019, Nat Commun). Therefore, I would rather say that the proportion of immature (cries) contact calls increases vs the mature (phee, trill, twitters) contact calls in the ACC group. Cries are also "isolated-induced contact calls" to attract the attention of the caregivers.

      The reviewer is correct in that cries are directed towards caregivers but in our sample, cry behavior was highly variable between the infants. Consequently, in Fig. 3E social contact calls include phee, twitter and trill calls but does not include cries which were separated (see also response to reviewer #1). Many of the calls made during babbling were immature in their spectral pattern (compare phee calls between Fig. 3A and 3B). Cries typically transitioned into phees, twitters or trills before they fully matured. Fig 3E shows that the controls made more isolation-induced social contact calls at postnatal week 6 which were presumably maturing at this time point. Thus, if anything, there was an increase in the proportion of mature contact calls vs immature contact calls with increasing age.

      Figure 4D. Animal location and head direction within the recording incubator can have significant effects on the perceived amplitude of a call. Were these factors taken into account?

      The reviewer makes an excellent observation. Unfortunately, we did not account for location and head direction because the infants were quite mobile in the incubator. The directional microphone was hidden from view because the infants were distracted by it, and positioned ~12 cm from the marmoset, and placed in the exact same location for every recording. In addition, calls with phantom frequencies were eliminated during visual inspection of spectrograms. Beyond these details, location and head direction were not taken into account.

      Figure 4E. When a phee call has a higher amplitude, as is the case for the ACC group (Figure 4D), the energy of the signal will be concentrated more strongly at the phee call frequency ~8KHz. This concentration of the energy reduces the variability in the frequency distribution, leading to lower entropy. The interpretation of the results should be reconsidered. A faint call (control group) can exhibit more variability in the frequency content since the energy is distributed across a wider range of frequencies contributing to higher entropy. It can still be "fixed, regular, and stereotyped" if the behavior is consistent or predictable with little variation. Also, to define ACC calls as "monotonic" I would rather search for the lack of frequency modulation, amplitude variation, or narrower bandwidth.

      We very much appreciate this explanation. We were able to identify the maximum frequency that closely matched pitch of a sound for each syllable in a multisyllabic phee. New Fig. 4E shows that the peak frequency for each phee syllable was lower in the ACC-lesioned monkeys which may directly translate to the low entropy observed in this group. The term “monotonic” was used to relate our data to the classical and long-standing evidence of human ACC lesions causing monotonous intonation of speech. When all factors are taken into account, it is evident that the vocal phee signature of the ACC-lesioned animal was structurally different to the controls implicating a less complex and stereotyped ACC signal. Further studies are needed to systematically explore the relationship between entropy and emotional quality of vocalizations

      Apart from the changes in the vocal behavior, did the AAC lesions manifest in any other observable cognitive, emotional, or social behavior? ACC plays a role in processing pain and modulating pain perception. Could that be the reason for the observed increase in the proportion of cries in the ACC group and the increase in the phee call amplitude? Did the cries in the ACC group also display a higher amplitude than the cries in the control group?

      It was our intention to acquire as much data as possible from these infants as they matured from a cognitive, social and emotional perspective. Unfortunately, our study was hampered by variety of reasons including the COVID-19 pandemic which imposed major restrictions on our ability to continue with the experiment in a time sensitive manner. In addition, the development and construction of the custom apparatus to measure these behaviors was stalled during this period further preventing us from collecting behavioral data at regular time intervals. As for the cry behavior, the number of cries, in the ACC group were very low especially at postnatal week 5 and 6. Consequently, there were very few data points to work with.

      Discussion. Louder calls have the potential to travel longer distances compared to fainter calls, possess higher energy levels, and can propagate through the environment more effectively. If the ACC group produced louder phee syllables, how could be the message conveyed over long distances "deficient, limited, and/or indiscriminate"?

      Thanks for raising this interesting concept. Not all calls emitted by the animals were loud. We specifically examined the long-distance phee call in this regard. The phee syllables emitted by the ACC group were high amplitude with low frequencies, short duration and low entropy. Taking these factors into account, it is conceivable that the phee calls produced by the ACC group could not effectively convey their message over long distances despite their propagation through the environment. We have made reference to this in the discussion where we focus is specifically on the phee calls only (pages 12).

      Abstract: Do marmosets have syntax? Consider replacing "syntactical" with a more appropriate term (maybe "syntax-like").

      Thanks for this suggestion. We have replaced the term syntactical with ‘sequential’ as per the recommendation of reviewer #1.

      Introduction: "...cries that infants normally issue when separated from its mother". Please replace "its" with "their".

      This has been corrected.

      Results: Is the reference to Fig 1B related to the text?

      We have included and referred to Fig. 1B in the text (results and methods) to show other researchers how they can use this technique as a reliable and safe means of monitoring tidal volume under anesthesia in small infant marmoset without intubation.

      I understand that both "spectrograph" and "spectrogram" are used to analyze the frequency content of a signal. Nevertheless, "spectrogram" refers to the visual representation of the frequency content of a signal over time, and this term is commonly used in audio signal processing and specifically in the vocal communication field. I would recommend replacing "spectrograph" with "spectrogram".

      Thanks for this suggestion. We have corrected this throughout the manuscript.

      (Concerning the previous comment in the public review). Cries are uttered to attract the attention of the caregivers. The increase in the proportion of cries in the ACC group does not match the sentence: "...these infants appeared to make little effort in using vocalizations to solicit social contact when socially isolated".

      We apologize for the confusion. It is not the case that the ACC animals make more cries. Cry calls were highly variable amongst the animals. Consequently, although Fig 3D gives the impression that the proportion of cries in higher in ACC animals they did not differ significantly from the controls. Due to their high variability, cries were removed in the measurement of social contact. Accordingly, Fig. 3E does not include cry behavior; it shows that the ACC animals engage less in social contact calls.

      Related to Figure 3. What is the difference between "egg" and "eck" calls? Do you mean "ock"?

      We apologize. This was a typo. It should be ock calls.

      Figure 4B. Is the sample size five animals per group and per week? Overlapping data points seem to be placed next to each other. Why in some groups (e.g. ACC 6 weeks) less than five dots are visible?

      The sample size differed per week because of the lack of recording during the COVID restrictions. In Fig 4b, we have now separated the overlapping dots. We have also added the sample size of the groups in the figure legends.

      Would the authors expect to see stronger differences between the lesioned and the control groups when comparing a later developmental stage? The animals were euthanized at the age of

      These speculation is certainly feasible and yes, we were hoping to establish this level of detail with testing at later developmental stages. This is an aspect of development we are currently pursuing.

      Could these experiments be conducted?

      I’m afraid these animals are longer available, but we are currently conducting experiments in other animals with early life neurochemical manipulations who show behavioral changes into early adulthood.

      ACC lesion: It is reported that the lesions extended past 24b into motor area 6M. Did the animal display any motor control disability?

      Surprisingly, despite the lesion encroaching into 6M, these animals showed no observable motor impairment. We assessed the animals grip strength and body weight and discovered normal strength and growth in weight in both controls and the lesioned group. We have added this data as supplemental information (Fig. S3).

    1. Author response:

      The following is the authors’ response to the original reviews

      Reviewer #1 (Public review):

      Summary:

      This study investigates what happens to the stimulus-driven responses of V4 neurons when an item is held in working memory. Monkeys are trained to perform memory-guided saccades: they must remember the location of a visual cue and then, after a delay, make an eye movement to the remembered location. In addition, a background stimulus (a grating) is presented that varies in contrast and orientation across trials. This stimulus serves to probe the V4 responses, is present throughout the trial, and is task-irrelevant. Using this design, the authors report memory-driven changes in the LFP power spectrum, changes in synchronization between the V4 spikes and the ongoing LFP, and no significant changes in firing rate.

      Strengths:

      (1) The logic of the experiment is nicely laid out.

      (2) The presentation is clear and concise.

      (3) The analyses are thorough, careful, and yield unambiguous results.

      (4) Together, the recording and inactivation data demonstrate quite convincingly that the signal stored in FEF is communicated to V4 and that, under the current experimental conditions, the impact from FEF manifests as variations in the timing of the stimulus-evoked V4 spikes and not in the intensity of the evoked activity (i.e., firing rate).

      Weaknesses:

      I think there are two limitations of the study that are important for evaluating the potential functional implications of the data. If these were acknowledged and discussed, it would be easier to situate these results in the broader context of the topic, and their importance would be conveyed more fairly and transparently.

      (1) While it may be true that no firing rate modulations were observed in this case, this may have been because the probe stimuli in the task were behaviorally irrelevant; if anything, they might have served as distracters to the monkey's actual task (the MGS). From this perspective, the lack of rate modulation could simply mean that the monkeys were successful in attending the relevant cue and shielding their performance from the potentially distracting effect of the background gratings. Had the visual probes been in some way behaviorally relevant and/or spatially localized (instead of full field), the data might have looked very different.

      Any task design involves tradeoffs; if the visual stimulus was behaviorally relevant, then any observed neurophysiological changes would be more confounded by possible attentional effects. We cannot exclude the possibility that a different task or different stimuli would produce different results; we ourselves have reported firing rate enhancements for other types of visual probes during an MGS task (Merrikhi et al. 2017). We have added an acknowledgement of these limitations in the discussion section (lines 323-330 in untracked version). At minimum, our results show a dissociation between the top-down modulation of phase coding, which is enhanced during WM even for these task-irrelevant stimuli, and rate coding. Establishing whether and how this phase coding is related to perception and behavior will be an important direction for future work.

      With this in mind, it would be prudent to dial down the tone of the conclusions, which stretch well beyond the current experimental conditions (see recommendations).

      We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

      (2) Another point worth discussing is that although the FEF delay-period activity corresponds to a remembered location, it can also be interpreted as an attended location, or as a motor plan for the upcoming eye movement. These are overlapping constructs that are difficult to disentangle, but it would be important to mention them given prior studies of attentional or saccade-related modulation in V4. The firing rate modulations reported in some of those cases provide a stark contrast with the findings here, and I again suspect that the differences may be due at least in part to the differing experimental conditions, rather than a drastically different encoding mode or functional linkage between FEF and V4.

      We have added a paragraph to the discussion section addressing links to attention and motor planning (lines 315-333), and specifically acknowledging the inherent difficulties of fully dissociating these effects when interpreting our results (lines 323-330).

      Reviewer #2 (Public review):

      Summary:

      It is generally believed that higher-order areas in the prefrontal cortex guide selection during working memory and attention through signals that selectively recruit neuronal populations in sensory areas that encode the relevant feature. In this work, Parto-Dezfouli and colleagues tested how these prefrontal signals influence activity in visual area V4 using a spatial working memory task. They recorded neuronal activity from visual area V4 and found that information about visual features at the behaviorally relevant part of space during the memory period is carried in a spatially selective manner in the timing of spikes relative to a beta oscillation (phase coding) rather than in the average firing rate (rate code). The authors further tested whether there is a causal link between prefrontal input and the phase encoding of visual information during the memory period. They found that indeed inactivation of the frontal eye fields, a prefrontal area known to send spatial signals to V4, decreased beta oscillatory activity in V4 and information about the visual features. The authors went one step further to develop a neural model that replicated the experimental findings and suggested that changes in the average firing rate of individual neurons might be a result of small changes in the exact beta oscillation frequency within V4. These data provide important new insights into the possible mechanisms through which top-down signals can influence activity in hierarchically lower sensory areas and can therefore have a significant impact on the Systems, Cognitive, and Computational Neuroscience fields.

      Strengths:

      This is a well-written paper with a well-thought-out experimental design. The authors used a smart variation of the memory-guided saccade task to assess how information about the visual features of stimuli is encoded during the memory period. By using a grating of various contrasts and orientations as the background the authors ensured that bottom-up visual input would drive responses in visual area V4 in the delay period, something that is not commonly done in experimental settings in the same task. Moreover, one of the major strengths of the study is the use of different approaches including analysis of electrophysiological data using advanced computational methods of analysis, manipulation of activity through inactivation of the prefrontal cortex to establish causality of top-down signals on local activity signatures (beta oscillations, spike locking and information carried) as well as computational neuronal modeling. This has helped extend an observation into a possible mechanism well supported by the results.

      Weaknesses:

      Although the authors provide support for their conclusions from different approaches, I found that the selection of some of the analyses and statistical assessments made it harder for the reader to follow the comparison between a rate code and a phase code. Specifically, the authors wish to assess whether stimulus information is carried selectively for the relevant position through a firing rate or a phase code. Results for the rate code are shown in Figures 1B-G and for the phase code are shown in Figure 2. Whereas an F-statistic is shown over time in Figure 1F (and Figure S1) no such analysis is shown for LFP power. Similarly, following FEF inactivation there is no data on how that influences V4 firing rates and information carried by firing rates in the two conditions (for positions inside and outside the V4 RF). In the same vein, no data are shown on how the inactivation affects beta phase coding in the OUT condition.

      Per the reviewer’s suggestion, we have added several new supplementary figures. We now show the F-statistic for discriminability over time for the LFP timecourse (Fig. S2), and as a function of power in various frequencies (Fig. S4). We have added before/after inactivation comparisons of the LFP and spiking activity, and their respective F-statistics for discrimination between contrasts and orientations in Fig. S9. Lastly, we added a supplementary figure evaluating the impact of FEF inactivation on beta phase coding in the OUT condition, showing no significant change (Fig. S11).

      Moreover, some of the statistical assessments could be carried out differently including all conditions to provide more insight into mechanisms. For example, a two-way ANOVA followed by post hoc tests could be employed to include comparisons across both spatial (IN, OUT) and visual feature conditions (see results in Figures 2D, S4, etc.). Figure 2D suggests that the absence of selectivity in the OUT condition (no significant difference between high and low contrast stimuli) is mainly due to an increase in slope in the OUT condition for the low contrast stimulus compared to that for the same stimulus in the IN condition. If this turns out to be true it would provide important information that the authors should address.

      We have updated the STA slope measurement, excluding the low contrast condition which lacks a clear peak in the STA. Additionally, we equalized the bin widths and aligned the x-axes for better visual comparability. Then, we performed a two-way ANOVA, analyzing the effects of spatial features (IN vs. OUT) and visual conditions (contrast and orientation). The results showed a significant effect of the visual feature on both orientation (F = 3.96, p=0.046) and contrast (F = 14.26, p<10<sup>-3</sup>). However, neither the spatial feature nor the spatial-visual interaction exhibited significant effects for orientation (F = 0.52, p=0.473, F=1.56, p=0.212) or contrast (F = 2.19, p=0.139, F=1.15, p=0.283).

      There are also a few conceptual gaps that leave the reader wondering whether the results and conclusion are general enough. Specifically,

      (1) The authors used microstimulation in the FEF to determine RFs. It is thus possible that the FEF sites that were inactivated were largely more motor-related. Given that beta oscillations and motor preparatory activity have been found to be correlated and motor sites show increased beta oscillatory activity in the delay period, it is possible that the effect of FEF inactivation on V4 beta oscillations is due to inactivation of the main source of beta activity. Had the authors inactivated sites with a preponderance of visual neurons in the FEF would the results be different?

      We do not believe this to be likely based on what is known anatomically and functionally about this circuitry. Anatomically, the projections from FEF to V4 arise primarily from the supragranular layers, not layers which contain the highest proportion of motor activity (Barone et al. 2000, Pouget et al. 2009, Markov et al. 2013). Functionally, based on electrical identification of V4-projecting FEF neurons, we know that FEF to V4 projections are predominantly characterized by delay rather than motor activity (Merrikhi et al. 2017). We have now tried to emphasize these points when we introduce the inactivation experiments (lines 185-186).

      Experimentally, the spread of the pharmacological effect with our infusion system is quite large relative to any clustering of visual vs. motor neurons within the FEF, with behavioral consequences of inactivation spreading to cover a substantial portion of the visual hemifield (e.g., Noudoost et al. 2014, Clark et al. 2014), and so our manipulation lacks the spatial resolution to selectively target motor vs. other FEF neurons.

      (2) Somewhat related to this point and given the prominence of low-frequency activity in deeper layers of the visual cortex according to some previous studies, it is not clear where the authors' V4 recordings were located. The authors report that they do have data from linear arrays, so it should be possible to address this.

      Unfortunately, our chamber placement for V4 has produced linear array penetration angles which do not reliably allow identification of cortical layers. We are aware of previous results showing layer-specific effects of attention in V4 (e.g., Pettine et al. 2019, Buffalo et al. 2011), and it would indeed be interesting to determine whether our observed WM-driven changes follow similar patterns. We may be able to analyze a subset of the data with current source density analysis to look for layer-specific effects in the future, but are not able to provide any information at this time.

      (3) The authors suggest that a change in the exact frequency of oscillation underlies the increase in firing rate for different stimulus features. However, the shift in frequency is prominent for contrast but not for orientation, something that raises questions about the general applicability of this observation for different visual features.

      While the shift in peak frequency across contrasts is more prominent than that across orientations (Fig. S3A-B), the relationship between orientation and peak frequency is also significant (one-way ANOVA for peak frequency across contrasts, F<sub>Contrast</sub>=10.72, p<10<sup>-4</sup>; or across orientations, F<sub>Orientation</sub>=3, p=0.030; stats have been added to Fig. S3 caption). This finding also aligns with previous studies, which reported slight peak frequency shifts (~1–2 Hz) in the context of attention (Fries, 2015). To address the question of whether the frequency-firing rate correlation generalizes to orientation-driven changes, we now examine the relationship between peak frequency and firing rate separately for each contrast level (Fig. S14). The average normalized response as a function of peak frequency, pooled across subsamples of trials from each of 145 V4 neurons (100 subsamples/neuron), IN vs. OUT conditions, shows a significant correlation during the delay period for each contrast (contrast low (F<sub>Condition</sub>=0.03, p=0.867; F<sub>Frequency</sub>=141.86, p<10<sup>-18</sup>; F<sub>Interaction</sub>=10.70, p=0.002, ANCOVA), contrast middle (F<sub>Condition</sub>=7.18, p=0.009; F<sub>Frequency</sub>=96.76, p<10<sup>-14</sup>; F<sub>Interaction</sub>=0.13, p=0.716, ANCOVA), contrast high (F<sub>Condition</sub>=12.51, p=0.001; F<sub>Frequency</sub>=333.74, p<10<sup>-29</sup>; F<sub>Interaction</sub>=7.91, p=0.006, ANCOVA).

      (4) One of the major points of the study is the primacy of the phase code over the rate code during the delay period. Specifically, here it is shown that information about the visual features of a stimulus carried by the rate code is similar for relevant and irrelevant locations during the delay period. This contrasts with what several studies have shown for attention in which case information carried in firing rates about stimuli in the attended location is enhanced relative to that for stimuli in the unattended location. If we are to understand how top-down signals work in cognitive functions it is inevitable to compare working memory with attention. The possible source of this difference is not clear and is not discussed. The reader is left wondering whether perhaps a different measure or analysis (e.g. a percent explained variance analysis) might reveal differences during the delay period for different visual features across the two spatial conditions.

      We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

      The use of the memory-guided saccade task has certain disadvantages in the context of this study. Although delay activity is interpreted as memory activity by the authors, it is in principle possible that it reflects preparation for the upcoming saccade, spatial attention (particularly since there is a stimulus in the RF), etc. This could potentially change the conclusion and perspective.

      We have added a new discussion paragraph addressing the relationship to attention and motor planning (lines 315-333). We have also moderated the language used to describe our conclusions throughout the manuscript in light of this ambiguity.

      For the position outside the V4 RF, there is a decrease in both beta oscillations and the clustering of spikes at a specific phase. It is therefore possible that the decrease in information about the stimuli features is a byproduct of the decrease in beta power and phase locking. Decreased oscillatory activity and phase locking can result in less reliable estimates of phase, which could decrease the mutual information estimates.

      Looking at the SNR as a ratio of power in the beta band to all other bands, there is no significant drop in SNR between conditions (SNRIN = 4.074+-984, SNROUT = 4.333+-0.834 OUT, p=0.341, Wilcoxon signed-rank). Therefore, we do not think that the change in phase coding is merely a result of less reliable phase estimates.

      The authors propose that coherent oscillations could be the mechanism through which the prefrontal cortex influences beta activity in V4. I assume they mean coherent oscillations between the prefrontal cortex and V4. Given that they do have simultaneous recordings from the two areas they could test this hypothesis on their own data, however, they do not provide any results on that.

      This paper only includes inactivation data. We are working on analyzing the simultaneous recording data for a future publication.

      The authors make a strong point about the relevance of changes in the oscillation frequency and how this may result in an increase in firing rate although it could also be the reverse - an increase in firing rate leading to an increase in the frequency peak. It is not clear at all how these changes in frequency could come about. A more nuanced discussion based on both experimental and modeling data is necessary to appreciate the source and role (if any) of this observation.

      As the reviewer notes, it is difficult to determine whether the frequency changes drive the rate changes, vice versa, or whether both are generated in parallel by a common source. We have adjusted our language to reflect this (lines 291-293). Future modeling work may be able to shed more light on the causal relationships between various neural signatures.

      Reviewer #3 (Public review):

      Summary:

      In this report, the authors test the necessity of prefrontal cortex (specifically, FEF) activity in driving changes in oscillatory power, spike rate, and spike timing of extrastriate visual cortex neurons during a visual-spatial working memory (WM) task. The authors recorded LFP and spikes in V4 while macaques remembered a single spatial location over a delay period during which task-irrelevant background gratings were displayed on the screen with varying orientation and contrast. V4 oscillations (in the beta range) scaled with WM maintenance, and the information encoded by spike timing relative to beta band LFP about the task-irrelevant background orientation depended on remembered location. They also compared recorded signals in V4 with and without muscimol inactivation of FEF, demonstrating the importance of FEF input for WM-induced changes in oscillatory amplitude, phase coding, and information encoded about background orientations. Finally, they built a network model that can account for some of these results. Together, these results show that FEF provides meaningful input to the visual cortex that is used to alter neural activity and that these signals can impact information coding of task-irrelevant information during a WM delay.

      Strengths:

      (1) Elegant and robust experiment that allows for clear tests for the necessity of FEF activity in WM-induced changes in V4 activity.

      (2) Comprehensive and broad analyses of interactions between LFP and spike timing provide compelling evidence for FEF-modulated phase coding of task-irrelevant stimuli at remembered location.

      (3) Convincing modeling efforts.

      Weaknesses:

      (1) 0% contrast background data (standard memory-guided saccade task) are not reported in the manuscript. While these data cannot be used to consider information content of spike rate/time about task-irrelevant background stimuli, this condition is still informative as a 'baseline' (and a more typical example of a WM task).

      We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL when remembering the V4 RF location) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

      (2) Throughout the manuscript, the primary measurements of neural coding pertain to task-irrelevant stimuli (the orientation/contrast of the background, which is unrelated to the animal's task to remember a spatial location). The remembered location impacts the coding of these stimulus variables, but it's unclear how this relates to WM representations themselves.

      Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

      Recommendations for the authors:

      Reviewer #1 (Recommendations for the authors):

      As mentioned above, the two points I raised in the public review merit a bit of development in the Discussion. In addition, the authors should revise some of their conclusions.

      For instance (L217):

      "The finding that WM mainly modulates phase coded information within extrastriate areas fundamentally shifts our understanding of how the top-down influence of prefrontal cortex shapes the neural representation, suggesting that inducing oscillations is the main way WM recruits sensory areas."

      In my opinion, this one is over-the-top on various counts.

      Here is another exaggerated instance (L298):

      "...leading us to conclude that representations based on the average firing rate of neurons are not the primary way that top-down signals enhance sensory processing."

      Again, as noted above, the problem is that one could make the case that the top-down signals are, in fact, highly effective, since they are completely quashing any distracter-related modulation in firing rate across RFs. There is only so much that one can conclude from responses to stimuli that are task-irrelevant, uniform across space, and constant over the course of a trial.

      I think even the title goes too far. What the work shows, by all accounts, is that the sustained activity in FEF has a definitive impact on V4 *even* with respect to a sustained, irrelevant background stimulus. The result is very robust in this sense. However, this is quite different from saying that the *primary* means of functional control for FEF is via phase coding. Establishing that would require ruling out other forms of control (i.e., rate coding) in all or a wide range of experimental conditions. That is far from the restricted set of conditions tested here and is also at variance with many other experiments demonstrating effects of attention or even FEF microstimulation on V4 firing activity.

      To reiterate, in my opinion, the work is carefully executed and the data are interesting and largely unambiguous. I simply take issue with what can be reliably concluded, and how the results fit with the rest of the literature. Revisions along these lines would improve the readability of the paper considerably.

      We have edited the title (removing the word ‘primarily’) and key sentences throughout to tone down the conclusions, generally to state that the importance of a phase code in WM modulations is *possible* given the observed results, rather than certain (see abstract lines 26-27, introduction lines 59-62, conclusion lines 310-311).

      Reviewer #3 (Recommendations for the authors):

      (1) My primary comment that came up multiple times as I read the manuscript (and which is summarized above) is that I wasn't ever sure why the authors are focused on analyzing neural coding of task-irrelevant sensory information during a WM task as a function of WM contents (remembered location). Most studies of neural codes supporting WM often focus on coding the remembered information - not other information. Conceptually, it seems that the brain would want to suppress - or at least not enhance - representations of task-irrelevant information when performing a demanding task, especially when there is no search requirement, and when there is no feature correspondence between the remembered and viewed stimuli. (i.e., the interaction between WM and visual input is more obvious for visual search for a remembered target). Why, in theory, would a visual region need to improve its coding of non-remembered information as a function of WM? This isn't meant to detract from the results, which are indeed very interesting and I think quite informative. The authors are correct that this is certainly relevant for sensory recruitment models of WM - there's clear evidence for a role of feedback from PFC to extrastriate cortex - but what role, specifically, each region plays in this task is critical to describe clearly, especially given the task-irrelevance of the input. Put another way: what if the animal was remembering an oriented grating? In that case, MI between spike-based measures and orientation would be directly relevant to questions of neural WM representations, as the remembered feature is itself being modeled. But here, the focus seems to be on incidental coding.

      Indeed, here we have focused on how maintaining spatial WM impacts visual processing of incoming sensory information, rather than on how the spatial WM signal itself is represented and maintained. Behaviorally, this impact on visual signals could be related to the effects of the content of WM on perception and reaction times (e.g., Soto et al. 2008, Awh et al. 1998, Teng et al. 2019), but no such link to behavior is shown in our data.

      Whether similar phase coding is also used to represent the content of object WM (for example, if the animal was remembering an oriented grating), or whether phase coding is only observed for WM’s modulation of the representation of incoming sensory signals, is an important question to be addressed in future work.

      (2) Related to the above, the phrasing of the second sentence of the Discussion (lines 291-292) is ambiguous - do the authors mean that the FEF sends signals that carry WM content to V4, or that FEF sends projections to V4, and V4 has the WM content? As presently phrased, either of these are reasonable interpretations, yet they're directly opposing one another (the next sentence clarifies, but I imagine the authors want to minimize any confusion).

      We have edited this sentence to read, “Within prefrontal areas, FEF sends direct projections to extrastriate visual areas, and activity in these projections reflects the content of WM.”

      (3) I'm curious about how the authors consider the spatial WM task here different from a cued spatial attention task. Indeed, both require sustained use of a location for further task performance. The section of the Discussion addressing similar results with attention (lines 307-311) presently just summarizes the similarities of results but doesn't offer a theoretical perspective for how/why these different types of tasks would be expected to show similar neural mechanisms.

      We have added discussion regarding the relationship of these results to previous findings during attention in the discussion section (lines 315-333).

      (4) As far as I can tell, there is no consideration of behavioral performance on the memory-guided saccade task (RT, precision) across the different stimulus background conditions. This should be reported for completeness, and to determine whether there is an impact of the (likely) task-irrelevant background on task performance. This analysis should also be reported for Figure 3's results characterizing how FEF inactivation disrupts behavior (if background conditions were varied, see point 7 below).

      We have added the effect of inactivation on behavioral RT and % correct across the different stimulus background conditions (Fig. S8). Background contrast and orientation did not impact either RT or % correct.

      (5) Results from Figure 2 (especially Figures 2A-B) concerning phase-locked spiking in V4 should be shown for 0%-contrast trials as well, as these trials better align with 'typical' WM tasks.

      We have added a new supplementary figure to show the effect of WM on V4 LFP power and SPL in 0% contrast trials (Fig. S6). These results (increases in beta LFP power and SPL) match our previous report for the effect of spatial WM on LFP power and SPL within extrastriate area MT (Bahmani et al. 2018).

      (6) The magnitude of SPL difference in aggregate (Figure 2B) is much, much smaller than that of the example site shown (Figure 2A), such that Figure 2A's neuron doesn't appear to be visible on Figure 2B's scatterplot. Perhaps a more representative sample could be shown? Or, the full range of x/y axes in Figure 2B could be plotted to illustrate the full distribution.

      We have updated Fig. 2A with a more representative sample neuron.

      (7) I'm a bit confused about the FEF inactivation experiments. In the Methods (lines 512-513), the authors mention there was no background stimulus presented during the inactivation experiment, and instead, a typical 8-location MGS task was employed. However, in the results on pg 8 (Lines 201-214), and Figure 3G, the authors quantify a phase code MI. The previous phase code MI analysis was looking at MI between each spike's phase and the background stimulus - but if there's no background, what's used to compute phase code MI? Perhaps what they meant to write was that, in addition to the primary task with a manipulation of background properties, an 8-location MGS task was additionally employed.

      The reviewer is correct that both tasks were used after inactivation (the 8-location task to assess the spread of the behavioral effect of inactivation, and the MGS-background task for measuring MI). We have edited the methods text to clarify.

      (8) How is % Correct defined for the MGS task? (what is the error threshold? Especially for the results described in lines 192-193).

      The % correct is defined as correct completed trials divided by the total number of trials; the target window was a circle with radius of 2 or 4 dva (depending on cue eccentricity). These details have been added to the Methods.

      (9) The paragraph from lines 183-200 describes a number of behavioral results concerning "scatter" and "RT" - the RT shown seems extremely high, and perhaps is normalized. Details of this normalization should be included in the Methods. The "scatter" is listed as dva, but it's not clear how scatter is quantified (std dev of endpoint distribution? Mean absolute error), nor how target eccentricity is incorporated (as scatter is likely higher for greater target eccentricity).

      We have renamed ‘scatter’ to ‘saccade error’ in the text to match the figure, and now provide details in the Methods section. Both RT and saccade error are normalized for each session, details are now provided in the Methods. Since error was normalized for each session before performing population statistics, no other adjustment for eccentricity was made.

    1. Reviewer #3 (Public review):

      Summary:

      This paper points out an inconsistency of the roles of the striatal spiny neurons projecting to the indirect pathway (iSPN) and the synaptic plasticity rule of those neurons expressing dopamine D2 receptors, and proposes a novel, intriguing mechanisms that iSPNs are activated by the efference copy of the chosen action that they are supposed to inhibit.

      The proposed model was supported by simulations and analysis of the neural recording data during spontaneous behaviors.

      Strengths:

      Previous models suggested that the striatal neurons learn action values functions, but how the information about the chosen action is fed back to the striatum for learning was not clear. The author pointed out that this is a fundamental problem for iSPNs that are supposed to inhibit specific actions and its synaptic inputs are potentiated with dopamine dips.

      The authors proposes a novel hypothesis that iSPNs are activated by efference copy of the selected action which they are supposed to inhibit during action selection. Even though intriguing and seemingly unnatural, the authors demonstrated that the model based on the hypothesis can circumvent the problem of iSPNs learning to disinhibit the actions associated with negative reward errors. They further showed by analyzing the cell-type specific neural recording data by Markowitz et al. (2018) that iSPN activities tend to be anti-correlated before and after action selection.

      Weaknesses:

      (1) It is not correct to call the action value learning using the externally-selected action as "off-policy." Both off-policy algorithm Q-learning and on-policy algorithm SARSA update the action value of the chosen action, which can be different from the greedy action implicated by the present action values. In standard reinforce learning terminology, on-policy or off-policy is regarding the actions in the subsequent state, whether to use the next action value of (to be) chosen action or that of greedy choice as in equation (7).<br /> It is worth noting that this paper suggested that dopamine neurons encode on-policy TD errors: Morris G, Nevet A, Arkadir D, Vaadia E, Bergman H (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9, 1057-63. https://doi.org/10.1038/nn1743

      (2) It is also confusing to contract TD learning and Q-learning, as the latter is considered as on type of TD learning. In the TD error signal by state value function (6) is dependent on the chosen action a_{t-1} implicitly in r_t and s_t based on the reward and state transition function.

      (3) It is not clear why interferences of the activities for action selection and learning can be avoided, especially when actions are taken with short intervals or even temporal overlaps. How can the efference copy activation for the previous action be dissociated with the sensory cued activation for the next action selection?

      (4) Although it may be difficult to single out the neural pathway that carries the efference copy signal to the striatum, it is desired to consider their requirements and difference possibilities. A major issue is that the time delay from actions to reward feedback can be highly variable.

      An interesting candidate is the long-latency neurons in the CM thalamus projecting to striatal cholinergic interneurons, which are activated following low-reward actions:<br /> Minamimoto T, Hori Y, Kimura M (2005). Complementary process to response bias in the centromedian nucleus of the thalamus. Science, 308, 1798-801. https://doi.org/10.1126/science.1109154

      (5) In the paragraph before Eq. (3), Eq (1) should be Eq. (2) for the iSPN.

      Here are comments back to the authors' replies with the revised version:

      (1) I do not agree on the use of inaccurate technical terms. On-policy does not require that the policy is greedy with respect to the actions values, as authors seem to assume here.

      In fact, the policy (10) is just a standard soft-max action selection based on the action values by the difference of dSPN and iSPN outputs.

      Furthermore, in the immediate reward setting tested in this paper, action values are independent of the policy, so there is no distinction between on-policy vs. off-policy. This is also apparent from the "TD" errors in (19) and (21), where there is no TD.

      (2) To really compare the different forms of TD, multi-step RL tasks should be used.

      (3) This fundamental limitation should be explicitly documented in the manuscript. This is not just the same as any RL algorithms. Having two action representations within each action step make temporal credit assignment more difficult.

    1. Chronologie des principaux événements abordés dans la source :

      • Période contemporaine (avant la conférence) :Constat d'un angle mort dans l'éducation en France : Mise en évidence d'une attention insuffisante portée aux compétences sociales et comportementales des élèves (confiance, coopération, autonomie).
      • Identification d'une faiblesse des élèves français : Les enquêtes (notamment PISA et PIAC) révèlent un déficit notable des élèves français (et des adultes) en matière de compétences sociales et comportementales (état d'esprit de développement, anxiété, sentiment de compétence, persévérance, ouverture à la résolution de problèmes, locus de contrôle, coopération).
      • Mesure des compétences sociales et comportementales : Utilisation de divers outils pour évaluer ces compétences, allant de questionnaires déclaratifs (estime de soi, sentiment d'efficacité personnelle, locus de contrôle, état d'esprit de développement, sentiment d'appartenance) à des tâches comportementales (jeux de confiance, jeux de coopération).
      • Importance des études de jumeaux (Twin studies) : Ces études, bien que méthodologiquement perfectibles, suggèrent que la part génétique dans l'intelligence (QI) se situe entre un tiers et la moitié, laissant une marge de manœuvre significative pour le développement.
      • Développement du concept d'état d'esprit de développement par Carole Dweck : Cette théorie met en avant l'idée que l'intelligence est malléable et peut progresser avec les efforts.
      • Comparaisons internationales via les enquêtes PISA : La France se situe en dessous de la moyenne de l'OCDE pour l'état d'esprit de développement et présente des niveaux d'anxiété plus élevés, un sentiment de compétence en mathématiques plus faible (non corrélé à leur niveau réel), une persévérance moindre, un manque d'ouverture à la résolution de problèmes et un locus de contrôle plus externe que d'autres pays comparables. La coopération est particulièrement problématique, la France étant le dernier pays de l'OCDE dans cet indice.
      • Multiplication d'études sur l'impact du développement de l'état d'esprit : Des recherches en Norvège, aux États-Unis, en Turquie et en France montrent des effets positifs et significatifs de la formation à l'état d'esprit de développement sur les résultats scolaires des élèves.
      • Expérimentation en France dans les collèges REP+ : Une expérimentation contrôlée a montré des effets positifs d'un programme de formation à l'état d'esprit de développement (12 sessions sur 4 ans) sur l'état d'esprit, le comportement en classe et les résultats scolaires des élèves.
      • Travaux de l'Education Endowment Foundation (EEF) : Cet organisme met en évidence l'impact variable des actions éducatives (taille des classes, apprentissage coopératif, feedback) sur l'apprentissage des élèves, soulignant l'efficacité de l'apprentissage coopératif et surtout du feedback individualisé et positif.
      • Programme intensif à Montréal pour enfants défavorisés (7-9 ans) : Ce programme axé sur le contrôle de soi et les habiletés sociales a eu des effets significatifs à long terme sur la réussite scolaire (augmentation de l'obtention du bac), l'insertion professionnelle (augmentation des revenus, baisse de l'inactivité) et la société (baisse de la criminalité, de la dépendance aux prestations sociales). Une analyse de rentabilité a montré un rendement de 11 dollars pour 1 dollar investi.
      • Nécessité de réformer l'évaluation des élèves : La méthode d'évaluation actuelle est jugée contradictoire avec le feedback positif nécessaire à la progression.
      • Importance de la formation des enseignants et des parents : Il est souligné le besoin de former les enseignants aux pédagogies favorisant les compétences sociales et comportementales (apprentissage coopératif, feedback, développement de l'état d'esprit) et d'impliquer également les parents dans ce processus.
      • Mise en place d'expérimentations à grande échelle en France : Les programmes "Motiveaction" pour les enseignants et "1, 2, 3 CP" pour les parents visent à développer ces compétences chez les enfants.

      Cast des personnages principaux mentionnés dans la source :

      • Élise Huillery : Économiste française spécialiste des questions d'éducation. Elle est l'auteure de la présentation dont sont extraits les propos, et ses recherches portent notamment sur l'importance et le développement des compétences sociales et comportementales des élèves. Elle a participé à des expérimentations sur ce sujet en France.
      • Stan (mentionné au début) : Personne non identifiée dans la source, probablement un interlocuteur ou une audience à qui Élise Huillery s'adresse de manière informelle au début de sa présentation. Elle l'interpelle en plaisantant sur la possibilité de tenir compte de ses propos pour une éventuelle réforme.
      • Philippe Lagon (mentionné) : Personne non identifiée dans la source, probablement une personne ayant introduit Élise Huillery ou ayant participé à une discussion antérieure mentionnée lors de la présentation. Il est cité en référence à une discussion sur la nature expérimentale de certaines recherches.
      • Yan Algan (mentionné) : Économiste, ayant collaboré avec Élise Huillery et Corine Prost sur une étude utilisant les enquêtes PISA pour analyser les compétences comportementales des élèves français. Il a également mené des recherches avec Élisabeth Bisley sur le suivi à long terme du programme de Montréal.
      • Corine Prost (mentionnée) : Chercheuse ayant collaboré avec Élise Huillery et Yan Algan sur une étude utilisant les enquêtes PISA concernant les compétences comportementales.
      • Carole Dweck (mentionnée) : Psychologue américaine ayant développé le concept d'état d'esprit de développement (mindset), qui postule que l'intelligence est malléable et peut être développée par l'effort et l'apprentissage.
      • Élisabeth Bisley (mentionnée) : Chercheuse ayant collaboré avec Yan Algan sur le suivi à long terme du programme intensif de Montréal évaluant l'impact des compétences sociales sur la vie adulte.
    2. Briefing Document : Développer la coopération, la confiance et l’autonomie des élèves

      Source : Excerpts from "Développer la coopération, la confiance et l’autonomie des élèves - Élise Huillery"

      Thème Central : La nécessité de reconnaître et de développer les compétences sociales et comportementales (CSC) des élèves comme un levier essentiel de la réussite scolaire, de l'insertion professionnelle et du bien-être sociétal. Élise Huillery argumente que ces compétences sont actuellement un "angle mort" du système éducatif français, bien que leur importance soit de plus en plus reconnue.

      Points Clés et Arguments Principaux :

      1. Définition et Importance des Compétences Sociales et Comportementales (CSC) :

      • Compétences Comportementales (Rapport à soi) : Estime de soi, optimisme, état d'esprit de développement (croire en la malléabilité de l'intelligence), locus de contrôle (sentiment de maîtrise sur ce qui nous arrive), contrôle de l'impulsivité, autodiscipline.
      • Huillery précise : "les compétences comportementales ça va référer à tout ce qui est rapport à soi-même donc on va retrouver dans les compétences comportementales dans le rapport à soi des des l'estime de soi une forme d'optimisme par rapport à à ses chances de réussite un état d'esprit de développement [...] un locus de contrôle [...] et la capacité en fait tout tout ce que je viens de citer ça réfère à un sentiment qu'on est capable de progresser qu'on est capable en faisant des efforts d'y arriver et puis il y a également de des des compétences très importantes qui sont le contrôle de son impulsivité et l'autodiscipline."
      • Compétences Sociales (Rapport aux autres) : Capacité à coopérer, empathie, respect, tolérance, contrôle de l'impulsivité et de l'agressivité envers les autres, sentiment d'appartenance.
      • Elle explique : "dans les champs des compétences sociales donc là on va parler du rapport aux autres et de la qualité du rapport aux autres donc entre autres on va avoir la capacité à coopérer à être empathique à respecter à tolérer à contrôler également l'impulsivité l'agressivité par rapport aux autres mais également le sentiment d'appartenance le sentiment de faire partie d'une équipe que l'on soutient et par laquelle on est soutenu".
      • Terminologie : Huillery clarifie l'utilisation de termes variés tels que "soft skills", "compétences non cognitives" ou "compétences socio-émotionnelles", soulignant qu'il s'agit bien de compétences cognitives qui se développent dans le cerveau. Elle préfère l'appellation "compétences sociales et comportementales" pour éviter les connotations réductrices de certains termes.

      2. Le Déficit Français en Matière de CSC :

      • État d'esprit de développement : Les élèves français ont un score d'état d'esprit de développement inférieur à la moyenne des pays de l'OCDE, indiquant une moindre croyance en la capacité de l'intelligence à évoluer.
      • "ce qu'on voit c'est que la France est en dessous de la moyenne des pays de l'OCDE concernant le score d'état d'esprit de développement donc la croyance que l'intelligence c'est quelque chose qui peut évoluer et qui peut s'entraîner et se développer en fait."
      • Anxiété, Sentiment de Compétence, Persévérance, Ouverture à la Résolution de Problèmes, Locus de Contrôle : Les données PISA montrent que les élèves français sont plus anxieux, ont un sentiment de compétence en mathématiques plus faible (même en étant dans la moyenne des performances), sont moins persévérants, moins ouverts à la résolution de problèmes et ont un locus de contrôle plus externe (sentiment que ce qui leur arrive dépend de facteurs extérieurs).
      • Concernant l'anxiété : "ce qu'on voit c'est que la France a une les élèves français [...] ont un niveau d'anxiété qui est nettement au-dessus des pays de l'OCDE".
      • Sur le locus de contrôle : "l'écart par rapport à la moyenne des autres pays est très fort on a des élèves pour qui le locus de contrôle est beaucoup plus externe et donc bah par contraposer moins interne donc en gros nos élèves pensent plus qu'ailleurs que ce qui leur arrive dépend de l'extérieur de contrainte extérieure et non pas de leur stratégie de leur travail de leurs efforts à eux."
      • Coopération : La France se situe au dernier rang des pays de l'OCDE en termes de capacité et de perception de la pratique coopérative.
      • "pour la coopération c'est la catastrophe nous sommes le dernier pays dernier pays de tous les pays de l'OCDE dans notre dans la capacité en fait l'indice de coopération il mesure la comment les élèves voient la pratique coopérative et comment ils l'utilisent et alors là on est les derniers."
      • Sentiment d'Appartenance et Résolution Collaborative de Problèmes : Bien que moins marqué, la France est également en dessous de la moyenne de l'OCDE dans ces domaines.
      • Similitudes chez les Adultes : Huillery souligne que ces déficits observés chez les élèves se retrouvent également chez les adultes, d'après les enquêtes comme le PIAC.

      3. L'Impact des CSC sur la Réussite Scolaire, l'Insertion Professionnelle et la Société :

      • État d'esprit de développement et réussite scolaire : Des études menées dans plusieurs pays, dont la France, montrent que le développement d'un état d'esprit de développement chez les élèves a des effets positifs et significatifs sur leurs résultats scolaires et leurs apprentissages, même à long terme. Une expérimentation menée dans des collèges REP+ en France a confirmé ces effets positifs sur l'état d'esprit, le comportement en classe et les résultats scolaires des élèves.
      • "ces programmes ont tous montré des effets positifs et significatifs sur des tests scolaires sur les notes des élèves sur des apprentissages même 2 ans et demi après les interventions".
      • Apprentissage Coopératif et Feedback : L'apprentissage coopératif est une pratique pédagogique à fort impact sur l'apprentissage des élèves, avec un coût relativement faible (principalement de la formation des enseignants). Le feedback individualisé et positif est identifié comme l'intervention éducative ayant l'impact le plus élevé sur la progression des élèves.
      • Concernant l'apprentissage coopératif : "l'apprentissage coopératif ça fait partie des pratiques pédagogiques qui ont été le plus étudié avec le un niveau de de confiance dans les résultats élevés et qui montre des effets qui sont bien plus importants euh par rapport au au à la réduction des tailles de classe".
      • Sur le feedback positif : "le feedback doit aussi être positif c'est-à-dire que le feedback doit dire à l'élève pas seulement ce sur quoi il faut qu'il retravaille [...] il faut il faut faire aussi beaucoup de feedback positif en disant c'est super tu as progressé là-dessus je suis très contente que tu aies réussi à faire ce cette chose-là même si là et on va essayer de retravailler et cetera."
      • Programme de Montréal (Contrôle de Soi et Habilités Sociales) : Un programme ciblant des enfants défavorisés âgés de 7 à 9 ans, axé sur le contrôle de soi et les habiletés sociales, a eu des effets spectaculaires à long terme : amélioration des compétences sociales, augmentation du taux d'obtention du baccalauréat, hausse des revenus annuels, baisse de l'inactivité, diminution de la criminalité et de la dépendance aux prestations sociales. Ce programme s'est avéré être un investissement très rentable pour la société.
      • "ce programme se finance lui-même fois 11 donc pour 1 € dépensé dans le programme on a 11 enfin c'est des dollars on a 11 dollars de gagner entre guillemets si on fait toute la somme de ces bénéfices à la fois individuels et sociaux."
      • Les CSC comme facteurs instrumentaux : Huillery insiste sur le fait que le développement des CSC n'est pas seulement souhaitable en soi, mais qu'elles sont des compétences "instrumentales" qui favorisent la réussite scolaire et l'insertion professionnelle, générant des "bénéfices publics" importants.

      4. Recommandations pour l'École :

      • Développer l'apprentissage coopératif : Mettre en place des pratiques pédagogiques favorisant la coopération et la collaboration entre les élèves.
      • Travailler sur l'état d'esprit de développement : Intégrer la promotion de cet état d'esprit au cœur du système éducatif, en formant les enseignants et les parents.
      • Réformer l'évaluation des élèves : Adopter des méthodes d'évaluation qui favorisent un feedback positif et encourageant, aligné sur la progression et non uniquement sur le constat des erreurs.
      • Former les enseignants (initiale et continue) : Renforcer la formation des enseignants aux pédagogies et aux attitudes qui développent les CSC.
      • Impliquer et former les parents : Reconnaître le rôle central des parents et les former aux pratiques et attitudes favorisant le développement des CSC chez leurs enfants.

      Conclusion Principale :

      Le discours d'Élise Huillery met en lumière un enjeu crucial pour le système éducatif français : la prise en compte et le développement actif des compétences sociales et comportementales des élèves.

      En s'appuyant sur des données de recherche solides, elle démontre que ces compétences ne sont pas des "soft skills" secondaires, mais des leviers fondamentaux pour améliorer la réussite scolaire, favoriser l'insertion professionnelle et générer des bénéfices significatifs pour la société dans son ensemble.

      Elle appelle à une transformation des pratiques pédagogiques, de la formation des enseignants et de l'implication des parents pour combler le déficit français dans ce domaine et exploiter pleinement le potentiel des élèves.

    3. Résumé de la vidéo [00:00:00][^1^][1] - [00:21:53][^2^][2]: * 00:00 Début * 01:03 Sommaire * 02:11 I es compétences * 04:18 Les mesures * 09:48 Déficit de compétences * 16:58 Compétences sociales * 17:34 Expérimentation au collège * 20,57 Impact des politiques éducatives * 24:38 Impact sur l'insertion professionnelle * 26:43 Calcul de rentabilité * 28:07 Recommandations

      Cette vidéo, présentée par Élise Huillery, aborde l'importance des compétences sociales et comportementales dans l'éducation, telles que la confiance, la coopération et l'autonomie des élèves. Elle souligne que ces compétences sont souvent négligées dans le système éducatif français, mais qu'elles sont essentielles pour la réussite scolaire, l'insertion professionnelle et les bénéfices publics. Huillery propose des recommandations pour développer ces compétences chez les élèves.

      Points forts: + [00:00:22][^3^][3] L'importance des compétences sociales * Nécessité de développer la confiance, la coopération et l'autonomie * Impact sur la réussite scolaire et l'insertion professionnelle * Bénéfices pour la société en général + [00:02:14][^4^][4] Les compétences comportementales et sociales * Comprend l'estime de soi, l'optimisme et le contrôle de l'impulsivité * Inclut la capacité à coopérer, l'empathie et le sentiment d'appartenance + [00:09:51][^5^][5] Le déficit des compétences chez les élèves français * Les élèves français montrent un manque de compétences comparé à l'OCDE * Importance de l'état d'esprit de développement et de la persévérance + [00:17:36][^6^][6] L'impact des compétences sur la réussite éducative * Les programmes visant à développer un état d'esprit de croissance montrent des résultats positifs * Amélioration du comportement en classe et des performances scolaires Résumé de la vidéo [00:21:56]¹[1] - [00:30:56]²[2]:

      La vidéo présente une conférence d'Élise Huillery sur l'amélioration de l'apprentissage des élèves en France. Elle discute de l'importance de réduire la taille des classes, mais souligne que cela ne suffit pas sans transformer les pédagogies et motiver les élèves. Elle met en avant l'apprentissage coopératif et le feedback positif comme des leviers efficaces pour améliorer l'éducation.

      Points forts: + [00:22:00]³[3] Réduction de la taille des classes * Effets limités sans changement pédagogique * Coûts élevés par rapport aux bénéfices * Nécessité d'autres actions pour améliorer l'efficacité + [00:22:39]⁴[4] Apprentissage coopératif * Pratiques pédagogiques efficaces et peu coûteuses * Formation des enseignants requise * Impact significatif sur la progression des élèves + [00:23:24]⁵[5] Feedback positif * Retours individualisés pour guider les progrès * Importance de l'encouragement et de la reconnaissance des réussites * Distinction entre feedback positif et négatif + [00:24:46]⁶[6] Programme à Montréal * Ciblage des compétences sociales et comportementales * Effets à long terme sur l'éducation et l'insertion professionnelle * Rentabilité élevée pour la société + [00:28:14]⁷[7] Déficit en compétences sociales en France * Rôle crucial dans la réussite scolaire et professionnelle * Possibilité de transformation par des programmes éducatifs + [00:29:01]⁸[8] Recommandations pour l'école * Développement de l'apprentissage coopératif et de l'état d'esprit de développement * Réforme de l'évaluation des élèves * Formation continue des enseignants et des parents

    1. Résumé de la vidéo [00:00:00][^1^][1] - [00:25:06][^2^][2]:

      Cette vidéo présente comment Anne-Marie enseigne les soft skills à ses élèves au CFA de La Roche-sur-Yon. Elle explique l'importance des compétences comportementales, relationnelles et cognitives dans le monde professionnel et comment elle les intègre dans son enseignement.

      Points forts : + [00:00:00][^3^][3] Introduction aux soft skills * 83 % des employeurs les considèrent essentiels * Anne-Marie enseigne au CFA depuis plus de 30 ans * Importance des compétences comportementales et relationnelles + [00:01:59][^4^][4] Enseignement des soft skills * Compétences non étudiées en classe ou en entreprise * Importance de la ponctualité et de la politesse * Adaptation au monde professionnel et au CFA + [00:05:08][^5^][5] Évaluation des soft skills * Utilisation d'un tableau pour l'auto-évaluation * Trois domaines : cognitif, relationnel, comportemental * Importance de l'autonomie et de l'initiative + [00:07:00][^6^][6] Méthodes d'enseignement * Classe flexible et travail en autonomie * Importance de la coopération et de l'entraide * Encouragement à la réflexion et à la créativité + [00:11:00][^7^][7] Suivi et progression des élèves * Auto-évaluation et évaluation par l'enseignant * Importance de la motivation et de la ponctualité * Récompense des élèves pour leur progression et leur auto-évaluation précise

      Résumé de la vidéo [00:25:08][^1^][1] - [00:25:51][^2^][2]:

      Cette vidéo présente une discussion avec Anne-Marie sur l'enseignement des compétences interpersonnelles à ses élèves en CFA. Elle partage ses méthodes et l'impact de ces compétences sur les étudiants.

      Points forts : + [00:25:08][^3^][3] Importance de la répétition * Répéter jusqu'à 10 fois * Satisfaction après plusieurs essais * Encouragement à persévérer + [00:25:18][^4^][4] Remerciements à Anne-Marie * Accueil chaleureux * Interaction avec les élèves * Contribution à l'éducation + [00:25:31][^5^][5] Crédits de la production * Podcast animé par Nathalie Drefus * Enregistrement et montage par l'équipe * Ressources supplémentaires sur êtreprof.fr

    1. 标准模型中的规范群(如 ​SU(3)×SU(2)×U(1))的表示由特征标理论分析

      以下是关于标准模型中规范群 \( \text{SU}(3) \times \text{SU}(2) \times \text{U}(1) \) 的表示及其通过特征标理论分析的详细解释:


      1. 标准模型规范群的结构

      标准模型的规范群是三个群的直积: [ \text{G} = \text{SU}(3)\text{C} \times \text{SU}(2)\text{L} \times \text{U}(1)\text{Y}, ] 其中: - \( \text{SU}(3)\text{C} \):强相互作用,描述夸克之间的色荷对称性。 - \( \text{SU}(2)\text{L} \times \text{U}(1)\text{Y} \):弱电相互作用,通过希格斯机制破缺为电磁相互作用 \( \text{U}(1)_\text{EM} \)。


      2. 规范群的表示与特征标

      (1) SU(3) 的表示

      • 不可约表示:由最高权 \( (\lambda, \mu) \) 标记,维度为: [ \frac{1}{2} (\lambda + 1)(\mu + 1)(\lambda + \mu + 2). ]
      • 基本表示
      • 3:夸克的色三重态(红、绿、蓝)。
      • \(\overline{3}\):反夸克的反三重态。
      • 特征标:SU(3) 的特征标 \( \chi_{(\lambda, \mu)} \) 由表示的最高权决定,例如:
      • \( \chi_3(g) = \text{tr}(\rho(g)) \),其中 \( \rho \) 是SU(3)的3维表示。

      (2) SU(2) 的表示

      • 不可约表示:由自旋 \( j \) 标记,维度为 \( 2j + 1 \)。
      • 基本表示
      • 2:左旋费米子(如上/下夸克、电子/中微子)的双旋量。
      • 1:右旋费米子(无SU(2)作用)。
      • 特征标:SU(2) 的特征标为: [ \chi_j(g) = \frac{\sin((j + 1/2)\theta)}{\sin(\theta/2)}, ] 其中 \( g \) 对应旋转角度 \( \theta \)。

      (3) U(1) 的表示

      • 不可约表示:由电荷 \( Y \)(超荷)标记,特征标为: [ \chi_Y(g) = e^{iY\theta}, ] 其中 \( g \) 对应相位因子 \( e^{i\theta} \)。

      3. 直积群的表示与特征标

      规范群 \( \text{G} \) 的表示是三个子群表示的直积: [ \rho = \rho_{\text{SU}(3)} \otimes \rho_{\text{SU}(2)} \otimes \rho_{\text{U}(1)}. ] 其特征标为各分量特征标的乘积: [ \chi_\rho(g_1, g_2, g_3) = \chi_{\text{SU}(3)}(g_1) \cdot \chi_{\text{SU}(2)}(g_2) \cdot \chi_{\text{U}(1)}(g_3). ]


      4. 特征标理论的应用

      (1) 粒子的量子数

      标准模型中的粒子由规范群的表示决定其量子数: - 夸克:属于 \( 3 \otimes 2 \otimes Y \) 表示,例如: - 上夸克 \( u \):\( Y = \frac{1}{3} \),电荷 \( Q = T_3 + Y = \frac{2}{3} \)(通过希格斯机制)。 - 下夸克 \( d \):\( Y = \frac{1}{3} \),电荷 \( Q = -\frac{1}{3} \)。

      (2) 场的相互作用

      • 规范玻色子
      • \( \text{SU}(3) \):8种胶子(\( \text{adj}(3) \) 表示)。
      • \( \text{SU}(2) \):3种 \( W^\pm, Z \) 玻色子(\( \text{adj}(2) \) 表示)。
      • \( \text{U}(1) \):光子(\( \text{adj}(1) \) 为平凡表示)。

      (3) 希格斯机制

      • 希格斯场:属于 \( 1 \otimes 2 \otimes Y=-1 \) 表示。
      • 对称性破缺:希格斯场的真空期望值 \( \langle H \rangle \) 破缺 \( \text{SU}(2)L \times \text{U}(1)_Y \to \text{U}(1)\text{EM} \),导致:
      • \( W^\pm, Z \) 获得质量。
      • 光子 \( A_\mu \) 的电荷 \( Q = T_3 + Y \sin^2\theta_W \)。

      (4) 粒子的组合与衰变

      • 夸克反夸克对:\( 3 \otimes \overline{3} = 8 \oplus 1 \),对应八重态和单重态介子。
      • 费米子与玻色子的耦合:通过特征标分析场的相互作用顶点,例如: [ \text{上夸克} \otimes \text{下夸克} \otimes W^+ \sim (2 \otimes 2) \ni 1 \text{(通过SU(2)分解)}. ]

      5. 具体例子:费米子的表示

      (1) 上夸克 \( u_L \)

      • 表示:\( 3 \otimes 2 \otimes Y = \frac{1}{3} \)。
      • 特征标: [ \chi_{u_L}(g) = \chi_3(g_3) \cdot \chi_{1/2}(g_2) \cdot e^{i(Y\theta)}. ]

      (2) 电子 \( e_R \)

      • 表示:\( 1 \otimes 1 \otimes Y = -1 \)(右旋电子无SU(2)作用)。
      • 电荷:\( Q = T_3 + Y = 0 + (-1) = -1 \)。

      (3) \( W^+ \) 玻色子

      • 表示:\( 1 \otimes 3 \otimes Y = 0 \)(\( \text{SU}(2) \) 的伴随表示)。
      • 质量:通过希格斯场 \( H \) 的真空期望值 \( \langle H \rangle \) 获得。

      6. 特征标理论的数学工具

      (1) 分解表示

      通过特征标正交性分解直积表示: [ \text{SU}(3) \otimes \text{SU}(2) \otimes \text{U}(1) \text{ 的表示} \to \bigoplus_{i,j,k} N_{ijk} \cdot (i,j,k), ] 其中 \( N_{ijk} \) 是非负整数,由特征标内积计算。

      (2) 电荷的计算

      电磁荷 \( Q \) 由弱超荷 \( Y \) 和SU(2)第三分量 \( T_3 \) 组合而来: [ Q = T_3 + \frac{Y}{2}. ] 例如,上夸克 \( u_L \): [ Q = \frac{1}{2} + \frac{1}{3} \cdot \frac{1}{2} = \frac{2}{3}. ]


      7. 总结:特征标理论在标准模型中的核心作用

      | 应用 | 特征标理论的作用 | |------------------------|------------------------------------------------------------------------------------| | 粒子分类 | 确定粒子属于哪个表示,计算其量子数(如电荷、色荷)。 | | 相互作用分析 | 通过表示的张量积分解,确定粒子间可能的相互作用(如弱相互作用的费米子-玻色子耦合)。 | | 对称性破缺 | 分析希格斯场如何破缺对称性,导致规范玻色子获得质量。 | | 实验预测 | 预测新粒子的存在(如顶夸克、希格斯玻色子)及其性质。 |


      8. 扩展思考:更复杂的表示与超对称

      特征标理论还可推广到: - 超对称模型:引入超对称群 \( \text{SU}(3) \times \text{SU}(2) \times \text{U}(1) \times \text{SU}(N)_\text{SUSY} \)。 - GUT理论:如 \( \text{SU}(5) \) 或 \( \text{SO}(10) \),其表示通过分支规则分解为标准模型的子表示。


      通过特征标理论,标准模型的规范群结构得以系统化分析,成为现代粒子物理中连接对称性、相互作用和粒子性质的核心工具。

    1. 矩阵群的商群

      以下是关于矩阵群商群的详细解释,以 GL(n, ℝ)SL(n, ℝ) 为例:


      1. 基本定义

      (1)原群:一般线性群 \( \text{GL}(n, \mathbb{R}) \)

      • 定义:所有 \( n \times n \) 可逆实矩阵的集合,运算为矩阵乘法。
      • 性质:这是一个非阿贝尔群(当 \( n \geq 2 \) 时,矩阵乘法不满足交换律)。

      (2)正规子群:特殊线性群 \( \text{SL}(n, \mathbb{R}) \)

      • 定义:行列式为 1 的所有 \( n \times n \) 可逆实矩阵的集合: [ \text{SL}(n, \mathbb{R}) = { A \in \text{GL}(n, \mathbb{R}) \mid \det(A) = 1 }. ]
      • 正规性:\( \text{SL}(n, \mathbb{R}) \) 是 \( \text{GL}(n, \mathbb{R}) \) 的正规子群,因为它是行列式同态的核(见后文)。

      2. 商群 \( \text{GL}(n, \mathbb{R})/\text{SL}(n, \mathbb{R}) \)

      (1)陪集的构造

      • 左陪集形式:对于任意 \( A \in \text{GL}(n, \mathbb{R}) \),其左陪集定义为: [ A \cdot \text{SL}(n, \mathbb{R}) = { A \cdot S \mid S \in \text{SL}(n, \mathbb{R}) }. ]
      • 几何意义:每个陪集 \( A \cdot \text{SL}(n, \mathbb{R}) \) 包含所有行列式为 \( \det(A) \) 的矩阵。这是因为:
      • 若 \( S \in \text{SL}(n, \mathbb{R}) \),则 \( \det(A \cdot S) = \det(A) \cdot \det(S) = \det(A) \cdot 1 = \det(A) \)。
      • 因此,所有行列式为 \( \det(A) \) 的矩阵构成一个陪集。

      (2)陪集的划分

      • 互不相交且覆盖
      • 如果两个陪集 \( A \cdot \text{SL}(n, \mathbb{R}) \) 和 \( B \cdot \text{SL}(n, \mathbb{R}) \) 有公共元素,则它们必相等(因为它们的行列式相同)。
      • 每个可逆矩阵 \( A \) 的行列式 \( \det(A) \) 是一个非零实数,因此所有陪集覆盖了 \( \text{GL}(n, \mathbb{R}) \)。

      3. 商群与非零实数乘法群的同构

      (1)行列式同态的作用

      • 同态定义:行列式映射: [ \det: \text{GL}(n, \mathbb{R}) \to \mathbb{R}^* \quad (\text{非零实数乘法群}), ] 其中 \( \det(A) \) 是矩阵 \( A \) 的行列式。
      • 核与像
      • :\( \ker(\det) = { A \in \text{GL}(n, \mathbb{R}) \mid \det(A) = 1 } = \text{SL}(n, \mathbb{R}) \)。
      • :\( \operatorname{Im}(\det) = \mathbb{R}^ \),因为对任意 \( c \in \mathbb{R}^ \),存在矩阵 \( A \)(如对角矩阵 \( \text{diag}(c, 1, \dots, 1) \))使得 \( \det(A) = c \)。

      (2)同态基本定理的应用

      根据同态基本定理: [ \text{GL}(n, \mathbb{R}) / \ker(\det) \cong \operatorname{Im}(\det). ] 代入上述结果: [ \text{GL}(n, \mathbb{R}) / \text{SL}(n, \mathbb{R}) \cong \mathbb{R}^*. ]


      4. 具体同构映射

      (1)构造同构

      定义映射: [ \phi: \text{GL}(n, \mathbb{R}) / \text{SL}(n, \mathbb{R}) \to \mathbb{R}^*, ] 将每个陪集 \( A \cdot \text{SL}(n, \mathbb{R}) \) 映射到其行列式值: [ \phi(A \cdot \text{SL}(n, \mathbb{R})) = \det(A). ]

      (2)验证同构

      • 井然有序(Well-defined):若 \( A \cdot \text{SL}(n, \mathbb{R}) = B \cdot \text{SL}(n, \mathbb{R}) \),则 \( \det(A) = \det(B) \),因此 \( \phi \) 不依赖于陪集代表元的选择。
      • 满射:对任意 \( c \in \mathbb{R}^* \),存在矩阵 \( A \) 使得 \( \det(A) = c \),故 \( \phi \) 满射。
      • 单射:若 \( \phi(A \cdot \text{SL}(n, \mathbb{R})) = \phi(B \cdot \text{SL}(n, \mathbb{R})) \),则 \( \det(A) = \det(B) \),因此 \( A^{-1}B \in \text{SL}(n, \mathbb{R}) \),即 \( A \cdot \text{SL}(n, \mathbb{R}) = B \cdot \text{SL}(n, \mathbb{R}) \)。
      • 保持运算:对任意陪集 \( A \cdot \text{SL}(n, \mathbb{R}) \) 和 \( B \cdot \text{SL}(n, \mathbb{R}) \),它们的乘积为: [ (A \cdot \text{SL}(n, \mathbb{R})) \cdot (B \cdot \text{SL}(n, \mathbb{R})) = (AB) \cdot \text{SL}(n, \mathbb{R}), ] 其映射值为: [ \phi((AB) \cdot \text{SL}(n, \mathbb{R})) = \det(AB) = \det(A)\det(B) = \phi(A \cdot \text{SL}(n, \mathbb{R})) \cdot \phi(B \cdot \text{SL}(n, \mathbb{R})). ] 因此,\( \phi \) 是群同构。

      5. 性质与直观解释

      (1)几何直观

      • 行列式为“缩放因子”:在 \( \mathbb{R}^n \) 中,矩阵 \( A \) 的行列式 \( \det(A) \) 表示其线性变换的体积缩放因子。
      • 商群的元素:商群 \( \text{GL}(n, \mathbb{R})/\text{SL}(n, \mathbb{R}) \) 的每个元素对应一个“缩放因子” \( c \in \mathbb{R}^* \),即所有行列式为 \( c \) 的矩阵构成一个陪集。
      • 运算对应:两个陪集的乘积对应其缩放因子的乘积。

      (2)代数意义

      • 商群去除了“体积缩放”信息:通过模去 \( \text{SL}(n, \mathbb{R}) \),商群仅保留了矩阵的行列式信息(即缩放因子),而忽略了具体的线性变换方向和形状。

      6. 具体例子

      例子:\( n = 2 \)

      • 原群:\( \text{GL}(2, \mathbb{R}) \) 是所有 \( 2 \times 2 \) 可逆实矩阵。
      • 子群:\( \text{SL}(2, \mathbb{R}) \) 是行列式为 1 的矩阵。
      • 陪集示例
      • 取矩阵 \( A = \begin{pmatrix} 2 & 0 \ 0 & 1 \end{pmatrix} \),其行列式为 2。
      • 陪集 \( A \cdot \text{SL}(2, \mathbb{R}) \) 包含所有行列式为 2 的 \( 2 \times 2 \) 矩阵。
      • 同构映射
      • 陪集 \( A \cdot \text{SL}(2, \mathbb{R}) \) 对应实数 2,乘法运算在商群中对应实数的乘法。

      总结

      商群 \( \text{GL}(n, \mathbb{R})/\text{SL}(n, \mathbb{R}) \) 的本质是通过行列式同态将矩阵群的“体积缩放信息”与“形状信息”分离,使得商群仅保留缩放因子(即行列式),从而与非零实数乘法群 \( \mathbb{R}^* \) 同构。这一过程体现了群论中“同态基本定理”的核心思想:通过核和像的分析,将复杂结构分解为更简单的部分。

    1. 平行线空间

      让我们通过具体的例子和几何直观来详细解释这一概念:


      1. 子空间 \( H \) 的定义

      在二维实数空间 \( \mathbb{R}^2 \) 中,子群 \( H \) 定义为: [ H = {(x, 0) \mid x \in \mathbb{R}} ] 这是一个一维子空间,即 x 轴。它满足以下性质: - 对于任意 \( (x, 0), (y, 0) \in H \),它们的和 \( (x + y, 0) \in H \)。 - 对于任意实数 \( c \),\( c \cdot (x, 0) = (cx, 0) \in H \)。


      2. 陪集 \( g + H \) 的构造

      取 \( \mathbb{R}^2 \) 中的一个点 \( g = (a, b) \),则陪集 \( g + H \) 定义为: [ g + H = {(a, b) + (x, 0) \mid (x, 0) \in H} ] 具体计算每个元素: [ (a, b) + (x, 0) = (a + x, b + 0) = (a + x, b) ] 因此,陪集 \( g + H \) 的所有元素形式为: [ {(a + x, b) \mid x \in \mathbb{R}} ] 这是一条 水平直线,其 \( y \) 坐标固定为 \( b \),而 \( x \) 坐标可以取任意实数值。


      3. 几何解释:平行于 \( H \) 的仿射子空间

      • 子空间 \( H \) 是 x 轴:它过原点,方向为水平方向。
      • 陪集 \( g + H \) 是平行于 \( H \) 的直线:因为:
      • \( g + H \) 的方向与 \( H \) 完全相同(水平方向)。
      • \( g + H \) 的位置由点 \( g = (a, b) \) 决定,即它沿着 \( y = b \) 的方向平移了 \( b \) 单位。

      例如: - 若 \( g = (2, 3) \),则陪集 \( g + H \) 是直线 \( y = 3 \),与 x 轴平行。 - 若 \( g = (0, 0) \),则陪集 \( g + H = H \) 本身(即 x 轴)。


      4. 陪集的性质

      (1)平行性

      陪集 \( g + H \) 的方向与 \( H \) 完全相同,因此它们是平行的。这是因为: - \( H \) 的方向由其基向量 \( (1, 0) \) 决定。 - \( g + H \) 的方向同样由 \( (1, 0) \) 决定,只是位置平移了 \( (a, b) \)。

      (2)划分整个空间

      所有陪集 \( g + H \)(对于不同的 \( g \in \mathbb{R}^2 \))将 \( \mathbb{R}^2 \) 分解为无数条平行的水平线,且这些直线: - 互不相交:如果两条水平线 \( y = b_1 \) 和 \( y = b_2 \) 满足 \( b_1 \neq b_2 \),则它们没有公共点。 - 覆盖整个空间:每个点 \( (x, y) \in \mathbb{R}^2 \) 都属于唯一的陪集 \( (x, y) + H = y \text{-水平线} \)。

      (3)仿射子空间

      陪集 \( g + H \) 是一个仿射子空间,它与子空间 \( H \) 平行,但不经过原点(除非 \( g \) 是原点)。仿射子空间的几何意义是: - 它是子空间 \( H \) 经过平移后的结果。 - 它的结构与 \( H \) 相同(维度、方向),但位置不同。


      5. 代数与几何的对应关系

      (1)代数定义

      陪集 \( g + H \) 的代数定义: [ g + H = {g + h \mid h \in H} ] 对应几何上的水平直线 \( y = b \),其中 \( g = (a, b) \) 是平移向量。

      (2)等价关系

      陪集的划分源于等价关系 \( x \sim y \iff x - y \in H \)。在 \( \mathbb{R}^2 \) 中: - 两点 \( (x_1, y_1) \) 和 \( (x_2, y_2) \) 等价当且仅当它们的 \( y \) 坐标相同(即 \( y_1 = y_2 \))。 - 因此,等价类正是水平直线 \( y = b \)。


      6. 例子验证

      例子 1:\( g = (0, 0) \)

      • 陪集 \( g + H = (0, 0) + H = H \),即 x 轴本身。

      例子 2:\( g = (2, 3) \)

      • 陪集 \( g + H = {(2 + x, 3) \mid x \in \mathbb{R}} \),即水平直线 \( y = 3 \)。

      例子 3:\( g = (5, -1) \)

      • 陪集 \( g + H = {(5 + x, -1) \mid x \in \mathbb{R}} \),即水平直线 \( y = -1 \)。

      7. 总结

      在 \( \mathbb{R}^2 \) 中: - 子空间 \( H \) 是 x 轴(一维子空间)。 - 陪集 \( g + H \) 是一条水平直线,与 x 轴平行,其位置由点 \( g = (a, b) \) 的 \( y \) 坐标 \( b \) 决定。 - 所有陪集构成 \( \mathbb{R}^2 \) 的水平直线族,彼此平行且覆盖整个平面。

      这种几何解释不仅直观,还揭示了代数结构(子群、陪集)与几何对象(子空间、仿射空间)之间的深刻联系。希望这个解释能帮助您更好地理解这一概念!如果有其他问题,欢迎继续提问!

    2. 复平面空间

      好的,让我们通过一个几何直观的例子来解释陪集的概念,这个例子涉及复数乘法群及其子群:


      例子:复数乘法群与射线陪集

      1. 群与子群的定义

      • 群 \( G \):复数乘法群 \( \mathbb{C}^* \)(所有非零复数,运算为乘法)。
      • 子群 \( H \):正实数群 \( \mathbb{R}^+ \)(所有正实数,运算为乘法)。

      2. 陪集的构造

      取 \( G \) 中的一个复数 \( g = re^{i\theta} \)(极坐标形式,\( r > 0 \),\( \theta \in [0, 2\pi) \)),则: - 左陪集 \( gH \) 定义为: [ gH = { g \cdot h \mid h \in \mathbb{R}^+ } = { re^{i\theta} \cdot s \mid s > 0 }. ] 这是一个复数集合,所有元素的模(绝对值)为 \( r \cdot s \)(\( s \) 是任意正实数),而辐角(角度)固定为 \( \theta \)。

      3. 几何解释

      • 陪集 \( gH \) 的几何意义
      • 所有模为任意正实数、辐角固定的复数构成一条从原点出发的射线,方向由 \( \theta \) 决定。
      • 例如:

        • 若 \( g = 1 \)(即 \( r = 1 \),\( \theta = 0 \)),则陪集 \( gH = \mathbb{R}^+ \) 是实轴上的正半轴。
        • 若 \( g = i \)(\( r = 1 \),\( \theta = \pi/2 \)),则陪集 \( gH \) 是虚轴上的正半轴。
      • 不同陪集的划分

      • 每个陪集 \( gH \) 对应复平面上一条以原点为起点的射线,方向由 \( \theta \) 确定。
      • 所有陪集共同覆盖整个复平面(除去原点),且彼此互不相交
        • 如果两条射线的方向不同(即 \( \theta_1 \neq \theta_2 \mod 2\pi \)),它们只有原点(但原点不属于 \( \mathbb{C}^* \))交点。
        • 每个非零复数 \( z \) 属于唯一一个陪集(即确定其辐角方向)。

      4. 陪集空间的几何意义

      • 陪集空间 \( \mathbb{C}^* / \mathbb{R}^+ \)
      • 所有陪集 \( gH \) 构成的集合,称为商群(或陪集空间),可以看作是复平面上所有以原点为起点的射线的集合。
      • 这个商群与单位圆 \( S^1 \) 同构,因为每个射线的方向由单位圆上的一个点(\( e^{i\theta} \))唯一确定: [ \mathbb{C}^* / \mathbb{R}^+ \cong S^1. ]
      • 每个陪集 \( gH \) 对应单位圆上的一个点 \( e^{i\theta} \),而陪集本身是这条射线的所有模长缩放。

      5. 性质验证

      (1)陪集的平行性

      • 子群 \( \mathbb{R}^+ \) 是实轴上的正半轴(角度 \( 0 \) 的射线)。
      • 每个陪集 \( gH \) 是另一条与 \( \mathbb{R}^+ \) 平行(方向相同,但角度不同)的射线,但方向由 \( \theta \) 决定。

      (2)划分整个空间

      • 每个非零复数 \( z \) 可以唯一表示为 \( z = re^{i\theta} \),因此属于唯一的陪集 \( e^{i\theta} \cdot \mathbb{R}^+ \)。
      • 所有陪集的并集覆盖了整个 \( \mathbb{C}^* \),且互不相交。

      (3)仿射子空间的类比

      • 在这个例子中,陪集类似于“方向固定但模长可变”的射线,而子群 \( \mathbb{R}^+ \) 是方向为 \( 0 \) 的射线。
      • 每个陪集是子群 \( \mathbb{R}^+ \) 的一个旋转(通过角度 \( \theta \))后的“平行”版本。

      6. 总结

      在复数乘法群 \( \mathbb{C}^ \) 中: - 子群 \( \mathbb{R}^+ \) 是实轴上的正半轴。 - 陪集 \( gH \) 是复平面上以原点为起点、方向由 \( g \) 的辐角确定的射线。 - 陪集空间 \( \mathbb{C}^ / \mathbb{R}^+ \)** 同构于单位圆 \( S^1 \),每个陪集对应单位圆上的一个方向。

      这个例子通过复数的几何表示,直观地展示了陪集如何将群分解为方向固定但模长可变的“平行”射线,进一步体现了代数结构与几何直观的联系。希望这个例子能帮助您更深入理解陪集的概念!如果有其他问题,请随时提问!

    1. 如中心 ​Z(G)、换群 ​G′、或指数为2的子群(如 ​An​ 是 ​Sn​ 的正规子群)

      以下是关于特殊子群(中心 \( Z(G) \)、换群 \( G' \)、指数为2的子群)的详细分析,结合知识库中的内容进行阐述:


      1. 中心 \( Z(G) \)

      定义与性质

      • 定义:<br /> 群 \( G \) 的中心 \( Z(G) \) 是所有与 \( G \) 中每个元素可交换的元素的集合:<br /> [ Z(G) = { z \in G \mid zg = gz \text{ 对所有 } g \in G }. ]
      • 正规性:<br /> \( Z(G) \) 总是 \( G \) 的正规子群,因为对任意 \( z \in Z(G) \) 和 \( g \in G \),有 \( gzg^{-1} = zgg^{-1} = z \in Z(G) \)。<br /> 知识库支持:见[4][7][9]中关于中心的讨论。

      例子

      • 阿贝尔群:<br /> 若 \( G \) 是阿贝尔群,则 \( Z(G) = G \),显然正规。
      • 四元数群 \( Q_8 \):<br /> \( Q_8 = { \pm 1, \pm i, \pm j, \pm k } \),其中心 \( Z(Q_8) = { \pm 1 } \),是正规子群。商群 \( Q_8/Z(G) \cong \mathbb{Z}_2 \times \mathbb{Z}_2 \)(见知识库[7])。
      • 一般线性群 \( GL(n, \mathbb{R}) \):<br /> 其中心 \( Z(GL(n, \mathbb{R})) \) 是所有标量矩阵 \( { \lambda I \mid \lambda \in \mathbb{R}^\times } \),是正规子群。

      2. 换群(换位子群) \( G' \)

      定义与性质

      • 定义:<br /> 换群 \( G' \)(或导群)是所有换位子 \( aba^{-1}b^{-1} \)(\( a,b \in G \))生成的子群:<br /> [ G' = \langle aba^{-1}b^{-1} \mid a,b \in G \rangle. ]
      • 正规性:<br /> \( G' \) 是 \( G \) 的正规子群,因为换位子的共轭仍为换位子:<br /> [ g(aba^{-1}b^{-1})g^{-1} = (gag^{-1})(gbg^{-1})(gag^{-1})^{-1}(gbg^{-1})^{-1}. ]
      • 关键作用:<br /> 商群 \( G/G' \) 是阿贝尔群,且是 \( G \) 的最大阿贝尔商群(见知识库[4][7])。

      例子

      • 对称群 \( S_n \):<br /> \( S_n \) 的换群 \( G' = A_n \)(\( n \geq 3 \)),因为所有换位子是3-循环,而 \( A_n \) 是 \( S_n \) 的正规子群(见知识库[2][3][9])。
      • 特殊线性群 \( SL(n, \mathbb{R}) \):<br /> \( SL(n, \mathbb{R}) \) 的换群是 \( SL(n, \mathbb{R}) \) 本身(当 \( n \geq 2 \)),因为它是完美群(\( G' = G \))。

      3. 指数为2的子群

      定义与性质

      • 定义:<br /> 若子群 \( H \lhd G \) 满足 \( [G:H] = 2 \),则 \( H \) 是指数为2的子群。
      • 正规性:<br /> 指数为2的子群必然是正规子群,因为其陪集只有 \( H \) 和 \( G \setminus H \),且左陪集与右陪集重合。
      • 商群结构:<br /> 商群 \( G/H \cong \mathbb{Z}_2 \),即二元群。

      经典例子

      • 交错群 \( A_n \):<br /> \( A_n \) 是 \( S_n \) 的正规子群,且 \( [S_n : A_n] = 2 \)(见知识库[2][3][9])。
      • 克莱因四元群 \( V_4 \):<br /> 在 \( S_4 \) 中,\( V_4 = { e, (12)(34), (13)(24), (14)(23) } \),但 \( V_4 \) 不是正规子群(除非 \( n=4 \) 时考虑特定情况)。
      • 实数加群 \( \mathbb{R} \):<br /> \( 2\mathbb{Z} \) 是 \( \mathbb{Z} \) 的指数为2的正规子群,商群 \( \mathbb{Z}/2\mathbb{Z} \cong \mathbb{Z}_2 \)(见知识库[3][6])。

      4. 特殊子群的综合应用

      (1)中心与商群

      • 阿贝尔化:<br /> 商群 \( G/Z(G) \) 揭示了群的“非交换性”。例如:
      • 若 \( G/Z(G) \) 是循环群,则 \( G \) 是阿贝尔群(因为循环群是阿贝尔的)。
      • 例子
      • 对于 \( S_3 \),\( Z(S_3) = { e } \),故 \( S_3/Z(S_3) \cong S_3 \),说明其非阿贝尔性。

      (2)换群与可解性

      • 可解群:<br /> 若群 \( G \) 的合成列的商群均为阿贝尔群,则 \( G \) 是可解群。换群 \( G' \) 是判断可解性的关键:
      • 若 \( G' = { e } \),则 \( G \) 是阿贝尔群(显然可解)。
      • \( S_n \) 的可解性取决于 \( n \),例如 \( S_4 \) 是可解群,而 \( S_5 \) 不可解(因 \( A_5 \) 是单群)。

      (3)指数为2的子群与群的结构

      • 二元商群:<br /> 若 \( H \) 是指数为2的子群,则 \( G \) 的结构可由 \( H \) 的性质和 \( \mathbb{Z}_2 \) 的作用决定。例如:
      • \( S_n \) 的结构由 \( A_n \) 和 \( \mathbb{Z}_2 \) 的组合决定(见知识库[2][9])。
      • 应用:<br /> 在晶体学中,\( S_n \) 的商群 \( \mathbb{Z}_2 \) 可用于分析分子的对称性(如手性区分)。

      5. 知识库中的关键案例

      • 知识库[1][7][9]:<br /> 讨论了中心 \( Z(G) \) 的正规性,如 \( GL(n, \mathbb{R}) \) 的中心是标量矩阵群。
      • 知识库[2][3][9]:<br /> 详细说明了 \( A_n \) 是 \( S_n \) 的正规子群,且指数为2。
      • 知识库[4][7]:<br /> 强调换群 \( G' \) 在构造阿贝尔商群中的核心作用,例如 \( S_3/A_3 \cong \mathbb{Z}_2 \)。

      6. 总结:特殊子群的分类与作用

      | 子群类型 | 定义与性质 | 典型例子 | 作用 | |---------------------|-----------------------------------------------------------------------------|-----------------------------|--------------------------------------------------------------------------| | 中心 \( Z(G) \) | 与所有元素可交换的元素组成的子群,总是正规。 | \( Q_8 \) 的中心 \( {\pm1} \) | 揭示群的交换性,商群 \( G/Z(G) \) 研究非交换性。 | | 换群 \( G' \) | 换位子生成的子群,正规且商群 \( G/G' \) 是最大阿贝尔商群。 | \( S_n \) 的换群 \( A_n \) | 判断群的可解性,分解群的非阿贝尔结构。 | | 指数为2的子群 | 指数为2的子群必为正规,商群为 \( \mathbb{Z}_2 \)。 | \( A_n \) 是 \( S_n \) 的子群 | 用于二元对称性分析,如群的分解或对称性破缺(如手性分子)。 |


      7. 深入思考:特殊子群的统一性

      这些特殊子群(中心、换群、指数为2的子群)共同体现了群论的核心思想:<br /> - 正规性:通过子群的共轭不变性或指数性质,确保商群的构造合法性。<br /> - 结构分解:通过商群 \( G/N \) 分解群的复杂性,例如:<br /> - 中心 \( Z(G) \) 分解出群的“交换部分”。<br /> - 换群 \( G' \) 分解出群的“非阿贝尔本质”。<br /> - 指数为2的子群分解出二元对称性。<br /> - 应用延伸:在物理(如量子力学中的对称性破缺)、几何(如晶体对称群)等领域,这些子群的分析至关重要。

      希望这些分析能帮助你深入理解这些特殊子群的数学本质和实际应用!如果有具体问题或需要进一步探讨某个案例,请随时告诉我。

    1. 循环群(Cyclic Group)

      好的,我们来详细解释一下循环群 (Cyclic Group)。循环群是群论中最简单也是最基础的一类群。

      1. 回顾群的定义:

      首先,我们知道一个群 (Group) 是一个集合 G,它配备了一个二元运算(通常记为 *+),满足以下四个公理:

      • 封闭性 (Closure): 对于任意 a, b ∈ Ga * b ∈ G
      • 结合律 (Associativity): 对于任意 a, b, c ∈ G(a * b) * c = a * (b * c)
      • 单位元 (Identity Element): 存在一个元素 e ∈ G,对于任意 a ∈ G,都有 e * a = a * e = a
      • 逆元 (Inverse Element): 对于任意 a ∈ G,都存在一个元素 a⁻¹ ∈ G,使得 a * a⁻¹ = a⁻¹ * a = e

      2. 循环群的定义:

      一个群 G 被称为循环群 (Cyclic Group),如果存在一个元素 g ∈ G,使得 G 中的每一个元素都可以表示为 g 的某个整数幂次(当群运算是乘法时)或者某个整数倍数(当群运算是加法时)。这个元素 g 就被称为该循环群的生成元 (generator)

      3. 乘法表示法:

      如果群 G 的运算用乘法表示,那么循环群 G 可以写成:

      G = {gⁿ | n ∈ ℤ} = {..., g⁻², g⁻¹, g⁰=e, g¹, g², ...}

      其中: * g⁰ = e 是群的单位元。 * gⁿ = g * g * ... * g (n 个 g 相乘,当 n > 0 时)。 * g⁻ⁿ = (g⁻¹)ⁿ = g⁻¹ * g⁻¹ * ... * g⁻¹ (n 个 g⁻¹ 相乘,当 n < 0 时),其中 g⁻¹g 的逆元。

      4. 加法表示法:

      如果群 G 的运算用加法表示(通常用于阿贝尔群),那么循环群 G 可以写成:

      G = {ng | n ∈ ℤ} = {..., -2g, -g, 0, g, 2g, ...}

      其中: * 0g = 0 是群的单位元(加法单位元)。 * ng = g + g + ... + g (n 个 g 相加,当 n > 0 时)。 * (-n)g = -(ng) = (-g) + (-g) + ... + (-g) (n 个 -g 相加,当 n < 0 时),其中 -gg 的加法逆元。

      5. 循环群的重要性质:

      • 阿贝尔群 (Abelian Group): 所有的循环群都是阿贝尔群(即满足交换律)。

        • 证明(使用乘法表示): 对于任意两个元素 gᵃgᵇ,其中 a, b ∈ ℤ,有 gᵃ * gᵇ = gᵃ⁺ᵇ = gᵇ⁺ᵃ = gᵇ * gᵃ
      • 子群 (Subgroups): 循环群的每一个子群也都是循环群。

      • 生成元的个数: 一个有限循环群的生成元的个数由欧拉函数 φ(n) 给出,其中 n 是群的阶数(元素的个数)。φ(n) 表示小于等于 n 且与 n 互质的正整数的个数。

      • 同构 (Isomorphism):

        • 每一个无限循环群都同构于整数群 (ℤ, +)。这意味着它们在结构上是完全相同的,只是元素的名称和运算符号可能不同。
        • 每一个有限循环群,如果其阶数为 n,都同构于模 n 整数群 (ℤ<0xE2><0x82><0x99>, +)
      • 结构定理 (Structure Theorem): 循环群是有限生成阿贝尔群的基本组成部分。有限生成阿贝尔群基本定理指出,每一个有限生成的阿贝尔群都同构于若干个循环群的直和。

      6. 循环群的例子:

      • 整数群 (ℤ, +): 整数集合在加法运算下构成一个无限循环群,生成元可以是 1 (因为任何整数 n 都可以表示为 n * 1)或者 -1 (因为任何整数 n 都可以表示为 n * (-1))。

      • 模 n 整数群 (ℤ<0xE2><0x82><0x99>, +):n 整数集合 {0, 1, 2, ..., n-1} 在模 n 加法运算下构成一个有限循环群,阶数为 n。生成元是与 n 互质的元素,例如 1 通常是一个生成元。例如,在 ℤ₅ 中,1 可以生成所有元素:1*1 = 1, 2*1 = 2, 3*1 = 3, 4*1 = 4, 5*1 = 0 (mod 5)。

      • 单位根群: n 次单位根的集合在复数乘法下构成一个有限循环群,阶数为 n。生成元是本原 n 次单位根,例如 e^(2πi/n) = cos(2π/n) + i sin(2π/n)

      • 二阶循环群 (Z₂, +): 集合 {0, 1} 在模 2 加法下是一个循环群,生成元是 1

      7. 不是循环群的例子:

      • 克莱因四元群 (V₄): 这是一个有四个元素的群 {e, a, b, c},其中每个非单位元的阶都是 2 (a² = b² = c² = e)。它是一个阿贝尔群,但不是循环群,因为没有一个元素可以生成所有四个元素。

      • 对称群 S₃: 这是三个元素的置换群,包含 6 个元素。它不是阿贝尔群,因此也不可能是循环群。

      • 有理数群 (ℚ, +) 和实数群 (ℝ, +): 这些群在加法下是阿贝尔群,但不是循环群。你无法找到一个有理数(或实数),通过对其进行有限次加法或减法得到所有的有理数(或实数)。

      8. 循环群的阶:

      循环群的阶(群中元素的个数)等于其生成元的阶。如果生成元 g 的阶是无限的(即 gⁿ ≠ e 对于所有非零整数 n),那么循环群也是无限的。如果生成元 g 的阶是有限的,为 n(即 gⁿ = e 是使得该等式成立的最小正整数 n),那么循环群的阶也是 n,并且 G = {e, g, g², ..., gⁿ⁻¹}

      9. 循环群的子群:

      循环群的每一个子群都是循环的。对于一个阶为 n 的循环群,它的每一个子群的阶都是 n 的因子,并且对于 n 的每一个正因子 d,都存在唯一一个阶为 d 的子群。

      10. 循环群的重要性:

      循环群作为最简单的群结构,在群论中扮演着重要的角色,它们:

      • 是理解更复杂群的基础。
      • 在数论、密码学(例如,椭圆曲线密码学中使用的循环群)、编码理论等领域有广泛的应用。
      • 为研究群的同态和结构提供了重要的例子。

      希望这个详细的解释能够帮助你理解循环群的概念。

    1. (1) access to screens: portable monitor devices, excluded from bedrooms, (2) frequency of use: age of exposure to screens, limitation to 1 hour per day, (3) content: nonviolent, slow pace, (4) dialogue: promote parent-child interaction and co-viewing

      unfortunately i think if you were to ask the average parent if they follow literally any of these rules with screens with their children, most of them would say no

    1. 除环

      好的,我们来详细介绍一下除环 (Division Ring)。除环是抽象代数中环论的一个重要概念,它与我们熟悉的域的概念非常接近。

      1. 回顾环的定义:

      首先,我们再次强调一下环的定义。一个环 (Ring) 是一个集合 D,它配备了两个二元运算,通常称为加法 (+)乘法 (*),满足一系列我们之前讨论过的公理(阿贝尔加法群、半群乘法、分配律)。

      2. 除环的定义:

      一个环 D 被称为除环 (Division Ring)(有时也称为斜域 (Skew Field)),如果它满足以下条件:

      • 存在乘法单位元: D 中存在一个元素 1,称为乘法单位元,满足对于所有 a ∈ D,都有 1 * a = a * 1 = a
      • 每个非零元素都有乘法逆元: 对于每个 a ∈ D,如果 a ≠ 0(这里的 0 是加法单位元),那么存在一个元素 a⁻¹ ∈ D,称为 a 的乘法逆元,满足 a * a⁻¹ = a⁻¹ * a = 1

      3. 除环与域的区别:

      除环与我们更熟悉的域 (Field) 的定义非常相似。域的定义是在除环的定义基础上,额外要求乘法运算是可交换的。也就是说,一个域是一个乘法可交换的除环。

      因此,所有的域都是除环,但并非所有的除环都是域。 那些乘法不可交换的除环就是非交换的除环,它们不是域。

      4. 除环的性质:

      • 非平凡环: 除环必须至少包含两个元素:加法单位元 0 和乘法单位元 1,并且 0 ≠ 1
      • 无零因子: 每个除环都是无零因子环。证明如下: 假设在除环 D 中存在非零元素 ab 使得 a * b = 0。由于 a ≠ 0,它存在乘法逆元 a⁻¹。两边同时左乘 a⁻¹,得到 a⁻¹ * (a * b) = a⁻¹ * 0。利用结合律,(a⁻¹ * a) * b = 0,即 1 * b = 0,所以 b = 0。这与我们假设 b 是非零元素矛盾。因此,除环中不可能存在非零的零因子。
      • 逆元的唯一性: 每个非零元素的乘法逆元在除环中是唯一的。

      5. 除环的例子:

      • 域 (Field): 如有理数域 (ℚ)、实数域 (ℝ)、复数域 (ℂ) 等,它们都是乘法可交换的除环,所以它们都是域,同时也是除环的例子。
      • 四元数环 (ℍ): 这是最著名的非交换除环的例子,由威廉·罗文·汉密尔顿发现。四元数可以表示为 a + bi + cj + dk,其中 a, b, c, d 是实数,i, j, k 是满足特定乘法规则的单位。四元数的乘法是不可交换的,例如 i * j = kj * i = -k。然而,每个非零四元数都有乘法逆元。

      6. 不是除环的环的例子:

      • 整数环 (ℤ): 整数环有乘法单位元 1,但不是每个非零整数都有整数乘法逆元(例如,2 的逆元是 1/2,不是整数)。
      • 模 n 整数环 (ℤ<0xE2><0x82><0x99>):n 是合数时,ℤ<0xE2><0x82><0x99> 含有零因子,因此不是除环。当 n 是素数时,ℤ<0xE2><0x82><0x99> 是一个有限域,所以是除环。
      • 矩阵环 (M<0xE2><0x82><0x99>(F)),当 n > 1 时: 矩阵环有单位矩阵作为乘法单位元,但不是所有非零矩阵都有逆矩阵(只有行列式非零的矩阵才有逆矩阵)。而且,矩阵乘法通常不可交换。

      7. 有限除环(Wedderburn 小定理):

      一个非常重要的结果是 Wedderburn 小定理,它指出:每个有限的除环都是域。 这意味着所有非交换的除环都必须是无限的。四元数环就是一个无限的非交换除环的例子。

      8. 除环的重要性:

      除环是比域更一般的代数结构,它放宽了乘法交换性的要求。它们在抽象代数的研究中扮演着重要的角色,尤其是在那些需要考虑非交换乘法的场合。例如,四元数在物理学、计算机图形学和表示三维空间中的旋转等方面都有应用。

      希望这个详细的解释能够帮助你理解除环的概念。

    2. 无零因子环

      好的,我们来详细解释一下无零因子环 (Zero-Divisor Free Ring)。这是一个抽象代数中环论的基本概念。

      1. 回顾环的定义:

      首先,我们简单回顾一下环的定义。一个环 (Ring) 是一个集合 R,它配备了两个二元运算,通常称为加法 (+)乘法 (*),满足以下条件:

      • (R, +) 是一个阿贝尔群(即满足封闭性、结合律、存在加法单位元 0、存在加法逆元、交换律)。
      • (R, *) 是一个半群(即满足封闭性和结合律)。
      • 乘法对加法满足分配律:对于所有 a, b, c ∈ R,有 a * (b + c) = (a * b) + (a * c)(b + c) * a = (b * a) + (c * a)

      2. 零因子的定义:

      在一个环 R 中,如果存在两个非零元素 a ∈ Rb ∈ R (即 a ≠ 0b ≠ 0),使得它们的乘积等于环的加法单位元 0 (即 a * b = 0),那么我们称 ab 为这个环的零因子 (Zero Divisor)

      注意: 零因子必须是非零元素。

      3. 无零因子环的定义:

      一个环 R 被称为无零因子环 (Zero-Divisor Free Ring),如果它不包含任何零因子。换句话说,在一个无零因子环中,如果两个元素的乘积等于 0,那么这两个元素中至少有一个必须是 0

      用逻辑符号表示:对于所有 a, b ∈ R,如果 a * b = 0,那么 a = 0b = 0 (或两者都为零)。

      4. 无零因子环的重要性质:消去律

      无零因子环最重要的性质之一是乘法消去律 (Cancellation Law for Multiplication) 成立。具体来说:

      • 如果 a * b = a * ca ≠ 0,那么 b = c
      • 如果 b * a = c * aa ≠ 0,那么 b = c

      证明:

      • 假设 a * b = a * ca ≠ 0。 那么 a * b - a * c = 0。 利用分配律,得到 a * (b - c) = 0。 由于环是无零因子的,且 a ≠ 0,所以必须有 b - c = 0,即 b = c
      • 第二个结论的证明类似。

      5. 无零因子环的例子:

      • 整数环 (ℤ): 整数环是最典型的无零因子环。如果两个整数的乘积是 0,那么它们中至少有一个是 0。
      • 有理数环 (ℚ): 有理数环也是无零因子环。
      • 实数环 (ℝ): 实数环是无零因子环。
      • 复数环 (ℂ): 复数环是无零因子环。
      • 整环 (Integral Domain): 一个交换环 R (乘法满足交换律) 且具有乘法单位元 1 (1 ≠ 0),并且是无零因子环,则称 R 为整环。因此,所有整环都是无零因子环。例如,多项式环 F[x],其中 F 是一个域,就是一个整环,所以也是无零因子环。

      6. 含有零因子的环的例子:

      • 模 n 整数环 (ℤ<0xE2><0x82><0x99>),当 n 是合数时: 例如,在模 6 整数环 ℤ₆ 中,2 ≠ 0 (mod 6) 且 3 ≠ 0 (mod 6),但是 2 * 3 = 6 ≡ 0 (mod 6)。所以,2 和 3 是 ℤ₆ 中的零因子。
      • 矩阵环 (M<0xE2><0x82><0x99>(F)),当 n > 1 时: 考虑 2x2 的实数矩阵环。令 A = [[1, 0], [0, 0]]B = [[0, 0], [0, 1]]。则 A ≠ [[0, 0], [0, 0]]B ≠ [[0, 0], [0, 0]],但是它们的乘积 A * B = [[1, 0], [0, 0]] * [[0, 0], [0, 1]] = [[0, 0], [0, 0]]。所以,AB 是这个矩阵环中的零因子。
      • 环的直积: 如果 RS 是两个非零环,那么它们的直积 R × S 总是含有零因子。例如,在 ℤ₂ × ℤ₃ 中,(1, 0) ≠ (0, 0)(0, 1) ≠ (0, 0),但是 (1, 0) * (0, 1) = (1 * 0, 0 * 1) = (0, 0)
      • 函数环: 考虑从实数集 ℝ 到自身的所有连续函数构成的环。令函数 f(x) 定义为当 x ≥ 0f(x) = x,当 x < 0f(x) = 0。令函数 g(x) 定义为当 x ≥ 0g(x) = 0,当 x < 0g(x) = x。这两个函数都不是零函数,但是它们的乘积 (f * g)(x) = f(x) * g(x) = 0 对于所有 x ∈ ℝ。因此,fg 是这个函数环中的零因子。

      7. 与整环的关系:

      一个整环 (Integral Domain) 被定义为一个满足以下条件的环:

      • 它是一个交换环(乘法满足交换律)。
      • 它具有乘法单位元 1,且 1 ≠ 0
      • 它是无零因子环。

      因此,无零因子环是定义整环的一个关键组成部分。

      8. 无零因子环的重要性:

      无零因子环在抽象代数中非常重要,因为它们具有一些类似于我们熟悉的数系(如整数、有理数、实数)的性质,例如乘法消去律。它们是研究更复杂的环结构的基础,并且在理解域和整环的概念中起着关键作用。

      希望这个详细的解释能够帮助你理解无零因子环的概念。