Reviewer #1 (Public review):

Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the subesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the subesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

(1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

(2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose, yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

(3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

(4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

(5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

(6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

(7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

Author response:

Reviewer #1 (Public review):

Wang et al. investigated how sexual failure influences sweet taste perception in male Drosophila. The study revealed that courtship failure leads to decreased sweet sensitivity and feeding behavior via dopaminergic signaling. Specifically, the authors identified a group of dopaminergic neurons projecting to the suboesophageal zone that interacts with sweet-sensing Gr5a+ neurons. These dopaminergic neurons positively regulate the sweet sensitivity of Gr5a+ neurons via DopR1 and Dop2R receptors. Sexual failure diminishes the activity of these dopaminergic neurons, leading to reduced sweet-taste sensitivity and sugar-feeding behavior in male flies. These findings highlight the role of dopaminergic neurons in integrating reproductive experiences to modulate appetitive sensory responses.

Previous studies have explored the dopaminergic-to-Gr5a+ neuronal pathways in regulating sugar feeding under hunger conditions. Starvation has been shown to increase dopamine release from a subset of TH-GAL4 labeled neurons, known as TH-VUM, in the suboesophageal zone. This enhanced dopamine release activates dopamine receptors in Gr5a+ neurons, heightening their sensitivity to sugar and promoting sucrose acceptance in flies. Since the function of the dopaminergic-to-Gr5a+ circuit motif has been well established, the primary contribution of Wang et al. is to show that mating failure in male flies can also engage this circuit to modulate sugar-feeding behavior. This contribution is valuable because it highlights the role of dopaminergic neurons in integrating diverse internal state signals to inform behavioral decisions.

An intriguing discrepancy between Wang et al. and earlier studies lies in the involvement of dopamine receptors in Gr5a+ neurons. Prior research has shown that Dop2R and DopEcR, but not DopR1, mediate starvation-induced enhancement of sugar sensitivity in Gr5a+ neurons. In contrast, Wang et al. found that DopR1 and Dop2R, but not DopEcR, are involved in the sexual failure-induced decrease in sugar sensitivity in these neurons. I wish the authors had further explored or discussed this discrepancy, as it is unclear how dopamine release selectively engages different receptors to modulate neuronal sensitivity in a context-dependent manner.

Our immunostaining experiments showed that three dopamine receptors, DopR1, Dop2R, and DopEcR were expressed in Gr5a⁺ neurons in the proboscis, which was consistent with previous findings by using RT-PCR (Inagaki et al 2012). As the reviewer pointed out, we found that DopR1 and Dop2R were required for courtship failure-induced suppression of sugar sensitivity, whereas Marella et al 2012 and Inagaki et al 2012 found that Dop2R and DopEcR were required for starvation-induced enhancement of sugar sensitivity. These results may suggest different internal states (courtship failure vs. starvation) modulate peripheral sensory system via different signaling pathways (e.g. different subsets of dopaminergic neurons; different dopamine release mechanisms; and different dopamine receptors). We will further discuss these possibilities in the revised manuscript.

The data presented by Wang et al. are solid and effectively support their conclusions. However, certain aspects of their experimental design, data analysis, and interpretation warrant further review, as outlined below.

(1) The authors did not explicitly indicate the feeding status of the flies, but it appears they were not starved. However, the naive and satisfied flies in this study displayed high feeding and PER baselines, similar to those observed in starved flies in other studies. This raises the concern that sexually failed flies may have consumed additional food during the 4.5-hour conditioning period, potentially lowering their baseline hunger levels and subsequently reducing PER responses. This alternative explanation is worth considering, as an earlier study demonstrated that sexually deprived males consumed more alcohol, and both alcohol and food are known rewards for flies. To address this concern, the authors could remove food during the conditioning phase to rule out its influence on the results.

We think this is a valid concern. We will conduct courtship conditioning in the absence of food and test if courtship failure can still suppress sugar sensitivity in the revised manuscript.

(2) Figure 1B reveals that approximately half of the males in the Failed group did not consume sucrose yet Figure 1-S1A suggests that the total volume consumed remained unchanged. Were the flies that did not consume sucrose omitted from the dataset presented in Figure 1-S1A? If so, does this imply that only half of the male flies experience sexual failure, or that sexual failure affects only half of males while the others remain unaffected? The authors should clarify this point.

Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose in the revised manuscript. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

(3) The evidence linking TH-GAL4 labeled dopaminergic neurons to reduced sugar sensitivity in Gr5a+ neurons in sexually failed males could be further strengthened. Ideally, the authors would have activated TH-GAL4 neurons and observed whether this restored GCaMP responses in Gr5a+ neurons in sexually failed males. Instead, the authors performed a less direct experiment, shown in Figures 3-S1C and D. The manuscript does not describe the condition of the flies used in this experiment, but it appears that they were not sexually conditioned. I have two concerns with this experiment. First, no statistical analysis was provided to support the enhancement of sucrose responses following activation of TH-GAL4 neurons. Second, without performing this experiment in sexually failed males, the authors lack direct evidence to confirm that the dampened response of Gr5a+ neurons to sucrose results from decreased activity in TH-GAL4 neurons.

We think this is also a valid suggestion. We will directly examine whether activating TH⁺ neurons in sexually conditioned males would enhance sugar responses of Gr5a⁺ neurons in sexually failed males. We will also add in statistical analysis.

Nevertheless, we would still argue our current experiments using Naive males (Figure 3, S1C-D) are adequate to show a functional link between TH⁺ neurons and Gr5a⁺ neurons. Combining with the results that these neurons form active synapses (Figure 3, S1B) and that the activity of TH⁺ neurons was dampened in sexually failed males (Figure 3G-I), our current data support the notion that sexual failure suppresses sweet sensitivity via TH-Gr5a circuity.

(4) The statistical methods used in this study are poorly described, making it unclear which method was used for each experiment. I suggest that the authors include a clear description of the statistical methods used for each experiment in the figure legends. Furthermore, as I have pointed out, there is a lack of statistical comparisons in Figures 3-S1C and D, a similar problem exists for Figures 6E and F.

We will add detailed information of statistical analysis in each figure legend.

(5) The experiments in Figure 5 lack specificity. The target neurons in this study are Gr5a+ neurons, which are directly involved in sugar sensing. However, the authors used the less specific Dop1R1- and Dop2R-GAL4 lines for their manipulations. Using Gr5a-GAL4 to specifically target Gr5a+ neurons would provide greater precision and ensure that the observed effects are directly attributable to the modulation of Gr5a+ neurons, rather than being influenced by potential off-target effects from other neuronal populations expressing these dopamine receptors.

We agree with the reviewer that manipulating Dop1R1 and Dop2R genes (Figure 4) and the neurons expressing them (Figure 5) might have broader impacts. In fact, we have also tested the role of Dop1R1 and Dop2R in Gr5a⁺ neurons by RNAi experiments (Figure 6). As shown by both behavioral and calcium imaging experiments, knocking down Dop1R1 and Dop2R in Gr5a⁺ neurons both eliminated the effect of sexual failure to dampen sweet sensitivity, further confirming the role of these two receptors in Gr5a⁺ neurons.

(6) I found the results presented in Fig. 6F puzzling. The knockdown of Dop2R in Gr5a+ neurons would be expected to decrease sucrose responses in naive and satisfied flies, given the role of Dop2R in enhancing sweet sensitivity. However, the figure shows an apparent increase in responses across all three groups, which contradicts this expectation. The authors may want to provide an explanation for this unexpected result.

We agree that there might be some potential discrepancies. However, our current data are not adequate for the clarification given the experiments shown in Figure 6E-F and the apparent control (Figure 3C) were not conducted under identical settings at the same (that’s why we did not directly compare these results). One way to address the issues is to conduct these calcium imaging experiments again with a head-to-head comparison with the control group (Gr5a-GCaMP, +/- Dop1R1 and Dop2R RNAi). We will conduct the experiments and present the data in the revised manuscript.

(7) In several instances in the manuscript, the authors described the effects of silencing dopamine signaling pathways or knocking down dopamine receptors in Gr5a neurons with phrases such as 'no longer exhibited reduced sweet sensitivity' (e.g., L269 and L288), 'prevent the reduction of sweet sensitivity' (e.g., L292), or 'this suppression was reversed' (e.g. L299). I found these descriptions misleading, as they suggest that sweet sensitivity in naive and satisfied groups remains normal while the reduction in failed flies is specifically prevented or reversed. However, this is not the case. The data indicate that these manipulations result in an overall decrease in sweet sensitivity across all groups, such that a further reduction in failed flies is not observed. I recommend revising these descriptions to accurately reflect the observed phenotypes and avoid any confusion regarding the effects of these manipulations.

We will change our expressions in the revised manuscript. In brief, we think that these manipulations (suppressing Dop1R1⁺ and Dop2R⁺ neurons) have two consequences: suppressing the overall sweet sensitivity and eliminating the effect of sexual failure.

Reviewer #2 (Public review):

Summary:

The authors exposed naïve male flies to different groups of females, either mated or virgin. Male flies can successfully copulate with virgin females; however, they are rejected by mated females. This rejection reduces sugar preference and sensitivity in males. Investigating the underlying neural circuits, the authors show that dopamine signaling onto GR5a sensory neurons is required for reduced sugar preference. GR5a sensory neurons respond less to sugar exposure when they lack dopamine receptors.

Strengths:

The findings add another strong phenotype to the existing dataset about brain-wide neuromodulatory effects of mating. The authors use several state-of-the-art methods, such as activity-dependent GRASP to decipher the underlying neural circuitry. They further perform rigorous behavioral tests and provide convincing evidence for the local labellar circuit.

Weaknesses:

The authors focus on the circuit connection between dopamine and gustatory sensory neurons in the male SEZ. Therefore, it is still unknown how mating modulates dopamine signaling and what possible implications on other behaviors might result from a reduced sugar preference.

We agree with the reviewer that in the current study, we did not examine how mating experience suppressed the activity of dopaminergic neurons in the SEZ. The current study mainly focused on the behavioral characterization (sexual failure suppresses sweet sensitivity) and the downstream mechanism (TH-Gr5a pathway). We think that examining the upstream modulatory mechanism may be more suitable for a separate future study.

We believe that a sustained reduction in sweet sensitivity (not limited to sucrose but extend to other sweet compounds, Figure 1, S1B-C) upon sexual failure suggests a generalized and sustained consequence on reward-related behaviors. Sexual failure may thus resemble a state of “primitive emotion” in fruit flies. We will further discuss this possibility in the revised manuscript.

Reviewer #3 (Public review):

Summary

In this work, the authors asked how mating experience impacts reward perception and processing. For this, they employ fruit flies as a model, with a combination of behavioral, immunostaining, and live calcium imaging approaches.

Their study allowed them to demonstrate that courtship failure decreases the fraction of flies motivated to eat sweet compounds, revealing a link between reproductive stress and reward-related behaviors. This effect is mediated by a small group of dopaminergic neurons projecting to the SEZ. After courtship failure, these dopaminergic neurons exhibit reduced activity, leading to decreased Gr5a+ neuron activity via Dop1R1 and Dop2R signaling, and leading to reduced sweet sensitivity. The authors therefore showed how mating failure influences broader behavioral outputs through suppression of the dopamine-mediated reward system and underscores the interactions between reproductive and reward pathways.

Concern

My main concern regarding this study lies in the way the authors chose to present their results. If I understood correctly, they provided evidence that mating failure induces a decrease in the fraction of flies exhibiting PER. However, they also showed that food consumption was not affected (Fig. 1, supplement), suggesting that individuals who did eat consumed more. This raises questions about the analysis and interpretation of the results. Should we consider the group as a whole, with a reduced sensitivity to sweetness, or should we focus on individuals, with each one eating more? I am also concerned about how this could influence the results obtained using live imaging approaches, as the flies being imaged might or might not have been motivated to eat during the feeding assays. I would like the authors to clarify their choice of analysis and discuss this critical point, as the interpretation of the results could potentially be the opposite of what is presented in the manuscript.

Here is a brief clarification of our experimental design and we will further clarify the details in the revised manuscript:

After the behavioral conditioning, male flies were divided for two assays. On the one hand, we quantified PER responses of individual flies. As shown in Figure 1C, Failed males exhibited decreased sweet sensitivity (as demonstrated by the right shift of the response curve).

On the other hand, we sought to quantify food consumption of individual flies by using the MAFE assay (Qi et al 2005). When presented with 400 mM sucrose, approximately 100% of the flies in the Naïve and Satisfied groups, and 50% of the flies in the Failed group, extended their proboscis and started feeding (Figure 1B). For these flies, we could quantify the consumed volumes and found there was no change (Figure 1, S1A). We should also note the consistency of these two experiments, e.g. in Figure 1C, only 50-60% of Failed males responded to 400 mM stimulation.  

These two experiments in combination suggest that sexual failure suppressed sweet sensitivity of the Failed males. Meanwhile, as long as they still initiated feeding, the volume of food consumption remained unchanged. These results led us to focus on the modulatory effect of sexual failure on the sensory system, the main topic of this present study.

In addition, to further clarify the potential misunderstanding, we plan to examine food consumption by using 800 mM sucrose instead. As shown in Figure 1C, 800 mM sucrose was adequate to induce feeding in ~100% of the flies.

Reviewer #2 (Public review):

Summary:

In this manuscript, Krishnan et al devised three paradigms to perform contextual fear conditioning in head-fixed mice. Each of the paradigms relied on head-fixed mice running on a treadmill through virtual reality arenas. The authors tested the validity of three versions of the paradigms by using various parameters. As described below, I think there are several issues with the way the paradigms are designed and how the data are interpreted. Moreover, as Paradigm 3 was published previously in a study by the same group, it is unclear to me what this manuscript offers beyond the validations of parameters used for the previous publication. Below, I list my concerns point-by-point, which I believe need to be addressed to strengthen the manuscript.

Major comments

(1) In the analysis using the LME model (Tables 1 and 2), I am left wondering why the mice had increased freezing across recall days as well as increased generalization (increased freezing to the familiar context, where shock was never delivered). Would the authors expect freezing to decrease across recall days, since repeated exposure to the shock context should drive some extinction? This is complicated by the analysis showing that freeing was increased only on retrieval day 1 when analyzing data from the first lap only. Since reward (e.g., motivation to run) is removed during the conditioning and retrieval tests, I wonder if what the authors are observing is related to decreased motivation to perform the task (mice will just sit, immobile, not necessarily freezing per se). I think that these aspects need to be teased out.

(2) Related to point 1, the authors actually point out that these changes could be due to the loss of the water reward. So, in line 304, is it appropriate to call this freezing? I think it will be very important for the authors to exactly define and delineate what they consider as freezing in this task, versus mice just simply sitting around, immobile, and taking a break from performing the task when they realize there is no reward at the end.

(3) In the second paradigm, mice are exposed to both novel and (at the time before conditioning) neutral environments just before fear conditioning. There is a big chance that the mice are 'linking' the memories (Cai et al 2016) of the two contexts such that there is no difference in freezing in the shock context compared to the neutral context, which is what the authors observe (Lines 333-335). The experiment should be repeated such that exposure to the contexts does not occur on the conditioning day.

(4) On lines 360-361, the authors conclude that extinction happens rapidly, within the first lap of the VR trial. To my understanding, that would mean that extinction would happen within the first 5-10 seconds of the test (according to Figure S1E). That seems far too fast for extinction to occur, as this never occurs in freely behaving mice this quickly.

(5) Throughout the different paradigms, the authors are using different shock intensities. This can lead to differences in fear memory encoding as well as in levels of fear memory generalization. I don't think that comparisons can be made across the different paradigms as too many variables (including shock intensity - 0.5/0.6mA can be very different from 1.0 mA) are different. How can the authors pinpoint which works best? Indeed, they find Paradigm 3 'works' better than Paradigm 2 because mice discriminate better between the neutral and shock contexts. This can definitely be driven by decreased generalization from using a 0.6mA shock in Paradigm 3 compared to 1.0 mA shock in Paradigm 2.

(6) There are some differences in the calcium imaging dataset compared to other studies, and the authors should perform additional testing to determine why. This will be integral to validating their head-fixed paradigm(s) and showing they are useful for modeling circuit dynamics/behaviors observed in freely behaving mice. Moreover, the sample size (number of mice) seems low.

(7) It appears that the authors have already published a paper using Paradigm 3 (Ratigan et al 2023). If they already found a paradigm that is published and works, it is unclear to me what the current manuscript offers beyond that initial manuscript.

(8) As written, the manuscript is really difficult to follow with the averages and standard error reported throughout the text. This reporting in the text occurred heterogeneously throughout the text, as sometimes it was reported and other times it was not. Cleaning this reporting up throughout the paper would greatly improve the flow of the text and qualitative description of the results.

Reviewer #2 (Public review):

Summary:

The authors developed a deep learning model based on a DenseNet CNN architecture to predict two cognitive functions: working memory and episodic memory, from functional connectivity matrices. These matrices were recorded under three conditions: during rest, a working memory task, and a movie, and were treated as images for the CNN algorithm. They tested their model's performance across different conditions and a separate dataset with a different age distribution (using the same MRI scanner, scanning configurations, and cognitive tests). They also calculated the "brain cognition gap" based on the model trained on resting functional connectivity to predict working memory. Extending from the commonly used index "brain age," the brain cognition gap was defined as the difference between the working memory score predicted by their model (predicted working memory) and the working memory score based on the working memory test itself (observed working memory). This brain cognition gap was found to be associated with physical activity, education, and cardiovascular risk. The authors also conducted additional mediation tests to examine whether regional functional variability mediated the relationship between PET-derived measures of dopamine and the brain cognition gap.

Strengths:

The major strength of this manuscript is the extensive effort the authors have put into creating a new 'biomarker' that links deep learning with fMRI, PET, physical activity, education, and cardiovascular risk across two studies. This effort is impressive.

Weaknesses:

There are several weaknesses in the current methods and results, making many of the claims unconvincing. These weaknesses include:

(1) The lack of baseline models to benchmark the predictive performance of their DenseNet models.

(2) The inappropriate calculation of the brain cognition gap due to the lack of control for regression-toward-the-mean and the influence of the working memory itself (a common practice in brain age studies).

(3) The lack of benchmarking of the brain cognition gap against the 'corrected' brain age gap and the direct prediction of physical activity, education, and cardiovascular risk.

(4) Minimal justification for their PET mediation analysis.

Regarding the impact of the work on the field and the utility of the methods and data to the community, I see its potential. However, addressing all the weaknesses listed above is crucial and likely to change the conclusions of the results.

It is important to note that many statements in the manuscript are overstated, making the contribution of the manuscript seem exaggerated.

For instance, the abstract claims "there is a lack of objective biomarkers to accurately predict cognitive function," and the discussion states, "across various studies, the correlation between predicted and actual fluid intelligence typically hovers around 0.25 (98-100)." However, a meta-analysis by Vieira and colleagues (2022 https://doi.org/10.1016/j.intell.2022.101654) found over 37 studies up to 2020 predicting cognitive abilities from fMRI with machine learning, with 24 studies published in 2019-20 alone. Since 2020, with the rise of machine learning and AI, even more studies have likely been published on this topic, all claiming to show objective biomarkers to accurately predict cognitive function. Vieira and colleagues also found an average performance of these objective biomarkers in predicting general cognition at r = .42, similar to what was found in this manuscript. Based on this alone, it is unclear how novel or superior their method is without a proper systematic benchmark.

Similarly, the authors claim superior performance of deep learning and mischaracterize machine learning algorithms: "In particular, deep neural networks (DNN) methods have been successfully applied to behavioral and disease prediction (24-26), and have been found to outperform other machine learning approaches (27-29)," and "Deep learning approaches overcome the limitation of predictive techniques that solely rely on linear associations between connectivity and behavioral phenotypes (17)." However, the superiority of deep learning is debatable. Studies show comparable performance between machine learning (such as kernel regression) and deep learning (such as fully-connected neural networks, BrainNetCNN, Graph CNN (GCNN), and temporal CNN), e.g., He and colleagues (2019) and Vieira and colleagues (2024) https://doi.org/10.1016/j.neuroimage.2019.116276 and Vieira and colleagues' https://doi.org/10.1101/2024.03.07.583858.

Moreover, many non-deep learning predictive techniques are non-linear, e.g., XGBoost, CatBoost, random forest, kernel ridge, and support vector regression with non-linear kernels (such as RBF and polynomial). Thus, stating that machine learning can only model linear relationships is incorrect. Moreover, for the small amount of data the authors had, some might argue that a linear algorithm might be more appropriate to balance the bias-variance trade-off in prediction. Again, without a proper systematic benchmark, it is unclear how well their DenseNet algorithm performs compared to other algorithms.

Regarding the Brain Age literature, the authors also misinterpreted recent findings: "However, a recent study suggests that brain age predictions contribute minimally compared to chronological age for explaining cognitive decline (65), implying that cognitive predictions are more reliable." In this study, Tetereva and colleagues (2024) (https://doi.org/10.7554/eLife.87297.4) showed that non-deep-learning machine learning can make good predictions from MRI on both chronological age (with r up to .88) and fluid cognition (with r up to .627). Using the combination of functional connectivity matrices across rest and tasks to predict fluid cognition, they found performance at r = .565, comparable to what was found in the current manuscript with deep learning. Nonetheless, while brain age predicted chronological age well (and brain cognition predicted fluid cognition well), it was problematic to predict fluid cognition from brain age. They showed that, because brain age, by design, shared so much common variance with chronological age, brain age and chronological age captured the same variance of fluid cognition. When chronological age was controlled for in the prediction of fluid cognition, brain age no longer had high predictive ability. In the case of the current manuscript, the brain cognition gap is not appropriately controlled for cognition (to be more precise, a working memory score). I expect the performance in predicting physical activity, education, and cardiovascular risk will drop dramatically once cognition is controlled for. There are at least two ways to control cognition according to Tetereva and colleagues' study (see more in the recommendations).

The authors mentioned, "The third aim of the current study is to uncover the contribution of dopamine (DA) integrity to brain-cognition gaps." However, I fail to see how mediation analysis would test this. The authors also mentioned, "Insufficient DA modulation can affect neurocognitive functions detrimentally (69, 74, 76-78)." They should test if DA levels are related to working memory scores in their study, and if so, whether the relationship is mediated by the "corrected" brain-cognition gaps. Note see more on the recommendation for the calculation of the "corrected" brain-cognition gaps.

Reviewer #3 (Public review):

Summary:

This paper by Esmaeili and co-authors presents a connectome prediction study to predict episodic memory and relate prediction errors to other phonotypic variables.

Strengths:

(1) A primary and external validation dataset.

(2) Novel use of prediction errors (i.e., brain-cognitive gap).

(3) A wide range of data was investigated.

Weaknesses:

(1) Lack of comparisons to other methods for prediction.

(2) Several different points are being investigated that don't allow any particular one to shine through.

(3) Some choices of analysis are not well-motivated.

(4) How do the n-back connectomes perform for prediction if the authors do not regress task activations from the n-back task?

(5) I am a little concerned about overfitting with the convolutional neural net. For example, the drop-off in prediction performance in the external sample is stark. How does the deep learning approach used here compare to something simpler, like a connectome-based predictive model or ridge regression?

(6) It may be nice to try the other models in the validation dataset. This would also provide a sense of the overfitting that may be going on with overfitting.

(7) While predictive models increase the power over association studies, they still require large samples to prevent overfitting. Do the authors have a sense of the power their main and external validation sample sizes provide?

(8) I am not sure that the Mann-Whitney is the correct test for comparing the distributions of prediction performances. The distributions are dependent on each other as they are each predicting the same outcomes. Using the typical degrees of freedom formula would overestimate the degrees of freedom.

(9) The brain cognition gap is interesting. It is very similar conceptually to the brain age gap. When associating the brain age gap with other phenotypes, typically age is regressed from the brain age gap and the other phenotype. In other words, age is typically associated with a brain age gap as individuals at the tail ages often show the largest gaps. Is the brain cognition gap correlated with episodic memory and do the group differences hold if episodic memory is controlled for?

(10) I have the same question for the dopamine results. Particularly, in the correlations that are divided by brain cognition gap sign. I could see these types of patterns arise due to a correlation with a third variable.

Pour structurer un document de synthèse pour un briefing, il est essentiel de saisir comment la forme scolaire influence le bien-être des élèves, en intégrant des perspectives critiques et des alternatives pédagogiques.

L'analyse de Henri Louis Go dans "La fabrique des écoliers, ou l'enfance enfermée" offre un cadre pertinent pour examiner l'évolution de l'école et son impact sur l'enfance.

L'école comme espace d'enfermement et d'aliénation

Historiquement, la forme scolaire s'est structurée comme un espace d'"enrégimentement" des enfants, les soumettant à une "orthopédie sociale".

L'école républicaine, héritière des modèles éducatifs chrétiens, a instauré une discipline stricte et une séparation entre l'école et la vie.

Cette séparation, défendue par des figures comme Jules Ferry et illustrée par la "simplicité monastique" prônée par le philosophe Alain, visait à concentrer l'attention des élèves sur le travail scolaire, souvent perçu comme une fin en soi.

Cette approche a conduit à un "confinement scolaire", où le monde extérieur est exclu de la salle de classe, créant un environnement artificiel et aliénant pour les élèves.

Foucault souligne que l'école, à l'instar d'autres institutions disciplinaires, exerce un pouvoir sur les corps et les esprits, normalisant les comportements et étouffant l'individualité.

L'immobilisation des corps, l'assujettissement de la parole et l'aliénation du temps des élèves contribuent à un "grand enfermement" de la jeunesse, produisant un mal-être généralisé.

La critique Arendtienne et ses contradictions

Hannah Arendt critique l'éducation moderne pour avoir créé un "monde de l'enfance" séparé du monde des adultes.

Elle estime que l'école doit préparer les enfants à comprendre le monde et à assumer leurs responsabilités civiques, mais met en garde contre toute tentative de politisation de l'école.

Pour Arendt, l'institution scolaire est censée s'intercaler entre la famille et le monde pour faire transition, relevant de la sphère prépolitique où les enfants ne peuvent ni ne doivent prendre part au monde.

Cependant, Go souligne les contradictions de la pensée arendtienne, notamment son opposition entre un "monde de l'enfant" illusoire et une exclusion de l'enfant du monde des adultes.

Arendt conteste l'existence d'un "monde de l'enfant", tout en déplorant l'exclusion des enfants du monde des adultes, ce qui crée une tension dans son argumentation.

Alternatives pédagogiques et reconstruction de la forme scolaire

Face aux limites de la forme scolaire classique, des alternatives pédagogiques émergent, comme la pédagogie Freinet.

L'École Freinet se veut un "conservatoire pour l'enfance", un lieu où les enfants peuvent s'épanouir et développer leur potentiel.

Contrairement à l'école traditionnelle, elle cherche à ouvrir l'école sur la vie, en favorisant les activités pratiques, les échanges avec le milieu environnant et l'expression libre des enfants.

Élise et Célestin Freinet insistent sur l'importance de prendre en compte le "monde de l'enfance", en préservant la sensibilité et la créativité des enfants.

Ils cherchent à dialectiser l'approche des problèmes, en tenant compte à la fois de la nécessité de protéger l'enfance et de celle de développer l'esprit critique des élèves.

L'École Freinet n'est ni un espace clos, protégé des bruits extérieurs, ni un espace seulement intermédiaire où le monde n'entrerait que représenté.

Freinet accorde une attention particulière aux conditions matérielles de l'existence des élèves, considérant que l'aménagement des locaux, l'accès à la nature et la qualité de l'alimentation sont essentiels pour favoriser le bien-être et l'épanouissement des enfants.

Pistes d'action pour le briefing

Pour structurer votre briefing, vous pourriez aborder les points suivants :

1. Évolution historique de la forme scolaire :

   • Analyser comment la forme scolaire a évolué depuis le XIXe siècle, en mettant en évidence les continuités et les ruptures.
   • Examiner comment les conditions matérielles et les pratiques disciplinaires ont influencé le vécu des élèves.

2. Critiques de la forme scolaire classique :

   • Présenter les critiques formulées par Foucault, Arendt et d'autres auteurs à l'encontre de la forme scolaire classique.
   • Analyser les notions d'enfermement, d'aliénation et de normalisation, et leurs effets sur le bien-être des élèves.

3. Alternatives pédagogiques et pistes de reconstruction :

   • Explorer les alternatives à la forme scolaire classique, comme la pédagogie Freinet et l'éducation nouvelle.
   • Mettre en évidence les principes clés de ces pédagogies, tels que l'ouverture sur la vie, la coopération, l'expression libre et le respect de l'enfance.

4. Conditions matérielles et bien-être :

   • Souligner l'importance des conditions matérielles de l'existence des élèves pour leur bien-être et leur épanouissement.
   • Proposer des pistes d'action concrètes pour améliorer l'aménagement des locaux, l'accès à la nature et la qualité de l'alimentation.

5. Reconstruction de l'école comme lieu de vie émancipateur :

*Comment repenser l'école comme un lieu de vie émancipateur, où les élèves peuvent développer un désir d'apprendre et s'ouvrir à l'avenir?

• Favoriser une approche pédagogique qui prenne en compte les besoins et les aspirations des enfants, tout en les préparant à devenir des citoyens actifs et responsables.
  • Encourager l'expérimentation de nouvelles formes d'organisation scolaire, plus ouvertes, coopératives et respectueuses de l'enfance.

En explorant ces questions, votre briefing permettra de nuancer l'idéologie du bien-être à l'école et de proposer des pistes d'action concrètes pour améliorer la vie scolaire des élèves.

Voici les éléments clés à considérer, organisés thématiquement pour faciliter la compréhension et la présentation.

Concepts et Définitions Clés

Distinction entre méthodologie et méthodes, ainsi que la notion de recherche créative.

• Méthodologie : Cadre large et cohérent, guidé par les valeurs et les croyances du chercheur, influençant les choix tout au long de la recherche.
• Méthodes : Outils utilisés à différents niveaux de la recherche (conception, collecte, analyse, rédaction, présentation). Un même outil, comme l'interview, peut être utilisé avec différentes méthodologies.
• Recherche Créative : Production visant à construire de nouvelles connaissances en adaptant les conventions et en s'ancrant dans le monde social.

La créativité n'est pas synonyme d'innovation et n'est pas réservée à l'art ou aux sciences sociales.

Il faut également distinguer la recherche création (partant des pratiques artistiques) de la méthodologie de recherche créative (utilisant une pratique créative à différentes étapes de la recherche).

Types de Méthodes de Recherche Créative

Il existe un large éventail d'approches, souvent basées sur l'art, visant à perturber les habitudes, susciter le changement, rechercher l'empathie et explorer l'identité des participants. Hélène Kara (2020) identifie cinq grands types de méthodes:

1. Méthodes basées sur les arts : Incluant les arts visuels, du spectacle et textiles.
2. Recherches incarnées : Liées à une implication totale du corps, notamment pour les thèmes liés au toucher ou à l'écoute.
3. Recherches basées sur la technologie : Utilisant les médias sociaux, applications, jeux et vidéos.
4. Approches multimodales : Combinant différentes méthodes qualitatives et quantitatives.
5. Approches transformatives : Visant à questionner ou changer un statu quo, en lien avec les recherches participatives, actions ou communautaires.

Intérêts et Enjeux des Approches Créatives

Ces méthodes présentent plusieurs avantages:

• Facilitation de l'écoute de points de vue multiples.
• Amélioration de la réflexivité des chercheurs.
• Vision holistique des expériences.
• Promotion de l'autonomisation des participants.
• Positionnement du participant comme expert.
• Perturbation des relations de pouvoir.

Cependant, elles soulèvent également des enjeux importants:

• Gestion de la relation chercheurs-participants.
• Production et analyse de données de différentes formes (2D, 3D, audio, etc.).
• Accessibilité des résultats pour les participants et les non-participants.
• Mobilisation des connaissances en conservant leur complexité.

Exemples Concrets

La présentation illustre l'utilisation de ces méthodes à travers une recherche sur la transition des jeunes enfants ayant des besoins éducatifs particuliers vers l'école. Deux exemples sont particulièrement détaillés:

1. Approche mosaïque avec les enfants:

   • Méthode participative où les enfants sont considérés comme experts.
   • Basée sur six composantes centrales : participation, réflexivité, adaptabilité, expérience, intégration à la pratique et multiméthode.
   • Utilisation d'outils variés : marionnette, micro, appareil photo, matériel créatif.
   • Collecte de données à travers des observations, dialogues, photos et créations.

   • Focus groupes créatifs avec des professionnels:

   • Utilisation de la métaphore du voyage pour faciliter la participation.

   • Fourniture de matériel artistique varié (papier, couleurs, ciseaux, colle, etc.).
   • Analyse des données en quatre étapes : description des créations, familiarisation avec les présentations, codage et élaboration de récits analytiques.
   • Prise en compte des aspects visuels, textuels et sensoriels des créations.

Forces et Questions

Les chercheuses soulignent trois forces principales dans leur utilisation des approches créatives:

• Promotion d'une participation significative et éthique.
• Création de portraits holistiques et empathiques.
• Importance des représentations visuelles dans toutes les phases de la recherche.

Elles mentionnent également des questions importantes qui les habitent:

• Comment reconnaître et affronter les dilemmes éthiques ?
• Dans quelle mesure le matériel fourni facilite-t-il l'élaboration du sens ?
• Comment s'assurer de la pertinence des outils cocréés pour les différents acteurs ?

En structurant votre briefing autour de ces thèmes, vous offrirez une vue d'ensemble claire et approfondie des méthodes de recherche créatives présentées, tout en soulignant leurs avantages, leurs défis et les questions qu'elles soulèvent.

This figure is essential to previewing the text because it helps you see what the author used for his data, in this case he drew a table with a straight forward x and y axis. It shows the number of Selections.

This function determines whether the system should retry memory compaction during page allocation. 1. If the allocation order is 0, no need for compaction 2. If compaction was skipped because of a lack of free order-0 pages: Check if memory reclaim can help. 3. If compaction succeeded but allocation still failed: retry 1/4 times. 4. If the compaction failed. retry with increase priority

whether it has a (yuh) sound 1. yes 2. yes 3. no 4. yes 5. no 6. yes 7. yes 8. no 9. no 10. yes 11. yes 12. no 13. no 14. yes

yes: p, k, b, g, m, f, v, h no: t,d,n,th,s differences: place of articulation; coronals don't get a (yuh)

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

Learn more at Review Commons

Reply to the reviewers

I would like to thank the reviewers for their comments and interest in the manuscript and the study.

Reviewer #1

1) I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.

As the reviewer pointed out, a wet experimental validation of the results of this study would give an opportunity for more biological researchers to have an interest in the study. I plan to promote the wet experimental analysis in collaboration with biological experimental researchers as a next step of this study. The same analysis in this study can be performed in immortalized cells for CRISPR experiment (e.g. Guo Y et al. Cell 2015).

2) Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.

Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.

3) Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.

As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality is their overall tendency, and it may be difficult to notice the directionality from each binding site.

I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.

4) The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.

According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value < 0.05). The comparison between the splice sites of both ends of first and last introns and those of other introns showed the similar statistical significance of enrichment and number of splice sites with the insulator-associated DNA-binding proteins (Table 2 and Table S9).

5) Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.

I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.

6) Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.

Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2’s comments.

Reviewer #2

1) Introduction, line 95: CTCF appears two times, it seems redundant.

On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".

2) Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?

Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.

I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.

3) Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.

On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".

4) Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.

On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines”. On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".

5) Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.

The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.

6) Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?

As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).

In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.

I added the following sentence on lines 561-569: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.

FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.

7) In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?

Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.

The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.

As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.

Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.

8) Do the authors think that the identified DBPs could work in that way as well?

The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).

Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.

9) Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?

Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.

10) Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?

Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 – 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.

To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).

Reviewer #3

1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.

When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 22 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.

2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.

As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 – 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.

3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.

Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 – 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 – 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).

4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.

The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.

1.PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g.,https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.

The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.

As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.

2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.

In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.

Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.

Reviewer #1 (Public review):

Summary:

The authors investigate the relationship between 3D chromatin architecture and innate immune gene regulation in monocytes from patients with alcohol-associated hepatitis (AH). Using Hi-C technology, they attempt to identify structural changes in the genome that correlate with altered gene expression. Their central claim is that genome restructuring contributes to the hyper-inflammatory phenotype associated with AH.

Strengths:

(1) The manuscript employs Hi-C technology, which, in principle, is a powerful approach for studying genome organization.

(2) The focus on disease-relevant genes, particularly innate immune loci, provides a contextually important angle for understanding AH.

Weaknesses:

(1) Sample Size: The study relies on an exceptionally small cohort (4 AH patients and 4 healthy controls), rendering the results statistically underpowered and highly susceptible to variability.

(2) Hi-C Resolution unpaired to RNA seq: The data are presented at a resolution of 100kb, which is insufficient to uncover meaningful chromatin interactions at the level of individual genes. This data is unpaired.

(3) Functional Validation: The manuscript lacks experiments to directly link changes in chromatin architecture with gene expression or monocyte function, leaving the claims speculative.

(4) Data Integration: The lack of Hi-C with ATAC and RNA-seq data handicaps the analysis and really makes it superficial. In short, it does not convincingly demonstrate a functional relationship.

(5) Confounding Factors: The manuscript neglects critical confounding variables such as comorbidities, medications, and lifestyle factors, which could influence chromatin structure and gene expression independently of AH.

Appraisal of the Aims and Results:

The manuscript sets out to establish a connection between chromatin architecture and AH pathology. However, the study fails to achieve its stated aims due to inadequate methods and insufficient data. The conclusions drawn from the Hi-C analyses alone are poorly supported, and the lack of functional validation undermines the credibility of the proposed mechanisms. Overall, the results do not provide compelling evidence to substantiate the authors' claims.

Impact on the Field and Utility to the Community:

The work, in its current form, is unlikely to have a meaningful impact on the field. The limited scope, methodological shortcomings, and lack of robust data significantly diminish its potential utility. Without addressing these critical gaps, the study does not offer new insights into the role of genome architecture in AH or provide useful methodologies or datasets for the community.

Additional Context:

The manuscript would benefit from a more comprehensive analysis of potential mechanisms underlying the observed changes, including the interplay between chromatin architecture and epigenetic modifications. Furthermore, longitudinal studies or therapeutic interventions could provide insights into the dynamic aspects of genome restructuring in AH. These considerations are entirely absent from the current study.

Conclusion:

The manuscript does not achieve its stated goals and does not present sufficient evidence to support its conclusions. The limitations in sample size, resolution, and experimental rigor severely hinder its contribution to the field. Addressing these fundamental flaws will be essential for the work to be considered a meaningful addition to the literature.

Author response:

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

eLife Assessment

This valuable study combined whole-head magnetoencephalography (MEG) and subthalamic (STN) local field potential (LFP) recordings in patients with Parkinson's disease undergoing deep brain stimulation surgery. The paper provides solid evidence that cortical and STN beta oscillations are sensitive to movement context and may play a role in the coordination of movement redirection.

We are grateful for the expert assessment by the editor and the reviewers. Below we provide pointby-point replies to both public and private reviews. We have tried to keep the answers in the public section short and concise, not citing the changed passages unless the point does not re-appear in the recommendations. There, we did include all of the changes to the manuscript, such that the reviewers need not go back and forth between replies and manuscript.

The reviewer comments have not only led to numerous improvements of the text, but also to new analyses, such as Granger causality analysis, and to methodological improvements e.g. including numerous covariates in the statistical analyses. We believe that the article improved substantially through the feedback, and we thank the reviewers and the editor for their effort.

Public Reviews

Reviewer #1 (Public review):

Summary:

Winkler et al. present brain activity patterns related to complex motor behaviour by combining wholehead magnetoencephalography (MEG) with subthalamic local field potential (LFP) recordings from people with Parkinson's disease. The motor task involved repetitive circular movements with stops or reversals associated with either predictable or unpredictable cues. Beta and gamma frequency oscillations are described, and the authors found complex interactions between recording sites and task conditions. For example, they observed stronger modulation of connectivity in unpredictable conditions. Moreover, STN power varied across patients during reversals, which differed from stopping movements. The authors conclude that cortex-STN beta modulation is sensitive to movement context, with potential relevance for movement redirection.

Strengths:

This study employs a unique methodology, leveraging the rare opportunity to simultaneously record both invasive and non-invasive brain activity to explore oscillatory networks.

Weaknesses:

It is difficult to interpret the role of the STN in the context of reversals because no consistent activity pattern emerged.

We thank the reviewer for the valuable feedback to our study. We agree that the interpretation of the role of the STN during reversals is rather difficult, because reversal-related STN activity was highly variable across patients. Although there seem to be consistent patterns in sub-groups of the current cohort, with some patients showing event-related increases (Fig. 3b) and others showing decreases, the current dataset is not large enough to substantiate or even explain the existence of such clusters. Thus, we limit ourselves to acknowledging this limitation and discussing potential reasons for the high variability, namely variability in electrode placement and insufficient spatial resolution for the separation of specialized cell ensembles within the STN (see Discussion, section Limitations and future directions).

Reviewer #2 (Public review):

Summary:

This study examines the role of beta oscillations in motor control, particularly during rapid changes in movement direction among patients with Parkinson's disease. The researchers utilized magnetoencephalography (MEG) and local field potential (LFP) recordings from the subthalamic nucleus to investigate variations in beta band activity within the cortex and STN during the initiation, cessation, and reversal of movements, as well as the impact of external cue predictability on these dynamics. The primary finding indicates that beta oscillations more effectively signify the start and end of motor sequences than transitions within those sequences. The article is well-written, clear, and concise.

Strengths:

The use of a continuous motion paradigm with rapid reversals extends the understanding of beta oscillations in motor control beyond simple tasks. It offers a comprehensive perspective on subthalamocortical interactions by combining MEG and LFP.

Weaknesses:

(1) The small and clinically diverse sample size may limit the robustness and generalizability of the findings. Additionally, the limited exploration of causal mechanisms reduces the depth of its conclusions and focusing solely on Parkinson's disease patients might restrict the applicability of the results to broader populations.

We thank the reviewer for the insightful feedback. We address these issues one by one in our responses to points 2, 4 and 6, respectively.

(2) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size and cohort stratification. We now acknowledge this in the revised discussion (section Limitations and future directions). Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (Discussion, section Limitations and future directions). Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

(3) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

Thanks for the suggestion. As measures of effect size, we have added partial eta squared (η_p<sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up t-tests.

(4) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band. The detailed results can be viewed on p. 16 in the section on Granger causality. For statistical testing, we conducted an rmANCOVA, similar to those for power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6-figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

(5) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

(6) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS in our centre makes this an unfeasible option in practical terms. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication and dopamine has been found to normalize pathological alterations of beta activity. Further, the general pattern of movement-related beta and gamma oscillations reported here has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants.

Reviewer #3 (Public review):

Summary:

The study highlights how the initiation, reversal, and cessation of movements are linked to changes in beta synchronization within the basal ganglia-cortex loops. It was observed that different movement phases, such as starting, stopping briefly, and stopping completely, affect beta oscillations in the motor system.

It was found that unpredictable cues lead to stronger changes in STN-cortex beta coherence. Additionally, specific patterns of beta and gamma oscillations related to different movement actions and contexts were observed. Stopping movements was associated with a lack of the expected beta rebound during brief pauses within a movement sequence.

Overall, the results underline the complex and context-dependent nature of motor-control and emphasize the role of beta oscillations in managing movement according to changing external cues.

Strengths:

The paper is very well written, clear, and appears methodologically sound.

Although the use of continuous movement (turning) with reversals is more naturalistic than many previous button push paradigms.

Weaknesses:

The generalizability of the findings is somewhat curtailed by the fact that this was performed perioperatively during the period of the microlesion effect. Given the availability of sensing-enabled DBS devices now and HD-EEG, does MEG offer a significant enough gain in spatial localizability to offset the fact that it has to be done shortly postoperatively with externalized leads, with an attendant stun effect? Specifically, for paradigms that are not asking very spatially localized questions as a primary hypothesis?

We appreciate the reviewer’s feedback and acknowledge the valid point raised on the timing of our measurements. Indeed, sensing-enabled devices offer a valid alternative to peri-operative recordings, circumventing the stun effect. We acknowledge this in the revised discussion, section Limitations and future directions (p. 23): “Additionally, future research could capitalize on sensingcapable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).” This alternative strategy, however, was not an option here because we did not have a sufficient number of patients implanted with sensing-enabled devices at the time when the data collection was initialized.

That being said, we would like to highlight that in the present study, our goal was not to study pathology related to Parkinson’s disease. Rather, we aimed to learn about motor control in general. The stun effect may have facilitated motor performance in our patients, which is actually beneficial to the research goals at hand.

Further investigation of the gamma signal seems warranted, even though it has a slightly lower proportional change in amplitude in beta. Given that the changes in gamma here are relatively wide band, this could represent a marker of neural firing that could be interestingly contrasted against the rhythm account presented.

We appreciate the reviewer’s interest and we have extended the investigation of gamma oscillations. We now provide statistics regarding the influence of predictability on gamma power and gamma coherence (no significant effects) and explore Granger causality in the gamma (and beta) band (see comment 4 of reviewer 2). Unfortunately, we cannot measure spiking via the DBS electrode, and therefore we cannot investigate correlations between gamma oscillatory activity and action potentials. We do agree with the reviewer, however, that action potentials rather than oscillations form the basis of motor control in the brain. This view of ours is now reflected in the revised discussion, section Limitations and future directions (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

This is a well-conducted study and overall the results are clear. I only have one minor suggestion for improvement of the manuscript. I found the order of appearance of the results somewhat confusing, switching from predictability-related behavioral effects to primarily stopping and reversal-related neurophysiological effects, back to predictability but starting with coherence. I would suggest that the authors try to follow a systematic order focused on the questions at hand. E.g. perhaps readability could be improved if the results section is split into reversal vs. stopping related effects, reporting behavior, power, and coherence in this order, followed by a predictability section, again reporting behavior, power, and coherence. Obviously, this is an optional suggestion. Apart from that, I just missed a more direct message related to the absence of statistical significance related to STN power changes during reversal. I think this could be made more clear in the text.

We thank the reviewer for the feedback to our study. In order to ease reading, we modified the order and further added additional sub-titles to the results section. We start with Behavior (p. 4) and then move on to Power (general movement effects on power – movement effects on STN power – movement effects on cortical power – predictability effects on power). Next, we move on to Connectivity (movement effects on connectivity – predictability effects on connectivity – Granger causality). We hope that these adaptations will help guide the reader.

Additionally, we thank the reviewer for noting that we did not explicitly mention the lack of statistical significance of reversal-related beta power modulations in the STN. We have adapted the section on modulation of STN beta power associated with reversals (p. 8) to: “In the STN, reversals were associated with a brief modulation of beta power, which was weak in the group-average spectrum and did not reach significance (Fig. 3a).”

Reviewer #2 (Recommendations for the authors):

(1) The small sample size and variability in clinical characteristics among patients may limit the robustness of the study's conclusions. It would be beneficial for the authors to acknowledge this limitation and propose strategies for addressing it in future research. Additionally, incorporating patient-specific factors as covariates in the ANOVA could help mitigate the confounding effects of heterogeneity.

Thank you for this comment. The challenges associated with recording brain activity peri-operatively can be a limiting factor when it comes to sample size. We now acknowledge this in the revised discussion, section Limitations and future directions (p. 20):

“Invasive measurements of STN activity are only possible in patients who are undergoing or have undergone brain surgery. Studies drawing from this limited pool of candidate participants are typically limited in terms of sample size and cohort stratification, particularly when carried out in a peri-operative setting. Here, we had a sample size of 20, which is rather high for a peri-operative study, but still low in terms of absolute numbers.”

Furthermore, we suggest using sensing-capable devices in the future as a measure to increase sample sizes (p. 21):

“Additionally, future research could capitalize on sensing-capable devices to circumvent the necessity to record brain activity peri-operatively, facilitating larger sample sizes and circumventing the stun effect, an immediate improvement in motor symptoms arising as a consequence of electrode implantation (Mann et al., 2009).”

Lastly, we appreciate the idea of adding patient-specific factors as covariates to the ANOVAs and have thus included age, disease duration and pre-surgical UPDRS score into our models. This did not lead to any qualitative changes of statistical effects.

Revised article

Methods, Statistical analysis:

“To account for their potential influence on brain activity, we added age, pre-operative UPDRS score, and disease duration as covariates to all ANOVAs. Covariates were standardized by means of zscoring.”

(2) The author may consider using standardized statistics, such as effect size, that would provide a clearer picture of the observed effect magnitude and improve comparability.

Thanks for this useful suggestion. As measures of effect size, we have added partial eta squared (η_p<sup2</sup>) to the results of all ANOVAs and Cohen’s d to all follow-up _t-_tests.

(3) Although the study identifies relevance between beta activity and motor events, it lacks causal analysis and discussion of potential causal mechanisms. Given the valuable datasets collected, exploring or discussing causal mechanisms would enhance the depth of the study.

We appreciate this idea and have conducted Granger causality analyses in response to this comment. This new analysis reveals that there is a strong cortical drive to the STN for all movements of interest and predictability conditions in the beta band, but no directed interactions in the gamma band. For statistical testing, we conducted an rmANCOVA, similar to the analysis of power and coherence (see p. 46-48 and 54-56 for the corresponding tables), as well as t-tests assessing directionality (Figure 6 figure supplement 2 on p. 35). In the discussion section, we connect these results with prior findings suggesting that the frontal cortex drives the STN in the beta band, likely through hyperdirect pathway fibers (p. 17).

Revised article

Methods Section, Granger Causality Analysis

“We computed beta and gamma band non-parametric Granger causality (Dhamala, Rangarajan, & Ding, 2008) between cortical ROIs and the STN in the hemisphere contralateral to movement for the post-event time windows (0 – 2 s with respect to start, reversal, and stop). Because estimates of Granger causality are often biased, we compared the original data to time-reversed data to suppress non-causal interactions. True directional influence is reflected by a higher causality measure in the original data than in its time-reversed version, resulting in a positive difference between the two, the opposite being the case for a signal that is “Granger-caused” by the other. Directionality is thus reflected by the sign of the estimate (Haufe, Nikulin, Müller, & Nolte, 2013). Because rmANCOVA results indicated no significant effects for predictability and movement type, and post-hoc tests did not detect significant differences between hemispheres, we averaged Granger causality estimates over movement types, hemispheres and predictability conditions in Figure 6-figure supplement 2.”

Results, Granger causality

“In general, cortex appeared to drive the STN in the beta band, regardless of the movement type and predictability condition. This was reflected in a main effect of ROI on Granger causality estimates (F_ROI(7,9) = 3.443, p_ROI = 0.044, η_p<sup2</sup> = 0.728; refer to Supplementary File 4 for the full results of the ANOVA). In the hemisphere contralateral to movement, follow-up t-tests revealed significantly higher Granger causality estimates from M1 to the STN (t = 3.609, one-sided p < 0.001, d = 0.807) and from MSMC to the STN (t = 2.051, one-sided p < 0.027, d = 0.459) than the other way around. The same picture emerged in the hemisphere ipsilateral to movement (M1 to STN: t = 3.082, one-sided p = 0.003, d = 0.689; MSMC to STN: t \= 1.833, one-sided p < 0.041, d = 0.410). In the gamma band, we did not detect a significant drive from one area to the other (F_ROI(7,9) = 0.338, p_ROI = 0.917, η_p<sup2</sup> = 0.208, Supplementary File 6). Figure 6-figure supplement 2 demonstrates the differences in Granger causality between original and time-reversed data for the beta and gamma band.”

Discussion, The dynamics of STN-cortex coherence

“Considering the timing of the increase observed here, the STN’s role in movement inhibition (Benis et al., 2014; Ray et al., 2012) and the fact that frontal and prefrontal cortical areas are believed to drive subthalamic beta activity via the hyperdirect pathway (Chen et al., 2020; Oswal et al., 2021) it seems plausible that the increase of beta coherence reflects feedback of sensorimotor cortex to the STN in the course of post-movement processing. In line with this idea, we observed a cortical drive of subthalamic activity in the beta band.”

(4) The study cohort focused on senior adults, who may exhibit age-related cortical responses during movement planning in neural mechanisms. These aspects were not discussed in the study.

We appreciate the comment and agree that age may have impacted neural oscillatory activity of patients in the present study. We now acknowledge this in the limitations section, and point out that our approach to handling these effects was including age as a covariate in the statistical analyses.

Revised article

Discussion, Limitations and Future Directions

“Further, most of our participants were older than 60 years. To diminish any confounding effects of age on movement-related modulations of neural oscillations, such as beta suppression and rebound (Bardouille & Bailey, 2019; Espenhahn et al., 2019), we included age as a covariate in the statistical analyses.”

(5) Including a control group of patients with other movement disorders who also undergo DBS surgery would be beneficial. Because we cannot exclude the possibility that the observed findings are specific to PD or can be generalized. Additionally, the current title and the article, which are oriented toward understanding human motor control, may not be appropriate.

We thank the reviewer for this comment and fully agree that it cannot be ruled out that the present findings are, in part, specific to PD. We acknowledge this limitation in the Limitations and future directions section (p. 20-21). Indeed, including a control group of patients with other disorders would be ideal, but the scarcity of patients with diseases other than PD who receive STN DBS makes this an unfeasible option. We do suggest that future research may address this issue by extending our approach to different disorders or healthy participants on the cortical level (p. 21). Lastly, we appreciate the idea to adjust the title of the present article. The adjusted title is: “Context-Dependent Modulations of Subthalamo-Cortical Synchronization during Rapid Reversals of Movement Direction in Parkinson’s Disease”.

That being said, we do believe that our findings at least approximate healthy functioning and are not solely related to PD. For one, patients were on their usual dopaminergic medication for the study and dopamine has been found to normalize pathological alterations of beta activity. More importantly, the general pattern of movement-related beta and gamma oscillations has been observed in numerous diseases and brain structures, including cortical beta oscillations measured non-invasively in healthy participants. Thus, it is not unlikely that the new aspects discovered here are also general features of motor processing.

Revised article

Discussion, Limitations and future directions

“Furthermore, we cannot be sure to what extent the present study’s findings relate to PD pathology rather than general motor processing. We suggest that our approach at least approximates healthy brain functioning as patients were on their usual dopaminergic medication. Dopaminergic medication has been demonstrated to normalize power within the STN and globus pallidus internus, as well as STN-globus pallidus internus and STN-cortex coherence (Brown et al., 2001; Hirschmann et al., 2013). Additionally, several of our findings match observations made in other patient populations and healthy participants, who exhibit the same beta power dynamics at movement start and stop (Alegre et al., 2004) that we observed here. Notably, our finding of enhanced cortical involvement in face of uncertainty aligns well with established theories of cognitive processing, given the cortex' prominent role in managing higher cognitive functions (Altamura et al., 2010). Yet, transferring our approach and task to patients with different disorders, e.g. obsessive compulsive disorder, or examining young and healthy participants solely at the cortical level, could contribute to elucidating whether the synchronization dynamics reported here are indeed independent of PD and age.”

Reviewer #3 (Recommendations for the authors):

Despite the strengths of the "rhythm" account of cognitive processes, the paper could possibly be improved by making it less skewed to rhythms explaining all of the movement encoding.

Thank you for this comment - the point is well taken. There is a large body of literature relating neural oscillations to spiking in larger neural populations, which itself is likely the most relevant signal with respect to motor control. In our eyes, it is this link that justifies the rhythm account, i.e. we agree with the reviewer that action potentials are the basis of movement encoding in the brain, not oscillations. Unfortunately, we cannot measure spiking with the method at hand.

To better integrate this view into the current manuscript, we make the following suggestion for future research in the Limitations and future directions section (p. 21): “Lastly, given the present study’s focus on understanding movement-related rhythms, particularly in the beta range, future research could further explore the role of gamma oscillations in continuous movement and their relation to action potentials in motor areas (Fischer et al., 2020; Igarashi, Isomura, Arai, Harukuni, & Fukai, 2013), which form the basis of movement encoding in the brain.”

In Figure 5 - is the legend correct? Is it really just a 0.2% change in power only? That would be a very surprisingly small effect size.

We thank the reviewer for noting this. Indeed, the numbers on the scale quantify relative change (post - pre)/pre and should be multiplied by 100 to obtain %-change. We have adjusted the color bars accordingly.

The dissociation between the effects of unpredictable cues in coherence versus raw power is interesting and could potentially be directly contrasted further in the discussion (here they are presented separately with separate discussions, but this seems like a pretty important and novel finding as beta coherence and power usually go in the same direction).

We appreciate the reviewer’s interest in our findings on the predictability of movement instructions. In case of coherence, the difference between pre- and post-event was generally more positive in the unpredictable condition, meaning that suppressions (negative pre-post difference) were diminished whereas increases (positive pre-post difference) were enhanced. With respect to power, we also observed less suppression in the unpredictable condition at movement start. Therefore, the direction of change is in fact the same. We made this clearer in the revised version by adapting the corresponding sections of the abstract, results and discussion (see below).

The only instance of coherence and power diverging (on a qualitative level) was observed during reversals: here, we noted post-event increases in coherence and post-event decreases in M1 power in the group-average spectra. However, when comparing the pre- and post-event epochs statistically by means of permutation testing, the coherence increase did not reach significance. Hence, we did not highlight this aspect.

Revised version

Abstract

“… Event-related increases of STN-cortex beta coherence were generally stronger in the unpredictable than in the predictable condition. … “

Results, Effects of predictability on beta power  

“With respect to the effect of predictability of movement instructions on beta power dynamics (research aim 2), we observed an interaction between movement type and condition (F_cond*mov (2,14) = 4.206, p_cond*mov = 0.037, η_p<sup2</sup> = 0.375), such that the beta power suppression at movement start was generally stronger in the predictable (M = -0.170, SD = 0.065) than in the unpredictable (M \= -0.154, SD = 0.070) condition across ROIs (t = -1.888, one-sided p \= 0.037, d = -0.422). We did not observe any modulation of gamma power by the predictability of movement instructions (F_cond (1,15) = 0.792, p_cond = 0.388, η_p<sup2</sup> = 0.050, Supplementary File 5).”

Effects of predictability on STN-cortex coherence

“With respect to the effect of predictability of movement instructions on beta coherence (research aim 2), we found that the pre-post event differences were generally more positive in the unpredictable condition (main effect of predictability condition; F_cond(1,15) = 8.684, p_cond = 0.010, η_p<sup2</sup> = 0.367; Supplementary File 3), meaning that the suppression following movement start was diminished and the increases following stop and reversal were enhanced in the unpredictable condition (Fig. 6a). This effect was most pronounced in the MSMC (Fig. 6b). When comparing regionaverage TFRs between the unpredictable and the predictable condition, we observed a significant difference only for stopping (t_clustersum = 142.8, p = 0.023), suggesting that the predictability effect was mostly carried by increased beta coherence following stops. When repeating the rmANCOVA for preevent coherence, we did not observe an effect of predictability (F_cond(1,15) = 0.163, p_cond = 0.692, η_p<sup2</sup> = 0.011), i.e. the effect was most likely not due to a shift of baseline levels. The increased tendency for upward modulations and decreased tendency for downward modulations rather suggests that the inability to predict the next cue prompted intensified event-related interaction between STN and cortex. STN-cortex gamma coherence was not modulated by predictability (F_cond(1,15) = 0.005, p_cond = 0.944, η_p<sup2</sup> = 0.000, Supplementary File 5).”

Discussion, Beta coherence and beta power are modulated by predictability

“In the present paradigm, patients were presented with cues that were either temporally predictable or unpredictable. We found that unpredictable movement prompts were associated with stronger upward modulations and weaker downward modulations of STN-cortex beta coherence, likely reflecting the patients adopting a more cautious approach, paying greater attention to instructive cues. Enhanced STN-cortex interactions might thus indicate the recruitment of additional neural resources, which might have allowed patients to maintain the same movement speed in both conditions. […]”

With respect to power, we observed reduced beta suppression in the unpredictable condition at movement start, consistent with the effect on coherence, likely demonstrating a lower level of motor preparation.

Given that you have a nice continuous data task here - the turning of the wheel, it might be interesting to cross-correlate the circular position (and separately - velocity) of the turning with the envelope of the beta signal. This would be a nice finding if you could also show that the beta is modulated continuously by the continuous movements. In the natural world, we rarely do a continuous movement with a sudden reversal, or stop, most of the time we are in continuous movement. Look at this might also be a strength of your dataset.

We could not agree more. In fact, having a continuous behavioral output was a major motivation for choosing this particular task. We are very interested in state space models such as preferential subspace identification (Sani et al., 2021), for example. These models relate continuous brain signals to continuous behavioral target variables and should be of great help for questions such as: do oscillations relate to moment-by-moment adaptations of continuous movement? Which frequency bands and brain areas are important? Is angular position encoded by different brain areas/frequency bands than angular speed? These analyses are in fact ongoing. This project, however, is too large to fit into the current article.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study is an important follow-up to their prior work - Wong et al. (2019), starting with clear questions and hypotheses, followed by a series of thoughtful and organized experiments. The method and results are convincing. Experiment 1 demonstrated the sensory preconditioned fear with few (8) or many (32) sound-light pairings. Experiments 2A and 2B showed the role of PRh NMDA receptors during conditioning for online integration, revealing that this contribution is present only after a few sound-light pairings, not after many sound-light pairings. Experiments 3A and 3B showed the contribution of PRh-BLA communication to online integration, again only after a few but not after many. Contrary to Experiments 3A and 3B, Experiments 4A and 4B showed the contribution of PRh-BLA communication to integration at test only after many but not few sound-light pairings.

Strengths:

Throughout the manuscript, the methods and results are clearly organized and described, and the use of statistics is solid, all contributing to the overall clarity of the research. The discussion section was also well-written, effectively comparing the current research with the prior work and offering insightful interpretations and potential future directions for this line of research. I have only a limited amount of concerns about some results and some details of experiments/statistics.

We thank the reviewer for their positive assessment.

Weaknesses:

Could you provide further interpretation regarding line 171: the observation that sensory preconditioned fear increased with the number of sound-light pairings? Was this increase due to better sound-light association learning during Stage 1? Additionally, were there any experimental differences between Experiment 1 and the other experiments that might explain why freezing was higher in the P32 group compared to the P8 group? This pattern seemed to be absent in the other experiments. If we consider the hypothesis that the online integration mechanism is more active with fewer pairings and the chaining mechanism at the test is more prominent with many pairings, we wouldn't expect a difference between the P8 and P32 groups. Given the relatively small sample size in Experiment 1, the authors might consider conducting a cross-experiment analysis or something similar to investigate this further.

We appreciate the reviewer’s point and thank them for the question. The heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment (Group P32) may reflect the combined effects of both mediated learning and chaining at test. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated in the subsequent experiments: i.e., the levels of freezing to the sensory preconditioned stimulus at test were almost identical among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B; and this was also true in Experiments 3A and 3B, and again in Experiments 4A and 4B. A key difference between the initial and subsequent experiments is that, in contrast to the initial experiment, rats in subsequent experiments underwent surgery for one reason or another (implantation of cannulas, lesion of the perirhinal cortex). The implication is that surgical interventions in the perirhinal cortex and/or basolateral amygdala might affect the way that rats integrate the sound-light and light-shock associations in sensory preconditioning: i.e., they may force rats to rely on one type of integration strategy or the other. This is, of course, purely speculative – it will be addressed in future research.

Reviewer #2 (Public review):

This manuscript builds on the authors' earlier work, most recently Wong et al. 2019, in which they showed the importance of the perirhinal cortex (PRh) during the first-order conditioning stage of sensory preconditioning. Sensory preconditioning requires learning between two neutral stimuli (S2-S1) and subsequent development of a conditioned response to one of the neutral stimuli after pairing of the other stimulus with a motivationally relevant unconditioned stimulus (S1-US). One highly debated question regarding the mechanisms of learning of sensory preconditioning has been whether conditioned responses evoked by the indirectly trained stimulus (S2) occur through a mediated representation at the time of the first-order US training, or whether the conditioned responses develop through a chained evoked representation (S2--> S1 --> US) at the time of test. The authors' prior findings provided strong evidence for PRh being involved in mediated learning during the first-order training. They showed that protein synthesis was required during the first-order S1-US learning to support the conditioned response to the indirectly trained stimulus (S2) at the test.

One question remaining following the previous paper was whether certain conditions may promote a chaining mechanism over mediated learning, as there is some evidence for chained representations at the time of the test. In this paper, the authors directly address this important question and find unambiguous results that the extent of training during the preconditioning stage impacts the involvement of PRh during the first-order conditioning or stage 2. They show that putative blockade of synaptic changes in PRh, using an NMDA antagonist, disrupts responding to the preconditioned cue at test during shorter duration preconditioning training (8 trials), but not during extended training (32 trials). They also show that this is the case for communication between the PRh and BLA during the same stage of training using a contralateral inactivation approach. This confirms their previous findings in 2019 of connectivity between these regions for the short-duration training, while they observe here for the first time that this is not the case for extended training. Finally, they show that with extended training, communication between BLA and the PRh is required at the final test of the preconditioned stimulus, but not for the short duration training.

The results are clear and extremely consistent across experiments within this paper as well as with earlier work. The experiments here are thorough, and well-conceived, and address an important and highly debated question in the field regarding the neural and psychological mechanisms underlying sensory preconditioning. This work is highly impactful for the field as the debate over mediated versus chaining mechanisms has been an important topic for more than 70 years.

We thank the reviewer for their kind assessment.

Reviewer #3 (Public review):

The authors tested whether the number of stimulus-stimulus pairings alters whether preconditioned fear depends on online integration during the formation of the stimulus-outcome memory or during the probe test/mobilization phase, when the original stimulus, which was never paired with aversive events, elicits fear via chaining of stimulus-stimulus and stimulus-outcome memories. They found that sensory preconditioning was successful with either 8 or 32 stimulus-stimulus pairings. Perirhinal cortex NMDA receptor blockade during stimulus-outcome learning impaired preconditioning following 8 but not 32 pairings during preconditioning. Therefore, perirhinal cortex NMDA activity is required for online integration or mediated learning. Perirhinal-basolateral amygdala had nearly identical effects with the same interpretation: these areas communicate during stimulus-outcome learning, and this online communication is required for later expressing preconditioned fear. Disconnection prior to the probe test, when chaining might occur, had different effects: it impaired the expression of preconditioned fear in rats that received 32, but not 8, pairings during preconditioning. The study has several strengths and provides a thoughtful discussion of future experiments. The study is highly impactful and significant; the authors were successful in describing the behavioral and neurobiological mechanisms of mediated learning versus chaining in sensory preconditioning, which is often debated in the learning field. Therefore this study will have a significant impact on the behavioral neurobiology and learning fields.

Strengths:

Careful, rigorous experimental design and statistics.

The discussion leaves open questions that are very much worth exploring. For example - why did perirhinal-amygdala disconnection prior to the probe have no effect in the 8-pairing group, when bilateral perirhinal inactivation did (in Wong et al, 2019)? The authors propose that perirhinal cortex outputs bypass the amygdala during the probe test, which is an excellent hypothesis to test.

The authors provide evidence that both mediated learning and chaining occur.

Thank you for the positive assessment – we fully intend to identify the circuitry that regulates retrieval/expression of sensory preconditioned fear when it is based on mediated learning in stage 2.

Weaknesses:

This is inherent to all neural interference and behavioral experiments: biological/psychological functions do not typically operate binarily. There is no single clear number or parameter at which mediated learning or chaining happens, and both probably happen to some extent. Addressing this is even more difficult given behavioral variability across subjects, implant sites, etc. Thus, this is not so much a weakness particular to this study as much as an existential problem, which the authors were able to work around with careful experimental design and appropriate controls.

We completely agree with the point raised here and thank the reviewer for their assessment.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) It appears that the method description for Sensory Preconditioning was copied from their previous Wong et al. (2019) paper, which is fine, but in the current research, the authors use 8 or 32 presentations, which is not reflected in the description.

Thank you for bringing this to our attention. This is now addressed in the method section on page 27 (beginning at line 655):

“Rats received either eight presentations of the sound and eight of the light in a single session, or 32 presentations of the sound and 32 of the light across four daily sessions. On Day 3, all rats received eight presentations of the sound and eight of the light. Each presentation of the sound was 30 s in duration and each presentation of the light was 10 s in duration. The first stimulus presentation occurred five min after rats were placed into the chambers. The offset of one stimulus co-occurred with the onset of the other stimulus for groups that received paired presentations of the sound and the light, while these stimuli were presented separately for groups that received explicitly unpaired presentations. The interval between each paired presentation was five min while the interval between each separately presented stimulus was 150 s. After the last stimulus presentation, rats remained in the chambers for an additional one min. They were then returned to their home cages. This training was repeated on Days 4-6 for rats that received 32 presentations of the sound and 32 of the light. All rats proceeded to first-order conditioning (details below) the day after their final session of sound and light exposures, which was Day 4 for rats exposed to eight presentations of the sound and light and Day 7 for rats exposed to 32 presentations of the sound and light.”

(2) Line 148: Could the authors clarify how the "significant linear increase" was assessed? From similar descriptions in later experiments, it seems it was based on a comparison of freezing across the four presentations, but the F(1,26) statistic suggests there seemed to be a half-split test. The same questions exist in all the experiments. Please clarify.

Conditioning data were analysed using contrasts with repeated measures in ANOVA. The repeated measures (or within-subject) factor was “trial” as all rats were exposed to four light-shock pairings in this stage of training. We examined whether there was a significant linear increase in freezing across trials using a standard within-subject contrast. The specific coefficients for this contrast, given the four trials, were -3, -1, 1, and 3. The reason that the degrees of freedom remain 1 and 26 in this analysis is because the within-subject contrast is part of a set of planned orthogonal contrasts. That is, in any planned analysis of the sort conducted here, the df1 will always be 1, indicating the very nature of the analysis. There was no splitting of the data, or comparisons between the split halves.

(3) Line 154: Could the authors clarify what is meant by "other main effects and their interactions"? It is not clearly inferable from the context.

Apologies for the confusion here. “Other main effects” refer to the two between-subject factors in isolation: i.e., the overall comparison of freezing to the light (averaged across the four trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1 à main effect 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2 à main effect 2). “Their interaction” refers to the assessment of whether the overall difference in freezing to the light (averaged across the four trials) between Groups P8 and U8 differs from the overall difference in freezing to the light (averaged across the four trials) between Groups P32 and U32. We have edited the text near line 153 to indicate that:

“The overall comparisons of freezing to the light (averaged across the four conditioning trials) between groups that received either paired or unpaired stimulus presentations in stage 1 (factor 1), and between groups that received either eight or 32 sound and light exposures in stage 1 (factor 2), were not significant (Fs < .45, p > .508). The interaction between these two between-subject factors was also not significant (F < .45, p > .508).”

(4) The use of sound and light as preconditioned and conditioned cues are counterbalanced. Was there any difference in the increase of freezing during conditioning depending on the type of conditioned cues? Was there any difference in the preconditioned fear? While it is hard to assess statistical significance due to the sample size limit, even observing a trend could be interesting.

We examined whether the levels of freezing to the conditioned and preconditioned stimuli depend on their physical identity. In general, there was a slight trend towards more freezing to the preconditioned stimulus when it was a tone, and less freezing to the conditioned stimulus when it was a tone. These are, however, simply indications. None of the statistical comparisons between rats for which the preconditioned stimulus was the tone (and, thereby, conditioned stimulus was the light) and rats for which the preconditioned stimulus was the light (and, thereby, conditioned stimulus was the tone) reached the conventional level of significance.

(5) General suggestion on reporting non-significant statistics: the authors reported a small F statistic value a few times to suggest non-significance. But without clearly specifying degrees of freedom, it is hard to get a sense of statistical significance (e.g. Line 227, largest F<3.10). I recommend adding p values alongside the F statistics and reporting exact statistics whenever possible.

Apologies for the omission. The p values have now been included alongside all non-significant F statistics.

(6) Another general suggestion is to use non-parametric statistical testing with such small sample sizes. I recommend using the Kruskal-Wallis H test (the non-parametric equivalent of F-statistic) to replace the ANOVA result. Also, given many tests only involve comparing two independent groups, using Mann-Whitney U test (the non-parametric equivalent of independent t-test) would be sufficient.

We understand that small sample sizes can occasionally lead to unequal variances between groups, which necessitates the use of non-parametric statistics. However, as non-parametric statistics raise a different set of issues for data analysis (e.g., power) and interpretation, our general view for the type of data collected in this study is that parametric analyses are appropriate and should be retained (particularly in the absence of unequal variances between groups). We hold this view for two reasons. First, the hypotheses tested in the present series were derived from past work in which parametric analyses revealed meaningful patterns of results at the same level of statistical power. Second, the application of these analyses then yielded results consistent with our hypotheses: for the most part, we observed between-group differences where we expected there to be such differences and did not observe between-group differences where we did not expect there to be such differences. As such, we have not switched from a parametric to non-parametric analysis strategy. We do, however, appreciate the suggestion and will apply a non-parametric approach where it is warranted in our future work.

Reviewer #2 (Recommendations for the authors):

I have a few very minor comments for the authors regarding the discussion and interpretation of the very nice experimental results.

(1) In Figures 4 and 5, the authors provide a schematic of the experiment. It's very clearly indicated whether the BLA inactivation is ipsi- or contralateral, but the unilateral PRh lesion isn't mentioned. I'd recommend including that here so that someone reading through the figures can more easily understand the experiment. The hypothesis is clear and the experiment is so well designed that a read through of the figures can relay most information to an experienced reader.

Thank you for this suggestion – we have included information about the unilateral PRh lesion in the schematic for Figures 4 and 5.

(2) The authors have an extended description of backward conditioning in the discussion. It seems like the authors are suggesting this as an important future direction, but they never explicitly say this, resulting in a bit of confusion as to what this section refers to. Also, Ward-Robinson and Hall 1996 showed backward sensory preconditioning using a serial auditory-visual association and argued for a mediated solution based on their results. It may be worth citing that paper here.

Apologies for the lack of clarity. We have revised this point in the discussion (page 18, beginning line 434) and referenced Ward-Robinson and Hall (1996):

“Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993). This order hypothesis is consistent with evidence showing that the way in which animals represent an audio-visual compound changes across repeated compound exposures (e.g., Bellingham & Gillette, 1981; Holmes & Harris, 2009). It can be tested using a so-called “backward” sensory preconditioning protocol, which reverses the order of stimulus presentations in stage 1 (e.g., Ward-Robinson & Hall, 1996). That is, rather than rats being exposed to the “forward” sound-light pairings used here and by Wong et al. (2019), rats in a backward protocol are exposed to light-sound pairings. Increasing the number of light-sound pairings in this protocol should result in rats learning that the light is followed by the sound (light→sound) and that the sound is followed by nothing (sound→nothing). Hence, during the session of light-shock pairings in stage 2, the light should continue to activate the memory of the sound, resulting in formation of the mediated sound-shock association (e.g., Ward-Robinson & Hall, 1996). That is, if our order hypothesis is correct, increasing the number of light-sound pairings in the backward protocol should preserve the likelihood of mediated learning in stage 2 and, if anything, diminish the likelihood of chaining at test in stage 3 (as the sound is never followed by a light). Hence, PRh manipulations that fail to affect fear of the sound when administered after many sound-light pairings (e.g., infusion of DAP5) should disrupt that fear when administered after many light-sound pairings in the backward protocol. This will be assessed in future work.”

(3) Line 467 in the discussion suggests that the results are surprising that PRh-BLA communication is not needed at test when learning putatively occurs through a mediated mechanism during first-order conditioning. I was a bit surprised by this comment since I was under the assumption that only BLA was required at this point after consolidation of the mediated learning. Holmes et al., 2013 showed that BLA is required for extinction to S2 after first-order conditioning. In that experiment they inactivated BLA during S2- presentations (typically considered the extinction test), and showed that reduction to S2 did not occur the subsequent day, indicating the memory was stored in BLA and may not necessarily require PRh-BLA communication.

The result noted here was somewhat surprising as our past studies showed that silencing activity in the PRh prior to testing attenuates freezing to a sensory preconditioned stimulus (i.e., an S2). We took this to mean that the PRh is necessary for retrieval/expression of fear to S2 and supposed that this retrieval/expression would be achieved through communication between the PRh and BLA. However, the results of the PRh-BLA disconnection at test show that this communication is not required, leaving us to speculate that retrieval/expression of fear to S2 may be achieved through communication between the PRh and CeA.

We have edited the opening of the relevant paragraph to clarify why the result noted here was surprising (page 20, beginning line 485):

“While the PRh and BLA clearly communicate to support mediated learning about the sound, this communication is not required for retrieval/expression of the mediated sound-shock association at the time of testing. This result is somewhat surprising as activity in the PRh is needed for expression of fear to the sound (Holmes et al., 2013; Wong et al., 2019) and raises the question: how does the PRh-dependent sound-shock association come to be expressed in fear responses?”

(4) The authors reference Holland 1981 and 1998, yet there's not much discussion of these findings. I think there should be a bit more emphasis on these studies since they show how mediated learning greatly depends on the extent of training. Also, it may be worth considering Holland's theory of why mediated conditioning is more effective with shorter training. His theory may be consistent with the authors, but I believe he suggests that early in training a stronger mediated representation is evoked which tends to dissipate with time. I think this is a valid hypothesis to consider in this paper.

The Holland papers show that rats form mediated associations (Holland, 1981) and that the likelihood of them doing so decreases with the amount of training (Holland, 1998). These findings are paralleled by those reported in the present series of experiments. However, the protocols used by Holland were very different to those used in the present study; and the explanation for his 1998 findings (which is the more relevant of the two papers) simply does not apply to the case of sensory preconditioning.

To be clear: Holland (1998) exposed rats to either “few” or “many” tone-food pairings in stage 1, tone-lithium chloride pairings in stage 2 and, finally, tested rats with the food alone in stage 3. He predicted and showed that those exposed to few tone-food pairings showed an aversion to the food at test (i.e., they consumed less of the food than controls) whereas those exposed to many tone-food pairings showed no such aversion (i.e., they consumed the same amount of food as the controls). This was taken to mean that, across the series of tone-lithium pairings, the tone activated the memory of food among rats in the few condition, resulting in a mediated food-lithium association; but failed to do so among rats in the many condition, resulting in no food-lithium association. According to Holland, the tone failed to activate the memory of food in the many condition because, by the end of training in stage 1, it was not needed for them to know what to do when the tone was presented: they simply had to run to the magazine to collect the food when delivered. That is, the tone eventually associated with the responses that rats emitted in the training situation, thereby obviating any need for activation of the food memory.

While this explanation is both elegant and interesting, it cannot be applied to the results obtained in the present study where the initial stage of training involved few or many sound-light pairings. That is, unlike in the Holland study where rats in the many condition eventually learned a stimulus-“run to magazine” association that maintained performance in the absence of any mental image of food, in the present study, any stimulus-response association acquired in stage 1 (e.g., orienting responses towards the sources of the auditory and visual stimuli) cannot have contributed to the expression of sensory preconditioned fear at test. Hence, stimulus-response learning in the many condition cannot be invoked to explain the pattern of results in the present study, even if it adequately explains what-appears-to-be a similar finding in the Holland study.

Nonetheless, we have included a reference to the general style of explanation that was considered and rejected by Holland in his 1998 and 2005 papers. This appears on page 18 (beginning line 434) and reads:

“Why does increasing the number of sound-light pairings change the way that rats integrate the sound-light and light-shock memories? One possibility is that increasing the number of sound-light pairings in stage 1 reduces the ability of each stimulus to activate the memory of the other. This is consistent with findings by Holland (1998), who showed that the likelihood of mediated learning in rats decreases with the amount of training (see also Holland, 2005); but inconsistent with our findings that, after extended training, rats continue to integrate the sound-light and light-shock associations through chaining at the time of testing (as chaining is predicated on the sound activating the memory of the light after extended training). Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

(5) There is also a Holland 2005 paper in which he tests whether extended training of the initial stimulus associations may result in a reduced associability of those stimuli. This would potentially result in lower mediated learning due to a decreased associability of the mediated representation, thereby explaining why extended training reductions in mediated learning occur. Using a probabilistic design, Holland shows that this reduction in mediated learning is likely not due to a change in associability.

We appreciate the note re Holland (2005) and have included a reference to it in our General Discussion. We agree with Holland that the reduction in mediated learning across extended training is not due to reduced associability of the retrieved stimulus representation. If this were the case, it would remain to explain why stimulus representations continue to be activated at test, which must occur for successful chaining of the sound-light and light-shock associations upon presentations of the sound alone. This is included in the modified text on page 18 (beginning line 434), which is part of our response to point 4.

Reviewer #3 (Recommendations for the authors):

(1) I think the 4th intro paragraph is essentially saying that more pairings during preconditioning encourage chaining as opposed to mediated learning - I might recommend clarifying this a bit. It took me a while to put it together.

Apologies for the confusion. We have clarified the argument at this point in the Introduction with the following insertion on page 4 (beginning line 84):

“That is, increasing the number of sound-light pairings may allow rats to encode information about stimulus order in stage 1 and, thereby, shift the locus of integration from mediated conditioning in stage 2 to chaining at test in stage 3 (Holmes et al., 2022).”

(2) In analyzing test data I am assuming percent freezing is the average of the entire 30s or 10s CS period - could this be clarified?

This is correct and has been clarified in the section for ‘Scoring and Statistics’ on page 29 (beginning line 708):

“Freezing data were collected using a time-sampling procedure in which each rat was scored as either ‘freezing’ or ‘not freezing’ every two seconds by an observer blind to the rat’s group allocation. A percentage score was then calculated by dividing the number of samples scored as freezing by the total number of samples. The baseline level of freezing was established by scoring the first two min at the start of each experimental session: i.e., we divided the total number of samples scored as freezing by the total number of observed samples, which was 60. The levels of freezing to the 10 s conditioned stimulus and 30 s preconditioned stimulus were established in a similar manner: we scored the entire period of each stimulus presentation and divided the number of samples scored as freezing by the total number of observed samples, which was 5 for each presentation of the conditioned stimulus and 15 for each presentation of the preconditioned stimulus.”

(3) Complementary to the above - during the probe test is there a difference during the first/last 2s of the CS? This would be interesting with respect to understanding the associative structure encoded.

We have previously examined whether freezing responses change across the duration of a 30 s preconditioned stimulus and a 10 s conditioned stimulus. We have never seen any such changes: in our past work and in the present series of experiments, the expression of freezing is largely uniform across each presentation of a preconditioned or conditioned stimulus.

(4) It is sort of unclear to me why more CS-CS pairings produced stronger preconditioned fear - is it that both mediated learning and chaining occur and giving 32 pairings permits both processes more than 8 pairings?

This is a very reasonable explanation for the heightened level of sensory preconditioned fear among rats that received many sound-light pairings in the initial control experiment. We are, however, reluctant to offer a strong interpretation of this result as it was not replicated across subsequent experiments in the series: i.e., the levels of freezing to the sensory preconditioned stimulus at test were largely the same among vehicle-injected controls that received either few (8) or many (32) sound-light pairings in Experiments 2A and 2B, and again in Experiments 3A and 3B as well as Experiments 4A and 4B.

(5) I would suggest individual data points overlaid on the bars, violin plots, or box and whisker plots to provide a better visualization of the data.

We appreciate the suggestion – these have been included overlaid on bars in each histogram_._

(6) There are other citations that would strengthen arguments for the idea that unidirectional/temporal associative structure can be acquired during (appetitive) sensory preconditioning: Leising 2007 Learning and Behavior, Hart 2022 Current Biology, for example.

Thank you for these citations. We have included references to the Leising et al (2007) and Hart et al (2022) papers in our discussion on page 18-19 (beginning line 442):

“Instead, we propose that the change in integration occurs because the increased number of sound-light pairings allows the rats to learn about the order in which the sound and light are presented (Figure 1; for evidence that rats acquire order information in sensory preconditioning, see Barnet et al., 1997; Hart et al., 2022; Leising et al., 2007; Miller & Barnet, 1993)…”

Editor's note:

We agree with the suggestions about full statistical reporting for non-significant results and about putting individual data points, perhaps coded to identify sex, on top of the bar graphs. Both will increase the transparency of the rigor of the work for readers.

We thank the editors and authors for their suggestions. We have included full statistical reporting for non-significant results and overlaid individual data points on the bars in each histogram.

Author response:

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

Joint Public Review:

Summary:

The behavioral switch between foraging and mating is important for resource allocation in insects. This study investigated the role of the neuropeptide, sulfakinin, and of its receptor, the sulfakinin receptor 1 (SkR1), in mediating this switch in the oriental fruit fly, Bactrocera dorsalis. The authors use genetic disruption of sulfakinin and of SkR1 to provide strong evidence that changes in sulfakinin signaling alter odorant receptor expression profiles and antennal responses and that these changes mediate the behavioral switch. The combination of molecular and physiological data is a strength of the study. Additional work would be needed to determine whether the physiological and molecular changes observed account for the behavioral changes observed.

Strengths:

(1) The authors show that sulfakinin signaling in the olfactory organ mediates the switch between foraging and mating, thereby providing evidence that peripheral sensory inputs contribute to this important change in behavior.

(2) The authors' development of an assay to investigate the behavioral switch and their use of different approaches to demonstrate the role of sulfakinin and SkR1 in this process provides strong support for their hypothesis.

(3) The manuscript is overall well-organized and documented.

Weaknesses:

(1) The authors claim that sulfakinin acts directly on SkR1-positive neurons to modulate the foraging and mating behaviors in B. dorsalis. The authors also indicated in the schematic that satiation suppresses SkR1 expression. Additional experiments and more a detailed discussion of the results would help support these claims.

(2) The findings reported could be strengthened with additional experimental details regarding time of day versus duration of starvation effects and additional genetic controls, amongst others.

Recommendations for the authors:

Major issues

(1) As written the introduction is somewhat fragmented and does not lay out a clear rationale for the current study in the species used by the authors. Others, including Guo et al. (2021) and Wang et al. (2022), have previously shown that sulfakinin signaling pathways are important for feeding and receptivity regulation in D. melanogaster. Thus, the novelty of this study should be more clearly articulated.

The introduction in the revision is significantly changed to improve the description for the rationale of study (lines 60-66 in the revision).

(2) In addition, the Introduction should provide more specific background information on the pheromonal activity of oriental fruit fly body extract, the odor-preferences, and the sex pheromone of this species compared to that of model insects such as Drosophila melanogaster.

The revision contains a paragraph of introduction for chemical ecology of oriental fruit fly that is related to this study (lines 67-75).

(3) It isn't clear what the first image in Figure 1C represents - is this a schematic of the area or does it represent data?

The Fig 1C and the associated figure caption are revised. The figure is more visible by changing the track colors. The figure caption is revised as “Representative foraging trajectories in the 100 mm diameter arenas within a 15-min observation period of flies starved for different durations.”

(4) The authors should include examples of the EAG recordings following the stimulation with food volatiles or pheromones, not only the results of their analyses. This could be included in the main figures or even in supporting information.

As suggested, we added the examples of the EAG recordings following the stimulation with food odors and body extracts in the Figure 1 and Figure 3.

(5) The demonstration that removal of the antennae severely impairs mating is dispensable because the antennae are required for other functions in addition to olfaction.

We agree that the roles of the antennae are likely more than the olfactory function. As suggested, we removed the data.

(6) It is currently difficult to understand how the authors measured successful rates of foraging. Please provide more details.

In the revision, we added a sentence describing the method for measuring in detail. See line 269-273.

(7) The expression of sulfakinin does not change significantly in the antennae following starvation (Figure 2A). Do the authors know whether they change in the central nervous system under these conditions? Have the authors (or has anyone else) checked the expression pattern of sulfakinin in the antennae? This information would help determine whether the sulfakinin signal that acts on SkR1 is released from neurons in the central nervous system (Figure S4C) or whether it is also released from the neurons in the olfactory organs. Based on the immunochemistry results shown in Figure S4C, it would also be interesting to determine whether the intensity of anti-sulfakinin immunoreactivity changes before versus after starvation. This could help establish whether sulfakinin is released during starvation.

We added the expression data showing the the mRNA level of Sk in the head that is higher after refeeding in Fig. S3. The change in the expression of Sk is also added in the text (lines 107-110). We were unable to identify the Sk neurons in the antennae suggesting possibility of the direct action of humoral Sk on the antennae.

(8) In Figure 2A, the authors show that the expression levels of some neuropeptides system components change during starvation. However, it would be helpful if the authors could include more detailed information on how the results are shown in the figure legends (e.g., the expression level of each candidate in fed flies was set as 1, etc).

We revised the figure caption to explain the Figure 2 with the expression values in the figure legend.

(9) In Figure 2D, null mutant males of sulfakinin and SkR1 consume more food at all times compared to the wild type. However, the corresponding mutant females consume more food only at night. Is this because the wild-type female flies eat more food during the day? In a related issue, Figure 2D shows differences in food consumption measured at different times of day, however, this is not directly addressed in the text, which instead mentions that "the amount of excess food consumed by the mutants was dependent on the duration of the starvation period in both sexes".

Thank you for the important suggestions. We speculate that the difference of feeding amounts of females occurring only at night is due to the high basal feeding rate of females during the daytime, masking the increase in feeding in the knockout of Sk signaling. As suggested, we have added a relevant description of the difference in food consumption. In addition, we changed the Y-axis scale in the figure for a justified comparison between males and females. See line 123-128.

(10) It isn't clear how the time of day relates to the duration of starvation. This suggests that mutant females only consume more at 21:00 (presumably at night) whereas males consume more throughout the day. Does this suggest an interaction with the circadian system? What is the duration of starvation in Figure 3A? In a related issue, in Figure 4 it would be useful to know what time of day the EAG analysis was done because the data shown in Figure 2D suggests that the time of day significantly impacts behavioral responses. And does the red versus blue color scheme of the OR subunits represent up/downregulated levels in wild-type animals? Please define this for the reader.

In addition to the response to the point 9, responding to the issue of feeding amount in females. As the reviewer noted, there was indeed a diurnal difference in food amount consumed by B. dorsalis. However, whether this is related to circadian rhythms is something we haven't studied for further in-depth. Measuring food intake at these 3 times of day, we all ensured that the duration of starvation was the same 12 h. The duration of starvation in Figure 3A is 12h. We have mentioned this in the manuscript. See line 267-268.

The EAG for sex pheromones and body surface extracts were measured form 21:00-23:00, and food odor was measured from 9:00-11:00. The times of the experiments are described in the revision. See line 309-311.

Accordingly, we made a revision of the figure caption for explaining the colored fonts. Red color represents a set of ORs related with foraging and blue color is for a set of ORs related with mating. Therefore, the ORs with red color were upregulated in starved wild-type animals and the ORs with blue color were downregulated in starved wild-type flies. We have defined this in the revised manuscript. See line 672-673.

(11) The authors convincingly show that SKR1 is present in the antennae and is co-expressed with orco. It would be useful to discuss whether this receptor is also expressed in other tissues where there may be additional sites of action of this pathway.

Indeed, SkR1 is also expressed in the Drosophila brain. We added the discussion on the expression and additional sites of action of SKR1 within the central nervous system. See line 200-205.

(12) It isn't clear what the dotted arrows in the model shown in Figure 5 represent.

Dashed arrows represent the additional possible pathways that have not been tested in this study, but not excluded in the model. Please see the discussion for details of additional possible factors modulating odorant sensitivity relevant to satiety. See line 210-229.

(13) In Figure 5, the authors indicate that satiation suppresses SkR1 expression. It would be helpful if the authors tested the expression level of SkR1 in re-fed flies (by feeding the flies after 12h starvation) to see whether levels of expression are rapidly restored to the levels seen in satiated animals. Such a result could further support the claims made by the authors.

Thank for your suggestions. Indeed, refeeding after 12h starvation significantly decreased SkR1. We added the result in supporting information (Fig. S3). See line 713. Results see line 107-110.

(14) The authors show that locomotor activity is unaffected in the mutants but body size comparison would be more useful here since this could also contribute to baseline differences in meal size.

In the revision, we provided a comparison between WT and Sk-/- in the supplementary data. Results showed that mutant flies have the same body size as the WT flies. (Fig. S7) See line 742. Results see line 120-121.

(15) Have the authors tested the behavioral phenotypes of heterozygotes mutant of both Sk and SkR1 flies? This may reveal whether a reduced expression of Sk-SkR1 will also cause significant changes in the foraging and mating behaviors seen during starvation.

We tested the behavioral phenotypes of heterozygous mutant of Sk knockout flies. The results showed that foraging and mating behaviors of Sk heterozygous mutants were unaffected during starvation, suggesting the mutants are completely recessive. We have added the results in supporting information (Fig. S8). See line 746. Results see line 132-135.

(16) It would be useful to provide information about which SK peptide is detected by the antibody used in Figure S4C. In Figures S4C and S5D, it would be useful to include a counterstain to show that the general morphology is unaffected in the mutants.

As suggested, we added a detailed description for rabbit anti-BdSk antibody. See line 362-363. We have improved the background image to be available to show the general structure. So counter staining would not be essential.

(17) The figure legends for supporting figures need to be improved as they are currently difficult to understand. For example, in S2: what is the meaning of "different removal of antennae"? In S3: it isn't clear how the authors evaluated the responses in EAG experiments; in S4A: there are several DNA sequences that do not appear in the main text of the manuscript; in S4C: the meaning of the boxes and the dots is unclear, as is the figure to the left; in S5D, the authors explain only the suppression of SKR1, yet the figure indicates some images for SKR IHC. These are only a few examples; we ask that the authors revise and improve the legends for supporting figures.

For S2, we removed the data as suggested. For S3, we added a sentence describing the method for measuring in detail. See line 707-709. For S4, the figure in the revision is significantly changed and added a detailed description in the legend (lines 717-724 in the revision). For S5, we have improved our description. See line 731-734. In addition, we have checked all the figure legends of our manuscript and changes were displayed in track version.

Minor issues

(1) It isn't clear what the meaning of "the complexity of sulfakinin pathways" is. Please explain.

We have rewritten the sentence in the revised manuscript by adding the description as “…complexity of Sk pathways, special and temporal dynamics and multiple ligands and receptors, is…”. See line 61-65.

(2) Please double-check the calls to the various figures in the text.

We have double-checked the calls to all the figures in the text to make sure they were correct.

(3) L125: What is the meaning of "olfactory reprogramming"? Please explain.

We rephrased it to “alteration of olfactory sensitivities”. See line 145.

(4) L135: After mentioning qRT-PCR the authors should include a call to a figure that shows these results.

Thank you for your suggestion, the qRT-PCR results are shown in Figure 4B, and we have added it as suggested. See line 154.

(5) L270: Details are provided for the extraction of the pheromone. However, more details are needed on how the EAG and other functional assays were done.

We have described the assay procedures in detail in the materials and method part. See line 298-311.

(6) Figure 2B. Please remove the period(".") at the C-terminal end of WT sk.

We are sorry for our mistake. We have corrected it.

Author response:

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

eLife Assessment

This important study identifies the "H-state" as a potential conformational marker distinguishing amyloidogenic from non-amyloidogenic light chains, addressing a critical problem in protein misfolding and amyloidosis. By combining advanced techniques such as small-angle X-ray scattering, molecular dynamics simulations, and H-D exchange mass spectrometry, the authors provide convincing evidence for their novel findings. However, incomplete experimental descriptions, limitations in SAXS data interpretation, and the way HDX MS data is presented aHect the strength and generalizability of the conclusions. Strengthening these aspects would enhance the impact of this work for researchers in amyloidosis and protein misfolding.

We thank eLife editors and reviewers for their constructive feedback. The manuscript has been improved to provide a more complete description of the experiments and to strengthen the interpretation and presentation of all data. Updated Figures (Figure 2 and Figure 5) and a new Table (Table 2) in the main text provide a more complete and clearer comparison of the SAXS data with MD simulations as well as a clearer representation of the HDX MS data. Additional figures have been added in SI. The text has been extended accordingly and complete materials and methods are now included in the main text. Abstract, introduction and discussion have been revised to improve the overall readability of the manuscript.

Public Reviews:

Reviewer #1 (Public review):

The study investigates light chains (LCs) using three distinct approaches, with a focus on identifying a conformational fingerprint to diHerentiate amyloidogenic light chains from multiple myeloma light chains. The study's major contribution is identifying a low-populated "H state," which the authors propose as a unique marker for AL-LCs. While this finding is promising, the review highlights several strengths and weaknesses. Strengths include the valuable contribution of identifying the H state and using multiple approaches, which provide a comprehensive understanding of LC structural dynamics. However, the study suHers from weaknesses, particularly in interpreting SAXS data, lack of clarity in presentation, and methodological inconsistencies. Critical concerns include high error margins between SAXS profiles and MD fits, unclear validation of oligomeric species in SAXS measurements, and insuHicient quantitative cross-validation between experimental (HDX) and computational data (MD). This reviewer calls for major revisions including clearer definitions, improved methodology, and additional validation, to strengthen the conclusions.

We thank the reviewer for the supportive comments, in the revised version of the manuscript we have focused on improving the clarity and completeness of our work. We are sorry for example to not have made previously clear enough that the comparison of SAXS with MD simulation was not that shown in the main text in Figure 1 and Table 1 (this is the comparison with single structures) but that reported in the SI (previously Figure S1 and Table S2, showing very good fits). These data have been moved in the main text in the reworked Figure 2 and new Table 2. We have also improved the presentation of the HDX MS data in Figure 5 and in the text adding also additional analysis in SI. Materials and methods are now completely moved in the main text. We generally revised the manuscript for clarity.

Reviewer #2 (Public review):

Summary:

This well-written manuscript addresses an important but recalcitrant problem - the molecular mechanism of protein misfolding in Ig light chain (LC) amyloidosis (AL), a major life-threatening form of systemic human amyloidosis. The authors use expertly recorded and analyzed smallangle X-ray scattering (SAXS) data as a restraint for molecular dynamics simulations (called M&M) and to explore six patient-based LC proteins. The authors report that a highly populated "H-state" determined computationally, wherein the two domains in an LC molecule acquire a straight rather than bent conformation, is what distinguishes AL from non-AL LCs. They then use H-D exchange mass spectrometry to verify this conclusion. If confirmed, this is a novel and interesting finding with potentially important translational implications.

We thank the reviewer for the supportive comments.

Strengths:

Expertly recorded and analyzed SAXS data combined with clever M&M simulations lead to a novel and interesting conclusion. Regardless of whether or not the CL-CL domain interface is destabilized in AL LCs explored in this (Figure 6) and other studies, stabilization of this interface is an excellent idea that may help protect at least a subset of AL LCs from misfolding in amyloid. This idea increases the potential impact of this interesting study.

We thank the reviewer for the supportive comments.

Weaknesses:

The HDX analysis could be strengthened.

We have extended the analysis and improved the presentation of the HDX data. Figure 5 has been reworked, text has been improved accordingly and additional analysis have been reported in SI.

Reviewer #3 (Public review):

Summary:

This study identifies conformational fingerprints of amyloidogenic light chains, that set them apart from the non-amyloidogenic ones.

We thank the reviewer for the supportive comments.

Strengths:

The research employs a comprehensive combination of structural and dynamic analysis techniques, providing evidence that conformational dynamics at the VL-CL interface and structural expansion are distinguished features of amyloidogenic LCs.

We thank the reviewer for the supportive comments.

Weaknesses:

The sample size is limited, which may aHect the generalizability of the findings. Additionally, the study could benefit from deeper analysis of specific mutations driving this unique conformation to further strengthen therapeutic relevance.

We agree, we tried to maximise the size of the sample and this was the best we could do. With respect to the analysis of the mutations, while we tried to discuss some of them also in view of previous works, because our set covers multiple germlines instead than focusing on a single one, this limit our ability to discuss single point mutations systematically, at the same time the discussion of single points mutations has been the focus of many recent works, while our approach provide a diNerent point of view.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

This study provides an investigation of light chains (LCs) using three distinct approaches, focusing primarily on identifying a conformational fingerprint to distinguish amyloidogenic light chains (AL-LCs) from multiple myeloma light chains (MM-LCs). The authors propose that the presence of a low-populated "H state," characterized by an extended quaternary structure and a perturbed CL-CL interface, is unique to AL-LCs. This finding is validated through hydrogendeuterium exchange mass spectrometry (HDX-MS). The study makes a valuable contribution to understanding the structural dynamics of light chains, particularly with the identification of the H state in AL-LCs. However, significant concerns regarding the interpretation of the SAXS data, clarity in presentation, and methodological rigor must be addressed. I recommend major revisions and resubmission of the work.

Major concerns:

(1) A critical concern is how the authors ensure that the SAXS profiles represent only dimeric species, given the high propensity of LCs to aggregate. If higher-order aggregates or monomers were present, this would significantly impact the SAXS data and SAXS-MD integration. Some measurements are bulk SAXS, while others are SEC-SAXS, making the study questionable. The authors need to clarify how only dimeric species were measured for the SEC-SAXS analysis, and all assessments of the dimeric state should be shown in the SI. Additionally, complementary techniques such as DLS or SEC-MALS should be used to verify the oligomeric state of the samples. Without this validation, the SAXS profiles may not be reliable.

We added SEC-MALS and SEC-SAXS data in the SI (Figures S20 and S21) as well the SAXS curves shown in log-log plot (Figure S1) that display a flat trend at low q that exclude aggregation. SAXS is very sensitive to oligomers and aggregates and our data do not indicate the presence of those species. When we had indication of possible aggregation in the sample we used SEC-SAXS.

(2) A major problem with the paper is that the claim of the "H state," which is the novelty of the study and serves as a marker of aggregation, is derived from samples where the error between the SAXS profiles and MD fits is extremely high. This casts doubt on whether the structure is indeed resolved by MD. The main conclusion of the paper is derived from weak consistency between experiment and simulation. In AL55, the error between experiment and simulation is greater than 5; for H7, it is higher than 2.8. The residuals show significant error at mid-q values, suggesting that long-range distance correlations (20-10 Å, CL, VL positioning) are not consistent between simulation and experiment. Furthermore, the FES plots of two independent replicas show deviation in the existence of the H state. One shows a minimum in that region, while the other does not. So, how robust is this conclusion? What is the chi-squared value if each replica is used independently? A separate experimental cross-validation is necessary to claim the existence of the H state.

We apologise for the misunderstanding underlying this reviewer comment. The poor agreement mentioned is not between the SAXS and MD simulations, but with the individual structures, and this disagreement led us to perform MD simulations that are in much better agreement with the data (previously Fig. S1 and Table S2). To avoid this misunderstanding, which would indeed weaken our work, we have now moved both the figure and the table in the main text to the updated Figure 2 and the new Table 2.

Regarding the robustness of the sampling, we believe that Table 3 (previously Table 2) clearly shows the statistical convergence of the data, diNerences in the presentation of the free energy are purely interpolation issues. The chi-squares of each replicate are reported in Table 2 (previously Table S2).

(3) There is insuHicient discussion about SAXS computations from MD trajectories. The accuracy of these calculations is crucial to deriving the existing conclusions, and the study's reliance on the PLUMED plugin, which is known to give inaccurate results for SAXS computations, raises concerns. How the solvent is treated in the SAXS computations needs to be explained. Alternative methods like WAXSiS or Crysol should be explored to check whether the SAXS profiles derived from the MD trajectory are consistent across other SAXS computation methods for the major conformers of the proteins.

We have now clarified that while the SAXS calculation to perform Metainference MD were done using PLUMED (that to our knowledge is as accurate as crysol) SAXS curves used for analysis were calculated using crysol.

(4) The HDX and MD results do not seem to correlate well, and there is a disconnect between Figure 2 (SAXS profiles) and Figure 5 (HDX structural interpretation). The authors should quantitatively assess residue-level dynamics by comparing HDX signals with MD-derived HDX signals for each protein. This would provide a cross-validation between the experimental and computational data.

In our opinion our SAXS, MD and HDX MS data provide a consistent picture. Our HDX-MS do not provide per residue data, making a quantitative comparison out of scope. RMSF data do not necessarily need to correlate with the deuterium uptake.

(5) MD simulations are only used to refine the structure of AlphaFold predictions, but the trajectories could help explain why these structures diHer, what stabilizes the dimer, or what leads to the conformational transition of the H state. A lack of analysis regarding the physical mechanism behind these structural changes is a weakness of the study. The authors should dedicate more eHort to analyzing their data and provide physical insights into why these changes are observed.

Our aim was to identify a property that could discriminate between AL and MM LCs. We used MD simulations, not to refine structures, but to explore the conformational dynamics of LCs (starting from either X-ray structures, homology or AlphaFold models), because SAXS data suggested that conformational dynamics could discriminate between AL- and MM-LCs. Simulations allowed us to propose a hypothesis, which we tested by HDX MS. While more insight is always welcome, we believe that we have achieved our goal for now. In the discussion, we present additional analysis of the simulations to connect with previous literature, we agree that more analysis can be done, and also for this reason, all our data are publicly available.

Minor concerns

(6) The abstract leans heavily on describing the problem and methods but lacks a clear presentation of key results. Providing a concise summary of the main findings (e.g., the identification of the H state) would better balance the abstract.

We agree with the reviewer and we rewrote the abstract.

(7) In the abstract, the term "experimental structure" is used ambiguously. Since SAXS also provides an experimental structure, it is unclear what the authors are referring to. This should be clarified.

We agree with the reviewer and we rewrote the abstract.

(8) Abbreviations such as VL (variable domain) and CL (constant domain) are not defined, making it harder for readers unfamiliar with the field to follow. Abbreviations should be defined when first mentioned.

We agree with the reviewer and we rewrote the abstract.

(9) The introduction provides a good general context but fails to explicitly define the knowledge gap. Specifically, the structural and dynamic determinants of LC amyloidogenicity are not well established, and this study could be framed as addressing that gap.

We thank the reviewer and we agree this could be better framed, we improved the introduction accordingly.

(10) The introduction does not present the novel discovery of the H state early enough. The unique contribution of identifying this state as a marker for AL-LCs should be mentioned upfront to guide the reader through the significance of the study.

We thank the reviewer and we have now made more explicit what we found.

(11) The therapeutic implications of this research should be highlighted more clearly in the discussion. Examples of how these findings could be utilized in drug design or therapeutic approaches would enhance the study's impact.

We thank the reviewer, but while we think that the H-state could be targeted for drug design, since we do not have data yet we do not want to stress this point more than what we are already doing.

(12) There is an overwhelming use of abbreviations such as H3, H7, H18, M7, and M10 without proper introduction. This makes it diHicult for readers to follow the results, and the average reader may become lost in the details. An introductory figure summarizing the sequences under study, along with a schematic of the dimeric structure defining VL and CL domains, would significantly aid comprehension.

We agree and we tried to better introduce the systems and simplify the language without adding a figure that we think would be redundant.

(13) In Figure 1, add labels to each SAXS curve to indicate which protein they correspond to. Also, what does online SEC-SAXS mean?

Done

(14) The caption of Figure 3 is unclear, particularly with abbreviations like Lb, Ls, G, and H, which are not mentioned in the captions. The authors should define these terms for clarity.

Done

(15) The study claims that the dominant structure of the dimer changes between diHerent LCs. However, Figure 5 shows identical structures for all proteins, raising questions about the consistency between the SAXS and HDX data. This inconsistency is a general problem between the MD and HDX sections, where cross-communication and comparisons are not properly addressed.

We do not claim that the dominant structure of the dimer changes between diNerent LCs, this would also be in contradiction with current literature. We claim a diNerence in a low-populated state. From this point of view using always the same structure is consistent and should simplify the representation of the results. We agree that the manuscript may be not always easy to follow and we thank the reviewer in helping us improving it.

(16) The authors show I(q) vs q and residuals for each protein. The Kratky plots are not suHicient to compare the SAXS computations with the measured profile.

Showing Kratky and residuals is a standard and complementary way to present and compare SAXS data to structures. Chi-square values are also reported. Log-log plots have been added to SI in response to previous comments.

(17) The authors need to explain how they estimate the Rg values (from simulation or SAXS profiles). If they are using simulations, they should compute the Rg values from the simulations for comparison.

Rg values reported in Table 1 are derived from SAXS. Rg from simulations have been added in Table 2.

(18) The evolution of the sampling is unclear. The authors need to show the initial starting conformation in each case and the most likely conformation after M&M in the SI, to demonstrate that their approach indeed caused changes in the initial predictions.

Our approach is not structure refinement and as such the proposed analysis would be misleading. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. DiNerences (or not) between initial and selected configurations will not be particularly informative in this context.

(19) The authors should also provide a running average of chi-squared values over time to demonstrate that the conformational ensemble converged toward the SAXS profile.

Our simulations are not driven to improve the agreement with SAXS over time, this is not structure refinement. Metainference is meant to generate a statistical ensemble representing the equilibrium conformations that as whole reproduce the data. The suggested analysis would be a misinterpretation of our simulations. The comparison with SAXS is provided in Figure 2 and Table 2 as mentioned above.

(20) The aggregate simulation time of 120 microseconds is misleading, as each replica was only run for 2-3 microseconds. This should be clarified.

The number reported in the text is accurate and represent the aggregated sampling. The number of replicas for each metainference simulation and their length is reported in Table 2 now moved for clarity from the SI to main text.

(21) It is not clear how the replicas were weighted to compute the SAXS profiles and FES. There are two independent runs in each case, and each run has about 30 replicas. How these replicas are weighted needs to be discussed in the SI.

Done

(22) The methods section is unevenly distributed, with detailed explanations of LC production and purification, while other key methodologies like SAXS+MD integration and HDX are not even mentioned in the main text (they are in the Supporting Information). The authors should provide a brief overview of all methodologies in the main text or move everything to the SI for consistency.

We agree with the reviewer, all methods are now in main text.

Reviewer #2 (Recommendations for the authors):

(1) Computational M&M evidence is strong (Figure 3) and is supported by SAXS (used as restraints). However, Kratky plots reported in the main MS Figure 1 show significant diHerences between the data and the structural model only for one protein, AL-55. It is hard for the general reader to see how these SAXS data support a clear diHerence between AL and non-AL proteins. If possible, please strengthen the evidence; if not, soften the conclusions.

We thank the reviewer for the comments. The chi-square (Table 1) and the residuals (Figure 1) are a strong indication of the diNerence. To strengthen the evidence, following also the comment from reviewer 3 we calculated the p-value (<10^-5) on the significance of the radius of gyration to discriminate AL and MM LCs. We agree that SAXS alone was not enough and this is indeed what prompted us to perform MD simulations.

(2) HDX MS results are cursory and not very convincing as presented. The butterfly plots in Figure 5 are too small to read and are unlabeled so it is unclear which protein is which.

Figure 5 has been reworked for readability. More data have been added in SI.

(3) What labeling time was selected to construct these plots and why?

The deuterium uptakes at 30 min HDX time showed the most pronounced diNerences between diNerent proteins, which were chosen to illustrate the key structural features in the main figure panel (Figure 5).

How diHerent are the results at other labeling times? Showing uptake curves (with errors) for more than just two peptides in the supplement Figure S12 might be helpful.

We found a continuous increase in deuterium uptake as we increased the exchange time from 0.5 to 240 min, which reached saturation at 120 min. Therefore, the exchange follows the same pattern at all time points. Butterfly plots at diNerent HDX times of 0.5 to 240 min are shown in gradient of light blue to dark blue which clearly shows the pattern of deuterium uptake at increasing incubation times (Figure 5). The HDX uptake kinetics of selected peptides with corresponding error bars are shown in Figure S12.

How redundant are the data, i.e. how good is the peptide coverage/resolution in key regions at the domain-domain interface that the authors deem important? Mapping the maximal deuterium uptake on the structures in Figure 5 is not very helpful. Perhaps mapping the whole range of uptake using a gradient color scheme would be more informative.

Overall coverage and redundancy for all four proteins are> 90% and > 4.0, respectively, with an average error margin in fractional uptake among all peptides is 0.04-0.05 Da, which suggests that our data is reliable (Table S3). We modified the main panel figures showing the gradient of deuterium uptake in blue-white-red for 0 to 30% of deuterium uptake on the chain A of the dimeric LCs.

(3) Is the conformational heterogeneity depicted in M&M simulations consistent with HDX results? The authors may want to address this by looking at the EX1/EX2 exchange kinetics for AL vs. non-AL proteins. Do AL proteins show more EX1?

No, we don’t see any EX1 exchange kinetics in our analysis. This is compatible with the prediction of the H-state that is a native like state and not an unfolded/partially folded state.

(4) Perhaps the main conclusion could be softened given the small number of proteins (six), esp. since only four (3 AL and 1 non-AL) could be explored by HDX. Are other HDX MS data of AL LCs from the same Lambda6 family (e.g. PMID: 34678302) consistent with the conclusions that a particular domain-domain interface is weakened in AL vs. non-AL LCs?

We thank the reviewer for this suggestions. A diNerence in HDX MS data is indeed visible between AL and MM proteins for peptide 33-47 in the suggested paper (Figures 4, S5 and S8). The diNerence is reduced by the mutation identified in the paper as driving the aggregation in that specific case. We now mention this in the discussion.

(5) Please clarify if the H* state is the same for a covalent vs. non-covalent LC dimer.

We do not know because our data are only for covalent dimers. But, interestingly, the state is very similar to what was observed for a model kappa light-chain in Weber, et al., we have better highlighted this point in the discussion.

(6) Please try and better explain why a smaller distance between CL domains in H7 protein and a larger distance in other AL proteins both promote protein misfolding.

We do not have elements to discuss this point in more detail.

(7) Please comment on the Kratky plots data vs. model agreement (see comments above).

Done.

(8) Please find a better way to display, describe, and interpret the HD exchange MS data.

We have generated new main text (new Figure 5) and SI figures that we think allow the reader to better appreciated our observations. Corresponding results sections have been also improved.

Minor points:

(9) Is the population of the H-state with perturbed CL-CL domain interface, which was obtained in M&M simulations, suHicient to be observable by HDX MS?

While populations alone are not enough to determine what is observable by HDX MS, a 10% population correspond roughly to 6 kJ/mol of ΔG and is compatible with EX2 kinetics. Previous works suggested that HDX-MS data should be sensitive to subpopulations of the order of 10%, (https://doi.org/10.1016/j.bpj.2020.02.005, https://doi.org/10.1021/jacs.2c06148)

(10) Typically, an excited intermediate in protein unfolding is a monomer, while here it is an LC dimer. Is this unusual?

This is a good point, we think that intermediates have mostly been studied on monomeric proteins because these are more commonly used as model systems, but we do not feel like discussing this point.

(11) Low deuterium uptake is consistent with a rigid structure but may also reflect buried structure and/or structure that moves on a time scale greater than the labeling time.

We agree.

Reviewer #3 (Recommendations for the authors):

(1) The p-value (statistical significance) of Rg diHerence should be computed.

We thank the reviewer for the suggestion, we calculated the p-value that resulted quite significant.

(2) The significance of mutations (SHM?) at the interface, such as A40G should be compared with previous observations. (Garrofalo et al., 2021).

We thank the reviewer for the suggestion, a sentence has been added in the discussion.

Reviewer #3 (Public review):

Summary:

Ishii et al used molecular genetics, behavioral analyses, in vivo neural activity imaging, and neural activity manipulations in mice to study the functional role of a subset of medial preoptic area (MPOA) neurons in the regulation of female sexual drive. They first employed a self-paced mating assay during which a female could control the amount of interaction time with a male to assess female sexual drive after completion of mating. The authors observed that after mating completion (i.e., male ejaculation) females spend significantly less time interacting with males, indicating that their sexual drive is reduced. Next, the authors performed a brain-wide analysis of neurons activated following male ejaculation and identified the MPOA as a strong candidate region. One caveat is that the activity labeling was not exclusive to neurons activated following male ejaculation but included all neurons activated before, during, and after the mating encounter. However, in this revised version of the manuscript, the authors have included a key control group that labels all neurons activated up to but not including male ejaculation. Comparison of the number of activated neurons in these two groups revealed a significant additional set of neurons in the female MPOA following ejaculation. Importantly, the authors also provided in vivo calcium imaging data showing that a subset of MPOA neurons responds significantly and specifically to male ejaculation and not other behaviors during the social encounter. The authors performed these studies in both excitatory and inhibitory populations of the MPOA. Their analysis identified a subpopulation of inhibitory neurons that exhibit sustained increased activity for 90 sec following male ejaculation. Finally, the authors used chemogenetics to activate MPOA neurons during home cage mating, condition place preference, pup retrieval, and the self-paced mating assay. They found that activation of female MPOA neurons that were previously activated following male ejaculation significantly reduces mating behaviors and time spent interacting with a male during the self-paced mating assay. Whereas, activation of female MPOA neurons that were previously activated during consummatory behaviors but not male ejaculation does not alter mating behaviors and time spent interacting with a male. Therefore, MPOA neurons activated following ejaculation are sufficient to suppress female sexual motivation.

The authors' experimental execution is rigorous and well performed. Their data identify inhibitory neurons in the female MPOA as a neural locus that is activated following male ejaculation and whose prolonged activity plays a key role in the regulation of female sexual motivation. The addition of some key control groups to this revised version of the manuscript greatly strengthens the interpretation of the authors' findings.

Strengths:

(1) The use of the self-paced mating assay in combination with neural imaging and manipulation to assess female sexual drive is innovative. The authors correctly assert that relatively little is known about how male ejaculation affects sexual motivation in females as compared to males. Therefore, the data collected from these studies is important and valuable.

(2) The authors provide convincing histological data and analyses to verify and validate their brain-wide activity labeling, neural imaging, and chemogenetic studies.

(3) The single cell in vivo calcium imaging data are well performed and analyzed. They provide key insights into the activity profiles of both excitatory and inhibitory neurons in the female MPOA during mating encounters. The authors identification of an inhibitory subpopulation of female MPOA neurons that is selectively activated following completion of mating is fundamental for future experiments which could potentially find a molecular marker for this population and specifically manipulate these neurons to understand their role in female sexual motivation in greater detail.

(4) The authors provide convincing evidence that activation of female MPOA neurons activated following male ejaculation is sufficient to suppress female sexual motivation. Importantly, the authors addition of the consummatory-hM3Dq group demonstrates that activation of female MPOA neurons activated during mating behaviors prior to male ejaculation is not sufficient to suppress female sexual motivation.

Weaknesses:

In this revised version of the manuscript, the authors have added important controls as well as additional clarifying text that adequately address the weaknesses that were present in the original version of the manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Summary:

In this manuscript, Corso-Diaz et al, focus on the NRL transcription factor (TF), which is critical for retinal rod photoreceptor development and function. The authors profile NRL's protein interactome, revealing several RNA-binding proteins (RBPs) among its components. Notably, many of these RBPs are associated with R-loop biology, including DHX9 helicase, which is the primary focus of this study. R-loops are three-stranded nucleic acid structures that frequently form during transcription. The authors demonstrate that R-loop levels increase during photoreceptor maturation and establish an interaction between NRL TF and DHX9 helicase. The association between NRL and RBPs like DHX9 suggests a cooperative regulation of gene expression in a cell-type-specific manner, an intriguing discovery relevant to photoreceptor health. Since DHX9 is a key regulator of R-loop homeostasis, the study proposes a potential mechanism where a cell-type-specific TF controls the expression of certain genes by modulating R-loop homeostasis. This study also presents the first data on R-loop mapping in mammalian retinas and shows the enrichment of R-loops over intergenic regions as well as genes encoding neuronal function factors. While the research topic is very important, there is some concern regarding the data presented: there are substantial data supporting the interaction between NRL and DHX9, including pull-down experiments and proximity labeling assay (PLA), however, the data showing an interaction between NRL and DDX5, another R-loop-associated helicase, are inadequate. Importantly, the data supporting the claim that NRL interacts with R-loops are absolutely insufficient and at best, correlative. The next concerns are regarding the R-loop mapping data analysis and visualization.

Strengths:

There is compelling evidence that the NRL transcription factor interacts with several RNA binding proteins, and specifically, sufficient data supporting the interaction of NRL with DHX9 helicase.

A major strength is the use of the single-stranded R-loop mapping method in the mouse retina.

Weaknesses:

(1) Figure S1A: There is a strong band in GST-IP (control IP) for either HNRNPUI1 or HNRNPU, although the authors state in their results that there is a strong interaction of these two RBPs with NRL.

Under our experimental conditions, most RNA-binding proteins displayed higher binding to glutathione beads (Fig. S1A). However, GST-NRL purifications showed much stronger signals for respective RBPs. In the case of HNRNPU and HNRNPUl1, white bands that are indicative of substrate depletion due to higher protein levels are observed in GST-NRL lanes. Additionally, in Figures 1B and 1C, there is a clear enrichment of HNRNPU and HNRNPUl1 above the background signal. We added this to the text. See page 5.

Both DHX9 and DDX5 samples have a faint band in the GST-IP.

RNA-binding proteins may display some background as observed in other studies (e.g. PMID: 32704541). We think that showing the raw data without decreasing the exposure time is useful and that there is a clear enrichment compared to controls.  In addition, we tested the interaction in multiple systems.

There is an extremely faint band for HNRNPA2B1 in the GST-NRL IP lane. Given this is a pull-down with added benzonase treatment to remove all nucleic acids, these data suggest, that previously observed NRL interactions with these particular RBPs are mediated via nucleic acids. Similarly, there is a loss of band signal for HNRNM in this assay, although it was identified as an NRL-interacting protein in three assays, which again suggests that nucleic acids mediate the interaction.

Thank you for highlighting this point. We mention in the manuscript that the interaction between HNRNPM and A1 depends on nucleic acids, as noted by the reviewer, since there is no obvious band after the pull-down. We have now added that the interaction of NRL with HNRNPA1B1 is likely dependent on nucleic acids as well, given its weak signal. See page 5.

(2) The data supporting NRL-DDX5 interaction in rod photoreceptor nuclei is very weak. In Figure 2D, the PLA signal for DDX5-NRL is very weak in the adult mouse retina and is absent in the human retina, as shown in Figure 2H.

We agree with the reviewer. We think that the signal for DDX5 is weak, and we addressed this in the text. We noted on page 7: “Taken together, these findings suggest a strong interaction between NRL and DHX9 throughout the nuclear compartment in the retina and that a transient and/or more regulated interaction of NRL with DDX5 may require additional protein partners.”  We have modified this sentence to add that the data also suggest transient interaction or the requirement of additional protein partners for stable interaction. See page 7.

Given that there is no NRL-KO available for the human PLA assay, the control experiments using single-protein antibodies should be included in the assay. Similarly, the single-protein antibody control PLA experiments should be included in the experimental data presented in Figure 2J.

Thank you for the suggestion. We performed PLAs using both DHX9 and IgG in the human retina and observed no specific amplification signal. Some background is observed outside the nucleus and in the extracellular space. We added these results to the text and to the supplementary information. See page 7 and Fig.S2B.

(3) The EMSA experiment using a probe containing NRL binding motif within the DHX9 promoter should include incubation with retina nuclear extracts depleted for NRL as a control.

In EMSA experiments, we used bovine retina to obtain enough protein quantities. As suggested by the reviewer, using NRL depleted extract would increase the specificity of observed gel shift and complement our pre-immune serum as a negative control. However, removal of all the NRL protein using the antibodies available was not feasible. In the future, we will use enough mice to obtain large quantities of protein for this experiment and will collect retinas from Nrl knockout as negative control.

(4) There is a reduced amount of DHX9 pulled down in NRL-IP in HEK293 cells, but there is no statistically significant difference in the reciprocal IP (DHX9-IP and blotting for NRL) (Figure 4C).

We believe the reviewer is referring to the data in Figure 4C showing that RNase H treatment led to significantly reduced pulldown of DHX9 as compared to control, but the reciprocal IP in Figure 4D showed no statistical significance between control and RNase H treatment. In Figure 4D, we hypothesize that NRL may account for only a small proportion of DHX9’s interactome, so the change in NRL levels could not be detected due to the sensitivity of our assay. DHX9 likely constitutes a large proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious when pulling down with NRL. We added this information to the results. See page 8.

(5) The only data supporting the claim that NRL interacts with R-loops are presented in Figure 5A.

Additional evidence that NRL interacts with R-loops comes from DRIP-Seq experiments where signals from R-loops overlap with NRL ChIP-Seq signals (Figure 7A). This shows that R-loops and NRL co-occur on multiple genomic regions. In addition, indirect evidence of NRL and R-loops’ interaction is shown in pull down experiments and PLA assays where R-loops influence DHX9 and NRL binding. We clarified this in the discussion. See page 14.

This is a co-IP of R-loops and then blotting for NRL, DHX9, and DDX5. Here, there is no signal for DDX5, quantification of DHX9 signal shows no statistically significant difference between RNase H treated and untreated samples, while NRL shows a signal in RNase H treated sample. These data are not sufficient to make the statement regarding the interaction of NRL with R-loops.

Thank you for this comment. We respectfully disagree as we observe statistically significant enrichment for both NRL and DHX9 in these experiments (See Fig5A). Some NRL continues to bind to DNA that is pulled down nonspecifically, which may be expected since NRL is a transcription factor. See for example R-loop binding by the transcription factor Sox2 (PMID: 32704541). However, binding to R-loops is evidenced by an enrichment compared to RNase H-treated sample. We clarified this in Results section (See page 9).

(6) Regarding R-loop mapping, the data analysis is quite confusing. The authors perform two different types of analyses: either overall narrow and broad peak analysis or strand-specific analysis. Given that the authors used ssDRIP-seq, which is a method designed to map R-loops strand specifically, it is confusing to perform different types of analyses.

Thank you for highlighting this point. This has enhanced the clarity of the methods and enriched the discussion. We aimed to identify R-loops as accurately as possible. We conducted two types of analyses to capture different aspects of R-loops: one that looks at overall patterns (narrow and broad peaks) and another that focuses on specific strands of DNA.

Using ssDRIP-seq, which is designed to map R-loops on specific strands, allowed us to examine R-loops formed in only one strand and those formed on both strands. To identify strand-specific R-loops, we filtered our RNase-H enriched peaks for those enriched on one strand compared to the opposite strand. We clarified the analysis in the results section, and Figure 6B. See page 10 and methods section page 25.

Next, the peak analysis is usually performed based on the RNase H treated R-loop mapping; what does it mean then to have a pool of "Not R-loops", see Figure 6B?

The “Not R-loop” group refers to peaks called using the opposite strand that are not observed when calling peaks using RNase H as control. We modified this figure for clarity (Figure 6B).

In that regard, what does the term "unstranded" R-loops mean? Based on the authors' definition, these are R-loops that do not fall within the group of strand-specific R-loops. The authors should explain the reasons behind these types of analyses and explain, what the biological relevance of these different types of R-loops is.

Thank you for helping us clarify this point. Unstranded R-loops are DNA regions containing DNA:RNA hybrids on both plus and minus strands and possibly representing bidirectional transcription by Pol II. We observed that unstranded R-loops are enriched only in intergenic regions, H3K9me3 regions, and downstream of the transcriptional termination site (TTS). We added to the discussion the possible implications of these enrichments, including regulation of Pol II termination and transcription of long genes.  See Page 13.

(7) It would be more useful to show the percent distribution of R-loops over the different genomic regions, instead of showing p-value enrichment, see Figure 6C.

Since most of the genome is non-coding, plotting the distribution as a proportion was not informative since the vast proportion of the data falls in intergenic regions. However, we created a new figure showing observed vs. expected ratio that seems to be more informative and moved the current p-value figure to the supplement in revised version. See Figure 6C and S6D.

(8) Based on the model presented, NRL regulates R-loop biology via interaction with RBPs, such as DHX9, a known R-loop resolution helicase. Given that the gene targets of NRL TF are known, it would be useful to then analyze the R-loop mapping data across this gene set.

Thank you for this suggestion. We performed an analysis of R-loops on NRL-regulated genes. Interestingly, NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B). We added a table containing all NRL-regulated genes we used for this analysis (table S5) and a figure showing this result (Fig. S7B).

Reviewer #2 (Public review):

Summary:

The authors utilize biochemical approaches to determine and validate NRL protein-protein interactions to further understand the mechanisms by which the NRL transcription factor controls rod photoreceptor gene regulatory networks. Observations that NRL displays numerous protein-protein interactions with RNA-binding proteins, many of which are involved in R-loop biology, led the authors to investigate the role of RNA and R-loops in mediating protein-protein interactions and profile the co-localization of R-loops with NRL genomic occupancy.

Strengths:

Overall, the manuscript is very well written, providing succinct explanations of the observed results and potential implications. Additionally, the authors use multiple orthogonal techniques and tissue samples to reproduce and validate that NRL interacts with DHX9 and DDX5. Experiments also utilize specific assays to understand the influence of RNA and R-loops on protein-protein interactions. The authors also use state-of-the-art techniques to profile R-loop localization within the retina and integrate multiple previously established datasets to correlate R-loop presence with transcription factor binding and chromatin marks in an attempt to understand the significance of R-loops in the retina.

Weaknesses:

In general, the authors provide superficial interpretations of the data that fit a narrative but fail to provide alternative explanations or address caveats of the results. Specifically, many bands are present in interaction studies either in control lanes (GST controls) of Westerns or large amounts of background in PLA experiments.

We have added additional information to the text regarding the presence of background signals in pull downs. We wish to note that experimental samples always exceeded background signals.  We believe that reporting these raw findings (rather than showing shorter exposures) is valuable for the scientific community. We did not observe any background in the proximity ligation assay (PLA) that exceeded what is typically expected, and the signals were clearly discernible. Cases where signals are weaker, such as with DDX5, have been highlighted. In addition, we added a DHX9-IgG negative control for the human PLA experiment. See page 5 and Fig. S2B.

Additionally, the lack of experiments testing the functional significance of Nrl interactions or R-loops within the developing retina fails to provide novel biological insights into the regulation of gene regulatory networks other than, 'This could be a potentially important new mechanism'.

We agree that functional experiments are necessary to understand the molecular mechanisms behind R-loop regulation in the retina; however, we believe it goes beyond the scope of this initial characterization (as this is the first report on R-loops in the retina). We are currently pursuing these studies.

We performed new analysis on NRL-regulated genes as suggested by reviewer 1. We show that NRL target genes have an enrichment of stranded R-loops at the promoter/TSS and unstranded R-loops on the gene body compared to all Ensembl genes (Figure S7B), providing further evidence of the functional  interaction between NRL and R-loops. See table S5 and Fig. S7B, and discussion.

Additionally, the authors test the necessity of RNA for NRL/DHX9 interactions but don't show RNA binding of NRL or DHX9 or the sufficiency of RNA to interfere/mediate protein-protein interactions. Recent work has highlighted the prevalence of RNA binding by transcription factors through Arginine Rich Motifs that are located near the DNA binding domains of transcription factors.

We agree that the role of RNA in these complexes is very exciting, and we are currently pursuing these studies. However, we believe that they fall outside the scope of this initial report on R-loops in the retina.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

There are a couple of minor comments:

(1) Unfinished sentence; page 11, the end of the first paragraph.

Thank you for catching this error. We removed the unfinished text.

(2) Page 6: Figure S2A should be Figure S2.

In general, the manuscript would benefit from a deeper explanation of the biological relevance of R-loop formation and the connection to NRL TF and the expression of genes regulated by NRL. In this regard, a more substantial description of the model would be useful.

We have modified the discussion for clarity and included new ideas on possible roles of R-loops in gene regulation of photoreceptors.

Reviewer #2 (Recommendations for the authors):

(1) The specificity of interactions needs to be addressed:

- Figure 1B - HNRNPUI1 bands present in GST control.

- Figure 1C - Bands present in the Empty Vector control IP for HNRNPU and DHX9.

- Supplemental Figure 1A - most proteins are present in GST control suggesting prevalent binding to GST and lack of specificity for other interactions.

Thank you for your comment. RNA-binding proteins can have more background as observed in other studies (e.g. PMID: 32704541) but there is always a higher signal in experimental samples compared to controls. While we agree that we can enhance the conditions for immunoprecipitation (IP) by optimizing washing buffers, exposure and other parameters, we believe the current methods tell the story. We have added additional text explaining this. See page 5.

(2) Use of the term 'Strongest' interaction - IPs don't directly address the strength of interaction, but depend on levels of expression AND affinity. The strength of interaction should be tested using techniques like an OCTET or SPR assay. One can also quantify the effect that RNA would have in such an assay.

Thank you for your suggestion. We replaced the term 'stronger' with “higher signal” and “robust” at most places. The source of protein lysates is the same for experiments and controls, thus the amount of protein is consistent in both conditions, and not dependent on level of gene expression.

(3) In supplemental tables, please use the proper gene names, not the UniProt peptide name. For example, there are no genes named ELAV1-ELAV4. These should be ELAVL1-ELAVL4. A short glance identifies >10 gene name errors.

Thank you for the suggestion. We updated current gene names in all tables.

(4) Please provide the rationale for the choice of DNA sequence for the DHX9 nucleotide sequence used for EMSA assays. In the human DHX9 locus, the NRL ChIP-seq peak looks to be contained in Intron1 whereas the NRL ChIP-seq peak in mouse DHX9 looks to be in the proximal upstream promoter. Did the authors choose an evolutionarily conserved sequence in the promoter region that contained the NRL motif or does the probe sequence arise from the sequence that has known NRL binding as assayed by NRL ChIP-seq? A zoomed-in image of the NRL ChIP-seq pile-ups in the DHX9 locus in each species would be beneficial.

Thank you for this suggestion. The probe was chosen by scanning for NRL binding motifs on the Chip-Seq peak at the human DHX9 promoter. We added a Zoom-in image of the ChIP-Seq or CUT&RUN reads for NRL on both human and mouse retinas. Figure 3D shows NRL binding in both species in regions containing the homologous motif. The sequence is partially conserved and shown in the figure.

(5) Normalization in RNaseH/RNaseA Co-IP experiments. Why does RNAseH treatment result in increased NRL IP (increased NRL expression?) or does RNaseA treatment cause reduced IP of DHX9? These differences seem to cause a 'denominator' effect, leading the Authors to conclude decreased co-IP of DHX9 with NRL when R-loops are inhibited or increased co-IP of NRL with DHX9 when RNA is degraded. An alternate interpretation would be that inhibiting the R-loop binding of NRL unmasks the epitope for antibody recognition. The authors should test NRL binding to RNA and determine if RNA binding affects the co-IP of NRL with DHX9.

We agree that removing total RNA by RNase A or R-loops by RNase H may alter the accessibility of our antibodies to the epitopes, resulting in the differences in the level of total protein pulled down. However, we quantified the relative level of the associating protein to the total protein and confirmed, in reciprocal assays, that RNase A treatment led to increased interaction between NRL and DHX9. However, the quantification was not consistent between the reciprocal IPs upon RNase H treatment. We reason that in Figure 4D, as NRL may account for only a small proportion of DHX9’s interactome, the change in NRL level could not be detected due to the sensitivity of our assay. However reciprocally, DHX9 can constitute a larger proportion of NRL’s interactome in HEK293 cells, hence the change in DHX9 level was more obvious. We added this information to the text. See page 8.

(6) Figure 7 - Malat1 - there doesn't seem to be an overlap of NRL with Stranded R-loop peaks in this image. Nrl seems to flank the region of R-loops.

We changed Malat1 for Mplkip that shows a direct overlap of Nrl binding and R-loops. See Figure 7C.

(7) Results end with 'A Model'. Seems like some concluding remarks and references to Figure 8 were mistakenly left out.

Thank you for catching this typo. We removed the misplaced text.

(8) Model and Discussion - authors should show raw data for RHO with respect to NRL binding and R-loops. No evidence was provided regarding R-loops (or lack thereof) in the Rhodopsin locus. Additionally, conclusions stating that "R-loops... are specifically depleted from genes, such as Rhodopsin, with high expression levels" go against Figures 7B and 7C. Malat1 is one of the highest expressed genes in the retina and contains R-loops.

Thank you for helping us clarify our hypothesis. We added a genome browser view of Rhodopsin showing the absence of R-loops (Fig. S8). We hypothesize that R-loops could interfere with achieving higher rates of transcription, however we did not mean to say that all high expressed genes lack R-loops. We have rephrased the discussion to clarify this point.

(9) Neuronal genes, particularly those involved in synaptic transmission are known to be, on average, longer than most genes (Gabel, 2015; PMID: 25762136). Is it possible that R-loops are detected at genes involved in synaptic function/structure solely because of transcript length, as it takes longer for transcription termination to resolve in genes that are longer? A plot showing R-loop enrichment and transcript length would address this.

We added a plot showing gene length in relation to R-loops and expression levels. We observed that R-loops are more common over long genes regardless of their expression levels. We also observed that the concomitant presence of stranded and unstranded R-loops is restricted to the longest genes in most cases. We added this to Figure 7D.

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12. p v ~s 17.**
13. s v R

Reviewer #2 (Public review):

Summary:

Generating biophysically detailed computational models that capture the characteristic physiological properties of biological neurons for diverse cell types is an important and difficult problem in computational neuroscience. One major challenge lies in determining the large number of parameters of such models, which are notoriously difficult to fit to experimental data. Thereby, the computational and energy costs can be significant. The study 'ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses' by Kim et al. describes a computationally efficient approach for predicting model parameters of Hodgkin-Huxley neuron models using Generative Adversarial Networks (GANs) trained on simulation data. The method is applied to generate models for 9 non-spiking neurons in C. elegans based on electrophysiological recordings. While the generated models capture the responses of these neurons to some degree, they generally show significant deviations from the empirically observed responses in important features. Although EP-GAN shows clear benefits under limited compute, the results do not yet demonstrate the quality needed to match other state-of-the-art methods. Future work examining extended training, larger datasets, or hybrid approaches would help clarify whether EP-GAN can generate models of high quality. If so, this would indeed be a major step forward; if not, the computationally more expensive methods will remain essential.

Strengths:

The authors work on an important and difficult problem. A noteworthy strength of their approach is that once trained, the GANs can generate models from new empirical data with very little computational effort. The generated models reproduce the response to current injections reasonably well.

Weaknesses:

Major 1: Models do not faithfully capture empirical responses. While the models generated with EP-GAN reproduce the average voltage during current injections reasonably well, the dynamics of the response are generally not well captured. For example, for the neuron labeled RIM (Figure 2), the most depolarized voltage traces show an initial 'overshoot' of depolarization, i.e. they depolarize strongly within the first few hundred milliseconds but then fall back to a less depolarized membrane potential. In contrast, the empirical recording shows no such overshoot. Similarly, for the neuron labeled AFD, all empirically recorded traces slowly ramp up over time. In contrast, the simulated traces are mostly flat. Furthermore, all empirical traces return to the pre-stimulus membrane potential, but many of the simulated voltage traces remain significantly depolarized, far outside of the ranges of empirically observed membrane potentials. The authors trained an additional GAN (EP-GAN Extended) to improve the fit to the resting membrane potential. Interestingly, for one neuron (AWB), this improved the response during stimulation, which now reproduced the slowly raising membrane potentials observed empirically, however, the neuron still does not reliably return to its resting membrane potential. For the other two neurons, the authors report a decrease in accuracy in comparison to EP-GAN. While such deviations may appear small in the Root mean Square Error (RMSE), they likely indicate a large mismatch between the model and the electrophysiological properties of the biological neuron. The authors added a second metric during the revision - percentages of predicted membrane potential trajectories within empirical range. I appreciate this additional analysis. As the empirical ranges across neurons are far larger than the magnitude of dynamical properties of the response ('slow ramps', etc.), this metric doesn't seem to be well suited to quantify to which degree these dynamical properties are captured by the models.

Major 2: Comparison with other approaches is potentially misleading. Throughout the manuscript, the authors claim that their approach outperforms the other approaches tested. But compare the responses of the models in the present manuscript (neurons RIM, AFD, AIY) to the ones provided for the same neurons in Naudin et al. 2022 (https://doi.org/10.1371/journal. pone.0268380). Naudin et al. present models that seem to match empirical data far more accurately than any model presented in the current study. Naudin et al. achieved this using DEMO, an algorithm that in the present manuscript is consistently shown to be among the worst of all algorithms tested. I therefore strongly disagree with the authors claim that a "Comparison of EP-GAN with existing estimation methods shows EP-GAN advantage in the accuracy of estimated parameters". This may be true in the context of the benchmark performed in the study (i.e., a condition of very limited compute resources - 18 generations with a population size of 600, compare that to 2000 generations recommended in Naudin et al.), but while EP-GAN wins under these specific conditions (and yes, here the authors convincingly show that their EP-GAN produces by far the best results!), other approaches seem to win with respect to the quality of the models they can ultimately generate.

Major 3: As long as the quality of the models generated by the EP-GAN cannot be significantly improved, I am doubtful that it indeed can contribute to the 'ElectroPhysiome', as it seems likely that dynamics that are currently poorly captured, like slow ramps, or the ability of the neuron to return to its resting membrane potential, will critically affect network computations. If the authors want to motivate their study based on this very ambitious goal, they should illustrate that single neuron model generation with their approach is robust enough to warrant well-constrained network dynamics. Based on the currently presented results, I find the framing of the manuscript far too bold.

Major 4: The conclusion of the ablation study 'In addition the architecture of EP-GAN permits inference of parameters even when partial membrane potential and steady-state currents profile are given as inputs' does not seem to be justified given the voltage traces shown in Figure 3. For example, for RIM, the resting membrane potential stays around 0 mV, but all empirical traces are around -40mV. For AFD, all simulated traces have a negative slope during the depolarizing stimuli, but a positive slope in all empirically observed traces. For AIY, the shape of hyperpolarized traces is off. While it may be that by their metric neurons in the 25% category are classified as 'preserving baseline accuracy', this doesn't seem justified given the voltage traces presented in the manuscript. It appears the metric is not strict enough.

Author response:

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

We thank the reviewers for valuable feedback and comments. Based on the feedback we revised the manuscript and believe that we addressed most of the reviewers' raised points. Below we include a summary of key revisions and point-by-point responses to reviewers comments.

Abstract/Introduction

We further emphasized EP-GAN strength in parameter inference of detailed neuron parameters vs specialized models with reduced parameters.

Results

We further elaborated on the method of training EP-GAN on synthetic neurons and validating on both synthetic and experimental neurons.

We added a new section Statistical Analysis and Loss Extension which includes:

- Statistical evaluation of baseline EP-GAN and other methods on neurons with multi recording membrane potential responses/steady-state currents data: AWB, URX, HSN

- Evaluation of EP-GAN with added resting potential loss + longer simulations to ensure stability of membrane potential (EP-GAN-E)

Methods

We added a detailed explanation on "inverse gradient process"

We added detailed current/voltage-clamp protocols for both synthetic and experimental validation and prediction scenarios (table 6)

Supplementary

We added error distribution and representative samples for synthetic neuron validations (Fig S1)

We added membrane potential response statistical analysis plots for existing methods for AWB, URX, HSN (Fig S6)

We added steady-state currents statistical analysis plots on EP-GAN + existing methods for AWB, URX, HSN (Fig S7)

We added mean membrane potential errors for AWB, URX, HSN normalized by empirical standard deviations for all methods (Table S4)

Please see our point-by-point responses to specific feedback and comment below.

Reviewer 1:

First, at the methodological level, the authors should explain the inverse gradient operation in more detail, as the reconstructed voltage will not only depend on the evaluation of the right-hand side of the HH-equations, as they write but also on the initial state of the system. Why did the authors not simply simulate the responses?

We thank the reviewer for the feedback regarding the need for further explanation. We have revised the Methods section to provide a more detailed description of the inverse gradient process. The process uses a discrete integration method, similar to Euler’s formula, which takes systems’ initial conditions into account. For the EP-GAN baseline, the initial states were picked soon after the start of the stimulus to reconstruct the voltage during the stimulation period. For EP-GAN with extended loss (EP-GAN-E), introduced in this revision in sub-section Statistical Analysis and Loss Extension, initial states before/after stimulations were also taken into account to incorporate resting voltage states into target loss.

Since EP-GAN is a neural network and we want the inverse gradient process to be part of the training process (i.e., making EP-GAN a “model informed network”), the process is expected to be implemented as a differentiable function of generated parameter p. This enables the derivatives from reconstructed voltages to be traced back to all network components via back-propagation algorithm.

Computationally, this requires the implementation of the process as a combination of discrete array operations with “auto-differentiation”, which allows automatic computation of derivatives for each operation. While explicit simulation of the responses using ODE solvers provides more accurate solutions, the algorithms used by these solvers typically do not support such specialized arrays nor are they compatible with neural network training. We thus utilized PyTorch tensors [54], which support both auto-differentiation and vectorization to implement the process.

The authors did not allow the models time to equilibrate before starting their reconstruction simulations, as testified by the large transients observed before stimulation onset in their plots. To get a sense of whether the models reproduce the equilibria of the measured responses to a reasonable degree, the authors should allow sufficient time for the models to equilibrate before starting their stimulation protocol.

In the added Statistical Analysis and Loss Extension under the Results section, we added results for EP-GAN-E where we simulate the voltage responses with 5 seconds of added stabilization period in the beginning of simulations. The added period mitigates voltage fluctuations observed during the initial simulation phase and we observe that simulated voltage responses indeed reach stable equilibrium for both prior stimulations and for the zero stimulus current-clamp protocol (Figure 5 bottom, Column 3).

In fact, why did the authors not explicitly include the equilibrium voltage as a target loss in their set of loss functions? This would be an important quantity that determines the opening level of all the ion channels and therefore would influence the associated parameter values.

EP-GAN baseline does include equilibrium voltage as a target loss since all current-clamp protocols used in the study (both synthetic and experimental) include a membrane potential trace where the stimulus amplitude is zero throughout the entire recording duration (see added Table 6 for current clamp protocols), thus enforcing EP-GAN to optimize resting membrane potential alongside with other non-zero stimulus current-clamp scenarios.

To further study EP-GAN’s accuracy in resting potential, we evaluated EP-GAN with supplemental resting potential target loss and evaluated its performance in the sub-section Statistical Analysis and Loss Extension. The added loss, combined with 5 seconds of additional stabilization period, improved accuracy in predicting resting potentials by mitigating voltage fluctuations during the early simulation phase and made significant improvements to predicting AWB membrane potential responses where EP-GAN baseline resulted in overshoot of the resting potential.

The authors should provide a more detailed evaluation of the models. They should explicitly provide the IV curves (this should be easy enough, as they compute them anyway), and clearly describe the time-point at which they compute them, as their current figures suggest there might be strong transient changes in them.

We included predicted IV-curve vs ground truth plots in addition to the voltages in the supplementary materials (Figure S2, S5) in the original submitted version of the manuscript. In this revision, we added additional IV-curve plots with statistical analysis for the neurons with multi-recording data (AWB, URX, HSN) in the supplementary materials (Figure S7).

For the evaluation of predicted membrane potential responses, we added further details in Validation Scenarios (Synthetic) under Results section such that it clearly explains on the current-clamp protocols used for both synthetic and experimental neurons and which time interval the RMSE evaluations were performed.

In the sub-section Statistical Analysis and Loss Extension, we introduced a new statistical metric in addition to RMSE, applied for neurons AWB, URX, HSN which evaluates the percentage of predicted voltages that fall within the empirical range (i.e., mean +- 2 std) and voltage error normalized by empirical standard deviations (Table S4).

The authors should assess the stability of the models. Some of the models exhibit responses that look as if they might be unstable if simulated for sufficiently long periods of time. Therefore, the authors should investigate whether all obtained parameter sets lead to stable models.

In the sub-section Statistical Analysis and Loss Extension, we included individual voltage traces generated by both EP-GAN baseline and EP-GAN-E (extended) with longer simulation (+5 seconds) to ensure stability. EP-GAN-E is able to produce equilibrium voltages that are indeed stable and within empirical bounds throughout the simulations for the zero-stimulus current-clamp scenario (column 3) for the 3 tested neurons (AWB, URX, HSN).

Minor:

The authors should provide a description of the model, and it's trainable parameters. At the moment, it is unclear which parameter of the ion channels are actually trained by the methodology.

The detailed description of the model and its ion channels can be found in [7]. Supplementary materials also include an excel table predicted parameters which lists all EP-GAN fitted parameters for 9 neurons (+3 new parameter sets for AWB, URX, HSN using EP-GAN-E) included in the study, the labels for trainability, and their respective lower/upper bounds used during training data generation. In the revised manuscript, we further elaborated on the above information in the second paragraph of the Results section.

Reviewer 2:

Major 1: While the models generated with EP-GAN reproduce the average voltage during current injections reasonably well, the dynamics of the response are not well captured. For example, for the neuron labeled RIM (Figure 2), the most depolarized voltage traces show an initial 'overshoot' of depolarization, i.e. they depolarize strongly within the first few hundred milliseconds but then fall back to a less depolarized membrane potential. In contrast, the empirical recording shows no such overshoot. Similarly, for the neuron labeled AFD, all empirically recorded traces slowly ramp up over time. In contrast, the simulated traces are mostly flat. Furthermore, all empirical traces return to the pre-stimulus membrane potential, but many of the simulated voltage traces remain significantly depolarized, far outside of the ranges of empirically observed membrane potentials. While these deviations may appear small in the Root mean Square Error (RMSE), the only metric used in the study to assess the quality of the models, they likely indicate a large mismatch between the model and the electrophysiological properties of the biological neuron.

EP-GAN main contribution is targeted towards parameter inference of detailed neuron model parameters, in a compute efficient manner. This is a difficult problem to address even with current state-of-the-art fitting algorithms. While EP-GAN is not perfect in capturing the dynamics of the responses and RMSE does not fully reflect the quality of predicted electrophysiological properties, it’s a generic error metric for time series that is easily interpretable and applicable for all methods. Using such a metric, our studies show that EP-GAN overall prediction quality exceeds those of existing methods when given identical optimization goals in a compute normalized setup.

In our revised manuscript, we included a new section Statistical Analysis and Loss Extension under Results section where we performed additional statistical evaluations (e.g., % of predicted responses within empirical range) of EP-GAN’s predictions for neurons with multi recording data. The results show that predicted voltage responses from EP-GAN baseline (introduced in original manuscript) are in general, within the empirical range with ~80% of its responses falling within +- 2 empirical standard deviations, which were higher than existing methods: DEMO (57.9%), GDE3 (37.9%), NSDE (38%), NSGA2 (60.2%).

Major 2: Other metrics than the RMSE should be incorporated to validate simulated responses against electrophysiological data. A common approach is to extract multiple biologically meaningful features from the voltage traces before, during and after the stimulus, and compare the simulated responses to the experimentally observed distribution of these features. Typically, a model is only accepted if all features fall within the empirically observed ranges (see e.g. https://doi.org/10.1371/journal.pcbi.1002107). However, based on the deviations in resting membrane potential and the return to the resting membrane potential alone, most if not all the models shown in this study would not be accepted.

In our original manuscript, due to all of our neurons’ recordings having a single set of recording data, RMSE was chosen to be the most generic and interpretable error metric. We conducted additional electrophysiological recordings for 3 neurons in prediction scenarios (AWB, URX, HSN) and performed statistical analysis of generated models in the sub-section Statistical Analysis and Loss Extension. Specifically, we evaluated the percentage of predicted voltage responses that fall within the empirical range (empirical mean +- 2 std, p ~ 0.05) that encompass the responses before, during and after stimulus (Figure 5, Table 5) and mean membrane potential error normalized by empirical standard deviations (Table S4).

The results show that EP-GAN baseline achieves average of ~80% of its predicted responses falling within the empirical range, which is higher than the other methods: DEMO (57.9%), GDE3 (37.9%), NSDE (38%), NSGA2 (60.2%). Supplementing EP-GAN with additional resting potential loss (EPGAN-E) increased the percentage to ~85% with noticeable improvements in reproducing dynamical features for AWB (Figure 5). Evaluations of membrane potential errors normalized by empirical standard deviations also showed similar results where EP-GAN baseline and EP-GAN-E have average error of 1.0 std and 0.7 std respectively, outperforming DEMO (1.7 std), GDE3 (2.0 std), NSDE (3.0 std) and NSGA (1.5 std) (Table S4).

Major 3: Abstract and introduction imply that the 'ElectroPhysiome' refers to models that incorporate both the connectome and individual neuron physiology. However, the work presented in this study does not make use of any connectomics data. To make the claim that ElectroPhysiomeGAN can jointly capture both 'network interaction and cellular dynamics', the generated models would need to be evaluated for network inputs, for example by exposing them to naturalistic stimuli of synaptic inputs. It seems likely that dynamics that are currently poorly captured, like slow ramps, or the ability of the neuron to return to its resting membrane potential, will critically affect network computations.

In the paper, EP-GAN is introduced as a parameter estimation method that can aid the development of ElectroPhysiome, which is a network model - these are two different method types and we do not claim EP-GAN is a model that can capture network dynamics. To avoid possible confusion, we made further clarifications in the abstract/introduction that EP-GAN is a machine learning approach for neuron HH-parameter estimation.

I find it hard to believe that the methods EP-GAN is compared to could not perform any better. For example, multi-objective optimization algorithms are often successful in generating models that match empirical observations very well, but features used as target of the optimization need to be carefully selected for the optimization to succeed. Likely, each method requires extensive trial and error to achieve the best performance for a given problem. It is therefore hard to do a fair comparison. Given these complications, I would like to encourage the authors to rethink the framing of the story as a benchmark of EP-GAN vs. other methods. Also, the number of parameters does not seem that relevant to me, as long as the resulting models faithfully reproduce empirical data. What I find most interesting is that EP-GAN learns general relationships between electrophysiological responses and biophysical parameters, and likely could also be used to inspect the distribution of parameters that are consistent with a given empirical observation.

We thank the reviewer for providing this perspective. While it is indeed difficult to have a completely fair comparison between existing optimization methods vs EP-GAN due to the fundamental differences in their algorithms, we believe that the current comparisons with other methods are justified as they provide baseline performance metrics to test EP-GAN for its intended use cases.

The main strength of EP-GAN, as previously mentioned, is in its ability to efficiently navigate large detailed HH-models with many parameters so that it can aid in the development of nervous system models such as ElectroPhysiome, potentially fitting hundreds of neurons in a time efficient manner.

While EP-GAN’s ability to learn the general relationship between electrophysiological responses and parameter distribution are indeed interesting and warrant a more careful examination, this is not the main focus of the paper since in this work we focus on introducing EP-GAN as a methodology for parameter inference.

In this context, we believe the comparisons with other methods conducted in a compute normalized manner (i.e., each method is given the same # of simulations) and identical optimization targets provides an adequate framework for evaluating the aforementioned EP-GAN aim. Indeed, while EPGAN excels with larger HH-models, it performs slightly worse than DE for smaller models such as the one used by [16] despite it being more compute efficient (Table S2).

To emphasize the EP-GAN aim, we revised the main manuscript description to focus on its intended use in parameter inference of detailed neuron parameters vs specialized models with reduced parameters.

I could not find important aspects of the methods. What are the 176 parameters that were targeted as trainable parameters? What are the parameter bounds? What are the remaining parameters that have been excluded? What are the Hodgkin-Huxley models used? Which channels do they represent? What are the stimulus protocols?

The detailed description and development of the HH-model that we use and its ion channel list can be found in [7]. Supplementary materials also include an excel table predicted parameters which lists all EP-GAN fitted parameters for 9 neurons (+3 new parameter sets for AWB, URX, HSN using EPGAN-E), the labels for trainability, and parameter bounds used for parameters during the generation of training data.

We also added a new Table which details the current/voltage clamp protocols used for 9 neurons including the ones used for evaluating EP-GAN-E, which was supplemented with longer simulation time to ensure voltage stability (please see Table 6).

I could not assess the validation of the EP-GAN by modeling 200 synthetic neurons based on the data presented in the manuscript since the only reported metric is the RMSE (5.84mV and 5.81mV for neurons sampled from training data and testing data respectively) averaged over all 200 synthetic neurons. Please report the distribution of RMSEs, include other biologically more relevant metrics, and show representative examples. The responses should be carefully investigated for the types of mismatches that occur, and their biological relevance should be discussed. For example, is the EP-GAN biased to generate responses with certain characteristics, like the 'overshoot' discussed in Major 1? Is it generally poor at fitting the resting potential?

We thank the reviewer for the feedback regarding the need for additional supporting data for synthetic neuron validations. In the revised supplementary materials Figure S1, we included the distribution of RMSE errors for both groups of synthetic neuron validations (validation/test set) and representative samples for both EP-GAN baseline and EP-GAN-E. Notably, the inaccuracies observed during the experimental neuron predictions (e.g., resting potential, voltage overshoot) do not necessarily generalize to synthetic neurons, indicating that such mismatches could stem from the differences between synthetic neurons used for training and experimental neurons for predictions. While synthetic neurons are generated according to empirically determined parameter bounds, some experimental neuron types are rarer than the others and may also involve other channels that have not been recorded or modeled in [7], which can affect the quality of predicted parameters (see 2nd and 4th paragraphs of Discussions section for more detail). Also, properties such as recording error/noise that are often present in experimental neurons are not fully accounted for in synthetic neurons.

To further study how these mismatches can be mitigated, in the revision we added an extended version of EP-GAN where target loss was supplemented with additional resting potential and 5 seconds of stabilization period during simulations (EP-GAN-E described in Statistical Analysis and Loss Extension). With such extensions, EP-GAN-E was able to improve its accuracies on both resting potentials and dynamical features with the most notable improvements on AWB where predicted voltage responses closely match slowly rising voltage response during stimulation. EPGAN-E is an example of further extensions to loss function that account for additional experimental features.

Furthermore, the conclusion of the ablation study ('EP-GAN preserves reasonable accuracy up to a 25% reduction in membrane potential responses') does not seem to be justified given the voltage traces shown in Figure 3. For example, for RIM, the resting membrane potential stays around 0 mV, but all empirical traces are around -40mV. For AFD, all simulated traces have a negative slope during the depolarizing stimuli, but a positive slope in all empirically observed traces. For AIY, the shape of hyperpolarized traces is off.

Since EP-GAN baseline optimizes voltage responses during the stimulation period, RMSE was also evaluated with respect to this period. From these errors, we evaluated whether the predicted voltage error for each ablation scenario fell within the 2 standard deviations from the mean error obtained from synthetic neuron test data (i.e. the baseline performance). We found that for input ablation for voltage responses, the error was within such range up to 25% reduction whereas for steady-state current input ablation, all 25%, 50% and 75% reductions resulted in errors within the range.

We extended the “Ablation Studies” sub-section so that the above reasoning is better communicated to the readers.

Additionally, I found a number of minor issues:

Minor 1: Table 1 lists the number of HH simulations as '32k (11k · 3)'. Should it be 33k, since 11.000 times 3 is 33.000? Please specify the exact number of samples.

Minor 2: x- and y-ticks are missing in Fig 2, Fig 3, Fig S1, Fig S2, Fig S3 and Fig S4.

Minor 3: All files in the supplementary zip file should be listed and described.

Minor 4: Code for training the GAN, generation of training datasets and for reproducing the figures should be provided.

Minor 5: In the reference (Figure 3A, Table 1 Row 2): should this refer to Table 2?

Minor 6: 'the ablation is done on stimulus space where a 50% reduction corresponds to removing half of the membrane potential responses traces each associated with a stimulus.' - which half is removed?

We thank the reviewer for pointing out these errors in the original manuscript. The revised manuscript includes corrections for these items. We will publish the python code reproducing the results in the public repository in the near future.

Author response:

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

Since multiple Reviewers requested that the results describing effects of TTX treatment on GluA2 receptor levels detected by immunofluorescence and confocal imaging be revised, we have made substantial changes, which are described below. We believe the changes have greatly improved the manuscript and thank the reviewers for their comments.

Lack of significant increase in GluA2 receptor data is due to too few cultures sampled; anything could have happened [in one] particular dissociation. A concern that the TTX effect might vary greatly from culture to culture was why we felt it was important to match the receptor measurements on the same cultures that we recorded mEPSCs. We now present the culture means in Figure 5A (mEPSCs) and 5B (GluA2 receptor cluster size). These plots make it clear that the variability in the GluA2 receptor cluster size effect is not attributable to a failure of that culture to show a homeostatic effect. That is, the variability in GluA2 receptor effect is independent of the variability in mEPSC effect. To increase sample size, we examined 2 additional cultures for synaptic GluA2 receptor levels in control vs. TTX treatment. These cultures showed very modest increases (Figure 5C). When cell means from these experiments were pooled with those from the 3 matched cultures, the TTX effect was still not statistically significant (Figure 5G).

Lack of significant increase in GluA2 receptor data is due to the choice to restrict our analysis to the primary dendrite, close to the cell body. We restricted our analysis to the primary dendrite because Figure 3 in Turrigiano et al, 1998, shows the increased response to exogenously applied glutamate after TTX treatment is greatest close to the cell body and wanes as the glutamate is applied further away (added to Results, new lines 388-389).

Variability in GluA2 receptor data is due to the much smaller number of synapses sampled, compared to mEPSCs. We matched the sampling for mEPSC amplitude data to that of imaging data by taking only 20 samples from each electrophysiological recording. Each mEPSC represents one synapse; in a set of 20 mEPSCs some might come from the same synapse, so that we are sampling from £ 20 synapses. The effect of TTX on mEPSC amplitudes remained significant despite the reduced samples per cell (Figure 5A).

Why do we fail to show a significant increase in receptors when this has been shown in many studies?

We have added to our discussion the point that several studies, including Wang et al. 2019, use the number of puncta, rather than the number of cells, as the sample number. We ran an analysis of GluA2 receptor cluster size where we sampled multiple synapses per cell, and used the number of clusters as the sample n. We found that even with as few as 6 synapses randomly selected from each cell, the effect of TTX on GluA2 receptor cluster size became highly significant (p = 0.001 for data from 3 cultures and p = 0.005 for data from 5 cultures) (see new lines 400-406 in Discussion). In sum, our data are not very different from that of some previous studies. We are not arguing that receptors do not increase. Instead our point is that the increase is more variable than the increase in MESPC amplitude and thus takes a much bigger sample size to detect. In sum, the difference between the mEPSC data and the receptor data is that the mEPSC data consistently show a ~20-25% increase, whereas the receptor data do not always show an increase and sometimes the increase is only ~10%. Finally, we added two matched culture experiments examining synaptic GluA1 receptor cluster characteristics. GluA1 receptor cluster size decreased in one culture, and increased very modestly in the other (Supplemental Figure 1B), whereas mEPSC amplitude robustly increased (Supplemental Figure 1A; Results, new lines 265-268).

We conclude that these data support the idea that there is another contributor to the TTXinduced increase in quantal size.

Other changes in presentation of GluA2 receptor results: Since the effects on intensity and integral are of lesser magnitude than that on cluster size, we have removed these results from the graphs, although they are presented in Table 1. We have removed Figure 6, the presentation of individual culture results, since these results are now conveyed in Figure 5A-C. We have removed graphs depicting GluA2 receptor cluster size in response to TTX in Rab3A-/- cultures, but these data are still presented in Table 1.

We address other detailed comments below.

Public Reviews:

Reviewer #1 (Public review):

(2) The effects of Rab3A on TTX-induced mini frequency modulation remains unclear, because TTX does not induce a change in mini frequency in the Rab3A+/Ebd control (Fig. 2). The respective conclusions should be revised accordingly (l. 427).

The effects on mini frequency were added for completeness, but given the lack of consistently significant changes with TTX treatment or changes in the KO or Rab3A^Ebd/Ebd cultures, we have removed comment on these results from the Discussion.

(3) The model is still not supported by the data. In particular, data supporting a negative regulation of Rab3A by APs, Rab3A-dependent release of a tropic factor, or a Rab3Adependent increase in GluA2 abundance are not presented.

We have removed the model from the manuscript.

(4) Data points are not overlapping and appear "quantal" in most box plots. How were the data rounded?

The appearance of quantal variation in cell amplitude means is due to the binning that is part of the creation of the box plot. We have not remade the figures without binning, because the binning provides a visual depiction of the distribution of the data points. We have added the bin sizes to the appropriate figure legends.

Reviewer #2 (Public review):

However, the authors still have not provided further investigation of the mechanisms behind the role of Rab3A in this form of plasticity, and the revision therefore has added little to the significance of the study. Moreover, the experimental design for the investigation of the mismatch between mEPSC amplitude and GluA2 cluster fluorescence remains questionable, making it difficult to draw any credible conclusions from groups of data that not only look similar to the eye but also show no significance statistically.

To our knowledge, no other study has matched measurements of mEPSC amplitude in the same cultures where synaptic receptor levels were assessed. As stated above, we have revised the presentation of GluA2 receptor results, concluding from the lack of significant effects on receptor levels that the mEPSC amplitude increase cannot be fully explained by the receptor data (which is strengthened by addition of two more cultures analyzed for GluA2 immunofluorescence). This is an important addition to the significance of the study.

In summary, this study establishes that neuronal Rab3A plays a role in homeostatic synaptic plasticity, but so do a number of other molecules that have been implicated in homeostatic synaptic plasticity in the past two decades (only will grow with the new techniques such as RNAseq). Without going beyond this finding and demonstrating how exactly Rab3A participates in the induction and/or expression of this form of plasticity, or maybe the potential Rab3A-mediated functional and behavioral defects in vivo, the contribution of the current study to the field is limited. However, given the presynaptic location of Rab3A, this finding could serve as a starting point for researchers interested in pre-postsynaptic cross-talk during homeostatic plasticity in general.

We previously published a review in which we list 19 molecules known at that time to be important for homeostatic synaptic plasticity (see Table 2, Koesters et al., 2024), and they fall into two categories: molecules involved in glutamate receptor expression or trafficking, and signaling molecules. Rab3A is the first synaptic vesicle protein to be implicated in homeostatic plasticity of quantal size. We have added this point to the Discussion, new lines 473-476. By demonstrating that Rab3A is not acting in glia (which release TNF, which regulates receptor expression), and that GluA2 receptor levels do not explain the homeostatic mEPSC increase in our experimental conditions, we have ruled out two major mechanisms.

Reviewer #3 (Public review):

Other questions arise from the NASPM experiments, used to justify looking at GluA2 (and not GluA1) in the immunostaining. First, there is a frequency effect that is unclear in origin. One would expect NASPM to merely block some fraction of the post-synaptic current, and not affect pre-synaptic release or block whole synapses. However the change in frequency seems to argue (as the authors do) that some synapses only have CP-AMPARs, while the rest of the synapses have few or none. Another possibility is that there are pre-synaptic NASPM-sensitive receptors that influence release probability. Further, the amplitude data show a strong trend towards smaller amplitude following NASPM treatment (Fig 3B). The p value for both control and TTX neurons was 0.08 - it is very difficult to argue that there is no effect. The decrease on average is larger in the TTX neurons, and some cells show a strong effect. It is possible there is some heterogeneity between neurons on whether GluA1/A2 heteromers or GluA1 homomers are added during HSP. This would impact the weakly supported conclusions about the GluA2 imaging vs mEPSC amplitude data.

We cannot rule out that the NAPSM-induced decrease in mEPSC frequency is due to a loss of presynaptic glutamate receptor enhancement of release probability, and have added this statement to the Results, new lines 202-204. Regarding the p value of 0.08—we are not arguing that NASPM has no effect on mEPSC amplitude, only that it has no effect on the homeostatic increase in amplitude after TTX treatment. An increase in GluA1/A2 heteromers should have been detected in our imaging studies.

Unaddressed issues that would greatly increase the impact of the paper:

(1) Is Rab3A acting pre-synaptically, post-synaptically or both? The authors provide good evidence that Rab3A is acting within neurons and not astrocytes. But where it is acting (pre or post) would aid substantially in understanding its role. They could use sparse knockdown of Rab3A, or simply mix cultures from KO and WT mice (with appropriate tags/labels). The general view in the field has been that HSP is regulated post-synaptically via regulation of AMPAR trafficking, and considerable evidence supports this view. The more support for their suggestion of a pre-synaptic site of control, the better.

We agree that doing co-cultures of Rab3A-/- and Rab3A+/+ neurons is the definitive experiment to determine the locus of action of Rab3A in homeostatic synaptic plasticity. We hope to examine this question in a future manuscript.

(2) Rab3A is also found at inhibitory synapses. It would be very informative to know if HSP at inhibitory synapses is similarly affected. This is particularly relevant as at inhibitory synapses, one expects a removal of GABARs (ie the opposite of whatever is happening at excitatory synapses). If both processes are regulated by Rab3A, this might suggest a role for this protein more upstream in the signaling; an effect only at excitatory synapses would argue for a more specific role just at these synapses.

We agree that it would be very interesting to determine if the homeostatic decrease in mIPSCs after activity blockade depends on Rab3A. We hope to address this question in the future.

Recommendations for the authors:

Reviewer #3 (Recommendations for the authors):

Minor points:

The abstract is a bit repetitive in places. Some editing would be advised.

We did not identify anything repetitive in the abstract except the parallel construction referring to the previous findings at the NMJ and current findings in cortical neurons. However, we have eliminated a section in the introduction which went into detail about the receptor imaging results (previous lines 103-110).

Line 77: 'shift toward early awakening' is unclear; do you mean shorter sleep/wake cycle? Other circadian issues? A more complete description is needed.

We have moved the additional detail about the Earlybird mutation’s effect on circadian period from the Results to the Introduction, new lines 77 to 79.

The results section has many passages that seem more like discussion, offering various interpretation and alternatives for the data. While some commentary is appropriate, to justify the next series of experiments and maintain a logical flow, this manuscript has rather a high amount of this. Some editing and shifting material to the discussion might be warranted.

We have reduced the commentary in the Results section.

Line 245: GluA2 homomers are really unlikely, as they won't pass current (unless unedited) and don't often if ever form. But GluA2/A3 heteromers are likely (and detected by their methods).

GluA2 homomers do conduct current, albeit less than heteromers (Swanson et al., 1997; Oh and Derkach, 2005; Coombs et al., 2019). [The Oh and Derkach paper shows a GluA2 homomer current in Supplementary Figure 3]. We have modified the text to acknowledge that the GluA2 receptor imaging will detect heteromers and homomers (Results, new lines 214 to 215).

Line 258: If the number of synaptic pairs analyzed was usually <20, what was the average and range of pairs? This gets into the sampling issue.

We have added the average number of synaptic sites (20.4 ± 6.5) and range (11-38) to the text, Results, new line 229.

Are the stats of the baseline mEPSC amplitude and frequency shifts (WT vs KO on WT feeder layer) given somewhere (lines 398-402)? If not, please add them.

These stats have been added to the text, mEPSC amplitude, (CON, WT on WT, 13.3 ± 0.5 pA; CON, KO on WT, 15.2 ± 1.1 pA, p = 0.23, Kruskal-Wallis test), new lines 325-326 and frequency, (CON, WT on WT, 2.54 ± 0.57 sec^-1; CON, KO on WT, 4.46 ± 1.21 sec^-1, p = 0.23, Kruskal-Wallis test), new lines, 329-330.

25mM K+ is going to be much more than 'mildly' depolarizing (line 697). Should just skip that word.

‘mildly’ has been removed.

The section on MiniAnalysis seems overly argumentative, and there is no need to discuss flaws in the Wu paper. The important thing (a bit buried at the end of this section) is that the manual mini selection was done blind to condition, which is the normal way of dealing with potential bias. It would be better to limit the methods to describing what was done.

The bulk of the justification of manual analysis has been removed from the text.

The discussion of potential conductance changes (lines 534-6) seems somewhat unwarranted.

Modification of GluA1 phosphorylation in the GluA1/A2 heteromer would not be detected by NASPM (and the NASPM data being a bit inconclusive anyway). Further, auxiliary subunits (like TARPs) can alter conductance of any of the AMPARs. So I don't think they have enough data to exclude such a possibility.

The discussion of contributions of conductance have been removed from the text.

Coombs ID, Soto D, McGee TP, Gold MG, Farrant M, Cull-Candy SG (2019) Homomeric GluA2(R) AMPA receptors can conduct when desensitized. Nat Commun 10:4312.

Oh MC, Derkach VA (2005) Dominant role of the GluR2 subunit in regulation of AMPA receptors by CaMKII. Nat Neurosci 8:853-854.

Swanson GT, Kamboj SK, Cull-Candy SG (1997) Single-channel properties of recombinant AMPA receptors depend on RNA editing, splice variation, and subunit composition. J Neurosci 17:5869.

Authors’ Response (27 January 2025)

Revised preprint

GENERAL ASSESSMENT

The revised manuscript by Henze et al. presents a novel and significant contribution to the field, demonstrating that lysophospholipids (LPC) act as endogenous activators of pannexin channels. The study provides compelling evidence that LPC activation of PANX1 and PANX2 channels facilitates the release of signaling molecules critical for immune responses, particularly in the context of inflammation and inflammasome activation. The removal of the cryo-EM data, addressing concerns about the binding site and mechanism of LPC activation, is a thoughtful revision that enhances the manuscript's focus. While the authors have addressed many of the previous critiques, key mechanistic questions remain regarding channel permeability and the specificity of LPC-induced metabolite release.

RECOMMENDATIONS

Essential revisions:

1. While the study suggests that LPC-induced PANX1 activation results in the release of metabolites via the pannexin channel pore, no direct evidence is provided to confirm this. It is possible that the release occurs through alternative pathways or that detected metabolites are by-products of other permeating substances. Additional experiments or a more thorough discussion of these possibilities would enhance the manuscript’s rigor.

We thank the reviewers for pointing this out. While the overlap between our current study and the published secretomics studies by Medina et al. supports the direct release of these metabolites, we agree that our experiments do not rule out the possibility of an alternative pathway. We have updated the discussion to acknowledge this limitation.

1. The manuscript lacks data on the ion selectivity of LPC-activated PANX1, an important aspect for understanding the channel’s permeability profile. A comparison of the selectivity of LPC-induced PANX1 currents to those activated by other stimuli, such as C-terminal cleavage or depolarization, would clarify whether LPC induces a unique or comparable open state. The reviewers appreciate the technical challenges of these experiments, however, thus a more thorough discussion of the uncertainties may be appropriate.

We agree that characterizing the channel’s permeability profile is essential. However, as the reviewers noted, prolonged or repeated lysophospholipid (LPC) perfusion often destabilizes the patch, making it difficult to apply conventional reversal potential analysis. Additionally, the application of voltage ramps would inevitably activate Panx1 channels, further complicating the interpretation of the results.

As a workaround, we analysed whole-cell current density before and after LPC treatment at -60 mV using different buffers containing various anions and cations. Both Panx1 and Panx2 channels produced significantly larger currents in NaCl or NMDG-Cl compared to NaGluconate, indicating that LPC-activated channels are more selective for anions under these conditions. Interestingly, currents in NMDG-Cl were slightly smaller than in NaCl, suggesting that NMDG may have an inhibitory effect on these channels activated by LPC. While we acknowledge that this analysis does not directly compare ion selectivity within the same patch, the almost negligible current observed in NaGluconate strongly suggests that small anion conductance through both Panx1 and Panx2 channels is greater than cation conductance. We have included these findings in the new Fig. 1.

Another important aspect of Panx1 channels is their ability to allow the permeation of cationic molecules, such as YOPRO-1, when the C-terminus is cleaved. In our experiments, we demonstrate that LPC-activated full-length Panx1 channels are permeable to both anions (e.g., ATP and Cl) and cations (e.g., YOPRO-1). Although the precise mechanism underlying ion selectivity remains to be elucidated, our data support the conclusion that LPC can facilitate the release of multiple signalling molecules through full-length Panx1.

1. The YO-PRO-1 uptake observed in the absence of LPC contradicts prior findings (e.g., Bayliss et al., eLife, 2021) that full-length PANX1 does not release large molecules like ATP or YO-PRO-1 under similar conditions. This discrepancy raises concerns about the interpretation of the proteoliposome experiments. Addressing this by ion selectivity experiments, or discussing differences in experimental conditions and reconstitution protocols, could provide clarity as well as strengthen the conclusions.

We are also aware of the discrepancy and currently lack a clear understanding of the underlying mechanism. However, there are substantial differences between the two experimental setups that may account for the divergent results.

First, we used human Panx1 tagged in the flexible intracellular loop, whereas the Bayliss group used frog Panx1 tagged with GFP at the C-terminus. This difference in tagging and species may have contributed to variations in basal activity. Second, the lipid compositions used for reconstitution were significantly different. In our experiments, we used 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG), and sphingomyelin, while the Bayliss group employed a mixture of 70% brain phosphatidylcholine, 15% total brain lipid extract, 14% cholesterol, and 1% phosphatidylinositol 4,5-bisphosphate. Given that the function of many ion channels is heavily influenced by lipid composition, these differences could have contributed to the observed discrepancy. Regardless, our functional reconstitution experiments clearly demonstrate that LPC stimulates YOPRO-1 uptake in a dose-dependent manner, which forms the foundation of our interpretation. We have included this discussion in the revised manuscript. discrepancy.

Optional suggestions:

1. The manuscript refers to experiments conducted in human monocytes, but the actual cell line used was THP-1, a human monocytic leukemia cell line. This should be made clearer in the text to avoid confusion.

We are confused by this comment. Our manuscript states that we used "phorbol 12-myristate 13-acetate (PMA)-differentiated human THP-1 monocytes" which should clearly indicate which cells we used in the study.

1. The behavior of LPC at concentrations exceeding its Critical Micelle Concentration (CMC) (4–8 μM for 16:0 Lyso-PC) should be considered. The authors should discuss whether micelle formation affects the observed channel activation and how this might influence the interpretation of the results.

Our dose-response experiments, shown in Supplementary Fig. 3, suggest that CMC does not appear to affect their activity. While we are eager to understand the delivery method and action mechanisms of lysophospholipids, such studies are beyond the scope of the current work.

(This is a response to peer review conducted by Biophysics Colab on version 2 of this preprint.)

Consolidated Peer Review Report (15 January 2025)

Revised preprint

GENERAL ASSESSMENT

The revised manuscript by Henze et al. presents a novel and significant contribution to the field, demonstrating that lysophospholipids (LPC) act as endogenous activators of pannexin channels. The study provides compelling evidence that LPC activation of PANX1 and PANX2 channels facilitates the release of signaling molecules critical for immune responses, particularly in the context of inflammation and inflammasome activation. The removal of the cryo-EM data, addressing concerns about the binding site and mechanism of LPC activation, is a thoughtful revision that enhances the manuscript's focus. While the authors have addressed many of the previous critiques, key mechanistic questions remain regarding channel permeability and the specificity of LPC-induced metabolite release.

RECOMMENDATIONS

Essential revisions:

1. While the study suggests that LPC-induced PANX1 activation results in the release of metabolites via the pannexin channel pore, no direct evidence is provided to confirm this. It is possible that the release occurs through alternative pathways or that detected metabolites are by-products of other permeating substances. Additional experiments or a more thorough discussion of these possibilities would enhance the manuscript’s rigor.
2. The manuscript lacks data on the ion selectivity of LPC-activated PANX1, an important aspect for understanding the channel’s permeability profile. A comparison of the selectivity of LPC-induced PANX1 currents to those activated by other stimuli, such as C-terminal cleavage or depolarization, would clarify whether LPC induces a unique or comparable open state. The reviewers appreciate the technical challenges of these experiments, however, thus a more thorough discussion of the uncertainties may be appropriate.
3. The YO-PRO-1 uptake observed in the absence of LPC contradicts prior findings (e.g., Bayliss et al., eLife, 2021) that full-length PANX1 does not release large molecules like ATP or YO-PRO-1 under similar conditions. This discrepancy raises concerns about the interpretation of the proteoliposome experiments. Addressing this by ion selectivity experiments, or discussing differences in experimental conditions and reconstitution protocols, could provide clarity as well as strengthen the conclusions.

Optional suggestions:

1. The manuscript refers to experiments conducted in human monocytes, but the actual cell line used was THP-1, a human monocytic leukemia cell line. This should be made clearer in the text to avoid confusion.
2. The behavior of LPC at concentrations exceeding its Critical Micelle Concentration (CMC) (4–8 μM for 16:0 Lyso-PC) should be considered. The authors should discuss whether micelle formation affects the observed channel activation and how this might influence the interpretation of the results.

REVIEWING TEAM

Reviewed by:

Jorge Contreras, Professor, University of California, Davis, USA: electrophysiology and ion channel mechanisms

Wei Lü, Professor, Northwestern University, USA: ion channel mechanisms, X-ray crystallography and cryo-EM

Xiaofeng Tan, Research Fellow, NINDS, NIH, USA: structural biology (X-ray crystallography and cryo-electron microscopy) and ion channel mechanisms

Curated by:

Kenton J. Swartz, Senior Investigator, NINDS, NIH, USA

(This consolidated report is a result of peer review conducted by Biophysics Colab on version 2 of this preprint. Comments concerning minor and presentational issues have been omitted for brevity.)

Author response:

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

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors investigate the role of BEND2, a novel regulator of meiosis, in both male and female fertility. Huang et al have created a mouse model where the fulllength BEND2 transcript is depleted but the truncated BEND2 version remains. This mouse model is fertile, and the authors used it to study the role of BEND2 on both male and female meiosis. Overall, the full-length BEND2 appears dispensable for male meiosis. The more interesting phenotype was observed in females. Females exhibit a lower ovarian reserve suggesting that full-length BEND2 is involved in the establishment of the primordial follicle pool.

Strengths:

The authors generated a mouse model that enabled them to study the role of BEND2 in meiosis. The role of BEND2 in female fertility is novel and enhances our knowledge of genes involved in the establishment of the primordial follicle pool.

Weaknesses:

The manuscript extensively explores the role of BEND2 in male meiosis; however, a more interesting result was obtained from the study of female mice. Only a few experiments were performed using female mice, therefore, more experiments should be performed to complete the story of the role of BEND2 on female fertility. In addition, the title and abstract of the manuscript do not align with the story, as female fertility is only a small portion of the data compared to the male fertility section.

We appreciate the reviewer’s thoughtful summary, recognition of the strengths of our study, and constructive feedback. In the revised manuscript, we have performed additional experiments to enhance our understanding of the role of BEND2 in female gametogenesis. These new experiments provide further insights into the establishment of the ovarian reserve and the role of BEND2 in female fertility.

Additionally, we have rewritten the title, abstract, and introduction to better align with the content of the manuscript and to reflect the balance between the male and female fertility results. We believe these changes address the reviewer’s concerns and improve the overall clarity and focus of the manuscript.

Reviewer #1 (Recommendations For The Authors):

• I recommend that the authors re-organize their abstract and introduction to accurately reflect the manuscript's primary focus on male fertility. Right now, the title of the manuscript is misleading. The manuscript does not investigate reproductive aging; rather, it primarily describes the depletion of primordial follicle number. The mechanism behind this depletion and whether this phenotype accelerates reproductive aging, are not explored. Clarifying these points will help align the title and content of the manuscript more accurately.

We thank the reviewer for this suggestion. We agree that the original title and abstract did not fully capture the focus of the study. In response, we have rewritten the title, abstract, and introduction to better align with the results presented, focusing more clearly on the implications of the effects of the full-length BEND2 depletion for spermatogenesis and oogenesis. These revisions ensure that the title, the abstract, and the manuscript's introduction are now more accurately reflective of the work performed.

• Figure 1: I couldn't find the validation of the polyclonal antibody against BEND2 that the authors generated.

Regarding this query about the validation of the polyclonal antibody against BEND2, we apologize for any confusion. We would like to clarify that this validation is indeed presented in Figure 2 of our manuscript. To ensure this information is easily accessible, we have revised the text to explicitly mention the validation in Figure 2.

• Figure 2A: Could you provide the actual numbers for the weight of the mice testis?

In response to this question regarding Figure 2A and the weights of the mice testis, we have now included this data in a graph in Fig 2A and Table S1 and added this information in the results section.

• Figure 2C and D: I am confused by the fact that in the WB we can appreciate a high expression of the p75 protein, but the signal is very low in the IF (Figure 2D).

We thank the reviewer for raising this point. We acknowledge the apparent discrepancy between the strong p75 signal observed in the Western blot (Fig. 2C) and the weaker signal seen in the immunofluorescence (Fig. 2D). We think several factors could contribute to this difference, such as differences in sensitivity and detection methods, epitope accessibility, protein localization or differences in sample preparation, antibody affinity, and experimental conditions between Western blot and IF.

• In the same figure, the authors also mention that the p75 protein is functional. On what basis do they rely on reaching this conclusion?

We acknowledge that we cannot definitively confirm the functionality of the p75 protein. Our assumption was based on the observed fertility of the male mice and existing literature indicating that BEND2 is essential for completing meiosis (Ma et al., 2022). However, we understand the importance of clarity in our claims. To avoid any potential confusion, we have revised the sentence to read: "The p75 BEND2 protein—likely corresponding to an exon 11-skipped transcript—is present and might be functional in our mutant testis, based on the observed phenotype (see below)."

• The phenotype in females is very interesting. The authors conclude that BEND2 influences primordial follicle formation, oocyte quality, fertility, and reproductive aging by (1) performing follicle counts, (2) analyzing the litter size, and (3) analyzing meiotic progression. Given that the authors build their story around these experiments, I strongly encourage them to expand the section on female fertility, or reorganize the manuscript, or be more cautious with some of their conclusions. They might consider performing additional experiments such as:

- Oocyte quality: To determine whether BEND2 impacts oocyte quality, mice should be stimulated with hormones and oocyte quality should be analyzed (GV, MI, MII progression, spindle morphology and/or fertilization, and embryo development). Does the decrease in primordial follicles correlate with the number of ovulated oocytes, or is the impact only on oocyte quality?

We appreciate the reviewer's suggestion to assess the impact of BEND2 on oocyte quality. Following the reviewer’s recommendation, we stimulated three control and three mutant mice. We analyzed the number of ovulated oocytes, their fertilization rate, and the percentage of embryos that developed to the blastocyst stage. These new results are included in the revised manuscript (see Results section and new Table 1). Our analyses indicate that for all parameters assessed, control and mutant oocytes behaved similarly. Specifically, there were no significant differences in the number of ovulated oocytes, fertilization rates, or the ability of embryos to progress to the blastocyst stage between the control and mutant groups. These findings suggest that mutant oocyte quality is comparable to control mice of a similar age. We have incorporated these new results into the manuscript.

- Reproductive aging: A fertility trial would provide more information on whether BEND2 depletion triggers an acceleration of reproductive aging. In addition, the oldest mice used by the authors are 9 months old, and at this point, fertility has not declined yet.

We appreciate the reviewer's suggestion regarding the assessment of reproductive aging. However, we respectfully disagree with the assertion that fertility has not declined by 9 months of age. In our colony, we have observed a significant decline in fertility around 10 months of age. Specifically, out of 18 10-month-old female mice placed in breeding cages, we observed only three pregnancies within the first 30 days (N.N. and I.R., data not published). Based on these observations, we determined that fertility begins to decline around this age in our colony, which informed our decision to use 9-month-old mice as the oldest age group for our analysis. Thus, this age is appropriate for evaluating the potential effects of BEND2 depletion on reproductive aging in our specific mouse population.

- The observation that the primordial follicle pool is already diminished in mice that are 1 week old is very interesting. Some experiments that the authors could perform to figure out the mechanism are: (1) Analyzing apoptosis. Are the primordial follicles dying during the pool's establishment, or is this an ongoing apoptotic process throughout the mice's lifespan? (2) If the authors still have ovaries from mice younger than 1 week of age (when the primordial pool is forming), they could perform DDX4 staining and quantify the number of oocytes in follicles and the total number of oocytes. These experiments would provide mechanistic insights into whether BEND2 impacts the formation of the primordial follicle pool or if the pool forms but is then depleted.

We appreciate the reviewer's suggestion to further explore the mechanism behind the reduced primordial follicle pool. In response, we have analyzed the number of DDX4positive cells (DDX4 labels oocytes) in newborn mutant and wild-type animals. Our results show that mutant ovaries contain significantly fewer oocytes compared to controls (see new Fig. 5). This finding supports the hypothesis that BEND2 is critical for the establishment of a normal ovarian reserve. We are grateful for this suggestion, as these additional data reinforce our conclusion that BEND2 is required to determine a normal ovarian reserve in mice.

• What is the red signal in Supplementary Figure 1C?

This image depicts the BEND2 staining pattern in 16 days post-coitum (dpc) wild-type mouse ovaries. To clarify this and prevent any confusion, we have updated the figure legend to explicitly state that the sample shown is from a wild-type mouse.

• Please spell out the full term of all the acronyms.

We apologize for the oversight in not fully spelling out some acronyms in the original manuscript. We have carefully reviewed the entire manuscript and have ensured that all acronyms are now spelled out in full upon their first use in the revised version. We want to thank the reviewer for bringing this to our attention.

• Is Line-1 also dysregulated in the ovary? This was one of the main findings from the male part. It would be interesting to perform the same analysis in the ovary since Line1 has a role in establishing the ovarian reserve (PDMI: 31949138).

We thank the reviewer for this insightful suggestion. We have analyzed the number of LINE1 and SYCP3-positive cells in wild-type and mutant newborn ovaries (new Fig. S4). Our results show no significant difference between the two genotypes, suggesting that LINE-1 is not dysregulated in newborn Bend2 mutant oocytes. These findings indicate that, at least in the context of the newborn ovary, LINE-1 does not appear to be affected by BEND2 depletion.

Reviewer #2 (Public Review):

In their manuscript entitled "BEND2 is a crucial player in oogenesis and reproductive aging", the authors present their findings that full-length BEND2 is important for repair of meiotic double strand break repair in spermatocytes, regulation of LINE-1 elements in spermatocytes, and proper oocyte meiosis and folliculogenesis in females. The manuscript utilizes an elegant system to specifically ablate the full-length form of BEND2 which has been historically difficult to study due to its location on the X chromosome and male sterility of global knockout animals.

While the manuscript is an overall excellent addition to the field, it would significantly benefit from a few additional experiments, as well as some additional clarification/elaboration.

The claim that BEND2 is required for ovarian reserve establishment is not supported, as the authors only look at folliculogenesis and oocyte abundance starting at one week of age, after the reserve is formed. Analysis of earlier time points would be much more convincing and would parse the role of BEND2 in the establishment vs. maintenance of this cell population. In spermatocytes, the authors demonstrate a loss of nuclear BEND2 in their mutant but do not comment on the change in localization (which is now cytoplasmic) of the remaining protein in these animals. This may have true biological significance and a discussion of this should be more thoroughly explored.

We thank the reviewer for their thoughtful feedback and constructive suggestions to improve our manuscript.

In response to the comment regarding the establishment of the ovarian reserve, we have now analyzed Bend2 mutant and control newborn ovaries. Our results show a significant reduction in the number of DDX4-positive cells in mutant ovaries compared to controls. These findings demonstrate that BEND2 is required for the establishment of the ovarian reserve, as the reduction is evident at birth.

Regarding the cytoplasmic staining of BEND2 in mutant spermatocytes, we did perform secondary-antibody-only controls using goat anti-rabbit Cy3 to address the specificity of the signal. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the cytoplasmic signal is nonspecific. Therefore, we do not believe this represents a meaningful change in the localization of BEND2 protein in the mutants. We have clarified this in the revised manuscript to address this point.

We hope these additional experiments and clarifications strengthen the manuscript and address the reviewer’s concerns.

Reviewer #2 (Recommendations For The Authors):

Major points:

(1) The title of the manuscript does not accurately capture the content of the work. The vast majority of the data presented here is from the male, which is not reflected at all in the title - perhaps considering revising it?

Thank you for your valuable suggestion. We agree that the original title did not fully reflect the focus of the manuscript. In response, we have revised the title, along with the abstract and introduction, to more accurately capture the content of the study and the emphasis on the male data. These changes ensure that the manuscript more clearly aligns with the results presented.

(2) In Figure 2D, the authors demonstrate that WT BEND2 expression and localization are lost in the mutant, but staining is still apparent, just in the cytoplasm. Did the authors perform secondary-antibody-only controls to determine if this was background staining or real staining? If real, can they comment on the change in localization of the protein?

We thank the reviewer for this insightful question. We have indeed performed secondary antibody-only controls using goat anti-rabbit Cy3. The staining observed in the Bend2 mutants closely resembles background staining, suggesting that the signal in the cytoplasm is not specific. Therefore, we do not believe this staining represents any real or meaningful expression of the BEND2 protein in the mutants.

(3) In Figure S2A, the authors show Ku70 staining and describe that it is similar between the genotypes, but - to my eye - it looks quite distinctly different. It appears to stain in patches in WT SYCP3+ spermatocytes, versus staining in patches in the more mature, SYCP3- germ cells closer to the lumen in the mutant. Can the authors please clarify, or provide arrows to point which foci they are referring to?

We apologize for the confusion caused by the image provided in the original submission. Upon review, we realized that the mutant image was not fully representative of the staining pattern observed in the majority of mutant samples. We have replaced this image with a new one in the revised manuscript, which more accurately reflects the similarity in Ku70 staining between wild-type and mutant testis. In this updated Figure S2, we have also included arrowheads to indicate the relevant foci, making it clearer to the reader. We have updated the figure legend to correspond with these changes as well.

(4) The authors state that BEND2 is "required to establish the ovarian reserve during oogenesis" but this has not been demonstrated. The authors do show a reduced density of primordial follicles at one week of age. While this is compelling data, the ovarian reserve is established earlier in the mouse, around postnatal days 0-1, so it is not clear from this manuscript whether BEND2 is required for the maintenance of this population after PND1, leading to reduced numbers by 1 week of age, OR if it is required for the establishment of this population, which would result in reduced numbers of oocytes around the time of birth. This is a critical experiment that should be performed in order to determine which of these possibilities is likely the case. Ideally, looking at embryonic through early postnatal time points during ovarian development would be very helpful.

We thank the reviewer for raising this important point. As mentioned earlier in response to Reviewer 1, we have performed the experiment suggested by Reviewer 2 and analyzed the number of DDX4-positive cells in newborn ovaries. Our results show that Bend2 mutant ovaries have fewer oocytes at birth than wild-type controls (Fig. 5H). This finding reinforces our conclusion that BEND2 is indeed required to establish the ovarian reserve, as the reduction in oocyte number is evident at the time of birth. We agree that this additional data strengthens our original claim, so we have included these results in the revised manuscript.

Reviewer #3 (Public Review):

Summary:

Huang et al. investigated the phenotype of Bend2 mutant mice which expressed a truncated isoform. This mutant male showed increasing apoptosis due to unrepaired double-strand breaks. However, this mutant male has fertility, and this enabled them to analyze Bend2 function in females. They revealed that Bend2 mutation in females showed decreasing follicle numbers which leads to loss of ovarian reserve.

Strengths:

Since their Bend2 mutant males were fertile, they were able to analyze the function of Bend2 in females and they revealed that loss of Bend2 causes less follicle formation.

Weaknesses:

Why the phenotype of their mutant male is different from previous work (Ma et al.) is not clear enough although they discuss it.

We appreciate the reviewer’s comment regarding the differences between our Bend2 mutant male phenotype and the previously reported phenotype by Ma et al., 2022. We believe this discrepancy is due to the fact that the Bend2 locus encodes two BEND2 isoforms: p140 and p80. In contrast to the previous study, where both proteins were ablated by mutation employed (the deletion of exons 12 and 13), our exon 11 deletion specifically ablates p140 expression while allowing the expression of p80 in the testis.

Based on the distinct phenotypes observed in the two Bend2 mutant mouse models, we hypothesize that p80 is sufficient to fulfill BEND2’s roles in meiosis, which could explain why our Bend2 mutant males remain fertile. We have rewritten the relevant sections in the results and discussion to better articulate this hypothesis and clarify the potential mechanisms behind the observed phenotypic differences.

We hope these clarifications and additional details adequately address the reviewer’s concerns.

Reviewer #3 (Recommendations For The Authors):

(1) The authors showed that Bend2 mutant females had decreased fertility. This may be due to decreased ovarian reserve. Did the authors check if the mutant mice decreased or lost fertility faster than WT? If the authors have the data, please refer to it in the manuscript.

We followed the breeding performance of a small number of control and Bend2 mutant females, and preliminary observations suggested no clear differences between the two groups. However, due to the limited sample size, we felt that these data were not conclusive enough to be included in the manuscript. We agree that a more thorough analysis of fertility decline over time would be valuable, and we plan to address this question in a future study.

(2) In Figure 1 A, there is no exon1 in the upper figure.

We thank the reviewer for pointing this out. We have revised Figure 1A to include exon 1 and ensure the schematic is accurate. The updated figure is included in the revised version of the manuscript.

(3) Figure 3A, it would be nice to show several tubules of the testis section as well as an enlarged one.

Following the reviewer's advice, we have revised Figure 3A to include new images showing several tubules and an enlarged view of one section of a tubule. These updates are included in the revised manuscript to better represent the testis sections.

(4) Please be consistent with the format of the graph, especially Supplemental figures 2C and 4D.

We have revised the figures, including Supplemental Figures 2C and 4D, to ensure consistency in the format throughout the manuscript. We have made modifications to the figures to align them more closely and improve the overall presentation.

这段文字对 Raoul Hausmann 的雕塑作品《Der Geist unserer Zeit – Mechanischer Kopf》（意为《我们时代的精神——机械头》）进行了深入的分析，揭示了其中复杂的哲学和政治含义。让我们一步步解析这段话的含义。

1. 哲学背景与黑格尔的思想

文本提到，Hausmann 的雕塑作品特别地唤起了德国哲学家 乔治·威廉·弗里德里希·黑格尔（Georg Wilhelm Friedrich Hegel） 的思想。黑格尔的哲学体系认为，“一切皆精神”（“everything is mind”）。在黑格尔的哲学中，历史和社会的演进由“绝对精神”推动，精神是现实和意识的最终主导力量。黑格尔的思想体系强调了心灵、思想在理解世界的核心地位。

2. 黑格尔与马克思的关系

黑格尔的思想深刻影响了后来的哲学家，尤其是 卡尔·马克思。马克思对黑格尔的思想进行了批判，并在其著作中提出了“唯物主义”的立场，主张历史的发展是由物质条件（如经济和生产方式）推动的，而非单纯的“精神”或“思想”主导社会的进程。马克思的这种观点对 Hausmann 的雕塑有直接影响。

3. 雕塑的马克思主义反转

在此语境下，Hausmann 的雕塑可以被看作是对黑格尔思想的“激进马克思主义式的反转”。黑格尔认为，思想和精神是社会和历史发展的核心力量，而 Hausmann 通过其雕塑《机械头》挑战这一观点。雕塑表现的不是一个主导思想的自由精神，而是一个“思想”受到外部物质力量决定的头部。这是通过雕塑中将机械部件直接固定在头部上，形象地表现了思想（头脑）受制于外部物质力量的主题。

4. 对西方文化更深层次的批判

然而，Hausmann 的雕塑不仅仅是对黑格尔思想的反动，它还触及了西方文化的其他深层次假设，揭示了西方哲学和文化中对“头部”作为理性象征的长期迷信。在传统西方文化中，头部和大脑被视为理性的座位，是人类智慧和思想的象征。这种观念在欧洲的肖像画艺术中也有深刻的体现，肖像画往往强调人物头部的表现，象征着理性和个人的内心世界。

Hausmann 的雕塑则颠覆了这种传统观念。他通过表现一个“被物质力量渗透和支配的头部”，直接挑战了头部作为理性象征的理念，揭示了思想并非独立于物质世界而存在。相反，思想和理性是受到外部力量和物质条件的支配。

5. 雕塑的现代性与力量

这件作品之所以被认为是“现代杰作”，正是因为它挑战了传统的哲学观念，尤其是对理性和个体主体性的传统理解。Hausmann 的雕塑不仅批判了西方哲学的理性至上主义，还通过其物质化的视觉表现，揭示了现代社会中个体思维被外部物质力量、社会机制甚至机器化力量所渗透和控制的现实。

总结

通过这件雕塑，Hausmann 传达了一个复杂的批判信息：他不仅挑战了黑格尔关于精神主导一切的观念，也揭示了西方文化中关于理性、思想与物质世界之间关系的根深蒂固的误解。《机械头》作品通过展示一个被外部物质控制的头部，反思并颠覆了西方传统文化中的理性观念，提出了思想和人的存在无法脱离物质世界的控制。

DOI: 10.1158/0008-5472.CAN-23-2785

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

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DOI: 10.1158/0008-5472.CAN-23-2785

Resource: Aperio ImageScope (RRID:SCR_020993)

Curator: @scibot

SciCrunch record: RRID:SCR_020993

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DOI: 10.1158/0008-5472.CAN-23-2785

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @scibot

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DOI: 10.1158/0008-5472.CAN-23-2785

Resource: Developmental Therapeutics Program (RRID:SCR_003057)

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SciCrunch record: RRID:SCR_003057

What is this?

Voici un sommaire de l'entretien avec Grégoire Borst, avec les horodatages correspondants :

• Introduction []
  • L'animateur présente Grégoire Borst, professeur de psychologie du développement et de neurosciences cognitives, directeur du laboratoire de psychologie du développement et de l'éducation de l'enfant (Lapsid), un laboratoire du CNRS.
  • Borst a fait sa thèse en psychologie en 2005, puis a passé 4 ans en post-doctorat à Harvard, avant de revenir en France en 2010.
  • Le Lapsid est le premier laboratoire français en psychologie scientifique, créé il y a 135 ans.
• Recherches du laboratoire []
  • Le laboratoire étudie le rôle des mécanismes de contrôle, des automatismes, de la détection de conflits et de doutes dans le développement cognitif et socio-émotionnel de l'enfant et de l'adolescent, ainsi que dans les apprentissages scolaires, en combinant approches comportementales et neuroimagerie.
  • L'objectif est d'intégrer différents niveaux d'explication, du génétique aux contextes sociaux et culturels.
  • Borst explique qu'il s'intéresse aux différences entre les individus et que l'adolescence est la période où l'hétérogénéité est la plus forte.
  • Il souligne l'importance de combiner les approches de la psychologie, des neurosciences, de la linguistique, de l'informatique, de la sociologie, de l'économie et de la didactique pour comprendre le développement de l'enfant.
• Interventions et publications []
  • Borst intervient auprès du monde de l'éducation et est membre du bureau international de l'éducation à l'UNESCO.
  • Il est l'auteur de nombreux articles scientifiques et de livres, dont "Le cerveau et les apprentissages" et "Explore ton cerveau" avec Olivier Houdé.
  • Il mentionne également "Mon cerveau questions-réponses" pour les moins de 10 ans et "C'est pas moi, c'est mon cerveau" avec Mathieu Cassoti pour les adolescents.
  • Ce dernier ouvrage décrypte le fonctionnement du cerveau des adolescents à travers 14 situations quotidiennes, en utilisant des jeux, des quiz et des récits.
• Le développement cérébral à l'adolescence []
  • Le cerveau se développe très tôt, dès les premiers jours après la fécondation, et continue de se transformer longtemps après la naissance.
  • Le cerveau n'est pas structuré comme un cerveau adulte avant 20 à 25 ans.
  • La plasticité cérébrale permet aux cerveaux de se transformer tout au long de la vie.
  • Une étude sur l'apprentissage du jonglage montre comment l'acquisition de nouvelles compétences transforme le cerveau.
  • Le cerveau humain contient environ 86 milliards de neurones connectés par 1 million de milliards de connexions.
  • L'adolescence est une période de forte plasticité cérébrale qui dure de 10 à 12 ans.
  • L'entrée dans la puberté marque le début de cette période, avec la réouverture du filet périneuronal qui augmente la plasticité cérébrale.
  • Le développement du cerveau est asynchrone, avec le système limbique (émotions, récompense) qui mature plus tôt que le cortex préfrontal (régulation).
  • Ce décalage rend difficile la régulation des émotions et l'impulsivité chez les adolescents.
• Gratification différée et prise de risque []
  • La tâche de gratification différée (test du marshmallow) montre la capacité des enfants à se maîtriser.
  • La capacité à différer son plaisir est un prédicteur de la réussite éducative future, plus que le milieu social ou le QI.
  • L'adolescence est une période de mortalité plus élevée que l'enfance en raison des risques suicidaires et des conduites à risque.
  • Les adolescents sont plus sensibles aux récompenses que les enfants et les adultes, comme le montre une expérience en IRM.
  • Les adolescents évaluent le ratio coût-bénéfice différemment, en accordant plus d'importance à la récompense, notamment sociale.
  • Les liens sociaux avec les pairs deviennent primordiaux à l'adolescence.
  • Les comportements à risque, comme le fire challenge, sont une façon de maximiser sa position sociale.
  • L'adolescence est une période où il est difficile de réguler ses émotions, comme une "cocotte-minute émotionnelle" sans valve.
  • Les adolescents sont plus orientés vers les récompenses immédiates, surtout en groupe.
  • Une expérience de conduite simulée montre que les adolescents prennent plus de risques lorsqu'ils sont observés par leurs pairs.
  • Les campagnes de prévention doivent tenir compte de la psychologie des adolescents.
• Influence sociale et altruisme []
  • Les adolescents sont très influençables par les autres et plus sujets au conformisme social.
  • L'altruisme devient plus stratégique à l'adolescence, avec un partage de ressources surtout avec les amis.
  • Le cerveau adolescent a une grande capacité d'apprentissage, mais est aussi très vulnérable.
  • La consommation d'alcool avant 15 ans a des effets irréversibles sur le développement cérébral, tout comme le cannabis, qui peut entraîner une perte de points de QI.
  • Après la COVID, 40% des adolescents présentent des symptômes dépressifs.
• Les écrans et les adolescents []
  • L'idée que les écrans sont responsables de la baisse du QI, des troubles de l'attention, des difficultés de concentration, des problèmes psychologiques et de l'addiction est fausse.
  • Il n'y a pas d'addiction aux écrans, ni de lien direct entre les réseaux sociaux et la dépression.
  • Les écrans peuvent même avoir un effet positif sur le développement de l'intelligence entre 8 et 10 ans et le développement de l'empathie.
  • Les écrans ont surtout un impact négatif sur le sommeil et la sédentarité.
  • La lumière artificielle des écrans perturbe la sécrétion de mélatonine, l'hormone du sommeil.
  • Il est recommandé de couper les écrans au moins une heure avant de se coucher.
  • Les rythmes scolaires ne sont pas adaptés au rythme de sommeil des adolescents, qui ont un décalage de phase de 2 heures.
  • Il faudrait décaler la première heure de cours d'une heure pour respecter leur rythme physiologique.
  • Un rapport de l'Élysée sur les enfants et les écrans propose 29 recommandations.
• Questions et réponses []
  • Les hormones et les transformations physiques expliquent en partie la fatigue et la maladresse des adolescents.
  • Les études sur les écrans ne sont pas encore assez probantes, et la question du contenu est complexe.
  • L'adolescence cérébrale est universelle, mais des différences culturelles et sociales peuvent exister.
  • L'impact des écrans sur les relations sociales est nuancé.
  • Il ne faut pas jouer sur les récompenses pour motiver les ados, mais plutôt développer leur motivation intrinsèque.
  • Les recommandations des 3-6-9-12 sont des repères mais ne sont pas basées sur des études scientifiques solides.
  • Il faut surtout un parcours de parentalité en France qui explique comment se développe un enfant et un adolescent.
  • Il est essentiel de développer les compétences psychosociales dès l'enfance pour prévenir les problèmes de santé mentale.

There are a number of ways to hook the reader: 1) relate the discussion to something about creativity that is in the news. Are there current popular artists or art or other creative work that is on the public radar that you can use as a means of starting the conversation? 2) You can attempt to define creativity is unusual or unorthodox terms --what is you personal definition and how might it differ from common and conventional ones? 3) you can relate creativity to education or technology. 4) You can tell a personal story of creativity.

Author response:

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

Reviewing Editor Comment:<br /> Please note that all three reviewers suggested this manuscript would best fit as a resource paper at eLife.

Reviewer #1 (Public review):

Summary:

This impressive study presents a comprehensive scRNAseq atlas of the cranial region during neural induction, patterning, and morphogenesis. The authors collected a robust scRNAseq dataset covering six distinct developmental stages. The analysis focused on the neural tissue, resulting in a highly detailed temporal map of neural plate development. The findings demonstrate how different cell fates are organized in specific spatial patterns along the anterior-posterior and medial-lateral axes within the developing neural tissue. Additionally, the research utilized high-density single-cell RNA sequencing (scRNAseq) to reveal intricate spatial and temporal patterns independent of traditional spatial techniques.

The investigation utilized diffusion component analysis to spatially order cells based on their positioning along the anterior-posterior axis, corresponding to the forebrain, midbrain, hindbrain, and medial-lateral axis. By cross-referencing with MGI expression data, the identification of cell types was validated, affirming the expression patterns of numerous known genes and implicating others as differentially expressed along these axes. These findings significantly advance our understanding of the spatially regulated genes in neural tissues during early developmental stages. The emphasis on transcription factors, cell surface, and secreted proteins provides valuable insights into the intricate gene regulatory networks underpinning neural tissue patterning. Analysis of a second scRNAseq dataset where Shh signaling was inhibited by culturing embryos in SAG identified known and previously unknown transcripts regulated by Shh, including the Wnt pathway.

The data includes the neural plate and captures all major cell types in the head, including the mesoderm, endoderm, non-neural ectoderm, neural crest, notochord, and blood. With further analyses, this high-quality data promises to significantly advance our understanding of how these tissues develop in conjunction with the neural tissue, paving the way for future breakthroughs in developmental biology and genomics.

Strengths:

The data is well presented in the figures and thoroughly described in the text. The quality of the scRNAseq data and bioinformatic analysis is exceptional.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #2 (Public review):

Summary:

Brooks et al. generate a gene expression atlas of the early embryonic cranial neural plate. They generate single-cell transcriptome data from early cranial neural plate cells at 6 consecutive stages between E7.5 to E9. Utilizing computational analysis they infer temporal gene expression dynamics and spatial gene expression patterns along the anterior-posterior and mediolateral axis of the neural plate. Subsequent comparison with known gene expression patterns revealed a good agreement with their inferred patterns, thus validating their approach. They then focus on Sonic Hedgehog (Shh) signalling, a key morphogen signal, whose activities partition the neural plate into distinct gene expression domains along the mediolateral axis. Single-cell transcriptome analysis of embryos in which the Shh pathway was pharmacologically activated throughout the neural plate revealed characteristic changes in gene expression along the mediolateral axis and the induction of distinct Shh-regulated gene expression programs in the developing fore-, mid-, and hindbrain.

Strengths:

This manuscript provides a comprehensive transcriptomic characterisation of the developing cranial neural plate, a part of the embryo that to my knowledge has not been extensively analysed by single-cell transcriptomic approaches. The single-cell sequencing data appears to be of high quality and will be a great resource for the wider scientific community. Moreover, the computational analysis is well executed and the validation of the sequencing data using published gene expression patterns is convincing. Taken together, this is a well-executed study that describes a relevant scientific resource for the wider scientific community.

Weaknesses:

Conceptually, the findings that gene expression patterns differ along the rostrocaudal, mediolateral, and temporal axes of the neural plate and that Shh signalling induces distinct target genes along the anterior-posterior axis of the nervous system are more expected than surprising. However, the strength of this manuscript is again the comprehensive characterization of the spatiotemporal gene expression patterns and how they change upon ectopic activation of the Shh pathway.

Reviewer #3 (Public review):

Summary:

The authors performed a detailed single-cell analysis of the early embryonic cranial neural plate with unprecedented temporal resolution between embryonic days 7.5 and 8.75. They employed diffusion analysis to identify genes that correspond to different temporal and spatial locations within the embryo. Finally, they also examined the global response of cranial tissue to a Smoothened agonist.

Strengths:

Overall, this is an impressive resource, well-validated against sets of genes with known temporal and spatial patterns of expression. It will be of great value to investigators examining the early stages of neural plate patterning, neural progenitor diversity, and the roles of signaling molecules and gene regulatory networks controlling the regionalization and diversification of the neural plate.

Weaknesses:

The manuscript should be considered a resource. Experimental manipulation is limited to the analysis of neural plate cells that were cultured in vitro for 12 hours with SAG. Besides the identification of a significant set of previously unreported genes that are differentially expressed in the cranial neural plate, there is little new biological insight emerging from this study. Some additional analyses might help to highlight novel hypotheses arising from this remarkable resource.

We thank all three reviewers for their thoughtful and constructive public reviews and believe they nicely capture the contributions of our study. We agree that this article represents a valuable resource for the community and agree with its designation as a Tools and Resources article.

We also thank the reviewers for their useful suggestions for improving the manuscript. In addition to addressing most of their comments, described below, we note that we have changed midbrain-hindbrain boundary (MHB) to rhombomere 1 (r1) throughout the paper and in Tables S4, S7, S10, and S11, as this designation is more closely aligned with the literature on this region. In addition, we added the anterior-posterior and mediolateral cluster identities from our wild-type analysis for the genes that were differentially expressed in SAG-treated embryos in Table S11. Lastly, we have added a new figure (Figure 5—figure supplement 2), as suggested by Reviewer 2, in which we compare our results with the published expression of genes in neural progenitor domains along the dorsal-ventral axis of the spinal cord.

Reviewer #1 (Recommendations for the authors):

I have a few small suggestions for improving the presentation of the data.

(1) It would be helpful to show illustrations and embryo images of all the stages utilized in the analysis in Figures 1A and B.

(2) It was difficult to distinguish all the different colors in Figures 3B and 4B. Could you label, as in Figure 4, supplements 1D, F?

(3) I was confused by the position of the color code key for Figure 7D-J, thinking it belonged to panels B and C. Could you put it under the figure/heatmap key so that it is clearly linked to panels D-J?

Thank you for these suggestions. We have incorporated the third suggestion to improve readability, but were not able to make the first two changes due to space limitations.

Reviewer #2 (Recommendations for the authors):

I only have a couple of minor additional suggestions/questions for the authors:

(1) The authors state that nearly half of the transcripts they found as differentially regulated in SAG-treated embryos were also characterized as spatially regulated in the wild-type embryos. It would be great if the authors could provide more detail here. How many of the transcripts that are differentially regulated along the mediolateral axis of the wild-type are characterized as differentially regulated in the SAG-treated embryos? How does this further break down into where these genes are expressed along the mediolateral and the anterior-posterior axes? I am aware that the authors answer some of these questions already by providing examples, but a more systematic characterisation would be appreciated here.

We have updated Table S11 to include the anterior-posterior and mediolateral cluster identities of differentially expressed genes in SAG-treated embryos, where applicable. In addition, we have added more discussion of the genes from our SAG analysis that were also found to be spatially patterned in wild-type embryos to the fourth paragraph of the last results section.

(2) Related to the previous question, the authors nicely demonstrate that SAG treatment of embryos causes many transcriptional changes, including the expression/repression of several transcription factors well-known to mediate spatial patterning, raising the question of which of these effects are directly due to gene regulation by the Shh pathway and which effects are secondary consequences of transcriptional changes of other transcription factors. Similarly, the authors' results also suggest that some genes are only induced in specific parts along the neuraxis, raising the question of why. The authors could attempt some type of regulon-interference approaches to identify further candidates that may mediate these effects.

This is an excellent suggestion for a future extension of this work, as we agree that validation of the predicted SHH targets, including which targets are direct, indirect, or region-specific, would be required to evaluate the predictions of this scRNA-seq analysis.

(3) The authors report that they observed 'a previously unreported inhibition of Scube2' upon SAG treatment of the embryos. At least in the spinal cord Scube2 is well-known to be expressed at a distance from the source of Shh secretion (e.g. Kawakami et al. Curr. Biol. 2005), thus the direct or indirect repression by Shh signalling is strongly expected. Moreover, a recent preprint (Collins et al. bioRxiv, https://doi.org/10.1101/469239 ) suggests that the interaction between Shh and Scube2 can mediate the scale-invariance of Shh patterning. Of note, the authors of this preprint also state that 'upregulation of Shh represses scube2 expression while Shh downregulation increases scube2 expression thus establishing a negative feedback loop.'

Thank you for this suggestion. We have added these references.

(4) The authors partition genes based on different diffusion components as being differentially expressed along the mediolateral axis. However, starting from ~e8.5, neural progenitors in the neural tube can be partitioned based on the expression of well-characterised combinatorial sets of transcription factors into molecularly defined progenitor domains that subsequently give rise to functionally distinct types of neurons. How much of this patterning process can the authors capture with their diffusion component analysis and does their data also allow them to capture these finer-grained differences in gene expression along the mediolateral and prospective dorsal-ventral axis of the neural tube that are known to exist?

This is a very interesting point. We have added a new figure showing UMAPs of the E8.5-9.0 cranial neural plate for a subset of 29 genes (described in Delile et al., 2019) that define distinct neural progenitor domains along the dorsal-ventral axis of the spinal cord (Figure 5—figure supplement 2). We observed that 18 of 20 genes that were detected in the midbrain/r1 region in our dataset were expressed in broad domains along the mediolateral axis of the cranial neural plate that were roughly consistent with their expression domains along the dorsal-ventral axis of the spinal cord. Of these 18 genes, 14 were patterned along both anterior-posterior and mediolateral axes, 2 were patterned only along the mediolateral axis, and 2 were patterned only along the anterior-posterior axis. These results suggest a general correspondence between mediolateral patterning in the cranial neural plate and dorsal-ventral patterning in the spinal cord. However, less refinement of these domains along the mediolateral axis was observed in the cranial neural plate, possibly because the relatively early, pre-closure stages captured by our dataset may be before the establishment of secondary feedback systems that lead to fine-scale patterning of mutually exclusive neural precursor domains. These results are described in the last paragraph of the results section titled “An integrated framework for analyzing cell identity in multiscale space.”

(5) The authors state that they will not only make the raw sequencing data but also the processed intermediate data files available. This is greatly appreciated as it strongly facilitates the re-use of the data. However, it would be also appreciated if the authors made the computational code publicly available that was used to analyze the data and generate the figure panels in the manuscript.

We have deposited the processed h5ad files in the GEO database, accession number GSE273804. Additionally, we have made interactive python notebooks available with the code used to analyze gene expression and generate the figures in this study, as well as code used to automatically generate customizable links to gene expression images in the Mouse Genome Informatics Gene Expression database, on our lab GitHub page (https://github.com/ZallenLab). We have updated the Data availability section to reflect these changes.

Reviewer #3 (Recommendations for the authors):

(1) Considering that individual progenitor domains in the developing neural tube are typically sharply delineated with few cells exhibiting mixed identities, it is interesting that clustering of single-cell data results in a largely continuous “cloud” of cells. Is this because the early neural plate cells have not yet crystallized their identity, or would clustering based on a smaller set of genes that exhibit high variance across only neural plate cells result in improved granularity, allowing for better characterization and quantification of distinct progenitor subtypes?

Thank you for raising this interesting point. The apparent continuity of gene expression in the cranial neural plate could reflect a gene signature shared by cranial neural plate cells and that cells may not be extensively regionalized into unique populations at these early stages. We now discuss these possibilities in the third paragraph of the discussion.

(2) Can the authors clarify how neural plate cells were identified and how they were distinguished from the anterior epiblast?

Cell typing was performed by supervised clustering based on known markers of fate. Cranial neural plate cells were identified by their expression of pan-neural factors (Sox2 and Sox3), early or late neural plate markers (Cdh1 or Cdh2), and the lack of markers associated with non-neural ectodermal cell fates (Grhl2, Krt18, Tfap2a) or other cell types (Ets1, T, Tbx6). Full gene sets used to identify all cell types in our analysis are provided in Supplementary Table 13.

(3) Did the study identify cells with cranial placode identity? Cranial placodes emerge during the same period, and it would be useful to highlight them in Figure 1.

Thank you for highlighting this point. Examination of the early placode markers Six1 and Eya1 indicates that cranial placode cells are a subset of the cells in PhenoGraph cluster 17 in our full dataset Figure 1—figure supplement 1). We now mention this along with other cell types of interest in the last paragraph of the discussion.

(4) It could be interesting to provide more information about the novel genes identified as differentially expressed along the AP or mediolateral axes. Do they belong to gene families that were not previously implicated in neural patterning, or do they point to novel biological mechanisms controlling neural patterning?

Diverse gene families are represented by the genes that are patterned along the anterior-posterior and mediolateral axes of the cranial neural plate at these stages, likely due to the large number of genes that are spatially patterned in this tissue. Further investigation of the biological mechanisms suggested by these patterns is an important direction for future work, both in terms of molecularly classifying the genes identified as well as directly investigating their roles in neural patterning using genetic analysis.

(5) It would be helpful to discuss how the data presented here compare to other relevant single-cell analyses, such as PMC10901739. This would help to highlight aspects that are unique to this study.

We have added this reference as well as an earlier study from these authors and we discuss how our study complements this work in the introduction.

(6) The inclusion of single-cell data from control embryos that were cultured for 12 hours is of great interest. The authors should identify the set of genes that are deregulated in cultured cells and, taking advantage of their detailed temporal series, examine whether the maturation of cultured embryos progresses normally or whether there are genes that fail to mature correctly in vitro.

We agree that an analysis of the impact of ex vivo culture on gene expression would be useful. However, the large difference in the number of cells in our wild-type and cultured embryo datasets, as well as the lack of time-course data for the cultured embryos, could make a comparison between our current cultured and non-cultured embryo datasets difficult to interpret.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The authors studied how hippocampal connectivity gradients across the lifespan, and how these relate to memory function and neurotransmitter distributions. They observed older age with less distinct transitions and observed an association between gradient de-differentiation and cognitive decline.

This is overall an innovative and interesting study to assess gradient alterations across the lifespan and its associations to cognition.

The paper is well-written, and the methods appear sound and thoughtful. There are several strengths, including the inclusion of two independent cohorts, the use of gradient mapping and alignment techniques, and an overall sound statistical and analysis framework. There are several areas for potential improvements in the paper, and these are listed below:

We thank the Reviewer for their positive assessment and summary of our work. We address each of the Reviewer’s comments below, and outline the revisions we have made to the manuscript based on the Reviewer’s suggestions.

(1) The reported D1 associations appear a bit post-hoc in the current work and I was unclear why the authors specifically focussed on dopamine here, as other transmitter systems are similar present at the level of the hippocampus and implicated in aging.

Other neurotransmitter systems may indeed be relevant in the context of hippocampal function in aging. In this study, however, we included a specific research question about the DA D1 receptor (D1DR) based on previous research 1) emphasizing the role of DA neuromodulation in maintaining functional network segregation in aging to support cognition (Pedersen et al., 2023), 2) reporting heterogeneous distribution of DA markers across the hippocampus, supporting efficient modulation of distinct behaviors (Dubovyk & ManahanVaughan, 2019; Edelmann & Lessmann, 2018; Gasbarri et al., 1994; Kempadoo et al., 2016), and 3) demonstrating the spatial distribution of D1DRs as varying across neocortex along a unimodal-transmodal gradient (Pedersen et al., 2024). To which degree this variation might be reflected in cortico-hippocampal connectivity, however, remained to be investigated. As such, one of the study’s specific aims was to evaluate the spatial distribution of D1DRs as a molecular correlate of the hippocampus’ functional organization. Importantly, we were interested in mapping associations between individual differences in the organization of connectivity and D1DRs. This was uniquely enabled by utilizing the DyNAMiC sample, as it includes structural and functional MRI data in combination with D1DR PET in the same individuals across the adult lifespan (n=180). However, after observing significant spatial correspondence between functional organization and D1DR expressed by the second hippocampal gradient (G2), we did indeed perform complimentary analyses with group-averaged data of additional dopamine markers (D2DR from a subsample of our participants, as well as DAT and FDOPA from open sources) to test the generalizability of the original finding. Taken together, the original analyses based on subject-level data and complimentary group-level analyses provided support for the interpretation of G2 as a dopaminergic mode.

We have updated the manuscript to clarify the focus on the D1 receptor and the contribution of including additional DA markers.

Updated paragraph in the Introduction, pages 5-6:

“Dopamine (DA) is one of the most important modulators of hippocampus-dependent function(47,48), and influences the brain’s functional architecture through enhancing specificity of neuronal signaling(49). Consistently, there is a DA-dependent aspect of maintained functional network segregation in aging which supports cognition(50). Animal models suggest heterogeneous patterns of DA innervation(51,52) and postsynaptic DA receptors(53), across both transverse and longitudinal hippocampal axes, likely allowing for separation between DA modulation of distinct hippocampus-dependent behaviors(47). Moreover, the human hippocampus has been linked to distinct DA circuits on the basis of long-axis variation in functional connectivity with midbrain and striatal regions(54,55). Taken together with recent findings revealing a unimodal-transmodal organization of the most abundantly expressed DA receptor subtype, D1 (D1DR), across cortex(56), we tested the hypothesis that the organization of hippocampal-neocortical connectivity partly reflects the underlying distribution of hippocampal DA receptors, predicting predominant spatial correspondence for any hippocampal gradient conveying a unimodal-transmodal pattern across cortex.”

Updated sections in the Results, page 13-14:

“Our next aim was to investigate to which extent the distribution of hippocampal DA D1 receptors (D1DRs), measured by [¹¹C]SCH23390 PET in the DyNAMiC(58) sample, may serve as a molecular correlate of the hippocampus’ functional organization.”

“Complimentary analyses were then conducted to further evaluate G2 as a dopaminergic hippocampal mode by utilizing additional DA markers at group-level.”

Moreover, the authors may be aware that multiple PET tracers are somewhat challenged in the mesiotemporal region. Is this the case for the D1 receptor as well? The hippocampus is a small and complex structure, and PET more of a low res technique so one would want to highlight and discuss the limitations of the correlations with PET maps here and/or evaluate whether the analysis adds necessary findings to the study.

We thank the Reviewer for raising this point. The lower resolution of PET is indeed a relevant aspect to consider when quantifying D1DR availability in the hippocampus, even though previous research indicate high test-retest reliability of [¹¹C]SCH23390 PET measurement in this region (Kaller et al., 2017). We have now elaborated on PET limitations in the Discussion of the revised manuscript.

In our study, we made efforts to reduce potential partial volume effects (PVE) by correcting our PET data, and tested spatial associations between our functional gradients and D1DR maps using trend-surface modelling (TSM), rather than through voxel-wise comparisons. This allowed us to evaluate the spatial correspondence between functional connectivity and D1DRs at a level of spatial trends, estimated using TSM models computed at increasing levels of complexity. The results showed consistent spatial overlap between G2 and D1DRs across these models, that is, across spatial trends described at coarser-to-finer scales. Furthermore, this was replicated across several DA markers with PET and SPECT data from independent samples.

Taken together, we agree with the Reviewer that the spatial correspondence observed between G2 and hippocampal D1DRs should be interpreted in the context of resolution-related limitations inherent to PET imaging. However, we strongly believe that our DA analyses offer valuable insight to the molecular underpinnings of hippocampal functional organization.

Updated paragraph in the Discussion, pages 25-26:

“We discovered that G2, specifically, manifested organizational principles shared among function, behavior, and neuromodulation. Meta-analytical decoding reproduced a unimodalassociative axis across G2 (Figure 3B), and analyses in relation to the distribution of D1DRs – which vary across cortex along a unimodal-transmodal axis(76,77) – demonstrated topographic correspondence both at the level of individual differences and across the group. It should, however, be acknowledged that PET imaging in the hippocampus is associated with resolutionrelated limitations, although previous research indicate high test-retest reliability of [¹¹C]SCH23390 PET to quantify D1DR availability in this region(78). As such, mapping the distribution of hippocampal D1DRs at a fine spatial scale remains challenging, and replication of our results in terms of overlap with G2 is needed in independent samples. Here, we evaluated the observed spatial overlap between G2 topography and D1DRs across multiple TSM model orders, showing correspondence between modalities from simple to more complex parameterizations of their spatial properties. Topographic correspondence was additionally observed between G2 and other DA markers from independent datasets (Figure 3B), suggesting that G2 may constitute a mode reflecting a dopaminergic phenotype, which contributes to the currently limited understanding of its biological underpinnings.”

From my (perhaps somewhat biased) perspective, it might be valuable to instead or in addition look at measures of hippocampal microstructure and how these relate to the functional aging effects. This could be done, if available, using data from the same subjects (eg based on quantitative MRI contrasts and/or structural MRI) and/or using contextualization findings as implemented in eg hippomaps.readthedocs.io

We thank the Reviewer for this suggestion. We performed additional analyses investigating the spatial overlap between our connectivity gradients and estimates of hippocampal microstructure, computed as the ratio of T1- over T2-weighted (T1w/T2w) images (Glasser & Von Essen, 2011; vos de Wael et al., 2018). Analyses of spatial correspondence then followed the TSM-based method used to test the spatial overlap between functional connectivity gradients and D1DR distribution. Applying TSM to the T1w/T2w image computed for each participant yielded subject-level model parameters describing microstructure topography, which were then entered as predictors of connectivity topography in multivariate GLMs (separate models for each gradient and hemisphere, 6 models in total).

Analyses revealed that microstructure of the right hippocampus significantly predicted gradient topography of right-hemisphere G1 (F = 1.325, p \= 0.034), while no other links between connectivity gradients and microstructure emerged as significant (F 0.930-1.184, ps 0.7060.079).

These results, suggesting an association along the anteroposterior axis, deviate from previous findings linking hippocampal microstructure to G3-like, medial-lateral, connectivity organization (vos de Wael et al., 2018). As we believe that comprehensive analyses of our gradients in relation to microstructure across the lifespan would be best addressed in future work, we have not included these analyses of microstructure in the revised manuscript.

(2) Can the authors clarify why they did not replicate based on cohorts that are more widely used in the community and open access, such as CamCAN and/or HCP-Aging? It might connect their results with other studies if an attempt was made to also show that findings persist in either of these repositories.

We agree with the Reviewer that replication in samples such as CamCAN and/or HCP-Aging would provide valuable opportunities to connect our findings with those of other studies using those datasets. Here, we included the Betula dataset (Nilsson et al., 2004) as our replication sample, as it was immediately available to us, included a large sample of adults in a comparable age, and a word recall episodic memory task closely aligned with the one included in DyNAMiC. Importantly, leveraging the Betula dataset as our replication sample allows us to link our findings to a wide range of previous studies central to the understanding of neurocognitive aging in general, and hippocampal aging in particular (Nyberg, 2017; Nyberg et al., 2020). Betula is a large longitudinal project that has been tracking individuals since 1988, and is part of the National E-infrastructure for Aging Research (NEAR: www.near-aging.se), through which data from several Swedish studies are made available to both national and international researchers. While we acknowledge the value of extending replication efforts to datasets like CamCAN and HCP-Aging, we emphasize the significant contribution of having replicated our connectivity gradients in the Betula dataset.

(3) The authors applied TSM and related these parameters to topographic changes in the gradients. I was wondering whether and how such an approach controls for autocorrelation present in both the PET map and gradients. Could the authors clarify?

The Reviewer raises an important topic in spatial autocorrelation. The TSM approach used to parameterize the topography of the functional gradients and D1DR distribution, and to test the spatial correspondence between modalities, did not include any specific method to control for autocorrelation. Here, we highlight two aspects of our study in relation to this point. First, we demonstrated in the Supplementary information (S. Figure 4) that autocorrelation induced by spatial smoothing likely has limited effects on overall gradient topography and the ability of TSM parameters to capture meaningful inter-individual differences in terms of age. Second, in the case of spatial overlap effects being significantly impacted by autocorrelation, we would expect the association between right-hemisphere G2 and D1DR topography to similarly emerge for G2 in the left hemisphere. The absence of such an association may speak to a limited effect of spatial autocorrelation.

(4) The TSM approach quantifies the gradients in terms of x/y/z direction in a cartesian coordinate system. Wouldn't a shape intrinsic coordinate system in the hippocampus also be interesting, and perhaps even be more efficient to look at here (see eg DeKraker 2022 eLife or Paquola et al 2020 eLife)?

This is a very relevant question and we appreciate the Reviewer’s suggestion. We recognize that there may be several benefits associated with adopting a shape-intrinsic coordinate system when characterizing effects in the hippocampus, given its curved/folded anatomy. Approaches like the ones adopted in DeKraker et al., 2022 and Paquola et al., 2020, utilizes geodesic coordinate frameworks to represent the hippocampus in surface space, enabling mapping of connectivity onto the hippocampal surface while respecting its inherent curvature and topology. We anticipate that quantifying gradients within such a framework would especially benefit identification of connectivity change across the hippocampal surface relative to reference points such as subfield boundaries, while minimizing effects of interindividual differences in hippocampal shape and folding. In our study, hippocampal gradients and their associated cortical patterns were computed in volumetric space, with TSM subsequently used to parameterize the change in connectivity along these gradients. This indeed yields a description of connectivity change within a coordinate system less specific to hippocampal anatomy, but may favor generalizability and integration with previous gradient findings within and beyond the hippocampus (e.g., Przeździk et al., 2019; Tian et al., 2020; Katsumi et al., 2023; Navarro-Schröder et al., 2015), as well as connections with broader neuroimaging frameworks through techniques such as meta-analytical decoding. In our view, the different coordinate frameworks offer complimentary insight to hippocampal organization, and while we have opted to not undertake novel analyses to explore our gradients within a geodesic coordinate system for the purposes of this paper, we recognize the importance of such evaluation of our gradients in future analyses. We have made updates to the Discussion in the revised manuscript on this topic (pages 23-24):

“Greater anatomical specificity, with more precise characterization of connectivity in relation to subfield boundaries while minimizing effects of inter-individual differences in hippocampal shape and folding, might be achieved by adopting techniques implementing a geodesic coordinate system to represent effects within the hippocampus(68,69).”

Reviewer #2 (Public Review):

Summary:

This paper derives the first three functional gradients in the left and right hippocampus across two datasets. These gradient maps are then compared to dopamine receptor maps obtained with PET, associated with age, and linked to memory. Results reveal links between dopamine maps and gradient 2, age with gradients 1 and 2, and memory performance.

Strengths:

This paper investigates how hippocampal gradients relate to aging, memory, and dopamine receptors, which are interesting and important questions. A strength of the paper is that some of the findings were replicated in a separate sample.

Weaknesses:

The paper would benefit from added clarification on the number of models/comparisons for each test. Furthermore, it would be helpful to clarify whether or not multiple comparison correction was performed and - if so - what type or - if not - to provide a justification. The manuscript would furthermore benefit from code sharing and clarifying which results did/did not replicate.

We thank the Reviewer for their positive assessment and suggestions regarding further clarifications. We have addressed the Reviewer’s comments in a point-by-point manner under the “Recommendations for the authors” section.

Reviewer #3 (Public Review):

Summary:

In this study, the authors analyzed the complex functional organization of the hippocampus using two separate adult lifespan datasets. They investigated how individual variations in the detailed connectivity patterns within the hippocampus relate to behavioral and molecular traits. The findings confirm three overlapping hippocampal gradients and reveal that each is linked to established functional patterns in the cortex, the arrangement of dopamine receptors within the hippocampus, and differences in memory abilities among individuals. By employing multivariate data analysis techniques, they identified older adults who display a hippocampal gradient pattern resembling that of younger individuals and exhibit better memory performance compared to their age-matched peers. This underscores the behavioral importance of maintaining a specific functional organization within the hippocampus as people age.

Strengths:

The evidence supporting the conclusions is overall compelling, based on a unique dataset, rich set of carefully unpacked results, and an in-depth data analysis. Possible confounds are carefully considered and ruled out.

Weaknesses:

No major weaknesses. The transparency of the statistical analyses could be improved by explicitly (1) stating what tests and corrections (if any) were performed, and (2) justifying the elected statistical approaches. Further, some of the findings related to the DA markers are borderline statistically significant and therefore perhaps less compelling but they line up nicely with results obtained using experimental animals and I expect the small effect sizes to be largely related to the quality and specificity of the PET data rather than the derived functional connectivity gradients.

We thank the Reviewer for the thoughtful summary and positive assessment of our work. To increase transparency of the statistical analyses, we have in the revised manuscript added information regarding statistical tests and corrections for multiple comparisons. In the Results, p-values were reported at an uncorrected statistical threshold, and we have in the revised manuscript included the corresponding p-values adjusted for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR). Finally, in the revised manuscript, we have now elaborated on the potential limitations of our PET analyses and we include the updated paragraph below.

Addition made to the Results section, page 13:

“Individual maps of D1DR binding potential (BP) were also submitted to TSM, yielding a set of spatial model parameters describing the topographic characteristics of hippocampal D1DR distribution for each participant. D1DR parameters were subsequently used as predictors of gradient parameters in one multivariate GLM per gradient (in total 6 GLMs, controlled for age, sex, and mean FD). Results are reported with p-values at an uncorrected statistical threshold and p-values after adjustment for multiple comparisons using the Benjamini-Hochberg method to control the false discovery rate (FDR).”

Addition made to the Results section, page 15:

“Effects of age on gradient topography were assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD). One model was fitted per gradient and hemisphere, each model including all TSM parameters belonging to a gradient (in total, 6 GLMs).”

Addition made to the Results section, page 17:

“Models were assessed separately for left and right hemispheres, across the full sample and within age groups, yielding eight hierarchical models in total. Results are reported with p-values at an uncorrected statistical threshold and p-values after FDR adjustment.”

Updated paragraph in the Discussion, pages 25-26:

“We discovered that G2, specifically, manifested organizational principles shared among function, behavior, and neuromodulation. Meta-analytical decoding reproduced a unimodalassociative axis across G2 (Figure 3B), and analyses in relation to the distribution of D1DRs – which vary across cortex along a unimodal-transmodal axis(76,77) – demonstrated topographic correspondence both at the level of individual differences and across the group. It should, however, be acknowledged that PET imaging in the hippocampus is associated with resolutionrelated limitations, although previous research indicate high test-retest reliability of [¹¹C]SCH23390 PET to quantify D1DR availability in this region(78). As such, mapping the distribution of hippocampal D1DRs at a fine spatial scale remains challenging, and replication of our results in terms of overlap with G2 is needed in independent samples. Here, we evaluated the observed spatial overlap between G2 topography and D1DRs across multiple TSM model orders, showing correspondence between modalities from simple to more complex parameterizations of their spatial properties. Topographic correspondence was additionally observed between G2 and other DA markers from independent datasets (Figure 3B), suggesting that G2 may constitute a mode reflecting a dopaminergic phenotype, which contributes to the currently limited understanding of its biological underpinnings.”

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Please see the comments in the public review.

We thank the Reviewer for their comments and recommendations, and have addressed them in the “Public review” section.

Reviewer #2 (Recommendations For The Authors):

(1) All statistical analyses are based on linear regressions using trend surface modeling (TSM) parameters that parameterize gradients at the subject level. These models resulted in 9 parameters for gradient 1 and 12 parameters each for gradients 2 and 3. The text states that 'Effects of age on gradient topography was assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD)'. Please clarify whether these GLMs were fitted separately for each TSM parameter (i.e., 9+12+12=33 models for both left and right = 66 total models) or on the overall model?

We appreciate the Reviewer’s request for clarification on this matter. These GLMs were fitted on the overall TSM model, that is, through one GLM per gradient (3) and hemisphere (2), each one including all TSM parameters belonging to a gradient (in total, 6 GLMs).

In the revised manuscript, we have added more details to the Results section, page 15: “Effects of age on gradient topography were assessed using multivariate GLMs including age as the predictor and gradient TSM parameters as dependent variables (controlling for sex and mean frame-wise displacement; FD). One model was fitted per gradient and hemisphere, each model including all TSM parameters belonging to a gradient (in total, 6 GLMs).”

(2) Similarly, for memory it appears that multiple models were performed (left and right, young, middle-aged, old, whole groups). Please clarify whether and how multiple comparison correction was performed in this case.

In the revised manuscript, we have now specified the number of analyses conducted in relation to memory performance. We have also clarified that p-values were reported at an uncorrected statistical threshold, and we have in the revised manuscript included the corresponding p-values adjusted for multiple comparisons using the Benjamini-Hochberg method to control the FDR.

Updated section in the Results, page 17:

“Models were assessed separately for left and right hemispheres, across the full sample and within age groups, yielding eight hierarchical models in total. Results are reported with p-values at an uncorrected statistical threshold and p-values after FDR adjustment.”

(3) Although I applaud the authors for their replication efforts, the results do not appear to replicate well. For example, memory was linked to gradient 2 in the whole group but to gradient 1 in the young group. Furthermore, dopamine was linked to gradient 2 in the right but not the left hemisphere. Although the overall group-level gradients were very stable between the two datasets, it is not clear whether the age findings replicated and the memory subgroup findings only replicated at trend level for memory and only partially replicated at the TSM parameter level.

We thank the Reviewer for highlighting the inclusion of a replication dataset as a strength of our study, and we appreciate the recommendation to clarify to which extent results replicated. We provide a response to the Reviewer’s points below, and specify the revisions made to the manuscript in relation to this topic.

The main aim of our study was to characterize the topographic organization of functional hippocampal-neocortical connectivity within the hippocampus across the adult lifespan, as previous studies have limited their focus to younger adults. Given the lack of previous studies for comparison, together with our identification of a novel secondary long-axis connectivity gradient (G2) taking precedence over the previously established medial-lateral G3, we included the Betula sample (Nilsson et al., 2004) for the purpose of replication. There was a high level of consistency between our main dataset and our replication dataset, with gradients 1-3 in left and right hemispheres identified in both samples.

Further use of the replication dataset, beyond the identification of the connectivity gradients, was originally not planned. As such, not all subsequent analyses in the main dataset were conducted in the replication dataset. However, we found it critical to evaluate the observation that older individuals who maintained a youth-like gradient topography also exhibited higher levels of memory performance in an independent sample. This was possible given that the replication dataset included a comparable number of participants in similar ages and a word recall episodic memory task corresponding well to the one used in DyNAMiC. Overall, we conclude that these analyses replicated well across samples. Firstly, topography of lefthemisphere G1 informed the classification of older adults into youth-like and aged subgroups in both samples. Furthermore, in both samples, we observed that the older subgroups identified based on G1 topography also exhibited the youth-like vs. aged pattern in G2 topography. This pattern was, however, evident also in G3 only in the main sample, possibly suggesting a limited contribution of G3 topography in determining overall functional profiles in older age. In terms of the behavioral relevance of maintaining youth-like gradient topography in older age, we observed effects on word recall performance in both samples; although the Reviewer correctly points out that, the difference between subgroups was significant at trend-level (p = 0.058) in the replication dataset. While this indeed underscores the importance of replication efforts in additional samples, we argue that the pattern observed in our replication dataset is overall consistent with, and conveys effects in the expected direction based on, the original observations in our main dataset.

In revising the manuscript, we have performed additional analyses for replication purposes in terms of memory. Originally, we observed a significant association between G2 topography and episodic memory across the main sample. However, this effect did not remain significant after FDR adjustment for multiple comparisons. To evaluate this association further, we conducted a corresponding hierarchical multiple regression analysis in the replication dataset, which supported a role of G2 in memory (Adj. R² = 0.368, ΔR² = 0.081, F= 1.992, p = 0.028). Together, these analyses suggest that inter-individual differences in episodic memory performance may in part be explained by the spatial characteristics of G2 across the adult lifespan, although increased statistical power in relation to the large number of TSM parameters included in the hierarchical regression models may be needed to explore this association in smaller, age-stratified, groups. Relatedly, it is worth mentioning that higher levels of memory performance in older age were linked to the maintenance of youth-like G2 topography in both our main and replication datasets.

In parallel, topographic parameters of G1 predicted memory performance in the younger adults, which successfully replicates TSM-based results previously reported in Przeździk et al., 2019. Although similar associations were not evident within the other age groups, a link between G1 topography and memory was demonstrated in older age based on a) the identification of individuals maintaining a youth-like G1 profile and higher levels of memory, within which b) memory performance was, as in young adults, significantly predicted by G1 topography.

The spatial correspondence between G2 topography and distribution of hippocampal D1DRs was lateralized to the right, and as the Reviewer points out, as such did not replicate across hemispheres. To which extent replication across hemispheres should be expected in this case is, however, difficult to determine. Lateralization and/or hemispheric asymmetry is commonly observed in numerous hippocampal features, from the molecular level to its functional involvement in behavior (Nematis et al., 2023; Persson & Söderlund, 2015), including various dopaminergic markers tested in the animal literature (Afonso et al., 1993; Sadeghi et al., 2017). Yet, potential differences between hemispheres in D1DR availability and the spatial distribution of receptors along hippocampal axes remain less studied in humans. More data is therefore needed to determine the nature of this right-hemisphere lateralization.

In sum, we argue that our results show a good level of replication across independent datasets and across analyses in our main dataset. Whereas this study did not attempt replication of all analyses conducted in the main dataset, it has through replication across independent samples provided support for its main findings – the organization of hippocampal-neocortical connectivity along three main hippocampal gradients across the adult lifespan, and the gradient topography-based identification of older individuals maintaining a youth-like hippocampal organization in older age.

The revised manuscript includes edits made to incorporate the new analyses and clarifications of observations in relation to memory.

In the Results, page 17:

“Observing that the association between G2 and memory did not remain significant after FDR adjustment, we performed the same analysis in our replication dataset, which also included episodic memory testing. Consistent with the observation in our main dataset, G2 significantly predicted memory performance (Adj. R² = 0.368, ΔR² = 0.081, F= 1.992, p = 0.028) over and above covariates and topography of G1. Here, the analysis also showed that G1 topography predicted performance across the sample (Adj. R² = 0.325, ΔR² = 0.112, F= 3.431, p < 0.001).”

In the Discussion, page 26:

“Results linked both G1 and G2 to episodic memory, suggesting complimentary contributions of these two overlapping long-axis modes. Considered together, analyses in the main and replication datasets indicated a role of G2 topography in memory across the adult lifespan, independent of age. A similar association with G1 was only evident across the entire sample in the replication dataset, whereas results in the main sample seemed to emphasize a role of youthlike G1 topography in memory performance. In line with previous research, memory was successfully predicted by G1 topography in young adults(30), and similarly predicted by G1 in older adults exhibiting a youth-like functional profile.”

(4) Please share the data and code and add a description of data and code availability in the manuscript.

We have now made our code available, and added a statement on data and code availability in the revised manuscript.

On page 37: “Data from the DyNAMiC study are not publicly available. Access to the original data may be shared upon request from the Principal investigator, Dr. Alireza Salami. The Matlab, R, and FSL codes used for analyses included in this study are openly available at https://github.com/kristinnordin/hcgradients. Computation of gradients was done using the freely available toolbox ConGrads: https://github.com/koenhaak/congrads.”

Reviewer #3 (Recommendations For The Authors):

Please see the comments in the public review.

We thank the Reviewer for their comments and recommendations, and have addressed them in the “Public review” section.

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印刷与色彩匹配是印刷行业中非常重要的一环，尤其是在需要高精度色彩还原的场景（如品牌标识、艺术作品、摄影画册等）中。以下是关于印刷与色彩匹配的详细解释，特别是针对 印刷品在不同光源下的色彩保持 和 RGB → CMYK 色彩转换的优化：

1. 印刷品在不同光源下的色彩保持

印刷品的色彩表现会受到光源的影响，这种现象称为 同色异谱（Metamerism）。为了在不同光源下保持色彩一致性，需要采取以下措施：

（1）同色异谱现象

• 定义：同色异谱是指两种颜色在某种光源下看起来相同，但在另一种光源下却显现出差异的现象。
• 原因：不同光源的光谱分布不同，会导致印刷品表面的反射光谱发生变化，从而影响人眼对色彩的感知。

（2）解决方案

• 标准化光源：在印刷品的设计和校对过程中，使用标准光源（如 D50 或 D65）进行色彩评估。这些光源模拟了自然光的色温，能够提供一致的观察条件。
• 多光源测试：在实际生产中，印刷品可能会在不同的光照环境下被观看（如日光、荧光灯、LED 灯）。因此，可以在多光源下测试印刷品的色彩表现，确保其在不同环境中的一致性。
• 使用特殊油墨：某些高端印刷会使用特殊油墨（如 Pantone 专色），这些油墨的光谱特性更稳定，能够减少同色异谱现象。

2. RGB → CMYK 色彩转换的优化

在印刷过程中，需要将数字图像从 RGB 色彩模式转换为 CMYK 色彩模式。由于 RGB 和 CMYK 的色彩空间不同，直接转换可能会导致色彩漂移（Color Shift）。以下是优化这一转换过程的方法：

（1）RGB 与 CMYK 的区别

• RGB：用于显示设备（如显示器、手机屏幕），基于光的加色混合原理，色彩范围较广。
• CMYK：用于印刷，基于油墨的减色混合原理，色彩范围较窄。

（2）色彩漂移的原因

• 色域差异：RGB 的色域比 CMYK 更广，某些 RGB 颜色无法在 CMYK 中准确再现。
• 转换算法：不同的色彩管理软件或工具可能使用不同的转换算法，导致色彩表现不一致。

（3）优化 RGB → CMYK 转换的方法

• 使用色彩配置文件（ICC Profile）：
• ICC 配置文件是描述设备色彩特性的文件，可以帮助在 RGB 和 CMYK 之间进行准确的色彩转换。
• 在转换时，选择与印刷设备和纸张匹配的 ICC 配置文件，以确保色彩还原的准确性。
• 软打样（Soft Proofing）：
• 在设计软件（如 Adobe Photoshop）中启用软打样功能，模拟印刷后的色彩效果。
• 通过软打样，可以提前发现并修正色彩漂移问题。
• 手动调整：
• 在转换后，手动调整图像的色彩平衡、饱和度和亮度，以弥补转换过程中丢失的色彩细节。
• 使用专色（Spot Color）：
• 对于品牌标识或其他需要高精度色彩还原的元素，可以使用 Pantone 专色，而不是依赖于 CMYK 的混合色。
• 测试印刷：
• 在实际印刷前，进行小样测试（如打样或数码打样），检查色彩表现是否符合预期。

3. 实际应用中的注意事项

• 设计阶段：在设计时尽量使用 CMYK 模式，避免后期转换带来的色彩损失。
• 沟通与协作：与印刷厂保持密切沟通，了解其设备和工艺特性，确保色彩管理的无缝衔接。
• 环境控制：在色彩校对和评估时，确保观察环境的光源和背景符合标准（如使用中性灰背景和标准光源）。

4. 总结

• 印刷品在不同光源下的色彩保持：通过标准化光源、多光源测试和使用特殊油墨，可以减少同色异谱现象，确保印刷品在不同光照环境下的色彩一致性。
• RGB → CMYK 色彩转换的优化：通过使用 ICC 配置文件、软打样、手动调整和测试印刷等方法，可以避免色彩漂移，确保印刷品的色彩还原准确。

这些技术和方法的结合，能够帮助设计师和印刷厂实现高质量的印刷效果，满足客户对色彩精度和一致性的要求。

电影调色（Color Grading）是电影制作中非常重要的一环，它不仅仅是让画面看起来更美观，还需要确保在不同光照环境下色彩的一致性。以下是对这一概念的具体解释：

1. 电影调色的基本目标

电影调色的主要目标是通过调整画面的色彩、对比度、亮度等参数，来实现以下效果： - 增强情感表达：通过色彩传递特定的情绪或氛围（如冷色调表现孤独，暖色调表现温馨）。 - 统一视觉风格：确保整部电影的视觉风格一致，避免不同场景之间的色彩跳跃。 - 修复拍摄问题：校正拍摄过程中因光照、设备等原因导致的色彩偏差。

然而，除了这些美学和技术目标外，电影调色还需要解决一个关键问题：在不同光照环境下保持色彩一致性。

2. 不同光照环境下的挑战

在电影拍摄和后期制作中，光照环境可能会发生显著变化，例如： - 自然光与人造光的差异：自然光（如日光）和人造光（如灯光）的色温和光谱分布不同，会导致色彩表现不一致。 - 场景切换：同一场景在不同时间拍摄（如白天和夜晚），或者不同场景之间的光照条件不同，可能会导致色彩偏差。 - 设备差异：不同摄影机或显示设备的色彩响应特性不同，可能会导致色彩还原不一致。

这些光照环境的变化会直接影响画面的色彩表现，如果不加以处理，可能会导致观众在观看时感到不连贯或失真。

3. 保持色彩一致性的重要性

在电影调色中，保持色彩一致性的意义在于： - 叙事连贯性：色彩是电影叙事的重要元素之一。如果同一场景或同一角色的色彩在不同镜头中不一致，可能会破坏叙事的连贯性，让观众感到困惑。 - 视觉沉浸感：一致的色彩表现能够增强观众的沉浸感，让他们更专注于故事情节，而不是被色彩跳跃所干扰。 - 品牌与风格：许多导演和摄影师有自己独特的视觉风格（如 Wes Anderson 的高饱和度色彩，或 Christopher Nolan 的低饱和度暗色调）。保持色彩一致性有助于强化这种风格，形成品牌识别度。

4. 如何在不同光照环境下保持色彩一致性

为了实现色彩一致性，电影调色师通常会采取以下步骤：

（1）色彩管理

• 使用色彩管理系统（如 ACES）确保从拍摄到后期制作的整个流程中色彩的一致性。
• 在拍摄时使用灰卡或色卡进行色彩校准，为后期调色提供参考。

（2）色彩匹配

• 对同一场景的不同镜头进行色彩匹配，确保它们看起来像是在相同光照条件下拍摄的。
• 对不同场景的色彩进行调整，使它们在视觉上保持连贯。

（3）动态范围调整

• 在高动态范围（HDR）内容中，调色师需要特别关注高亮度和低亮度区域的色彩表现，确保它们在不同光照条件下的一致性。

（4）参考显示设备

• 使用经过校准的专业显示设备进行调色，确保色彩还原的准确性。
• 在不同的显示设备（如影院投影、电视、手机）上进行测试，确保色彩在不同平台上的表现一致。

（5）光照模拟

• 在后期制作中，调色师可以通过数字手段模拟不同光照条件的效果，并调整色彩以保持一致性。例如，模拟日光下的阴影效果或夜晚的灯光效果。

5. 实际案例

• 《银翼杀手2049》：这部电影在调色时特别注重在不同光照环境下保持色彩的一致性。例如，在室外场景中，尽管光照条件复杂（如雾霾、霓虹灯），但通过精细的调色，画面依然保持了统一的冷色调风格。
• 《疯狂的麦克斯：狂暴之路》：这部电影以高饱和度的色彩风格著称，调色师通过精确的色彩管理，确保了在沙漠、夜晚、室内等不同光照环境下色彩的一致性。

6. 总结

电影调色中保持色彩一致性的核心在于：通过色彩管理、色彩匹配和技术手段，确保在不同光照环境下画面的色彩表现连贯且真实。这不仅是为了提升画面的美观度，更是为了增强叙事的连贯性和观众的沉浸感。调色师在这一过程中扮演了至关重要的角色，他们需要结合艺术感知和技术能力，为电影赋予统一的视觉风格和情感表达。

Author response:

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

eLife Assessment 

This study presents valuable finding regarding the role of life history differences in determining population size and demography. The evidence for the claims is still partially incomplete, with concerns about generation times and population structure. Nonetheless, the work will be of considerable interest to biologists thinking about the evolutionary consequences of life history changes.  

Thank you. We have addressed the generation time and population structure issues in detail in our revision and hope that you, like us, find them to be of sufficiently low concern (i.e., they are not driving the results) that they do not overshadow the main findings and conclusions.

The opportunity to make in-depth revisions also helped the manuscript in two ways unanticipated by both us and the reviewers. First, KW made a mistake in the original analysis of phylogenetic signal, and catching that error simplifies that aspect of the study (there is none in our measured variables). Second, in June 2024 Hilgers et al. (2024; https://doi.org/10.1101/2024.06.17.599025) posted an important manuscript to bioRxiv noting the possibility of false population size peaks in PSMC analyses using the standard default settings. Our results had three of those, which we have eliminated. N_either of these issues affect the overall conclusions, but their resolution improves the work.  

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

This interesting study applies the PSMC model to a set of new genome sequences for migratory and nonmigratory thrushes and seeks to describe differences in the population size history among these groups. The authors create a set of summary statistics describing the PSMC traces - mean and standard deviation of N_e, plus a set of metrics describing the shape of the oldest N_e peak - and use these to compare across migratory and resident species (taking single samples sequenced here as representative of the species). The analyses are framed as supporting or refuting aspects of a biogeographic model describing colonization dynamics from tropical to temperate North and South America. 

Strengths: 

At a technical level, the sequencing and analysis up through PSMC looks good and the paper is engaging and interesting to read as an introduction to some verbal biogeographic models of avian evolution in the Pleistocene.

The core findings - higher and more variable N_e in migratory species - seem robust, and the biogeographic explanation is plausible.  

Thanks. We thought so as well. Our analyses go beyond being simply descriptive and test some simple hypotheses, including a biogeographic+ecological expansion opportunity gained in some lineages through the adoption of a seasonal migration life-history strategy.  

Weaknesses: 

I did not find the analyses particularly persuasive in linking specific aspects of clade-level PSMC patterns causally to evolutionary driving forces. To their credit, the authors have anticipated my main criticism in the discussion. This is that variation in population size inferred by methods like PSMC is in "effective" terms, and the link between effective and census population size is a morass of bias introduced by population structure and selection so robustly connecting specific aspects of PSMC traces to causal evolutionary forces is somewhere between extremely difficult and impossible.  

As R1 notes, we do not attempt to link effective population sizes and census sizes (though we do discuss this), and we are also careful to discuss correlated rather than causative factors when going beyond the overarching hypotheses regarding life-history strategy.

Population structure is the most obvious force that can generate large N_e changes mimicking the census-sizefocused patterns the authors discuss. The authors argue in the discussion that since they focus on relatively deep time (>50kya at least, with most analyses focusing on the 5mya - 500kya range) population structure is "likely to become less important", and the resident species are usually more structured today (true) which might bias the findings against the observed higher N_e in migrants.  

To clarify, the patterns we discuss are entirely related to effective population size, not census size. But, yes, this is why we’ve given population structure its own section in the Discussion.

But is structure really unimportant in driving PSMC results at these specific timescales? There is no numerical analysis presented to support the claim in this paper. The biogeographic model of increased temperate-latitude land area supporting higher populations could yield high N_e via high census size, but shifts in population structure (for example, from one large panmictic population to a series of isolated refugial populations as a result of glaciation-linked climate changes) could plausibly create elevated and more variable N_e. Is it more land area and ecological release leading to a bigger and faster initial N_e bump, or is it changes in population connectivity over time at expanding range edges, or is the whole single-bump PSMC trace an artifact of the dataset size, or what? The authors have convinced me that the N_e history of migratory thrushes is on average very different from nonmigrant thrushes, but beyond that it's unclear what exactly we've learned here about the underlying process.  

We do not argue that population structure is unimportant, only that it is less important as one goes into deeper time. Further, we agree with the reviewer’s observation above that structure is more likely to bias nonmigrant estimates of N_e. In other words, following Li & Durbin’s (2011) simulations, we interpret that an inflated N_e due to structure should occur more often among residents. We have clarified this in the revision. We also agree that what we’ve learned about the underlying process is not entirely clear, but as we stated, population structure does not seem to be the main driver, and there is evidence that both biogeographic and ecological factors are involved. With this being the first time that these questions have been asked, we think we’ve made an important advance and that we’ve opened a number of avenues for future study.

It also important to consider the time scales involved and the sampling regime. Glacial-interglacial cycles averaged ~100 Kyr back to 0.74 Mya and then averaged ~41 Kyr from then back to 2.47 Mya; about 50-60 of these cycles occurred (Lisiecki & Raymo 2005: fig. 4). This probably caused a lot of population structuring and mixing in these lineages. In addition, in the PSMC output from one of our lineages, C. ustulatus swainsonii, we find that there are 54 time segments sampled for the Pleistocene, indicating the inadequacy of this method to reflect fine-scale changes and suggesting that each estimate is capturing a lot of both phenomena, structuring and mixing. We have added this to the revision.

I generally agree with the authors that "at present there is no way to fully disentangle the effects of population structure and geographic space on our results". But given that, I think there are two options - either we can fully acknowledge that oversimplified demographic models like PSMC cannot be interpreted as supporting evidence of any particular mechanistic or biogeographic hypothesis and stop trying to use them to do that, or we have to do our best to understand specifically which models can be distinguished by the analyses we're employing. 

Short of developing some novel theory deep in the PSMC model, I think readers would need to see simulations showing that the analyses employed in this paper are capable of supporting or refuting their biogeographic hypothesis before viewing them as strongly supporting a specific biogeographic model. Tools like msprime and stdpopsim can be used to simulate genome-scale data with fairly complex biogeographic models. Running simulations of a thrush-like population under different biogeographic scenarios and then using PSMC to differentiate those patterns would be a more convincing argument for the biogeographic aspects of this paper. The other benefit of this approach would be to nail down a specific quantitative version of the taxon cycles model referenced in the abstract, and it would allow the authors to better study and explain the motivation behind the specific summary statistics they develop for PSMC posthoc analysis.  

These could very well be fruitful pursuits for future work, but they are beyond the scope of this paper. The impossibility of reconstructing ranges through deep time makes anything other than the very general biogeographic hypothesis we’ve posed an uncertain pursuit. Also, a purely biogeographic approach neglects the likelihood of ecological expansion also being involved. We get at the importance of the latter in the “Geography and evolutionary ecology” section of the Discussion. Below, the editor states that discussions among reviewers indicate that simulations are not warranted at this time. We agree that the complexities involved are substantial, to the point of making direct relevance to this empirical study uncertain (especially in such an among-lineage context). Regarding taxon cycles, we merely point out that that conceptual framework seems relevant given our findings. This was not even remotely anticipated at the outset of the study, so we are reluctant to do anything more than point out its possible relevance in several aspects of the results. Finally, the motivation for the study’s summary statistics were entirely driven by the hypotheses, as given in Methods, and due to an earlier error (noted above), there are no post-hoc analyses in the revision. Sorry for the needless confusion.

Reviewer #2 (Public Review): 

Summary: 

Winker and Delmore present a study on the demographic consequences of migratory versus resident behavior by contrasting the evolutionary history of lineages within the same songbird group (thrushes of the genus Catharus). 

Strengths: 

I appreciate the test-of-hypothesis design of the study and the explicit formulation of three main expectations to test. The data analysis has been done with appropriate available tools. 

Weaknesses: 

The current version of the paper, with the case study chosen, the results, and the relative discussion, is not satisfying enough to support or reject the hypotheses here considered.  

Given the stated strengths, the weaknesses noted seem a little incongruous, but we understand from the comments below that the reviewer would like to see the study redesigned and expanded.  

The authors hypothesized that the wider realized breeding and ecological range characterising migrants versus resident lineages could be a major drive for increased effective population size and population expansion in migrants versus residents. I understand that this pattern (wider range in migrants) is a common characteristic across bird lineages and that it is viewed as a result of adapting to migration. A problem that I see in their dataset is that the breeding grounds range of the two groups are located in very different geographic areas (mainly South versus North America). The authors could have expanded their dataset to include species whose breeding grounds are from the two areas, regardless of their migratory behaviour, as a comparison to disentangle whether ecological differences of these two areas can affect the population sizes or growth rates.

Because the questions are about the migratory life history strategy and the best way to get at this is in a phylogenetic framework, we’re not sure how we could effectively add species “regardless of their migratory behavior.” Further, we know that migration causes lineages to experience variable ecological conditions that include breeding, migration, and wintering conditions. Obligate migrants are going to have different breeding ranges from their close relatives, and the more distantly related species are, the less likely it is that they respond to particular ecological conditions the same way. So we do not think that an approach that included miscellaneous species from northern and southern regions would strengthen this study. Here, the comparative framework of closely related lineages that possess or lack the trait of interest is a study design strength. We do agree, however, that future work is needed that does encompass more lineages (we would argue in a phylogenetic context), and that disentangling the effects of geography and ecology will also be an important future endeavor. 

As I understand from previous literature, the time-scale to population growth and estimates of effective population sizes considered in the present paper for the resident versus migratory clades seem to widely predate the times to speciation for the same lineages, which were reported in previous work of the same authors (Everson et al 2019) and others (Termignoni-Garcia et al 2022). This piece of information makes the calculation of species-specific population size changes difficult to interpret in the light of lineages' comparison. It is unclear what the authors consider to be lineage-specific in these estimates, as the clades were likely undergoing substantial admixture during the time predating full isolation.  

We do recognize that timing estimates vary among studies. Differences among studies in important variables like markers, methods, generation time, and mutation or substitution rates create much of this uncertainty. Also, we are not confident in prior dating efforts in this group, largely because of gene flow and its effects on bringing estimates closer to the present. As we point out (line 485), differences among studies on these issues do not detract from the strengths here for within-study, among-lineage contrasts. In short, the timing could be off in an among-study context (and likely is with prior work, given gene flow), but relative performance of among-lineage N_e differences is less susceptible to these factors. This was shown fairly well in Li & Durbin’s initial use of the method among human populations. Regarding substantial admixture, PSMC curves often unite at their origins with sister lineages (when they were the same lineage). A good example is with the two C. guttatus E & W curves in Fig. S3, which still have substantial gene flow today (they are subspecies and in contact), yet they show remarkably different N_e curves through their history. It is not possible to mark a cutoff point for each lineage that represents the cessation of admixture with another lineage (e.g., Everson et al. 2019 showed substantial admixture between three full species in this group); that period can be very long (Price et al. 2008), varies among lineages, and will not be available for deeper lineage divergences in the phylogeny. We therefore chose to use all of the time intervals retrievable from the genomic data in each lineage, considering that this uniform treatment is the best approach for our among-lineage comparison. And note that we were careful to label these as “the lineages’ PSMC inception” (line 190).  

Regarding the methodological difficulties in interpreting the impact of population structure on the estimates of effective population sizes with the PSMC approach, I would think that performing simulations to compare different scenarios of different degrees of structured populations would have helped substantially understand some of the outcomes.  

The complexities of such modeling in a system like this are daunting. The different degrees of structuring among all of these lineages across just a single glacial-interglacial cycle would necessitate a lot of guesswork; projecting that back across 50-60 such cycles just in the Pleistocene would probably end up being fiction. Disentangling the effects of structure versus changes in N_e in a system like this would probably not be possible with that approach and these data. As noted above and below, there was agreement among reviewers and the editor that simulations in this case are not warranted for revision. We have added the nature of the glacialinterglacial cycles and the PSMC sampling time segments to help readers understand this better (see above in response to R1, and lines 272-278).

Additionally, I have struggled to understand if migratory behaviour in birds is considered to be acquired to relieve species competition, or as a consequence of expanded range (i.e., birds expand their range but their feeding ground is kept where speciation occurred as to exploit a ground with higher quality and abundance of seasonal local resources).  

The origins of migration have been a struggle for researchers since the subject was taken up. But how the trait was acquired among these species does not really matter for our study. Here, migratory lineages possess different biogeographic+ecological attributes than their close relatives that are sedentary. Our focus is on the presence and absence of this life-history trait.

The points raised above could be considered to improve the current version of the paper. 

Thank you. We appreciate the opportunity to guide our revision using your comments.  

Reviewer #3 (Public Review): 

Summary: 

This paper applies PSMC and genomic data to test interesting questions about how life history changes impact long-term population sizes. 

Strengths: 

This is a creative use of PSMC to test explicit a priori hypotheses about season migration and N_e. The PSMC analyses seem well done and the authors acknowledge much of the complexity of interpretation in the discussion. 

Weaknesses: 

The authors use an average generation time for all taxa, but the citations imply generation time is known for at least some of them. Are there differences in generation time associated with migration? I am not a bird biologist, but quick googling suggests maybe this is the case (https://doi.org/10.1111/1365-2656.13983). I think it important the authors address this, as differences in generation time I believe should affect estimates of N_e and growth.  

Good point. The study cited by the reviewer encompasses a much higher degree of variation in body size and thus generation time. Differences in generation time in similarly sized close relatives, as in our study, should be small, and our approach has been to average those that are known. Unfortunately, generation times are not known for all of these species, but given their similarity in size we can have reasonable confidence in their being similar. We used data from the life-history research available (as cited) to obtain our average; there are not appropriate data for the residents, though. However, there is thought to be a generation time cost to seasonal migration in birds, and Bird et al. (2020) included this in their estimates to provide modeled values for all of the lineages we studied. We’re leery of using modeled values where good data for the nonmigrants in this group don’t exist (and the basis for quantifying this cost is tiny), but we recognize that this second approach is available and could leave some doubt in our results if not pursued. So we re-did everything with the modeled generation times of Bird et al. (2020). As expected, most of the differences are time-related. Importantly, our overall results are not different. We present them as Table S2 and have added the details on this to the Methods.

The writing could be improved, both in the introduction for readers not familiar with the system and in the clarity and focus of the discussion.  

We have added a phylogeny (new Fig. 1) to help readers better understand the system, and we’ve re-worked the Discussion to make it clearer what is clarified by our results and what remains unclear.  

Recommendations for the authors:

Reviewing Editor comment: 

I note that discussion among the reviewers made clear that simulations are probably not the right answer given the complexity of the modeling required.  

We appreciate this conclusion, with which we agree.  

Reviewer #2 (Recommendations For The Authors): 

Apologies for the delay with the review, which came at a very busy time. I hope you will find my comments helpful.

Thanks. Your comments are helpful, and we fully understand how reviews (and our revisions!) have to wait until more pressing needs are addressed.

I enjoyed reading the manuscript but I believe that the discussion sections could be heavily rewritten for better clarity. The discussion is sometimes redundant and lacks some flow/clarity. In a nutshell, I had the feeling that a bit of everything is thrown in the discussion but clear conclusions are not made.  

Yes, the Discussion has been difficult to write, because more issues arose in the Results than we anticipated at the outset. We feel that discussing them is relevant, but we agree that much remains unclear. This coupling of paleodemographics with geography and ecology is a new area, which opens some important new (and relevant) areas to consider. So clarity is not possible in some areas. We’ve revised to point out where we do have clarity (e.g., in migrant lineages having different paleodemographic attributes than nonmigrants) and where only further study can provide clarity (e.g., in the roles of geography versus ecology). The journal format does not seem to have secondary subheaders, but we’ve used bold in one place to highlight ‘ecological mechanisms’ to offset that section, one of the more complex. We’ve also added a paragraph in the conclusions to clarify where we have clear takeaways and where uncertainties remain. 

Reviewer #3 (Recommendations For The Authors): 

The introduction should engage the reader with biology, not the use of demographic methods or genomics (both of which have been around for more than a decade). I would drop the first paragraph and considerably expand the second. What has previous research on ecology/behavior/genetics found regarding the demographic effects of seasonal migration?

There are two important aspects to our study: 1) using paleodemographic methods to test hypotheses about adoption of a major life-history trait—an important biological question regardless of system, and so far (surprisingly) unaddressed; and 2) using this novel approach to study the effects of one such trait, seasonal migration. At these timescales, nothing exists on this subject, so there is really nothing to expand with. If there is relevant literature that we’ve missed, we’d be happy to add it.

What is the missing bit of information or angle the current study addresses (other than just doing it larger and fancier with genomics)?  

The effects of major life-history traits on paleodemographics has not been addressed before, to our knowledge. The whole context is new, so we’re not doing something “larger and fancier” with genomics. We are doing something that has not been done before: testing hypotheses about the effects of a major life-history trait on population sizes in evolutionary time. We’re not sure how this can be made clearer. To us this seems like a very engaging biological question with wide applicability. We hope that this study is just the first of many to come, in a diversity of biological systems.

A figure showing the phylogenetic relationships of these taxa which are migratory would help the reader immensely. Although this is shown in Fig S3 I think it might be nice to have a map of the species and their ranges alongside a phylogeny as a main figure early on.  

Thank you. This is a good suggestion. We can’t fit a phylogeny and all the distribution maps (Fig. S1) onto a page, but we can include a phylogeny as one of the main figures with nonmigrants highlighted. We’ve inserted this as a new Fig. 1. 

If I understand correctly, the authors' arguments for why migratory species should show more growth hinge on large range size and geographic expansion. Yet they argue in the discussion that these forces are unlikely to be important (L226). I found the discussion on this confusing (e.g. L231 then says maybe it does matter). I think more clarity here would be helpful.

Our argument and predictions are based both on geographic and ecological expansion. This was clearly stated as our third prediction “3) early population growth would be higher as seasonal migration opens novel ecological and geographic space…” We have gone back through and reiterated the coupling of these two factors. The line mentioned concludes the first paragraph in the section ‘Geography and evolutionary ecology,’ which focuses on the difficulty of decoupling these in this system. As the paragraph relates, geography alone does not seem to be driving our results (we do not argue that it is unimportant). 

I also would have liked more time in the discussion addressing why variation in N_e may be higher in migratory lineages.

In addition to re-clarifying this in the Introduction, we have touched back on this now at line 221: “We attribute the higher variation in N_e among migrants to be the result of the relative instability of northern biomes compared with tropical ones through glacial-interglacial cycles (e.g., Colinvaux et al., 2000; Pielou, 1991).”

Minor comments: 

L 62: Presumably PSMC is limited by the coalescent depth of the genelaogy, which may be younger or older than population "origins" depending on the history of colonization, lineage splitting, gene flow, etc.  

We were careful to phrase these as “the lineages’ PSMC inception” (line 190), and responded to this issue in more detail above in response to R2’s public review. 

L 338: I think a few more details on PSMC would be helpful. Was no maskfile used?  

We did not use a maskfile, choosing instead to generate data of decent coverage and aligning reads to a single closely related relative. 

Did the consensus fasta include all species?  

No, we used a single reference high-quality fasta of Catharus ustulatus , as reported (lines 434-37). We have added that “Identical treatment of all lineages in these respects should provide a strong foundation for a comparative study like this among close relatives.” 

L 361: Fair to assume the authors used a weighted average of N_e from the output, rather than just averaging the N_e values from each time segment?  

No – we used all the values of N_e produced by PSMC output. The PSMC method uses nonoverlapping portions of the genome in its analyses (which we’ve added to make that clear), and portions in juxtaposition will often provide data for very different periods in the time segments. Further, time segments are uneven within and among taxa, so it is not clear how a uniform and comparable weighting scheme could be implemented. We consider a uniform approach to be of primary importance, including for future comparisons among studies. 

L 383 "delta" typo

Thank you for catching this.

L 93: I'd be tempted to present the questions (how does seasonal migration affect population size trajectory, means, and variation) and rationale before presenting the hypotheses. I found myself reading the hypotheses and wondering "why?"  

We’ve tried this change in the revision. It makes the hypotheses a little harder to pull out (they are no longer numbered in a short sequence), but it is shorter and solves this concern.  

L 337 read depth is usually expressed as X (e.g. "23X") rather than bp.

Changed.

Author response:

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

eLife Assessment

This important study further validates DNAH12 as a causative gene for asthenoteratozoospermia and male infertility in humans and mice. The data supporting the notion that DNAH12 is required for proper axonemal development are generally convincing, although more experiments would solidify the conclusions. This work will interest reproductive biologists working on spermatogenesis and sperm biology, as well as andrologists working on male fertility.

We thank the editor and the two reviewers for their time and careful evaluation of our manuscript. We sincerely appreciate their encouraging feedback and insightful guidance on improving our study. In the revised manuscript, we have performed additional experiments and provided quantitative data regarding the reviewers' comments.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Even though this is not the first report that the mutation in the DNAH12 gene causes asthenoteratozoospermia, the current study explores the sperm phenotype in-depth. The authors show experimentally that the said mutation disrupts the proper axonemal arrangement and recruitment of DNALI1 and DNAH1 - proteins of inner dynein arms. Based on these results, the authors propose a functional model of DNAH12 in proper axonemal development. Lastly, the authors demonstrate that the male infertility caused by the studies mutation can be rescued by ICSI treatment at least in the mouse. This study furthers our understanding of male infertility caused by a mutation of axonemal protein DNAH12, and how this type of infertility can be overcome using assisted reproductive therapy.

Strengths:

This is an in-depth functional study, employing multiple, complementary methodologies to support the proposed working model.

Thank you for your recognition of the strength of this study. Your positive feedback motivates us to continue refining our research and methodological rigor in future studies.

Weaknesses:

The study strength could be increased by including more controls such as peptide blocking of the inhouse raised mouse and rat DNAH12 antibodies, and mass spectrometry of control IP with beads/IgG only to exclude non-specific binding. Objective quantifications of immunofluorescence images and WB seem to be missing. At least three technical replicates of western blotting of sperm and testis extracts could have been performed to demonstrate that the decrease of the signal intensity between WT and mutant was not caused by a methodological artifact.

Thank you for your comments. In order to study in-depth, we have analyzed the protein sequence features of DNAH12 protein, 1-200 amino acids of DNAH12 were selected as the ideal antigen considering its good performance (1. high immunogenicity; 2. High hydrophilicity; 3. Good Surface Leakage Groups; 4. Sequence homology analysis to avoid unspecific recognition to other proteins;). The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have tried to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were discard after the service. Luckily, we have got the target band of DNAH12 protein in western blotting experiment while the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, we have tested that the inhouse raised rabbit antibody were suitable for IP experiment. The IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12^+/+ mice but not in Dnah12^-/- mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies.  In IP assay, we have added the IgG group in the IP-mass spectrometry to exclude non-specific binding. And the experimental design was described in Figure 6B. The raw data were deposited in iProX partner repository (accession number: PXD051681), and we have coordinated with the repository manager to make the data publicly accessible (https://www.iprox.cn/page/subproject.html?id=IPX0008674001).  

Besides, we have conducted replicates of western blotting of sperm and testis extracts at least 3 times and added the objective quantifications of immunofluorescence signals and WB images. The quantifications of the blot were shown in figures to help readers understand these results easily.

Reviewer #2 (Public Review):

Summary:

The authors first conducted whole exome sequencing for infertile male patients and families where they co-segregated the biallelic mutations in the Dynein Axonemal Heavy Chain 12 (DNAH12) gene.

Sperm from patients with biallelic DNAH12 mutations exhibited a wide range of morphological abnormalities in both tails and heads, reminiscing a prevalent cause of male infertility, asthenoteratozoospermia. To deepen the mechanistic understanding of DNAH12 in axonemal assembly, the authors generated two distinct DNAH12 knockout mouse lines via CRISPR/Cas9, both of which showed more severe phenotypes than observed in patients. Ultrastructural observations and biochemical studies revealed the requirement of DNAH12 in recruiting other axonemal proteins and that the lack of DNAH12 leads to the aberrant stretching in the manchette structure as early as stage XI-XII. At last, the authors proposed intracytoplasmic sperm injection as a potential measure to rescue patients with DNAH12 mutations, where the knockout sperm culminated in the blastocyst formation with a comparable ratio to that in WT.

Strengths:

The authors convincingly showed the importance of DNAH12 in assembling cilia and flagella in both human and mouse sperm. This study is not a mere enumeration of the phenotypes, but a strong substantiation of DNAH12's essentiality in spermiogenesis, especially in axonemal assembly.

The analyses conducted include basic sperm characterizations (concentration, motility), detailed morphological observations in both testes and sperm (electron microscopy, immunostaining, histology), and biochemical studies (co-immunoprecipitation, mass-spec, computational prediction). Molecular characterizations employing knockout animals and recombinant proteins beautifully proved the interactions with other axonemal proteins.

Many proteins participate in properly organizing flagella, but the exact understanding of the coordination is still far from conclusive. The present study gives the starting point to untangle the direct relationships and order of manifestation of those players underpinning spermatogenesis. Furthermore, comparing flagella and trachea provides a unique perspective that attracts evolutional perspectives.

Thank you for your thoughtful and positive feedback. We are delighted that you found our study to be a strong substantiation of DNAH12's essential role in spermiogenesis, particularly in axonemal assembly. We believe that this study represents a meaningful step toward unraveling the intricate coordination of axonemal proteins during spermatogenesis, and your comments further inspire us to continue exploring these complex mechanisms in future work. Thank you once again for your valuable insights and summary of this work.

Weaknesses:

Seemingly minor, but the discrepancies found in patients and genetically modified animals were not fully explained. For example, both knockout mice vastly reduced the count of sperm in the epididymis and the motility, while phenotypes in patients were rather milder. Addressing the differences in the roles that the orthologs play in spermatogenesis would deepen the comprehensive understanding of axonemal assembly.

This is an interesting question. Actually, it seems that although humans and mice share the male infertility phenotypes with deficiency in dynein proteins essential for sperm flagellar development, they are different in some ways. For instance, it has been reported that deficiency in DNAH17 (Clin Genet. 2021. PMID: 33070343) or DNAH8 (Am J Hum Genet. 2020. PMID: 32619401; PMCID: PMC7413861), two other members of Dynein Axonemal Heavy Chain family, also cause more severe phenotype in mice, comparing with that of human patients carrying bi-allelic DNAH17 or DNAH8 loss-of-function mutations. In knockout mice, sperm counts are lower, and the proportion of abnormal sperm morphology is higher, whereas the phenotypes in human patients tend to be milder. These observations suggest that orthologs may influence spermatogenesis to slightly different extents in humans and mice. We plan to investigate the mechanisms underlying these discrepancies in future studies, which will provide deeper insights into axonemal assembly and the evolutionary aspects of spermatogenesis. Thank you again for bringing up this important issue.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

This reviewer is impressed by the study's depth and the extent of the methodology used in the study. The study is well-designed, and the results are very interesting. The reviewer's enthusiasm was reduced by the lack of some controls (provided that the reviewer did not miss them). Further are point-to-point suggestions that this reviewer believes will increase the merit of the present study.

Title:

(1) Why a "special" dynein? What makes it special when compared to other dyneins? I suggest removing the word special.

Through phylogenetic and protein domain analyses of the DNAH family, we found that DNAH12 is the shortest member and the only one that lacks a typical microtubule-binding domain (MTBD) in the DNAH family, thus we want to describe it as a “special” dynein. We have fully considered your valuable suggestion and decided to remove it from the title.

Abstract:

(2) L23: same as above, why special?

We identified DNAH12 as the shortest member of the DNAH family and uniquely lacking the typical microtubule-binding domain (MTBD). This distinct characteristic prompted us to describe it as a 'special' dynein in the abstract part.

(3) L37: the reviewer did not find a figure (neither main nor supplementary) that would demonstrate the proper organization of microtubules in cilia. Figure S11 only shows the presence of cilia in DNAH12-/- mouse. A TEM image of cilia is required to confirm or reject the claim that DNAH12 does not play a crucial role in proper microtubule organization in cilia.

We have now added TEM images of cilia in wild-type and Dnah12^-/- mice. The ultra-structures of cilia axonemes were comparable in wild-type and Dnah12^-/- groups, suggesting that DNAH12 may not play crucial role in proper microtubule organization. The results have now been added to Supplemental Figure 11F.

(4) L122-6: Did the authors also confirm these structures by cryo-EM? If not, this needs to be pointed out as a shortcoming in the discussion, that the structures and interactions are predicted in silico only.

Thank you for your comment. Due to resource limit, we do not perform cryo-EM to confirm these structures. We will pursue the structures details at an atomic resolution structure in further study. We understand this point and now we have addressed this as a shortcoming in the discussion part.

(5) L134: Be more specific about what characteristics of DNAH12 were analyzed.

Thank you for your comment. We have now updated these in the method part. The characteristics of the DNAH12 including its region immunogenicity, hydrophilicity, surface leakage groups, and sequence homology were analyzed.

(6) L137: Be more specific about how the antibodies validated were. Were the antibodies validated for both immunofluorescence and western blotting? I suggest doing peptide blocking of the antibody, for instance for ICC, preincubation of ab with immunizing peptide followed by primary ab incubation with studied cells/tissues.

Thank you for your comments and suggestions. We validated the antibodies for both immunofluorescence and western blotting to ensure their effectiveness in our experiments. The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have attempted to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were disposed after the service. Luckily, we have got the target band of DNAH12 protein which showed strong signal in western blotting experiment and the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, the IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12^+/+ mice but not in Dnah12^-/- mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies. We sincerely admire your suggestion and will require for the peptide material if we develop new antibodies.

(7) L142: This reviewer is unfamiliar with using TRIzol for sperm protein extraction. Is there a specific reason for not using PAGE loading buffer for human sperm protein extraction?

Thanks for your suggestions. TRIzol reagent can be used for small amounts of samples (5×10⁶ cells) as well as large amounts of samples (>10⁷ cells). It is suitable for extraction of RNA and proteins at the same time. Our lab has adopted these methods in our previous work (Hum Reprod Open. 2023; PMID: 37325547; PMCID: PMC10266965.). This method is very useful to process valuable small amounts of samples for scientific work. The human sperm protein extraction was added with SDS-sample buffer [PAGE loading buffer] before SDS-PAGE separation. We have added this detail in the method part. We are sorry for making this misunderstanding.

(8) L144: Were these the final concentrations of the SDS loading buffer? 1 × Laemmli buffer contains 62.5 mM TRIS, 2% (w/w) SDS, 10 % (w/v) glycerol, and 5% 2-mercaptoethanol. Please, amend accordingly.

Thanks for your suggestions.  We apologized for incorrect labelling of concentrations (The previous one is 3× SDS loading buffer).  We have now amended the SDS loading buffer to 1 × Laemmli buffer as suggested.

(9) L151: Table S2 contains other homemade antibodies than DNAH12. Please, include references to the studies where the generation and validation of these antibodies is described.

Thank you for your suggestions. We have developed a DNAH1 antibody for use in Western blot assays, with its generation and validation detailed in Frontiers in Endocrinology (Lausanne), 2021 (PMID: 34867808; PMCID: PMC8635859). Additionally, we have produced a DNAH17 antibody for both immunofluorescence (IF) and Western blot, as described in Journal of Experimental Medicine, 2020 (PMID: 31658987; PMCID: PMC7041708). These references have now been included.

(10) L167: Please, spell out ICR at its first appearance.

Done as suggested, Thank you. The full name of ICR is Institute of Cancer Research.

(11)L169: This reviewer is confused. It seems that the mouse encodes DNAH12 on exons 5 and 18 simultaneously. Each mouse model has only one exon targeted for a knockout. Would not this mean that the expression of DNAH12 in both models is not completely knocked down? Please, give more background in this paragraph for those less familiar with CRISPR/Cas9.

Thank you for your insightful comment. We appreciate your attention to detail. To clarify, while the mouse model does indeed encode DNAH12 on exons 5 and 18 simultaneously, we specifically targeted the key exon 5 or exon 18 in each model to achieve different knockout strategies. This approach allows us to assess the functional implications of the remaining DNAH12 expression in both models. We have checked the DNAH12 expression in both models, and the result showed both models present with undetected DNAH12 proteins, indicating both models were completely knocked out of DNAH12 proteins. Additionally, we will revise the manuscript to include further details on the CRISPR/Cas9 methodology, ensuring accessibility for readers less familiar with this technique. Thank you again for your valuable feedback, which we believe will greatly enhance our manuscript.

(12) L201: 50 % PBS? As in 0.5 x concentrated PBS? Please, rewrite for clarity.

The term "50% PBS" refers to a 1:1 dilution of phosphate-buffered saline (PBS) with an appropriate diluent, resulting in a final concentration of 0.5x PBS. We will revise the text to explicitly clarify this, ensuring it is clear to all readers. Thank you for highlighting this point.

(13) L224: Please, state what beads those were (magnetic/agarose, conjugated to protein A/G...) Include catalog # and manufacturer.

Thank you for your suggestion. We have updated the manuscript to include this information. The beads used were Protein A/G Magnetic Beads (Catalog #B23202, Bimake, Texas, USA).

(14) L227: What was the reason for adding a proteasomal inhibitor? What concentration was used? Please, add this information to the text.

We adding MG132 in cell immunoprecipitation (IP) experiments is to inhibit proteasomal activity, thereby preventing the degradation of the target protein. This helps maintain the stability of the target protein during the experiment (Sci Adv. 2022. PMID: 35020426; PMCID: PMC8754306.), enhancing its detectability in subsequent analyses. MG132 (5 μM) was added. We have added this information in the revised the manuscript

(15) L233: in vivo IP of mouse testis lysate? This does not make sense. I suggest removing "in vivo".

Thank you for your careful review and comments on our manuscript. We have modified as suggested.

(16) L317: Supplemental Figure 6 precedes Supplemental Figure 5 in the text, which is neither logical nor orderly.

Thank you for your suggestion. Since the N-terminal DNAH12 antibody is already described in the Methods section (L317), we propose removing Supplemental Figure 6 from the content to improve the logical flow and maintain an orderly presentation.

(17) L345 and elsewhere: how did the authors quantify the decrement of the signal? This needs to be measured objectively.

Thank you for your valuable suggestion. We quantified the signal intensity using Fiji (Nat Methods. 2012. PMID: 22743772; PMCID: PMC3855844), which allows for precise analysis of pixel intensity. The results are presented in the figures to effectively illustrate the decrement in signal intensity. We appreciate your suggestion, and we have provided a description of the method in our methodology section.

(18) L371: I recommend: ...and elongated spermatids; the abnormal...

Done as suggested. Thank you.

(19) L412-4: Cilia in both Dnah12^mut/mut and Dnah12^-/- are developed, but are they motile or immotile? This needs to be investigated. Is the DNAH12 in cilia truncated while still fulfilling its function?

Thanks for your comment. We have checked the ciliary motility using an inverted microscope, and no significant difference of ciliary motility were observed between the knockout group and the control group. These results indicated that the ciliary motility was not affected by DNAH12 deficiency. The N-terminal DNAH12 antibody was developed to detect whether a truncated protein in mice tissues while we do not detect DNAH12 signals through immunofluorescence assay on trachea sections of the Dnah12^-/- mice. These results indicate that DNAH12 may exert little influence on cilia, comparing to its important function in flagella.

(20) L414-6: The results do not support this claim as the authors do not show that cilia are motile.

Thanks for your comment. The supplemental videos 3-4 of trachea live of Dnah12^+/+ and Dnah12^-/- mice have been uploaded to support this conclusion.

(21) L421-3: Did the authors perform a negative test, where they let the testis lysate interact with beads/IgG only and performed the MS to identify non-specific binding? This is a crucial specificity test for this approach.

We have performed negative test. In IP assay, we have added the IgG group in the IP-mass spectrometry to exclude non-specific binding. And the experimental design was described in Figure 6B. The raw data were deposited in iProX partner repository (PXD051681), which we have required the manager soon to update the status to public, so it will be visible to readers. 

(22) L462: same as #18 the authors need to show that cilia are also motile. The mere presence of cilia in DNAH12-/- as shown in Fig S11C&D is not sufficient to conclude that the mice do not manifest PCD symptoms.

Thanks for your comment. We do not observe obvious differences between the cilia of Dnah12^+/+ and Dnah12^-/- mice.  The supplemental videos 3-4 of trachea live of Dnah12^+/+ and Dnah12^-/- mice have been uploaded to show the motility of the trachea.

(23) L529: MTBD region instead of domain, as "domain" is already part of the abbreviation.

Done as suggested

(24) L875: Sperm is both the singular and plural form. Spermatozoon vs spermatozoa can be used where the distinction between singular and plural needs to be made.

Thanks for your suggestion. We have checked and changed this usage.

(25) Figure 3H: Is there a specific reason why P11 is not shown?

Because limited smear slides of P11 were available, the P11 were not stained for DNAH17 antibody previously. We have now updated the experiment, which showed that DNAH17 expression were not affected in patient P11. We have now added this result to Figure 3H.

(26) Figure 8H: The authors in their MS do not describe what is happening to N-DRC proteins, yet they suggest in their model that it's unaffected in the mutant mouse/human. Please, address this in the MS and clearly state in the model that N-DRC needs further exploration in future studies.

Thanks for your suggestion, we have checked the MS data but do not observe the enrichment of nexin-dynein regulatory complex (N-DRC) protein, just one known N-DRC protein DRC1 present with only 1 unique peptide. Instead, enrichment of inner dynein arm proteins and radial spoke proteins were observed. However, we cannot determine the N-DRC structures maybe affected or not. We have stated this in the discussion part and will pursue this with high resolution technology like cryo-EM in the future.

(27) Figure 5F: Is it possible to choose a different Dnah12^-/- spermatozoon to see a reduced level of DNALI1 so that it corresponds with the WB detection in Fig 5B?

Thanks for your suggestion, we have chosen a Dnah12^-/- spermatozoon with faint remnants of the DNALI1 signal as the representative picture.

(28) Figure S2 and elsewhere: How were the authors able to resolve and calibrate 356 kDa protein using SDS PAGE? Agarose electrophoresis protein electrophoresis is more suitable for resolution of high molecular proteins. Most of the protein standards have as high molecular standard as 250 kDa.

We have found that high molecular proteins (like 356kDa) were able to resolve in concentration 4-12% gradient gel of polyacrylamide gels and employ appropriate voltages and more time during electrophoresis to improve resolution of high molecular weight proteins. The DNAH12 proteins were calibrated by the using of a HiMark™ Pre-Stained High Molecular Weight Protein Standard (30-460 kDa). We have now updated the blot images to show the size of the DNAH12 protein (Fig S6B,). The target band is obvious between 268 kDa and 460 kDa, which make it easy to calculate the target band of DNAH12 antibody elsewhere. Thanks for your suggestion.

(29) Figure S5: similar to #24: Why P10 and P11 are not shown?

Because limited smear slides of P10 or P11 were available, we did not stain ODF2 antibody previously. We have now updated the experiments, which showed that ODF2 expression were not affected in patient P10 or P11. We have now added this result to Figure S5.

(30) Figure S6B: The specificity of the anti-DNAH12 antibody against mouse DNAH12 seems to be questionable since the authors detect multiple bands on WB. I recommend doing peptide blocking to show that these are non-specific binding as opposed to off-target binding.

Thank you for your comments. In order to study in-depth, we have analyzed the protein sequence features of DNAH12 protein, 1-200 amino acids of DNAH12 were selected as the ideal antigen considering its good performance (1. high immunogenicity; 2. High hydrophilicity; 3. Good Surface Leakage Groups; 4. Sequence homology analysis to avoid unspecific recognition to other proteins;). The two different anti-DNAH12 antibodies were developed with the help Dia-An Biotech company in 2022, we have attempted to acquire the polypeptide fragments of target proteins to do peptide blocking but the material were disposed after the service. Luckily, we have got the target band of DNAH12 protein which showed strong signal in western blotting experiment and the band was not detected in knockout mice group; the immunofluorescence signals of DNAH12 were strong but not present in knockout mice group. Besides, we have tested that the inhouse raised rabbit antibody was suitable for IP experiment. The IP experiment also showed the raised rabbit antibody were able to immunoprecipitated the DNAH12 band in the Dnah12^+/+ mice but not in Dnah12^-/- mice. Collectively, these data could support the specificity of the raised DNAH12 antibodies. We admire your suggestion and will require for the peptide material if we develop new antibodies.

Reviewer #2 (Recommendations For The Authors):

Recruitment of DNAH1 and DNALI1 to the flagella is dependent on DNAH12 expression, according to the data. What would be the mechanism that locates DNAH12 which lacks MTBD to the flagella?

Thank you for your insightful question. We are currently investigating the mechanisms that facilitate the loading of DNAH12 to the flagella. Based on existing data, we hypothesize that CCDC39 and/or CCDC40 may play a critical role in the recruitment of DNAH12 to sperm flagella during spermiogenesis (Nat Genet. 2011, PMID: 21131972; PMCID: PMC3509786; Nat Genet. 2011, PMID: 21131974; PMCID: PMC3132183). Furthermore, a structural study by Walton et al. showed that DNAH12 associates with CCDC39/CCDC40 proteins (Nature. 2023, PMID: 37258679; PMCID: PMC10266980). These findings suggest that CCDC39 and/or CCDC40 may play a role in facilitating the localization of DNAH12 to the flagella. Additional studies are needed to identify other potential factors involved in this process and to further elucidate the mechanisms underlying this complex biological phenomenon.

Reviewer #1 (Public review):

Summary:

The paper demonstrated through a comprehensive multi-omics study of the oviduct that the transcriptomic and proteomic landscape of the oviduct at 4 different preimplantation periods was dynamic during natural fertilization, pseudopregnancy, and superovulation using three independent cell/tissue isolation and analytical techniques. This work is very important for understanding oviductal biology and physiology. In addition, the authors have made all the results available in a web search format, which will maximize the public's access and foster and accelerate research in the field.

Strengths:

(1) The manuscript addresses an important and interesting question in the field of reproduction: how does the oviduct at different regions adapt to the sperm and embryos for facilitating fertilization and preimplantation embryo development and transport?<br /> (2) Authors used cutting-edge techniques: Integrated multi-modal datasets followed with in vivo confirmation and machine learning prediction.<br /> (3) RNA-seq, scRNA-seq and proteomic results are immediately available to the scientific community in a web search format<br /> (4) Substantiated results indicate the source of inflammatory responses was the secretory cell population in the IU region when compared to other cell types; sperm modulate inflammatory responses in the oviduct; the oviduct displays immuno-dynamism.

In addition, the revised version has addressed weaknesses adequately.<br /> (1) The revised version provided a clear explanation and the rationale for using the superovulation model.<br /> (2) The revised version generated a graphic abstract/summary of their major findings.

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Reply to the reviewers

Manuscript number: RC-2024-02788

Corresponding author(s): Kazuhiro, Aoki and Yuhei, Goto

1. General Statements [optional]

We sincerely thank all reviewers for their insightful comments and constructive suggestions that have substantially improved our manuscript. We provide point-to-point responses to each comment and added detailed explanations in the preliminary revised manuscript. The reviewers' comments are shown in dark blue italics, followed by our responses.

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.

• *

Reviewer #1

Major Concerns

1. • Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes. While Cdc2 protein levels are indeed stable in whole cells as you noted, we specifically measured nuclear Cdc2-miRFP670 levels. A previous study has shown that nuclear Cdc2 levels fluctuate throughout the cell cycle, increasing during interphase and decreasing during mitosis (Curran et al*., 2022). This known behavior of nuclear Cdc2 is consistent with our observation.

To address your concerns about potential artifacts from fluorescent protein tagging to endogenous Cdc2 and Cdc13, we will perform two additional experiments:

1. Compare protein expression levels between wild-type and fluorescently tagged strains for Cdc2 and Cdc13 using western blot analysis.

2. Examine whether the fluorescent tags affect cell cycle progression by measuring cell cycle duration in tagged versus untagged strains using time-lapse imaging.

3. The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.*

Thank you for this valuable suggestion. We will expand our photobleaching analysis to include green fluorescent proteins, specifically mEGFP and the recently developed mStayGold as well as mNeonGreen. These measurements will be conducted under identical experimental conditions to our red fluorescent protein analysis, allowing for direct comparison of their photostability properties. This additional data will provide a more comprehensive evaluation of fluorescent protein options for FCCS.

• In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.*

We plan to conduct FCCS measurements with reversed fluorophore combinations in HeLa cells to validate our experiments. Specifically, we will compare Kd values between:

1. cyclin D1-miRFP670 and CDK4-mNG pair versus cyclinD1-mNG and CDK4-miRFP670 pair
2. cyclin D3-miRFP670 and CDK6-mNG pair versuscyclin D3-mNG and CDK6-miRFP670 pair.
3. We also plan to do it in fission yeast cells comparing Kd values between: Cdc13-miRFP670 and Cdc2-mNG pair versus Cdc13-mNG and Cdc2-miRFP670 pair Reviewer #2

SectionA

Major Comments

(ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 EGFexpression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?

We would like to thank you for raising these important comments and suggestions about cell cycle stage determination. We agree that using Cdc13-mNG levels alone as a cell cycle marker requires more rigorous validation.We will incorporate cell length measurements as an independent cell cycle stage indicator for FCCS measurements. However, it is important to note that traditional cell cycle stage classification is limited in fission yeast cells due to its unique cell cycle characteristics; a brief G1 phase, continuous S phase during cell separation, and an extended G2 phase. Cdc13 expression keeps at the undetectable level during G1 and S phases, and therefore this inevitably restricts our FCCS measurements to G2 and M phases. G2 and M phase cells can be distinguished by the characteristic relocalization of Cdc2 and Cdc13 to the mitotic spindle during the M phase (Sugiyama et al., 2024). In the revised manuscript, we will demonstrate the FCCS data with both quantitative (cell length) and qualitative (G2 and M phase localization pattern) indicators for more precise cell cycle staging.

(iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.

We agree with the reviewer's comment, which suggests validation of the functionality of tagged proteins. We will perform two key control experiments:

1. Compare Cdc13 protein expression levels between wild-type cells and cells expressing Cdc13-mNG and Cdc2-miRFP670 using western blot analysis with anti-Cdc13 antibody.
2. Measure cell cycle duration in both strains through time-lapse microscopy to assess any potential effect of the fluorescent tags on cell cycle progression. Major points

(ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.

Thank you for highlighting the need for more rigorous statistical analysis. We will calculate and add the confidence intervals for all Kd values of each cyclin-CDK pair.

(iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?

We appreciate the reviewer's valuable comment regarding the determination of low affinity interactions. As mentioned above, we are currently calculating confidence intervals for our curve fitting analyses across all measurements. Based on these statistical analyses, we will carefully evaluate the reliability of the >10 µM designations and revise our descriptions accordingly in the manuscript to ensure accurate representation of the binding parameters.

• *

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

Reviewer #1

Major Concerns

• The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase).*

These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.

We acknowledge the important issues about the limitations of using overexpressed proteins for Kd measurement. Indeed, several factors affect the reliability of our measurements. At first, competition between overexpressed and endogenous proteins varies throughout the cell cycle due to cell cycle-dependent fluctuations in endogenous cyclin levels. Indeed, we had analyzed the effects of the overexpression on in vivo Kd measurements with FCCS (Sadaie, Mol Cell Biol, 2014), showing that not only endogenous proteins but also competitive binding proteins affect Kd values quantified in living cells. Second, variable expression levels from transient transfection may impact T-loop phosphorylation of CDKs, which is known to be rate-limiting (Merrick et al., 2008). We have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 25, line 16-18). We also note that our overexpression experiments may not fully capture the formation of previously reported unconventional complexes, such as those between CDK1 and D- or E-type of cyclins (Koff et al. 1992; Zhang et al. 1993) (page 26, line 8-10).

• Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.*

We agree that the observed persistence of Cyclin E into mitosis differs from the known behavior of endogenous Cyclin E, which is typically degraded during late S and G2 phases. This discrepancy is likely due to our overexpression system overwhelming the normal degradation machinery. In the revised manuscript, we have explicitly acknowledged this limitation and discuss how overexpression may alter the typical cell cycle-dependent regulation of cyclin proteins (page 26, line 12-16). This observation further highlights the importance of considering expression levels when interpreting protein-protein interaction data from overexpression systems.

Minor Comments

1. • The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).* Thank you for your suggestion. We have cited these two papers in introduction (page 6, line 5-8).

2. The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".*

We removed the word "better".

• In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.*

We have changed the color codes to make them easy to distinguish.

• The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.*

We apologize for the unclear description. All experiments involving miRFP670 imaging, including those in Figure 1, were performed using fission yeast cells expressing SynPCB2.1 under the control of the adh1 promoter. We have clarified these important experimental details in the revised manuscript under the section "miRFP670, a near-infrared fluorescent protein, is suitable for simultaneous imaging with mNeonGreen."

• The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.*

Our cell volume estimate was based on HeLa cells reported by our previous work (Aoki, PNAS, 2011). In our study, total cell volume was determined using differential interference contrast microscopy, while nuclear volume was measured through Höechst 33258 fluorescence imaging. While we reported average volumes from 20 cells, we acknowledge that the cell cycle stage was not specified in our measurement. We have added these experimental details to the revised manuscript (page 15, line 7-9), noting that cell volumes vary with cell cycle stage.

• Including page and/or line numbers would facilitate future revisions.*

We have added page numbers and line numbers throughout the revised manuscript.

Reviewer #2

Section A

Major Comments

(i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"

We apologize for not providing sufficient details about the fitting constraints. In the revised Material and Methods section (page 11, line 20-21) and (page 13, line 8-9), we have specified the initial parameter estimates and their boundary conditions used in our fitting process. These have included explicit numerical values for all parameters and the physical reasoning behind each constraint.

Minor points

*(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (We have changed the color palette to make them easy to distinguish.

SectionB

Major points

(i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.

We acknowledge this important point about the complexity of cyclin-CDK interactions in cellular context. Our current analysis, which assumes simple binary interactions between overexpressed proteins, has several limitations as the reviewer suggested:

1. As demonstrated by Pines laboratory's work with CDK1-cyclin B1 FCCS, dissociation constant can vary throughout the cell cycle, suggesting regulation by additional factors.
2. Both cyclins and CDKs interact with multiple binding partners in cells, and therefore the analysis with binary interaction does not account for.
3. Overexpression of exogenous proteins may alter the balance of these interactions. While our previous studies (Sadaie, MCB, 2014; Komatsubara, JBC, 2019) cited in the manuscript have addressed similar considerations, we agree that this aspect requires more thorough explanation. We have expanded our explanation in the results section (page 16, line 26-page17, line 8) and discussion part (page 26, line 7-23).

(iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.

We acknowledge the limitations of the current approach that averages dissociation constants across the cell cycle. The Pines laboratory's work revealed cell cycle-dependent variations in the dissociation constant for Cyclin B1-CDK1, suggesting complex regulation beyond simple binary interactions. These variations likely reflect both changes in cyclin expression levels and the involvement of additional regulatory factors throughout the cell cycle. While our comprehensive survey of multiple cyclin-CDK pairs provides a useful overview of relative binding preferences, we agree that a more focused analysis of selected pairs across different cell cycle stages would offer deeper mechanistic insights. We have expanded our discussion to address the significance of cell cycle-dependent changes in binding affinities and the potential role of additional regulatory factors as well as the trade-offs between breadth and depth in studying cyclin-CDK interactions (page 26, line 7-23).

Minor Points

(i) For both Figures 3 and 4 address red/green color pair choice.

We have modified the color codes in Figures 3 and 4.

**Referee cross-commenting**

I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.

We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:

(i) Competition between tagged and endogenous proteins

(ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).

(iii) Cell cycle dependent protein expression

Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.

As mentioned above, we have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 26, line 7-23).

Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.

We are sorry, but we could not fully understand what the reviewer wanted to ask.

We also have shared concerns about the data presentation in Figure 4.

According to the suggestion, we have modified Figure 4.

• *

4. Description of analyses that authors prefer not to carry out

Reviewer #2

Major Comments

(iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.

We appreciate the suggestion to analyze cell cycle-dependent changes in dissociation constants using synchronized cells. However, we have deliberately chosen not to use cell synchronization methods in fission yeast for several important reasons. During cell cycle arrest, cells continue to grow and synthesize proteins, leading to cell elongation and abnormal accumulation of Cdc13. These unphysiological perturbations are evidenced by the unusually rapid progression through the subsequent cell cycle following release. Such conditions deviate significantly from normal cellular physiology. One of the key advantages of FCCS is its ability to measure protein-protein interactions in individual, asynchronous cells. While traditional biochemical analyses require cell synchronization to obtain population-averaged measurements, they inherently suffer from the artifacts mentioned above.

Instead, as described in (ii), we will utilize cell length as a natural indicator of cell cycle progression in fission yeast, allowing us to examine the relationship between cell cycle stage and Kd values while maintaining normal cellular physiology.

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Referee #2

Evidence, reproducibility and clarity

Summary

In the first part of the manuscript the authors present a thorough description of the background and theoretical basis to the identification of a fluorescent pair that permits both FCS and FCCS measurements at the single cell level to enable the determination of Kd values between labelled protein pairs (Figures 1 and 2). The generation of the reagents and subsequent experimental details are thorough and would permit the experiments to be repeated. The first two sections are well argued and appropriately controlled.

They then tag the endogenous S. pombe cdk1 and cdc13 genes at their 3' ends with sequences that encode miRFP670 (a near infrared fluorescent protein) and mNG (mNeonGreen) respectively and from measurements collected on 13 cells derive a mean Kd value calculated for each of the 13 cells of 0.31{plus minus}0.22 μM. They note that this value agrees with that reported by the Pines lab following labelling of cyclin B1 and CDK1 with genome editing in RPE-1/hTERT cells.

The final part of the manuscript then extends the technique to a pair-wise analysis of 9 cyclins and 4 CDKs in a human cell line.

Major Comments

(i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"

(ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 expression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?

(iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.

(iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.

Minor points

(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (< 5%) (Fig. 1C)." be supported by additional labelling on the figure panel.

The manuscript then goes on to describe the measurement of Kds for 36 CDK-cyclin pairs in HeLa cells by overexpression of labelled CDKs and cyclins following transient overexpression by plasmid co-transfection. This last section of the manuscript requires significant revision.

Major points

(i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.

(ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.

(iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?

(iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.

Minor Points

(i) For both Figures 3 and 4 address red/green color pair choice.

Referee cross-commenting

I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.

We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:

(i) Competition between tagged and endogenous proteins

(ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).

(iii) Cell cycle dependent protein expression

Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.

Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.

We also have shared concerns about the data presentation in Figure 4.

Significance

Technology: The paper describes a technical advance in identifying a fluorescent probe pair suitable for FCCS in living cells.

Cell cycle: The ability of CDKs and cyclins to discriminate each other and pair to form complexes that characterise different cell cycle stages and drive progression has long been appreciated. The formation of non-cognate pairings when the cell cycle is perturbed has also been noted and a greater understanding of the in-cell affinities of all possible CDK-cyclin complexes would be a significant advance in our understanding. However, this manuscript currently does not (i) provide statistically validated measures of apparent differences in affinity between different CDK-cyclin pairs and (ii) address whether the measurements are cell cycle dependent. (iii) Interpretation of the results has to take into consideration that both the CDK and cyclin components are transiently over expressed in cells and therefore the values that are measured are difficult to interpret in terms of CDK and cyclin function. These considerations would dampen interest in the findings by cell cycle biologists.

Expertise: CDKs, cyclin, cell cycle biology.

Non-expert in technical aspects of fluorescence microscopy

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Referee #1

Evidence, reproducibility and clarity

Major Concerns

1. Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes.
2. The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.
3. In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.
4. The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase). These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.
5. Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.

Minor Comments

1. The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).
2. The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".
3. In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.
4. The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.
5. The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.
6. Including page and/or line numbers would facilitate future revisions.
7. Fig. 4I would benefit from providing actual Kd values alongside the color-coded representation.

Significance

In this study, Toyama and colleagues characterize a novel low-bleaching fluorophore pair to detect protein-protein interactions through FCCS. They demonstrate that while red-fluorescent proteins bleach rapidly, NeonGreen and iRFP670 are relatively stable over time and applicable to both yeast and mammalian cells. Furthermore, they apply their system to cyclin-Cdk pairs and describe a clever approach to enhance the brightness of iRFP670 in mammalian cells. The data are clear and the identification of suitable fluors for FCCS will be of value to the field; however, there are several major concerns that need to be addressed before publication.

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Reply to the reviewers

Reply to the Reviewers

We sincerely appreciate your insightful and constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript.

Reviewer #1

Evidence, reproducibility and clarity

The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).

2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.

Response:

Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.

2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?

Response:

As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.

The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.

As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 7).

3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?

Response:

In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 12). Therefore, by E21, the sound localization circuit is largely established.

On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume.

Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.

4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

Response:

In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.

As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).

Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).

Significance

This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

Response:

This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the post-functional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.

__ __

Reviewer #2

Evidence, reproducibility and clarity

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major points, detailed below, need to be addressed to overcome some limitations of the study.

Major comments:

1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

Response:

eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.

One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.

Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 {plus minus} 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 5).

2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

Response:

Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 {plus minus} 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.

On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.

To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we plan to add a new Supplementary Figure. Specifically, we will evaluate the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. Preliminary data indicate that, unlike the results in Figure 6D, sparse eTeNT expression did not contribute to an increase in heminodes and unmyelinated segments. This result would further support our argument that the increase in unmyelinated segments by A3V-eTeNT was due to a disruption of synaptic transmission between NM axons and NL neurons, which in turn altered the pericellular microenvironment at the NL region.

3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

__Response: __

Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.

3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

__Response: __

We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.

3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

__Response: __

In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.

3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

__Response: __

We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman's rank correlation coefficient (rₛ) was used for evaluation.

3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

__Response: __

We have now clarified what "n" represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.

In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.

To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592-019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.

If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.

3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

__Response: __

We have now incorporated individual data points into the bar graphs in Figures 5 and 6.

4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

__Response: __

The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 32).

5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

__Response: __

As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.

The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.

In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.

6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812). 6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

__Response: __

Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.

Minor comments:

7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.

__Response: __

Thank you for your suggestion. We have corrected the units in Figure 1G to µm

8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

__Response: __

Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.

Significance

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

__Response: __

The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density.

__ __

Reviewer #____3

Evidence, reproducibility and clarity

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:

1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

__Response: __

Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).

Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.

However, a key issue to be discussed in this study is not merely the presence or absence of pre-nodal clusters, but rather whether pre-nodal clusters-if present-would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression. If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.

2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

__Response: __

As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision.

In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions.

3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

__Response: __

The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).

Significance

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

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 #2

Evidence, reproducibility and clarity

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major points, detailed below, need to be addressed to overcome some limitations of the study.

Major comments:

1. It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.
2. Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?
3. The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:

3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?

3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.

3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)

3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).

3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6). 4. The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit? 5. Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended. 6. The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:

6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)

6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)

6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).

6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

Minor comments:

1. Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.
2. The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?

Significance

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

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 manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:

1. Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
2. Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.

These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:

1. The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
2. In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
3. The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
4. The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?

Significance

This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.

Author response:

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

Reviewer #1:

Regarding the manuscript's clarity, the sentence on page 5, "We also stained VTA sections for Tyrosine hydroxylase (TH) to estimate the rate of ChR2 colocalization with DA neurons," reads awkwardly. Removing the word "rate" could improve clarity.

We have made the recommended clarifying edit (page 5, lines 30-31).

Additionally, the anatomical data and findings are largely non-quantitative in nature. However, solid microscopy images are presented to support each claim. Additional quantification would strengthen the paper, specifically the quantification of projection density for each population and the proportion of each subpopulation that projects to their regions of interest.

To rigorously quantify the projection density of each subpopulation would require a level of exhaustivity our study was not designed for. This is because during microscopy we focused efforts on imaging regions containing dense signals but did not exhaustively image regions receiving apparently weak or no input. While we considered including a semi-quantitative table of projection density, based on the data available we could not discriminate with confidence between, e.g., regions recipient of minimal input versus no input from VTA populations. Thus, while we stand by our descriptive statements we do not expand on those further.

The authors should consider discussing the possibility that subpopulations of these cells could still be true interneurons especially if cells were looked at the single neuron level of resolution.

We agree that some of the VTA populations we studied could include subpopulations that are bona fide interneurons. The identification of alternate markers or combinations of markers, or use of single-cell imaging approaches may indeed support this possibility in future. This is discussed in the context of currently available evidence on page 5 lines 32-34, page 11 lines 2-4, page 12 lines 2-11, and page 12 lines 15-16.

Overall, the paper is well-written and important for the field and beyond.

Thank you!

Reviewer #2:

Weaknesses:

While the authors use several Cre driver lines to identify GABAergic projection neurons, they then use wild-type mice to show that projection neurons synapse onto neighboring cells within the VTA. This does not seem to lend evidence to the idea that previously described "interneurons" are projection neurons that collateralize within the VTA.

We think the use of WT mice is a strength because it allows us to measure both GABA and non-GABA synapses made by VTA projections on to the same cells within VTA. However, we have also done this experiment targeting NAc-projecting VTA VGAT-Cre neurons, and VP-projecting VTA MOR-Cre neurons. Consistent with the WT dataset, we find that these defined projection neurons also make intra-VTA synapses. These data are now included as Figure 7.

More broadly. Our review of the literature finds very little evidence to support the notion of a VTA interneuron as we define it: VTA neurons that makes only local connections. But the absence of evidence need not imply evidence of absence, thus we do not claim that all VTA neurons previously presumed to be interneurons must be projection neurons. We do express confidence in our findings that VTA projection neurons (that include GABA-releasing neurons) make local synapses in VTA. We argue that in the absence of compelling positive evidence for the existence of VTA interneurons, such as a selective marker, “we”, “the field”, should not presume their existence.

Other suggestions:

(1) While the authors present evidence that some projection neurons also synapse locally, there is no quantification as to the proportion of each neuronal subtype that collateralizes within the VTA. This would be a useful analysis.

We agree this would be useful information. But our experiments were not designed to answer this question. Indeed, we have not conceived of a feasible method to discriminate between collateralizing and non-collateralizing VTA projection neurons at the single-cell level, thus we do not know how we would calculate such proportions.

(2) There is significant interest in the molecular heterogeneity and spatial topography of the VTA. Additional analyses of the spatial topography of labeled projectors would be useful. For example, knowing if Pvalb+ projection neurons are distributed throughout the VTA or located along the midline would be a useful analysis.

Prior studies and public databases (e.g., Allen brain atlas, GENSAT) allow one to visualize the location of VTA neurons positive for Pvalb and the other markers we investigated (Olson & Nestler, 2007). However, these label the entire population of neurons and thereby include those that project to any of the various projection targets. There are also studies that have used retrograde labeling approaches to map the distribution of labeled VTA cells projecting to one or another target (Beier et al., 2015; Lammel et al., 2008; Margolis et al., 2006). For example, finding that LHb-projecting neurons (a major target of Pvalb+ VTA neurons) are enriched in medial VTA (Root et al., 2014). From this evidence we might infer that Pvalb+ VTA neurons that project to LHb are likely to be medially biased. Future studies may more carefully map the intersection of specific projection targets for each VTA subpopulation.  

Reviewer #3 (Recommendations For The Authors):

Weaknesses:

This study has a few modest shortcomings, of which the first is likely addressable with the authors' existing data, while the latter items will likely need to be deferred to future studies:

(1) Some key anatomical details are difficult to discern from the images shown. In Figure 1, the low-magnification images of the VTA in the first column, while essential for seeing what overall section is being shown, are not of sufficient resolution to distinguish soma from processes. A supplemental figure with higher-resolution images could be helpful.

We uploaded a higher resolution file for figure 1.

Also, where are the insets shown in the second column obtained from? There is not a corresponding marked region on the low-magnification images. Is this an oversight, or are these insets obtained from other sections that are not shown?

This was an oversight, we added the corresponding marked region to the low-magnification images.

Lastly, there is a supplemental figure showing the NAc injection sites corresponding to Figure 5, but not one showing VP or PFC injection sites in Figure 6. Why not?

We added a figure with histology examples for the VP and the PFC injection sites as done for Figure 5, included as Supplemental Figure 3.

(2) Because multiple ChR2 neurons are activated in the optogenetic experiments, it is not clear how common is it for any specific projection neuron to make local connections. Are the observed synaptic effects driven by just a few neurons making extensive local collateralizations (while other projection neurons do not), or do most VTA projection neurons have local collaterals? I realize this is a complex question, that may not have an easy answer.

This is a great question but, indeed, we don’t know the answer. As mentioned in response to Reviewer #2, we are not convinced there is a currently feasible way to discriminate between collateralizing and non-collateralizing cells at the single cell level.

(3) There is something of a conceptual disconnect between the early and later portions of this paper. Whereas Figures 1-4 examine forebrain projections of genetic subtypes of VTA neurons, the optogenetic studies do not address genetic subtypes at all. I do realize that is outside of the scope of the author's intent, but it does give the impression of somewhat different (but related) studies being stitched together. For example, the MOR-expressing neurons seem to project strongly to the VP, but it is not addressed whether these are also the ones making local projections. Also, after showing that PV neurons project to the LHb, the opto experiments do not examine the LHb projection target at all.

This too was raised by Reviewer #2. While addressing this question for all the populations we investigated feels redundant, we now include optogenetic data showing that NAc-projecting VTA VGAT-Cre and VP-projecting VTA MOR-Cre neurons also make local collaterals (Figure 7). We think this allows us to connect the two approaches to a greater degree. Based on our findings using a dual virus approach to express Syn:Ruby in each population of VTA projection neuron, we think it very likely that we’d continue to find similar results using optogenetics-assisted slice electrophysiology for each population.

Other suggestions:

(1) I appreciated the extensive and high-quality anatomical figures shown in Figures 2-4. However, the layout was sometimes left-to-right, and sometimes right-to-left, which felt distracting. At some point, the text refers to "Fig. 3KJ", i.e. with the letters being in backward alphabetical order, and Figures 3I and 3L do not appear mentioned anywhere in the main text, leading me to wonder if that text was intended to read "Fig. 3I-L".

Thank you for noting this. We have harmonized the layout of Figures 2-4 and adjusted the in-text Figure call-outs.

Also, the inset in Figure 3J appears to show local collaterals of NTS neurons in the VTA, since there is no soma in that inset. This is interesting, and worth reporting, but is not explained in either the main text or Figure legend.

We added a more complete description in the result section (page 6 line 25-30).

(2) Perhaps I missed it, but I could not find any mention of the intensity of the LED light delivered during the optogenetic experiments. While acknowledging that this can be variable, do the authors have at least a rough range?

We have added this information to the methods, page 17 line 8.

Editor's Note:

Should you choose to revise your manuscript, please double check that you have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

We confirm that we have fully reported all statistics including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals.

Note to Editor and Readers

While reanalyzing our data for resubmission, we discovered that some of the short-latency optogenetic evoked postsynaptic currents (oPSCs) we detected were erroneously categorized. Specifically, some VTA cells that showed large outward currents (oIPSCs) when held at 0 mV, also had small inward currents when held at -60 mV. These small inward currents were initially categorized as oEPSCs, suggesting these VTA cells received input from populations of VTA projection neurons that released GABA and/or glutamate. However, the kinetics of these small inward currents were slow and aligned with the within-cell kinetics of the oIPSCs, indicating that these were very likely mediated by GABA<SUB>A</SUB> receptors. In one case the opposite was apparent, with a small PSC initially miscategorized as an oIPSC. These miscategorized oEPSCs and oIPSC were presumably detected because our holding potentials were not precisely identical to the reversal potentials for GABA<SUB>A</SUB> and AMPA receptors, respectively. For this reason, we removed these 14 oEPSCs and 1 oIPSCs from our analyses in the revised version. The revised dataset suggests that VTA glutamate projection neurons may be less likely to collateralize widely within VTA compared to GABA projection neurons. But, importantly, this correction does not affect any of our conclusions.

Citations:

Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015). Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping. Cell, 162(3), 622-634. https://doi.org/10.1016/j.cell.2015.07.015

Lammel, S., Hetzel, A., Hackel, O., Jones, I., Liss, B., & Roeper, J. (2008). Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron, 57(5), 760-773. https://doi.org/10.1016/j.neuron.2008.01.022

Margolis, E. B., Lock, H., Chefer, V. I., Shippenberg, T. S., Hjelmstad, G. O., & Fields, H. L. (2006). Kappa opioids selectively control dopaminergic neurons projecting to the prefrontal cortex. Proc Natl Acad Sci U S A, 103(8), 2938-2942. https://doi.org/10.1073/pnas.0511159103

Olson, V. G., & Nestler, E. J. (2007). Topographical organization of GABAergic neurons within the ventral tegmental area of the rat. Synapse, 61(2), 87-95. https://doi.org/10.1002/syn.20345

Root, D. H., Mejias-Aponte, C. A., Zhang, S., Wang, H. L., Hoffman, A. F., Lupica, C. R., & Morales, M. (2014). Single rodent mesohabenular axons release glutamate and GABA. Nat Neurosci, 17(11), 1543-1551. https://doi.org/10.1038/nn.3823

Reviewer #2 (Public review):

In this work the authors develop a mathematical model that incorporates three contributions to cellular force generation in 3D matrices: (1) actively generated contractile forces via myosin motors and consumption of ATP; (2) the energy stored in the extracellular matrix as it is deformed by the contractile cell; and (3), the energy associated with the interactions at the interface between the matrix and the cell, e.g. at focal adhesions. The authors make predictions about the dependence of cell shape on these three contributions.

The authors succeed in making a number of predictions of how cell shapes will depend on these contributions to force generation. However, these predictions seem to be largely buried in the supplemental material and come in a form that will be accessible to a certain type of physicist and modeler but will likely not be accessible to many experimentalists who may want to test the predictions of the model. The authors show a comparison between their expected cell shape distributions and those predicted by the model, under multiple regimes: cells in two different concentrations of collagen (Figure 4c), cells with inhibited myosin and therefore reduced contractility (Figure 4d), cells with impaired interactions with the ECM (Figure 4e), and for cells with both contractility and ECM interactions impaired. They find a strong agreement between the experiments and their predictions. However, it should be noted that there are multiple "tuning parameters" in their model, so the ability to match experiment and theory may not be ultimately so surprising.

While the authors do achieve their aim of building this modeling and testing it in comparison to experimental data, the text is frequently unclear and doesn't seem to have the right information at the right place and time to allow the reader to most clearly understand the motivation, the approach, or the results. A number of elements of this manuscript were confusing to this reviewer, and I discuss these below in the hopes that raising these points here can bring more clarity in future revisions, and/or that readers will be able to provide additional insight or attention to these questions.

There are certain elements of the writing that obscure, rather than clarify, the model and the results. For example, the authors frequently refer to "matrix stiffening" and "strain stiffening", which are typically used in the literature to describe the phenomenon whereby an applied force changes the mechanical properties of the substrate; here, for example in regard to the discussion of Figure 4C, these terms instead seem to be simply referring to the experimental intervention of exposing different cells to different concentrations of the collagen matrix. While there may be some element of classically understood strain stiffening, incorporated into the model as the function f(λ_i), this doesn't seem to match the experimental validation - which, as described above, is not about strain stiffening but instead simply uses softer vs. stiffer gels. Therefore, it is unclear what exactly is meant throughout the manuscript by strain stiffening - does it mean "difference in stiffness between two conditions" or does it mean "change in substrate stiffness upon application of force"?

Furthermore, while the introductory text emphasizes collective migration, the model itself focuses on the interactions between single cells and their environments. The emphasis on collective migration and cell shape in the introduction invokes previous literature focusing on collective phase transitions, but that is misleading. This paper is all about individual cell mechanics, not about collective migration or unjamming.

The experimental validation seems to have a significant flaw. The mechanics and interactions of the cellular extensions seem to be completely ignored. We see, in Figure 4, that cell bodies are outlined to determine cell shape, but that the extremely long extensions are simply ignored. We know from previous studies that these extensions are generating quite a bit of traction and are contractile, and yet they've been excluded from the analysis. This doesn't make physical sense or fit with previous literature, and would seem to indicate that the regimes predicted by the model are missing an essential component of force generation and cell-matrix interaction.

亚里士多德的色彩理论

亚里士多德（Aristotle，公元前384–322年）是古希腊哲学家和科学家，他的色彩理论是古代色彩研究的重要组成部分。尽管他的理论在现代科学视角下存在局限性，但在当时具有开创性意义，并对后世的色彩研究产生了深远影响。

一、理论背景

1. 哲学基础：
2. 亚里士多德的色彩理论基于他的自然哲学，尤其是四因说（质料因、形式因、动力因、目的因）和元素理论（土、水、火、气）。

3. 他认为色彩是物体表面的一种属性，与光线的相互作用密切相关。

4. 观察与实验：

5. 亚里士多德通过观察自然现象（如彩虹、光线透过不同介质的变化）提出了色彩的形成机制。
6. 他的理论虽然缺乏现代实验手段，但体现了早期科学思维的萌芽。

二、核心理论内容

1. 色彩的基本分类：

2. 亚里士多德将色彩分为简单色和复合色：

   • 简单色：白、黑、黄、红、绿、蓝、紫。
   • 复合色：由简单色混合而成，如橙色（红+黄）。

3. 色彩的形成机制：

4. 亚里士多德认为色彩是光线与物体表面相互作用的结果。

5. 他提出透明介质理论：光线通过透明介质（如空气、水）时，会被物体表面反射或吸收，从而产生不同的颜色。

6. 色彩与光的关系：

7. 亚里士多德认为光是一种透明介质的状态，而非独立实体。

8. 色彩是光与物体表面相互作用的结果，白色是光的完全反射，黑色是光的完全吸收。

9. 彩虹的解释：

10. 亚里士多德对彩虹的形成提出了早期解释，认为彩虹是阳光通过雨滴折射和反射的结果。
11. 他将彩虹的颜色分为红、黄、绿、蓝、紫五种。

三、理论的特点与局限性

1. 特点：
2. 系统性：亚里士多德首次尝试将色彩现象系统化，提出了色彩的分类和形成机制。
3. 哲学性：他的理论与其自然哲学紧密结合，体现了古希腊哲学的特点。

4. 观察性：基于对自然现象的观察，尽管缺乏实验验证，但具有早期科学思维的萌芽。

5. 局限性：

6. 缺乏实验支持：亚里士多德的理论主要基于观察和推理，缺乏现代科学的实验验证。
7. 色彩分类不完整：他的色彩分类未能涵盖所有可见光谱颜色，且对色彩混合的解释较为简单。
8. 光与色彩关系的误解：亚里士多德未能理解光的本质（如波长和频率），导致对色彩形成机制的解释存在偏差。

四、对后世的影响

1. 中世纪色彩研究的基础：
2. 亚里士多德的色彩理论在中世纪被广泛接受，成为欧洲色彩研究的基础。

3. 许多中世纪学者（如罗杰·培根）在其理论基础上进行了进一步研究。

4. 文艺复兴时期的启发：

5. 文艺复兴时期的艺术家和科学家（如达·芬奇）借鉴了亚里士多德的理论，探索光与色彩的关系。

6. 达·芬奇在其著作《论绘画》中多次引用亚里士多德的观点。

7. 现代色彩科学的铺垫：

8. 尽管亚里士多德的理论在现代科学视角下存在局限性，但其系统化的思维方式为后来的色彩研究提供了启发。
9. 牛顿的光谱实验（17世纪）部分修正了亚里士多德的理论，但也借鉴了其色彩分类的思想。

五、与现代色彩理论的对比

| 方面 | 亚里士多德理论 | 现代色彩理论 | |--------------|------------------------------------|----------------------------------| | 色彩分类 | 简单色（7种）与复合色 | 可见光谱（连续波长） | | 光与色彩关系 | 光为透明介质状态，色彩为光与物体相互作用 | 光为电磁波，色彩由波长决定 | | 彩虹解释 | 阳光通过雨滴折射和反射 | 阳光通过雨滴折射、反射和色散 | | 实验基础 | 基于观察和推理 | 基于实验和数学模型 |

六、总结

亚里士多德的色彩理论是古代色彩研究的重要成果，尽管在现代科学视角下存在局限性，但其系统化的思维方式和观察方法为后世的色彩研究奠定了基础。他的理论不仅影响了中世纪的学者，也对文艺复兴时期的艺术家和科学家产生了深远影响。亚里士多德的工作标志着人类对色彩现象从神话解释向科学探索的转变。

需要进一步了解亚里士多德理论的具体应用或与现代理论的对比案例吗？

亚里士多德的色彩理论

亚里士多德（Aristotle，公元前384–322年）是古希腊哲学家和科学家，他的色彩理论是古代色彩研究的重要组成部分。尽管他的理论在现代科学视角下存在局限性，但在当时具有开创性意义，并对后世的色彩研究产生了深远影响。

一、理论背景

1. 哲学基础：
2. 亚里士多德的色彩理论基于他的自然哲学，尤其是四因说（质料因、形式因、动力因、目的因）和元素理论（土、水、火、气）。

3. 他认为色彩是物体表面的一种属性，与光线的相互作用密切相关。

4. 观察与实验：

5. 亚里士多德通过观察自然现象（如彩虹、光线透过不同介质的变化）提出了色彩的形成机制。
6. 他的理论虽然缺乏现代实验手段，但体现了早期科学思维的萌芽。

二、核心理论内容

1. 色彩的基本分类：

2. 亚里士多德将色彩分为简单色和复合色：

   • 简单色：白、黑、黄、红、绿、蓝、紫。
   • 复合色：由简单色混合而成，如橙色（红+黄）。

3. 色彩的形成机制：

4. 亚里士多德认为色彩是光线与物体表面相互作用的结果。

5. 他提出透明介质理论：光线通过透明介质（如空气、水）时，会被物体表面反射或吸收，从而产生不同的颜色。

6. 色彩与光的关系：

7. 亚里士多德认为光是一种透明介质的状态，而非独立实体。

8. 色彩是光与物体表面相互作用的结果，白色是光的完全反射，黑色是光的完全吸收。

9. 彩虹的解释：

10. 亚里士多德对彩虹的形成提出了早期解释，认为彩虹是阳光通过雨滴折射和反射的结果。
11. 他将彩虹的颜色分为红、黄、绿、蓝、紫五种。

三、理论的特点与局限性

1. 特点：
2. 系统性：亚里士多德首次尝试将色彩现象系统化，提出了色彩的分类和形成机制。
3. 哲学性：他的理论与其自然哲学紧密结合，体现了古希腊哲学的特点。

4. 观察性：基于对自然现象的观察，尽管缺乏实验验证，但具有早期科学思维的萌芽。

5. 局限性：

6. 缺乏实验支持：亚里士多德的理论主要基于观察和推理，缺乏现代科学的实验验证。
7. 色彩分类不完整：他的色彩分类未能涵盖所有可见光谱颜色，且对色彩混合的解释较为简单。
8. 光与色彩关系的误解：亚里士多德未能理解光的本质（如波长和频率），导致对色彩形成机制的解释存在偏差。

四、对后世的影响

1. 中世纪色彩研究的基础：
2. 亚里士多德的色彩理论在中世纪被广泛接受，成为欧洲色彩研究的基础。

3. 许多中世纪学者（如罗杰·培根）在其理论基础上进行了进一步研究。

4. 文艺复兴时期的启发：

5. 文艺复兴时期的艺术家和科学家（如达·芬奇）借鉴了亚里士多德的理论，探索光与色彩的关系。

6. 达·芬奇在其著作《论绘画》中多次引用亚里士多德的观点。

7. 现代色彩科学的铺垫：

8. 尽管亚里士多德的理论在现代科学视角下存在局限性，但其系统化的思维方式为后来的色彩研究提供了启发。
9. 牛顿的光谱实验（17世纪）部分修正了亚里士多德的理论，但也借鉴了其色彩分类的思想。

五、与现代色彩理论的对比

| 方面 | 亚里士多德理论 | 现代色彩理论 | |--------------|------------------------------------|----------------------------------| | 色彩分类 | 简单色（7种）与复合色 | 可见光谱（连续波长） | | 光与色彩关系 | 光为透明介质状态，色彩为光与物体相互作用 | 光为电磁波，色彩由波长决定 | | 彩虹解释 | 阳光通过雨滴折射和反射 | 阳光通过雨滴折射、反射和色散 | | 实验基础 | 基于观察和推理 | 基于实验和数学模型 |

六、总结

亚里士多德的色彩理论是古代色彩研究的重要成果，尽管在现代科学视角下存在局限性，但其系统化的思维方式和观察方法为后世的色彩研究奠定了基础。他的理论不仅影响了中世纪的学者，也对文艺复兴时期的艺术家和科学家产生了深远影响。亚里士多德的工作标志着人类对色彩现象从神话解释向科学探索的转变。

需要进一步了解亚里士多德理论的具体应用或与现代理论的对比案例吗？

pg 4 - 75 year deficit equal to 3.5% of taxable payrolls The shift from 3.61-3.5% from 2023-2-24 is is the result of 3 assumptions 1) Economy: Greater than anticipated growth last year led to an increase in assumed level of productivity growth over the projection period 2) Disability Incidence: It has continued to drop driven largely by the strength of the economy and a stricter process for awarding benefits on appeal 3) Fertility: Has declined sharply because of lower marriage rates, high childcare, and lower fertility from which new immigrants arrive from

À partir de la transcription de la vidéo YouTube, voici un document de synthèse pour un briefing sur les classes préparatoires TSI, incluant des horodatages pour faciliter la référence aux témoignages spécifiques.

Thèmes Généraux

• Présentation des Parcours [0:00-0:03]:

  • Les anciens élèves de TSI présentent leurs parcours diversifiés après la prépa.
  • Importance du bac d'origine, souvent STI2D ou STL, comme base de leur formation.
  • Expériences variées en école d'ingénieur, en BUT ou en réorientation.

  • Difficultés et Défis en Prépa [1:00-2:00]

  • Le "choc" initial est un thème récurrent, lié à une charge de travail accrue.

  • Certains élèves rencontrent des difficultés dès la première année, d'autres en deuxième année.
  • La pression et la charge de travail sont identifiées comme des défis majeurs.

  • Concours, Réussite et Échecs [2:00-3:00]:

  • Les résultats aux concours varient, certains réussissent, d'autres échouent.

  • L'importance de la persévérance face à l'échec est soulignée.
  • La réorientation vers d'autres formations comme les BUT est une option viable.

  • Expérience en École d'Ingénieur [3:00-4:00]:

  • Le travail par projet et la pratique sont valorisés en école d'ingénieur.

  • La vie associative est un atout majeur pour le développement personnel.
  • Certains regrettent le manque d'encadrement comparé à la prépa.
  • Les écoles demandent de plus en plus d'exigence.

  • Stages et Expérience Professionnelle [4:00-5:00]:

  • Les stages sont vus comme des opportunités d'apprentissage et de découverte.

  • Ils permettent de développer des compétences et de préciser son projet professionnel.

  • Importance des Langues, Anglais en Tête [5:00-6:00]:

  • L'anglais est crucial pour l'obtention du diplôme et les opportunités à l'étranger.

  • Il ne faut pas négliger les langues.

  • Vie Associative et Engagement Étudiant [6:00-7:00]:

  • La vie associative développe des compétences et valorise le CV.

  • Elle offre des opportunités de gestion de projet et de travail en équipe.

  • Réflexions sur l'ENIM [7:00-8:00]:

  • L'ENIM est perçue comme une école à double tranchant, avec des réorientations possibles.

  • Réorientation et Adaptabilité [8:00-9:00]:

  • Des anciens élèves témoignent de leur réorientation après la prépa ou l'école d'ingénieur.

  • Conseils aux Futurs Élèves [9:00-10:00]:

  • Bien choisir son orientation et travailler régulièrement sont essentiels.

  • Il ne faut pas négliger les langues et s'entraider.
  • Il faut profiter de la vie étudiante.

  • Aptitudes acquises en prépa [10:00-11:00]:

  • La prépa apprend à apprendre, elle apprend la méthodologie pour maîtriser des sujets et pour sortir de sa zone de confort.

  • La prépa apprend à s'adapter.

  • Le rôle des professeurs [11:00-12:00]:

  • Les professeurs sont disponibles et offrent un bon encadrement.

Points Spécifiques et Anecdotes

• Expériences Personnelles :

  • Certains étudiants ont intégré la prépa un peu par hasard, poussés par leurs professeurs.
  • D'autres avaient ce choix en tête dès la seconde.

  • Stages à l'étranger:

  • De nombreuses écoles d'ingénieurs proposent des stages à l'étranger.

  • VIE:

  • Le Volontariat International en Entreprise est un contrat de 10 à 24 mois.

  • Il permet de travailler à l'étranger pour une entreprise tout en étant sous la protection de l'État.

  • L'importance du réseau:

  • Les anciens élèves peuvent aider les nouveaux à trouver des stages.

  • L'IA:

  • L'intelligence artificielle va transformer le monde du travail, il faut apprendre à l'utiliser.

Conclusion

Ce briefing vise à donner une vue d'ensemble des classes préparatoires TSI à travers les expériences de ceux qui les ont vécues. Il met en évidence les défis, les opportunités et les compétences clés pour réussir dans cette voie exigeante.

上图展示了绘画色彩混合术语的流程图，具体说明了如何通过将不同颜色（如白色、黑色、灰色等）与纯色混合来生成不同的色调。以下是对每个术语的详细解释：

1. Pure Color（纯色）

• 纯色是未经过任何其他颜色调整的原色。它是完全饱和且没有任何添加的颜色（如白色、黑色或灰色）。

2. Tint（色调）

• Tint是通过向纯色中添加白色来生成的颜色。这个过程使得颜色变得更浅，但保持了原始的色相。Tint表示了一种明亮的、带有白色成分的颜色。

3. Tone（调子）

• Tone是通过向纯色中添加灰色来改变的颜色。添加灰色会降低颜色的饱和度，减少它的鲜艳度，使颜色更加柔和或温和。Tone的形成使得颜色不那么鲜艳，但仍然保持了其基本的色相。

4. Shade（阴影）

• Shade是通过向纯色中添加黑色来生成的颜色。这个过程使得颜色变得更暗，增加深度和阴影效果。Shade表示了一种深色调，通常用于给颜色增加深度感。

5. Gray（灰色）

• Gray是由黑色和白色的混合物形成的颜色，它没有明显的色相，是一种中性颜色。灰色可以通过调整黑白比例来改变，通常用于调和其他颜色。

6. Black（黑色）

• Black是缺乏任何颜色的完全黑暗的颜色。它常常用于通过减少亮度或添加深度来调整其他颜色。

7. White（白色）

• White代表颜色的完全亮度，是一种没有色相的颜色。它是最明亮的颜色，常常用于通过增加亮度来调整其他颜色。

总结：

这个图解释了绘画中的色彩混合过程，展示了如何通过将白色、黑色或灰色与纯色进行不同的混合，生成色调（tint）、调子（tone）和阴影（shade）。这些术语帮助艺术家精确地控制颜色的亮度、饱和度和深度，从而创造出丰富的视觉效果。

RGB到HSV的转换是色彩模型中的一个重要操作，用于将RGB颜色空间（常用于屏幕显示）转换到HSV颜色空间（通常用于图形设计和图像处理）。RGB颜色模型是基于红、绿、蓝三种基色来表示颜色，而HSV颜色模型则通过色相（Hue）、饱和度（Saturation）和明度（Value）来表示颜色。

1. RGB到HSV转换的原理

RGB颜色模型是通过红色、绿色和蓝色的光强度来表示颜色，而HSV模型通过色相、饱和度和明度来表示颜色的属性。为了将RGB转换为HSV，我们需要计算这三个分量：H、S和V。

2. RGB到HSV的步骤

假设我们有一个RGB颜色值，其中： - R：红色分量，范围是0到255 - G：绿色分量，范围是0到255 - B：蓝色分量，范围是0到255

步骤如下：

2.1 归一化RGB值

首先，需要将RGB的值从0到255的范围归一化到0到1的范围。这是为了便于后续的计算。

[ R' = \frac{R}{255}, \quad G' = \frac{G}{255}, \quad B' = \frac{B}{255} ]

其中，\( R' \)、\( G' \)和\( B' \)是归一化后的RGB值，范围是 0到1。

2.2 计算最大值和最小值

计算归一化后的RGB分量的最大值（Max）和最小值（Min）：

[ \text{Max} = \max(R', G', B') ] [ \text{Min} = \min(R', G', B') ]

2.3 计算明度（V）

HSV中的明度（V）代表了颜色的亮度，它就是RGB值的最大值：

[ V = \text{Max} ]

2.4 计算饱和度（S）

饱和度（S）表示颜色的纯度，值越高表示颜色越鲜艳。饱和度的计算公式为：

[ S = \frac{\text{Max} - \text{Min}}{\text{Max}} \quad \text{if Max} \neq 0 ] [ S = 0 \quad \text{if Max} = 0 ]

如果Max为0，说明颜色是黑色，因此饱和度也为0。

2.5 计算色相（H）

色相（H）表示颜色的类型或色调，范围是 0° 到 360°。根据RGB的最大值和最小值，色相的计算方式不同。具体计算方法如下：

• 如果 Max = R'（即红色最大）： [ H = 60^\circ \times \left( \frac{G' - B'}{\text{Max} - \text{Min}} \right) ] 如果计算得到的 H 为负值，则加上360°（使其在0°到360°之间）。

• 如果 Max = G'（即绿色最大）： [ H = 60^\circ \times \left( \frac{B' - R'}{\text{Max} - \text{Min}} \right) + 120^\circ ]

• 如果 Max = B'（即蓝色最大）： [ H = 60^\circ \times \left( \frac{R' - G'}{\text{Max} - \text{Min}} \right) + 240^\circ ]

这些公式根据RGB的最大分量来确定色相的角度，确保它落在色环上合适的位置。

2.6 最终HSV值

• H：色相，范围是 0° 到 360°。
• S：饱和度，范围是 0 到 1。
• V：明度，范围是 0 到 1。

3. 完整的RGB到HSV转换公式

总结一下，将RGB值转换为HSV值的步骤：

1. 归一化RGB： [ R' = \frac{R}{255}, \quad G' = \frac{G}{255}, \quad B' = \frac{B}{255} ]

2. 计算Max和Min： [ \text{Max} = \max(R', G', B'), \quad \text{Min} = \min(R', G', B') ]

3. 计算明度（V）： [ V = \text{Max} ]

4. 计算饱和度（S）： [ S = \frac{\text{Max} - \text{Min}}{\text{Max}} \quad \text{if Max} \neq 0, \quad S = 0 \quad \text{if Max} = 0 ]

5. 计算色相（H）：

6. 如果 Max = R'：\( H = 60^\circ \times \left( \frac{G' - B'}{\text{Max} - \text{Min}} \right) \)
7. 如果 Max = G'：\( H = 60^\circ \times \left( \frac{B' - R'}{\text{Max} - \text{Min}} \right) + 120^\circ \)

8. 如果 Max = B'：\( H = 60^\circ \times \left( \frac{R' - G'}{\text{Max} - \text{Min}} \right) + 240^\circ \)

9. 最终结果：

10. H：色相（0°到360°）
11. S：饱和度（0到1）
12. V：明度（0到1）

4. RGB到HSV转换的示例

假设我们有一个RGB值为 (R=255, G=100, B=50)，我们将其转换为HSV。

1. 归一化RGB： [ R' = \frac{255}{255} = 1.0, \quad G' = \frac{100}{255} \approx 0.392, \quad B' = \frac{50}{255} \approx 0.196 ]

2. 计算Max和Min： [ \text{Max} = 1.0, \quad \text{Min} = 0.196 ]

3. 计算明度（V）： [ V = 1.0 ]

4. 计算饱和度（S）： [ S = \frac{1.0 - 0.196}{1.0} = 0.804 ]

5. 计算色相（H）：

6. 因为Max = R'，我们使用红色的公式： [ H = 60^\circ \times \left( \frac{0.392 - 0.196}{1.0 - 0.196} \right) = 60^\circ \times \left( \frac{0.196}{0.804} \right) \approx 14.6^\circ ]

7. 最终HSV值为： [ H = 14.6^\circ, \quad S = 0.804, \quad V = 1.0 ]

5. 总结

RGB到HSV的转换涉及到： - 归一化RGB值， - 计算颜色的明度（V）和饱和度（S）， - 根据最大值和最小值来计算色相（H）。

转换后的HSV模型使得我们可以直观地调节颜色的亮度（通过V），饱和度（通过S）以及色调（通过H）。这种模型非常适合图像编辑和颜色选择，因为它能够轻松地操作颜色的不同属性。

Voici un sommaire minuté avec des horodatages basés sur la transcription de la vidéo YouTube, mettant en évidence les points clés de la présentation sur Parcoursup:

• 0:00-2:37: Introduction et présentation des intervenants.

• 2:37-3:14: Présentation générale de Parcoursup.

• 3:14-4:04: Transparence des critères de sélection et des profils des admis.

• 4:04-4:24: Parcoursup agit pour l'égalité d'accès et la réussite des étudiants.

• 4:24-5:03: Mise à jour de Parcoursup et dates des portes ouvertes. Les portes ouvertes permettent aux élèves d'obtenir des réponses à leurs questions.

• 5:03-5:52: Calendrier Parcoursup et étape actuelle.

• 5:52-7:12: Accompagnement à l'orientation au lycée. L'accompagnement à l'orientation est obligatoire pour les élèves.

• 7:12-7:57: Ressources en ligne pour l'orientation (ONISEP, Parcoursup).

• 7:57-9:22: Types de formations : statut étudiant (sélectives et non sélectives) et statut apprenti. Il est conseillé de diversifier les vœux en incluant des formations sélectives et non sélectives.

• 9:22-11:04: Informations sur les formations en apprentissage et rôle des CFA. Les CFA peuvent aider à trouver un employeur pour les formations en apprentissage.

• 11:04-12:00: Complément sur l'apprentissage et possibilité de passer d'un statut scolaire à apprenti en cours d'année. Il est possible de commencer une formation sous statut scolaire et de continuer en apprentissage si une entreprise est trouvée.

• 12:00-15:34: Focus sur les cartes d'identité des formations et les six onglets principaux. Il est important de prendre le temps d'examiner chaque formation.

• 15:34-16:20: Nouveautés 2025 concernant les chiffres d'accès et les données chiffrées sur les poursuites d'études.

• 16:20-17:18: Modalités d'examen et rôle des commissions d'analyse des dossiers. Les commissions d'analyse des dossiers sont composées d'enseignants.

• 17:18-17:54: Visibilité des critères de candidature et données chiffrées.

• 17:54-18:26: Rappel de l'étape 2 : inscription, formulation des vœux et finalisation du dossier.

• 18:26-21:45: Activation du dossier Parcoursup et vérification des informations. L'activation du compte doit être faite par l'élève.

• 21:45-24:58: Formulation des vœux : liberté de choix, conseils des enseignants et données chiffrées.

• 24:58-27:41: Nombre de vœux possibles et vœux multiples. Il est possible de formuler jusqu'à 10 vœux sous statut scolaire et 10 vœux supplémentaires en apprentissage.

• 27:41-31:46: Focus sur les vœux multiples et les sous-vœux. Un vœu multiple compte pour un seul vœu parmi les 10 possibles.

• 31:46-34:23: Exemples de vœux multiples (BTS Métiers de la Chimie) et exceptions (IFSI).

• 34:23-35:55: Concours et regroupements d'écoles (écoles d'ingénieurs).

• 35:55-37:11: Vœux en apprentissage et absence de date limite pour les formations en apprentissage.

• 37:11-38:00: Le secteur géographique de recrutement et son impact.

• 38:00-41:41: Secteur géographique : formations sélectives vs non sélectives et académies de rattachement. Pour les formations non sélectives, une priorité est donnée aux candidats de l'académie.

• 41:41-44:34: Gestion des situations particulières : parents résidant dans des académies différentes.

• 44:34-45:34: Secteur géographique : cas de l'Île-de-France.

• 45:34-48:24: Année de césure : conditions et démarches. L'année de césure n'est pas accordée de droit et nécessite l'accord de la formation.

• 48:24-50:06: Rappel de la date limite pour formuler les vœux (13 mars) et importance de ne pas attendre le dernier moment.

• 50:06-51:15: Accompagnement pour les lettres de motivation.

• 51:15-54:48: Finalisation du dossier et confirmation des vœux : rôle du lycée et du candidat.

• 54:48-57:22: Conseils pour la lettre de motivation et importance de l'adapter à la formation. Il est déconseillé d'utiliser des intelligences artificielles pour rédiger la lettre de motivation.

• 57:22-58:46: La rubrique "Autres projets" : caractère obligatoire et confidentialité des informations.

• 58:46-1:01:20: Activités et centres d'intérêt : valorisation du profil et engagements extrascolaires.

• 1:01:20-1:02:32: Attestation spécifique pour les licences de droit.

• 1:02:32-1:07:25: Éléments transmis par l'établissement : bulletins scolaires, fiche Avenir, appréciations des enseignants. La fiche Avenir est remplie par les professeurs principaux et le chef d'établissement.

• 1:07:25-1:08:33: Fiche Avenir : compétences transversales et avis du chef d'établissement.

• 1:08:33-1:10:20: Parcours spécifiques, certifications (Pix) et importance de la certification Pix.

• 1:10:20-1:11:41: Précisions sur les absences et leur prise en compte.

• 1:11:41-1:13:03: Fiche Avenir : consultation à partir du 2 juin 2025.

• 1:13:03-1:13:42: Accompagnement pour les élèves en situation de handicap.

• 1:13:42-1:14:13: Rappel de la date limite du 2 avril et conséquences du non-respect de cette date.

• 1:14:13-1:15:16: Ce n'est pas Parcoursup qui décide de l'affectation, mais une commission.

• 1:15:16-1:15:40: Question sur la remontée de la certification Pix.

• 1:15:40-1:17:11: Politique d'égalité des chances et places priorisées pour certains bacs. Des places sont priorisées pour les bacs professionnels en BTS et les bacs technologiques en BUT.

• 1:17:11-1:18:03: Prochaine étape : accompagnement spécifique pour anticiper les réponses.

• 1:18:03-1:20:28: STAPS : formation sélective ou non sélective et demandes de bourse.

• 1:20:28-1:21:29: Bourses : le CROUS gère les bourses et il est possible de faire une simulation.

• 1:21:29-1:22:33: Calendrier du CROUS et contact pour les questions sur les bourses.

• 1:22:33-1:23:00: Remerciements et prochain YouTube live.

详细解释：芒塞尔对传统色彩教学方法的不足感到不满，认为色彩需要一种更科学、更系统化的表达方式

芒塞尔对传统色彩教学方法的批评和改进，源于他在艺术教育实践中发现的一系列问题。以下是详细分析：

一、传统色彩教学方法的不足

1. 主观性强，缺乏科学依据
2. 传统色彩教学主要依赖艺术家的经验和直觉，缺乏系统化的理论支持。

3. 示例：教师通常通过“感觉”指导学生调配颜色，而非基于可量化的规则。

4. 色彩描述模糊

5. 传统方法使用模糊的语言描述颜色（如“深红”“浅蓝”），无法精确传达色彩属性。

6. 示例：不同人对“深红”的理解可能存在显著差异。

7. 缺乏统一标准

8. 不同艺术家和学派对色彩的分类和命名方式各异，难以形成通用标准。

9. 示例：同一颜色在不同文化或艺术流派中可能有不同名称。

10. 忽视色彩的三维特性

11. 传统方法通常只关注色相（如红、蓝），而忽略了明度和彩度的重要性。

12. 示例：无法准确描述“明亮的深红”与“暗淡的深红”之间的区别。

13. 教学工具单一

14. 传统教学主要依赖色轮和有限的色卡，无法全面展示色彩的复杂关系。
15. 示例：色轮无法直观表现色彩的明暗变化和饱和度差异。

二、芒塞尔的改进思路

1. 科学化色彩定义
2. 引入色相（Hue）、明度（Value）和彩度（Chroma）三个维度，将色彩定义为可量化的科学对象。

3. 示例：用5YR 6/8精确描述一种黄红色相、中等明度、高饱和度的颜色。

4. 系统化色彩空间

5. 构建三维色彩空间模型，直观展示色彩之间的关系。

6. 示例：色相环展示色相变化，明度轴展示明暗变化，彩度轴展示饱和度变化。

7. 视觉均匀性优化

8. 通过实验确保相邻色块的视觉差异一致，使色彩空间更符合人眼感知。

9. 示例：在色相环中，红色到黄色的过渡与黄色到绿色的过渡在视觉上均匀。

10. 标准化色彩命名

11. 开发统一的色彩标号系统，避免模糊描述。

12. 示例：用10B 5/6代替“中等亮度的蓝绿色”。

13. 开发教学工具

14. 设计芒塞尔色立体模型和色彩图谱，帮助学生直观理解色彩关系。
15. 示例：色立体模型展示色彩的三维结构，色彩图谱提供具体颜色示例。

三、《A Color Notation》的核心内容

1. 色彩三维理论
2. 详细阐述色相、明度、彩度的定义及其相互关系。

3. 示例：色相环分为10种主色相，每种色相进一步细分为10级。

4. 色彩标号系统

5. 提出H V/C格式的色彩标号方法。

6. 示例：5YR 6/8表示黄红色相、中等明度、高饱和度的颜色。

7. 视觉均匀性实验

8. 描述如何通过实验调整色彩空间，确保视觉均匀性。

9. 示例：通过大量视觉匹配实验确定色块间距。

10. 教学应用案例

11. 展示如何将芒塞尔系统应用于艺术教育和色彩实践。
12. 示例：学生通过色立体模型理解色彩关系，并通过标号系统调配颜色。

四、芒塞尔系统的意义

1. 科学化色彩研究

2. 为色彩科学提供了可量化的研究工具，推动了色彩理论的发展。

3. 革新艺术教育

4. 系统化的教学方法帮助学生更高效地掌握色彩知识。

5. 跨学科应用

6. 在土壤科学、工业设计、数字色彩管理等领域得到广泛应用。

7. 标准化色彩表达

8. 统一的色彩标号系统解决了传统方法中的模糊性问题。

五、与传统方法的对比

| 方面 | 传统方法 | 芒塞尔系统 | |--------------|------------------------------|------------------------------| | 色彩定义 | 主观描述（如“深红”） | 科学化标号（如5YR 6/8） | | 教学工具 | 色轮、有限色卡 | 色立体模型、系统化色彩图谱 | | 色彩关系 | 模糊、难以量化 | 三维空间展示，视觉均匀 | | 应用领域 | 主要限于艺术创作 | 跨学科应用（科学、设计等） |

六、总结

芒塞尔通过科学化和系统化的方法，解决了传统色彩教学中的模糊性、主观性和局限性问题。他的工作不仅革新了艺术教育，还为色彩科学和跨学科应用奠定了基础。《A Color Notation》的出版标志着色彩研究进入了一个新的时代。

需要进一步了解芒塞尔系统的具体技术细节或应用案例吗？

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-2024-02831

Corresponding author(s): Charisios Tsiairis

1. General Statements [optional]

We are very pleased that all three reviewers found our work to be solid, well-supported by the data, and free of major flaws. It is particularly gratifying that they did not request additional experimental work to support our conclusions. Instead, their comments focused on clarifications, textual improvements, and refinements in data presentation, which we have carefully addressed.

• *

We have made revisions to improve the clarity of the manuscript, incorporating insightful suggestions from the reviewers. These include refining key explanations, adjusting figure annotations, and modifying the structure of certain sentences. Additionally, we have addressed specific points regarding statistical significance, genome assembly references, and phylogenetic comparisons, ensuring that all aspects of our study are as precise and informative as possible.

• *

We are confident that these revisions have strengthened the manuscript.

2. Point-by-point description of the revisions

*Reviewer #1 (Evidence, reproducibility and clarity (Required)): *

• *

*Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes. *

• *

*General Points: *

• *

• The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood. *

• *

The observed differences in upregulation are primarily technical. Expression levels are measured relative to unperturbed tissue, and in the control, Zic4 expression in the foot is detected only at noise levels (see figure 2C). As a result, any increase in Zic4 expression upon Gata3 knockdown appears relatively high when normalized to the minimal control levels. In contrast, Gata3 is already present at detectable levels in control samples from the upper body, head, and tentacles (See Fig 2D). Therefore, while its upregulation following Zic4 RNAi appears more modest, we interpret this as a qualitative indication of increased gene expression in the absence of the opposing transcription factor. That said, we acknowledge the possibility of additional regulatory layers contributing to these differences.

• *

• Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced. *

• *

This is a valid point; however, the interpretation is complicated by the technical limitations of RNAi-based knockdown rather than a complete knockout of Gata3. Over time, the effect of RNAi diminishes, and we have observed that GFP expression returns within four weeks following GFP RNAi, indicating a temporal limit to RNAi-mediated knockdown. Therefore, while an extended time course would be informative, the transient nature of the knockdown makes it challenging to definitively track long-term cell replacement dynamics.

• *

• The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers? *

• *

We appreciate the reviewer’s comment. In our previous publication (Vogg et al., 2022), we provided evidence supporting this phenomenon. As demonstrated in our data published there, markers of the peduncle, rather than the basal disc—such as manacle (gene ID 100212761) (Bridge et al., 2000) and Bmp5-8 (gene ID 100206618) (Reinhardt et al., 2004)—are also upregulated, suggesting a transition towards a peduncle-like state. However, we opted not to elaborate on this aspect in the current manuscript to maintain focus and avoid redundancy with previously published findings.

• *

*Specific Points: *

• *

*Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles. *

• *

Our intention was to illustrate the general presence of Zic4 in the oral domain, but we acknowledge the reviewer’s point that this could be misleading regarding its precise expression pattern. To address this concern, we have updated the figure panels to more accurately reflect the available in situ hybridization and scRNA-seq data.

• *

*Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend. *

• *

We thank the reviewer for catching this error. We have now corrected the references, replacing the numbering with the first authors' last names and publication dates.

• *

Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

• *

We appreciate the reviewer’s observation. The label refers to the budding zone, and we acknowledge our omission in specifying this. We have now updated the figure and its legend to clarify this.

• *

*Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper? *

• *

We appreciate the reviewer’s request for clarification. These conditions are indeed similar to those in the previously published Vogg et al. paper. The boxes in the figure represent proximal and distal tentacle regions, each with four replicates. We have now updated the figure and its legend to make this explicit.

• *

*Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative. *

• *

*We added the percentage of cells expressing Zic4, Gata3 and both genes on the panel. *

• *

*Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control. *

• *

To make the figure clearer, we modified it and grouped each experimental sample with its corresponding control.

• *

*Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups. *

• *

We have now expanded the statistical analyses, ensuring that significance tests are presented in all relevant instances. However, we note that while statistical significance is important, it should be interpreted alongside other factors such as the magnitude of the effect, consistent trends across replicates, and biological relevance. Additionally, high standard deviations in certain conditions may influence absolute p-values, and we encourage consideration of the broader context of the data when interpreting these results.

• *

*Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway). *

• *

We understand the reviewer’s concern. The p-value for the qPCR data is slightly above 0.05, primarily due to high standard deviation. As the reviewer notes, qPCR on RNAi samples can be noisy, so the data should be interpreted in context. Importantly, the consistent qualitative increase in Gata3 levels after Zic4 knockdown aligns with the RNA-seq results, which, as the reviewer correctly points out, provide a more reliable measurement. Additionally, qPCR samples include a broader portion of head tissue, likely diluting the Gata3 signal from the tentacles and contributing to the observed variability.

• *

*Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A. *

• *

We appreciate the reviewer’s suggestion. However, we believe that displaying co-expression UMAPs for Zic4 would be redundant. Additionally, for genes with greater positional overlap, such as FoxI1 and Nfat5, co-expression UMAPs make visualization more challenging. To ensure clarity and optimize the interpretability of the data, we have chosen to present the expression profiles of each gene separately.

• *

*Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly. *

• *

Indeed, the study does not focus on the functional role of Gata3 in these neurons. We have revised the sentence, replacing "involvement of Gata3 in specifying neurons" with "expression expression of Gata3 in neurons emerging*" to more accurately reflect the study’s findings. *

• *

*Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences. *

• *

We appreciate the reviewer’s concern. However, we have chosen to use the Hydra_RP_1.0 assembly for Figure 1 to maintain consistency with previously published data, which were also mapped to this assembly. Since these publications predate the newer assembly, using the same reference ensures comparability in our analysis. Importantly the assembly used is still downloadable and accessible to every researcher. That said, for the phylogenetic analysis in Figure 2, we have used the latest available genome assemblies and annotations for all species, including Hydra. We have now clarified this in the Methods section.

• *

*The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper). *

• *

We appreciate the reviewer’s suggestion and have updated the references accordingly to include Siebert et al., 2019, for data collection and Cazet et al., 2023, for the analysis used in this paper.

• *

Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

• *

We agree with the reviewer that the nematocyte marker HCR provides a more specific identification of nematocytes. This is why we have also used the nematocilin marker in separate samples. However, actin staining adds important information on the morphology of the surrounding epithelial cells, which become indistinguishable from battery cells in Gata3 KDs. Unfortunately, combining actin staining with HCR is technically challenging, as the tissue preparation protocols for these two approaches are not compatible, and we have therefore decided to show both stainings next to each other.

• *

*Minor Wording Issues: *

• *

*Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear. *

• *

We have made the suggested change.

• *

*Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset. *

• *

We are grateful for the suggestion, we have changed the text accordingly.

• *

*p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed. *

• *

We are referring to the upregulation of gata3 expression in tentacles upon Zic4 knockdown. To make this clearer, we have revised the wording to: “Q-PCR performed on dissected oral and aboral body regions confirmed the upregulation of gata3 upon Zic4 knockdown (Fig. 2C-D).”

• *

*Reviewer #1 (Significance (Required)): *

• *

*This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination. *

• *

*We appreciate the reviewer’s thoughtful summary and recognition of our study’s significance. *

• *

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

• *

*Summary: *

*The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia. *

• *

*Major comments: *

*- Are the key conclusions convincing? *

*The key conclusions are convincing. *

• *

*- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? *

*The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. *

• *

*- 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. *

*No. *

*- Are the data and the methods presented in such a way that they can be reproduced? *

*Yes, *

*- Are the experiments adequately replicated and statistical analysis adequate? *

*Yes. *

• *

*Minor comments: *

*- Specific experimental issues that are easily addressable. *

*None. *

*- Are prior studies referenced appropriately? *

*Yes. *

*- Are the text and figures clear and accurate? *

*Yes. The figures are very nice. *

• *

*- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *

• *

*1) Move the phylogenetic comparisons to the end *

*2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results. *

• *

We thank the reviewer for their positive assessment and constructive suggestions. Below, we comment on each point:

• Placement of cross-species comparison: This suggestion concerns the emphasis and structure of the manuscript. We appreciate the reviewer's interest in the evolutionary aspects of our work. However, we believe that moving this analysis to the end would dilute the main message, which is reinforced by the schematic in Figure 4E-F. We aim to conclude with the experimental results demonstrating the minimization of phenotypic consequences when both factors are knocked down. Therefore, we have chosen to retain the cross-species comparison in its current position to emphasize the conservation of the double-negative interaction before presenting the functional consequences of its perturbation.
• Reordering of Gata3 KD results: We understand the rationale behind this suggestion. However, our sequencing is guided by the fact that foot regeneration deficiency under Gata3 kd has already been documented and presented in previous work (Ferenc et al., 2021). For this reason, we begin with that reference, then build upon it with a deeper examination of the phenotype.
• *

We are grateful for the reviewer’s feedback and for recognizing the clarity of our figures and analysis.

• *

***Referee cross-commenting** *

• *

*I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1. *

• *

*Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity. *

• *

We appreciate Reviewer’s #3 remark about the nuance of transcription factor homology. The situation is exactly as described here by Reviewer #2 - The gene names in Figure 1 are based on the results of NCBI automated homology annotation, which we have now clarified in a note in the legend of Figure 1.

• *

*Reviewer #2 (Significance (Required)): *

• *

*- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. *

*This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. *

*- Place the work in the context of the existing literature (provide references, where appropriate). *

*The manuscript does a good job of placing the work into the appropriate context. *

• *

*- State what audience might be interested in and influenced by the reported findings. *

*Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. *

• *

*- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. *

*Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution *

• *

We greatly appreciate the reviewer’s positive feedback and recognition of our study's focus on gene expression in cell fate decisions. We're pleased that our findings on the mutual knockdown and the broader context were well received. Thank you for highlighting the relevance of our work to gene regulation and cell specification.

• *

*Reviewer #3 (Evidence, reproducibility and clarity (Required)): *

• *

*Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two. *

• *

*A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry. *

• *

The naming of the gene as Sry was indeed based on the NCBI automated homology annotation, and we have clarified this in the revised manuscript. Since we did not pursue further analysis of this gene, we believe that a deeper phylogenetic analysis may not be necessary and could potentially divert attention from the main focus of our study on Gata3's role.

• *

*Reviewer #3 (Significance (Required)): *

• *

*This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers. *

• *

We thank the reviewer for the thoughtful assessment of our work. We appreciate their feedback and the recognition of the quality and significance of our findings.

• *

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

Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes.

General Points:

1. The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood.
2. Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced.
3. The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers?

Specific Points:

Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles.

Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend.

Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?

Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper?

Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative.

Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control.

Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups.

Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway).

Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A.

Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly.

Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences.

The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper).

Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.

Minor Wording Issues:

Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear.

Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset.

p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed.

Significance

This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination.

Reviewer #1 (Public review):

Summary:

In this manuscript submitted by Qi et al., the authors study the RNA methylation mechanism by the METTL3-METTL14 complex. This complex catalyzes the major epitranscriptome methylation mark of nuclear RNA, including mRNA and lncRNAs. They catalyze the transfer of methyl group from SAM to convert the N6 of adenosine in RNA to m6A. Mutations in this complex have been associated with several diseases, such as type 2 diabetes and several types of cancer. The primary focus of this study was to understand the post-catalytic state of the METTL3-14 bound to a structural mimic of a reaction product known as N6-methyladenosine monophosphate (m6A) using X-ray crystallography. The authors show that the m6A occupies a novel pocket at the interface of the METTL3-14 complex and identified that residues interacting with m6A are mutated in several cancers. Furthermore, the authors demonstrate that the mutations lead to a significant loss in catalytic activity, alter RNA binding, and hinder the proper positioning of the substrate adenine in the active site. Lastly, the authors perform supervised molecular dynamics simulations to understand the effect of the mutations on the interaction network with m6A. The evidence for this study is good, with the combination of X-ray, functional assays, and molecular dynamics justifying their overall conclusions. This structure is significant as it provides new insights into the structural determinants of known cancer-associated mutations of this important class of enzymes. However, some issues need to be addressed.

Strengths:

(1) The X-ray structure is well determined, and the density map has the quality to observe all the interactions of the METTL3-14 complex with m6A.

(2) The structure reveals a novel 'cryptic pocket' in the complex that is 16 Å away from the SAM binding site. It is a functional m6A-sensor, illustrating a mechanism where the complex switches its functionality from an m6A writer to a reader.

(3) The structure illustrates that the residues forming cryptic pockets are found in multiple Cancer-associated mutations and are well conserved across several organisms.

(4) The functional assays (methyl transferase, RNA binding, kinetic, and SPR assays) provide a complete picture of the effect of the mutations on the activity of the METTL3-14 complex.

(5) Molecular dynamics simulations were done to understand the impact of the mutations on the pocket structure and its dynamics and support the X-ray structure findings.

Weaknesses:

(1) Although the X-ray structure is well determined, the statistics are a bit troubling, particularly the Ramachandran, Sidechain and RSRZ outliers. It is well above the average for structures at that resolution. Maybe the use of alternative software such as ISOLDE may be adequate to improve those parameters.

(2) The authors should expand their discussion as to why the affinity for the product is higher than the substrate and the implications on the mechanism.

(3) The SPR profiles of the association kinetics look to have several minor association-dissociation events occurring. Multiple binding sites? Authors should provide an explanation for such behavior. Also, what is the structural explanation of the difference in binding modes between the wt vs. mutant (one vs. two-state binding modes)?

(4) In materials and methods, it shows the data in Figure 2a was fitted to a Michaelis-Menten equation, however, the Y axis shows Normalized methylation and not initial rates. The authors should elaborate on their approach. In addition, more than three initial velocity rate points per protein are needed to fit a Michaelis-Menten curve confidently. Additionally, where can the Michaelis-Menten parameters be found?

Reviewer #2 (Public review):

Summary:

Kargulyan et al. investigate the function of the transsynaptic adhesion molecule RTN4RL2 in the formation and function of ribbon synapses between type I spiral ganglion neurons (SGNs) and inner hair cells. For this purpose, they study constitutive RTN4RL2 knock-out mice. Using immunohistochemistry, they reveal defects in the recruitment of protein to ribbon synapses in the knockouts. Serial block phase EM reveals defects in SGN projections in mutants. Electrophysiological recordings suggest a small but statistically significant depolarized shift in the activation of Cav1.3 Ca2+ channels. Auditory thresholds are also elevated in the mutant mice. The authors conclude that RTN4RL2 contributes to the formation and function of auditory afferent synapses to regulate auditory function.

Strengths:

The authors have excellent tools to analyze ribbon synapses.

Weaknesses:

However, there are several concerns that substantially reduce my enthusiasm for the study.

(1) The analysis of the expression pattern of RTN4RL2 in Figure 1 is incomplete. The authors should show a developmental time course of expression up into maturity to correlate gene expression with major developmental milestones such as axon outgrowth, innervation, and refinement. This would allow the development of models supporting roles in axon outgrowth versus innervation or both.

(2) It would be important to improve the RNAscope data. Controls should be provided for Figure 1B to show that no signal is observed in hair cells from knockouts. The authors apparently already have the sections because they analyzed gene expression in SGNs of the knock-outs (Figure 1C).

(3) It is unclear from the immunolocalization data in Figure 1D if all type I SGNs express RTN4RL2. Quantification would be important to properly document the presence of RTN4RL2 in all or a subset of type I SGNs. If only a subset of SGNs express RTN4RL2, it could significantly affect the interpretation of the data. For example, SGNs selectively projecting to the pillar or modiolar side of hair cells could be affected. These synapses significantly differ in their properties.

(4) It is important to show proper controls for the RTN4RL2 immunolocalization data to show that no staining is observed in knockouts.

(5) The authors state in the discussion that no staining for RTN4RL2 was observed at synaptic sites. This is surprising. Did the authors stain multiple ages? Was there perhaps transient expression during development? Or in axons indicative of a role in outgrowth, not synapse formation?

(6) In Figure 2 it seems that images in mutants are brighter compared to wildtypes. Are exposure times equivalent? Is this a consistent result?

(7) The number of synaptic ribbons for wildtype in Figure 2 is at 10/IHCs, and in Figure 2 Supplementary Figure 2 at 20/IHCs (20 is more like what is normally reported in the literature). The value for mutant similarly drastically varies between the two figures. This is a significant concern, especially because most differences that are reported in synaptic parameters between wild-type and mutants are far below a 2-fold difference.

(8) The authors report differences in ribbon volume between wild-type and mutant. Was there a difference between the modiolar/pillar region of hair cells? It is known that synaptic size varies across the modiolar-pillar axis. Maybe smaller synapses are preferentially lost?

(9) The authors show in Figure 2 - Supplement 3 that GluA2/3 staining is absent in the mutants. Are GluA4 receptors upregulated? Otherwise, synaptic transmission should be abolished, which would be a dramatic phenotype. Antibodies are available to analyze GluA4 expression, the experiment is thus feasible. Did the authors carry out recordings from SGNs?

(10) The authors use SBEM to analyze SGN projections and synapses. The data suggest that a significant number of SGNs are not connected to IHCs. A reconstruction in Figure 3 shows hair cells and axons. It is not clear how the outline of hair cells was derived, but this should be indicated. Also, is this a defect in the formation of synapses and subsequent retraction of SGN projections? Or could RTN4RL2 mutants have a defect in axonal outgrowth and guidance that secondarily affects synapses? To address this question, it would be useful to sparsely label SGNs in mutants, for example with AAV vectors expression GFP, and to trace the axons during development. This would allow us to distinguish between models of RTN4RL2 function. As it stands, it is not clear that RTN4RL2 acts directly at synapses.

(11) The authors observe a tiny shift in the operation range of Ca2+ channels that has no effect on synaptic vesicle exocytosis. It seems very unlikely that this difference can explain the auditory phenotype of the mutant mice.

(12) ABR recordings were conducted in whole-body knockouts. Effects on auditory thresholds could be a secondary consequence of perturbation along the auditory pathway. Conditional knockouts or precisely designed rescue experiments would go a long way to support the authors' hypothesis. I realize that this is a big ask and floxed mice might not be available to conduct the study.

Reviewer #3 (Public review):

In this study, the authors used RNAscope and immunostaining to confirm the expression of RTN4RL2 RNA and protein in hair cells and spiral ganglia. Through RTN4RL2 gene knockout mice, they demonstrated that the absence of RTN4RL2 leads to an increase in the size of presynaptic ribbons and a depolarized shift in the activation of calcium channels in inner hair cells. Additionally, they observed a reduction in GluA2/3 AMPA receptors in postsynaptic neurons and identified additional "orphan PSDs" not paired with presynaptic ribbons. These synaptic alterations ultimately resulted in an increased hearing threshold in mice, confirming that the RTN4RL2 gene is essential for normal hearing. These data are intriguing as they suggest that RTN4RL2 contributes to the proper formation and function of auditory afferent synapses and is critical for normal hearing. However, a thorough understanding of the known or postulated roles of RTN4Rl2 is lacking.

While the conclusions of this paper are generally well supported by the data, several aspects of the data analysis warrant further clarification and expansion.

(1) A quantitative assessment is necessary in Figure 1 when discussing RNA and protein expression. It would be beneficial to show that expression levels are quantitatively reduced in KO mice compared to wild-type mice. This suggestion also applies to Figure 2-supplement 3.D, which examines expression levels.

(2) In Figure 2, the authors present a morphological analysis of synapses and discuss the presence of "orphan PSDs." I agree that Homer1 not juxtaposed with Ctbp2 is increased in KO mice compared to the control group. However, in quantifying this, they opted to measure the number of Homer1 juxtaposed with Ctbp2 rather than directly quantifying the number of Homer1 not juxtaposed with Ctbp2. Quantifying the number of Homer1 not juxtaposed with Ctbp2 would more clearly represent "orphan PSDs" and provide stronger support for the discussion surrounding their presence.

(3) In Figure 2, Supplementary 3, the authors discuss GluA2/3 puncta reduction and note that Gria2 RNA expression remains unchanged. However, there is an issue with the lack of quantification for Gria2 RNA expression. Additionally, it is noted that RNA expression was measured at P4. While the timing for GluA2/3 puncta assessment is not specified, if it was assessed at 3 weeks old as in Figure 2's synaptic puncta analysis, it would be inappropriate to link Gria2 RNA expression with GluA2/3 protein expression at P4. If RNA and protein expression were assessed at P4, please indicate this timing for clarity.

(4) In Figure 3, the authors indicate that RTN4RL2 deficiency reduces the number of type 1 SGNs connected to ribbons. Given that the number of ribbons remains unchanged (Figure 2), it is important to clearly explain the implications of this finding. It is already known that each type I SGN forms a single synaptic contact with a single IHC. The fact that the number of ribbons remains constant while additional "orphan PSDs" are present suggests that the overall number of SGNs might need to increase to account for these findings. An explanation addressing this would be helpful.

(5) In Figure 4F and 5Cii, could you clarify how voltage sensitivity (k) was calculated? Additionally, please provide an explanation for the values presented in millivolts (mV).

(6) In Figure 6, the author measured the threshold of ABR at 2-4 months old. Since previous figures confirming synaptic morphology and function were all conducted on 3-week-old mice, it would be better to measure ABR at 3 weeks of age if possible.

Author response:

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

Reviewer #1 (Public review):

Summary:

This is by far the phylogenetic analysis with the most comprehensive coverage for the Nemacheilidae family in Cobitoidea. It is a much-lauded effort. The conclusions derived using phylogenetic tools coincide with geological events, though not without difficulties (Africa pathway).

Strengths:

Comprehensive use of genetic tools

Weaknesses:

Lack of more fossil records

Thank you for appreciating the comprehensiveness of our study.

We agree that additional nemacheilid fossils would have provided valuable support for reconstructing the evolutionary history of the family. However, the nemacheilid fossil used in our study is currently the only fossil species of the family, which precludes the possibility of including more. To address this limitation, we incorporated fossils from closely related fish families, as well as a geological event, to calibrate the time tree. We have added further details on this point in “Divergence time estimations and ancestral range reconstruction” section of the Methods. The reconstruction of the pathway by which loaches reached northeast Africa, is further complicated by the extensive aridification of the Arabian Peninsula and the Nile valley, leaving no fossil or extant Nemacheilidae species of Nemacheilidae to provide insights into the distribution of the family during late Miocene.

Reviewer #2 (Public review):

Summary:

The authors present the results of molecular phylogenetic analysis with very comprehensive samplings including 471 specimens belonging to 250 species, trying to give a holistic reconstruction of the evolutionary history of freshwater fishes (Nemacheilidae) across Eurasia since the early Eocene. This is of great interest to general readers.

Strengths:

They provide very vast data and conduct comprehensive analyses. They suggested that Nemacheilidae contain 6 major clades, and the earliest differentiation can be dated to the early Eocene.

Weaknesses:

The analysis is incomplete, and the manuscript discussion is not well organized. The authors did not discuss the systematic problems that widely exist. They also did not use the conventional way to discuss the evolutionary process of branches or clades, but just chronologically described the overall history.

In the revised version, we address the systematic issues within Nemacheilidae in a new paragraph. The polyphyly of the genus Schistura and the polyphyly or paraphyly of many other nemacheilid genera are wellknown challenges in ichthyology. However, the large size of the family Nemacheilidae and the absence of a clear basal classification system has made systematic work difficult.

The chronological concept in the description of events is in accordance with the sequence in which the events occurred over time and corresponds with Figure 8. Additionally, a clade-by-clade description would make it challenging to capture the periods before all clades were formed. As a compromise, the revised version includes a new table where each clade is represented by a column, allowing readers to trace the history of each clade in a clear overview. With this table, we make both the chronological and clade-by-clade perspectives to enhance reader understanding

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

I have no major comments, except for Figure 8, where the colour code for Sunda is not consistent, appearing as light purple and then dark purple. I was trying to locate the colour legend, maybe include this for all figures or refer to it.

Figure 8 has been revised to improve matching of the colours.

Reviewer #2 (Recommendations for the authors):

(1) It is better to discuss the evolutionary history of the major inner groups. For example, why the Branch A and B differentiated? How are the 6 major clades differentiated?

As mentioned above, the new table provides an overview of the evolutionary history of the major clades and, where known, the mechanism that led to their differentiation. For branches A and B, the underlying causes of differentiation remain known. Currently, the extensive morphological variability within each clade prevents a definitive morphological diagnosis, but such a study is planned for the future.

(2) In this study, there are still some phylogenetic or systematic problems unresolved. For example, the Genus Schistura remains polyphyletic even in different major clades. The situation is similar for the Genus Tripophysa though not so serious. These need to be discussed or at least partially solved before discussing the evolutionary history.

We discuss these topics now in a new paragraph ‘Taxonomic implications’.

(3) In Table S1, what is the meaning of "-". Does this mean no data available? If so, how do the authors treat this in their phylogenetic analysis?

Indeed, in Table S1, a ‘-‘ indicates that no sequence was available for the given species and gene. In the phylogenetic analyses, these cases were treated as missing data.

(4) What is the source of Figure 8? There are different opinions on the geological events. The authors need to indicate the source of their information.

The sources of Fig. 8 are now provided in the figure caption.

(5) The Eastern Clade forms continuous distribution in Figure 6, but discontinuous in Figure 8. Is this correct?

Figure 6 does not display the distribution areas for the clades, but illustrates the biogeographic regions used in the biogeographic analysis.

Reviewer #1 (Public review):

Summary:

The manuscript by Anbarcia et al. re-evaluates the function of the enigmatic Rete Ovarii (RO), a structure that forms in close association with the mammalian ovary. The RO has generally been considered a functionless structure in the adult ovary. This manuscript follows up on a previous study from the lab) that analyzed ovarian morphogenesis using high-resolution microscopy (McKey et al., 2022). The present study adds finer details to RO development and possible function by 1) identifying new markers for OR sub-regions (e.g. GFR1a labels the connecting rete) suggesting that the sub-regions are functionally distinct, 2) showing that the OR sub-regions are connected by a luminal system that allows transport of material from the extra-ovarian rete (EOR) to the inter-ovarian rete (IOG), 3) identifies proteins that are secreted into the OR lumen and that may regulate ovarian homeostasis, and finally, 4) better defines how the vasculature, nervous, and immune system integrates with the OR.

Strengths:

The data is beautifully present and convincing. They show that the RO is composed of three distinct domains that have unique gene expression signatures and thus likely are functionally distinct.

Author response:

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

Reviewer #1 (Public review):

Weaknesses: 

It is not always clear what the novel findings are that this manuscript is presenting. It appears to be largely similar to the analysis done by McKey et al. (2022) but with more time points and molecular markers. The novelty of the present study's findings needs to be better articulated. 

The previous study focused on placing the Rete Ovarii in the context of ovarian development. The current study focuses on the novel findings that the EOR is a active structure that sends fluid/information to the ovary. We show this by characterizing the presence of secretory proteins in the RO epithelial cells, by dye injections into the EOR and observing transport of the dye to the ovary, and by collection of EOR fluid followed by proteomic analysis. We also show that RO is embedded in an elaborate vascular network and contacted by neurons. None of this data was not discussed in the McKey 2022 paper. 

Reviewer #2 (Public Review):

Clarifications: 

(1) Is there any comparative data on the proteomics of RO and rete testis in early development? With some molecular markers also derived from rete testis, it would be better to provide the data or references.

To the best of our knowledge, there are no available proteomic datasets of the embryonic or early postnatal mouse Rete Testis or Epididymis. The authors agree that having this information would be very useful. 

(2) Although the size of RO and its components is quite small and difficult to operate, the researchers in this article had already been able to perform intracavitary injection of EOR and extract EOR or CR for mass spectrometry analysis. Therefore, can EOR, CR, or IOR be damaged or removed, providing further strong evidence of ovarian development function?

We attempted to genetically ablate the RO by expressing the diphtheria toxin receptor (DTR) in RO cells and adding DT. This approach was not successful in ablating the RO. We also tried to use Pax2/8 homo- and heterozygous mutants for ablation (as used in the McKey 2022 paper), but so far, we cannot find a genetic combination that ablates the RO, but not the oviduct, uterus and/or kidneys. We have also embarked on a study to surgically remove the RO. This assay is taking some time to optimize. The goal of the current study was to characterize the cells along the length of the RO and to present evidence that it is a secretory appendage of the ovary.

(3) Although IOR is shown on the schematic diagram, it cannot be observed in the immunohistochemistry pictures in Figure 1 and Figure 3. The authors should provide a detailed explanation.

An annotation has been added to Figure 1 to indicate the IOR. As the images within the panels are of maximum intensity projections, it is often difficult to clearly see the IOR as it is deeper within the ovary. In Figure 3, the view of the ovary is from the ventral side:  this view does not allow for clear visualization of the IOR.

Reviewer #3 (Public Review):

Weaknesses: 

There is a lack of conclusive data supporting many conclusions in the manuscript. Therefore, the paper's overall conclusions should be moderated until functional validations are conducted.

We have moderated the conclusions where appropriate

Reviewer #1 (Recommendations For The Authors):

(1) The introduction is relatively brief and does not mention some historical data/hypotheses on the role of the RO in ovarian function (e.g. regulation of meiotic entry) or development (e.g. Mayère et al., 2022).

Mayere 2022 was cited in line 57. Steins hypothesis about entry into meiosis has been added line 58.

(2) L82-84: It is stated that KRT8 was first identified as a potential RO marker by sc/snRNAseq (Anbarci et al., 2023) and then validated in this manuscript. However, KRT8 was used by McKey et al. (2022) as a RO marker, and they noted there that KRT8 was enriched in the EOR. It is not clear why McKey et al. is not cited as the primary reference validating KRT8 as an EOR marker.

The embryonic and neonatal timecourse description from KRT8 expression is first identified in this paper. McKey 2022 only highlights KRT8 at E18.5 A reference has been added to address this line 85

(3) Figure 1: Can the IOR be seen in these images? If so, please label. 

The label has been added.

(4) L107: It is hypothesized that "the RO may respond to or interpret homeostatic cues." Can transcriptomics data shed light on what signals the RO may be capable of responding to? E.g. what receptors are expressed by cells of the RO (e.g. ER, LHCGR, FSHR)?

The RO expresses ESR1, PGR, INSR, IGF1R. The IOR exclusively expresses LHCGR and FSHR.This has been added to the manuscript line 309

(5) L152: Mass spec was used to identify proteins secreted into the lumen of the RO. These proteins were then compared to the mammalian secretome to filter out possible nonsecreted protein contaminants. Finally, the candidates were compared to the RO scRNAseq data from Anbarci et al., (2023). This method gives a very conservative candidate list. However, it may also be informative to compare the sc/snRNA-seq gene list directly to the secretome to ID other possible candidate-secreted proteins that may not have been detected in the mass spec data set. 

There are quite a number of secreted proteins that are also not actively secreted. This is a good suggestion for future analysis. For the current study we wanted to take a more conservative approach, and chose to do proteomics to determine proteins that are actively secreted. 

(6) L195: It is not clear if IGFBP2 is expressed by both OR and granulosa cells or only granulosa cells. It would be informative to know what ovarian cell types express both IGFBP2 and IGF1R (e.g. from sc/snRNA-seq)? This information is referenced in the discussion (L285-287) but would be better to reference it in the results section for clarity.

Both RO and granulosa cells express IGFBP2 and IGF1R. A sentence has been added to results for clarity. (Line 197)

(7) L295: "...the RO participates in endocrine signaling..." might be more accurate to say "...the RO responds to endocrine signaling...".

The authors agreed that this statement is more accurate and the changes have been made. 

Reviewer #3 (Recommendations For The Authors): 

Several issues significantly affect the paper's quality in the current version. Firstly, there is a lack of conclusive data supporting many conclusions in the manuscript. For instance, the assertion in line 105 that "EOR was directly innervated by neurons" lacks substantial evidence beyond basic immunofluorescent staining. 

We agree that the term “innervated” might be a step too far since we rely on IF evidence.  We changed the wording of this sentence to say, “The EOR was directly contacted by neurons”.

In another pivotal experiment illustrated in Figure 3, the provided images lack temporal continuity and quantitative analysis, suggesting the incorporation of time-lapse imaging for improved sequential presentation in Figure 3.

The microscope where we can perform injections cannot record movies.  We have tried moving the rete to another microscope after injection, but so far, we have been unable to capture dextran moving through the RO. We therefore believe that transport is rapid, but future experiments will be needed to optimize this imaging.

Moreover, relying solely on proteomics analysis, as seen in lines 188-189, makes it challenging to assert conclusions such as "EOR actively secretes proteins." Therefore, the paper's overall conclusions should be moderated until functional validations are conducted. 

The findings that (1) the cells of the EOR express SNARE complex proteins at their apical surfaces and (2) luminal fluid expelled from the EOR contains abundant secreted proteins strongly suggest that the RO is involved in active secretion. We use the word “suggest” in this sentence, lines 188-189 as we realize that further experiments should be done to validate this conclusion.

Furthermore, the predominant methods in this study involve immunostaining and imaging. However, the current images exhibit a notable inconsistency in color definitions for different markers by the authors. For instance, in Figure 2.A/C, PAX8 is portrayed as cyan, while in D, it is represented in yellow. Similarly, in Figure 4, E-CAD is depicted using both cyan and yellow. Utilizing different colors for the same protein within a figure can significantly confuse readers' interpretation of the experiments. Rectifying these inconsistencies is essential to enhance the clarity and comprehension of the experimental results.

These colors were chosen to be visible to those with color image impairments. We typically used cyan and magenta to emphasize the most important markers in the image. When E-Cad and KRT8 were often used to emphasized or landmark a structure by localization of these protein. When KRT8 and E-Cad were highlighted, they were represented in cyan and magenta for visibility. When these proteins were used as a landmark to orient the reader and not as the main point, they were labeled in yellow.

At last, many markers in this study are derived from bulk and single-cell sequencing of developing RO. However, it seems that these important data were separated into another paper as a preprint. If this data were incorporated into the current manuscript, the manuscript would become more comprehensive for guiding future research on the RO.

Since we have single cell and single nuclei data from fetal and adult estrus and metestrus stages, we found that incorporating all this data into the present manuscript was overwhelming. Instead, we devoted another manuscript to presenting and validating that data. We believe a quick look at the sequencing manuscript will make this clear.

Are there any reasons to drop the term \(\frac{n_T + n_C - 2}{n_T + n_C - 4}\) from Eq 24, Morris and to replace the last term \(\frac{d^2}{c^2}\) by \(d^2\)?

Author response:

We appreciate the reviewers’ thoughtful and constructive feedback, which has provided valuable insights to refine our manuscript. Below, we outline the planned revisions in response to the public reviews.

Response to Reviewer #1

We are grateful for the reviewer’s recognition of our methodological approach and the potential significance of CD47 as a novel MSC marker for cartilage repair. To address the concerns raised:

(1) Clarifying the proteomics data supporting CD47 as an MSC marker

· The manuscript will be revised to clearly indicate where the proteomics data demonstrate elevated CD47 expression in MSCs compared to non-MSCs.

· Additional figure annotations or a supplemental figure may be included to enhance clarity.

(2) Providing further details on CD47hi and CD47lo MSC populations

· Information on the number of isolated CD47hi and CD47lo cells, along with any necessary expansion steps before in vivo use, will be explicitly detailed.

(3) Expanding the characterization of CD47hi MSCs in vitro

· A more comprehensive analysis of the chondrogenic differentiation capacity of CD47hi MSCs will be incorporated to strengthen the findings.

(4) Clarifying experimental details of the in vivo rat OA model

· The methodology section will be updated to specify the number of injected cells and their labeling strategies.

· Representative histological images will be added to support the results.

· To further substantiate the cartilage repair potential of CD47hi MSCs, additional staining for Collagen Type II will be included alongside Sox9 expression.

Response to Reviewer #2

We appreciate the reviewer’s enthusiasm for the study and recognition of its rigor and translational significance. The following revisions are planned to address the feedback:

(1) Addressing additional assessments for OA phenotype in the rat model

· While this study primarily relied on histology, the limitations of this approach will be acknowledged in the discussion.

· The absence of microCT and behavioral assessments will be explained, with suggestions for incorporating these methods in future studies.

(2) Justifying the focus on CD47

· The rationale behind prioritizing CD47 over other proteomics-identified markers will be expanded to provide better context for this choice.

(3) Clarifying MSC engraftment patterns

· The manuscript will include a discussion on whether CD47hi MSCs specifically engraft in articular cartilage or contribute to ectopic cartilage formation (e.g., osteophytes).

(4) Contextualizing findings within recent research on synovial progenitors

· Additional discussion will highlight recent studies on DPP4+ PI16+ CD34+ stromal cells and how the identified MSC populations may relate to these universal fibroblasts.

We are confident that these revisions will strengthen the manuscript and enhance its clarity and impact. The reviewers’ insights have been invaluable, and we look forward to refining the study accordingly.

Intro pg 2 - 27% of direct care workers in the U.S. are immigrants vs. 17% of total U.S. labor force (and increasing)

4 areas of federal policy intervention: 1. Providing work authorizations and pathways to citizenship that allow more immigrants to work in direct care and other essential sectors 2. Supporting the creation, testing, and replication of workforce innovations that recruit, retain, train, 3. Support immigrant direct care workers strengthening immigrant specific supports for these workers 4. Improving data collection and research to fully capture the realities of immigrant direct care workers.

I wonder how inflation would make these things worth today