Author Response

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

Response to Reviewer 1:

• We agree with the reviewer’s overall assessment of this manuscript.

• Because multiple secreted proteins are changed between the control and experimental groups, some of them could be causal and others corelative in the context of enhancing compensatory glucose production in response to elevated glycosuria. Through future studies we will determine the causal factors that trigger the increase in glucose production.

• Yes, we will correct the typographical errors in a revised version of this manuscript.

Response to Reviewer 2:

• We agree with reviewer on their comment about potential sex differences we may have missed in this study. Therefore, we will include this limitation in discussion section of a revised manuscript.

• The reviewer’s statement ‘The methods of that publication indicate that all experiments were completed within 14 days of inducing the Glut2 knockout’ is incorrect. In the referred publication, we had explicitly mentioned in methods that ‘All of the experiments, except those using a diet-induced obesity mouse model or noted otherwise, were completed within 14 days of inducing the Glut2 deficiency.’ Please see figures 5h-l and 6 in that previous publication, which demonstrate that all the experiments were not completed within 14 days of inducing renal Glut2 deficiency. Per the reviewer’s advice, in the present manuscript we will include the timeline of the experiments (which in some cases is 4 months beyond inducing glycosuria) with all the figure legends. In addition, for a separate project (which is unpublished) we have measured glycosuria up to 1 year after inducing renal Glut2 deficiency. Therefore, the glycosuria observed in the renal Glut2 KO mice is not temporary.

• In our previous response to the reviewer, we had already mentioned which control group was used in this study. Please see our response to the second reviewer’s point 3. As mentioned to the reviewer, we had used Glut2-loxp/loxp mice as the control group, which is also described multiple times in the figure legends of our previous paper that reported the phenotype of renal Glut2 KO mice and is cited in this manuscript so we don’t have to repeat the same information. Per the reviewer’s advice, we will also include the information in a revised version of this manuscript.

• We request the reviewer to look at figure 1, showing an increase in glucose production in renal Glut2 KO mice and figure 3, which demonstrates that an afferent renal denervation reduces blood glucose levels by 50%. The afferent renal denervation (ablation of afferent renal nerves) does reduce blood glucose levels in renal Glut2 KO mice. Therefore, the use of the word ‘promote’ in the title is accurate and appropriate to reflect the role of the afferent renal nerves in contributing to about 50% increase in blood glucose levels in renal Glut2 KO mice. Regarding the reviewer's comment on changes in Crh gene expression, please look at figure 3. Ablation of renal afferent nerves decreases hypothalamic Crh gene expression and other mediators of the HPA axis by 50%. Therefore, the afferent renal nerves do contribute to regulating blood glucose levels, at least in part, by the HPA axis (which is widely known to change blood glucose levels). The use of words such as ‘required’ or ‘necessary’ in the title may have indicated causal role or could have been misleading here; therefore we have purposely used ‘promote’ in the title to accurately reflect the findings of this study.

• Because we observed an increase in hepatic glucose production in renal Glut2 KO mice (Fig. 1) - which was reduced by 50% after selective afferent renal denervation (Fig. 3) - in the graphical abstract we are suggesting a neural connection between the kidney-brain-liver or an endocrine factor(s) to account for these changes in blood glucose levels as also described in the discussion section. We can include a question mark ‘?’ in the graphical abstract to show that further studies are need to validate these proposed mechanisms; however, we cannot just remove the arrow as advised by the reviewer.

• Per the reviewer’s advice, in the methods we will include the dilutions used for each assay.

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

Reviewer #1 (Recommendations For The Authors):

It would be helpful to the reader to specify in Figure 1a-c whether data were directly measured or calculated.

We have now clarified this in method section of the revised manuscript. The glucose production was directly measured and then fractional contribution of the tissues was calculated from the former data. We have also included a reference research paper to further clarify the method.

The methods section would be strengthened by clarifying the order in which experiments were performed, the age of the mice at each time point, and whether different cohorts were used for different techniques.

We have included additional details in the method section with proper citations. For in-depth protocols we have cited our previous publications.

It would be helpful to explain or provide a reference for how the post-mortem background activity measurement was performed.

We have included this explanation in the revised manuscript.

Similarly, details regarding the collection of blood for ACTH and corticosterone measurement are needed for the reader to evaluate whether the results are confounded by stress at the time of collection.

We have added these details in the method section.

I recommend stating, if accurate, that you used mixed-sex groups because your previous study found no sex differences in the phenotype of renal Glut2 KO mice.

Yes, we have included these details in the revised manuscript.

Sentence 239 is difficult to follow. Also, line 287 contains a contraction.

We have revised the sentence per the reviewer’s advice.

A graphical abstract would be helpful, bearing in mind conclusive vs suggestive findings.

Yes, we have included the graphical abstract with the revised manuscript.

Reviewer #2 (Recommendations For The Authors):

Minor Comments to the Authors

(1) The Methods also need to specify more of the critical details of the ELISAs, including the dilution factors used, and whether the values reported are dilution-corrected. Also, there is no description of how insulin was measured.

We have included these details in the method section. The assay dilutions were performed per manufacturers’ instructions.

(2) The Methods do not sufficiently describe how Crh mRNA was quantified in the hypothalamus. Presumably, they examined only the paraventricular nucleus? How many sections were used for in situ hybridization? How were the brains processed? What thickness of section was used? When were the brains collected?

We have included these details in the method section and cited our previous publications for in-depth protocols. Some of the information is also available in the figure legends.

(3) The number of mice that were used for plasma proteomics is not indicated.

The number of mice is indicated using individual symbols or points presented on the bar graphs.

Author Response

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

eLife assessment

This valuable study addresses the long-term effect of warming and altered precipitation on microbial growth, as a proxy for understanding the impact of global warming. While the methods are compelling and the evidence supporting the claims is solid, additional analysis of the data would strengthen the study, which should be of broad interest to microbial ecologists and microbiologists.

We sincerely appreciate your assessment and thoughtful comments, which are valuable and very helpful for improving our manuscript. We have carefully considered all comments, and made extensive, thorough corrections and additional analysis of the data, which we hope to meet with approval.

Reviewer #1 (Public Review):

Warming and precipitation regime change significantly influences both above-ground and below-ground processes across Earth's ecosystems. Soil microbial communities, which underpin the biogeochemical processes that often shape ecosystem function, are no exception to this, and although research shows they can adapt to this warming, population dynamics and ecophysiological responses to these disturbances are not currently known. The Qinghai-Tibet Plateau, the Third Pole of the Earth, is considered among the most sensitive ecosystems to climate change. The manuscript described an integrated, trait-based understanding of these dynamics with the qSIP data. The experimental design and methods appear to be of sufficient quality. The data and analyses are of great value to the larger microbial ecological community and may help advance our understanding of how microbial systems will respond to global change. There are very few studies in which the growth rates of bacterial populations from multifactorial manipulation experiments on the Qinghai-Tibet Plateau have been investigated via qSIP, and the large quantity of data that comprises the study described in this manuscript, will substantially advance our knowledge of bacterial responses to warming and precipitation manipulations.

We appreciate the encouragement and positive comments.

Specific comments:

(1) Please add some names of microbial groups with most common for the growth rates.

We have added the sentence “The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes” In the Abstract (Line 57-58).

(2) L47-48, consider changing "microbial growth and death" to "microbial eco-physiological processes (e.g., growth and death)", and changing "such eco-physiological traits" to "such processes".

Done (Line 47 and 48).

(3) L50-51, the author estimated bacterial growth in alpine meadow soils of the Tibetan Plateau after warming and altered precipitation manipulation in situ. Actually, the soil samples were collected and incubated in the laboratory rather than in the field like the previous experiment conducted by Purcell et al. (2021, Global Change Biology). "In situ" would lead me to believe that the qSIP incubation was conducted in the field, so I think the use of the word in situ is inappropriate here. [https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.15911]

Agreed. We have deleted “in situ”.

(4) L52, what does "interactive global change factors" mean?

We have revised this sentence to “the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation” (Line 52-53).

(5) L61, in my opinion, "Microbial diversity" belongs to the category of species composition, rather than ecosystem functional services. Please revise it.

Agree. We have deleted it.

(6) L69, consider changing "further" to "thus".

Done (Line 70).

(7) L82, delete "The evidence is overwhelming that".

Done.

(8) L85-90, these two sentences have similar meanings, please express them concisely.

We have deleted the sentence “Altered precipitation, particularly drought or heavy precipitation events, also tends to negatively influence soil processes and biodiversity”.

(9) L91, the effect of drought on soil microorganisms is lacking here.

We have added the sentence “Reduced precipitation affects soil processes notably by directly stressing soil organisms, and also altering the supply of substrates to microbes via dissolution, diffusion, and transport” in the Introduction (Line87-89).

(10) L102, "Growth" should be highlighted here, as changes in relative abundance can also be classified as population dynamics. The use of the term "population dynamics" will eliminate the highlight of this study in calculating the growth rate of microbial species in in-situ soil based on qSIP. Consider changing "population dynamics" to "population-growth responses" or something like that.

Done (Line 98).

(11) L105, please note that this citation focuses on plant physiological characteristics.

We have revised the reference (Line 102).

(12) L115, "soil temperature, water availability" should be considered as a direct impact of climate change, rather than an indirect impact on microorganisms.

We have deleted them.

(13) L134-135, please clarify the interaction types between which climate factors.

We have deleted this sentence.

(14) L135-138, suggest modifying or deleting this sentence. The results in this study are already eco-physiological data and do not need to be further "understood and predicted".

We have deleted this sentence.

(15) L150, "The experimental design has been described in previously". I think this refers to another study and not the actual incubations in this study. Also in L198, suggest a change to "Incubation conditions were similar to those previously described". So, it's clear it's not the same experiment.

We have revised these sentences to “has been described previously in (Ma et al., 2017)” (Line 136) and “according to a previous publication” (Line 194).

Reference:

Ma, Z., Liu, H., Mi, Z., Zhang, Z., Wang, Y., Xu, W. et al. (2017). Climate warming reduces the temporal stability of plant community biomass production. Nature Communications, 8, 15378.

(16) L188, change "pre-wet soil samples" to "pre-wet samples" and change "soil samples for 48h incubation" to "incubation samples". What does "pre-wet" mean? Does it represent soil pre-cultivation?

Done. The pre-wet samples, i.e., the soil samples before incubation (T = 0 d), were used to estimate the initial microbial composition. "pre-wet" does not mean soil pre-cultivation. We have added the description “A portion of the air-dried soil samples was taken as the pre-wet treatment (i.e., before incubation without H2O addition)” in MATERIALS AND METHODS (Line 174-175).

(17) Unify the time unit of incubation (hour or day). Consider changing "48 h" to "2 d" in Materials and Methods.

Done.

(18) L247, what version of RDP Classifier was used?

We used RDP v16 database for taxonomic annotation. We have added this information in the revision (Line 246).

(19) L270, "average molecular weights".

Done (Line 268).

(20) L272-275, based on the preceding description, it appears that the culture period was limited to 48 hours. Please confirm it.

Apologize for this mistake. We have revised it (Line 273).

(21) L297, switch the order of the first two sentences of this paragraph.

Done (Line 297).

(22) L331, change "smaller-than-additive" to "smaller than their expected additive effect".

Done (Line 331).

(23) L374 and 381, I struggle with why "larger combined effects" than single factor effects represent higher degree of antoninism, and I think it should be "smaller combined effects".

Agree. We have revised it according to this suggestion (Line 369 and 374).

(24) L375, remove "than that of drought and warming".

Done.

(25) L405, simplify the expression, change "between different warming and rainfall regimes" to "between climate regimes"

We have deleted this sentence.

(26) L406-408, species are already on the phylogenetic tree and they can not "clustered at the phylogenetic branches", but the functional traits of microbes can. Please revise it.

We have revised this sentence to “Overall, the most incorporators whose growth was influenced by the antagonistic interaction of T × P showed significant phylogenetic clustering (i.e., species clustered at the phylogenetic branches; NTI > 0, P < 0.05)” (Line 402-404).

(27) L409, the same as above, and consider removing "The incorporators subjected to". We have revised this sentence to “The incorporators whose growth subjected to the additive interaction of warming × drought also showed significant phylogenetic clustering (P < 0.05)” (Line 404-406).

(28) L412, consider changing "incorporators subjected to the synergistic interaction" to "the synergistic growth responses under multifactorial changes".

We have revised the sentence to “incorporators whose growth is influenced by the synergistic interaction showed phylogenetically random distribution under both climate scenarios (P > 0.05)” (Line 407-409).

(29) L505-506, please add a reference for this sentence.

Done (Line 488).

(30) L511-514, It should be noted that the production of MBC does not necessarily imply a net change in the C pool size. The accelerated growth rates may result in expedited turnover of MBC, rather than an increase in carbon sequestration.

Thanks. We have deleted this sentence.

(31) Language precision. In the discussion section there must be some additional caveats introduced to some of the claims the authors are making. For instance, L518, the author should clarify that "in this study, the bacterial growth in alpine grassland may be influenced by antagonistic interactions between multiple climatic factors after a decadal-long experiment". Because other studies may exhibit different results due to the focus on different ecosystem functions as well as environmental conditions. As such, softening of the language is recommended- lines are noted below- and these will not adjust the outcomes of this study, but support more precise interpretation.

We have revised the sentence to “In this study, a decade-long experiment revealed that bacterial growth in alpine meadows is primarily influenced by the antagonistic interaction between T × P” (Line 497-499).

(32) Picrust analysis is a good way to connect species and their functions, especially Picrust2, which updated the reference database and optimized the algorithm to improve its prediction accuracy (Douglas et al., 2020, Nature Biotechnology). However, the link between microbial taxonomy and microbial metabolism is still not straightforward, especially in diverse microbial communities like soils. The authors should introduce caveats within discussion that they know the limitations of their methods. For context, as a reader who does metabolisms in soils, I found myself somewhat disappointed when piecrust data was introduced and not properly caveated. Particularly, it might be helpful to introduce briefly in the last paragraph of the results. These caveats are necessary to not potentially overstate the author's findings, and to make sure the reader knows the authors understand the very clear limitations of these methods. [https://www.nature.com/articles/s41587-020-0548-6]

Thanks. We have introduced caveats in DISCUSSION, that is “This is, however, still to be verified, as the functional output from PICRUSt2 is less likely to resolve rare environment-specific functions (Douglas et al., 2020)” (Line 540-542).

Reference:

Douglas, G., Maffei, V., Zaneveld, J., Yurgel, S., Brown, J., Taylor, C. et al. (2020). PICRUSt2 for prediction of metagenome functions. Nature Biotechnology, 38, 1-5.

(33) Although the author has explained the potential causes for the negative effects of different climate change factors (i.e., warming, drought, and wet) on microbial growth, there seems to be a lack of a summary assertion and an extension on how climate change affects microbial growth and related ecosystem functions. It is recommended to make a general summary of the results in the last part of Discussion.

We have added a general summary in the last paragraph of DISCUSSION, that is “Our results demonstrated that both warming and altered precipitation negatively affect the growth of grassland bacteria; However, the combined effects of warming and altered precipitation on the growth of ~70% soil bacterial taxa were smaller than the single-factor effects, suggesting antagonistic interaction. This suggests the development of multifactor manipulation experiments in precise prediction of future ecosystem services and feedbacks under climate change scenarios” (Line 552-558).

(34) L546, please add the taxonomic information for "OTU 14".

Done (Line 533).

(35) L800, change "The phylogenetic tree" to "A phylogenetic tree".

Done (Line 762).

Reviewer #2 (Public Review):

Summary:

The authors aimed to describe the effect of different temperature and precipitation regimes on microbial growth responses in an alpine grassland ecosystem using quantitative 18O stable isotope probing. It was found that all climate manipulations had negative effects on microbial growth, and that single-factor manipulations exerted larger negative effects as compared to combined-factor manipulations. The degree of antagonism between factors was analyzed in detail, as well as the differential effect of these divergent antagonistic responses on microbial taxa that incorporated the isotope. Finally, a hypothetical functional profiling was performed based on taxonomic affiliations. This work gives additional evidence that altered warming and precipitation regimes negatively impact microbial growth.

Strengths:

A long term experiment with a thorough experimental design in apparently field conditions is a plus for this work, making the results potentially generalisable to the alpine grassland ecosystem. Also, the implementation of a qSIP approach to determine microbial growth ensures that only active members of the community are assessed. Finally, particular attention was given to the interaction between factors and a robust approach was implemented to quantify the weight of the combined-factor manipulations on microbial growth.

We appreciate the reviewer’s positive comments.

Weaknesses:

The methodology does not mention whether the samples taken for the incubations were rhizosphere soil, bulk soil or a mix between both type of soils. If the samples were taken from rhizosphere soil, I wonder how the plants were affected by the infrared heaters and if the resulting shadow (also in the controls with dummy heaters) had an effect on the plants and the root exudates of the parcels as compared to plants outside the blocks? If the samples were bulk soil, are the results generalisable for a grassland ecosystem? In my opinion, it is needed to add more info on the origin of the soil samples and how these were taken.

The samples taken for the incubations can be considered as a mixture of rhizosphere and bulk soils. During soil sampling, we did not use conventional rhizosphere soil collection methods. However, there is a certain proportion of fragmented roots in the soil samples we collected, indicating that soil properties are influenced by plants. We have added this description in MATERIALS AND METHODS (Line 158).

To minimize the impact of physical shading on the plants, each sampling point was as far away from infrared heaters as possible. We have added this information of soil collection in MATERIALS AND METHODS, that is “In each plot, three soil cores of the topsoil (0-5 cm in depth) were randomly collected and combined as a composite sample, which can be considered as a mixture of rhizosphere and bulk soils. Each sampling point was as far away from infrared heaters as possible to minimize the impact of physical shading on the plants. The fresh soil samples were shipped to the laboratory and sieved (2-mm) to remove root fragments and stones.” (Line 157-162).

Previous studies based on our field experiment assessed the effects of warming and altered precipitation on soil microbial communities (Zhang et al., 2016), the temporal stability of plant community biomass (Ma et al., 2017), shifting plant species composition and grassland primary production (Liu et al., 2018). These studies provide guidance for the experiment design and execution.

Reference:

Zhang, KP., Shi, Y., Jing, X. et al. (2016). Effects of Short-Term Warming and Altered Precipitation on Soil Microbial Communities in Alpine Grassland of the Tibetan Plateau. Frontiers in Microbiology, 7, 1-11.

Ma ZY., Liu, HY., Mi, ZR. et al. (2017). Climate warming reduces the temporal stabilityof plant community biomass production. Nature Communications, 8, 15378.

Liu, HY., Mi, ZR., Lin, L. et al. (2018). Shifting plant species composition in response to climate change stabilizes grassland primary production. Proceedings of the National Academy of Sciences, 115, 4051-4056.

The qSIP calculations reported in the methodology for this work are rather superficial and the reader must be experienced in this technique to understand how the incorporators were identified and their growth quantified. For instance, the GC content of taxa was calculated for reads clustered in OTUs, and it is not discussed in the text the validity of such approach working at genus level.

We have added the description of qSIP calculations in Supplementary Materials.

The approach of GC content calculation can be used at genus level (Koch et al., 2018). The GC content of each bacterial taxon (Gi) was calculated using the mean density for the unlabeled (WLIGHTi) treatments (Hungate et al. 2015), rather than OTU sequence information. We have revised the sentence in MATERIALS AND METHODS, that is “the number of 16S rRNA gene copies per OTU taxon (e.g., genus or OTU) in each density fraction was calculated by multiplying the relative abundance (acquisition by sequencing) by the total number of 16S rRNA gene copies (acquisition by qPCR)” (Line 255-258).

Reference:

Hungate, B., Mau, R., Schwartz, E., Caporaso, J., Dijkstra, P., Van Gestel, N. et al. (2015). Quantitative microbial ecology through stable isotope probing. Applied and Environmental Microbiology, 81, 7570-7581.

Koch, B., McHugh, T., Hayer, M., Schwartz, E., Blazewicz, S., Dijkstra, P. et al. (2018). Estimating taxon-specific population dynamics in diverse microbial communities. Ecosphere, 9, e02090.

The selection of V4-V5 region over V3-V4 region to quantify the number of copies of the 16S rRNA gene should be substantiated in the text. Classic works determined one decade ago that primer pairs that amplify V3-V4 are most suitable to assess soil bacterial communities. Hungate et al. (2015), worked with the V3-V4 region when establishing the qSIP method. Maybe the number of unassigned OTUs is related with the selection of this region.

Both primer sets (V3-V4 and V4-V5 regions), are widely used across various sample sets, with highly similar in representing the total microbial community composition (Fadeev et al., 2021; Zhang et al., 2018).

A previous study based on our Field Research Station of Alpine Grassland Ecosystem used V4-V5 primer pairs to investigated the effect of warming and altered precipitation on the overall bacterial community composition (Zhang et al., 2016).

Another reason for choosing the V4-V5 primer set in this study was to integrate and compare the data with that of two previous qSIP studies (Ruan et al., 2023; Guo et al., submitted), both of them focused on the growth responses of active species to global change and used V4-V5 primer pairs.

We have added an explanation about primer selection as “The V4-V5 primer pairs were chosen to facilitate integration and comparison with data from previous studies (Ruan et al., 2023; Zhang et al., 2016)” (Line 213-215).

Reference:

Fadeev, E., Cardozo-Mino, M.G., Rapp, J.Z. et al. (2021). Comparison of Two 16S rRNA Primers (V3–V4 and V4–V5) for Studies of Arctic Microbial Communities. Frontiers in Microbiology, 12

Zhang, J.Y., Ding, X., Guan, R. et al. (2018). Evaluation of different 16S rRNA gene V regions for exploring bacterial diversity in a eutrophic freshwater lake. Science of The Total Environment, 618, 1254-1267.

Zhang, K.P., Shi, Y., Jing, X. et al. (2016). Effects of Short-Term Warming and Altered Precipitation on Soil Microbial Communities in Alpine Grassland of the Tibetan Plateau. Frontiers in Microbiology, 7, 1-11.

Ruan, Y., Kuzyakov, Y., Liu, X. et al. (2023). Elevated temperature and CO2 strongly affect the growth strategies of soil bacteria. Nature Communications, 14, 1-12.

Guo, J.J., Kuzyakov, Y., Li, L. et al. (2023). Bacterial growth acclimation to long-term nitrogen input in soil. The ISME Journal, Submitted.

Report of preprocessing and processing of the sequences does not comply state of the art standards. More info on how the sequences were handled is needed, taking into account that a significant part of the manuscript relies on taxonomic classification of such sequences. Also, an OTU approach for an almost species-dependent analysis (GC contents) should be replaced or complemented with an ASV or subOTUs approach, using denoisers such as DADA2 or deblur. Usage of functional prediction tools underestimates gene frequencies, including those related with biogeochemical significance for soil-carbon and nitrogen cycling.

(1) We have complemented the information about sequence processing as “The raw sequences were quality-filtered using the USEARCH v.11.0 (Edgar, 2010). In brief, the paired-end sequences were merged and quality filtered with “fastq_mergepairs” and “fastq_filter” commands, respectively. Sequences < 370 bp and total expected errors > 0.5 were removed. Next, “fastx_uniques” command was implemented to remove redundant sequences. Subsequently, high-quality sequences were clustered into operational taxonomic units (OTUs) with “cluster_otus” commandat a 97% identity threshold, and the most abundant sequence from each OTU was selected as a representative sequence.” (Line 238-245).

(2) We have complemented the zero-radius OTU (ZOTU) analysis by the unoise3 command in USEARCH (https://drive5.com/usearch/manual/pipe_otus.html), as shown in Fig. S1-S2. The results showed that overall growth responses of soil bacteria to warming and precipitation changes were similar based on OTU and ZOTU analyses, i.e., warming and altered precipitation tend to negatively affect the growth of grassland bacteria and the prevalence of antagonistic interactions of T × P. The similarity of results between the different methods is reflected at the overall community level, the phylum level, the genus level and the species (i.e., OTU or ZOTU) level (Fig. S1 and S2).

Author response image 1.

The growth responses of grassland bacteria to warming and altered precipitation based on ZOTU analysis. The results of growth rates at the community level (A), the phylum level (B), and the ZOTU level (C and D) were similar to those based on OTU analysis. C the single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. D the proportions of species growth influenced by different interaction types of T × P. T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0+P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. Values represent mean and the error bars represent standard deviation. Different letters indicate significant differences between climate treatments.

Author response image 2.

The growth responses of grassland bacteria at the genus level to warming and altered precipitation based on OTU analysis (A and C) and ZOTU analysis (B and D). A and B the single and combined factor effects of climate factors on growth in genera, by comparing with those in T0nP. C and D the proportions of genera whose growth influenced by different interaction types of T × P.

(3) Agreed. We have introduced the caveat about the limitation of usage of functional prediction tools to the end of DISCUSSION, that is “This is, however, still to be verified, as the functional output from PICRUSt2 is less likely to resolve rare environment-specific functions (Douglas et al., 2020)” (Line 540-542). The caveat ensures that the reader knows the limitations of these methods, and are not potentially overstate our findings.

Reference:

Douglas, G.M., Maffei, V.J., Zaneveld, J.R. et al. (2020) PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 38, 685–688.

Reviewer #2 (Recommendations For The Authors):

General suggestions:

Regarding the qSIP method, would be of help to see the differences in density vs number of 16S rRNA gene abundance for the most responsive bacterial groups in the different treatments, taking into account that with only 7 fractions the entire change in bacterial growth was resolved.

We have selected three representative bacterial taxa (OTU1 belonging to Bradyrhizobium, OTU14 belonging to Solirubrobacter, OTU15 belonging to Pseudoxanthomonas), which have high growth rates in climate change treatments. The result showed that the peaks in the 18O treatment are shifted "backwards" (greater average weighted buoyancy density) compared to the 16O treatment, indicating that these species assimilates the 18O isotope into the DNA molecules during growth.

Author response image 3.

The distribution of 16S rRNA gene abundance of three representative bacterial taxa (OTU1- Bradyrhizobium, OTU14-Solirubrobacter, and OTU15-Pseudoxanthomonas) in different buoyant density fractions. Values represent mean and the error bars represent standard deviation.

Seven fractionated DNA samples were selected for sequencing because they contained more than 99% gene copy numbers of each samples (please see the Figure below). The DNA concentrations of other fractions were too low to construct sequencing libraries.

Author response image 4.

Relative abundance of 16S rRNA gene copies in each fraction. The fractions with density between 1.703 and 1.727 g ml-1 were selected because they contained more than 99% gene copy numbers of each sample. T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0+P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. Values represent mean and the error bars represent standard deviation.

With such dataset additional multivariate analysis would be of help to better interpret the ecological framework.

Thanks for the suggestion. Interpreting the ecological framework is meaningful for understanding microbial responses to environmental changes.

The main objective of this study is to investigate the growth response of soil microbes in alpine grasslands to the temperature and precipitation changes, and the interaction between climate factors. Our results, as well as the results of complementary analyses (based on subOTU analyses, SHOWN BELOW), indicate that warming and altered precipitation tend to negatively affect the growth of grassland bacteria, and the prevalence of antagonistic interactions of T × P.

We have emphasized our research objectives and main conclusions in the revised manuscript: “The goal of current study is to comprehensively estimate taxon-specific growth responses of soil bacteria following a decade of warming and altered precipitation manipulation on the alpine grassland of the Tibetan Plateau” (Line 112-114);

“Our results demonstrated that both warming and altered precipitation negatively affect the growth of grassland bacteria; However, the combined effects of warming and altered precipitation on the growth of ~70% soil bacterial taxa were smaller than the single-factor effects, suggesting antagonistic interaction” (Line 552-556).

Extension of interaction analysis and its conclusions should be shortened, summarizing the most relevant findings. In my opinion, it becomes a bit redundant.

We have shortened the discussion of Extension of interaction analysis by deleting the little relevant contents.

Below are some, but not all, examples that have been deleted or revised in the Discussion,

(1) Deleted “This result supports our second hypothesis that the interactive effects between warming and altered precipitation on soil microbial growth are not simply additive”;

(2) Deleted “A previous study suggested that multiple global change factors had negative effects on soil microbial diversity (Yang et al., 2021)”;

(3) Revised “A meta‐analysis of experimental manipulation revealed that the combined effects of different climate factors were usually less than expected additive effects, revealing antagonistic interactions on soil C storage and nutrient cycling processes (Dieleman et al., 2012; Wu et al., 2011). Moreover, two experimental studies on N cycling and net primary productivity demonstrated that the majority of interactions among multiple factors are antagonistic rather than additive or synergistic, thereby dampening the net effects (Larsen et al., 2011; Shaw et al., 2002)” to “A range of ecosystem processes have been revealed to be potentially subject to antagonistic interactions between climate factors, for instance, net primary productivity (Shaw et al., 2002), soil C storage and nutrient cycling processes (Dieleman et al., 2012; Wu et al., 2011; Larsen et al., 2011)” (Line 499-503);

(4) Revised “Previous evidences suggest that warming has a negative impact on soil carbon pools (Jansson & Hofmockel, 2020; Purcell et al., 2022). During the first phase of soil warming (~ 10 years), microbial activity increased, resulting in rapid soil carbon mineralization and respiration (Melillo et al., 2017)” to “Previous evidences suggest that warming has a negative impact on soil carbon pools (Jansson & Hofmockel, 2020; Purcell et al., 2022), mainly because of the rapid soil carbon mineralization and respiration (Melillo et al., 2017)” (Line 464-466).

I strongly suggest a functional analysis based on shotgun sequencing or RNAseq approaches. With this approach this work would be able to answer who is growing under altered T and Precipitation regimes and what are those that are growing doing.

Thanks for the suggestion. Metagenomic sequencing is a popular approach to evaluate potential functions of microbial communities in environment. However, there are two main reasons that limit the application of metagenomic or metatranscriptomic sequencing in this study: 1) Most of the fractionated samples in SIP experiment have low DNA concentration and do not meet the requirement of library construction for sequencing; 2) Metagenome and metatranscriptomics usually have relatively low sensitivity to rare species, reducing the diversity of detected active species.

This study focused on active microbial taxa and their growth in response to multifactorial climate change. We have added the prospect in DISCUSSION, that is “This suggests the development of methods combining qSIP with metagenomes and metatranscriptomes to assess the functional shifts of active microorganisms under global change scenarios” (Line 542-544).

Minor suggestions:

L121. _As

We have deleted this sentence and relocated the hypotheses in the last paragraph of INTRODUCTION (according to the suggestion of the reviewer #3).

Line150. Described previously in.

Done (Line 136).

Line500. Check whether it is better to use the word acclimatization (Coordinated response to several simultaneous stressors) in exchange of acclimation

We have revised it according to this suggestion (Line 481).

Fig.4C Drought

Done (Line 761).

Reviewer #3 (Public Review):

Summary:

In this paper, Ruan et al. studied the long-term impact of warming and altered precipitations on the composition and growth of the soil microbial community. The researchers adopted an experimental approach to assess the impact of climate change on microbial diversity and functionality. This study was carried out within a controlled environment, wherein two primary factors were assessed: temperature (in two distinct levels) and humidity (across three different levels). These factors were manipulated in a full factorial design, resulting in a total of six treatments. This experimental setup was maintained for ten years. To analyze the active microbial community, the researchers employed a technique involving the incorporation of radiolabeled water into biomolecules (particularly DNA) through quantitative stable isotope probing. This allowed for the tracking of the active fraction of microbes, accomplished via isopycnic centrifugation, followed by Illumina sequencing of the denser fraction. This study was followed by a series of statistical analysis to identify the impact of these two variables on the whole community and specific taxonomic groups. The full factorial design arrangement enabled the researchers to discern both individual contributions as well as potential interactions among the variables

Strengths:

This work presents a timely study that assesses in a controlled fashion the potential impact of global warming and altered precipitations on microbial populations. The experimental setup, experimental approach and data analysis seem to be overall solid. I consider the paper of high interest for the whole community as it provides a baseline to the assessment of global warming on microbial diversity.

Thanks for the encouragement and positive comments.

Weaknesses:

While taxonomic information is interesting, it would have been highly valuable to include transcriptomics data as well. This would allow us to understand what active pathways become enriched under warming and altered precipitations. Non-metabolic OTUs hold significance as well. The authors could have potentially described these non-incorporators and derived hypotheses from the gathered information. The work would have benefited from using more biological replicates of each treatment.

Thanks for the valuable suggestions.

(1) Metatranscriptomics can assess the functional profiles of the community, but it has relatively low sensitivity to rare species, which is difficult to correlate the function pathways with the assignment to the numerous active taxa identified by qSIP. Additionally, due to the low DNA concentration, most fractionated samples are difficult to construct sequencing libraries, while amplicon based sequencing analyses were allowed. This study therefore focused on active microbial taxa and their growth in response to multifactorial climate change. We have added the prospect in DISCUSSION, that is “This suggests the development of methods combining qSIP with metagenomes and metatranscriptomes to assess the functional shifts of active microorganisms under global change scenarios” (Line 542-544).

(2) 18O-qSIP can identify the growing microbial species (i.e., 18O incorporators) in the environment rather than metabolically active taxa. These non-incorporators in our study were likely to be metabolically active, i.e., maintaining life activities without reproduction, or recently deceased (Blazewicz et al., 2013). Therefore, it is hard to distinguish whether these non-incorporators possess metabolic activity.

(3) Agreed. The qSIP experiments involve the use of isotopes and the sequencing of a large number of DNA samples (90 samples per biological replicate in this study). Considering its high cost, we selected three replicates for analysis. We have explained this issue in MATERIALS AND METHODS, that is “Considering the cost of qSIP experiment (i.e., the use of isotopes and the sequencing of a large number of DNA samples), we randomly selected three out of the six plots, serving as three replicates for each treatment” (Line 154-157).

Reference:

Nuccio, E.E., Starr, E., Karaoz, U. et al. (2020) Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J 14, 999–1014.

Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K (2013). Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. The ISME Journal, 7, 2061–2068.

Reviewer #3 (Recommendations For The Authors):

Major comments:

The manuscript should be written in a clearer way. The language should be more direct, so the message is conveyed faster and clearer. Some sentences, for instance, could be shortened or re-organized. Below, you will find some examples.

We have rewritten the sentences to make the manuscript clearer. Below are some, but not all, examples that have been revised:

(1) Deleted “(reduced precipitation, hereafter ‘drought’, or enhanced precipitation, hereafter ‘wet’)” in INTRODUCTION;

(2) Deleted “Controlled experiments simulating climate change have investigated changes in microbial community composition as measured by shifts in the relative abundances (Evans & Wallenstein, 2014; Barnard et al., 2015). However, changes in relative abundances may be poor indicators of population responses to environmental change in some cases (Blazewicz et al., 2020). Another challenge is the presence of a large number of inactive microbial cells in the soil, which hinders the direct, quantitative measure of the ecological drivers in population dynamics (Fierer, 2017; Lennon & Jones, 2011).” in DISCUSSION;

(3) Deleted “This result supports our second hypothesis that the interactive effects between warming and altered precipitation on soil microbial growth are not simply additive” in DISCUSSION;

(4) Deleted “A previous study suggested that multiple global change factors had negative effects on soil microbial diversity (Yang et al., 2021)” in DISCUSSION;

(5) Revised “A meta‐analysis of experimental manipulation revealed that the combined effects of different climate factors were usually less than expected additive effects, revealing antagonistic interactions on soil C storage and nutrient cycling processes (Dieleman et al., 2012; Wu et al., 2011). Moreover, two experimental studies on N cycling and net primary productivity demonstrated that the majority of interactions among multiple factors are antagonistic rather than additive or synergistic, thereby dampening the net effects (Larsen et al., 2011; Shaw et al., 2002)” to “A range of ecosystem processes have been revealed to be potentially subject to antagonistic interactions between climate factors, for instance, net primary productivity (Shaw et al., 2002), soil C storage and nutrient cycling processes (Dieleman et al., 2012; Wu et al., 2011; Larsen et al., 2011)” in DISCUSSION (Line 499-503);

(6) Revised “Previous evidences suggest that warming has a negative impact on soil carbon pools (Jansson & Hofmockel, 2020; Purcell et al., 2022). During the first phase of soil warming (~ 10 years), microbial activity increased, resulting in rapid soil carbon mineralization and respiration (Melillo et al., 2017)” to “Previous evidences suggest that warming has a negative impact on soil carbon pools (Jansson & Hofmockel, 2020; Purcell et al., 2022), mainly because of the rapid soil carbon mineralization and respiration (Melillo et al., 2017)” in DISCUSSION (Line 464-466).

I'm curious about why, even though there were six replicates of the experiment, only three samples were collected for analysis. Metagenomic analyses tend to display high variability.

The qSIP experiments involve the use of isotopes and the sequencing of a large number of DNA samples (90 samples per biological replicate in this study). Considering its high cost, we selected three replicates for analysis..

In Fig. 3A, the absolute growth rates (16S copies/d*g) are shown. How do you know that the efficiency of DNA extraction was similar across all treatments and therefore the absolute numbers are comparable?

To avoid differences in extraction efficiency caused by experimental procedures, all DNA samples were extracted by the same person (the first author) within 2-3 hours, and a unifying procedure of cell lysis and DNA extraction was used, i.e., the mechanical cell destruction was attained by multi-size beads beating at 6 m s-1 for 40 s, and then FastDNA™ SPIN Kit for Soil (MP Biomedicals, Cleveland, OH, USA) was used for DNA extraction.

We have measured the concentration of extracted DNA and found no significant difference between treatments (Table for the response letter).

Author response table 1.

Soil DNA concentration in climate change treatments after qSIP incubation (measured by Qubit® DNA HS Assay Kits).

Values represent mean and standard deviation. T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0+P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. The results of ANOVA indicated no significant difference of extracted DNA concentration between treatments (p > 0.05).

We have introduced the caveat in the DISCUSSION, that is “Note that the experimental parameters such as DNA extraction and PCR amplification efficiencies also have significant effects on the accuracy of growth assessment. This alerts the need to standardize experimental practices to ensure more realistic and reliable results” (Line 544-547).

Line 96-99 and 121-124: "Hypotheses are typically placed at the end of the final paragraph in the Introduction section. It is advisable to relocate them there and provide a clearer description of the paper's main goal."

We have relocated the hypotheses at the end of INTRODUCTION, and the main goal of this study, that is “The goal of current study is to comprehensively estimate taxon-specific growth responses of soil bacteria following a decade of warming and altered precipitation manipulation on the alpine grassland of the Tibetan Plateau, by using the 18O-quantitative stable isotope probing (18O-qSIP)” (Line 112-115).

Line 399: Although you describe the classification among antagonistic interactions in the Methods section, I think you should describe this in further detail here. Can you clarify how you carried out this categorization and how these results were interpreted considering the phylogenetic classification.

We have added the description of antagonistic interactions, that is “The interaction type of T × P on the growth of ~70% incorporators was antagonistic (i.e., the combined effect size is smaller than the additive expectation) (Fig. 4C)” (Line 388-390).

The interaction types between factors can be classified into three categories: additive, synergistic and antagonistic. Additive interactions are those in which the combined effect size of factors is equal to the sum of the single effects of the factors (i.e., additive expectation, Fig. 1B). Synergistic interactions refer to the effect size was larger than the additive expectation by the combined manipulation of factors. On the contrary, antagonistic interactions refer to the combined effect size of factors is smaller than the additive expectation. In this study, the antagonistic interactions were further divided into three sub-categories: weak antagonistic interaction, strong antagonistic interaction, and neutralizing effect (Fig. 1B). The weak antagonistic interaction refers to the combined effect size smaller than the additive expectation and larger than any of the single factor effects. The strong antagonistic interaction refers to that the combined effect size is smaller than any of the single factor effects but larger than 0. The neutralizing effect refers to that the combined effect size is equal to 0, implying that the effects of different factors cancel each other out.

Methodologically, the single and combined effects of two climate factors and their interaction effects were calculated by the natural logarithm of response ratio (lnRR) and Hedges’ d, respectively (Yue et al., 2017).

We have added the result interpretation about the phylogenetic distribution patterns of incorporators, that is “The degree of phylogenetic relatedness can indicate the processes that influenced community assembly, like the extent a community is shaped by environmental filtering (clustered by phylogeny) or competitive interactions (life strategy is phylogenetically random distribution) (Evans & Wallenstein, 2014; Webb et al., 2002).The results showed that the incorporators whose growth was influenced by the antagonistic interaction of T × P showed significant phylogenetic relatedness, indicating the occurrence of taxa more likely shaped by environment filtering (i.e., selection pressure caused by changes in temperature and moisture conditions). In contrast, the growing taxa affected by synergistic interactions of T × P showed random phylogenetic distributions (Table S1), which may be explained by competition between taxa with similar eco-physiological traits or changes in genotypes (possibly through horizontal gene transfer) (Evans & Wallenstein, 2014). We also found that the extent of phylogenetic relatedness to which taxa groups of T × P interaction types varied by climate scenarios, suggesting that different climate history processes influenced the ways bacteria survive temperature and moisture stress” (Line 515-529).

Reference:

Evans, S.E. & Wallenstein, M.D. (2014). Climate change alters ecological strategies of soil bacteria. Ecology Letters, 17, 155-164.

Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J. (2002). Phylogenies and Community Ecology. Annual Review of Ecology and Systematics, 33, 475-505.

Yue, K., Fornara, D.A., Yang, W., Peng, Y., Peng, C., Liu, Z. et al. (2017). Influence of multiple global change drivers on terrestrial carbon storage: additive effects are common. Ecology Letters, 20, 663-672.

Line 407-8: What do you mean with "...clustered at the phylogenetic branches" and Line 410: "cluster near the tips of the phylogenetic tree". Can you please clarify?

Sorry for the unclear statement. We have added the explanation of NTI, that is “Nearest taxon index (NTI) was used to determine whether the species in a particular growth response are more phylogenetically related to one another than to other species (i.e., close or clustering on phylogenetic tree). NTI is an indicator of the extent of terminal clustering, or clustering near the tips of the tree (Evans & Wallenstein, 2014; Webb et al., 2002)” (Line 397-401).

Reference:

Evans, S.E. & Wallenstein, M.D. (2014). Climate change alters ecological strategies of soil bacteria. Ecology Letters, 17, 155-164.

Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J. (2002). Phylogenies and Community Ecology. Annual Review of Ecology and Systematics, 33, 475-505.

Could you provide some info about the biochemistry of the incorporation of heavy water into DNA molecules? What specific enzymes are typically involved?

Due to the low DNA concentration in most fractionated samples (less than 10 ng/μL, measured by Qubit DNA HS Assay Kits), only amplicon based sequencing analyses were allowed. This study therefore focused only on active microbial taxa and their growth in response to multifactorial climate change.

What might be the impact of soil desiccation on bacterial survival and subsequent water uptake?

Slow dehydration and air drying of soil is a very common phenomenon in nature (Koch et al., 2018). In this process, microorganisms will reduce metabolism, and shift towards a potentially active state (Blagodatskaya and Kuzyakov, 2013). A previous study suggested that the potentially active microbial population permanently existing in soil between the active and dormant physiological states. Even under long-term starvation the potentially active microorganisms maintain ‘physiological alertness’ to be ready to occasional substrate input (Blagodatskaya and Kuzyakov, 2013). These microorganisms are important participants in the biogeochemical cycle is the focus of this study.

Replacing the environmental water in the soil with 18O-labelled water is a typical practice for qSIP studies (Hungate et al. 2015; Koch et al., 2018). This process may cause disturbance to the microbial community. In this study, the soil samples were placed in a thermostatic incubator (14℃ and 16℃), rather than air-drying at 25℃ (as used in most studies). The incubation temperature is relatively low (compared to 25℃) and there is no violent air convection in the incubator, resulting slower evaporation and no significant discoloration caused by severe soil dehydration after 48 h. The process of soil drying in this study simulated the natural phenomenon, i.e., slow water loss in soil.

We have added the description in MATERIALS AND METHODS, that is “There is no violent air convection in the incubator and the incubation temperature is relatively low (compared to 25℃ used in previous studies), resulting slower evaporation and no significant discoloration caused by severe soil dehydration after 48 h” (Line 171-174).

Reference:

Blagodatskaya, E. & Kuzyakov, Y. (2013) Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biology and Biochemistry, 67, 192-211.

Hungate, B., Mau, R., Schwartz, E., Caporaso, J., Dijkstra, P., Van Gestel, N. et al. (2015). Quantitative microbial ecology through stable isotope probing. Applied and Environmental Microbiology, 81, 7570-7581.

Koch, B., McHugh, T., Hayer, M., Schwartz, E., Blazewicz, S., Dijkstra, P. et al. (2018). Estimating taxon-specific population dynamics in diverse microbial communities. Ecosphere, 9, e02090.

The analysis of the 180 incorporators is interesting as it defines what microbes are metabolically active and hence growing under the different conditions tested. Should not be worth to analyze the non-incorporators? Is it possible to identify a pattern to generate a hypothesis of why they are metabolically inactive based on this information? In the Methods section, the authors state that they identified a total of 6,938 OTUs, of which only 1,373 were found to be incorporators.

Microbes exist in a range of metabolic states: growing, active (non-growth), dormant and recently deceased (Blazewicz et al., 2013), and there is still a lack of clear threshold for their identification. 18O-DNA qSIP can identified the growing microbial species (i.e., 18O incorporators) rather than all metabolic active taxa, because some cells are measurably metabolizing (catabolic and/or anabolic processes) without reproduction. Therefore, the non-incorporators in our study may be metabolically active, or not (recently deceased microorganisms). This study focuses on the growing microorganisms identified by 18O-qSIP.

In this study, ~20% microbial taxa (1,373/6,938) were identified as 18O incorporators. Microorganisms in soils suffer from resource and energy constraints frequently (Blagodatskaya and Kuzyakov, 2013). The energy requirements of species in the growing state are much higher (~30 fold) than those in the non-growing state, so the percentage of growing bacterial taxa in soil tends to be low.

Reference:

Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K (2013). Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. The ISME Journal, 7, 2061–2068.

Blagodatskaya, E. & Kuzyakov, Y. (2013) Active microorganisms in soil: Critical review of estimation criteria and approaches. Soil Biology and Biochemistry, 67, 192-211.

Minor comments:

Fig. 3A and 3B. Please show the results of the multiple comparisons.

Done.

Author response image 5.

Bacterial growth responses to climate change and the interaction types between warming and altered precipitation. The growth rates (A), and responses (LnRR) of soil bacteria to warming and altered precipitation (B) at the whole community level. The growth rates (C), and responses of the dominant bacterial phyla (D) had similar trends with that of the whole community. Values represent mean and the error bars represent standard deviation. Different letters indicate significant differences between climate treatments.

Fig. 4. This figure should be self-explanatory. This diagram is challenging to understand.

We have revised Fig. 4 to improve clarity.

Author response image 6.

The growth responses and phylogenetic relationship of incorporators subjected to different interaction types under two climate scenarios. A phylogenetic tree of all incorporators observed in the grassland soils (A). The inner heatmap represents the single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. The outer heatmap represents the interaction types between warming and altered precipitation under two climate change scenarios. The proportions of positive or negative responses in species growth to single and combined manipulation of climate factors by summarizing the data from the inner heatmap (B). The proportions of species growth influenced by different interaction types of T × P by summarizing the data from the outer heatmap (C).

Fig. 4. It says "Dorought" instead of "drought"

Done (Line 760).

Line 109: "relieves" instead of "relieved"

Done (Line 102).

Line 129: Should be: "We classified the interaction types as additive, synergistic, antagonistic, null and neutralizing."

Done (Line 117).

Line 233: How were the 16S rRNA sequences from each density fraction analyzed?

(1) Raw sequencing data processing:

The raw 16S rRNA gene sequences of each density fraction were quality-filtered using the USEARCH v.11.0 (Edgar, 2010). The paired-end sequences were merged and quality filtered with “fastq_mergepairs” and “fastq_filter” commands, respectively. Sequences < 370 bp and total expected errors > 0.5 were removed. Next, “fastx_uniques” command was implemented to identify the unique sequences. Subsequently, high-quality sequences were clustered into operational taxonomic units (OTUs) with “cluster_otus” commandat a 97% identity threshold, and the most abundant sequence from each OTU was selected as a representative sequence. The taxonomic affiliation of the representative sequence was determined using the RDP classifier (Wang et al., 2007).

(2) qSIP calculation:

Sequencing data reflects the relative abundance of taxa in community. We multiply the OTU’s relative abundance (acquisition by sequencing) and the number of 16S rRNA gene copies (acquisition by qPCR) to obtain the number of gene copies per OTU in each fraction. Then, the proportion of gene copies of a specific OTU of each fraction relative to the total amount of gene copies in one sample was calculated and used as a weight value for further calculation of the average weighted buoyant density (the critical parameter for assessing microbial growth).

Line 366: "Three single-factor ... between warming and altered precipitation" -> "The individual impact of warming, drought, and wet conditions resulted in the most substantial negative effects on bacterial growth compared with the effects of warming x drought and warming x wet. A result that illustrates the negative interactions between warming and modified precipitations patterns."

Done (Line 365-368).

Line 376: "Similar with the result of whole growth of bacteria community, the growth responses of the major bacterial phyla were also negatively influenced by single climate factors". This sentence is hard to read. Maybe something like this: "Growth of the major bacterial phyla was also negatively influenced by the individual climate factors".

Done (Line 371-372).

Line 383: "In particular, the effects of wet and warming neutralized each other, resulting the net effects became zero on the growth rates of the phyla Actinobacteria and Bacteroidetes". "In Actinobacteria and Bacteroidetes, the effect of wet and warming neutralized each other, as the combined effect of these two factors had no effect on growth".

Done (Line 377-379).

Line 390: "The individual warming treatment (T+nP) reduced the growth rates of 75% incorporators..." "Warming (T+nP) reduced the growth of 75% of the taxonomic groups, which was followed by drought and wet.

Done (Line 384-385).

Line 392: "The combined manipulations of warming and altered precipitation lowered the percentages of incorporators with negative responses compared with single factor manipulation, especially warming and enhanced precipitation manipulation" -> "Warming x drought and warming x wet had a smaller impact on the growth of incorporators, compared with single effects."

Done (Line 385-387).

Line 468. This sentence "To the best ..." is not necessary.

We have deleted this sentence.

Line 476. Is it really "synthesis" the word you want to use?

We have deleted this sentence.

Line 477. Maybe should written like this: "Consistent with our findings, a recent experimental study demonstrated that 15 years of warming reduced the growth rate of soil bacteria in a montane meadow in northern Arizona."

Done (Line 459-461).

Line 490 and 502. Consider using "however" only once in a paragraph.

We have deleted the second “however” (Line 483).

Line 555-559. Based on genomic data you cannot predict the functional role of microbes in the environment. These sentences are speculative. Please, consider using less strong affirmations and focus more on the pathways that are enriched in the incorporators.

Agreed. We have deleted this part of content.

Author Response

Reviewer #2 (Public Review):

In this manuscript, Chen et al. reported that the core binding factor beta (Cbfβ), a heterodimeric subunit of the RUNX family transcription factors (TFs), is crucial in maintaining cartilage homeostasis and counteracting traumatic OA pathology. Using mouse models in which Cbfβ is conditionally inactivated in the Col2a1+ and Acan+ cells, the authors claimed that Cbfβ ablation led to articular cartilage (AC) degeneration, which is associated with aberrant cartilage gene expression and chondrocyte signaling, particularly the elevated Wnt/Catenin and the decreased Hippo/YAP and TGFβ signaling. The authors further showed that Cbfβ transcripts are decreased in human OA cartilage, and sustaining Cbfβ expression in mouse knee joints mitigated the severity of surgery-evoked OA.

On the whole, the work reported is interesting and exciting. Genetic and biochemical data support key statements. Both in vivo and in vitro experiments were well designed with proper controls; semiquantitative data were digitalized and processed for statistical significance. Furthermore, new findings were adequately discussed in contrast to the current available knowledge. However, the conceptual novelty of this study is slightly compromised by recent publications showing that Cbfβ reduction is associated with OA (Che et al. 2023; Li et al. 2021). Also, the authors claimed that multiple signaling pathways were affected by Cbfβ ablation in cartilage cells; many of them, however, are indirect effects given the nature of Cbfβ as a TF. The authors also showed that pSMAD2/3 and active βCatenin decreased and increased upon Cbfβ depletion in the mouse AC cartilage. However, how the deficiency of Cbfβ, a widely expressed TF, affected the posttranslational modification of SMAD2/3 and βCatenin is unclear and needs further discussion. Overall, Cbfβ's role in cartilage and OA pathology is an emerging area of study; the authors provided a set of genetic evidences showing that Cbfβ is indispensable for cartilage homeostasis.

We thank the reviewer for the positive appraisal of our manuscript. We greatly appreciate the insightful comments and critiques. In accordance with the reviewer’s suggestions, we have thoroughly revised all parts of the manuscript. We are glad that the reviewers considered our work to be of interest, and we are grateful for this opportunity to resubmit our manuscript. With regard to concerns of novelty of our study, Li et al’s study only reported the relationship between abnormal Cbfβ expression in human cartilage and osteoarthritis. Che et al’s study employed Cbfβf/fAggrecan-cre mice, while our study used a novel inducible Cbfβf/fCol2α1CreERT mouse model. While the Aggrecan-creERT system provides valuable insights into the role of Cbfβ in differentiated cartilage cells and its implications in the advanced stages of osteoarthritis, our current study also used Cbfβf/fCol2α1-CreERT aimed to explore the gene's function from a broader perspective. Previous study points out that Col2α1 is expressed in both early and late stage of chondrogenesis, including skeletal mesenchymal cells, perichondrium and presumptive joint cells, but aggrecan is expressed specifically in differentiated chondrocytes(1). However, studies show that not only differentiated chondrocytes but also chondrocyte progenitors are involved in OA pathogenesis(2). In our current study, the Col2α1-CreERT system allowed us to investigate Cbfβ's role not only in mature chondrocytes but also in early chondroprogenitor cells, offering a comprehensive view of Cbfβ’s involvement in cartilage in osteoarthritis. Therefore, the use of the Cbfβf/fCol2α1-CreERT mouse mutant strain was instrumental in expanding our understanding of Cbfβ's multifaceted role in osteoarthritis, highlighting its importance not only in mature cartilage but also in the early stages of cartilage formation and differentiation. In addition to the different types of Cre used compared to our previous study, our current study also used gain-of-function approach in ACLT-induced OA disease model to understand the potential therapeutic function of Cbfβ in OA pathological condition. Adding our current findings to our previous research, we can now piece together a more complete picture of Cbfβ's role across the entire spectrum of cartilage development in osteoarthritis.

We agree with the reviewer that how the deficiency of Cbfβ, a widely expressed TF, affected the posttranslational modification of SMAD2/3 and βCatenin is unclear and needs further exploration. So far there is no clear explanation of this, which is why we used RNA-seq and heatmap analysis to examine other genes expression which could help to uncover the mechanism underlying these results. Interestingly, Che et al’s result showed that TGFB signaling (P-Smad3) increased in Cbfβf/fAggrecan-cre mice, while our data showed that TGFB signaling (both PSmad3 and Smad3) decreased in Cbfβf/fCol2α1-CreERT mice as shown in our results in Figure 8. These results were also confirmed by RNA-seq analysis as shown in the heatmaps in figure 5.

These differences could be the result of different mouse ages used in our study and Che et al’s study.

1. Blaney Davidson EN, van de Loo FA, van den Berg WB, van der Kraan PM. How to build an inducible cartilagespecific transgenic mouse. Arthritis Res Ther. 2014;16(3):210.

2. Tong L, Yu H, Huang X, Shen J, Xiao G, Chen L, et al. Current understanding of osteoarthritis pathogenesis and relevant new approaches. Bone Res. 2022;10(1):60.

Reviewer #3 (Public Review):

The authors comprehensively demonstrated the Cbfβ gene, which is involved in articular cartilage homeostasis, can promote articular cartilage regeneration and repair in osteoarthritis (OA) through regulating Hippo/YAP signaling TGF-β signaling, and canonical Wnt signaling. First, the authors demonstrated the deletion of Cbfβ can induce the OA phenotypes including decreased articular cartilage and osteoblasts, and increased osteoclasts and subchondral bone hyperplasia, and induce the early onset of OA. Additionally, the authors showed that the deficiency of Cbfβ in cartilage can increase canonical Wnt signaling and decrease TGF-β and Hippo signaling. Finally, the authors demonstrated that the overexpression of Cbfβ can inhibit Wnt signaling and enhance Hippo/YAP signaling in knee joints articular cartilage of ACLT-induced OA mice and protect against ACLT-induced OA. The manuscript is overall well-constructed, and the authors provided evidence to support their findings.

In Fig. 7I, it could be better to show the statistical analysis between normal and AAV-mediated Cbfβ ACLT mice groups.

We thank the reviewer for bringing this to our attention. In the revised figure 7I, we have included the statistical analysis between normal and AAV-mediated Cbfβ ACLT mice groups.

In Fig. 9H-K, in the quantification analysis, the OARSI score in the DMM+AAV-YFP group is higher than in the sham group significantly. However, the SO staining results appear to show no significant difference between the DMM+AAV-luc-YFP group (Fig. 9I) and the sham group (Fig. 9H).

We thank the reviewer for bringing this to our attention. Although both the sham and DMM+AAV-luc-YFP group stain positive for SO, the SO stain intensity of the DMM+AAV-lucYFP group is noticeably lower. In addition, SO staining is not the only parameter which is included in the OARSI score. We also evaluated the cartilage thickness, proteoglycan structure, and Cartilage surface fibrillation index. Our evaluation to determine the OARSI score relies on the qualities of the whole joint, not only the magnified portion. For convenience we have also outlined the region of positive SO stain in the revised figure 9I

Author Response

eLife assessment

This important study provides a new, apparently high-performance algorithm for B cell clonal family inference. The new algorithm is highly innovative and based on a rigorous probabilistic analysis of the relevant biological processes and their imprint on the resulting sequences, however, the strength of evidence regarding the algorithm's performance is incomplete, due to (1) a lack of clarity regarding how different data sets were used for different steps during algorithm development and validation, resulting in concerns of circularity, (2) a lack of detail regarding the settings for competitor programs during benchmarking, and (3) method development, data simulation for method validation, and empirical analyses all based on the B cell repertoire of a single subject. With clarity around these issues and application to a more diverse set of real samples, this paper could be fundamental to immunologists and important to any researcher or clinician utilizing B cell receptor repertoires in their field (e.g., cancer immunology).

We apologize for the long delay in implementing the suggested changes. Some of the co-authors had some personal issues that made it hard to efficiently work on the revision.

We have addressed all the essential points below, as well as all the detailed comments of each reviewer in the following pages.

Due to the journal’s guidelines we have to upload an “all black” version of the manuscript as the main version. We have uploaded a revised manuscript with the changes marked in red as a “Related Manuscript file”, which appears at the very end of the Merged Manuscript File, after all the Figures, and at the end of the list of files on the webpage. We apologize for this inconvenience.

In addition, we have added an extension of HILARy to deal with paired-chain repertoires, and have benchmarked the new method on a recently published synthetic dataset. This new analysis is now presented in new Fig. 5.

Reviewer #1 (Public Review):

Identifying individual BCR/Ab chain sequences that are members of the same clone is a longstanding problem in the analysis of BCR/Ab repertoire sequencing data. The authors propose a new method designed to be scalable for application to huge repertoire data sets without sacrificing accuracy. Their approach utilizes Hamming Distance between CDR3 sequences followed by clustering for a fast, high-precision approach to classifying pairs of sequences as related or not, and then refines the classification using mutation information from germline-encoded regions. They compare their method with other state-of-the-art methods using synthetic data.

The authors address an important problem in an interesting, innovative, and rigorous way, using probabilistic representations of CDR3 differences, frequencies of shared and not-shared mutations, and the relationships between the two under hypotheses of related pairs and unrelated pairs, and from these develop an approach for determining thresholds for classification and lineage assignment. Benchmarking shows that the proposed method, the complete method including both steps, outperforms other methods.

Strengths of the method include its theoretical underpinnings which are consistent with an immunologist's intuition about how related and unrelated sequences would compare with each other in terms of the metrics to use and how those metrics are related to each other.

I have two high-level concerns:

(1) It isn't clear how the real and synthetic data are being used to estimate parameters for the classifier and evaluate the classifier to avoid circularity. It seems like the approach is used to assign lineages in the data from [1], and then properties of this set of lineages are used to estimate parameters that are then used to refine the approach and generate synthetic data that is used to evaluate the approach. This may not be a problem with the approach but rather with its presentation, but it isn't entirely clear what data is being used and where for what purpose. An understanding of this is necessary in order to truly evaluate the method and results.

The reviewer is correct in their understanding of the pipeline. It should be stressed that the lineages used to guide the generation of the synthetic data was done on VJl classes for which the clustering was easy and reliable, and should therefore be largely model independent.

We have added an explanation in the main text of why the re-use of real data lineages inferred by HILARy doesn’t bias the procedure, since it’s done on a subset of lineages within VJl classes that are easy to infer (section “Test on synthetic dataset”).

(2) Regarding the data used for benchmarking - given the intertwined fashion by which the classification approach and synthetic data generation approach appear to have been developed, it is not surprising that the proposed approach outperforms the other methods when evaluated on the synthetic data presented here. It would be better to include in the benchmark the data used by the other methods to benchmark themselves or also generate synthetic data using their data generation procedures.

We agree with the reviewer that a test of the method on an independent synthetic dataset is important for its applicability and to compare to other methods.

We have added a new synthetic dataset from the group that designed the partis method to our benchmark. Our method still performs competitively, on par with partis—which was developed and tested on that dataset—and better than other methods. The results are presented in revised Fig. 4 (panels E-G), and Figure 4–figure supplement 1 as a function of the mutation rate.

In addition, we have used that dataset to benchmark a new version of HILARy that also uses the light chain. We present the results in new Figures 5 and Figure 4–figure supplement 1.

An improved method for BCR/Ab sequence lineage assignment would be a methodologic advancement that would enable more rigorous analyses of BCR/Ab repertoires across many fields, including infectious disease, cancer, autoimmune disease, etc., and in turn, enable advancement in our understanding of humoral immune responses. The methods would have utility to a broad community of researchers.

Reviewer #2 (Public Review):

This manuscript describes a new algorithm for clonal family inference based on V and J gene identity, sequence divergence in the CDR3 region, and shared mutations outside the CDR3. Specifically, the algorithm starts by grouping sequences that have the same V and J genes and the same CDR3 length. It then performs single-linkage clustering on these groups based on CDR3 Hamming distance, then further refines these groups based on shared mutations.

Although there are a number of algorithms that use a similar overall strategy, a couple of aspects make this work unique. First, a persistent challenge for algorithms such as this one is how to set a cutoff for single-linkage clustering: if it is too low, then one separates clusters that should be together, and if too high one joins together clusters that should be separate. Here the authors leverage a rich collection of probabilistic tools to make an optimal choice. Specifically, they model the probability distributions of within- and between-cluster CDR3 Hamming distances, with parameters depending on CDR3 length and the "prevalence" of clonal sequence pairs (i.e. family size distribution). This allows the algorithm to make optimal choices for separating clusters, given the particular chosen distance metric, and assuming the sample in question has been accurately modeled. Second, the algorithm uses a highly efficient means of doing single-linkage clustering on nucleotide sequences.

This leads to a fast and highly performant algorithm on data meant to replicate the original sample used in algorithm design. The ideas are new and beautifully developed. The application to real data is interesting, especially the point about dN/dS.

However, the paper leaves open the question of how this inference algorithm works on samples other than the one used for simulation and as a template for validation. If I understand the simulation procedure correctly - that one takes a collection of inferred trees from the real data, then re-draws the root sequence and the identity of the mutations on the branches - then the simulated data should be very close to the data used to develop the methods in the paper. This consideration seems especially important given that key methods in this paper use mutation counts and overall mutation counts are preserved.

Repertoires come in all shapes and sizes: infants to adults, healthy to cancerous, and naive to memory to plasma-cell-just-after-vaccination. If this is being proposed as a general-purpose clonal inference algorithm rather than one just for this sample, then a more diverse set of validations are needed.

We agree that testing the method on a differently generated dataset is a useful check. We should point out, however, that our synthetic dataset is not as biased as it may seem. In particular, it is based on trees from VJl classes that we predicted are very easy to cluster, which means that they are truly faithful to the data, and not dependent on the particular algorithm used to infer them. The big advantage over this synthetic dataset over others is that it recapitulates the power law statistics of clone size distribution, as well as the diversity of mutation rates. To us, it still represents a more useful benchmark than synthetic datasets generated by population genetics models, which miss most of this very broad variability.

However, to check how the method generalizes to other datasets, we repeated our validation procedure on the dataset used to evaluate Partis in Ralph et al 2022. The new results are discussed in the main text and in new panels of Fig. 4 in the same form as the previous comparisons. We also added a comparison of performance as a function of mutation rate in the new Figure 4–figure supplement 1.

It is unclear how to run the code. The software repo has a nice readme explaining the file layout, dependencies, and input file format, but the repo seems to be lacking an inference.ipynb mentioned there which runs an analysis. Perhaps this is a typo and refers to inference.py, which in addition to the documented cdr3 clustering, seems to have functions to run both clustering methods. However, it does not seem to have any documentation or help messages about how to run these functions.

We have completely overhauled the github to provide a detailed step by step explanation of how to run the code. The code is now easily installable using pip.

The results are not currently reproducible, because the simulated data is not available. The data availability statement says that no data have been generated for this manuscript, however simulated data has been generated, and that is a key aspect of the analysis in the paper.

We have uploaded the simulated data to zenodo, as well as provided scripts in the github to run the benchmarks.

More detail is needed to understand the timing comparisons. The new software is clearly written to use many threads. Were the other software packages run using multiple threads? What type of machine was used for the benchmarks?

All timing comparisons were made based on a single VJl class on a 14 double-threaded CPU computer. HILARy uses all 28 threads, and other methods were run with default settings, with multi-threading allowed.

We have clarified the specifications of the computer.

Reviewer #3 (Public Review):

B cell receptors are produced through a combination of random V(D)J recombination and somatic hypermutation. Identifying clonal lineages - cells that descend from a common V(D)J rearrangement - is an important part of B cell repertoire analysis. Here, the authors developed a new method to identify clonal lineages from BCR data. This method builds off of prior advances in the field and uses both an adaptive clonal distance threshold and shared somatic hypermutation information to group B cells into clonal lineages.

The major strength of this paper is its thorough quantitative treatment of the subject and integration of multiple improvements into the clonal clustering process. By their simulation results, the method is both highly efficient and accurate.

The only notable weakness we identified is that much of the impact of the method will depend on its superiority to existing approaches, and this is not convincingly demonstrated by Fig. 4. In particular, little detail is given on how the other clonal clustering programs were run, and this can significantly impact their performance. More specifically:

We have added a new benchmark to address these concerns, presented in Fig. 4 and in new figure 4 – figure supplement 1 as a function of a controllable mutation rate.

(1) Scoper supports multiple methods for clonal clustering, including both adaptive CDR3 distance thresholds (Nouri and Kleinstein, 2018) and shared V-gene mutations (Nouri and Kleinstein, 2020). It is not clear which method was used for benchmarking. The specific functions and settings used should have been detailed and justified. Spectral clustering with shared V gene mutations would be the most comparable to the authors' method. Similar detail is needed for partis.

In the updated version I use the 2020 version. The 2018 is very similar to simple single linkage so will be removed from the benchmark.

(2) It is not clear how the adaptive thresholds and shared mutation analysis in the authors' method differ from prior approaches such as scoper and partis.

We have changed the paragraph in the discussion section about the benchmark to highlight the innovative aspects and differences with previous approaches.

(3) The scripts for performing benchmarking analyses, as well as the version numbers of programs tested, are not available.

We have added to the github all the scripts used for benchmarking. We have added details about the version numbers in the data and code availability section of the methods.

(4) Similar to above, P. 10 describes single linkage hierarchical clustering with a fixed threshold as a "crude method" that "suffers from inaccuracy as it loses precision in the case of highlymutated sequences and junctions of short length." As far as we could tell, this statement is not backed up by either citations or analyses in the paper. It should not be difficult for the authors to test this though using their simulations, as this method is also implemented in scoper.

We have added this method to our benchmark to support that point. The results are presented in Figure 4 – figure supplement 2.

References

Nouri N, Kleinstein SH. 2020. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data. PLOS Comput Biol 16:e1007977. doi:10.1371/journal.pcbi.1007977

Nouri N, Kleinstein SH. 2018. A spectral clustering-based method for identifying clones from high- throughput B cell repertoire sequencing data. Bioinformatics 34:i341-i349. doi:10.1093/bioinformatics/bty235

We have changed citation [22] to refer to the 2018 paper. The 2020 paper is citation [18].

Author Response

We acknowledge the editors and reviewers for their careful and thoughtful review of the preprint. Their comments and suggestions will be very useful in improving the manuscript's revised version, which we plan to submit in the coming weeks.

Author Response

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

Reviewing Editor

We thank you for clarifying several of the questions raised by the reviewers. Since the study has otherwise largely stayed unchanged, we will leave the eLife assessment as “before”:

We respectfully disagree because we addressed all concerns raised by the two reviewers except one (below), which was not satisfactorily answered according to reviewer 1; it has now been addressed (new S3 Fig).

Reviewer #1 (Recommendations For The Authors):

The authors addressed most of my previous comments. However, there is one important point that was not satisfactorily addressed "The band intensities on Western blots in Fig. 4 and Fig. 5 are not quantified, and the numbers of repeats are also not provided" The response that "It is not straightforward to quantify and describe the intensity of the bands of these numerous with different fate outcomes." In the revision, they mentioned at least three repeats were performed. If so, it's not entirely clear why they couldn't quantify the western blots results. Including quantitative data will strengthen the rigor of the findings.

Quantitative data from Fig. 4 and Fig. 5 are now provided as S3 Fig and described in the manuscript (lines 170-175; 184-188).

Author Response

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

Reviewer #1:

(1) It is not entirely clear why a tumor-free model is chosen to study immune responses, as immune responses can differ significantly with or without tumor-bearing. A more detailed explanation is needed.

We appreciate the question. As stated in the original submission, tumor-free mouse models are commonly used to assess off-target outcomes of anti-neoplastic therapies. We have expanded on this point and acknowledged this shortcoming in the revised manuscript (lines 264-265).

(2) Immune responses in isolated macrophages, neutrophils, and bone marrow cells require priming with LPS, while such responses are not observed in vivo. There is no explanation for these differences.

The reviewer raises an excellent point. The assembly of inflammasomes such as those nucleated by NLRP3 requires priming signals, which increase the levels of this sensor, which are kept low in homeostatic conditions to prevent spontaneous unwanted inflammation. While LPS is commonly used in vitro as an inducer of priming signals, these cues are triggered in vivo by various molecules, including pro-inflammatory cytokines. We have provided a rationale for the use of LPS in vitro in the revised manuscript (lines 144-145).

(3) The band intensities on Western blots in Fig. 4 and Fig. 5 are not quantified, and the numbers of repeats are also not provided. This additional information is recommended.

While caspase-1, caspase-3, GSDMD, and GSDME but not AIM 2 and NLRP3 are activated upon proteolytic cleavage. It is not straightforward to quantify and describe the intensity of the bands of these numerous with different fate outcomes. We regret for not mentioning the numbers of repeats in the original submission. This information has now been provided in figure legends where necessary.

(4) Many abbreviations are used throughout the text, and some of the full names are not provided.

Full names are required at the first introduction.

We agree. We have provided full names at the first introduction (lines 21, 23, 86).

(5) Fig. 5B needs a label on the X axis.

We regret the confusion: X axis was for both Fig. 5B and 5C. We have made the change in the new Fig. 5.

Reviewer #2:

The following specific points could be addressed to further improve the quality of the manuscript:

(1) Concerning data presented in Figure 1, 3D micro-CT reconstructions of the entire femurs could be shown instead of just the trabecular bone. Data on cortical bone loss are important. It would be important to show histological (sagittal) sections of the bones at baseline, treated with Doxorubicin or vehicle, and quantify osteoblasts in addition to osteoclasts. Is there increased bone marrow adiposity in Doxorubicin-treated mice? The data with vehicle should be shown in the main figures not just in the supplemental data.

We thank the reviewer for the suggestion. We have now provided 3 D micro-CT reconstructions of a representative femur containing both trabecular and cortical bones (S1B Fig). Only the metaphyseal area is shown because we did not originally scan the entire femur.

Quantification of osteoblast number is not a reliable measurement, the reason why we carried out dynamic histomorphometry to assess the effect of doxorubicin on bone formation (original S1D Fig/new S1E Fig).

Unfortunately, we did not determine the effects of doxorubicin on bone marrow adiposity. However, to address the reviewer’s comment, we have mentioned in the revised manuscript adipogenic effects of doxorubicin based on the literature (lines 264-265).

(2) Concerning data presented in Figure 2, how long after Doxorubicin injection is leukopenia observed (beyond the 72-hour timepoint)? Does cell-count return to baseline 4 weeks after treatment (when the bone phenotype is characterized)? Why use 12-week-old mice here and 10week-old animals for the rest of the study?

We appreciate the question. We did not measure leukopenic effects of doxorubicin beyond the 72-hour timepoint based on the following: i) bones are analyzed in mice injected only once with a single dose of doxorubicin; ii) leukopenia is a side effect of doxorubicin whose blood levels should be undetectable 4 weeks after its administration although we did not measure them experimentally. Our premise is that osteopenia observed in doxorubicin-exposed mice is the result of early events that occur after the administration of the drug.

We apologize for the confusion. We assessed baseline bone mass by VivaCT using 10-week-old mice; doxorubicin was injected 2 weeks when mice were 12-week-old. We have clarified this point in the revised manuscript (line 301).

(3) It would be important to evaluate local inflammation in bones collected from wild-type and mutant mice. Are ASC specks, Cit-H3, and MPO present in the bone marrow? The expression of some components of the inflammasomes or relevant pathways could be assessed in bone samples deprived of bone marrow and in the bone marrow.

This is a good point. Although we were not able to reliably measure Cit-H3 and MPO in bone marrow fluid, our data shown in Figs. 3-6, 7A-D are from bone marrow cells.

(4) Data presented in western blots should be quantified. The ratio of signal intensity obtained for beta-actin over the signal obtained for a given protein should be calculated for each experimental condition (especially in Figure 5, where beta-actin levels fluctuate a lot).

Please see the response to question #1. Fluctuations in β-actin levels are likely related to doxorubicin cytotoxic effects as mentioned in the original submission (lines 150, 194, 253). Despite this caveat, IL-1β levels are stimulated by this drug.

(5) In Figure 7, BV/TV of WT and mutant mice at baseline should be quantified and shown. Sagittal histological sections of the femur should be shown. 3D micro-CT reconstructions of the entire femur could be shown instead of just the trabecular bone. Osteoblasts and bone resorption should be quantified. Data obtained with vehicle should be quantified and shown in the main figure. The control and LPS conditions should be better defined. Does it include vehicle?

Please see the response to reviewer 1’s question #1.

We have now provided 3 D micro-CT reconstructions of a representative femur containing both trabecular and cortical bone (S3A, B Fig).

LPS was dissolved in PBS (vehicle), which was used as control. We have now replaced vehicle with PBS in Fig. 7.

(6) For all figures, the number of biological replicates should be mentioned in the legends, as well as the statistical tests used for the analyses.

We have now included this information in the legends where necessary.

(7) Some of the scientific rationales are not totally clear and could be better explained in the text. For example, it is written on page 6 "studies mainly on male mice and revolved around innate immune responses" and "we focused on neutrophils because of their high turnover rate and short lifespan", but it is not clear why. The rationale (page 10) for assessing bone mass in "mice globally lacking AIM2 and/or NLRP3" is not totally clear either. The argument is that systemic inflammation leads to bone loss but the effects obtained with the total ablation of AIM2 and NLRP3 do not prove strictly speaking that systemic inflammation really matters (in this current study, although we know from many other studies that it clearly does matter). We could imagine, for example, that bone mass would be preserved in AIM2 KO mice only because the inflammasome is impaired in osteoblasts and/or osteoclasts, but not in any other cell types. Conversely one could imagine that bone would be preserved only because inflammation is preserved in the gut, for example. The use of global knockouts unfortunately does not tell us much about the importance of systemic versus local effects of the inflammasomes. It shows that reducing inflammation, either in specific organs or globally, limits bone loss in doxorubicin-treated mice. This result is important but it was fully expected since doxorubicin has been reported to induce systemic inflammation, and since many studies have shown that systemic inflammation leads to bone loss.

We appreciate the comments. We have clarified the rationale for focusing on neutrophils (lines 129-130) and AIM2 and NLRP inflammasomes (lines 209-211). We have also now down played the concept of inflammasome-mediated systemic inflammation in doxorubicin-induced bone loss.

Author Response

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

Public Reviews:

Roget et al. build on their previous work developing a simple theoretical model to examine whether ageing can be under natural selection, challenging the mainstream view that ageing is merely a byproduct of other biological and evolutionary processes. The authors propose an agent-based model to evaluate the adaptive dynamics of a haploid asexual population with two independent traits: fertility timespan and mortality onset. Through computational simulations, their model demonstrates that ageing can give populations an evolutionary advantage. Notably, this observation arises from the model without invoking any explicit energy tradeoffs, commonly used to explain this relationship.

The model’s results are based on both numerical simulations and formal mathematical analysis.

Additionally, the theoretical model developed here indicates that mortality onset is generally selected to start before the loss of fertility, irrespective of the initial values in the population. The selected relationship between the fertility timespan and mortality onset depends on the strength of fertility and mortality effects, with larger effects resulting in the loss of fertility and mortality onset being closer together. By allowing for a trans-generational effect on ageing in the model, the authors show that this can be advantageous as well, lowering the risk of collapse in the population despite an apparent fitness disadvantage in individuals. Upon closer examination, the authors reveal that this unexpected outcome is a consequence of the trans-generational effect on ageing increasing the evolvability of the population (i.e., allowing a more effective exploration of the parameter landscape), reaching the optimum state faster.

The simplicity of the proposed theoretical model represents both the major strength and weakness of this work. On one hand, with an original and rigorous methodology, the logic of their conclusions can be easily grasped and generalised, yielding surprising results. Using just a handful of parameters and relying on direct competition simulations, the model qualitatively recapitulates the negative correlation between lifespan and fertility without requiring energy tradeoffs. This alone makes this work an important milestone for the rapidly growing field of adaptive dynamics, opening many new avenues of research, both theoretically and empirically.

We thank the reviewers and editor for highlighting the importance of the work presented here.

On the other hand, the simplicity of the model also makes its relationship with living organisms difficult to gauge, leaving open questions about how much the model represents the reality of actual evolution in a natural context.

We presented both in results and discussion how the mathematical trade-offs between fertility and survival time give rise to (xb, xd) configuration representative of existing aging modes.

In particular, a more explicit discussion of how the specifics of the model can impact the results and their interpretation is needed. For example, the lack of mechanistic details on the trans-generational effect on ageing makes the results difficult to interpret.

We discussed the role of the transgenerational Lansing effect played to its function, there is no need for a particular mechanism beyond that function of transgenerational negative effect. We reinforce this in the discussion by adding the following sentence “Regarding the nature of the transgenerational effect, our model is agnostic and the mere transmission of any negative effect would be sufficient to exert the function.“

Even if analytical results are obtained, most of the observations appear derived from simulations as they are currently presented. Also, the choice of parameters for the simulations shown in the paper and how they relate to our biological knowledge are not fully addressed by the authors.

The long time limit of the system with and without the Lansing effect is based on analytical results later confirmed using numerical simulations. The choice of parameters is explained in the introduction as being the minimum ones for defining a living organism. As for the parameters’ values, our numerical analysis gives a solution for any ib, id, xb and xd on R+, making the choice of initial value a mere random decision.

Finally, the conclusions of evolvability are insufficiently supported, as the authors do not show if the wider genotypic variability in populations with the ageing trans-generational effect is, in fact, selected.

We do not show nor claim that evolvability per se is selected for but that the apparent advantage given by this transgenerational effect seems to be mediated by an increased genotypic/phenotypic variability conferred to the lineage that we interpreted as evolvability.

Recommendations for the authors

(1) The authors could use the lineage tracing results for the evolvability aspect. Specifically, within subpopulations featuring the Lansing effect, it would be valuable to explore whether individuals with parental age greater than the mortality onset (a > x_d) demonstrate higher fitness compared to individuals with a < x_d. Additionally, an examination of how this variation evolves over time could provide further insights into the dynamics of the proposed model.

We thank the reviewer for this suggestion. This is an ongoing work in the group, especially in the context of varying environmental conditions.

(2) In all simulations, I_b = I_d = 1, resulting in total fertility (x_b * I_b) equating to x_b, while x_d is proportional to life expectancy. Considering an exploration of the implications of this parameter setting, the authors could frame x_d as a 'lifespan cost', potentially allowing for the model to be conceptualised in terms of energetic tradeoffs. This might offer additional perspectives on the dynamics of the model and its alignment with biological principles.

We discuss how the apparent trade-offs given by the model depending on ib and id values can be related to the interpretation of such trade-offs that has been accepted for most of the past century. Our claim here in the discussion is that one does not need such energetic trade-off for the fertility/longevity trade-offs to appear. Such energetic trade-off is not a “biological principle” but merely an accepted interpretation of a fertility/longevity trade-off that is not even a general mechanism.

(3) Considering the necessity of variation in x_d for the observed patterns, an exploration could be undertaken by the authors to examine a model where x_d is simply variable without inheritance. This could involve centring x_d at some value d with some variance σ_d for all individuals. In such a scenario, it may be observed whether the same convergence of x_b - x_d occurs without requiring x_d to be selected. Furthermore, similar consequences of the Lansing effect could potentially be identified.

This was done early on during our work and did not show any major changes in the model’s behaviour beyond the time of convergence. We did not include it to the final manuscript because of the low added value to an already long and complex manuscript.

(4) While it may not be necessary to alter the model itself, it is suggested that the authors consider acknowledging the potential consequences of certain modelling decisions that might be perceived as biologically unrealistic. Notable examples include assumptions such as fertility from birth and zero mortality prior to x_d. These assumptions, such as infertility from birth, could be viewed as distinctive features, and it might be worth mentioning that parental care of offspring could have co-evolved with such features. This is particularly relevant considering the energy tradeoff hypothesis that has been postulated.

Although inspired from results obtained in Drosophila, mice, nematodes and zebrafish, the model is so far haploid and asexual, thus involving individuals likely more similar to unicellulars. In these conditions, infertility from birth did not seem relevant to us. However, the model and codes are accessible online and we hope that others will use it to address such questions. It is interesting though to notice that ageing appears here without such constraint.

Additionally, the consideration that all organisms face a non-trivial mortality rate at every age, not solely from physiological causes, reflects the reality within which selection operates.

We thought this was the best way to reflect, an environment with a limited carrying capacity. A more complex model is under construction to take into account the fact that older individuals might be more sensitive to it than younger ones.

(5) While acknowledging the technical rigour applied by the authors, it is suggested that further attention be given to conducting a comprehensive 'reality check' associated with the chosen parameters, particularly regarding the biological relevance of the results. For instance, the authors argue that offspring of old organisms do not, on average, live similarly to their parents. However, it is noted that studies in the haploid asexual organism yeast, akin to what the authors model (albeit not necessarily yeast), revealed that the average lifespan of yeast progeny born from young or old mothers is very similar.

We do not claim that progeny of old parents live less long than that of younger parents on average, we say that it happens in the progeny of physiologically old parents, representing at most 10% of the population in our numerical simulations.

The authors cite experimental evolution in Drosophila progeny conceived later in the life of the parent, indicating that the onset of mortality in these progeny occurs late, sometimes even after the end of the fertility period (Burke et al., 2016; Rose et al., 2002). While the authors report their own previous studies with divergent results, independent experiments have suggested an increase of x_d following an artificial increase of x_b (Luckinbill and Clare, 1985; Sgro et al., 2000). A more in-depth consideration of these contrasting observations and their potential implications for the current model could enhance the overall robustness of the study.

The increase of x_d following an artificial increase of x_b is predicted by our model as discussed. The divergence of observations between studies is alas hard to assess.

(6) To enhance readability and maintain consistency, it is suggested that the authors homogenise the description of key parameters, specifically x_b and x_d, throughout the text. This could contribute to improved clarity and rigour. One recommendation is to refer to x_b consistently as the 'fertility span' and x_d as the 'mortality onset' for the sake of uniformity in terminology.

We have modified the text accordingly.

(7) At various points in the text, the assertion is made that observations have indicated a tradeoff between fertility and longevity. It is recommended that the authors provide references or data to substantiate this claim. This addition would contribute to the empirical grounding of the mentioned tradeoff and strengthen the overall support for the assertions made in the study.

We added the following references to the discussion Lemaitre et al., 2015, Kirkwood, 2005 and Rodrigues and Flatt 2016.

(8) The statement claiming that the model is 'able to describe all types of ageing observed in the wild' should be moderated. As the authors themselves acknowledge, the model is referred to as a 'toy model,' and it is made clear that it cannot capture, nor is intended to capture, the entire diversity observed in life. Adjusting this statement to reflect the limited scope and purpose of the model would enhance precision and accuracy in the presentation of its capabilities.

Although a toy model, its possible configurations encompass all the possible configurations described so far across the diversity of ageing throughout the tree of life from negligible senescence with no loss of fertility (x_b and x_d >> 0) to menopause-like configurations (x_b >> x_d) through fast mortality increase post reproduction (x_b = x_d). Replacing our current square functions would allow age-dependant decrease or increase of fertility and/or risks of mortality onsets.

(9) To bolster the biological relevance of the study, it is strongly recommended that the authors cross-check the results of their simulations with previously published experimental findings. This approach would serve to strengthen the alignment between the model outcomes and observed biological phenomena. Additionally, placing greater emphasis on the biological relevance aspects throughout the text would contribute to a more robust and comprehensive exploration of the study's implications.

In the present manuscript we have tried to cite a certain number of results from artificial selection experiments on life history traits in order to strengthen the interpretations of our model’s behaviour. There are numerous other studies, going in the same direction or not, but we do not think that it would be relevant to add an exhaustive list of them. Nevertheless, we added Stearns et al., 2000 that adds extrinsic high mortality to the evolution of life history traits.

(1) For enhanced clarity, it is suggested that the x-axis in Figure 1 be labelled as 'age.' Considering this adjustment could contribute to clearer visual communication of the data.

We agree with the reviewer and modified the figure accordingly.

(!!) The addition of graphical legends is recommended for Figures 3-5, as well as the supplementary figures. Including these legends would provide essential context and improve the interpretability of the figures for readers.

We agree with the reviewer and modified the figure accordingly.

(12) For improved distinction of the ranges indicated by quantiles in Figure 3, it is suggested that the authors consider enhancing visual clarity. One approach could involve making the middle quantile thicker or using a different line type. Additionally, it is recommended to explore the calculation of the highest density 90% intervals rather than the 1-9 deciles. This adjustment could contribute to a clearer representation of the data distribution in the figure.

We named the different deciles directly on the figure to improve readability.

(13) It is observed that the mathematical proofs in Annex 1 are not displaying properly in the PDF. Additionally, there seem to be missing and broken references for the Annex. This issue may be related to LaTeX formatting. The authors could consider revisiting the formatting of Annex 1 to ensure the correct display of mathematical proofs and address the referencing concerns, possibly by checking and rectifying any LaTeX-related issues.

The latex file of the supplementary was not correctly compiled. It is now corrected.

(14) There is inconsistency in the text regarding the reference to the Annex, with both 'Annex' and 'Annexe' being used interchangeably. To maintain uniformity, it is suggested that the authors consistently use either 'Annex' or 'Annexe' throughout the text. This adjustment would contribute to a more polished presentation of the supplementary material.

We corrected them accordingly.

(15)There appears to be a typographical error in the name of Supplementary Figure 3.

We corrected it accordingly.

Author Response

eLife assessment

The authors present evidence that small extracellular vesicles can be secreted from cells inside larger vesicles that they call amphiectosomes, which then tear to release their small vesicle contents. There are questions and concerns relating to the quality of the data and the in vivo significance of the observations. The findings are potentially important but the data are incomplete and the claims are only partially supported.

We agree that the in vivo significance and details of the molecular background of amphiectosome release remains to be studied further. However, as Reviewer 2 indicated, our data in this Short Report may have a substantial impact on our understanding of EV biogenesis. Therefore, we considered it was important to publish our data as soon as possible because it may significantly impact other EV biogenesis studies.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors' research group had previously demonstrated the release of large multivesicular body-like structures by human colorectal cancer cells. This manuscript expands on their findings, revealing that this phenomenon is not exclusive to colorectal cancer cells but is also observed in various other cell types, including different cultured cell lines, as well as cells in the mouse kidney and liver. Furthermore, the authors argue that these large multivesicular body-like structures originate from intracellular amphisomes, which they term "amphiectosomes." These amphiectosomes release their intraluminal vesicles (ILVs) through a "torn-bag mechanism." Finally, the authors demonstrate that the ILVs of amphiectosomes are either LC3B positive or CD63 positive. This distinction implies that the ILVs either originate from amphisomes or multivesicular bodies, respectively.

Strengths:

The manuscript reports a potential origin of extracellular vesicle (EV) biogenesis. The reported observations are intriguing.

Weaknesses:

It is essential to note that the manuscript has issues with experimental designs and lacks consistency in the presented data. Here is a list of the major concerns:

(1) The authors culture the cells in the presence of fetal bovine serum (FBS) in the culture medium. Given that FBS contains a substantial amount of EVs, this raises a significant issue, as it becomes challenging to differentiate between EVs derived from FBS and those released by the cells. This concern extends to all transmission electron microscopy (TEM) images (Figure 1, 2P-S, S5, Figure 4 P-U) and the quantification of EV numbers in Figure 3. The authors need to use an FBS-free cell culture medium.

(1) Although FBS indeed contains bovine EVs, however, the presence of very large multivesicular EVs (amphiectosomes) that our manuscript focuses on has never been observed and reported. For reported size distributions of EVs in FBS, please find a few relevant references below:

PMID: 29410778, PMID: 33532042, PMID: 30940830 and PMID: 37298194

All the above publications show that the number of lEVs > 350-500 nm is negligible in FBS. The average diameter of MV-lEVs (amphiectosomes) described in our manuscript is around 1.00-1.50 micrometre.

(1) When we demonstrated the TEM of isolated EVs, we consistently used serum- free conditioned medium (Fig2 P-S, Fig2S5 J, O) as described previously (Németh et al 2021, PMID: 34665280).

(2) Our TEM images show cells captured in the process of budding and scission of large multivesicular EVs excluding the possibility that these structures could have originated from FBS.

(3) In addition, in our confocal analysis, we studied Palm-GFP positive, cell-line derived MV-lEVs. Importantly, in these experiments, FBS-derived EVs are non-fluorescent, therefore, the distinction between GFP positive MV-lEVs and FBS-derived EVs was evident.

(4) In addition, culturing cells in FBS-free medium (serum starvation) significantly affects autophagy. Given that in our study, we focused on autophagy related amphiectosome secretion, we intentionally chose to use FBS supplemented medium.

(5) Even though the authors of this manuscript are not familiar with the technological details how FBS is processed before commercialization, it is reasonable to assume that the samples are subjected to sterile filtration (through a 0.22 micron filter) after which MV-lEVs cannot be present in the commercial FBS samples.

(2) The data presented in Figure 2 is not convincingly supportive of the authors' conclusion. The authors argue that "...CD81 was present in the plasma membrane-derived limiting membrane (Figures 2B, D, F), while CD63 was only found inside the MV-lEVs (Fig. 2A, C, E)." However, in Figure 2G, there is an observable CD63 signal in the limiting membrane (overlapping with the green signals), and in Figure 2J, CD81 also exhibits overlap with MV-IEVs.

Both CD63 and CD81 are tetraspanins known to be present both in the membrane of sEVs and in the plasma membrane of cells (for references, please see Uniprot subcellular location maps: https://www.uniprot.org/uniprotkb/P08962/entry#subcellular_location https://www.uniprot.org/uniprotkb/P60033/entry#subcellular_location). However, according the feedback of the reviewer, for clarity, we will delete the implicated sentence from the text.

(3) Following up on the previous concern, the authors argue that CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs, respectively (Figure 2-A-M). However, in lines 104-106, the authors conclude that "The simultaneous presence of CD63, CD81, TSG101, ALIX, and the autophagosome marker LC3B within the MV-lEVs..." This statement indicates that CD63 and CD81 co-localize to the MV-IEVs. The authors need to address this apparent discrepancy and provide an explanation.

There must be a misunderstanding because we did not claim or implicate in the text that that “CD81 and CD63 are exclusively located on the limiting membrane and MV-IEVs”. Here we studied co-localization of the above proteins in the case intraluminal vesicles (ILVs). In Fig 2. we did not show any analysis of limiting membrane co-localization.

(4) The specificity of the antibodies used in Figure 2 should be validated through knockout or knockdown experiments. Several of the antibodies used in this figure detect multiple bands on western blots, raising doubts about their specificity. Verification through additional experimental approaches is essential to ensure the reliability and accuracy of all the immunostaining data in this manuscript.

We will consider this suggestion during the revision of the manuscript.

(5) In Figures 2P-R, the morphology of the MV-IEVs does not resemble those shown in Figures 1-A, H, and D, indicating a notable inconsistency in the data.

EM images in Figure2 P-R show sEVs separated from serum-free conditioned media as opposed to MV-lEVs, which were in situ captured in in fixed tissue cultures (Fig1). Therefore, the two EV populations necessarily have different size and structure. Furthermore, Fig. 1 shows images of ultrathin sections while in Figure 2P-R, we used a negative-positive contrasting of intact sEV-s without embedding and sectioning.

(6) There are no loading controls provided for any of the western blot data.

Not even the latest MISEV 2023 guidelines give recommendations for proper loading control for separated EVs in Western blot (MISEV 2023 , DOI: 10.1002/jev2.12404 PMID: 38326288). Here we applied our previously developed method (PMID: 37103858), which in our opinion, is the most reliable approach to be used for sEV Western blotting. For whole cell lysates, we used actin as loading control (Fig3_S2B).

Additionally, for Figures 2-S4B, the authors should run the samples from lanes i-iii in a single gel.

Please note that in Figure 2- S4B, we did run a single gel, and the blot was cut into 4 pieces, which were tested by anti-GFP, anti-RFP, anti-LC3A and anti-LC3B antibodies. Full Western blots are shown in Fig.3_S2 B, and lanes “1”, “2” and “3” correspond to “i”, “ii” and “iii” in Fig.2_S4, respectively.

(7) In Figure 2-S4, is there co-localization observed between LC3RFP (LC3A?) with other MV-IFV markers? How about LC3B? Does LC3B co-localize with other MV-IFV markers?

In the Supplementary figure Figure 2-S4 we showed successful generation of HEK293T-PalmGFP-LC3RFP cell line. In this case we tested the cells, and not the released MV-lEVs. LC3A co-localized with the RFP signal as expected.

(8) The TEM images presented in Figure 2-S5, specifically F, G, H, and I, do not closely resemble the images in Figure 2-S5 K, L, M, N, and O. Despite this dissimilarity, the authors argue that these images depict the same structures. The authors should provide an explanation for this observed discrepancy to ensure clarity and consistency in the interpretation of the presented data.

As indicated in Material and Methods, Fig 2_S5 F, G, H and I are conventional TEM images fixed by 4% glutaraldehyde 1% OsO4 2h and embedded into Epon resin with a post contrasting of 3.75% uranyl acetate 10 min and 12 min lead citrate. Samples processed this way have very high structure preservation and better image quality, however, they are not suitable for immune detection. In contrast, Fig.2._S5 K,L,M,N shows immunogold labelling of in situ fixed samples. In this case we used milder fixation (4% PFA, 0.1% glutaraldehyde, postfixed by 0.5% OsO4 30 min) and LR-White hydrophilic resin embedding. This special resin enables immunogold TEM analysis. The sections were exposed to H2O2 and NaBH4 to render the epitopes accessible in the resin. Because of the different applied techniques, the preservation of the structure is not the same. In the case of Fig.2 J, O, separated sEVs were visualised by negative-positive contrast and immunogold labelling as described previously (PMID: 37103858).

(9) For Figures 3C and 3-S1, the authors should include the images used for EV quantification. Considering the concern regarding potential contamination introduced by FBS (concern 1), it is advisable for the authors to employ an independent method to identify EVs, thereby confirming the reliability of the data presented in these figures.

In our revised manuscript, we will provide all the images used for EV quantification in Figure 3C. Given that Figures 3C and 3-S1 show MV-lEVs released by HEK293T-PlamGFP cells, the possible interference by FBS-derived non-fluorescent EVs can be excluded.

(10) Do the amphiectosomes released from other cell types as well as cells in mouse kidneys or liver contain LC3B positive and CD63 positive ILVs?

Based on our confocal microscopic analysis, in addition the HEK293T-PalmGFP cells, HT29 and HepG2 cells also release similar LC3B and CD63 positive MV-lEVs. Preliminary evidence shows MV-lEV secretion by additional cell types.

Author Response

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

Reviewer #1 (Public Review):

Wang et al investigated the evolution, expression, and function of the X-linked miR-506 miRNA family. They showed that the miR-506 family underwent rapid evolution. They provided evidence that miR-506 appeared to have originated from the MER91C DNA transposons. Human MER91C transposon produced mature miRNAs when expressed in cultured cells. A series of mouse mutants lacking individual clusters, a combination of clusters, and the entire X-linked cluster (all 22 miRNAs) were generated and characterized. The mutant mice lacking four or more miRNA clusters showed reduced reproductive fitness (litter size reduction). They further showed that the sperm from these mutants were less competitive in polyandrous mating tests. RNA-seq revealed the impact of deletion of miR-506 on the testicular transcriptome. Bioinformatic analysis analyzed the relationship among miR-506 binding, transcriptomic changes, and target sequence conservation. The miR-506-deficient mice did not have apparent effect on sperm production, motility, and morphology. Lack of severe phenotypes is typical for miRNA mutants in other species as well. However, the miR-506-deficient males did exhibit reduced litter size, such an effect would have been quite significant in an evolutionary time scale. The number of mouse mutants and sequencing analysis represent a tour de force. This study is a comprehensive investigation of the X-linked miR-506 miRNA family. It provides important insights into the evolution and function of the miR-506 family.

The conclusions of this preprint are mostly supported by the data except being noted below. Some descriptions need to be revised for accuracy.

L219-L285: The conclusion that X-linked miR-506 family miRNAs are expanded via LINE1 retrotransposition is not supported by the data. LINE1s and SINEs are very abundant, accounting for nearly 30% of the genome. In addition, the LINE1 content of the mammalian X chromosome is twice that of the autosomes. One can easily find flanking LINE1/SINE repeat. Therefore, the analyses in Fig. 2G, Fig. 2H and Fig. S3 are not informative. In order to claim LINE1-mediated retrotransposition, it is necessary to show the hallmarks of LINE1 retrotransposition, which are only possible for new insertions. The X chromosome is known to be enriched for testis-specific multi-copy genes that are expressed in round spermatids (PMID: 18454149). The conclusion on the LINE1-mediated expansion of miR-506 family on the X chromosome is not supported by the data and does not add additional insights. I think that the LINE1 related figure panels and description (L219-L285) need to be deleted. In discussion (L557558), "...and subsequently underwent sequence divergence via LINE1-mediated retrotransposition during evolution" should also be deleted. This section (L219-L285) needs to deal only with the origin of miR506 from MER91C DNA transposons, which is both convincing and informative.

Reply: Agreed, the corresponding sentences were deleted.

Fig. 3A: can you speculate/discuss why the miR-506 expression in sperm is higher than in round spermatids?

Reply: RNAs are much less abundant in sperm than in somatic or spermatogenic cells (~1/100). Spermborne small RNAs represent a small fraction of total small RNAs expressed in their precursor spermatogenic cells, including spermatocytes and spermatids. Therefore, when the same amount of total/small RNAs are used for quantitative analyses, sperm-borne small RNAs (e.g., miR-506 family miRNAs) would be proportionally enriched in sperm compared to other spermatogenic cells. We discussed this point in the text (Lines 550-556).

**Reviewer #2 (Public Review):

In this paper, Wang and collaborators characterize the rapid evolution of the X-linked miR-506 cluster in mammals and characterize the functional reference of depleting a few or most of the miRNAs in the cluster. The authors show that the cluster originated from the MER91C DNA transposon and provide some evidence that it might have expanded through the retrotransposition of adjacent LINE1s. Although the animals depleted of most miRNAs in the cluster show normal sperm parameters, the authors observed a small but significant reduction in litter size. The authors then speculate that the depletion of most miRNAs in the cluster could impair sperm competitiveness in polyandrous mating. Using a successive mating protocol, they show that, indeed, sperm lacking most X-linked miR-506 family members is outcompeted by wild-type sperm. The authors then analyze the evolution of the miR-506 cluster and its predicted targets. They conclude that the main difference between mice and humans is the expansion of the number of target sites per transcript in humans.

The conclusions of the paper are, in most cases, supported by the data; however, a more precise and indepth analysis would have helped build a more convincing argument in most cases.

(1) In the abstracts and throughout the manuscript, the authors claim that "... these X-linked miRNA-506 family miRNA [...] have gained more targets [...] " while comparing the human miRNA-506 family to the mouse. An alternative possibility is that the mouse has lost some targets. A proper analysis would entail determining the number of targets in the mouse and human common ancestor.

Reply: This question alerted us that we did not describe our conclusion accurately, causing confusion for this reviewer. Our data suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis. In other words, mice never lost any targets compared to humans, but per the miR-506 family miRNA tends to target more genes in humans than in mice.

We revised the text to more accurately report our data. The pertaining text (lines 490-508) now reads: “Furthermore, we analyzed the number of all potential targets of the miR-506 family miRNAs predicted by the aforementioned four algorithms among humans, mice, and rats. The total number of targets for all the X-linked miR-506 family miRNAs among different species did not show significant enrichment in humans (Fig. S9C), suggesting the sheer number of target genes does not increase in humans. We then compared the number of target genes per miRNA. When comparing the number of target genes per miRNA for all the miRNAs (baseline) between humans and mice, we found that on a per miRNA basis, human miRNAs have more targets than murine miRNAs (p<0.05, t-test) (Fig. S9D), consistent with higher biological complexity in humans. This became even more obvious for the X-linked miR-506 family (p<0.05, t-test) (Fig. S9D). In humans, the X-linked miR-506 family, on a per miRNA basis, targets a significantly greater number of genes than the average of all miRNAs combined (p<0.05, t-test) (Fig. S9D). In contrast, in mice, we observed no significant difference in the number of targets per miRNA between X-linked miRNAs and all of the mouse miRNAs combined (mouse baseline) (Fig. S9D). These results suggest that although the sheer number of target genes remains the same between humans and mice, the human X-linked miR-506 family targets a greater number of genes than the murine counterpart on a per miRNA basis.”

We also changed “have gained” to “have” throughout the text to avoid confusion.

(2) The authors claim that the miRNA cluster expanded through L1 retrotransposition. However, the possibility of an early expansion of the cluster before the divergence of the species while the MER91C DNA transposon was active was not evaluated. Although L1 likely contributed to the diversity within mammals, the generalization may not apply to all species. For example, SINEs are closer on average than L1s to the miRNAs in the SmiR subcluster in humans and dogs, and the horse SmiR subcluster seems to have expanded by a TE-independent mechanism.

Reply: Agreed. We deleted the data mentioned by this reviewer.

(3) Some results are difficult to reconcile and would have benefited from further discussion. The miR-465 sKO has over two thousand differentially expressed transcripts and no apparent phenotype. Also, the authors show a sharp downregulation of CRISP1 at the RNA and protein level in the mouse. However, most miRNAs of the cluster increase the expression of Crisp1 on a reporter assay. The only one with a negative impact has a very mild effect. miRNAs are typically associated with target repression; however, most of the miRNAs analyzed in this study activate transcript expression.

Reply: Both mRNA and protein levels of Crisp1 were downregulated in KO mice, and these results are consistent with the luciferase data showing overexpression of these miRNAs upregulated the Crisp1 3’UTR luciferase activity. We agree that miRNAs usually repress target gene expression. However, numerous studies have also shown that some miRNAs, such as human miR-369-3, Let-7, and miR-373, mouse miR-34/449 and the miR-506 family, and the synthetic miRNA miRcxcr4, activate gene expression both in vitro (1, 2) and in vivo (3-6). Earlier reports have shown that these miRNAs can upregulate their target gene expression, either by recruiting FXR1, targeting promoters, or sequestering RNA subcellular locations (1, 2, 6). We briefly discussed this in the text (Lines 605-611).

(4) More information is required to interpret the results of the differential RNA targeting by the murine and human miRNA-506 family. The materials and methods section needs to explain how the authors select their putative targets. In the text, they mention the use of four different prediction programs. Are they considering all sites predicted by any method, all sites predicted simultaneously by all methods, or something in between? Also, what are they considering as a "shared target" between mice and humans? Is it a mRNA that any miR-506 family member is targeting? Is it a mRNA targeted by the same miRNA in both species? Does the targeting need to occur in the same position determined by aligning the different 3'UTRs?

Reply: Since each prediction method has its merit, we included all putative targets predicted by any of the four methods. The "shared target" refers to a mRNA that any miR-506 family member targets because the miR-506 family is highly divergent among different species. We have added the information to the “Large and small RNA-seq data analysis” section in Materials and Methods (Lines 871-882).

(5) The authors highlight the particular evolution of the cluster derived from a transposable element. Given the tendency of transposable elements to be expressed in germ cells, the family might have originated to repress the expression of the elements while still active but then remained to control the expression of the genes where the element had been inserted. The authors did not evaluate the expression of transcripts containing the transposable element or discuss this possibility. The authors proposed an expansion of the target sites in humans. However, whether this expansion was associated with the expansion of the TE in humans was not discussed either. Clarifying whether the transposable element was still active after the divergence of the mouse and human lineages would have been informative to address this outstanding issue.

Reply: Agreed. The MER91C DNA transposon is denoted as nonautonomous (7); however, whether it was active during the divergence of mouse and human lineages is unknown. To determine whether the expansion of the target sites in humans was due to the expansion of the MER91C DNA transposon, we analyzed the MER91C DNA transposon-containing transcripts and associated them with our DETs. Of interest, 28 human and 3 mouse mRNAs possess 3’UTRs containing MER91C DNA sequences, and only 3 and 0 out of those 28 and 3 genes belonged to DETs in humans and mice, respectively (Fig. S9E), suggesting a minimal effect of MER91C DNA transposon expansion on the number of target sites. We briefly discussed this in the text (Lines 511-518).

Post-transcriptional regulation is exceptionally complex in male haploid cells, and the functional relevance of many regulatory pathways remains unclear. This manuscript, together with recent findings on the role of piRNA clusters, starts to clarify the nature of the selective pressure that shapes the evolution of small RNA pathways in the male germ line.

Reply: Agreed. We appreciate your insightful comments.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors conducted a comprehensive study of the X-linked miR-506 family miRNAs in mice on its origin, evolution, expression, and function. They demonstrate that the X-linked miR-506 family, predominantly expressed in the testis, may be derived from MER91C DNA transposons and further expanded by retrotransposition. By genetic deletion of different combinations of 5 major clusters of this miRNA family in mice, they found these miRNAs are not required for spermatogenesis. However, by further examination, the mutant mice show mild fertility problem and inferior sperm competitiveness. The authors conclude that the X-linked miR-506 miRNAs finetune spermatogenesis to enhance sperm competition.

Strengths:

This is a comprehensive study with extensive computational and genetic dissection of the X-linked miR506 family providing a holistic view of its evolution and function in mice. The finding that this family miRNAs could enhance sperm competition is interesting and could explain their roles in finetuning germ cell gene expression to regulate reproductive fitness.

Weaknesses:

The authors specifically addressed the function of 5 clusters of X-link miR-506 family containing 19 miRNAs. There is another small cluster containing 3 miRNAs close to the Fmr1 locus. Would this small cluster act in concert with the 5 clusters to regulate spermatogenesis? In addition, any autosomal miR-506 like miRNAs may compensate for the loss of X-linked miR-506 family. These possibilities should be discussed.

Reply: The three FmiRs were not deleted in this study because the SmiRs are much more abundant than the FmiRs in WT mice (Author Response image 1, heatmap version of Fig. 5C). Based on small RNA-seq, some FmiRs, e.g., miR-201 and miR-547, were upregulated in the SmiRs KO mice, suggesting that this small cluster may act in concert with the other 5 clusters and thus, worth further investigation. To our best knowledge, all the miR-506 family miRNAs are located on the X chromosome, although some other miRNAs were upregulated in the KO mice, they don’t belong to the miR-506 family. We briefly discussed this point in the text (Lines 635-638).

Author response image 1.

sRNA-seq of WT and miR-506 family KO testis samples.

Direct molecular link to sperm competitiveness defect remains unclear but is difficult to address.

Reply: In this study, we identified a target of the miR-506 family, i.e. Crisp1. KO of Crisp1 in mice, or inhibition of CRISP1 in human sperm (7, 8), appears to phenocopy the quinKO mice, displaying largely normal sperm motility but compromised ability to penetrate eggs. The detailed mechanism warrants further investigation in the future.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Lines 84-85: "Several cellular events are unique to the male germ cells, e.g., meiosis, genetic recombination, and haploid male germ cell differentiation (also called spermiogenesis)". This statement is not accurate. Please revise. Meiosis and genetic recombination are common to both male and female germ cells. They are highly conserved in both sexes in many species including mouse.

Reply: Agreed. We have revised the sentence and it now reads: “Several cellular events are unique to the male germ cells, e.g., postnatal formation of the adult male germline stem cells (i.e., spermatogonia stem cells), pubertal onset of meiosis, and haploid male germ cell differentiation (also called spermiogenesis) (9)” (Lines 83-86).

Lines 163-164: "we found that Slitrk2 and Fmr1 were syntenically linked to autosomes in zebrafish and birds (Fig. 1A), but had migrated onto the X chromosome in most mammals". This description is not accurate. Chr 4 in zebrafish and birds is syntenic to the X chromosome in mammals. The term "migrated" is not appropriate. Suggestion: Slitrk2 and Fmr1 mapped to Chr 4 (syntenic with mammalian X chromosome) in zebrafish and birds but to the X chromosome in most mammals.

Reply: Agreed. Revised as suggested.

Reviewer #2 (Recommendations For The Authors):

(1) In the significance statement, the authors mention that the mutants are "functionally infertile," although the decrease in competitiveness is partial. I suggest referring to them as "functionally sub-fertile."

Reply: Agreed. Revised as suggested.

(2) I will urge the authors to explain in more detail how some figures are generated and what they mean. Some critical information needs to be included in various panels.

(2a) Figure S1. The phastCons track does not seem to align as expected with the rest of the figure. The highest conservation peak is only present in humans, and the sequence conserved in the sea turtle has the lowest phastCons score. I was expecting the opposite from the explanation.

Reply: The tracks for phyloP and phastCons are the scores for all 100 species, whereas the tracks with the species names on the left are the corresponding sequences aligned to the human genome. We have revised our figure to make it clearer.

(2b) Figure 2A and Figure S2C. Although all the functional analysis of the manuscript has been done in mice, the alignments showing sequence conservation do not include the murine miRNAs. Please include the mouse miRNAs in these panels.

Reply: The mouse has Mir-506-P7 with the conserved miRNA-3P seed region, which was included in the lower panel in Figure S2C. However, mice do not have Mir-506-P6, which may have been lost or too divergent to be recognized during the evolution and thus, were not included in Figure 2A and the upper panel in Figure S2C.

(2c) Figure S7H. The panel could be easier to read.

Reply: Agreed. We combined all the same groups and turned Figure S7H (now Figure S6H) into a heatmap.

(2d) The legend of Figure 6G reads, "The number of target sites within individual target mRNAs in both humans and mice ." Can the author explain why the value 1 of the human "Number of target sites" is connected to virtually all the "Number of target sites" values in mice?

Reply: Sorry for the confusion. For example, for gene 1, we have 1 target site in the human and 1 target site in the mouse; but for gene 2, we have 1 target site in the human and multiple sites in the mouse; therefore, the value 1 is connected to more than one value in the mouse.

Reviewer #3 (Recommendations For The Authors):

CRISP1 and EGR1 protein localization in WT and mutant sperm by immunostaining would be helpful.

Reply: Agreed. We performed immunostaining for CRISP1 on WT sperm, and the new results are presented in Figure S8D. CRISP1 seems mainly expressed in the principal piece and head of sperm.

The detailed description of the generation of various mutant lines should be included in the Methods.

Reply: We added more details on the generation of knockout lines in the Materials and Methods (686701).

References:

(1) S. Vasudevan, Y. Tong, J. A. Steitz, Switching from repression to activation: microRNAs can upregulate translation. Science 318, 1931-1934 (2007).

(2) R. F. Place, L. C. Li, D. Pookot, E. J. Noonan, R. Dahiya, MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 105, 1608-1613 (2008).

(3) Z. Wang et al., X-linked miR-506 family miRNAs promote FMRP expression in mouse spermatogonia. EMBO Rep 21, e49024 (2020).

(4) S. Yuan et al., Motile cilia of the male reproductive system require miR-34/miR-449 for development and function to generate luminal turbulence. Proc Natl Acad Sci U S A 116, 35843593 (2019).

(5) S. Yuan et al., Oviductal motile cilia are essential for oocyte pickup but dispensable for sperm and embryo transport. Proc Natl Acad Sci U S A 118 (2021).

(6) M. Guo et al., Uncoupling transcription and translation through miRNA-dependent poly(A) length control in haploid male germ cells. Development 149 (2022).

(7) V. G. Da Ros et al., Impaired sperm fertilizing ability in mice lacking Cysteine-RIch Secretory Protein 1 (CRISP1). Dev Biol 320, 12-18 (2008).

(8) J. A. Maldera et al., Human fertilization: epididymal hCRISP1 mediates sperm-zona pellucida binding through its interaction with ZP3. Mol Hum Reprod 20, 341-349 (2014).

(9) L. Hermo, R. M. Pelletier, D. G. Cyr, C. E. Smith, Surfing the wave, cycle, life history, and genes/proteins expressed by testicular germ cells. Part 1: background to spermatogenesis, spermatogonia, and spermatocytes. Microsc Res Tech 73, 241-278 (2010).

Author Response

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

Reviewer #1 (Public Review):

The present study by Berger et al. analyzes to what extent memory formation is dependent on available energy reserves. This has been dealt with extensively in the case of aversive memory formation, but only very sparsely in the case of appetitive memory formation. It has long been known that an appetitive memory in flies can only be formed by starvation. However, the authors here additionally show that not only the duration of starvation plays a role, but also determines which form of memory (short- or long-term memory) is formed. The authors demonstrated that internal glycogen stores play a role in this process and that this is achieved through insulin-like signaling in octopaminergic reward neurons that integrates internal energy stores into memory formation. Here, the authors suggest that octopamine plays a role as a negative regulator of different forms of memory.

The study sheds light on an old question, to what extent the octopaminergic neuronal system plays a role in the formation of appetitive memory, since in recent years only the dopaminergic system has been in focus. Furthermore, the data are an interesting contribution to the ongoing debate whether insulin receptors play a role in neurons themselves or in glial cells. The experiments are very well designed and the authors used a variety of behavioural experiments, genetic tools to manipulate neuronal activity and state-of-the-art imaging techniques. In addition, they not only clearly demonstrated that octopamine is a negative regulator of appetitive memory formation, but also proposed a mechanism by which the insulin receptor in octopaminergic neurons senses the internal energy status and then controls the activity of those neurons. The conclusions are mostly supported by the data, but some aspects related to the experimental design, some explanations and literature references need more clarification and revision.

(1) Usually, long-term memory (LTM) is tested 24 hours after training. Here, the authors usually refer to LTM as a memory that is tested 6 hours after training. The addition of a control experiment to show that LTM that the authors observe here lasts longer would increase the power of this study immensely.

We thank the reviewer for this comment, as it helped greatly to clarify the matter.

We measured memory of control and mutant flies 24 h after the training and included the data into the manuscript (Figure 1B and summarized in a model in Figure 2C). We show that control flies develop an intermediate type of memory, that is depending on the length of starvation either anesthesia-sensitive or resistant. Mutants lacking octopamine develop either anesthesia-sensitive or resistant long-term memory.

(2) The authors define here another consolidated memory component as ARM, when they applied a cold-shock 2 hours after training. However, some publications showed that LTM is formed after only one training cycle (Krashes et al 2008, Tempel et al 1983). This makes it difficult to determine, whether appetitive ARM can be formed. Furthermore, one study showed that appetitive ARM is absent after massed training (Colomb et al 2009). Therefore, the conclusion could be also, that different starvation protocols, would lead to different stabilities of LTM. Therefore, additional experiments could help to clarify this opposing explanation. From these results, it can then be concluded either that different stable forms of LTM are formed depending on the starvation state, or that two differently consolidated memory phases (LTM, ARM) are formed, as has already been shown for aversive memory. This is also important for other statements in the manuscript, and therefore the authors should address this. For example, the findings about the insulin receptor (is it two opposing memories or different stabilities of LTM).

The flies indeed develop different types of memory depending on the length of starvation and the internal energy supply.

Reviewer #2 (Public Review):

How organism physiological state modulates establishment and perdurance of memories is a timely question that the authors aimed at addressing by studying the interplay between energy homeostasis and food-related conditioning in Drosophila. Specifically, they studied how starvation modulates the establishment of short-term vs long-term memories and clarified the role of the monoamine Octopamine in food-related conditioning, showing that it is not per se involved in formation of appetitive short-term memories but rather gates memory formation by suppressing LTM when energy levels are high. This work clarifies previously described phenotypes and provides insight about interconnections between energy levels, feeding and formation of short-term and long-term food-related memories. In the absence of population-specific manipulation of octopamine signaling, it however does not reach a circuit-level understanding of how these different processes are integrated.

Strengths

• Previous studies have documented the impact of Octopamine on different aspects of food-related behaviors (regulation of energy homeostasis, feeding, sugar sensing, appetitive memory...), but we currently lack a clear understanding of how these different functions are interconnected. The authors have used a variety of experimental approaches to systematically test the impact of internal energy levels in establishment of appetitive memory and the role of Octopamine in this process.

• The authors have used a range of approaches, performed carefully controlled experiments and produced high quality data.

Weaknesses

(1) In the tbh mutant flies, Tyramine -to- Octopamine conversion is inhibited, resulting not only in a lack of Octopamine, but also in elevated levels of Tyramine. If and how elevated levels of Tyramine contributes to the described phenotypes is unclear. In the current version of the manuscript, only one set of experiments (Figure 2) has been performed using Octopamine agonist. This is particularly important in light of recent published data showing that starvation modifies Tyramine levels. (2) Octopamine (and its precursor Tyramine) have been implicated in numerous processes, complicating the analysis of the phenotypes resulting from a general inhibition of tbh.

We thank the reviewer for raising these points. The observed memory defects of the Tbh mutants can be solely explained by loss of octopamine. We included models into the manuscript to illustrate this (Figure 2 C and Figure 7E).

To address whether the elevated levels of tyramine observed in Tbh mutants interfere with food consumption, we analyzed the effect of increased levels of tyramine and octopamine on food consumption. We included the data (Figure S2). An increase in tyramine levels did not result in a change in food intake, rather the increase in octopamine levels reduced food intake. Our data show that the reduction of food intake observed in starved Tbh mutants is due to the increased internal energy supply.

(3) The manuscript explores various aspects of the impact of energy levels on food-related behaviors and the underlying sensing and effector mechanism, both in wild-type and tbh mutants, making it difficult to follow the flow of the results.

We included models illustrating the results to clarify the content of the manuscript.

Reviewer #3 (Public Review):

In this manuscript, Berger et al. study how internal energy storage influence learning and memory. Since in Drosophila melanogaster, octopamine (OA) is involved in the regulation of energy homeostasis they focus on the roles of OA. To do so they use the tyramine-β-hydroxylase (Tbh) mutant that is lacking the neurotransmitter OA and study short term memory (STM), long-term memory (LTM) and anesthesia-resistant memory (ARM). They show that the duration of starvation affects the magnitude of both short- and long-term memory. In addition, they show that OA has a suppressive effect on learning and memory. In terms of energy storage, they show that internal glycogen storage influences how long sucrose is remembered and high glycogen suppresses memory. Finally, they show that insulin-like signaling in octopaminergic neurons, which is also related to internal energy storage, suppresses learning and memory.

This is an important study that extends our knowledge on OA activity in learning and memory and the effects the metabolic state has on learning and memory. The authors nicely use the genetic tools available in flies to try and unravel the complex circuitry of metabolic state level, OA activity and learning and memory.

Nevertheless, I do have some comments that I think require attention:

(1) The authors use RNAi to reduce the level of glycogen synthase or glycogen phosphorylase. These manipulations are expected to affect the level of glycogen. Using specific drivers the authors attempt to manipulate glycogen level at the muscles and fat bodies and examine how this affects learning and memory. The conclusions of the authors arise solely from the manipulation intended (i.e. the genetics). However, the authors also directly measured glycogen levels at these organs and those do not follow the manipulation intended, i.e. the RNAi had very limited effect on the glycogen level. Nevertheless, these results are ignored.

We agreed with the reviewer and repeated the experiments. While we could not detect differences in whole animals, we detected differences in tissues enriched for muscles or fat, e.g. thorax or abdomen. We added the data.

(2) The authors claim in the summary that OA is not required for STM. However, according to one experiment OA is required for STM as Tbh mutants cannot form STM. In another experiment OA is suppressive to STM as wt flies fed with OA cannot form STM. Therefore, it is very difficult to appreciate the actual role of OA on STM.

During mild starvation, the internal energy supply is greater in Tbh mutants than in control flies. This information is integrated into the reward system via insulin receptor signaling. Therefore, the association between the odorant and sucrose is not meaningful to the mutants and no STM is formed. At the same time there is no release of octopamine and therefore no repression of LTM. In starved animals, octopamine suppresses food intake (we added the data). This is consistent with a function of Octopamine as a signal for the presence of food. Depending on when the signal comes, this might suppress the formation of STM or LTM.

(3) The authors use t-test and ANOVA for most of the statistics, however, they did not perform normality tests. While I am quite sure that most datasets will pass normality test, nevertheless, this is required.

Thanks for pointing this out. We have included a description in the “Materials and Methods” section that explains how we tested the data for normal distribution. We corrected the figure legends accordingly.

“We used the Shapiro-Wilk test (significance level P < 0.05) followed by a QQ-Plot chart analysis to determine whether the data were normal distributed. “

(4) While it is logical to assume that OA neurons are upstream to R15A04 DA neurons, I am not sure this really arises from the experiment that is presented here. It is well established that without activity in R15A04 DA neurons there is no LTM. Since OA acts to decrease LTM, can one really conclude anything about the location of OA effect when there is no learning?

Normally control flies did not form memory 6 h after training, only Tbh mutants. We wanted to investigate what kind of memory develops in Tbh mutants. During the experiments of the manuscript, we kept the training procedure constant.

(5) It is unclear how expression of a dominant negative form of insulin receptor (InR) in OA neurons can rescue the lack of OA due to the Tbh mutation. If OA neurons cannot release anything to the presumably downstream DA neurons, how can changing their internal signaling has any effect?

The expression of the dominant negative form of the insulin receptor signals no food or low energy levels and activation of the insulin receptor that there is enough food. The reward is a source of food, but the energy content is not high enough to fill the energy stores. The insulin receptor activation can activate at least three different signaling cascades, one of which might regulate octopamine release.

While I stressed some comments that need to be addressed, the overall take-home message of the manuscript is supported and the authors do show that the metabolic state of the animal affects learning and memory. I do think though, that some more caution is required for some of the conclusions.

We added additional data to address the points raised.

Recommendations for the authors:

We addressed all points raised by the reviewers, clarified the content or added more data.

Reviewer #1 (Recommendations For The Authors):

(1) Throughout the manuscript, the full stop of a sentence is always placed before the references.

We fixed this.

(2) I find the English in the manuscript not yet sufficient for publication. I suggest that the authors carefully revise the manuscript. I think if the sentences are structured a little more clearly, this paper has enormous potential to be read by your broad community.

We agree and revised the manuscript. We hope the manuscript is now clearer.

(3) Sentences l114 to l117 are misleading. The authors imply that they tested the same flies for changes in odor perception or sucrose sensitivity. I assume that the authors meant that they analyzed different groups of animals.

We clarified the sentence as follows:

“To ensure that the observed differences in learning and memory were not due to changes in odorant perception, odorant evaluation or sucrose sensitivity, different fly populations of the same genotypes were tested for their odorant acuity, odorant preference and their sucrose responsiveness (Table S1).”

(4) In the title as well as in the abstract the influence of octopamine on appetitive memory formation is described in more detail, this is also the main focus of this study. However, in the introduction, the influence of the insulin receptor on memory formation is discussed first. Personally, I would describe this later in the manuscript, ideally in the results section. At this point in the manuscript, this leads to an interruption in the flow of reading.

Thanks for the suggestion. We changed the order in the introduction.

(5) The authors could consider, since they only used Drosophila melanogaster, changing "Drosophila melanogaster" to "Drosophila" throughout the manuscript.

We modified the text accordingly.

(6) All evaluations and statistical tests are state of the art. However, I have one comment. For each statistical test, a correction should be made depending on the number of tests. However, I could not determine whether this was also done for the parametric or non-parametric one-sample t-test. From the results and the methods section, I would guess not. Here I would recommend a Bonferroni correction or even better a Sidak-Holm correction. Furthermore, the authors could also go into more detail about which non-parametric one-sample t-test they used.

We described the statistic used in more detail in the material and method section.

“We used the Shapiro-Wilk test (significance level P < 0.05) followed by a QQ-Plot chart analysis to determine whether the data were normal distributed. For normal distributed data, we used the Student’s t test to compare differences between two groups and the one-way ANOVA with Tukey’s post hoc HSD test for differences between more than two groups. For nonparametric data, we used the Mann-Whitney U test for differences between two groups and for more than two groups the Kruskal-Wallis test with post hoc Duenn analysis and Bonferroni correction. The nonparametric one-sample sign test was used to analyze whether behavior was not based on random choice and differed from zero (P < 0.5). The statistical data analysis was performed using statskingdom (https://www.statskingdom.com).”

(7) In nearly all figure legends the sentence "The letter "a" marks a significant difference from random choice as determined by a one-sample sign test (P < 0.05; P< 0.01)" occur. This is correctly indexed in the figures. However, I do not understand here what then P < 0.05; P**< 0.01 means. The significance level should be described here. I would strongly recommend the authors to make the definition clearer.

We corrected this in the figure legends (see also above).

(8) In Fig. 1B the labelling is a bit confusing. I interpret the two right groups as the mutants for octopamine, but there is still w[1118] in front.

We modified the Figure 1B.

Reviewer #2 (Recommendations For The Authors):

Suggestions

(1) Assessing the contribution of Tyramine in the observed phenotypes (for example by reducing the levels of Tyramine or its specific receptor) would help understand the contribution of Tyramine in the observed phenotypes.

See comments above.

We thank the reviewer for raising these points. The observed memory defects of the Tbh mutants can be solely explained by loss of octopamine. We included models into the manuscript to illustrate this (Figure 2 C and Figure 7E).

To address whether the elevated levels of tyramine observed in Tbh mutants interfere with food consumption, we analyzed the effect of increased levels of tyramine and octopamine on food consumption. We included the data (Figure S2). An increase in tyramine levels did not result in a change in food intake, rather the increased octopamine levels reduced food intake. Our data show that the reduction of food intake observed in starved Tbh mutants is due to the increased internal energy supply.

(2) Cell-specific inhibition of octopamine receptors should thus be performed to precisely interpret the observed phenotypes and dissect how interconnected the different phenotypes are, which is the object of this publication.

We observed that the time point and duration of octopamine application changes the behavioral output. The behavior analyzed depends on pulses of octopamine and differences of the internal energy status. A cell-specific inhibition via RNAi knock down of octopamine receptors might not clarify the issue.

(3) Defining of streamline and progressively integrating the different observations into a unifying model would improve the clarity and flow of the manuscript.

We included models explaining the observed results (Figure 2C and Figure 7E).

Minor comments

Line 129: Figure 1B should be mentioned, not 2B.

Figure 1 legend: E should be replaced by C (after A,B).

Figure S5: what are the arrows pointing to? Why are the Inr foci visible in A not seen in B? It should be mCD8-GFP and not mCD on top of the images.

We fixed this.

Reviewer #3 (Recommendations For The Authors):

Major:

(1) Can one really conclude from Figure 2A that OA acts on R15A04 DA neurons? It is well established that without activity in these DA neurons there is no LTM. Since OA acts to decrease learning, how one can conclude anything about the location of OA effect when there is no learning? With STM the situation was opposite, OA supported learning and this was abolished when DA neurons were silenced. I think some supporting experiment are required, i.e. how OA affects DA neurons activity or, alternatively, tone down a bit the writing.

Normally control flies did not form memory 6 h after training, only Tbh mutants. We wanted to investigate what kind of memory develops in Tbh mutants. During the experiments of the manuscript, we kept the training procedure constant. The inhibition of dopaminergic neurons blocks the memory of Tbh mutants. Taken together the duration of the memory, the cold-shock experiments and the inhibition of the dopaminergic neurons, Tbh develops LTM after training. This training does not evoke memory in controls.

The loss of STM in mildly starved Tbh mutants depends on the integration of the high internal energy levels via InR signaling. Reducing the internal energy levels further by extension of starvation result in STM supporting that OA is not directly involved in the formation of STM.

(2) Figure 4 requires some clarifications. In Supplementary Figure S2 the authors show that they could not manipulate glycogen levels in muscles. However, in Figure 4B they show that "Increasing glycogen levels in the muscles did not change short-term memory in 16 h starved flies, but the reduction in glycogen significantly improved memory strength (Figure 4B)" (lines 231-233). How can this be reconciled?

While we could not detect differences in whole animals, we detected differences in glycogen content in body parts enriched with muscles or fat, e.g. thorax or abdomen when using UAS-GlyP-RNAi or UAS-GlyS-RNAi under the control of the respective Gal4 drivers.

We added the data.

Likewise, the authors write that "Increasing or decreasing glycogen levels in the fat bodies had no effect on memory performance (Figure 4C)" Line (233-234). However, in Figure S2 they show that they can only increase glycogen levels but not decrease them.

As explained above the conclusion of Figure 4 "Thus, low levels of glycogen in the muscles upon starvation positively influence appetitive short-term memory, while high levels of glycogen in the muscles and fat body reduce short-term memory" lines 245-246, is not supported by the direct measurements of glycogen presented in Figure S2.

We added the data showing that the reduction or increase can be measured when analyzing the specific body parts enriched in muscles tissue or fat tissue.

(3) In cases where mutant flies do not display learning, a control should be done to see if they ate the sugar (with dye). Especially since the genetic manipulation affects metabolism.

We analyzed how much sucrose the animals consumed in the behavioral test. Tbh and controls fed and there was no difference in feeding behavior between the mutants and the controls.

“We next determined whether differences in preferences influence sucrose intake during training. Therefore, we measured the sucrose intake of starved flies in the behavioral set up. We used a food-colored sucrose solution and evaluated the presence of food in the abdomen of the fly after two 2 min (Table S1). Flies fed sucrose within 2 min and there was no difference between w1118 and TβhnM18 flies. “

(4) The use of t-test requires the data to be normally distributed. If I am not mistaken this was not demonstrated for any of the datasets used. I did a quick check on one of the datasets provided in the excel sheet and it is normally distributed. Therefore, please add normality test for all data sets. If some do not pass normality, please use a suitable non-parametric test.

We added normality test to all data sets and used non-parametric tests for non-normal distributed data. We clarify this in the material and method section and the figure legends.

(5) The authors show that OA suppresses also STM. This result is in contradiction to previous published results. This by itself is not a problem. However, this result also seems to me in contradiction to the authors own results. According to Figure 1B, OA is required for STM as it absence in the tbh mutant results in loss of STM. According to Figure 2C, OA is reducing STM as wt flies fed with OA just prior to learning do not form STM. This appears in other places in the manuscript as well.

In addition, in the text lines 178-180, the authors write "A short pulse of octopamine before the training inhibits the STM. Thus, octopamine is a negative regulator of appetitive dopaminergic neuron-dependent long-term memory and can block STM." But in the summary they write "Octopamine is not required for short-term memory, since octopamine deficient mutants form appetitive short-term memory to sucrose and to other nutrients depending on the internal energy status." So, the take-home message regarding OA and STM is unclear.

The authors need to better clarify this point.

We clarified these points. See comments above. The loss of memory in Tbh mutants is not due to loss of octopamine, but increased energy levels that changes the reward properties of sucrose.

(6) The manuscript is very difficult to follow. The authors constantly change between 16 and 40 hours starvation, short term memory, 3 hour memory and 6 hour memory. I think it would have been better to have a more focused manuscript. However, if this is not possible, I recommend preparing a diagram with the different neurons or signaling pathways (i.e. insulin) and how they affect each other. Also, perhaps add to each figure a panel describing exactly the experimental conditions. I think also simplifying the text and adding more conclusions throughout the results section will help the readers to follow. Finally, I think that it would help understanding the conclusions if the authors can add a diagram of the flow that they think occurs. For example, the authors show that glycogen suppresses learning as its reduction increases learning. They also show that InR activity receptor suppresses learning as its KD also increases learning. If I am not mistaken the link between the two is not straight forward (but I may be wrong here). A diagram of the flow would be very helpful.

We prepared diagrams summarizing and explaining the results.

Minor

(1) I may not have understood correctly as I am not sure that I found Table S1.

Also, there was no legend for Table S1.

Nevertheless, if I understood correctly, the authors write that "Before the experiments, flies were tested to determine whether they perceived the odorants, preferred one odorant over other and responded to the reward similarly to ensure that the observed differences in behavior were not due to changes in odorant perception or sucrose sensitivity (Table S1)." However, according to the Table that I found it seems that following 40h starvation wt flies show preference to OCT whereas this does not occur for the mutant. Also, it seems that at 16h the mutant has a much higher preference to the odors than after 40h. This is a bit odd. I am also not sure what the balance value refers to. Finally, the mutant shows really low 2M sucrose preference after 40h. In general, this set of experiments requires a bit more explanation.

I think it is better to show these experiments using graphs and add this to the supplementary figures.

We clarified the experiments in the result section as follows and added an explanation to the material and method section. We tested the odorant acuity and sucrose preference for all genotypes used in the manuscript and added the data to the Table S1.

“The flies of the different genotypes sensed the odorants and evaluated them as similar salient in comparison. This is important to a avoid a bias in the situation where flies have to choose between the two odorants after training. They also sensed sucrose. We next determined whether differences in preferences influence sucrose intake during training. Therefore, we measured the sucrose intake of starved flies in the behavioral set up. We used a food-colored sucrose solution and evaluated the presence of food in the abdomen of the fly after two 2 min (Table S1). Flies fed sucrose within 2 min and there was no difference between w1118 and TβhnM18 flies.”

(2) Line 129 should be Figure 1B

Is corrected.

(3) Line 133, Figure 1C, how can one explain the negative reinforcement? I can understand no reinforcement, but negative?

The effect of glucose might be doses dependent. 0.15 M sucrose is a much closer to a realistic concentration found in fruits than 2 M sucrose and might therefore elicit aversion. When animals are starved enough they might find any food source attractive, even when the concentrations of sucrose is unrealistic.

(4) Figure 1, why are the graphs different between panel B and C?

Is corrected.

(5) In Figure S1, are the TβhnM18 groups differ significantly from zero? I think they are, so better to state this somewhere. If not, the claims in lines 134-135 are not supported by the data.

We added the significance and added the data to Figure 1.

Figure S1 legend: there is no A panel. Also "below box blots" should be box plots.

Thanks for pointing that out. We corrected it.

(6) It is not clear what is the duration of starvation used in Figure 2A. I assume that 16h and sucrose 2M used were used, but I would state that explicitly.

We added the information to the figure legends.

(7) Figure 2A is missing a control of flies with both the driver and UAS shibirets at the permissive temperature.

We added the controls to the supplement (Figure S1).

(8) It seems to me that Figure 3B, in which the author state that "Only after 40 h of starvation did TβhnM18 mutants show a similar preference to control sucrose consumption" (line 198) is somewhat in contradiction to Table S1 in which I see Sucrose preference for wt 0.36 and for tbh 0.17. I think this comment arise because I did not understand Table S1 correctly, so please better explain.

We rewrote this section.

(9) In Figure 3C, consider not using std as this stands for standard deviation and may be confusing.

We now use the term “food” instead of “std” and explained in the legend that food means standard fly food.

We fixed this.

(10) Please check the Supplementary Figures. I think Figures S2 and S3 are switched.

We fixed this.

(11) There is a mistake in Figure S3A. The right column should have another "+" sign.

Thanks, we fixed this.

(12) I am somewhat puzzled by Figures 4 and 5. If I understand correctly figure 4B w1118 mef2-G4 is exactly the same experiment as Figure 5A w1118 mef2-G4 and yet in Figure 4B performance index is 0.2 and in Figure 5A about 0.4. According to other comparisons it seems to me that these will be significantly different and yet it is the same experiment.

They are two independent experiments done at different times. The controls were independently repeated.

(13) Line 273 should be Figure 5C.

Is corrected.

(14) I don't think this is a correct sentence "Virgin females remembered sucrose significantly better than mated females." Line 274.

Reads now:

“Virgin females remembered the odorant paired with sucrose significantly better than mated females.”

(15) Line 340 there is no Figure 1E

Is fixed (1 C)

(16) The data excel file is difficult to follow. In Figure 2 there are references to Figure 5. The graphs are pointing to other files. Text is not always in English. It is not clear what W stands for. I recommend making it more accessible.

We corrected the data excel files.

(17) The manuscript is difficult to follow. I recommend preparing a diagram with the different neurons or signaling pathways (i.e. insulin) and how they affect each other.

We improved the data presentation by

a) adding a model showing the kinetics of memory formation in controls and mutants (Figure 2C)

b) a model explaining how the internal state is integrated into the formation of memory (Figure 7D).

Author Response

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

This study reports important evidence that infants' internal factors guide children's attention and that caregivers respond to infants' attentional shifts during caregiver-infant interactions. The authors analyzed EEG data and multiple types of behaviors using solid methodologies that can guide future studies of neural responses during social interaction in infants. However, the analysis is incomplete, as several methodological choices need more adequate justification.

Reviewer #1

Public Review:

The authors bring together multiple study methods (brain recordings with EEG and behavioral coding of infant and caregiver looking, and caregiver vocal changes) to understand social processes involved in infant attention. They test different hypotheses on whether caregivers scaffold attention by structuring a child's behavior, versus whether the child's attention is guided by internal factors and caregivers then respond to infants' attentional shifts. They conclude that internal processes (as measured by brain activation preceding looking) control infants' attention, and that caregivers rapidly modify their behaviors in response to changes in infant attention.

The study is meticulously documented, with cutting-edge analytic approaches to testing alternative models; this type of work provides a careful and well-documented guide for how to conduct studies and process and analyze data for researchers in the relatively new area of neural response in infants in social contexts.

We are very pleased that R1 considers our work an important contribution to this developing field, and we hope that we have now addressed their concerns below.

Some concerns arise around the use of terms (for example, an infant may "look" at an object, but that does not mean the infant is actually "attending); collapsing of different types of looks (to people and objects), and the averaging of data across infants that may mask some of the individual patterns.

We thank the reviewer for this feedback and their related comments below, and we feel that our manuscript is much stronger as a result of the changes we have made. Please see blow for a detailed description of our rationale for defining and analysing the attention data, as well as the textual changes made in response to the author’s comments.

Recommendations For The Authors

This paper is rigorous in method, theoretically grounded, and makes an important contribution to understanding processes of infant attention, brain activity, and the reciprocal temporal features of caregiver-infant interactions. The alternative hypothesis approach sets up the questions well (although authors should temper any wording that suggests attention processes are one or the other. That is, certain bouts of infant attention can be guided by exogenous factors such as social input, and others be endogenous; so averaging across all bouts can actually mask the variation in these patterns). I appreciated the focus on multiple types of behavior (e.g., gaze, vocal fluctuations in maternal speech); the emphasis on contingent responding; and the very clear summaries of takeaways after each section. Furthermore, methods and analyses are well described, details on data processing and so on are very thorough, and visualizations aptly facilitate data interpretation. However, I am not an expert on infant neural responses in EEG and assume that a reviewer with such expertise will weigh in on the treatment and quality of the data; therefore, my comments should be interpreted in light of this lack of knowledge.

We thank R1 for these very positive and insightful comments on our analyses which are the result of a number of years of methodological and technical developmental work.

We do agree with R1 that we should more carefully word parts of our argument in the Introduction to make clear the fact that shifts in infant attention could be driven by a combination of interactive and endogenous influences. As a result of this comment, we have made direct changes to parts of the Introduction; removing any wording that suggests that these processes are ‘alternative’ or ‘separate’, and our overall aim states: ‘Here, recording EEG from infants during naturalistic interactions with their caregiver, we examined the (inter)-dependent influences of infants’ endogenous oscillatory neural activity, and inter-dyadic behavioural contingencies in organising infant attention’.

Examining variability between infant attention episodes in the factors that influence the length and timing of the attention episode is an important area for future investigation. We now include a discussion on this on page 38 of the Discussion section, with suggestions for how this could be examined. Investigating different subtypes of infant attention is methodologically challenging, given the number of infant behaviours that would need to inform such an analysis- all of which are time consuming to code. Developing automated methods for performing these kinds of analyses is an important avenue for future work.

Here, I review various issues that require revision or elaboration based on my reading of what I consider to otherwise be a solid and important research paper.

Problem in the use of the term attention scaffolding. Although there may be literature precedent in the use of this term, it is problematic to narrowly define scaffolding as mother-initiated guidance of attention. A mother who responds to infant behaviors, but expands on the topic or supports continued attention, and so on, is scaffolding learning to a higher level. I would think about a different term because it currently implies a caregiver as either scaffolding OR responding contingently. It is not an either-or situation in conceptual meaning. In fact, research on social contingency (or contingent responsiveness), often views the follow-in responding as a way to scaffold learning in an infant.

Yes, we agree with R1 that the term ‘attention scaffolding’ could be confusing given the use of this term in previous work conducted with children and their caregivers in problem-solving tasks, that emphasise modulations in caregiver behaviour as a function of infant behaviour. As a result of this suggestion, we have made direct edits to the text throughout, replacing the term attentional scaffold with terms such as ‘organise’ and ‘structure’ in relation to the caregiver-leading or ‘didactic’ perspective, and terms such as ‘contingent responding’ and ‘dynamic modulation’ in relation to the caregiver-following perspective. We feel that this has much improved the clarity of the argument in the Introduction and Discussion sections.

Do individual data support the group average trends? My concern with unobservable (by definition) is that EEG data averages may mask what's going on in individual brain response. Effects appear to be small as well, which occurs in such conditions of averaging across perhaps very variable response patterns. In the interest of full transparency and open science, how many infants show the type of pattern revealed by the average graph (e.g., do neural markers of infant engagement forward predict attention for all babies? Majority?). Non-parametric tests on how many babies show a claimed pattern would offer the litmus test of significance on whether the phenomenon is robust across infants or pulled by a few infants with certain patterns of data. Ditto for all data. This would bolster my confidence in the summaries of what is going on in the infant brain. (The same applies as I suggest to attention bouts. To what extent does the forward-predict or backward-predict pattern work for all bouts, only some bouts, etc.?). I recognize that to obtain power, summaries are needed across infants and bouts, but I want to know if what's being observed is systematic.

We thank R1 for this comment and understand their concern that the overall pattern of findings reported in relation to the infants’ EEG data might obscure inter-individual variability in the associations between attention and theta power. Averaging across individual participant EEG responses is, however, the gold standard way to perform both event-locked (Jones et al., 2020) and continuous methods (Attaheri et al., 2020) of EEG analysis that are reported in the current manuscript. EEG data, and, in particular, naturalistic EEG data is inherently noisy, and averaging across participants increases the signal to noise ratio (i.e. inconsistent, and, therefore, non-task-related activity is averaged out of the response (Cohen, 2014; Noreika et al., 2020)). Examining individual EEG responses is unlikely to tell us anything meaningful, given that, if a response is not found for a particular participant, then it could be that the response is not present for that participant, or that it is present, but the EEG recording for that participant is too noisy to show the effect. Computing group-level effects, as is most common in all neuroimaging analyses, is, therefore, most optimal to examining our main research questions.

The findings reported in this analysis also replicate previous work conducted by our lab which showed that infant attention to objects significantly forward-predicted increases in infant theta activity during joint table-top play with their caregiver, involving one toy object (compared to our paradigm which involved 3;Wass et al., 2018). More recent work conducted by our lab has also shown continuous and time-locked associations between infant look durations and infant theta activity when infants play with objects on their own (Perapoch Amadó et al., 2023). To reassure readers of the replicability of the current findings, we now reference the Wass et al. (2018) study at the beginning of the Discussion section.

Could activity artifacts lead to certain reported trends? Babies typically look at an object before they touch or manipulate the object, and so longer bouts of attention likely involve a look and then a touch for lengthier time frames. If active involvement with an object (touching for example) amplifies theta activity, that may explain why attention duration forward predicts theta power. That is, baby looks, then touches, then theta activates, and coding would show visual gaze preceding the theta activation. Careful alignment of infants' touches and other such behaviors with the theta peak might help address this question, again to lend confidence to the robustness of the interpretation.

Yes, again this is a very important point, and the removal of movement-related artifact is something we have given careful attention to in the analysis of our naturalistic EEG data (Georgieva et al., 2020; Marriott Haresign et al., 2021). As a result of this comment we have made direct changes to the Results section on page 18 to more clearly signal the reader to our EEG pre-processing section before presenting the results of the cross-correlation analyses.

As we describe in the Methods section of the main text, movement-related artifacts are removed from the data with ICA decomposition, utilising an automatic-rejection algorithm, specially designed for work with our naturalistic EEG data (Marriott Haresign et al., 2021). Given that ICA rejection does not remove all artifact introduced to the EEG signal, additional analysis steps were taken to reduce the possibility that movement artifacts influenced the results of the reported analyses. As explained in the Methods section, rather than absolute theta power, relative theta was used in all EEG analyses, computed by dividing the power at each theta frequency by the summed power across all frequencies. Eye and head movement-related artifacts most often associate with broadband increases in power in the EEG signal (Cohen, 2014): computing relative theta activity therefore further reduces the potential influence of artifact on the EEG signal.

It is also important to highlight that previous work examining movement artifacts in controlled paradigms with infants has shown that limb movements actually associate with a decrease in power at theta frequencies, compared to rest (Georgieva et al., 2020). It is therefore unlikely that limb movement artifacts explain the pattern of association observed between theta power and infant attention in the current study.

That said, examining the association between body movements and fluctuations in EEG activity during naturalistic interactions is an important next step, and something our lab is currently working on. Given that touching an object is most often the end-state of a larger body movement, aligning the EEG signal to the onset of infant touch is not all that informative to understanding how body movements associate with increases and decreases in power in the EEG signal. Our lab is currently working on developing new methods using motion tracking software and arousal composites to understand how data-derived behavioural sub-types associate with differential patterns of EEG activity.

The term attention may be misleading. The behavior being examined is infant gaze or looks, with the assumption that gaze is a marker of "attention". The authors are aware that gaze can be a blank stare that doesn't reflect underlying true "attention". I recommend substitution of a conservative, more precise term that captures the variable being measured (gaze); it would then be fine to state that in their interpretation, gaze taken as a marker for attention or something like that. At minimum, using term "visual attention" can be a solution if authors do not want to use the precise term gaze. As an example, the sentence "An attention episode was defined as a discrete period of attention towards one of the play objects on the table, or to the partner" should be modified to defined as looking at a play object or partner.

We thank the reviewer for this comment, and we understand their concern with the use of the term ‘attention’ where we are referring to shifts in infant eye gaze. However, the use of this term to describe patterns of infant gaze, irrespective of whether they are ‘actually attending’ or not is used widely in the literature, in both interactive (e.g. Yu et al., 2021) and screen-based experiments examining infant attention (Richards, 2010). We therefore feel that its use in our current manuscript is acceptable and consistent with the reporting of similar interaction findings. On page 39 of the Discussion we now also include a discussion on how future research might further investigate differential subtypes of infant looks to distinguish between moments where infants are attending vs. just looking.

Why collapse across gaze to object vs. other? Conceptually, it's unclear why the same hypotheses and research questions on neural-attention (i.e., gaze in actuality) links would apply to looks to a mom's face or to an object. Some rationale would be useful to the reader as to why these two distinct behaviors are taken as following the same principles in ordering of brain and behavior. Perhaps I missed something, however, because later in the Discussion the authors state that "fluctuations in neural markers of infants' engagement or interest forward-predict their attentiveness towards objects", which suggests there was an object-focused variable only? Please clarify. (Again, sorry if I missed something).

This is a really important point, and we agree with R1 that it could have been more clearly expressed in our original submission – for which, we apologise. In the cross-correlation analyses conducted in parts 2 and 3 which examines forwards-predictive associations between infant attention durations and infant endogenous oscillatory activity (part two), and caregiver behaviour (part three), as R1 describes, we include all infant looks towards objects and their partner. Including all infant look types is necessary to produce a continuous variable to cross-correlate with the other continuous variables (e.g. theta activity, caregiver vocal behaviours), and, therefore, does not concentrate only on infant attention episodes towards objects.

We take the reviewers’ point that different attention and neural mechanisms may be associated with looks towards objects vs. the partner, which we now acknowledge directly on page 10 of the Introduction. However, our focus here is on the endogenous and interactive mechanisms that drive fluctuations in infant engagement with the ongoing, free-flowing interaction. Indeed, previous work has shown increases in theta activity during sustained episodes of infant attention to a range of different stimuli, including cartoon videos (Xie et al., 2018), real-life screen-based interactions (Jones et al., 2020), as well as objects (Begus et al., 2016). In the second half of part 2, we go on to address the endogenous processes that support infant attention episodes specifically towards objects.

As a result of this comment, we have made direct changes to the Introduction on page 10 to more clearly explain the looking behaviours included in the cross-correlation analysis, and the rationale behind the analysis being conducted in this way – which is different to the reactive analyses conducted in the second half of parts one and three, which examines infant object looks only. Direct edits to the text have also been made throughout the Results and Methods sections as a result of this comment, to more clearly specify the types of looks included in each analysis. Now, where we discuss the cross-correlation analyses we refer only to infant ‘attention durations’ or infant ‘attention’, whilst ‘object-directed attention’ and ‘looks towards objects’ is clearly specified in sections discussing the reactive analyses conducted in parts 2 and 3. We have also amended the Discussion on page 31so that the cross-correlation analyses is interpreted relative to infant overall attention, rather than their attention towards objects only.

Why are mothers' gazes shorter than infants' gazes? This was the flip of what I'd expect, so some interpretation would be useful to understanding the data.

This is a really interesting observation. Our findings of the looking behaviour of caregivers and infants in our joint play interactions actually correspond to much previous micro-dynamic analysis of caregiver and infant looking behaviour during early table-top interactions (Abney et al., 2017; Perapoch Amadó et al., 2023; Yu & Smith, 2013, 2016). The reason for the shorter look durations in the adult is due to the fact that the caregivers alternate their gaze between their infant and the objects (i.e. they spend a lot of the interaction time monitoring their infants’ behaviours). This can be seen in Figure 2 (see main text) which shows that caregiver looks are divided between looks to their infants and looks towards objects. In comparison, infants spend most of their time focussing on objects (see Figure 2, main text), with relatively infrequent looks to their caregiver. As a result, infant looks are, overall, longer in comparison to their caregivers’.

Minor points

Use the term association or relation (relationships is for interpersonal relationships, not in statistics).

This has now been amended throughout.

I'm unsure I'd call the interactions "naturalistic" when they occur at a table, with select toys, EEG caps on partners, and so on. The term seems more appropriate for studies with fewer constraints that occur (for example) in a home environment, etc.

We understand R1s concern with our use of the term ‘naturalistic’ to refer to the joint play interactions that we analyse in the current study. However, we feel the term is appropriate, given that the interactions are unstructured: the only instruction given to caregivers at the beginning of the interaction is to play with their infants in the way that they might do at home. The interactions, therefore, measure free-flowing caregiver and infant behaviours, where modulations in each individual’s behaviour are the result of the intra- and inter-individual dynamics of the social exchange. This is in comparison to previous work on early infant attention development which has used more structured designs, and modulations in infant behaviour occur as a result of the parameters of the experimental task.

Reviewer #2

Public Review

Summary:

This paper acknowledges that most development occurs in social contexts, with other social partners. The authors put forth two main frameworks of how development occurs within a social interaction with a caregiver. The first is that although social interaction with mature partners is somewhat bi-directional, mature social partners exogenously influence infant behaviors and attention through "attentional scaffolding", and that in this case infant attention is reactive to caregiver behavior. The second framework posits that caregivers support and guide infant attention by contingently responding to reorientations in infant behavior, thus caregiver behaviors are reactive to infant behavior. The aim of this paper is to use moment-to-moment analysis techniques to understand the directionality of dyadic interaction. It is difficult to determine whether the authors prove their point as the results are not clearly explained as is the motivation for the chosen methods.

Strengths

The question driving this study is interesting and a genuine gap in the literature. Almost all development occurs in the presence of a mature social partner. While it is known that these interactions are critical for development, the directionality of how these interactions unfold in real-time is less known.

The analyses largely seem to be appropriate for the question at hand, capturing small moment-to-moment dynamics in both infant and child behavior, and their relationships with themselves and each other. Autocorrelations and cross-correlations are powerful tools that can uncover small but meaningful patterns in data that may not be uncovered with other more discretized analyses (i.e. regression).

We are pleased that R2 finds our work to be an interesting contribution to the field, which utilises appropriate analysis techniques.

Weaknesses

The major weakness of this paper is that the reader is assumed to understand why these results lead to their claimed findings. The authors need to describe more carefully their reasoning and justification for their analyses and what they hope to show. While a handful of experts would understand why autocorrelations and cross-correlations should be used, they are by no means basic analyses. It would also be helpful to use simulated data or even a simple figure to help the reader more easily understand what a significant result looks like versus an insignificant result.

We thank the reviewer for this comment, and we agree that much more detail should be added to the Introduction section. As a result of this comment, we have made direct changes to the Introduction on pages 9-11 to more clearly detail these analysis methods, our rationale for using these methods; and how we expect the results to further our understanding of the drivers of infant attention in naturalistic social interactions.

We also provide a figure in the SM (Fig. S6) to help the reader more clearly understand the permutation method used in our statistical analyses described in the Methods, on page 51, which depicts significant vs. insignificant patterns of results against their permutation distribution.

While the overall question is interesting the introduction does not properly set up the rest of the paper. The authors spend a lot of time talking about oscillatory patterns in general but leave very little discussion to the fact they are using EEG to measure these patterns. The justification for using EEG is also not very well developed. Why did the authors single out fronto-temporal channels instead of using whole brain techniques, which are more standard in the field? This is idiosyncratic and not common.

We very much agree with R2 that the rationale and justification for using EEG to understand the processes that influence infants’ attention patterns is under-developed in the current manuscript. As a result of this comment we have made direct edits to the Introduction section of the main text on pages 7-8 to more clearly describe the rationale for examining the relationship between infant EEG activity and their attention during the play interactions with their caregivers.

As we describe in the Introduction section, previous behavioural work conducted with infants has suggested that endogenous cognitive processes (i.e. fluctuations in top-down cognitive control) might be important in explaining how infants allocate their attention during free-flowing, naturalistic interactions towards the end of the first year. Oscillatory neural activity occurring at theta frequencies (3-6Hz), which can be measured with EEG, has previously been associated with top-down intrinsically guided attentional processes in both adulthood and infancy (Jones et al., 2020; Orekhova, 1999; Xie et al., 2018). Measuring fluctuations in infant theta activity therefore provides a method to examine how endogenous cognitive processes structure infant attention in naturalistic social interactions which might be otherwise unobservable behaviourally.

It is important to note that the Introduction distinguishes between two different oscillatory mechanisms that could possibly explain the organisation of infant attention over the course of the interaction. The first refers to oscillatory patterns of attention, that is, consistent attention durations produced by infants that likely reflect automatic, regulatory functions, related to fluctuations in infant arousal. The second mechanism is oscillatory neural activity occurring at theta frequencies, recorded with EEG, which, as mentioned above, is thought to reflect fluctuations in intrinsically guided attention in early infancy. We have amended the Introduction to make the distinction between the two more clear.

A worrisome weakness is that the figures are not consistently formatted. The y-axes are not consistent within figures making the data difficult to compare and interpret. Labels are also not consistent and very often the text size is way too small making reading the axes difficult. This is a noticeable lack of attention to detail.

This has now been adjusted throughout, where appropriate.

No data is provided to reproduce the figures. This does not need to include the original videos but rather the processed and de-identified data used to generate the figures. Providing the data to support reproducibility is increasingly common in the field of developmental science and the authors are greatly encouraged to do so.

This will be provided with the final manuscript.

Minor Weaknesses

Figure 4, how is the pattern in a not significant while in b a very similar pattern with the same magnitude of change is? This seems like a spurious result.

The statistical analysis conducted for all cross-correlation analyses reported follows a rigorous and stringent permutation-based temporal clustering method which controls for family-wise error rate using a non-parametric Monte Carlo method (see Methods in the main text for more detail). Permutations are created by shuffling data sets between participants and, therefore, patterns of significance identified by the cluster-based permutation analysis will depend on the mean and standard deviation of the cross-correlations in the permutation distribution. Fig. S6 now depicts the cross-correlations against their permutation distributions which should help readers to understand the patterns of significance reported in the main text.

The correlations appear very weak in Figures 3b, 5a, 7e. Despite a linear mixed effects model showing a relationship, it is difficult to believe looking at the data. Both the Spearman and Pearson correlations for these plots should be clearly included in the text, figure, or figure legend.

We thank the reviewer for this comment, and agree that reporting the correlations for these plots would strengthen the findings of the linear mixed effects models reported in text. As a result, we have added both Spearman and Pearson correlations to the legends of Figures 3b, 5a and 7e, corresponding to the statistically significant relationships examined in the linear mixed effects models. The strength of the relationships are entirely consistent with those documented in other previous research that used similar methods (e.g. Piazza et al., 2018). How strong the relationship looks to the observer is entirely dependent on the graphical representation chosen to represent it. We have chosen to present the data in this way because we feel that it is the most honest way to represent the statistically significant, and very carefully analysed, effects that we have observed in our data.

Linear mixed effects models need more detail. Why were they built the way they were built? I would have appreciated seeing multiple models in the supplementary methods and a reasoning to have landed on one. There are multiple ways I can see this model being built (especially with the addition of a random intercept). Also, there are methods to test significance between models and aid in selection. That being said, although participant identity is a very common random effect, its use should be clearly stated in the main text.

We very much agree with R2 that the reporting of the linear mixed effects models needs more detail and this has now been added to the Method section (page 54). Whilst it is true that there are multiple ways in which this model could be built, given the specificity of our research questions, regarding the reactive changes in infant theta activity and caregiver behaviours that occur after infant look onsets towards objects (see pages 9-11 of the Introduction), we take a hypothesis driven approach to building the linear mixed effects models. As a result, random intercepts are specified for participants, as well as uncorrelated by-participant random slopes (Brown, 2021; Gelman & Hill, 2006; Suarez-Rivera et al., 2019). In this way, infant look durations are predicted from caregiver behaviours (or infant theta activity), controlling for between participant variability in look durations, as well as the strength of the effect of caregiver behaviours (or infant theta activity) on infant look durations.

Some parentheses aren't closed, a more careful re-reading focusing on these minor textual issues is warranted.

This has now been corrected.

Analysis of F0 seems unnecessarily complex. Is there a reason for this?

Computation of the continuous caregiver F0 variable may seem complex but we feel that all analysis steps are necessary to accurately and reliably compute this variable in our naturalistic, noisy and free-flowing interaction data. For example, we place the F0 only into segments of the interaction identified as the mum speaking so that background noises and infant vocalisations are not included in the continuous variable. We then interpolate through unvoiced segments (similar to Räsänen et al., 2018), and compute the derivative in 1000ms intervals as a measure of the rate of change. The steps taken to compute this variable have been both carefully and thoughtfully selected given the many ways in which this continuous rate of change variable could be computed (cf. Piazza et al., 2018; Räsänen et al., 2018).

The choice of a 20hz filter seems odd when an example of toy clacks is given. Toy clacks are much higher than 20hz, and a 20hz filter probably wouldn't do anything against toy clacks given that the authors already set floor and ceiling parameters of 75-600Hz in their F0 extraction.

We thank the reviewer for this comment and we can see that this part of the description of the F0 computation is confusing. A 20Hz low pass filter is applied to the data stream after extracting the F0 with floor and ceiling parameters set between 75-600Hz. The 20Hz filter therefore filters modulations in the caregivers’ F0 that occur at a modulation frequency greater than 20Hz. The 20Hz filter does not, therefore, refer to the spectral filtering of the speech signal. The description of this variable has been rephrased on page 48 of the main text.

Linear interpolation is a choice I would not have made. Where there is no data, there is no data. It feels inappropriate to assume that the data in between is simply a linear interpolation of surrounding points.

The choice to interpolate where there was no data was something we considered in a lot of detail, given the many options for dealing with missing data points in this analysis, and the difficulties involved with extracting a continuous F0 variable in our naturalistic data sets. As R2 points out, one option would be to set data points to NaN values where no F0 is detected and/ or the Mum is not vocalising. A second option, however, would be to set the continuous variable to 0s where no F0 is detected and/ or the Mum is not vocalising (where the mum is not producing sound there is no F0 so rather than setting the variable to missing data points, really it makes most objective sense to set to 0).

Either of these options (setting parts where no F0 is detected to NaN or 0) makes it difficult to then meaningfully compute the rate of change in F0: where NaN values are inserted, this reduces the number of data points in each time window; where 0s are inserted this creates large and unreal changes in F0. Inserting NaN values into the continuous variable also reduces the number of data points included in the cross-correlation and event-locked analyses. It is important to note that, in our naturalistic interactions, caregivers’ vocal patterns are characterised by lots of short vocalisations interspersed by short pauses (Phillips et al., in prep), similar to previous findings in naturalistic settings (Gratier et al., 2015). Interpolation will, therefore, have largely interpolated through the small pauses in the caregiver’s vocalisations.

The only limitation listed was related to the demographics of the sample, namely saying that middle class moms in east London. Given that the demographics of London, even east London are quite varied, it's disappointing their sample does not reflect the community they are in.

Yes we very much agree with R2 that the lack of inclusion of caregivers from wider demographic backgrounds is disappointing, and something which is often a problem in developmental research. Our lab is currently working to collect similar data from infants with a family history of ADHD, as part of a longitudinal, ongoing project, involving families from across the UK, from much more varied demographic backgrounds. We hope that the findings reported here will feed directly into the work conducted as part of this new project.

That said, demographic table of the subjects included in this study should be added.

This is now included in the SM, and referenced in the main text.

References

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Author Response

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

We are grateful to the reviewers for their appreciation of our study and thoughtful comments. In response to the main concern raised by all reviewers regarding the potential influences of external noise factors on intuitive inference, such as external disturbances or imperfect observations, we have conducted three new experiments suggested by the reviewers. These experiments were designed to: (1) assess the influence of external forces on humans’ judgments by implementing a wall to block wind disturbances from one direction, (2) examine human accuracy in predicting the landing position of a falling ball when its trajectory is obscured, and (3) evaluate the effect of object geometry on human judgment of stability. The findings from these experiments consistently support our proposal of the stochastic world model on gravity embedded in human mind. Besides, we have also addressed the rest comments from the reviewers in a one-by-one fashion.

Reviewer #1 (Recommendations For The Authors):

As mentioned in the public review, I did not find it entirely convincing that the study shows evidence for a Gaussian understanding of gravity. There are two studies that would bolster this claim: 1. Replicate experiment 1, but also ask people to infer whether there was a hidden force. If people are truly representing gravity as proposed in the paper, you should get no force inferences. However, if the reason the Gaussian gravity model works is that people infer unseen forces, this should come out clearly in this study.

Author response image 1.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

R1: We thank the reviewer for this suggestion. To directly test whether participants’ judgments were influenced by their implicit assumptions about external forces, we duplicated the original experimental setup with the addition of a wall implemented on one side (Supplementary Figure 4A). Before the start of the experiment, we explicitly informed the participants that the wall was designed to block wind, ensuring that any potential wind forces from the direction of the wall would not influence the collapse. If participants’ judgments were affected by external noise, we would expect to observe a skewed angle distribution. Contrary to this prediction, our results showed a normal distribution across all three participants tested (1 female; ages: 24-30), similar to the experiment without the wall (Supplementary Figure 4B). Therefore, the stochastic nature of intuitive inference on objects’ stability is embedded in the mind, not shaped by external forces or explicit instructions.

This new experiment has been added to the revised manuscript

Line 166-168: “…, and remained unchanged with the addition of a wall on one side to block potential external disturbances from wind (Supplementary Figure 4).”

(2) Similarly, you can imagine a simple study where you drop an object behind a floating occluder and you check where people produce an anticipatory fixation (i.e., where do they think the object will come out?). If people have a stochastic representation of gravity, this should be reflected in their fixations. But my guess is that everyone will look straight down.

Author response image 2.

Trajectory experiment to test the stochastic nature of gravity represented in the mind. (a) Experiment design. In this experiment, participants were required to use a mouse to determine the landing point of a parabolic trajectory (marked by the green dot), obscured by a grey rectangle. Note that the parabolic trajectory was determined only by gravity, and no external disturbances were introduced. The parameters used in this experiment are detailed in the upper right corner. (b) Predictive errors from three participants. The predictive errors from all three participants conform to Gaussian distributions with non-negligible variances. These results suggest the notion of an inherent stochastic property of gravity represented in the mind.

R2: We thank the reviewer for suggesting this thought experiment. However, when predicting the landing point of a falling object, participants may rely more on learned knowledge that an unimpeded object continues to fall in a straight line, rather than drawing on their intuitive physics. To avoid this potential confounding factor, we designed a similar experiment where participants were asked to predict the landing point of a parabolic trajectory, obscured by an occluder (Author response image 2A). In each trial, participants used a mouse (clicking the left button) to predict the landing point of each parabolic trajectory, and there were 100 trials in total. This design not only limits the impact of direct visual cues but also actively engages the mental simulation of intuitive physics. All three participants (1 female; ages: 24-30) were unable to accurately predict the landing points of the trajectories, and the predictive errors conformed to Gaussian distributions with different variances (Author response image 2B). Therefore, this new experiment confirms the stochastic nature of intuitive physics.

(3) I believe the correct alternative model should be the one that has uncertainty over unseen forces, which better captures current proposals in the field, and controls for the amount of uncertainty in the models.

R3: We thank the reviewers for the above-mentioned suggestions, and the findings from these two new experiments reinforce our proposal regarding the inherent stochastic characteristic of how the mind represents gravity.

(4) I was not convinced that the RL framework was set up correctly to tackle the questions it claims to tackle. What this shows is that you can evolve a world model with Gaussian gravity in a setup that has no external perturbations. That does not imply that that is how humans evolved their intuitive physics, particularly when creatures have evolved in a world full of external perturbations. Showing that when (1) there are hidden perturbations, and (2) these perturbations are learnable, but (3) the model nonetheless just learns stochastic gravity, would be a more convincing result.

R4: We completely agree with the reviewer that the RL framework serves primarily as a theoretic model to explain the stochastic nature of the world model on gravity, rather than as a demonstration of the developmental origins of intuitive physics abilities. The genesis of such abilities is multifaceted and unlikely to be fully replicated through a simple simulation like RL. Therefore, the purpose of incorporating the RL framework in our study is to demonstrate that external perturbances are not necessary for the development of a stochastic representation of gravity. In fact, introducing additional external noise into the RL framework likely heightens the uncertainty in learning gravity’s direction, potentially amplifying, rather than diminishing, the stochastic nature of mental gravity.

In revision, we have clarified the role of the RL framework

Line 265-277: “While the cognitive impenetrability and the self-consistency observed in this study, without resorting to an external perturbation, favor the stochastic model over the deterministic one, the origin of this stochastic feature of the world model is unclear.

Here we used a reinforcement learning (RL) framework to unveil this origin, because our intelligence emerges and evolves under the constraints of the physical world. Therefore, the stochastic feature may emerge as a biological agent interacts with the environment, where the mismatches between external feedback from the environment and internal expectations from the world model are in turn used to fine-tune the world model (Friston et al., 2021; MacKay, 1956; Matsuo et al., 2022). Note that a key aspect of the framework is determining whether the stochastic nature of the world model on gravity emerges through this interaction, even in the absence of external noise.”

(5) Some comments on the writing:

The word 'normality' is used to refer to people's judgments about whether a tower collapsed looked 'normal'. I was a bit confused by this because normality can also mean 'Gaussian' and the experiments are also sampling from Gaussian distributions. There were several points where it took me a second to figure out which sense of 'normality' the paper was using. I would recommend using a different term.

R5: We are sorry for the confusion. In revision, the term “normality” has been replaced with “confidence level about normal trajectory”.

(6) One small comment is that Newton's laws are not a faithful replica of the "physical laws of the world" they are a useful simplification that only works at certain timescales. I believe some people propose Newtonian physics as a model of intuitive physics in part because it is a rapid and useful approximation of complex physical systems, and not because it is an untested assumption of perfect correspondence.

R6: We are sorry for the inaccurate expression. We have revised our statements in the manuscript Line 15-16: “We found that the world model on gravity was not a faithful replica of the physical laws, but instead encoded gravity’s vertical direction as a Gaussian distribution.”

(7) Line 49-50: Based on Fig 1d, lower bound of possible configurations for 10 blocks is ~17 in log-space, which is about 2.5e7. But the line here says it's 3.72e19, which is much larger. Sorry if I am missing something.

R7: We thank the reviewer to point out this error. We re-calculated the number of possible configurations using the formula (3) in the appendix, and the number of configurations with 10 blocks is:

Thus,

This estimated number is much larger than that in our previous calculation, which has been corrected in the revised text.

Line 827-829: “d) The lower bound of configurations’ possible number and the number of blocks in a stack followed an exponential relationship with a base of 10. The procedure can create at least 1.14×1050 configurations for stacks consisting of 10 blocks.”

Line 49-50: “… but the universal cardinality of possible configurations is at least 1.14×1050 (Supplementary Figure 1), …”

Line 1017-1018: “… the number of configurations can be estimated with formula (9), which is 1.14×1050.”

(8) Lines 77-78: "A widely adopted but not rigorously tested assumption is that the world model in the brain is a faithful replica of the physical laws of the world." This risks sounding like you are asserting that colleagues in the field do not rigorously test their models. I think you meant to say that they did not 'directly test', rather than 'rigorously test'. If you meant rigorous, you might want to say more to justify why you think past work was not rigorous.

R8: We apologize for the inappropriate wording, the sentence has been revised and we illustrate the motivation more comprehensively in the revised text,

Line 76-92: “A prevailing theory suggests that the world model in the brain accurately mirrors the physical laws of the world (Allen et al., 2020; Battaglia et al., 2013; Zhou et al., 2022). For example, the direction of gravity encoded in the world model, a critical factor in stability inference, is assumed to be straight downward, aligning with its manifestation in the physical world. To explain the phenomenon that tall and thin objects are subjectively perceived as more unstable compared to short and fat ones (Supplementary Figure 2), external noise, such as imperfect perception and assumed external forces, is introduced to influence the output of the model. However, when the brain actively transforms sensory data into cognitive understanding, these data can become distorted (Kriegeskorte and Douglas, 2019; Naselaris et al., 2011), hereby introducing uncertainty into the representation of gravity’s direction. In this scenario, the world model inherently incorporates uncertainty, eliminating the need for additional external noise to explain the inconsistency between subjective perceptions of stability and the actual stability of objects. Note that this distinction of these two theories is nontrivial: the former model implies a deterministic representation of the external world, while the latter suggests a stochastic approach.”

(9) Lines 79-84 States that past models encode gravity downward. It then says that alternatively there is consensus that the brain uses data from sensory organs and adds meaning to them. I think there might be a grammatical error here because I did not follow why saying there is 'consensus' on something is a theoretical alternative. I also had trouble following why those two statements are in opposition. Is any work on physics engines claiming the brain does not take data from sensory organs and add meaning to them?

R9: We are sorry for the confusion. Here we intend to contrast the deterministic model (i.e., the uncertainty comes from outside the model) with the stochastic model (i.e., the uncertainty is inherently built into the model). In revision, we have clarified the intention. For details, please see R8.

(10) Lines 85-88: Following on the sentence above, you then conclude that the representation of the world may therefore not be the same as reality. I did not understand why this followed. It seems you are saying that, because the brain takes data from sensory organs, therefore its representations may differ from reality.

R10: Again, we are sorry about the confusion. Please see the revised text in R8.

(11) Lines 190-191: I had trouble understanding this sentence. I believe you are missing an adjective to clarify that participants were more inclined to judge taller stacks as more likely to collapse.

R11: We are sorry for the confusion. What we intended to state here is that participants’ judgment was biased, showing a tendency to predict a collapse for stacks regardless of their actual stability. We have revised this confusing sentence in the revision. Line 202–204: “However, the participants showed an obvious bias towards predicting a collapse for stacks regardless of their actual stability, as the dots in Fig 2b are more concentrated on the lower side of the diagonal line.”

(12) Line 201: I don't think it's accurate to say that MGS "perfectly captured participants' judgments" unless the results are actually perfect.

R12: We agree, and in revision we have toned down the statement Line 213–214: “…, the MGS, in contrast to the NGS, more precisely reflected participants’ judgments of stability …”

Reviewer #2 (Recommendations For The Authors):

I think this is an impressive set of experiments and modeling work. The paper is nicely written and I appreciate the poetic license the authors took at places in the manuscript. I only have clarification points and suggest a simple experiment that could lend further support to their conclusions. 1. In my opinion, the impact of this work is twofold. First, the suggestion that gravity is represented as a distribution of the world and not a result of (inferred) external perturbations. Second, that the distribution is advantageous as it balances speed and accuracy, and lessens computational processing demands (i.e., number of simulations). The second point here is contingent on the first point, which is really only supported by the RL model and potentially the inverted scene condition. I am somewhat surprised that the RL model does not converge on a width much smaller than ~20 degrees after 100,000 simulations. From my understanding, it was provided feedback with collapses based on natural gravity (deterministically downward). Why is learning so slow and the width so large? Could it be the density of the simulated world model distribution? If the model distribution of Qs was too dense, then Q-learning would take forever. If the model distribution was too sparse, then its final estimate would hit a floor of precision. Could the authors provide more details on the distribution of the Qs for the RL model?

Author response image 3.

RL learning curves as a function of θ angle with different sampling densities and learning rates. Learning rates were adjusted to low (a), intermediate (b) and high (c) settings, while sampling densities were chosen at four levels: 5x5, 11x11, 31x31, and 61x61 shown from the left to the right. Two key observations emerged from the simulations as the reviewer predicted. First, higher learning rates resulted in a more rapid decline in learning curves but introduced larger variances. Second, increased sampling density necessitated more iterations for convergence. Note that in all simulations, we limited the iterations to 1,000 times (as opposed to 100,000 times reported in the manuscript) to demonstrate the trend without excessive computational demands.

R1: To illustrate the distribution of the Q-values for the RL model, we re-ran the RL model with various learning rates and sampling densities (Author response image 3). These results support the reviewer’s prediction that higher learning rates resulted in a more rapid decline in learning curves but introduced larger variances, and increased sampling density requires more iterations for convergence.

This simulation also elucidates the slower learning observed in the experiment described in the text, where the force sphere was divided into 61x61 angle pairs, and the learning rate was set to 0.15. This set of parameters ensured convergence within a reasonable brief timeframe while maintaining high-resolution force assessments.

Besides, the width of the Gaussian distribution is mainly determined by the complexity of stacks. As shown in Figure 3c and Supplementary Figure 9, stacks with fewer blocks (i.e., less complex) caused a larger width, whereas those with more blocks resulted in a narrower spread. In the study, we used a collection of stacks varying from 2 to 15 blocks to simulate the range of stacks humans typically encounter in daily life.

In revision, we have incorporated these insights suggested by the reviewer to clarify the performance of the RL framework:

Line 634-639: “The angle density and learning rate are two factors that affect the learning speed. A larger angle density prolongs the time to reach convergence but enables a more detailed force space; a higher learning rate accelerates convergence but incurs larger variance during training. To balance speed and convergence, we utilized 100,000 configurations for the training.”

Line 618-619: “…, separately divided them into 61 sampling angles across the spherical force space (i.e., the angle density).”

(2) Along similar lines, the authors discuss the results of the inverted science condition as reflecting cognitive impenetrability. However, do they also interpret it as support for an intrinsically noisy distribution of gravity? I would be more convinced if they created a different scene that could have the possibility of affecting the direction of an (inferred) external perturbation - a previously held explanation of the noisy world model. For example, a relatively simple experiment would be to have a wall on one side of the scene such that an external perturbation would be unlikely to be inferred from that direction. In the external perturbation account, phi would then be affected resulting in a skewed distribution of angle pairs. However, in the authors' stochastic world model phi would remain unaffected resulting in the same uniform distribution of phi the authors observed. In my opinion, this would provide more compelling evidence for the stochastic world model.

Author response image 4.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

R2: We thank the reviewer for this suggestion. Following the reviewer’s concern, we designed the experiment with the addition of a wall implemented on one side (Supplementary figure 4A). We explicitly informed the participants that the wall was designed to block wind before the start of the experiment, ensuring no potential wind forces from the direction of the wall to influence the collapse trajectory of configurations. Participants need to judge if the trajectory was normal. If participants’ judgments were influenced by external noises, we would expect to observe a skewed angle distribution. However, our results still showed a normal distribution across all participants tested, consistent with the experiment without the wall (Supplementary figure 4B). This experiment suggested the stochastic nature of intuitive inference on objects’ stability is embedded in the mind, rather than shaped by external forces or explicit instructions.

We revised the original manuscript, and added this new experiment

Line 166-168: “…, and remained unchanged with the addition of a wall on one side to block potential external disturbances from wind (Supplementary Figure 4).”

(3) I didn't completely follow the authors' explanation for the taller objects illusion. On lines 229-232, the authors state that deviations from gravity's veridical direction are likely to accumulate with the height of the objects. Is this because, in the stochastic world model account, each block gets its own gravity vector that is sampled from the distribution? The authors should clarify this more explicitly. If this is indeed the author's claim, then it would seem that it could be manipulated by varying the dimensions of the blocks (or whatever constitutes an object).

R3: We are sorry for the confusion caused by the use of the term ‘accumulate’. In the study, there is only one gravity vector sampled from the distribution for the entire structure, rather than each block having a unique gravity vector. The height illusion is attributed to the fact that the center of gravity in taller objects is more susceptible to influence when gravity deviates slightly from a strictly downward direction. This is especially true for objects consisting of multiple blocks stacked atop one another. In revision, we have removed the confusing term ‘accumulate’ for clarification.

Line 242-244: “…, because the center of gravity in taller objects is more susceptible to influence when gravity deviates slightly from a strictly downward direction during humans’ internal simulations.”

(4) The authors refer to the RL simulations as agent-environment interactions, but in reality, the RL model does not interact with the blocks. Would experience-dependent or observation be more apropos?

R4: We completely agree. Indeed, the RL model did not manipulate stacks; rather, it updated its knowledge of natural gravity based on the discrepancies between the RL model’s predictions and observed outcomes. In revision, we have removed the confusing term ‘agent-environment interactions’ and clarified its intended meaning.

Line 19-22: “Furthermore, a computational model with reinforcement learning revealed that the stochastic characteristic likely originated from experience-dependent comparisons between predictions formed by internal simulations and the realities observed in the external world, …”

Reviewer #3 (Public Review):

(1) In spite of the fact that the Mental Gravity Simulation (MGS) seems to predict the data of the two experiments, it is an untenable hypothesis. I give the main reason for this conclusion by illustrating a simple thought experiment. Suppose you ask subjects to determine whether a single block (like those used in the simulations) is about to fall. We can think of blocks of varying heights. No matter how tall a block is, if it is standing on a horizontal surface it will not fall until some external perturbation disturbs its equilibrium. I am confident that most human observers would predict this outcome as well. However, the MSG simulation would not produce this outcome. Instead, it would predict a non-zero probability of the block to tip over. A gravitational field that is not perpendicular to the base has the equivalent effect of a horizontal force applied on the block at the height corresponding to the vertical position of the center of gravity. Depending on the friction determined by the contact between the base of the block and the surface where it stands there is a critical height where any horizontal force being applied would cause the block to fall while pivoting about one of the edges at the base (the one opposite to where the force has been applied). This critical height depends on both the size of the base and the friction coefficient. For short objects this critical height is larger than the height of the object, so that object would not fall. But for taller blocks, this is not the case. Indeed, the taller the block the smaller the deviation from a vertical gravitational field is needed for a fall to be expected. The discrepancy between this prediction and the most likely outcome of the simple experiment I have just outlined makes the MSG model implausible. Note also that a gravitational field that is not perpendicular to the ground surface is equivalent to the force field experienced by the block while standing on an inclined plane. For small friction values, the block is expected to slide down the incline, therefore another prediction of this MSG model is that when we observe an object on a surface exerting negligible friction (think of a puck on ice) we should expect that object to spontaneously move. But of course, we don't, as we do not expect tall objects that are standing to suddenly fall if left unperturbed. In summary, a stochastic world model cannot explain these simple observations.

Author response image 5.

Differentiating Subjectivity from Objectivity. In both Experiment 1 (a) and Experiment 2 (b), participants were instructed to determine which shape appeared most stable. Objectively, in the absence of external forces, all shapes possess equal stability. Yet, participants typically perceived the shape on the left as the most stable because of its larger base area. The discrepancy between objective realities and subjective feelings, as we propose, is attributed to the human mind representing gravity’s direction as a Gaussian distribution, rather than as a singular value pointing directly downward.

R1: We agree with the reviewer that objects will remain stable until disturbed by external forces. However, in many cases, this is a clear discrepancy between objective realities and subjective feelings. For example, electromagnetic waves associated with purple and red colors are the farthest in the electromagnetic space, yet purple and red are the closest colors in the color space. Similarly, as shown in Supplementary Figure 4, in reality all shapes possess equal stability in the absence of external forces. Yet, humans typically perceive the shape on the left as more stable because of its larger base area. In this study, we tried to explore the mechanism underlying this discrepancy by proposing that the human mind represents gravity’s direction as a Gaussian distribution, rather than as a singular value pointing directly downward.

In revision, we have clarified the rationale of this study

Line 76-98: “A prevailing theory suggests that the world model in the brain accurately mirrors the physical laws of the world (Allen et al., 2020; Battaglia et al., 2013; Zhou et al., 2022). For example, the direction of gravity encoded in the world model, a critical factor in stability inference, is assumed to be straight downward, aligning with its manifestation in the physical world. To explain the phenomenon that tall and thin objects are subjectively perceived as more unstable compared to short and fat ones (Supplementary Figure 2), external noise, such as imperfect perception and assumed external forces, is introduced to influence the output of the model. However, when the brain actively transforms sensory data into cognitive understanding, these data can become distorted (Kriegeskorte and Douglas, 2019; Naselaris et al., 2011), hereby introducing uncertainty into the representation of gravity’s direction. In this scenario, the world model inherently incorporates uncertainty, eliminating the need for additional external noise to explain the inconsistency between subjective perceptions of stability and the actual stability of objects. Note that this distinction of these two theories is nontrivial: the former model implies a deterministic representation of the external world, while the latter suggests a stochastic approach. Here, we investigated these two alternative hypotheses regarding the construction of the world model in the brain by examining how gravity’s direction is represented in the world model when participants judged object stability.”

(2) The question remains as to how we can interpret the empirical data from the two experiments and their agreement with the predictions of the stochastic world model if we assume that the brain has internalized a vertical gravitational field. First, we need to look more closely at the questions posed to the subjects in the two experiments. In the first experiment, subjects are asked about how "normal" a fall of a block construction looks. Subjects seem to accept 50% of the time a fall is normal when the gravitational field is about 20 deg away from the vertical direction. The authors conclude that according to the brain, such an unusual gravitational field is possible. However, there are alternative explanations for these findings that do not require a perceptual error in the estimation of the direction of gravity. There are several aspects of the scene that may be misjudged by the observer. First, the 3D interpretation of the scene and the 3D motion of the objects can be inaccurate. Indeed, the simulation of a normal fall uploaded by the authors seems to show objects falling in a much weaker gravitational field than the one on Earth since the blocks seem to fall in "slow motion". This is probably because the perceived height of the structure is much smaller than the simulated height. In general, there are even more severe biases affecting the perception of 3D structures that depend on many factors, for instance, the viewpoint.

R2: We thank the reviewer for highlighting several potential confounding factors in our study. We address each of these concerns point-by-point:

(a) Misinterpretation of the 3D scene and motion. In Response Figure 4 shown above, there is no 3D structure, yet participants’ judgment on stability still deviated from objective realities. In addition, the introduction of 3D motion was to aid in understanding the stacks’ 3D structure. Previous studies without 3D motion have reported similar findings (Allen et al., 2020). Therefore, regardless of whether objects are presented in 2D or 3D, or in static or in motion formats, humans’ judgment on object stability appears consistent.

(b) Errors in perceived height. While there might be discrepancies between perceived and simulated heights, such errors are systematic across all conditions. Therefore, they may affect the width of the Gaussian distribution but do not fundamentally alter its existence.

(c) The viewpoint. In one experiment, we inverted gravity’s direction to point upward, diverging from common daily experience. Despite this change in viewpoint, the Gaussian distribution was still observed. That is, the viewpoint appears not a key factor in influencing how gravity’s direction is represented as a Gaussian distribution in our mental world.

In summary, both our and previous studies (Allen et al., 2020; Battaglia et al., 2013) agree that humans’ subjective assessments of objects’ stability deviate from actual stability due to noise in mental simulation. Apart from previous studies, we suggest that this noise is intrinsic, rather than stemming from external forces or imperfect observations.

(3) Second, the distribution of weight among the objects and the friction coefficients acting between the surfaces are also unknown parameters. In other words, there are several parameters that depend on the viewing conditions and material composition of the blocks that are unknown and need to be estimated. The authors assume that these parameters are derived accurately and only that assumption allows them to attribute the observed biases to an error in the estimate of the gravitational field. Of course, if the direction of gravity is the only parameter allowed to vary freely then it is no surprise that it explains the results. Instead, a simulation with a titled angle of gravity may give rise to a display that is interpreted as rendering a vertical gravitational field while other parameters are misperceived. Moreover, there is an additional factor that is intentionally dismissed by the authors that is a possible cause of the fall of a stack of cubes: an external force. Stacks that are initially standing should not fall all of a sudden unless some unwanted force is applied to the construction. For instance, a sudden gust of wind would create a force field on a stack that is equivalent to that produced by a tilted gravitational field. Such an explanation would easily apply to the findings of the second experiment. In that experiment subjects are explicitly asked if a stack of blocks looks "stable". This is an ambiguous question because the stability of a structure is always judged by imagining what would happen to the structure if an external perturbation is applied. The right question should be: "do you think this structure would fall if unperturbed". However, if stability is judged in the face of possible external perturbations then a tall structure would certainly be judged as less stable than a short structure occupying the same ground area. This is what the authors find. What they consider as a bias (tall structures are perceived as less stable than short structures) is instead a wrong interpretation of the mental process that determines stability. If subjects are asked the question "Is it going to fall?" then tall stacks of sound structure would be judged as stable as short stacks, just more precarious.

R3: Indeed, the external forces suggested by the reviewer certainly influence judgments of objects’ stability. The critical question, however, is whether humans’ judgments on objects’ stability accurately mirror the actual stability of objects in the absence of external forces. To address this question, we designed two new experiments.

Experiment 1: we duplicated the original experimental setup with the addition of a wall implemented on one side (Supplementary Figure 4A). We explicitly informed the participants that the wall could block wind, ensuring that no potential wind from the direction of the wall could influence the configuration. If participants’ judgments were affected by external noise, we would expect to observe a skewed angle distribution. Contrary to this prediction, our results showed a normal distribution across all three participants (Age: 25-30, two females), which is similar to the experiment without the wall (Supplementary Figure 4B).

Author response image 6.

Wall experiment to test the impact of external forces on the measurement of stochastic gravity. (a) Experimental setting. We replicated the original setup with the addition of a wall implemented on one side. Left: the overall experimental scene; Right, the scene shown to participants. (b) Human behaviors. Three participants conducted this experiment, and their responses consistently showed normal distributions without any skewness, suggesting that their judgments were not affected by the presence of the wall. These results support our claim that humans’ judgments on stability were not affected by potential concerns regarding external forces.

Experiment 2: The second experiment adopted another paradigm to test the hypothesis of stochastic mental simulation. Consider humans to infer the landing point of a parabolic trajectory that was obscured by an occlude (Author response image 2A), the stochastic mental simulation predicted that humans’ behavior follows a Gaussian distribution. However, if humans’ judgments were influenced by external noise, the landing points could not be Gaussian. The experiment consists of 100 trials in total, and in each trial participants used a mouse to predict the landing point of each trajectory by clicking the left button. Our results found all three participants (1 female; ages: 24-30) were unable to accurately predict the landing points of the trajectories, and the predictive errors conformed to Gaussian distributions with different variances (Author response image 2B). Therefore, this new experiment confirms the stochastic nature of intuitive physics.

Author response image 7.

Trajectory experiment to test the stochastic nature of gravity represented in the mind. (a) Experiment design. In this experiment, participants were required to use a mouse to determine the landing point of a parabolic trajectory (marked by the green dot), obscured by a grey rectangle. Note that the parabolic trajectory was determined only by gravity, and no external disturbances were introduced. The parameters used in this experiment are detailed in the upper right corner. (b) Predictive errors from three participants. The predictive errors from all three participants conform to Gaussian distributions with non-negligible variances. These results suggest the notion of an inherent stochastic property of gravity represented in the mind.

(4) The RL model used as a proof of concept for how the brain may build a stochastic prior for the direction of gravity is based on very strong and unverified assumptions. The first assumption is that the brain already knows about the force of gravity, but it lacks knowledge of the direction of this force of gravity. The second assumption is that before learning the brain knows the effect of a gravitational field on a stack of blocks. How can the brain simulate the effect of a non-vertical gravitational field on a structure if it has never observed such an event?

R4: We agree with the reviewer that the RL framework serves primarily as a theoretic model to explain the stochastic nature of the world model on gravity, rather than as a demonstration of the developmental origins of intuitive physics abilities. The genesis of such abilities is multifaceted and unlikely to be fully replicated through a simple simulation like RL. Therefore, the purpose of incorporating the RL framework in our study is to demonstrate that external perturbances are not necessary for the development of a stochastic representation of gravity.

In revision, we have clarified the role of the RL framework

Line 265-277: “While the cognitive impenetrability and the self-consistency observed in this study, without resorting to an external perturbation, favor the stochastic model over the deterministic one, the origin of this stochastic feature of the world model is unclear.

Here we used a reinforcement learning (RL) framework to unveil this origin, because our intelligence emerges and evolves under the constraints of the physical world. Therefore, the stochastic feature may emerge as a biological agent interacts with the environment, where the mismatches between external feedback from the environment and internal expectations from the world model are in turn used to fine-tune the world model (Friston et al., 2021; MacKay, 1956; Matsuo et al., 2022). Note that a key aspect of the framework is determining whether the stochastic nature of the world model on gravity emerges through this interaction, even in the absence of external noise.”

(5) The third assumption is that from the visual input, the brain is able to figure out the exact 3D coordinates of the blocks. This has been proven to be untrue in a large number of studies. Given these assumptions and the fact that the only parameters the RL model modifies through learning specify the direction of gravity, I am not surprised that the model produces the desired results.

Author response image 8.

Perception Uncertainty in 3D stacks structures. (a) Experimental design. A pair of two stacks with similar placements of blocks were presented sequentially to participants, who were instructed to judge whether the stacks were identical and to rate their confidence in this judgment. Each stack was presented on the screen for 2 seconds. (b) Behavior Performance. Three participants (2 males, age range: 24-30) were recruited to the experiment. The confidence in determining whether a pair of stacks remained unchanged rapidly decreased when each block had a very small displacement, suggesting humans could keenly perceive trivial changes in configurations. The x-axis denotes the difference in block placement between stacks, with the maximum value (0.4) corresponding to the length of a block’s short side. The Y-axis denotes humans’ confidence in reporting no change. The red curve illustrates the average confidence level across 4 runs, while the yellow curve is the confidence level of each run.

R5: Indeed, uncertainty is inevitable when perceiving the external world, because our perception is not a faithful replica of external reality. A more critical question pertains to the accuracy of our perception in representing the 3D coordinates of a stack’s blocks. To address this question, we designed a straightforward experiment (Author response image 5a), where participants were instructed to determine whether a pair of stacks were identical. The position of each block was randomly changed horizontally. We found that all participants were able to accurately identify even minor positional variations in the 3D structure of the stacks (Author response image 5b). This level of perceptual precision is adequate for locating the difference between predictions from mental simulations and actual observations of the external world.

(6）Finally, the argument that the MGS is more efficient than the NGS model is based on an incorrect analysis of the results of the simulation. It is true that 80% accuracy is reached faster by the MGS model than the 95% accuracy level is reached by the NGS model. But the question is: how fast does the NGS model reach 80% accuracy (before reaching the plateau)?

R6: Yes. The NGS model achieved 80% accuracy as rapidly as the MGS model. However, the NGS model required a significantly longer period to reach the plateau crucial for decision-making. In revision, this information is now included.

Line 348-350: “…, while the initial growth rates of both models were comparable, the MGS reached the plateau crucial for decision-making sooner than the NGS.”

We greatly appreciate the thorough and insightful review provided by all three reviewers, which has considerably improved our manuscript, especially in terms of clarity in the presentation of the approach and further validation of the robustness implications of our results.

Reference: Allen KR, Smith KA, Tenenbaum JB. 2020. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences 117:29302–29310.

Battaglia PW, Hamrick JB, Tenenbaum JB. 2013. Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences 110:18327–18332.

Friston K, Moran RJ, Nagai Y, Taniguchi T, Gomi H, Tenenbaum J. 2021. World model learning and inference. Neural Networks 144:573–590.

Kriegeskorte N, Douglas PK. 2019. Interpreting encoding and decoding models. Current opinion in neurobiology 55:167–179.

MacKay DM. 1956. The epistemological problem for automataAutomata Studies.(AM-34), Volume 34. Princeton University Press. pp. 235–252.

Matsuo Y, LeCun Y, Sahani M, Precup D, Silver D, Sugiyama M, Uchibe E, Morimoto J. 2022. Deep learning, reinforcement learning, and world models. Neural Networks.

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Zhou L, Smith K, Tenenbaum J, Gerstenberg T. 2022. Mental Jenga: A counterfactual simulation model of physical support.

Author Response

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

eLife assessment:

This important study combines a comparative approach in different synapses with experiments that show how synaptic vesicle endocytosis in nerve terminals regulates short-term plasticity. The data presented support the conclusions and make a convincing case for fast endocytosis as necessary for rapid vesicle recruitment to active zones. Some aspects of the description of the data and analysis are however incomplete and would benefit from a more rigorous approach. With more discussion of methods and analysis, this paper would be of great interest to neurobiologists and biophysicists working on synaptic vesicle recycling and short-term plasticity mechanisms.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The study examines the role of release site clearance in synaptic transmission during repetitive activity under physiological conditions in two types of central synapses, calyx of Held and hippocampal CA1 synapses. After the acute block of endocytosis by pharmacology, deeper synaptic depression or less facilitation was observed in two types of synapses. Acute block of CDC42 and actin polymerization, which possibly inhibits the activity of Intersectin, affected synaptic depression at the calyx synapse, but not at CA1 synapses. The data suggest an unexpected, fast role of the site clearance in counteracting synaptic depression.

Strengths:

The study uses an acute block of the molecular targets with pharmacology together with precise electrophysiology. The experimental results are clear-cut and convincing. The study also examines the physiological roles of the site clearance using action potential-evoked transmission at physiological Ca and physiological temperature at mature animals. This condition has not been examined.

Weaknesses:

Pharmacology may have some off-target effects, though acute manipulation should be appreciated. Although this is a hard question and difficult to address experimentally, reagents may affect synaptic vesicle mobilization to the release sites directly in addition to blocking endocytosis.

To acutely block vesicle endocytosis, we utilized two different pharmacological tools, Dynasore and Pitstop-2, after testing their blocking spectra and potencies at the calyx presynaptic terminals and collected data of their common effects on target functions. Since the recovery from STD was faster at the calyx synapses in the presence of both endocytic blockers in physiological 1.3 mM [Ca2+] (Figure 2B), but not in 2.0 mM [Ca2+] (Figure S4), they might facilitate vesicle mobilization in physiological condition.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Mahapatra and Takahashi report on the physiological consequences of pharmacologically blocking either clathrin and dynamin function during compensatory endocytosis or of the cortical actin scaffold both in the calyx of Held synapse and hippocampal boutons in acute slice preparations

Strengths:

Although many aspects of these pharmacological interventions have been studied in detail during the past decades, this is a nice comprehensive and comparative study, which reveals some interesting differences between a fast synapse (Calyx of Held) tuned to reliably transmit at several 100 Hz and a more slow hippocampal CA1 synapse. In particular, the authors find that acute disturbance of the synaptic actin network leads to a marked frequency-dependent enhancement of synaptic depression in the Calyx, but not in the hippocampal synapse. This striking difference between both preparations is the most interesting and novel finding.

Weaknesses:

Unfortunately, however, these findings concerning the different consequences of actin depolymerization are not sufficiently discussed in comparison to the literature. My only criticism concerns the interpretation of the ML 141 and Lat B data. With respect to the Calyx data, I am missing a detailed discussion of the effects observed here in light of the different RRP subpools SRP and FRP. This is very important since Lee et al. (2012, PNAS 109 (13) E765-E774) showed earlier that disruption of actin inhibits the rapid transition of SRP SVs to the FRP at the AZ. The whole literature on this important concept is missing. Likewise, the role of actin for the replacement pool at a cerebellar synapse (Miki et al., 2016) is only mentioned in half a sentence. There is quite some evidence that actin is important both at the AZ (SRP to FRP transition, activation of replacement pool) and at the peri-active zone for compensatory endocytosis and release site clearance. Both possible underlying mechanisms (SRP to FRP transition or release site clearance) should be better dissected.

The concept of FRP and SRP are derived from voltage-clamp step-depolarization experiments at calyces of Held in pre-hearing rodents at RT, which cannot be directly dissected in data of action-potential evoked EPSCs at post-hearing calyces at physiological conditions. However, we dissected as much by referring to related literatures in new paragraphs in Result section (p9-10), particularly on the different effects of Latrunculin application and experimental conditions by adding a new supplementary Figure (now S5). Regarding F-actin role in vesicle replenishment at cerebellar synapses, we added sentences in Discussion section (p14, last paragraph).

Reviewer #3 (Public Review):

General comments:

(1) While Dynasore and Pitstop-2 may impede release site clearance due to an arrest of membrane retrieval, neither Latrunculin-B nor ML-141 specifically acts on AZ scaffold proteins. Interference with actin polymerization may have a number of consequences many of which may be unrelated to release site clearance. Therefore, neither Latrunculin-B nor ML-141 can be considered suitable tools for specifically identifying the role of AZ scaffold proteins (i.e. ELKS family proteins, Piccolo, Bassoon, α-liprin, Unc13, RIM, RBP, etc) in release site clearance which was defined as one of the principal aims of this study.

In this study, we focused our analysis on the downstream activity of scaffold protein intersectin by comparing the common inhibitory effects of CDC42 and actin polymerization, by use of ML141 and Latrunculin B, respectively, on vesicle endocytosis and synaptic depression/ facilitation without addressing diverse individual drug effects. To avoid confusion we removed “AZ” from scaffold protein.

(2) Initial EPSC amplitudes more than doubled in the presence of Dynasor at hippocampal SC->CA1 synapses (Figure S2). This unexpected result raises doubts about the specificity of Dynasor as a tool to selectively block SV endocytosis.

It is possible that Dynasore might have unknown or off-target effects. However, the main conclusion is backed up by Pitstop-2.

(3) In this study, the application of Dynasore and Pitstop-2 strongly decreases 100 Hz steady-state release at calyx synapses while - quite unexpectedly - strongly accelerates recovery from depression. A previous study found that genetic ablation of dynamin-1 actually enhanced 300 Hz steady-state release while only little affecting recovery from depression (Mahapatra et al., 2016). A similar scenario holds for the Latrunculin-B effects: In this study, Latrunculin-B strongly increased steady-state depression while in Babu et al. (2020), Latrunculin-B did not affect steady-state depression. In Mahapatra et al. (2016), Latrunculin-B marginally enhanced steady-state depression. The authors need to make a serious attempt to explain all these seemingly contradicting results.

The latrunculin effect on STD can vary according to the condition of application and external [Ca2+], which we show in a new supplemental Figure S5. The latrunculin effect on the recovery from STD also varies with temperature, [Ca2+], and animal age, which affect Ca2+-dependent fast recovery component from depression. We added paragraphs for this issue in Results section (p9-10).

(4) The experimental conditions need to be better specified. It is not clear which recordings were obtained in 1.3 mM and which (if any?) in 2 mM external Ca. It is also unclear whether 'pooled data' are presented (obtained from control recordings and from separate recordings after pre-incubation with the respective drugs), or whether the data actually represent 'before'/'after' comparisons obtained from the same synapses after washing in the respective drugs. The exact protocol of drug application (duration of application/pre-incubation?, measurements after wash-out or in the continuous presence of the drugs?) needs to be clearly described in the methods and needs to be briefly mentioned in Results and/or Figure legends.

We added methodological explanations and reworded sentences in the text to be clear for pharmacological data derived from non-sequential separate experiments.

(5) The authors compare results obtained in calyx with those obtained in SC->CA1 synapses which they considered examples for 'fast' and 'slow' synapses, respectively. There is little information given to help readers understand why these two synapse types were chosen, what the attributes 'fast' and 'slow' refer to, and how that may matter for the questions studied here. I assume the authors refer to the maximum frequency these two synapse types are able to transmit rather than to EPSC kinetics?

Yes, the “fast and slow” naming features maximum operating frequency these synapses can transmit. We reworded “fast and slow” to “fast-signaling and slow-plastic” and added explanation in the text.

(6) Strong presynaptic stimuli such as those illustrated in Figures 1B and C induce massive exocytosis. The illustrated Cm increase of 2 to 2.5 pF represents a fusion of 25,000 to 30,000 SVs (assuming a single SV capacitance of 80 aF) corresponding to a 12 to 15% increase in whole terminal membrane surface (assuming a mean terminal capacitance of ~16 pF). Capacitance measurements can only be considered reliable in the absence of marked changes in series and membrane conductance. Since the data shown in Figs. 1 and 3 are central to the argumentation, illustration of the corresponding conductance traces is mandatory. Merely mentioning that the first 450 ms after stimulation were skipped during analysis is insufficient.

Conductance trace is shown with a trace of capacitance change induced by a square pulse in our previous paper (Yamashita et al, 2005 Science).

(7) It is essential for this study to preclude a contamination of the results with postsynaptic effects (AMPAR saturation and desensitization). AMPAR saturation limits the amplitudes of initial responses in EPSC trains and hastens the recovery from depression due to a 'ceiling effect'. AMPAR desensitization occludes paired-pulse facilitation and reduces steady-state responses during EPSC trains while accelerating the initial recovery from depression. The use of, for example, 1 mM kynurenic acid in the bath is a well-established strategy to attenuate postsynaptic effects at calyx synapses. All calyx EPSC recordings should have been performed under such conditions. Otherwise, recovery time courses and STP parameters are likely contaminated by postsynaptic effects. Since the effects of AMPAR saturation on EPSC_1 and desensitization on EPSC_ss may partially cancel each other, an unchanged relative STD in the presence of kynurenic acid is not necessarily a reliable indicator for the absence of postsynaptic effects. The use of kynurenic acid in the bath would have had the beneficial side effect of massively improving voltage-clamp conditions. For the typical values given in this MS (10 nA EPSC, 3 MOhm Rs) the expected voltage escape is ~30 mV corresponding to a change in driving force of 30 mV/80 mV=38%, i.e. initial EPSCs in trains are likely underestimated by 38%. Such large voltage escape usually results in unclamped INa(V) which was suppressed in this study by routinely including 2 mM QX-314 in the pipette solution. That approach does, however, not reduce the voltage escape.

Glutamate released during AP-evoked EPSCs does not saturate or desensitize postsynaptic receptors at post-hearing calyces of Held (Ishikawa et al, 2002; Yamashita et al, 2003) although it does in pre-hearing calyces (Yamashita et al, 2009). In fact, as shown in Figure S3, our results are essentially the same with or without kynurenate.

(8) In the Results section (pages 7 and 8), the authors analyze the time course into STD during 100 Hz trains in the absence and presence of drugs. In the presence of drugs, an additional fast component is observed which is absent from control recordings. Based on this observation, the authors conclude that '... the mechanisms operate predominantly at the beginning of synaptic depression'. However, the consequences of blocking or slowing site clearing are expected to be strongly release-dependent. Assuming a probability of <20% that a fusion event occurs at a given release site, >80% of the sites cannot be affected at the arrival of the second AP even by a total arrest of site clearance simply because no fusion has yet occurred. That number decreases during a train according to (1-0.2)^n, where n is the number of the AP, such that after 10 APs, ~90% of the sites have been used and may potentially be unavailable for new rounds of release after slowing site clearance. Perhaps, the faster time course into STD in the presence of the drugs isn't related to site clearance?

Enhanced depression at the beginning of stimulation indicates the block of rapid SV replenishment mechanism, which includes endocytosis-dependent site-clearance and scaffold-dependent vesicle translocation to release sites.

(9) In the Discussion (page 10), the authors present a calculation that is supposed to explain the reduced size of the second calyx EPSC in a 100 Hz train in the presence of Dynasore or Pitstop-2. Does this calculation assume that all endocytosed SVs are immediately available for release within 10 ms? Please elaborate.

We do not assume rapid endocytosed vesicle reuse within 10 ms as it requires much longer time for glutamate refilling (7s at PT; Hori & Takahashi, 2012). Instead, already filled reserved vesicles can rapidly replenish release sites if sites are clean and scaffold works properly. Results shown in Figure S6 also indicate that block of vesicle transmitter refilling has no immediate effect on synaptic responses.

(10) It is not clear, why the bafilomycin/folimycin data is presented in Fig. S5. The data is also not mentioned in the Discussion. Either explain the purpose of these experiments or remove the data.

These v-ATPase blockers, which block vesicular transmitter refilling, are reported to enhance EPSC depression at hippocampal synapses at RT and 2 mM [Ca2+] presumably because of lack of filled vesicles undergoing rapid vesicle recycling (eg Kiss & Run). We thought it important to determine whether these data have physiological relevance since such a mechanism might also regulate synaptic strength during repetitive transmission. However, our results did not support its physiological relevance. Since these results are not within our main questions, the negative results are shown it in supplementary Figure 6 and explained in the last paragraph of Result section (p11), but were not discussed further in Discussion section.

(11) The scheme in Figure 7 is not very helpful.

We updated the scheme to summarize our conclusion that vesicle replenishment through endocytosis-dependent site-clearance and scaffold-dependent mechanism independently co-operate to strengthen synaptic efficacy during repetitive transmission at calyx fast-signaling synapses. However, endocytic site clearance is solely required to support facilitation at slow-plastic hippocampal SC-CA1 synapses.

Recommendations for the authors:

First, my deep apologies for the long delay in reviewing your paper. All reviewers are now in agreement that the paper has valuable new information, but some methods are not described well and some results appear to be incompatible with previous results in the literature. The discussion of previous literature is also incomplete and not well-balanced. With more discussion of methods and literature strengthened this paper would be of great interest to neurobiologists and biophysicists working on synaptic vesicle recycling and short-term plasticity mechanisms. We ask that you address the comments and revise your paper before we can fully recommend the paper as being an important contribution with compelling evidence and a strong data set that supports the conclusions.

We explained methods more explicitly. Apparent incompatibility with previous results is now explained and discussed with new supplementary data.

Major:

(1) In this study, the application of Dynasore and Pitstop-2 strongly decreased 100 Hz steady-state release at calyx synapses while - quite unexpectedly - it strongly accelerated recovery from depression. A previous study found that genetic ablation of dynamin-1 actually enhanced 300 Hz steady-state release while only little affecting recovery from depression (Mahapatra et al., 2016). A similar scenario holds for the Latrunculin-B effects: In this study, Latrunculin-B strongly increased steady-state depression while in Babu et al. (2020), Latrunculin-B did not affect steady-state depression. In Mahapatra et al. (2016), Latrunculin-B marginally enhanced steady-state depression. The authors need to make a serious attempt to explain all these seemingly contradicting results.

Lack of change in the recovery from depression in dynamin-1 knockout mice by Mahapatra et al (2016) is consistent with results in Figure S4 in 2 mM [Ca2+], whereas accelerated recovery by Dynasore (Figure 2B2) is observed in 1.3 mM [Ca2+] suggesting that it is masked in 2 mM [Ca2+] but revealed in physiological [Ca2+] (p7, top paragraph). In both cases, however, recovery from STD is not prolonged unlike Hosoi et al (2009).

The latrunculin issues are discussed in Results section with newly added Supplementary Figure S5 (p9-10).

(2) The experimental conditions need to be better specified. It is not clear which recordings were obtained in 1.3 mM and which (if any?) in 2 mM external Ca. It is also unclear whether 'pooled data' are presented (obtained from control recordings and from separate recordings after pre-incubation with the respective drugs), or whether the data actually represent 'before'/'after' comparisons obtained from the same synapses after washing in the respective drugs. The exact protocol of drug application (duration of application/pre-incubation?, measurements after wash-out or in the continuous presence of the drugs?) needs to be clearly described in the methods and needs to be briefly mentioned in Results and/or Figure legends.

We made these points clearer in Method section and Result section.

(3) Please cite and discuss briefly previous papers that have shown fast endocytosis in the calyx of Held with membrane capacitance measurements like Renden and von Gersdorff, J Neurophysiology, 98:3349, 2007 and Taschenberger et al., Neuron, 2002. These papers first showed exocytosis and endocytosis kinetics in more mature (hearing) mice calyx of Held and at higher physiological temperatures.

One of these literatures relevant to the present study is quoted in p4.

(4) The findings concerning the different consequences of actin depolymerization are not sufficiently discussed in comparison to the literature. My only criticism concerns the interpretation of the ML 141 and Lat B data. With respect to the Calyx data, I am missing a detailed discussion of the effects observed here in light of the different RRP subpools SRP and FRP. This is very important since Lee et al. (2012, PNAS 109 (13) E765-E774) showed earlier that disruption of actin inhibits the rapid transition of SRP SVs to the FRP at the AZ. The whole literature on this important concept is missing. Likewise, the role of actin for the replacement pool at a cerebellar synapse (Miki et al., 2016) is only mentioned in half a sentence. There is quite some evidence that actin is important both at the AZ (SRP to FRP transition, activation of replacement pool) and at the peri-active zone for compensatory endocytosis and release site clearance. Both possible underlying mechanisms (SRP to FRP transition or release site clearance) should be better dissected.

We added discussions on the issue of latrunculin in Result section by quoting previous literatures (p9-10). Since there is no direct evidence (by vesicle imaging) for the presence of FRP and SRP, these definitions derived from voltage clamp step-depolarization studies are difficult to incorporate into the dissection of synaptic depression in physiological conditions.

Reviewer #1 (Recommendations For The Authors):

I have no major comments, but the following issues may be addressed.

(1) The term "fast and slow" synapses may be relative and a bit confusing. I do not think hippocampal synapses are slow synapses.

We have replaced “fast and slow” by “fast-signaling and slow-plastic” to represent their functions and added explanation in the text.

(2) Off-target effects of pharmacological effects may be discussed. In this respect, bafilomycin experiments can be used to argue against the slow effects of vesicle cycling such as endocytosis, and vesicle mobilization. However, the effects on rapid vesicle mobilization cannot be excluded entirely. Because I cannot exclude the absence of off-target effects either (can be addressed by looking at single vesicle imaging at nano-scale, which is hard to do or looking at EM level quantitatively?), I feel this is a matter of discussion.

It is possible that Dynasore might have unknown or off-target effects. However, the main conclusion is backed up by Pitstop-2.

(3) Fig2 A2, B2 and Fig 4 A2 and B2. It is easier to plot the recovery only normalized to the initial value. Subtracting steady-state is somewhat confusing because the recovery looks faster after deeper depression, but this may be just apparent.

We have given values for both types of plots in Table 2, which indicates no essential difference in the recovery parameters.

Reviewer #2 (Recommendations For The Authors):

Line 51: Rajappa et al. (2016) investigated clearance deficits in synaptophysin KO mice (not synaptobrevin).

Corrected.

Line 54: intersectin is introduced as AZ scaffold protein, although in most of the literature, it is referred to as an endocytic scaffold protein (also in the cited one, e.g. Sakaba et al. 2013). At least, this should be discussed.

Since blockers of intersectin downstream protein activity has no effect on vesicle endocytosis (Figure 3 and Sakaba et al, 2013), we called it (presynaptic) scaffold protein instead of endocytic scaffold protein.

Reviewer #3 (Recommendations For The Authors):

Minor comments

Page 1, Title: I don't think the presented data address the role of the presynaptic scaffold in SV replenishment. In addition, 'SV replenishment' and 'site clearance' should not be used synonymously as it seems to be implied here.

In this study our focus was on the downstream activity of scaffold protein intersectin and since block of its downstream effector proteins CDC42 and actin activities do not obstruct the endocytic activity (Fig 3, and Sakaba et al., 2013), instead of naming it as “endocytic scaffold protein”, we adopted “presynaptic scaffold protein”.

We have corrected it in the text.

Page 2, Abstract: Clarify 'physiologically optimized condition' here and elsewhere in the manuscript.

Abstract: in physiologically optimized condition → in physiological temperature and Ca2+.

Page 3, line 62: I don't think 'the site-clearance hypothesis is widely accepted'. There are very few models that implement such a mechanism. Examples would be Pan & Zucker (2009) Neuron and Lin, Taschenberger & Neher 2022 (PNAS) which could be cited.

62: the site-clearance hypothesis is “widely accepted”→ “well supported”

Page 3 line 77: Please clarify 'fast synapses

77: fast synapses→fast-signaling synapses, added clarification in the text.

Page 4, line 100: Please clarify 'in the maximal rate'.

100: in the maxima rate→reached during 1-Hz stimulation.

Page 6, line 136: Please clarify 'to reduce the gap'.

136: To reduce the gap between these different results→To explore the reason for these different results

Page 7, line 157: I don't consider ML141 and Latrunculin-B 'scaffold protein inhibitors'.

157: scaffold protein inhibitors had no effect on→ reworded as “none of these inhibitors affected fast or slow endocytosis”. 　

Page 7, line 162: P-value missing.

162: p < 0.001 added.

Page 8, line 184: "Since both endocytic blockers and scaffold inhibitors enhanced synaptic depression with a similar time course" consider rephrasing. Sounds like you refer to the time course by which these drugs exert their effect after being applied.

184: Since both endocytic blockers and scaffold inhibitors enhance synaptic depression with a similar time course→Since the enhancement of synaptic depression by endocytic blockers or scaffold inhibitor occurred mostly at the early phase of synaptic depression.

Same on page 11, line 250: "At the calyx of Held, scaffold protein inhibitors significantly enhanced synaptic depression with a time course closely matching to that enhanced by endocytic blocker" Please consider rephrasing.

At the calyx of Held, scaffold protein inhibitors significantly enhanced synaptic depression with a time course closely matching to that enhanced by endocytic blocker →the early phase of synaptic depression like endocytic blockers

Page 13, line 318: Please clearly state which experiments were performed at 1.3 mM and which at 2 mM external Ca if two different concentrations were used during recordings.

320: Added text “Unless otherwise noted, EPSCs were recorded in 1.3 mM [Ca2+] aCSF at 37oC” in the methods.

Page 15: line 346: Reference in the wrong format.

346; (25) → (Yamashita et al, 2005)

Page 15: line 351: Do you mean to say every 10 s and every 20 s? Please clarify.

No, averaged at 10 ms and 20 ms, respectively as written.

Page 16, line 369: 1 mM kyn was present in only very few experiments shown in the supplemental figures. Please clarify.

368: In some experiments, to test in the presence of 1 mM kyn, if there is any difference in enhanced STD following endocytic block. However, as shown in Figure S3, our results are essentially the same with or without kynurenate, suggesting glutamate released during AP-evoked EPSCs does not saturate or desensitize postsynaptic receptors at post-hearing calyces of Held (Ishikawa et al, 2002; Yamashita et al, 2003) unlike in pre-hearing calyces (Yamashita et al, 2009).

Page 16, line 387: You cannot simply use multiple t-tests to compare a single control to multiple test conditions which seems to be the scenario here. Please correct or clarify.

Experimental protocols are clarified in Methods as “Experiments were designed as population study using different cells from separate brain slices under control and drug treatment, rather than on a same cell before and after the drug exposure.”

Table S1: 'Endo decay rate'. It's either the 'Endo rate' or the 'Deacy rate of delta Cm'. Please correct.

Corrected as Endocytosis rate (Endo rate).

Author Response

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

Major change:

All three of our reviewers raised the possibility that changes in movement during the time spent at the center ports could have contributed to changes in SWR rates. Analyses to address this possibility, based on the examination of trials with high and low speeds, were originally included in the supplement but we did not sufficiently highlight and explain these results. To rectify this, we have moved these results into a new main Figure 3 and now include a paragraph describing our interpretation of these results (page 9). We also include a more detailed description of the subjects’ behavior during port times – namely, that all subjects must remain quite stationary while at the reward ports in order to keep their nose in a specific position which keeps the port triggered. As a result, all subjects maintain head speeds well below our typical speed threshold for immobility while at the ports. This leads us to predict that any feedback based on periods of immobility alone (as requested by Reviewer 3) would show results very similar to our Control cohort and would not alter SWR rates seen during neurofeedback trials.

Minor changes:

(1) Reviewer 1 observed our that reported statistics appeared to be missing an interaction term showing that neurofeedback differentially affected the SWR rate/count pre- and postreward. We apologize for a lack of clarity here: we fit pre- and post-reward times with separate linear mixed effects models, so this interaction term is neither expected nor defined in our model. We have added a sentence clarifying this aspect of our LME approach in the Methods section: “Each model is designed to compare samples from all trials of the control group to samples from neurofeedback and delay trials from the neurofeedback cohort for a specific time period (for instance, pre-reward-delivery at the center ports).” Combining both times in the same model would require adding an additional hierarchical level in order to preserve the pairing of the pre- and post-reward time period for each trial, which we are concerned would complicate the formulation and interpretation of the model. However, the reviewer raises a good point that the comparison between these two time periods reveals an additional difference between the trial types: SWR rate remains relatively consistent between the pre- and post-reward periods during neurofeedback trials, while delay and control trials show a clear increase in SWR rate between the two time periods. To visualize and quantify this effect, we calculated the difference in SWR rates between the two time periods and now include this plot as Supplementary Figure 2F, which is referenced in page 8 of the main text.

(2) Reviewer 2 found our original title, “Neurofeedback training can modulate task-relevant memory replay in rats” to be misleading and suggestive of a manipulation to memory content. We are in complete agreement with the Reviewer in that our manipulation does not alter replay content, so to be more specific and accurate, we have changed our title to their suggestion “Neurofeedback training can modulate task-relevant memory replay rate in rats” accordingly.

(3) Reviewer 2 also requested that we include analyses quantifying baseline SWR rates for each of our experimental subjects. Although we initially considered reporting our results in measures of change relative to each individual animal’s baseline, we decided against this approach for several reasons.

First, it is important to clarify that we extensively train the animals on the task prior to implant, so we do not have access to a truly naïve, pre-behavior baseline SWR rate for any of our subjects. However, because the pre-implant training is conducted consistently between our neurofeedback and our control cohort, we have no reason to believe that the behavioral training prior to implant would introduce differences in SWR rate between the cohorts. Indeed, we find no difference in post-reward SWR rate (or SWR rate at the home well) when we quantify the first 250 trials of post-implant behavior for each subject (see panel A below). Note that we cannot compare the pre-reward SWR rate at this point, because it is influenced by the task structure which guarantees at least one SWR in each neurofeedback trial pre-reward.

Further, we do find that SWR rate is quite consistent over many days of task performance in the control cohort (show for the post-reward period in panel B below). This suggests that comparing the post-neurofeedback training SWR rates for the neurofeedback cohort to SWR rates throughout the training for the control cohort is not likely to be confounded by differing amounts of training experience. This is supported by our analyses in Figure 2 which show no differences in SWR rate between the two cohorts when considering pre- and post-reward times combined.

Author response image 1.

(A) SWR rate calculated during the post-reward period at the center port for the first 250 trials of postimplant behavior for each animal. Trials of all types are included (ie both neurofeedback trials and delay trials for the manipulation cohort). Groupwise comparison p=0.192. (B) Mean SWR rate during the post-reward period at the center port for each behavioral training epoch shows no systematic change over time across subjects within the control cohort.

Finally, within each cohort, we found the overall SWR rates to be quite consistent across animals. If each subject in the neurofeedback cohort had shown dramatically different SWR rates at the beginning of neurofeedback training, we would have needed to express the effect of neurofeedback training relative to baseline for each animal. However, since the range of SWR rates were highly comparable, we felt that it was more accessible, and easier to place our results within the context of the literature, by expressing our results as simple SWR rates themselves rather than measures of relative change. Within the neurofeedback cohort, comparing neurofeedback to delay trials is inherently matched for baseline SWR rate since these comparisons are made within the same animal.

(4) Finally, Reviewer 2 raises the possibility that older animals or those with cognitive deficits might respond to neurofeedback differently. We entirely agree with this possibility, and note this in our Discussion section: “Since the neurofeedback paradigm depends on the occurrence of at least a low endogenous rate of SWR occurrence, it would be important to implement neurofeedback training as a relatively early interventional strategy prior to extensive neurodegeneration, and training may take longer in aged or impaired subjects.”

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

(1) The authors' primary research question revolves around the inquiry of "how far in advance semantic information might become available from parafoveal preview." In contrast to prior studies, the current research seeks to achieve a breakthrough in terms of timing by employing innovative technology. They mention in the manuscript that "most of these studies have been limited to measuring parafoveal preview from fixations to an immediately adjacent word... We tackle these core issues using a new technique that combines the use of frequency tagging and the measurement of magnetoencephalography (MEG)-based signals." However, the argumentation for how this new technology constitutes a breakthrough is not sufficiently substantiated. Specifically, there are two aspects that require further clarification. Firstly, the authors should clarify the importance of investigating the timing of semantic integration in their research question. They need to justify why previous studies focusing on the preview effect during fixations to an immediately adjacent word cannot address their specific inquiry about "how far in advance semantic information might become available from parafoveal preview," which requires examining parafoveal processing (POF). Secondly, in terms of the research methodology, the authors should provide a more comprehensive explanation of the advantages offered by MEG technology in the observation of the timing of semantic integration compared to the techniques employed in prior research. Indeed, the authors have overlooked some rather significant studies in this area. For instance, the research conducted by Antúnez, Milligan, Hernández-Cabrera, Barber, & Schotter in 2022 addresses the same research question mentioned in the current study and employs a similar experimental design. Importantly, they utilize a natural reading paradigm with synchronized ERP and eye-tracking recordings. Collectively, these studies, along with the series of prior research studies employing ERP techniques and RSVP paradigms discussed by the authors in their manuscript, provide ample evidence that semantic information becomes available and integrated from words before fixation occurs. Therefore, the authors should provide a more comprehensive citation of relevant research and delve deeper into explaining the potential contributions of their chosen technology to this field.

We express our gratitude to the reviewer for providing insightful comments. Firstly, we clarify the advantages of the RIFT technique. The revised paragraph is on Page 4 with tracked changes and is copied as follows:

“…… The RIFT technique provides a notable advantage by generating a signal — the tagging response signal — specifically yoked to just the tagged word. This ensures a clear separation in processing the tagged word from the ongoing processing of other words, addressing a challenge faced by eye tracking and ERP/FRP approaches. Moreover, RIFT enables us to monitor the entire dynamics of attentional engagement with the tagged word, which may begin a few words before the tagged word is fixated.”

We also rephase our research questions in the introduction section on Page 5 with tracked changes:

“This paradigm allows us to address three questions. First, we aimed to measure when in the course of reading people begin to direct attention to parafoveal words. Second, we sought to ascertain when semantic information obtained through parafoveal preview is integrated into the sentence context. Modulations of pre-target RIFT responses by the contextual congruity of target words would serve as evidence that parafoveal semantic information has not only been extracted and integrated into the sentence context but that it is affecting how readers allocate attention across the text. Third, we explored whether these parafoveal semantic attention effects have any relationship to reading speed.”

Secondly, we would like to elucidate the significance of investigating the timing of semantic integration and why this complements existing findings of parafoveal processing (POF) during reading. Our manuscript has been revised accordingly, with specific modifications highlighted on Page 2. The revised passage reads as follows:

“…… eye tracking-based evidence for the extraction of parafoveal semantic information …… was eventually extended into English …… For example, Schotter and Jia (2016) showed preview benefits on early gaze measures for plausible compared to implausible words, even for plausible words that were unrelated to the target. These results demonstrate that semantic information can indeed be extracted from parafoveal words. However, due to the limitations of the boundary paradigm, which only assesses effects after target words have been fixated, it is challenging to precisely determine when and how parafoveal semantic processing takes place. Furthermore, it is generally hard to distinguish between the effects of cross-saccade integration (e.g., mismatch between the preview and the word fixated) and the effects of how differing words fit into the context itself (Veldre and Andrews, 2016a, 2016b).”

Thirdly, we now better highlight the contributions of Antúnez et al. paper as they have provided important evidence for parafoveal semantic processing during natural reading. The relevant modifications are highlighted on Page 3. The revised passage is as follows: “Although many of these effects have been measured in the context of unnatural reading paradigms (e.g., the “RSVP flanker paradigm”), similar effects obtain during natural reading. Using the stimuli and procedures from Schotter and Jia (2016), Antúnez et al. (2022) showed that N400 responses, measured relative to the fixation before the target words (i.e., before the boundary change while the manipulated words were in parafoveal preview), were sensitive to the contextual plausibility of these previewed words. These studies suggest that semantic information is available from words before they are fixated, even if that information does not always have an impact on eye fixation patterns.”

References:

Schotter ER, Jia A. 2016. Semantic and plausibility preview benefit effects in English: Evidence from eye movements. J Exp Psychol Learn Mem Cogn 42:1839–1866. doi:10.1037/xlm0000281

Veldre A, Andrews S. 2016a. Is Semantic Preview Benefit Due to Relatedness or Plausibility? J Exp Psychol Hum Percept Perform 42:939–952. doi:10.1037/xhp0000200

Veldre A, Andrews S. 2016b. Semantic preview benefit in English: Individual differences in the extraction and use of parafoveal semantic information. J Exp Psychol Learn Mem Cogn 42:837–854. doi:10.1037/xlm0000212

Antúnez M, Milligan S, Andrés Hernández-Cabrera J, Barber HA, Schotter ER. 2022. Semantic parafoveal processing in natural reading: Insight from fixation-related potentials & eye movements. Psychophysiology 59:e13986. doi:10.1111/PSYP.13986

(2) Further, the authors emphasize semantic integration in their observed results but overlook the intricate relationship between access, priming, and integration. This assertion appears overly confident. Despite using low-constraint sentences and low-predicted targets (lines 439-441), differences between congruent and incongruent conditions may be influenced by word-level factors. For instance, in the first coherent sentence, such as "Last night, my lazy brother came to the party one minute before it was over" (line 1049), replacing the keyword "brother" with an incongruent word could create an incoherent sentence, possibly due to semantic violation, relation mismatch with "lazy," or prediction error related to animate objects. A similar consideration applies to the second example sentence, "Lily says this blue jacket will be a big fashion trend this fall" (line 1050), where the effect might result from a discrepancy between "blue" and an incongruent word. However, the authors do not provide incongruent sentences to substantiate their claims. I recommend that the authors discuss alternative explanations and potentially control for confounding factors before asserting that their results unequivocally reflect semantic integration. My intention is not to dispute the semantic integration interpretation but to stress the necessity for stronger evidence to support this assertion.

We agree with the reviewer that stimulus control is very critical for this kind of work and apologize for the lack of clarity in the original manuscript.

(1) We fully agree that word-level factors can be an important confound, which is why we carefully controlled word-level factors in the experimental design. As detailed in the Appendix of the original manuscript, each pair of target words has been strategically embedded into two sentences, allowing for the creation of both congruent and incongruent sentence pairs through the interchange of these words. We now have explicitly specified this design in all sentences, as reflected in the edited manuscript on Page 38. For example, considering the exemplar pair of “brother/jacket”,

“Last night, my lazy brother/jacket came to the party one minute before it was over.

Lily says this blue jacket/brother will be a big fashion trend this fall.”

In this design, the pair of target words is presented in both congruent and incongruent sentences. Participant A reads “lazy brother” and “blue jacket”, while Participant B reads “lazy jacket” and “blue brother”. This approach ensures that the same target words appear in both congruent and incongruent conditions across participants, serving as an effective control for word-level factors.

(2) We acknowledge that the consideration of word-level information is crucial when making claims about contextual integration in the current study. However, we don’t think there are many cases in the stimulus set where a single feature like animacy is enough to create the mismatch. Instead, the stimuli were written so that it is not possible to strongly predict any word or even a specific semantic feature, so that appreciating the mismatch requires the comprehender to integrate the word into the context (and especially to integrate the word with the immediately preceding one). However, this more local modifier/noun plausibility may behave differently from a more global contextual plausibility, which is a limitation of the stimulus set and has been discussed in the revised manuscript, as indicated by the tracked changes on Page 16, as copied below:

“Two noteworthy limitations exist in the current study. Firstly, the construction of pretarget–target word pairs consistently follows an adjective–noun phrase structure, potentially leading to semantic violations arising from immediate local incongruence rather than a broader incongruence derived from the entire sentential context. While the context preceding target words was deliberately minimized to ensure a pure effect of bottom-up parafoveal processing rather than the confounding impact of top-down prediction, it is essential to recognize that information from both local and global contexts can exert distinct effects on word processing during natural reading (Wong et al., 2022). Future investigations should incorporate more information-rich contexts to explore the extent to which the parafoveal semantic integration effect observed in this study can be generalized.”

References:

Wong R, Veldre A, Andrews S. 2022. Are There Independent Effects of Constraint and Predictability on Eye Movements During Reading? J Exp Psychol Learn Mem Cogn. doi:10.1037/XLM0001206

Reviewer #2 (Public Review):

This MEG study used co-registered eye-tracking and Rapid Invisible Frequency Tagging (RIFT) to track the effects of semantic parafoveal preview during natural sentence reading. Unpredictable target words could either be congruent or incongruent with sentence context. This modulated the RIFT response already while participants were fixating on the preceding word. This indicates that the semantic congruency of the upcoming word modulates visual attention demands already in parafoveal preview.

The quest for semantic parafoveal preview in natural reading has attracted a lot of attention in recent years, especially with the development of co-registered EEG and MEG. Evidence from dynamic neuroimaging methods using innovative paradigms as in this study is important for this debate.

We express our gratitude to the reviewer for recognizing the significance of our research question in the domain of natural reading.

Major points:

(1) The authors frame their study in terms of "congruency with sentence context". However, it is the congruency between adjective-noun pairs that determines congruency (e.g. "blue brother" vs "blue jacket", and examples p. 16 and appendix). This is confirmed by Suppl Figure 1, which shows a significantly larger likelihood of refixations to the pre-target word for incongruent sentences, probably because the pre-target word is most diagnostic for the congruency of the target word. The authors discuss some possibilities as to why there is variability in parafoveal preview effects in the literature. It is more likely to see effects for this simple and local congruency, rather than congruency that requires an integration and comprehension of the full sentence. I'm not sure whether the authors really needed to present their stimuli in a full-sentence context to obtain these effects. This should be explicitly discussed and also mentioned in the introduction (or even the abstract).

We have addressed this limitation of the study explicitly in the revised manuscript. The modifications can be found in the tracked changes on Page 16, and is copied as follows:

“Two noteworthy limitations exist in the current study. Firstly, the construction of pretarget–target word pairs consistently follows an adjective–noun phrase structure, potentially leading to semantic violations arising from immediate local incongruence rather than a broader incongruence derived from the entire sentential context. While the context preceding target words was deliberately minimized to ensure a pure effect of bottom-up parafoveal processing rather than the confounding impact of top-down prediction, it is essential to recognize that information from both local and global contexts can exert distinct effects on word processing during natural reading (Wong et al., 2022). Future investigations should incorporate more information-rich contexts to explore the extent to which the parafoveal semantic integration effect observed in this study can be generalized.”

References:

Wong R, Veldre A, Andrews S. 2022. Are There Independent Effects of Constraint and Predictability on Eye Movements During Reading? J Exp Psychol Learn Mem Cogn. doi:10.1037/XLM0001206

(2) The authors used MEG and provided a source estimate for the tagging response (Figure 2), which unsurprisingly is in the visual cortex. The most important results are presented at the sensor level. This does not add information about the brain sources of the congruency effect, as the RIFT response probably reflects top-down effects on visual attention etc. Was it necessary to use MEG? Would EEG have produced the same results? In terms of sensitivity, EEG is better than MEG as it is more sensitive to radial and deeper sources. This should be mentioned in the discussion and/or methods section.

Source estimation was exclusively provided for the tagging response rather than the congruency effect because we posit that this conditional contrast would emanate from the same brain regions exhibiting the tagging responses in general. As depicted in the following figure, source localization for the congruency effect was identified in the left association cortex (Brodmann area 18), the same area as the source localization for the tagging response (the negative cluster observed here is due to the incongruent minus congruent contrast). While we agree with the Reviewer that the RIFT result might indicate a top-down effect on visual attention, it is important to note that, due to the low-pass filter property of synapses, observing a tagging response at a high frequency beyond the visual cortex is challenging.

Author response image 1.

We discussed the necessity of using MEG in the edited manuscript with tracked changes on Page 20, and is copied as follows:

“While the current study was conducted using MEG, these procedures might also work with EEG. If so, this would make our approach accessible to more laboratories as EEG is less expensive. However, there are currently no studies directly comparing the RIFT response in EEG versus MEG. Therefore, it would be of great interest to investigate if the current findings can be replicated using EEG.”

(3) The earliest semantic preview effects occurred around 100ms after fixating the pre-target word (discussed around l. 323). This means that at this stage the brain must have processed the pre-target and the target word and integrated their meanings (at some level). Even in the single-word literature, semantic effects at 100 ms are provocatively early. Even studies that tried to determine the earliest semantic effects arrived at around 200 ms (e.g. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382728/, https://psycnet.apa.org/record/2013-17451-002). The present results need to be discussed in a bit more detail in the context of the visual word recognition literature.

We have incorporated this valuable suggestion into the discussion section to enhance the clarity of our key result regarding the timing of parafoveal semantic integration. The revised manuscript with tracked changes can be found on Page 14, and the relevant passage is provided below:

“Our results also provide information about the time course of semantic integration …… by as early as within 100 ms after fixating on the pre-target word. The timing of this parafoveal semantic effect appears remarkably early, considering that typical semantic access for a single word occurs no earlier than around 200 ms, as demonstrated in the visual word recognition literature (Carreiras et al., 2014). For instance, in a Go/NoGo paradigm, the earliest distinguishable brain activity related to category-related semantic information of a word occurs at 160 ms (Amsel et al., 2013; Hauk et al., 2012). Therefore, the RIFT results presented here suggest that natural reading involves parallel processing that spans multiple words. The level of (covert) attention allocated to the target word, as indexed by the significant difference in RIFT responses compared to the baseline interval, was observed even three words in advance (see Figure 2C). This initial increase in RIFT coincided with the target entering the perceptual span (McConkie and Rayner, 1975; Rayner, 1975; Underwood and McConkie, 1985), likely aligning with the initial extraction of lower-level perceptual information about the target. The emerging sensitivity of the RIFT signal to target plausibility, detected around 100 ms after the fixation on the pre-target word, suggests that readers at that time had accumulated sufficient semantic information about the target words and integrated that information with the evolving sentence context. Therefore, it is plausible that the initial semantic processing of the target word commenced even before the pre-target fixation and was distributed across multiple words. This parallel processing of multiple words facilitates rapid and fluent reading.”

References:

Carreiras M, Armstrong BC, Perea M, Frost R. 2014. The what, when, where, and how of visual word recognition. Trends Cogn Sci 18:90–98. doi:10.1016/j.tics.2013.11.005

Amsel BD, Urbach TP, Kutas M. 2013. Alive and grasping: Stable and rapid semantic access to an object category but not object graspability. Neuroimage 77:1–13. doi:10.1016/J.NEUROIMAGE.2013.03.058

Hauk O, Coutout C, Holden A, Chen Y. 2012. The time-course of single-word reading: Evidence from fast behavioral and brain responses. Neuroimage 60:1462. doi:10.1016/J.NEUROIMAGE.2012.01.061

McConkie GW, Rayner K. 1975. The span of the effective stimulus during a fixation in reading. Percept Psychophys 17:578–586. doi:10.3758/BF03203972

Rayner K. 1975. The perceptual span and peripheral cues in reading. Cogn Psychol 7:65–81.

Underwood NR, McConkie GW. 1985. Perceptual Span for Letter Distinctions during Reading. Read Res Q 20:153. doi:10.2307/747752

(4) As in previous EEG/MEG studies, the authors found a neural but no behavioural preview effect. As before, this raises the question of whether the observed effect is really "critical" for sentence comprehension. The authors provide a correlation analysis with reading speed, but this does not allow causal conclusions: Some people may simply read slowly and therefore pay more attention and get a larger preview response. Some readers may hurry and therefore not pay attention and not get a preview response. In order to address this, one would have to control for reading speed and show an effect of RIFT response on comprehension performance (or vice versa, with a task that is not close to ceiling performance). The last sentence of the discussion is currently not justified by the results.

We acknowledge that the correlation analysis between the RIFT effect and reading speed on the group level lacks causality, making it less ideal for addressing this question. We have incorporated this acknowledgment as one of the limitations of the current study in the revised manuscript on Page 16, as indicated by the tracked changes, and the relevant passage is provided below:

“Two noteworthy limitations exist in the current study. …… Secondly, the correlation analysis between the pre-target RIFT effect and individual reading speed (Figure 5) does not establish a causal relationship between parafoveal semantic integration and reading performance. Given that the comprehension questions in the current study were designed primarily to maintain readers’ attention and the behavioural performance reached a ceiling level, employing more intricate comprehension questions in future studies would be ideal to accurately measure reading comprehension and reveal the impact of semantic parafoveal processing on it.”

We reformulated the last sentence:

“These results support the idea that words are processed in parallel and suggest that early and deep parafoveal processing may be important for fluent reading.”

(5) L. 577f.: ICA components were selected by visual inspection. I would strongly recommend including EOG in future recordings when the control of eye movements is critical.

We appreciate the reviewer for providing this valuable suggestion. We acknowledge that EOG recordings were not included in the current study due to restrictions on MEG data collection from the University of Birmingham during the COVID-19 pandemic. In our future studies, we will follow the reviewer's suggestion to incorporate EOG recordings in data collection. This addition will facilitate optimal eye movement-related artifact rejection through ICA, as recommended by Dimigen in his methodological paper:

Dimigen, O. (2020). Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments. NeuroImage, 207, 116117.

(6) The authors mention "saccade planning" a few times. I would suggest looking at the SWIFT model of eye movement control, which is less mechanistic than the dominant EZ-Reader model (https://psycnet.apa.org/record/2005-13637-003). It may be useful for the framing of the study and interpretation of the results (e.g. second paragraph of discussion).

In the revised manuscript, we have provided a more comprehensive explanation eye movements/saccade planning, aligning it with the SWIFT model. Please refer to Page 15 with tracked changes, and the updated passage is provided below:

“The results of the present study are aligned with the SWIFT model of eye movement control in natural reading (Engbert et al., 2005), wherein the activation field linked to a given word is hypothesized to be both temporally and spatially distributed. Indeed, we found that the initial increase in covert attention to the target word occurred as early as three words before, as measured by RIFT responses (Figure 2C). These covert processes enable the detection of semantic incongruity (Figure 3B and Figure 3C). However, it may occur at the non-labile stage of saccade programming, preventing its manifestation in fixation measures of the currently fixated pre-target word (Figure 1B). Therefore, the RIFT technique’s capacity to yoke patterns to a specific word offers a unique opportunity to track the activation field of word processing during natural reading.”

References:

Engbert R, Nuthmann A, Richter EM, Kliegl R. 2005. Swift: A dynamical model of saccade generation during reading. Psychol Rev 112:777–813. doi:10.1037/0033-295X.112.4.777

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

While the manuscript is well-written and presents a structured analysis of the data, it requires further clarification and substantiation regarding the originality of the research questions, the advantages of the proposed methodology, and the interpretation of the results related to semantic integration. Additional references and a more thorough discussion of related research are needed to strengthen the manuscript's contribution to the field.

We appreciate the reviewer's kind words about this manuscript and the insightful comments and suggestions provided. In the revised manuscript, we have now placed additional emphasis on the importance of investigating semantic integration within the realm of parafoveal processing in natural reading. We have clarified the advantages of employing MEG and RIFT and expanded upon our results in the context of Antúnez et al.'s 2022 paper, as suggested by the reviewer.

Reviewer #2 (Recommendations For The Authors):

(1) L. 59: The "N400" has been linked to much more than "semantic access". I think it is widely accepted that "access" happens (or at least begins) earlier, and that the N400 reflects high-level integration processes etc.

Earlier debates about whether the N400 is more linked to access or integration have resolved in favour of an access account, but with a growing appreciation of the blurred boundaries between constructions like access, priming, and integration, as Reviewer 1 also pointed out in comment #2.

(2) L. 177: I wasn't sure about the selection of sensors. Were the same sensors used for all participants (whether they had a tagging response or not)?

We appreciate the reviewer for highlighting the confusion regarding the sensor selection procedure in the study. In response, we have added further clarifications about this procedure in the Method section of the revised manuscript. The relevant changes can be found on Page 25 with tracked changes, and the modified passage is reproduced below:

"Please note that the tagging response sensors may vary in number across participants (7.9 ± 4.5 sensors per participant, M ± SD). Additionally, they may have a different but overlapping spatial layout, primarily over the visual cortex. For the topography of all tagging response sensors, please refer to Figure 2A."

(3) Ll. 247ff.: I don't understand the idea of a "spill-over effect". The future cannot spill into the past. Or does this refer to possible artefacts or technical problems?

In the revised manuscript, we have rephrased this passage with tracked changes on Page 11, and the updated version is provided below:

“We conducted a similar analysis of the coherence measured when participants fixated the target word and found no significant modulations related to the contextual congruity of that target word. …… Thus, the parafoveal semantic integration effect identified during the pre-target intervals cannot be attributed to signal contamination from fixations on the target word induced by the temporal smoothing of filters.”

(4) I struggled to follow the "internal attention" explanation for the paradoxical RIFT effect (p. 11/12).

We appreciate the reviewer for pointing out the confusion, and we have rephrased the passage in the revised manuscript with tracked changes on Page 13. The revised version is provided below:

"Previous work has demonstrated that tagging responses decrease as attention shifts from an external task (e.g., counting visual targets) to an internal task (e.g., counting heartbeats) (Kritzman et al., 2022). Similarly, in a reading scenario, visually perceiving the flickering word constitutes an external task, while the internal task involves the semantic integration of previewed information into the context. If more attentional resources are internally directed when faced with the challenge of integrating a contextually incongruent word, fewer attentional resources would remain for processing the flickering word. This may be the kind of shift reflected in the reduction in RIFT responses."

References:

Kritzman L, Eidelman-Rothman M, Keil A, Freche D, Sheppes G, Levit-Binnun N. 2022. Steady-state visual evoked potentials differentiate between internally and externally directed attention. Neuroimage 254:119133.

(5) L. 572: Why was detrending necessary on top of a 0.5 Hz high-pass filter? Was detrending applied to the continuous raw data, or to epochs? Was it just the linear trend or other polynomial terms?

We agree with the Reviewer that, given the prior application of a 0.5Hz high-pass filter to the data, the detrending does not alter the data. Nonetheless, we included this procedure in the manuscript for the sake of completeness. In the revised manuscript, we have provided additional clarification on this point, as indicated by the tracked changes on Page 23. The modified passage is presented below:

"Subsequently, detrending was applied individually to each channel of the continuous raw data to factor out the linear trend."

(6) Source analysis, p. 25f.: How was the beamformer regularized?

This information was already included in the original manuscript on Page 26. The original text is provided below for reference:

“No regularisation was performed to the CSD matrices (lambda = 0).”

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Zhu, et al present a genome-wide histone modification analysis comparing patients with schizophrenia (on or off antipsychotics) to non-psychiatric controls. The authors performed analyses across the dorsolateral prefrontal cortex and tested for enrichment of nearby genes and pathways. The authors performed an analysis measuring the effect of age on the epigenomic landscape as well. While this paper provides a unique resource around SCZ and its epigenetic correlates, and some potentially intriguing findings in the antipsychotic response dataset there were some potential missed opportunities - related to the integration of outside datasets and genotypes that could have strengthened the results and novelty of the paper.

Major Comments

(1) Is there genotype data available for this cohort of donors or can it be generated? This would open several novel avenues of investigation for the authors. First the authors can test for enrichment of heritability for SCZ or even highly comorbid disorders such as bipolar. Second, it would allow the authors to directly measure the genetic regulation of histone markers by calculating QTLs (in this case histone hQTLs). The authors assert that although interesting, ATACseq approach does not provide the same chromatin state information as histone mods mapped by ChiP. Why do the authors not test this? There are several ATACseq datasets available for SCZ [https://pubmed.ncbi.nlm.nih.gov/30087329/]and an additional genomic overlap could help tease apart genetic regulation of the changes observed.

As detailed in our Methods section, brain samples have previous medical diagnosis, treatment record, and toxicological screening. Unfortunately, there was no genotype information on our brain sample collection. However, we examined overlap of differential enhancer and promoter peaks with genetic variants using linkage disequilibrium score regression (Fig. S10). Additionally, to assess agreement with the literature, we compared DEGs identified in our study with a previous snRNA-seq study in postmortem prefrontal cortex of schizophrenics and controls (Table S7).

Repressive histone marks tend to provide different information than ATAC-seq data. However, we examined only activating marks in this study. Thus, the sentence in the Introduction mentioning that “ATAC-seq approach does not provide the same chromatin state information as histone modifications mapped by chromatin immunoprecipitation sequencing (ChIP-seq) assays do” has been removed.

(2) Can the authors theorize why their analysis found significant effects for H3K27Ac for antipsychotic use when a recent epigenomic study of SCZ using a larger cohort of samples and including the same histone modifications did not [https://pubmed.ncbi.nlm.nih.gov/30038276/]? Given the lower n and lower number of cells in this group, it would be helpful if the authors could speculate on why they see this. Do the authors know if there is any overlap with the Girdhar study donors or if there are other phenotypic differences that could account for this?

As mentioned in the Methods sections, three strengths of this brain bank include i) inclusion of samples of schizophrenia subjects with antemortem diagnosis (i.e., based on clinical histories) and not with postmortem diagnosis (i.e., based on interviews with relatives and friends – a diagnostic approach used by many brain banks worldwide but with important limitations, see here: PMID: 15607306), ii) inclusion of control subjects individually matched by sex, age and PMD, and iii) our possibility to test the presence or absence of antipsychotic medications in blood samples as an independent experimental variable. This allowed us to obtained novel and statistically valid conclusions related to cell-type epigenetic alterations in the frontal cortex of schizophrenia subjects, and the impact of age and antipsychotic treatment on chromatin organization.

There is no overlap with Girdhar study donors.

(3) The reviewer is concerned about the low concordance between bulk nuclei RNA-seq and single-cell RNA-seq for SCZ (236 of 802 DEGs in NeuN+ and 63 of 1043 NEuN-). While it is not surprising for different cohorts to have different sets of DEGs these seem to be vastly different. Was there a particular cell type(s) that enriched for the authors' DEGs in the single-cell dataset? Do the authors know if any donors overlapped between these cohorts?

This overlap is acceptable considering that these are datasets originated from an entirely distinct cohort of postmortem human brain samples.

(4) Functional enrichment analyses: details are not provided by the authors and should be added. The authors need to consider a) providing a gene universe, ie only considering the sets of genes with nearby H3K4me3/ H3K27ac levels, to such pathway tools, and b) should take into account the fact that some genes have many more peaks with data. There are known biases in seemingly just using the best p-value per gene in other epigenetic analysis (ie. DNA methylation data) and software is available to run correct analyses: https://pubmed.ncbi.nlm.nih.gov/23732277.

GREAT was used to map differential peak loci to target genes using the whole genome as the background set and default basal extension as per Nord et al. http://dx.doi.org/10.1016/j.cell.2013.11.033. We argue that it is more biologically relevant than comparing against an artificially selected background. These gene sets were then passed to Panther for Gene Ontology enrichment analysis as per Liu et al. 10.1186/s12940-015-0052-5.

Additional details are provided in Materials and Methods section:

ChIP-seq annotation and functional enrichment

GREAT analysis (http://great.standford.edu) was performed on differential peaks using the whole genome as background and default basal extension from 5kb upstream to 1kb downstream of the TSS.

Significantly enriched Gene Ontology biological processes were identified using the Panther Classification tools using a hypergeometric test.

Reviewer #2 (Public Review):

The manuscript by Zhu has generated ChIP-seq and RNA-seq data from sizeable cohorts of SCZ patient samples and controls. The samples include 15 AF-SCZ samples and 15 controls, as well as 14 AT-SCZ samples and 14 controls. The genomics data was generated using techniques optimized for low-input samples: MOWChIP-seq and SMART-seq2 for histone profiles and transcriptome, respectively. The study has generated a significant data resource for the investigation of epigenomic alterations in SCZ. I am not convinced that the hierarchical pairwise design - first comparing AF-SCZ and AT-SCZ with their corresponding controls and secondarily contrasting the two comparisons is fully justified. The authors should repeat the statistical analysis by modeling all three groups simultaneously with an interaction effect for treatment or directly compare AF-SCZ to AT-SCZ groups and evaluate if the main conclusions remain supported.

Major comments

(1) The manuscript did not discuss (mention) the quality control of RNA-seq data shown in Fig. 1B. The color scheme choice for the heatmap visualization did not provide a quantitative presentation of the specificity of the RNA-seq data. I would recommend using bar plots to present the results more quantitatively.

QC of raw RNA-seq data including per sequence GC and adapter content was assessed with FastQC. Reads underwent soft-clipping during STAR alignment with on average 73.8% (+/- 0.08%) reads for neurons and 69.0% (+/- 0.99%) reads for glia being uniquely mapped. A new supplementary figure (Figure S5) has been included to show four bar plots representing the expression values more quantitatively.

These details are now provided in the RNA-seq data processing part of the Materials and Methods section:

RNA-seq data processing

The human genome (GRCh38) and comprehensive gene annotation were obtained from GENCODE (v29). Quality control of RNA-seq reads including per sequence GC and adapter content was assessed with FastQC. Reads were mapped with STAR (2.7.0f) with soft-clipping (average of 73.8% (+/- 0.08%) reads uniquely mapped for neurons and 69.0% (+/- 0.99%) reads for glia) and quantified with featureCounts (v2.0.1) using the default parameters.

(2) How does the specificity of this RNA-seq dataset compare to previous studies using a similar NeuN sorting strategy?<br /> As mentioned in the Results section, highly significant (median p-value = 6 ´ 10-7) pairwise differences in molecular marker expression were observed for all markers ranging from mature, functional and synaptic neuron markers to astrocyte, oligodendrocyte and microglial markers (Figure 1B; Figures S4 and S5; Table S5). This confirms neuronal and non-neuronal cell-type identities in the NeuN+ and NeuN- nuclei samples, respectively.

(3) I appreciate the effort to assess the ChIP-seq data quality using phantompeakqualtools. However, prior knowledge/experience with this tool is required to fully understand the QC results. The authors should additionally provide browser shots at different scales for key neuronal/glial genes, so readers can have a more direct assessment of data quality, such as the enrichment of H3K4me3 at promoters (but not elsewhere), and H3K27ac at promoters and enhancers. Existing browser views, such as Fig. 2B are too zoomed out for assessing the data quality.

A new Fig 2B has been generated with a magnified view for clearer examination.

(4) The pairwise regression model should be explicitly reported in methods.

Additional details are included in the Methods section:

Differential analysis for RNA-seq data

We analyzed the bulk RNA-seq data of 29 schizophrenia subjects and 29 controls. The initial step involved filtering out genes with low read counts (less than 20 reads in over 50% of samples). The analysis then employed a two-step method to estimate the technical and biological noise. The first step was identifying the top 10 principal components (PCs) of the dataset. Subsequently, the correlation between each PC and various experimental (alignment rate, unique rate, exon percentage, number of unique mapped reads) and demographic (sex, age at death, PMD, antemortem diagnosis) factors was calculated. Covariates with high correlation to the PCs were included in the analysis to minimize their impact. The analysis was conducted using the 'DESeq2' software package, and genes with a false discovery rate (FDR) below 0.05 were identified as differentially expressed.

(5) The statistical strategy to compare AF-SCZ and AT-SCZ to their corresponding control groups was unjustified. Why not model all three groups simultaneously with an interaction effect for treatment or directly compare AF-SCZ to AT-SCZ groups? If the manuscript argues that the antipsychotic effect is the main novelty, why not directly compare AF-SCZ and AT-SCZ?

This is an important point. As mentioned above, one of the main strengths of our experimental design is that schizophrenia subjects and controls were individually matched by sex and age and (if possible) postmortem delay and freezing storage time. Our study is also among the first to report the potential impact of antipsychotic treatment on chromatin organization using postmortem human brain samples. Because of this individual matching method, we only compared schizophrenia subjects (either antipsychotic-free or antipsychotic-treated) with their respective individually matched controls. This experimental design is supported by our previous publications with postmortem human brain samples (PMID: 36100039; PMID: 28783139; PMID: 26758213; PMID: 23129762; PMID: 22864611; PMID: 18297054). The rationale behind this experimental design – as well as potential limitations particularly related to the division of the schizophrenia group in antipsychotic-free and antipsychotic-treated – is mentioned in the Discussion:

Related to the effect of antipsychotic treatment, frontal cortex samples of schizophrenia subjects were divided into AF and AT based on postmortem toxicological analysis in both blood and when possible brain samples, which provides information about a longer retrospective drug-free period due to the high liposolubility of antipsychotic medications (Voicu and Radulescu, 2009). However, we cannot fully exclude the possibility of previous exposure to antipsychotic medications in the AF-schizophrenia group, and hence that the epigenetic alterations observed exclusively in the AF-schizophrenia group are a consequence of a potential period of decompensation, which typically occurs following voluntary treatment discontinuation (Liu-Seifert et al., 2005).

It is also worth mentioning here that data were analyzed both at the cohort level, as well as at an individual level (schizophrenia/cohort pairs). This is mentioned in the manuscript:

It should be noted that in the differential analyses here, the schizophrenia subjects (whether AF or AT) and their controls were compared at the cohort level, while matched schizophrenia/control pairs were examined individually in the TF-based analyses.

(6) The method of pairwise comparison to corresponding control groups, then further comparing the pairwise results opens the study to a number of statistical vulnerabilities. For example, on page 12, the studies identified 166 DEGs between AF and control, and 1273 DEGs between AT and control. Instead of implicating a greater amount of difference between AT and control, such a result can often be driven by differences in between-group variance, rather than between-group means, that is, are the SCZ-AF and SCZ-treated effect size magnitudes and directionalities similar (but the treated group has lower variance) or are the two groups truly different in terms of means? The result in Fig. 5A suggests effect sizes for the two comparisons (AF-Ctrl and AT-Ctrl) are similar but have lower variability in the treated group.

For a discussion regarding our approach, which involves a pairwise comparison, see above.

(7) The pairwise comparison further raised the possibility the results were driven by the difference in the two control cohorts rather than the two SCZ cohorts.

We clearly show that age is an important independent factor (Fig 7). Since controls are individually matched by sex and age, this limits the validity of the comparison among the two cohort groups including subjects of different age (see Tables S1 and S2).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor Comments

(1) Why not mention what histone modifications you measured by Chip-seq in the abstract? A certainly minor point but I felt I read for quite a while before I got to that point in the intro.

The two histone marks are now mentioned in the abstract.

(2) There are several places in the introduction where improper grammar is utilized and this should be edited.

Introduction has been edited.

(3) Related to major comments, how many donors overlapped with the PsychENCODE, CommonMind papers?

Our datasets were generated from an entirely distinct cohort of postmortem human brain samples. Our postmortem sample collection does not overlap with postmortem samples included in PsychENCODE and/or CommonMind publications.

(4) Since studies have already measured H3K4me3 and H3K27ac in the SCZ prefrontal cortex, why didn't the authors consider measuring changes in a related repressive marker? This is not to suggest the authors should do that now, but additional comments about other markers would help provide context for this analysis and point toward potential future studies.

This is an interesting question and will be the goal of our future investigation.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Why does stimulation at 0.15 Hz show a third harmonic signal (Figure 5A) but 0.25 Hz does not show a second harmonic signal?

Second and third harmonic signals were sometimes observed in 0.15 Hz and also in 0.25 Hz and other frequency stimulations. The second harmonic signal is easier to understand as vasomotion may be reacting to both directions of oscillating stimuli. The reason for the emergence of the third harmonics was totally unknown. These harmonic signals were not always observed, and the magnitude of these signals was variable. The frequency-locked signal was robust, thus, in this manuscript, we decided to describe only this signal. These observations are mentioned in the revised manuscript (Results, page 9, paragraph 2).

References for the windows are missing. Closed craniotomy: (Morii, Ngai, and Winn 1986). Thinned skull: (Drew et al. 2010).

These references were incorporated into the revised manuscript.

An explanation of, or at least a discussion on, why a flavoprotein or other intrinsic signal from the parenchyma might follow vasomotion with high fidelity would be most helpful.

We spend a large part of the Results describing that any fluorescence signal from the brain parenchyma follows the vasomotion because the blood vessels largely lack fluorescence signals within the filter band that we observe. This is described as “shadow imaging”. What was rather puzzling was that flavoprotein or other intrinsic signals were phase-shifted in time. This suggests that these autofluorescence signals have an anti-phase “shadow imaging” component and another component that is phase-shifted in time. This is described in the manuscript as the following.

(Results, page 13, paragraph 2)

“Production and degradation of flavin and other metabolites may be induced by the fluctuation in the blood vessel diameter with a fixed delay time. The phase shift in the autofluorescence could be due to the additive effect of “shadow” imaging of the vessel and to the concentration fluctuation of the autofluorescent metabolite”

Glucose and oxygen are likely to be abundantly delivered during the vasodilation phase compared to the vasoconstriction phase of vasomotion. These molecules will trigger cell metabolism and endogenous fluorescent molecules such as NADH, NADPH, and FAD may increase or decrease with a certain delay, which is required for the chemical reactions to occur. Therefore, the concentration fluctuation of these metabolites could lag in time to the changes in the blood flow. These discussions are added in the revised manuscript (Discussions, page 19, paragraph 2).

Reviewer #2 (Recommendations For The Authors):

Minor corrections to the text and figures:

(1) Figures 1 and 2- The single line slice basal and dilated traces are larger in Figure 2 (intact skull) than in Figure 1 (thinned skull)- have these been mixed up, as the authors state in the text that larger dilations are detected in the thinned skull preparation?

The example vessel described for the thinned skull (Figure 1) happened to be larger than that shown for the intact skull (Figure 2). We did not describe that larger dilations are observed in the thinned skull preparation. What was described was that the vessel profiles were shallower in the intact skull. This is because the presence of the intact skull blurs the fluorescence image.

(2) Figure 3- I think the lower panel of the amplitude spectrums from 3 individual animals included in D would benefit from being in its own panel within this Figure (i.e. E). The peak ratio is also used in this figure, but the equation to calculate this is not displayed until Figure 4.

We thank the reviewer for recommending making the figure more comprehensible. We have divided panel D into D and E and shifted the panel character accordingly. The manuscript text was also updated.

As the reviewer describes, the peak ratio of 0.25 Hz is used in Figure 3E (original). However, the equation to calculate this figure is described in the appropriate location within the main text of the manuscript (Results, page 10, paragraph 2) as well as in the figure legend.

(3) Figure 5- In the visual stimulation traces displayed in C you have included a 10-degree scale bar, which looks similar in amplitude to the trace but the text states these are 17-degree amplitude traces.

We thank the reviewer for noticing this mistake of labeling in the figure. We have corrected the error in the revised figure.

(4) Figure 6- For the Texas red fluorescence traces and image scales displayed in F, you have shown the responding traces on the right and non-responding on the left, but the figure legend states the amplitude is strong on the left and weak on the right.

We thank the reviewer for noticing the error in the figure legend text. We have corrected the error in the revised manuscript.

(5) Figure 6- It would be helpful for the reader if the r value was displayed on the graph in G.

We thank the reviewer for the suggestion. We have indicated the r value in Figure 6G as the reviewer recommended.

Reviewer #3 (Recommendations For The Authors):

Major

It is unclear to me if the authors are studying vasomotion per se. Vasomotion is an intrinsic, natural rhythm of blood vessel diameter oscillation that is entrained by endogenous rhythmic neural activity. Importantly, if you take neural activity away, the blood vessel (with flow and pressure) should still be capable of oscillating due to an intrinsic mechanism within the vessel wall. In contrast, if one increases neural activity by way of sensory stimulation and blood flow increases, this is the basis of functional hyperemia. If one stimulates the brain over and over again at a particular frequency, it is expected that blood flow will increase whenever neural activity increases to the stimulus, up to a particular frequency until the blood vessel cannot physically track the stimulus fast enough. Functional hyperemia does not depend on an intrinsic oscillator mechanism. It occurs when the brain becomes active above endogenous resting activity due to sensory or motor activity.

We thank the reviewer for stressing the importance of the distinction between “vasomotion” and functional “hyperemia”.

We recognized that the terminology used in our paper was not explicitly explained. Traditionally, “vasomotion” is defined as the dilation and constriction of the blood vessels that occurs spontaneously at low frequencies in the 0.1 Hz range without any apparent external stimuli. Sensory-induced changes in the blood flow are usually called “hyperemia”. However, in our paper, we used the term, vasomotion, literally, to indicate both forms of “vascular” “motion”. Therefore, the traditional vasomotion was called “spontaneous vasomotion” and the hyperemia, with both vasoconstriction and vasodilation, induced with slow oscillating visual stimuli was called “visually induced vasomotion”. This distinction in the terminology is now explicitly introduced in the revised manuscript (Introduction, page 3, paragraph 2-3; page 4, paragraph 1-2).

Using our newly devised methods, we show the presence of “spontaneous vasomotion”. However, this spontaneous vasomotion was often fragmented and did not last long at a specific frequency. With visual stimuli that slowly oscillated at temporal frequencies close to the frequency of spontaneous vasomotion, oscillating hyperemia, or “visually induced vasomotion” was observed. Importantly, this visually induced vasomotion is not observed in novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to neuronal activity in the primary visual cortex. We also do not know how the synchronized vasomotion can spread throughout the whole brain. Where the plasticity for vasomotion entrainment occurs is also unknown. How much of the visually induced vasomotion relies on the mechanisms of intrinsic spontaneous vasomotion is also undetermined. Discussion about the future directions of understanding the mechanisms of visually induced vasomotion and entrainment is described in better detail in the revised manuscript (Discussions, page 19, paragraph 1).

To me, one would need to silence the naturally occurring vasomotion to study it. As soon as one activates the brain with an external stimulus, functional hyperemia is being studied. One idea that would be interesting to look at is whether a single or perhaps a double stimulus, in an untrained vs trained mouse, shows vasodilation that occurs across the cortex and in the cerebellum. In other words, is there something special about repeating the signal over and over again that results in brain-wide synchronization, or does a single or double oscillation of the same frequency (0.25Hz) also transiently synchronize the brain? My guess is that a short stimulus would give you the same thing (especially in a trained mouse) and that there is nothing special about oscillating the signal over and over again (except for the learning component).

We thank the reviewer for the ideas of new experiments to understand whether the visually induced vasomotion shares the same mechanisms for creating spontaneous vasomotion or not.

We would like to emphasize again that the visually induced vasomotion is not observed in the Novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to the visual stimuli. Entrainment with repeated presentation of visual stimuli is required for this global synchronization phenomenon to occur.

We would also like to emphasize that, even in Expert animals, the visually induced vasomotion that is frequency-locked to the presented stimulus does not always occur immediately. As shown in Figure 3D lower panel (Figure 3E in the revised figure), the vasomotion did not always immediately frequency-lock. The vasomotion was also not always stable throughout the 15 min of visual stimulation presentation. These characteristics are emphasized in the revised manuscript (Results, page 10, paragraph 1).

Therefore, we would assume that a single or double frequency of the visual stimulation would not always be sufficient to transiently frequency-lock the visually induced vasomotion.

An alternative idea is to test frequencies lower than vasomotion. Vasomotion typically oscillates around a wide range of very low frequencies averaging around 0.1Hz, yet here the authors entrain blood vessel oscillations towards the top end of vasomotion, at 0.25Hz. What would happen if the authors tried synchronizing brain activity with 0.025Hz? Would the natural vasomotion frequency still be there, or would it be gone, dominated by the 0.025Hz entrainment?

We would assume that visually induced vasomotion will not be induced with 0.025 Hz visual stimuli. This is too slow to induce smooth pursuit of the visual stimuli with eye movement. We show that, even if smooth eye pursuit occurs, the visually induced vasomotion may or may not occur (Figure 6F). However, visually induced vasomotion does not largely occur without eye movement. Therefore, the proposed experiment by the reviewer is likely not doable.

Finally, perhaps the authors can see if there is a long-lasting change in natural vasomotion occurring after the animal has been trained to 0.25Hz. For example, is there greater power in the endogenous fluctuation at either 0.25Hz (or perhaps 0.1Hz) with no visual stimulation given but after the animal has been trained? These ideas would be interesting to test and could help clarify whether this is plasticity in functional hyperemia or plasticity in vasomotion.

It should also be mentioned that the frequency-locked vasomotion quickly dissipates as soon as the visual stimulation is halted (Figure 3D upper panel, middle). However, we agree with the reviewer that it would be interesting to see whether the fragmentation of the spontaneous vasomotion is observed less in the Trained or Expert mice compared to the Novice mice, to understand whether the entrainment effect would propagate to the properties of the spontaneous vasomotion.

This issue I have raised is not a fundamental flaw in the paper, it pertains more to the wording, phrasing, and pitch of the paper i.e. is this really entrained and plastic vasomotion? I am skeptical. Nevertheless, I think the authors should try some of these suggestions to better characterize this effect.

We agree that the phrasing used in the original manuscript was rather confusing, as “vasomotion” normally refers to spontaneous vascular movement. However, functional “hyperemia” may not adequately express the phenomenon that we observe either. The phenomenon that we observe is slowly oscillating vasodilation and vasoconstriction that is induced with visual stimuli with a temporal frequency similar to the spontaneously occurring “vasomotion”. This phenomenon is not a direct hyperemia response to the visual stimuli as it requires entrainment and it spreads globally throughout the whole brain. We revised our manuscript to define the terminology that we use.

An important question is if neural activity is entraining the CBF responses. The authors should do one experiment in a pan-neural GCaMP line to test if neural activity in the visual cortex (and other areas captured in the widefield microscope) shows a progressive and gradual synchronization (or not) to the vasomotion responses with training. It is possible to do this through a thinned skull window. This important to know if/how synchronized population neural activity scales with training. Perhaps they will not correlate and there is something more subtle going on.

In our paper, we mainly studied visually induced vasomotion (or visual stimulus-triggered vasomotion). Therefore, visual stimulation must first activate the neurons and, through neurovascular coupling, the initial drive for vasomotion is likely triggered. However, visually induced vasomotion is not observed in novice animals. Therefore, the visually induced vasomotion is not a simple sensory reaction of the vascular in response to neuronal activity in the primary visual cortex.

An important point that should be pointed out is that the neuronal visual response in the primary visual cortex could potentially decrease with repeated visual stimulation presentation as the adaptive movement of the eye should decrease the retinal slip. With repeated training sessions, a more static projection of the presented image will likely be shown to the retina. The neurovascular coupling could be enhanced with increased responsiveness of the vascules and vascular-to-vascular coupling could also be potentiated. This argument is now incorporated in the revised manuscript (Discussions, page 19, paragraph 1).

We agree with the reviewer that, to identify the extent of the neuronal contribution to the vasomotion triggering, whole brain synchronization, and vasomotion entrainment, simultaneous neuronal calcium imaging would be ideal. However, due to the fact that fluorescent Ca2+ indicators expressed in neurons would also be distorted by the “shadow” effect from the vasomotion, exquisite imaging techniques would be required. We recognize this “shadow” effect and we are currently developing methods to take out the “shadow” effect and the intracellular pH fluctuation effect from the fluorescence traces.

The authors nicely show that plasticity in vasomotion coincides with the mouse learning the HOKR task and that as eye movement tracks the stimulus, CBF gets entrained. However, there could also be a stress effect going on in the early trials, and as the mouse gets used to the procedure and stress comes down, the vasomotion entrainment can be seen. It could be the case that the vasomotion process is there on the first trial, but masked by stress-induced effects on neural and/or vascular activity. I did not see anything in the methods about how the mouse was habituated to head restraint. Was the first visual stim trial the first time the mouse was head restrained? If so, there could be a strong stress effect. The authors should address this either by clarifying that habituation to head restraint was done, or by doing a control experiment where each animal receives at least 1week of progressive and gradual head restraint before doing the same HOKR experiment using multiple trials.

We agree with the reviewer that stress could well affect spontaneous vasomotion as well as visually induced vasomotion (or visual stimulus-triggered vasomotion). As the reviewer suggested, we could have compared the habituated and non-habituated mice to the initial visually induced vasomotion response. In addition, whether the experimentally induced increase in stress would interfere with the vasomotion or not could also be studied. With the TexasRed experiments, we observed that tail-vein injection stress appeared to interfere with the HOKR learning process. In the experiments presented in Fig. 3, TexasRed was injected before session 1. Vasomotion entrainment likely progressed with sessions 2 and 3 training. Before session 4, TexasRed was injected again to visualize the vasomotion. The vasomotion was clearly observed in session 4, indicating that the stress induced by tail-vein injection could not interfere with the generation of visually induced vasomotion. This argument is included in the revised manuscript (Discussions, page 20, paragraph 2).

Minor

The first sentence of the introduction requires citations. It is also a somewhat irrelevant comparison to make.

Necessary citation was made in the revised manuscript, as the reviewer suggested. We think that describing how the energy is distributed in the brain would provide one of the most important breakthroughs to the understanding of how efficient information processing in the brain works. Therefore, we would like to keep this introduction.

The third and fourth sentence of the introduction equates vasodilation/vasoconstriction with vasomotion and it is not this simple. Vasomotion is a specific physiological process involving rhythmic changes to artery diameter. Also, the frequency of these slow oscillations needs to be stated. The authors only say they are slower than 10Hz.

The definition of spontaneous vasomotion with indication of typical temporal frequency is described in the revised manuscript, as the reviewer suggested.

More than half of the introduction is describing the paper itself, rather than setting the stage for the findings. The authors need a more thorough account of what is known and what is not known in this area. Some of this information is in the discussion, which should be moved up to the intro.

We have revised the introduction to include the definition of spontaneous vasomotion and visually induced vasomotion or functional hyperemia, as the reviewer suggested.

In the first paragraph of the results section, the authors should state in what way the mice are awake. Are they freely mobile? Are they head-restrained? Are they resting or moving or doing both at different times? This is clarified later but it should come up front as someone reads through the paper.

As the reviewer suggested, we clarified that the experiments were done in awake and head-restrained mice within the first paragraph for the Results section.

The authors say "As shown later, blood vessels on the surface...". There is no need to say "as shown later".

This is deleted as the reviewer suggested.

The use of "full width at 10% maximum" of the Texas red intensity for the diameter measure is a little odd, as it may actually overestimate the diameter, but I see what the authors were trying to do. A full-width half max is standard here and that is likely more appropriate. Also, the line profiles of intensity are not raw data. The authors say the trace is strongly filtered/smoothed. If so, this creates a somewhat artificial platform to make the diameter measurement. The authors should show raw data from a single experiment and make the measurement from that. The raw line profile should look almost square, where a full-width half-max would work well.

Contrary to what the reviewer observed, the raw line profile was not almost square. Even if there were almost no blur in the XY dimension in the optical imaging system, one would not expect to see a square line profile, as the thickness of the vessel increases in the Z dimension towards the center, as this is not a confocal or two-photon microscope image, and an ideal optical section was not created. Therefore, the full-width half-maximum value would definitely be an underestimate of the actual vessel diameter. It may be possible to equate an ideal value for cutoff if we have the 3D point spread function of the imaging. 10% is an arbitrary number but we think 10% is the minimum intensity that we can distinguish from the background intensity fluctuations. We did not attempt to derive the “true” diameter of the vessel and full-width at 10% maximum is just an index of the actual diameter. In most of the manuscript, we only deal with the change of the vessel diameter relative to the basal diameter, therefore, we considered that careful derivation of the absolute diameter estimate is not necessary. This argument is detailed in the Materials and Methods section in the revised manuscript (page 31, paragraph 2).

The raw line profile before filtering is shown overlaid in Figure 1C, as the reviewer suggested.

In Figures 1 and 2, state/label what brain region this is.

The blood vessels between the bregma and lambda on the cortex were observed and described in Figures 1 and 2. This is described in the revised manuscript, as the reviewer suggested.

Can the authors also show what a vein or venule looks like using their quantification method in Figures 1 and 2? This would be a helpful comparison to a static vein.

The methods shown in Figures 1 and 2 would not allow us to distinguish between vein and venule in our study. Methods that allow quantification of the relative blood vessel diameter fluctuation due to spontaneous or visually induced vasomotion activities are shown in Figures 1 and 2. Later in the manuscript, the whole intensity fluctuation of TexasRed or autofluorescence in the brain parenchyma is studied, and in this case, no distinction between vein and venules could be made.

Statements such as this are not necessary: "Later in the manuscript, we will be dealing with vasomotion dynamics observed with the optical fiber photometry methods, in which the blood vessel type under the detection of the fiber could not be identified". Simply talk about this data when you get to it.

We have deleted this statement in this part of the manuscript, as the reviewer suggested.

Same as this, please consider deleting: "Spontaneous vasomotion dynamic differences between different classes of blood vessels would be of interest to study using a more sophisticated in vivo two-photon microscope which we do not own." Just describe the data you have from the methods you have. There is no need to lament.

We deleted this sentence, as the reviewer suggested.

Figure 3 D the light blue boxes showing the time period of visual stimulation physically overlay with the frequency-time spectrograms. They should not overlay with this graph because it makes them more light blue, distorting the figure which also uses light blue in the heat map.

Figure 3D was modified, as the reviewer suggested.

The authors say: "The reason why the vasomotion detected in our system through the intact skull in awake in vivo mice was less periodic was unknown." Yes, but you are imaging an awake mouse. Many spontaneous behaviours such as whisking, grooming, twitching, and struggling will manifest as increased artery diameter. These will be functional hyperemia occurring events on top of rhythmic vasomotion. This can be briefly discussed.

As the reviewer comments, the vasomotion detected in awake mice was likely to be less periodic because the spontaneous animal behavior induces functional hyperemia and interrupts spontaneous vasomotion. This interpretation was included in the revised manuscript (Results, page 8, paragraph 1).

The authors say "extremely tuned" on page 8. They should not use words like "extremely". Perhaps say "more strongly tuned" or equivalent.

We have changed “extremely” to “more strongly”, as the reviewer suggested.

The authors say "First, the Texas Red fluorescence images were Gaussian filtered in the spatial XY dimension to take out the random noise presumably created within the imaging system." It is inadvisable to alter the raw data in this way unless there is a sound reason to do so. If there is random noise this should not affect the Fast Fourier Transform analysis. If there is regular noise caused by instrumentation artefact, which is picked up by the analysis then perhaps this could be filtered out. A static Texas red sample in a vial can be used to determine if there is artefactual noise.

We mainly used the Gaussian filter for better presentation of the imaged data. The TexasRed fluorescence was low in intensity and the acquired images were Gaussian filtered in the spatial XY dimesion to reduce the pixelated noise at the expense of spatial resolution reduction. This filter should not affect the temporal frequency of the observed vasomotion. This is now more clearly indicated in the revised manuscript (Results, page 10, paragraph 2).

There are endogenous fluorescent molecules in cell metabolism that change dynamically to neural activity: NADH, NADPH, and FAD. These are almost certainly a fraction of the auto-fluorescent signal the authors are measuring and it would be expected to see small fluctuations in these metabolites with neural activity. Perhaps this can be discussed, and the authors can likely argue that metabolic signals are much smaller than the change caused by vasodilation.

We found that the autofluorescence signal was phase-shifted in time relative to the vasomotion, which was visualized with TexasRed. This suggests that these autofluorescence signals have an anti-phase “shadow imaging” component and another component that is phase-shifted in time. Glucose and oxygen are likely to be abundantly delivered during the vasodilation phase compared to the vasoconstriction phase of vasomotion. These molecules will trigger cell metabolism and endogenous fluorescent molecules such as NADH, NADPH, and FAD may increase or decrease with a certain delay, which is required for the chemical reactions to occur. Therefore, the concentration fluctuation of these metabolites could lag in time to the changes in the blood flow. It is also expected that these metabolites may fluctuate according to the neuronal activity that triggers visually induced vasomotion or functional hyperemia. These discussions are added in the revised manuscript (Discussions, page 19, paragraph 2).

The authors say "however, we found that, if Texas Red had to be injected before every training session, the mouse did not learn very well." This is interesting. Why do the authors suppose this was the case? Stress from the injection? Or perhaps some deleterious effect on blood vessel function caused by the dye itself? Either way, I think this honest statement should remain. Others need to know about it.

We think that the stress from the injection interferes with the HOKR learning. However, as shown, TexasRed injection after the mouse had learned did not interfere with the eye movement or with the visually induced vasomotion. We do not know whether the injection stress directly interferes with the blood vessel function and affects the plastic vasomotion entrainment. These arguments are now described in the revised manuscript (Discussions, page 20, paragraph 2). The statement above remains as is, as the reviewer suggested.

YCnano50 is a calcium sensor and not really appropriate for the use employed by the authors. They are exciting YFP at 505nm but unless the authors are using a laser line, there is some bandwidth of excitation light that is likely exciting the CFP too which still absorbs light up to ~490nm. Here, calcium signalling may affect the YFP signal. This can be discussed.

Multiband-pass filter (Chroma 69008x with the relevant band of 503 nm / 19.5 nm (FWHM)) was used for direct excitation of YFP. Negligible light is passed below 490 nm. CFP excitation above 490 nm is assumed to be negligible and usually not defined in literature. We assume that with our optical system, fluorescence by direct YFP excitation dominates the effect from the minor CFP excitation effect. We explicitly describe this in the revised manuscript (Materials and Methods, page 28, paragraph 2).

The discussion is interesting but does not actually discuss much of the data or measurements in the paper. Most of the discussion reads more like a topical review, rather than a critical analysis of the effects/measurements and why the authors' interpretations are likely correct. This can be improved.

As the reviewer suggests, we have improved the discussion by starting with the summary of the results (Discussion, page 19, paragraph 1). We also included the possibility of stress affecting visually induced vasomotion (Discussion, page 20, paragraph 2).

Author Response

OVERVIEW OF RESPONSE TO REVIEWS

I thank the three anonymous reviewers for providing well-informed, constructive feedback on the initial version of this manuscript. Based on their comments I will revise the manuscript and hopefully improve it in several ways. I expected a great deal of resistance to the ideas proposed in this model because they break from traditional approaches. One of my goals in developing this model was to argue for a paradigm shift regarding the concept of a “receptive field”. Experimentally, the receptive field is defined as the set of preferred environmental sensory circumstances that cause a neuron to become highly active. Traditional interpretation of receptive fields implicitly assumes that the environmental circumstances that give rise to the receptive field do so in a purely bottom-up fashion (the cell is “receiving” its field), in which case the receptive field specifies the function of the cell. In other words, the receptive field is what the cell does. However, some brain regions (e.g., entorhinal cortex) receive substantial feedback from downstream regions (e.g., hippocampus), and feedback can play an important role in determining the receptive field. As applied to a memory account of MTL, this feedback is memory retrieval and reactivation. Thus, the multifield spatial response of grid cells doesn’t necessarily mean that their function is spatial. Consideration of bottom-up versus top-down signals gives rise to the proposal that the bottom-up preference of many grid cells is some non-spatial attribute even though they exhibit a spatial receptive field owing to retrieval in specific locations.

One thing I will emphasize in a revision is that this model can address findings in the vast literature on learning, memory, and consolidation. The question asked in this study is whether a memory model can also explain the rodent navigation literature. This is not an attempt to provide definitive evidence that this is a better account of the rodent navigation literature. Instead, the goal is to model the rodent navigation literature even though this is a memory model rather than a spatial/navigation model. Nevertheless, within the domain of rodent spatial/navigation, this model makes different predictions/explanations than spatial/navigation models. For instance, this is the only model predicting that many grid cells with spatial receptive fields are non-spatial (see predictions in Box 1). As reviewed in Box 1, this is the only model that can explain why head direction conjunctive grid cells become head direction cells in the absence of hippocampal feedback and it is the only model that can explain why some grid cells are also sensitive to sound frequency (see several other unique explanations in Box 1).

This study is an attempt to unify the spatial/navigation and learning/memory literatures with a relatively simply model. Given the simplicity of the model, there are important findings that the model cannot address -- it is not that the model makes the wrong predictions but rather that it makes no predictions. The role of running speed is one such variable for which the model makes no predictions. Similarly, because the model is a rate-coded model rather than a model of oscillating spiking neurons, it makes no predictions regarding theta oscillations. The model is an account of learning and memory for an adult animal, and it makes no predictions regarding the developmental or evolutionary time course of different cell types. This model contains several purely spatial representations such as border cells, head direction cells, and head direction conjunctive grid cells. In evolution and/or in development, it may be that these purely spatial cell types emerged first, followed by the evolution and/or development of non-spatial cell types. However, this does not invalidate the model. Instead, this is a model for an adult animal that has both episodic memory capabilities and spatial navigation capabilities, irrespective of the order in which these capabilities emerged.

Grid cell models that are purely spatial are agnostic regarding the thousands of findings in the literature on memory, learning, and consolidation whereas this model can potentially unify the learning/memory and spatial/navigation literatures. The reason to prefer this model is parsimony. Rather than needing to develop a theory of memory that is separate from a theory of spatial navigation, it might be possible to address both literatures with a unified account. There are other grid cell models that can explain non-spatial grid-like responses (Mok & Love, 2019; Rodríguez‐Domínguez & Caplan, 2019; Stachenfeld et al., 2017; Wei et al., 2015) and these models may be similarly positioned to explain memory results. However, these models assume that grid cells exhibiting spatial receptive fields serve the function of identifying positions in the environment (i.e., their function is spatial). As such, these models do not explain why most of the input to rodent hippocampus appears to be spatial (these models would need to assume that rodent hippocampus is almost entirely concerned with spatial navigation). This account provides an answer to this conundrum by proposing that grid cells with spatial receptive fields have been misclassified as spatial. Below I give responses to some of the specific comments made by reviewers, grouping these comments by topic:

COMMENTS RELATED TO THE NEED/MOTIVATION FOR THIS MODEL

In a revision, I will clarify that the non-spatial MTL cell types that are routinely found in primate and human studies are fully compatible with this model. The reported simulations are focused on the specific question of how it can be that most mEC and hippocampal cell types in the rodent literature appear to be spatial. It is known that perirhinal cortex is not spatial. However, entorhinal cortex is the gateway to hippocampus. If the hippocampus has the capacity to represent non-spatial memories, it must receive non-spatial input from entorhinal cortex. These simulations suggest that characterization of the rodent mEC cortex as primarily spatial might be incorrect if most grid cells (except perhaps head direction conjunctive grid cells) have been mischaracterized as spatial.

Lateral entorhinal cortex also projects to hippocampus, and one reviewer asks about the distinction between lateral versus medial entorhinal cortex. From this memory perspective, the important question is which part of the entorhinal cortex represents the non-spatial attributes common to the entire recording session, under the assumption that the animal is creating and retrieving memories during recording. If these non-spatial attributes are represented in lateral EC, there would be grid cells in lateral EC (but these are not found). There is evidence that lateral EC cells respond selectively in relation to objects (Deshmukh & Knierim, 2011), but in a typical rodent navigation study there are no objects in the enclosure.

One reviewer asks whether this model is built to explain the existing data or whether the assumptions of this model are made for theoretical reasons. The BVC model (Barry et al., 2006), which is a precursor to this model, is a theoretically efficient representation of space that could support place coding. If the distances to different borders are known, it’s not clear why the MTL also needs the two-dimensional Fourier-like representation provided by grid cells. This gives rise to the proposal that grid cells with spatial receptive fields are serving some function other than representing space. In the proposed model, the precise hexagonal arrangement of grid cells indicates a property that is found everywhere in the enclosure (i.e., a “tiling” of knowledge for where the property can be found). This arrangement arises from the well-documented learning process termed “differentiation” in the memory literature (McClelland & Chappell, 1998; Norman & O’Reilly, 2003; Shiffrin & Steyvers, 1997), which highlights differences between memories to avoid interference and confusion.

CONCERNS RELATED TO LIMITATIONS AND CONFLICTING RESULTS

One reviewer points out that individual grid cells will typically reveal a grid pattern regardless of the environmental circumstances, which, according to this model, indicates that all such circumstances have the same non-spatial attribute. This might seem strange at first, but I suggest that there is a great deal of “sameness” to the environments used in the published rodent navigation experiments. For instance, as far as I’m aware, the animal is never allowed to interact with other animals during spatial navigation recording. Furthermore, the animal is always attached to wires during recording. The internal state of the animal (fear, aloneness, the noise of electronics, etc.) is likely similar across all recording situations and attributes of this internal state are likely represented in the hippocampus as well as in the regions that provide excitatory drive to hippocampus. The claim of this model is that the grid cells are “tagging” different navigation enclosures as places where these things happen (fear, aloneness, electronics, metal floor, no objects, etc.). The interesting question is what happens when the animal is allowed to navigate in a more naturalistic setting that includes varied objects, varied food sources, varied surfaces, other animals, etc. Do grid cells persist in such a naturalistic environment? Or do they lose their regularity, or even become silent, considering that there is no longer a uniformity to the non-spatial attributes? The results of Caswell Barry et al. (2012), demonstrate that the grid pattern expands and becomes less regular in a novel environment. Nevertheless, the novel environment in that study was uncluttered rather than naturalistic. It remains to be seen what will happen with a truly naturalistic environment.

One reviewer asks how this model relates to non-grid multifield cells found in mEC (Diehl et al., 2017; see also the irregularly arranged 3D multifield cells reported by Ginosar et al., 2021). A full explanation of these cells would require a new simulation study. In a revision, I will discuss these cells, which reveal a consistent multifield spatial receptive field and yet the multiple fields are irregular in their arrangement rather than a precise hexagonal lattice. On this memory account, precise hexagonal arrangement of memories is something that occurs when there is a non-spatial attribute found throughout the enclosure. However, in a typical rodent navigation study, there may be some non-spatial attributes that are not found everywhere in the enclosure. For instance, consider the set of locations within the enclosure that afford a particular view of something outside of the enclosure or the set of locations corresponding to remembered episodic events (e.g., memory for the location where the animal first entered the enclosure). For non-spatial characteristics that are found in some locations but not others within in the enclosure, the cells representing those non-spatial attributes should reveal multifield firing at irregular locations, reflecting the subset of locations associated with the non-spatial attribute.

One reviewer suggests that this model cannot explain the finding that grid fields become warped (e.g., grid fields arranged in an ellipse rather than a circle) in the same manner that the enclosure is warped when a wall is moved (Barry et al., 2007). The way in which I would simulate this result would be to assume that the change in the boundary location was too modest to be noticed by the animal. Because the distances are calculated relative to the borders, an unnoticed change in the border would not change the model in terms of the grid field as measured by proportional distances between borders. However, because the real-world Euclidean positions of the border are changed, the grid fields would be changed in terms of real-world coordinates. This is what I was referring to in the paper when I wrote “For instance, perhaps one egocentric/allocentric pair of mEC grid modules is based on head direction (viewpoint) in remembered positions relative to the enclosure borders whereas a different egocentric/allocentric pair is based on head direction in remembered positions relative to landmarks exterior to the enclosure. This might explain why a deformation of the enclosure (moving in one of the walls to form a rectangle rather than a square) caused some of the grid modules but not others to undergo a deformation of the grid pattern in response to the deformation of the enclosure wall (see also Barry et al., 2007). More specifically, if there is one set of non-orthogonal dimensions for enclosure borders and the movement of one wall is too modest as to cause avoid global remapping, this would deform the grid modules based the enclosure border cells. At the same time, if other grid modules are based on exterior properties (e.g., perhaps border cells in relation to the experimental room rather than the enclosure), then those grid modules would be unperturbed by moving the enclosure wall.” Related to the question of enclosure geometry, the irregularity that can emerge in trapezoid shaped enclosures was discussed in the section of the paper that reads “As seen in Figure 12, because all but one of the place cells was exterior when the simulated animal was constrained to a narrow passage, the hippocampal place cell memories were no longer arranged in a hexagonal grid. This disruption of the grid array for narrow passages might explain the finding that the grid pattern (of grid cells) is disrupted in the thin corner of a trapezoid (Krupic et al., 2015) and disrupted when a previously open enclosure is converted to a hairpin maze by insertion of additional walls within the enclosure (Derdikman et al., 2009).”

CONCERNS THAT WILL BE ADDRESSED WITH GREATER CLARIFICATION

One reviewer asks why a cell representing a non-spatial attribute found everywhere in the enclosure would not fire everywhere in the enclosure. In theory, cells could fire constantly. However, in practice, cells habituate and rapidly reduce their firing rate by an order of magnitude when their preferred stimulus is presented without cessation (Abbott et al., 1997; Tsodyks & Markram, 1997). After habituation, the firing rate of the cell fluctuates with minor variation in the strength of the excitatory drive. In other words, habituation allows the cell to become sensitive to changes in the excitatory drive (Huber & O’Reilly, 2003). Thus, if there is stronger top-down memory feedback in some locations as compared to others, the cell will fire at a higher rate in those remembered locations. In brief when faced with constant excitatory drive, the cell accommodates, and becomes sensitive to change in the magnitude of the excitatory drive.

One reviewer asks for greater clarification regarding the simulation result of immediate stability for grid cells but not place cells. In a revision, I will provide a video showing a sped-up birds-eye view of the place cell memories for the 3D simulations that include head direction, showing the manner in which memories tend to linger in some locations more than others as they consolidate. This behavior was explained in the text that reads “Because the non-spatial cell’s grid field reflects on-average memory positions during the recording session (i.e., the locations where the non-spatial attribute is more often remembered, even if the locations of the memories are shifting), the grid fields for the non-spatial are immediately apparent, reflecting the tendency of place cells to linger in some locations as compared to other locations during consolidation. More specifically, the place cells tend to linger at the peaks and troughs of the border cell tuning functions (see the explanation above regarding the tendency of the grid to align with border cell dimensions). By analogy, imagine a time-lapsed birds-eye view of cars traversing the city-block structure of a densely populated city; this on-average view would show a higher density of cars at the cross-street junctions owing to their tendency to become temporarily stuck at stoplights. However, with additional learning and consolidation, the place cells stabilize their positions (e.g., the cars stop traveling), producing a consistent grid field for the head direction conjunctive grid cells.” The text describing why some locations are more “sticky” than others reads “Additional analyses revealed that this tendency to align with border cell dimensions is caused by weight normalization (Step 6 in the pseudocode). Specifically, connection weights cannot be updated above their maximum nor below their minimum allowed values. This results in a slight tendency for consolidated place cell memories to settle at one of the three peak values or three trough values of the sine wave basis set. This “stickiness” at one of 6 peak or trough values for each basis set is very slight and only occurred after many consolidation steps. In terms of biological systems, there is an obvious lower-bound for excitatory connections (i.e., it is not possible to have an excitatory weight connection that is less than zero), but it is not clear if there is an upper-bound. Nevertheless, it is common practice with deep learning models include an upper-bound for connection weights because this reduces overfitting (Srivastava et al., 2014) and there may be similar pressures for biological systems to avoid excessively strong connections.”

One reviewer points out that Border cells are not typically active in the center of enclosure. However, the model can be built without assuming between-border cells (early simulations with the model did not make this assumption). Regarding this issue, the text reads “Unlike the BVC model, the boundary cell representation is sparsely populated using a basis set of three cells for each of the three dimensions (i.e., 9 cells in total), such that for each of the three non-orthogonal orientations, one cell captures one border, another the opposite border, and the third cell captures positions between the opposing borders (Solstad et al., 2008). However, this is not a core assumption, and it is possible to configure the model with border cell configurations that contain two opponent border cells per dimension, without needing to assume that any cells prefer positions between the borders (with the current parameters, the model predicts there will be two border cells for each between-border cell). Similarly, it is possible to configure the model with more than 3 cells for each dimension (i.e., multiple cells representing positions between the borders).” The Solstad paper found a few cells that responded in positions between borders, but perhaps not as many as 1 out of 3 cells, such as this particular model simulation predicts. If the paucity of between-border cells is a crucial data point, the model can be reconfigured with opponent-border cells without any between border cells. The reason that 3 border cells were used rather than 2 opponent border cells was for simplicity. Because 3 head direction cells were used to capture the face-centered cubic packing of memories, the simulation also used 3 border cells per dimensions to allow a common linear sum metric when conjoining dimensions to form memories. If the border dimensions used 2 cells while head direction used 3 cells, a dimensional weighting scheme would be needed to allow this mixing of “apples and oranges” in terms of distances in the 3D space that includes head direction.

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Author Response

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

eLife assessment

This valuable paper presents a thoroughly detailed methodology for mesoscale-imaging of extensive areas of the cortex, either from a top or lateral perspective, in behaving mice. While the examples of scientific results to be derived with this method are in the preliminary stages, they offer promising and stimulating insights. Overall, the method and results presented are convincing and will be of interest to neuroscientists focused on cortical processing in rodents.

Authors’ Response: We thank the reviewers for the helpful and constructive comments. They have helped us plan for significant improvements to our manuscript. Our preliminary response and plans for revision are indicated below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors introduce two preparations for observing large-scale cortical activity in mice during behavior. Alongside this, they present intriguing preliminary findings utilizing these methods. This paper is poised to be an invaluable resource for researchers engaged in extensive cortical recording in behaving mice.

Strengths:

-Comprehensive methodological detailing:

The paper excels in providing an exceptionally detailed description of the methods used. This meticulous documentation includes a step-by-step workflow, complemented by thorough workflow, protocols, and a list of materials in the supplementary materials.

-Minimal movement artifacts:

A notable strength of this study is the remarkably low movement artifacts. To further underscore this achievement, a more robust quantification across all subjects, coupled with benchmarking against established tools (such as those from suite2p), would be beneficial.

Authors’ Response: This is a good suggestion. We have records of the fast-z correction applied by the ScanImage on microscope during acquisition, so we have supplied the online fast-z motion correction .csv files for two example sessions on our GitHub page as supplementary files:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/online_fast_z_correction

These files correspond to Figure S3b (2367_200214_E210_1) and to Figures 5 and 6 (3056_200924_E235_1). These are now also referenced in the main text. See lines ~595, pg 18 and lines ~762, pg 24.

We have also made minor revisions to the main text of the manuscript with clear descriptions of methods that we have found important for the minimization of movement artifacts, such as fully tightening all mounting devices, implanting the cranial window with proper, evenly applied pressure across its entire extent, and mounting the mouse so that it is not too close or far from the surface of the running wheel. See Line ~309, pg 10.

Insightful preliminary data and analysis:

The preliminary data unveiled in the study reveal interesting heterogeneity in the relationships between neural activity and detailed behavioral features, particularly notable in the lateral cortex. This aspect of the findings is intriguing and suggests avenues for further exploration.

Weaknesses:

-Clarification about the extent of the method in the title and text:

The title of the paper, using the term "pan-cortical," along with certain phrases in the text, may inadvertently suggest that both the top and lateral view preparations are utilized in the same set of mice. To avoid confusion, it should be explicitly stated that the authors employ either the dorsal view (which offers limited access to the lateral ventral regions) or the lateral view (which restricts access to the opposite side of the cortex). For instance, in line 545, the phrase "lateral cortex with our dorsal and side mount preparations" should be revised to "lateral cortex with our dorsal or side mount preparations" for greater clarity.

Authors’ Response: We have opted to not change the title of the paper, because we feel that adding the qualifier, “in two preparations,” would add unnecessary complexity. In addition, while the dorsal mount preparation allows for imaging of bilateral dorsal cortex, the side mount preparation does indeed allow for imaging of both dorsal and lateral cortex across the right hemisphere (a bit of contralateral dorsal cortex is also imageable), and the design can be easily “flipped” across a mirror-plane to allow for imaging of left dorsal and lateral cortex. Taken together, we do show preparations that allow for pan-cortical 2-photon imaging.

We do agree that imprecise reference to the two preparations can sometimes lead to confusion. Therefore, we made several small revisions to the manuscript, including at ~line 545, to make it clearer that we used two imaging preparations to generate our combined 2-photon mesoscope dataset, and that each of those two preparations had both benefits and limitations.

-Comparison with existing methods:

A more detailed contrast between this method and other published techniques would add value to the paper. Specifically, the lateral view appears somewhat narrower than that described in Esmaeili et al., 2021; a discussion of this comparison would be useful.

Authors’ Response: The preparation by Esmaeili et al. 2021 has some similarities to, but also differences from, our preparation. Our preliminary reading is that their through-the-skull field of view is approximately the same as our through-the-skull field of view that exists between our first (headpost implantation) and second (window implantation) surgeries for our side mount preparation, although our preparation appears to include more anterior areas both near to and on the contralateral side of the midline. We have compared these preparations more thoroughly in the revised manuscript. (See lines ~278.)

Furthermore, the number of neurons analyzed seems modest compared to recent papers (50k) - elaborating on this aspect could provide important context for the readers.

Authors’ response: With respect to the “modest” number of neurons analyzed (between 2000 and 8000 neurons per session for our dorsal and side mount preparations with medians near 4500; See Fig. S2e) we would like to point out that factors such as use of dual-plane imaging or multiple imaging planes, different mouse lines, use of different duration recording sessions (see our Fig S2c), use of different imaging speeds and resolutions (see our Fig S2d), use of different Suite2p run-time parameters, and inclusion of areas with blood vessels and different neuron cell densities, may all impact the count of total analyzed neurons per session. We now mention these various factors and have made clear that we were not, for the purposes of this paper, trying to maximize neuron count at the expense of other factors such as imaging speed and total spatial FOV extent.

We refer to these issues now briefly in the main text. (See ~line 93, pg 3).

-Discussion of methodological limitations:

The limitations inherent to the method, such as the potential behavioral effects of tilting the mouse's head, are not thoroughly examined. A more comprehensive discussion of these limitations would enhance the paper's balance and depth.

Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this configuration (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript at ~line 235, pg. 7.

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it is possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the Thorlabs mesoscope objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use ultrasound gel instead (which we found to be, to some degree, optically inferior to water), but without the horizontal light shield, light from the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult under these conditions because the camera would need the same optical access angle as the 2-photon objective, or would need to be moved downward toward the air table and rotated up at an angle of 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

-Preliminary nature of results:

The results are at a preliminary stage; for example, the B-soid analysis is based on a single mouse, and the validation data are derived from the training data set.

Authors’ Response: In this methods paper, we have chosen to supply proof of principle examples, without a complete analysis of animal-to-animal variance.

The B-SOiD analysis that we show in Figure 6 is based on a model trained on 80% of the data from four sessions taken from the same mouse, and then tested on all of a single session from that mouse. Initial attempts to train across sessions from different mice were unsuccessful, probably due to differences in behavioral repertoires across mice. However, we have performed extensive tests with B-SOiD and are confident that these sorts of results are reproducible across mice, although we are not prepared to publish these results at this time.

We now clarify these points in the main text at ~line 865, pg 27.

An additional comparison of the results of B-SOiD trained on different numbers of sessions to that of keypoint-MOSEQ (Weinreb et al, 2023, bioRxiv) trained on ~20 sessions can now be found as supplementary material on our GitHub site:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/Figure_SZZ_BSOID_MOSEQ_align.pdf

The discrepancy between the maps in Figures 5e and 6e might indicate that a significant portion of the map represents noise. An analysis of variability across mice and a method to assign significance to these maps would be beneficial.

Authors’ Response: After re-examination of the original analysis output files, we have indeed discovered that some of the Rastermap neuron density maps in Figure 6e were incorrectly aligned with their respective qualitative behaviors due to a discrepancy in file numbering between the images in 6e and the ensembles identified in 6c (each time that Rastermap is run on the same data, at least with the older version available at the time of creation of these figures, the order of the ensembles on the y-axis changes and thus the numbering of the ensembles would change even though the neuron identities within each group stayed the same for a given set of parameters).

This unfortunate panel alignment / graphical display error present in the original reviewed preprint has been fixed in the current, updated figure (i.e. twitch corresponds to Rastermap groups 2 and 3, whisk to group 6, walk to groups 5 and 4, and oscillate to groups 0 and 1), and in the main text at ~line 925, pg 29. We have also changed the figure legend, which also contained accurate but misaligned information, for Figure 6e to reflect this correction.

One can now see that, because the data from both figures is from the same session in the same mouse, as you correctly point out, Fig 5d left (walk and whisk) corresponds roughly to Fig 6e group R7, “walk”, and that Fig 5d right (whisk) corresponds roughly to Fig 6e group R4, “twitch”.

We have double-checked the identity of other CCF map displays of Rastermap neuron density and of mean correlations between neural activity and behavioral primitives in all other figures, and we found no other such alignment or mis-labeling errors.

We have also added a caveat in the main text at ~lines 925-940, pg. 30, pointing out the preliminary nature of these findings, which are shown here as an example of the viability of the methods. Analysis of the variability of Rastermap alignments across sessions is beyond the scope of the current paper, although it is an issue that we hope to address in upcoming analysis papers.

-Analysis details:

More comprehensive details on the analysis would be beneficial for replicability and deeper understanding. For instance, the statement "Rigid and non-rigid motion correction were performed in Suite2p" could be expanded with a brief explanation of the underlying principles, such as phase correlation, to provide readers with a better grasp of the methodologies employed.

Authors’ Response: We added a brief explanation of Suite2p motion correction at ~line 136, pg 4. We have also added additional details concerning CCF / MMM alignment and other analysis issues. In general we cite other papers where possible to avoid repeating details of analysis methods that are already published.

Reviewer #2 (Public Review):

Summary:

The authors present a comprehensive technical overview of the challenging acquisition of large-scale cortical activity, including surgical procedures and custom 3D-printed headbar designs to obtain neural activity from large parts of the dorsal or lateral neocortex. They then describe technical adjustments for stable head fixation, light shielding, and noise insulation in a 2-photon mesoscope and provide a workflow for multisensory mapping and alignment of the obtained large-scale neural data sets in the Allen CCF framework. Lastly, they show different analytical approaches to relate single-cell activity from various cortical areas to spontaneous activity by using visualization and clustering tools, such as Rastermap, PCA-based cell sorting, and B-SOID behavioral motif detection.

Authors’ Response: Thank you for this excellent summary of the scope of our paper.

The study contains a lot of useful technical information that should be of interest to the field. It tackles a timely problem that an increasing number of labs will be facing as recent technical advances allow the activity measurement of an increasing number of neurons across multiple areas in awake mice. Since the acquisition of cortical data with a large field of view in awake animals poses unique experimental challenges, the provided information could be very helpful to promote standard workflows for data acquisition and analysis and push the field forward.

Authors’ Response: We very much support the idea that our work here will contribute to the development of standard workflows across the field including those for multiple approaches to large-scale neural recordings.

Strengths:

The proposed methodology is technically sound and the authors provide convincing data to suggest that they successfully solved various problems, such as motion artifacts or high-frequency noise emissions, during 2-photon imaging. Overall, the authors achieved their goal of demonstrating a comprehensive approach for the imaging of neural data across many cortical areas and providing several examples that demonstrate the validity of their methods and recapitulate and further extend some recent findings in the field.

Weaknesses:

Most of the descriptions are quite focused on a specific acquisition system, the Thorlabs Mesoscope, and the manuscript is in part highly technical making it harder to understand the motivation and reasoning behind some of the proposed implementations. A revised version would benefit from a more general description of common problems and the thought process behind the proposed solutions to broaden the impact of the work and make it more accessible for labs that do not have access to a Thorlabs mesoscope. A better introduction of some of the specific issues would also promote the development of other solutions in labs that are just starting to use similar tools.

Authors’ Response: We have edited the motivations behind the study to clarify the general problems that are being addressed. However, as the 2-photon imaging component of these experiments were performed on a Thorlabs mesoscope, the imaging details necessarily deal specifically with this system.

We briefly compare the methods and results from our Thorlabs system to that of Diesel-2p, another comparable system, based on what we have been able to glean from the literature on its strengths and weaknesses. See ~lines 206-213, pg 6.

Reviewer #3 (Public Review):

Summary

In their manuscript, Vickers and McCormick have demonstrated the potential of leveraging mesoscale two-photon calcium imaging data to unravel complex behavioural motifs in mice. Particularly commendable is their dedication to providing detailed surgical preparations and corresponding design files, a contribution that will greatly benefit the broader neuroscience community as a whole. The quality of the data is high, but it is not clear whether this is available to the community, some datasets should be deposited. More importantly, the authors have acquired activity-clustered neural ensembles at an unprecedented spatial scale to further correlate with high-level behaviour motifs identified by B-SOiD. Such an advancement marks a significant contribution to the field. While the manuscript is comprehensive and the analytical strategy proposed is promising, some technical aspects warrant further clarification. Overall, the authors have presented an invaluable and innovative approach, effectively laying a solid foundation for future research in correlating large-scale neural ensembles with behaviour. The implementation of a custom sound insulator for the scanner is a great idea and should be something implemented by others.

Authors’ Response: Thank you for the kind words.

We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

The data is located here: https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with in-depth analysis papers that are currently in preparation.

This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other. This is described in the methods, but a visual representation would greatly benefit the readers looking to implement something similar.

Authors’ Response: This is an excellent suggestion. We have included a workflow diagram in the revised manuscript, in the form of a 3-part figure, for the methods (a), data collection (b and c), and analysis (d). This supplementary figure is now located on the GitHub page at the following link:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/pancortical_workflow_diagrams.pdf

We now reference this figure on ~lines 190-192, pg 6 of the main text, near the beginning of the Results section.

The authors should cite sources for the claims stated in lines 449-453 and cite the claim of the mouse's hearing threshold mentioned in lines 463.

Authors’ Response: For the claim stated in lines 449-453:

“The unattenuated or native high-frequency background noise generated by the resonant scanner causes stress to both mice and experimenters, and can prevent mice from achieving maximum performance in auditory mapping, spontaneous activity sessions, auditory stimulus detection, and auditory discrimination sessions/tasks”

,we can provide the following references: (i) for mice: Sadananda et al, 2008 (“Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain”, Neuroscience Letters, Vol 435, Issue 1, p 17-23), and (ii) for humans: Fletcher et al, 2018 (“Effects of very high-frequency sound and ultrasound on humans. Part I: Adverse symptoms after exposure to audible very-high frequency sound”, J Acoust Soc A, 144, 2511-2520). We will include these references in the revised paper.

For the claim stated on line 463:

“i.e. below the mouse hearing threshold at 12.5 kHz of roughly 15 dB”

,we can provide the following reference: Zheng et al, 1999 (“Assessment of hearing in 80 inbred strains of mice by ABR threshold analyses”, Vol 130, Issues 1-2, p 94-107).

We have included these two new references in the new, revised version of our paper. Thank you for identifying these citation omissions.

No stats for the results shown in Figure 6e, it would be useful to know which of these neural densities for all areas show a clear statistical significance across all the behaviors.

Authors’ Response: It would be useful if we could provide a statistic similar to what we provide for Fig. S6c and f, in which for each CCF area we compare the observed mean correlation values to a null of 0, or, in this case, the population densities of each Rastermap group within each CCF area to a null value equal to the total number of CCF areas divided by the total number of recorded neurons for that group (i.e. a Rastermap group with 500 neurons evenly distributed across ~30 CCF areas would contain ~17 neurons, or ~3.3% density, per CCF area.) Our current figure legend states the maximums of the scale bar look-up values (reds) for each group, which range from ~8% to 32%.

However, because the data in panel 6e are from a single session and are being provided as an example of our methods and not for the purpose of claiming a specific result at this point, we choose not to report statistics. It is worth pointing out, perhaps, that Rastermap group densities for a given CCF area close to 3.3% are likely not different from chance, and those closer to ~40%, which is our highest density (for area M2 in Rastermap group 7, which corresponds to the qualitative behavior “walk”), are most likely not due to chance. Without analysis of multiple sessions from the same mouse we believe that making a clear statement of significance for this likelihood would be premature.

We now clarify this decision and related considerations in the main text at ~line 920, pg 29.

While I understand that this is a methods paper, it seems like the authors are aware of the literature surrounding large neuronal recordings during mouse behavior. Indeed, in lines 178-179, the authors mention how a significant portion of the variance in neural activity can be attributed to changes in "arousal or self-directed movement even during spontaneous behavior." Why then did the authors not make an attempt at a simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc). These models are straightforward to implement, and indeed it would benefit this work if the model extracts information on par with what is known from the literature.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the current methods paper. We are following up with an in depth analysis of neural activity and corresponding behavior across the cortex during spontaneous and trained behaviors, but this analysis goes well beyond the scope of the present manuscript.

Here, we prefer to present examples of the types of results that can be expected to be obtained using our methods, and how these results compare with those obtained by others in the field.

Specific strengths and weaknesses with areas to improve:

The paper should include an overall cartoon diagram that indicates how the various modules are linked together for the sampling of both behaviour and mesoscale GCAMP. This is a methods paper, but there is no large diagram that shows how all the parts are connected, communicating, and triggering each other.

Authors’ Response: This is an excellent suggestion. We have included a workflow diagram in the revised manuscript, in the form of a 3-part figure, for the methods (a), data collection (b and c), and analysis (c). This supplementary figure is now located on the GitHub page at the following link:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/pancortical_workflow_diagrams.pdf

The paper contains many important results regarding correlations between behaviour and activity motifs on both the cellular and regional scales. There is a lot of data and it is difficult to draw out new concepts. It might be useful for readers to have an overall figure discussing various results and how they are linked to pupil movement and brain activity. A simple linear model that tries to predict the activity of their many thousands of neurons by employing the multitude of regressors at their disposal (pupil, saccades, stimuli, movements, facial changes, etc) may help in this regard.

Authors’ Response: This is an excellent suggestion, but beyond the scope of the present methods paper. Such an analysis is a significant undertaking with such large and heterogeneous datasets, and we provide proof-of-principle data here so that the reader can understand the type of data that one can expect to obtain using our methods. We will provide a more complete analysis of data obtained using our methodology in the near future in another manuscript.

Previously, widefield imaging methods have been employed to describe regional activity motifs that correlate with known intracortical projections. Within the authors' data it would be interesting to perhaps describe how these two different methods are interrelated -they do collect both datasets. Surprisingly, such macroscale patterns are not immediately obvious from the authors' data. Some of this may be related to the scaling of correlation patterns or other factors. Perhaps there still isn't enough data to readily see these and it is too sparse.

Authors’ Response: Unfortunately, we are unable to directly compare 1-photon widefield GCaMP6s activity with mesoscope 2-photon GCaMP6s activity. During widefield data acquisition, animals were stimulated with visual, auditory, or somatosensory stimuli (i.e. “passive sensory stimulation”), while 2-photon mesoscope data collection occurred during spontaneous changes in behavioral state, without sensory stimulation. The suggested comparison is, indeed, an interesting project for the future.

In lines 71-71, the authors described some disadvantages of one-photon widefield imaging including the inability to achieve single-cell resolution. However, this is not true. In recent years, the combination of better surgical preparations, camera sensors, and genetically encoded calcium indicators has enabled the acquisition of single-cell data even using one-photon widefield imaging methods. These methods include miniscopes (Cai et al., 2016), multi-camera arrays (Hope et al., 2023), and spinning disks (Xie et al., 2023).

Cai, Denise J., et al. "A shared neural ensemble links distinct contextual memories encoded close in time." Nature 534.7605 (2016): 115-118.

Hope, James, et al. "Brain-wide neural recordings in mice navigating physical spaces enabled by a cranial exoskeleton." bioRxiv (2023).

Xie, Hao, et al. "Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution." Nature Biomedical Engineering (2023): 1-14.

Authors’ Response: We have corrected these statements and incorporated these and other relevant references. There are advantages and disadvantages to each chosen technique, such as ease of use, field of view, accuracy, and speed. We will reference the papers you mention without an extensive literature review, but we would like to emphasize the following points:

Even the best one-photon imaging techniques typically have ~10-20 micrometer resolution in xy (we image at 5 micrometer resolution for our large FOV configuration, but the xy point-spread function for the Thorlabs mesoscope is 0.61 x 0.61 micrometers in xy with 970 nm excitation) and undefined z-resolution (4.25 micrometers for Thorlabs mesoscope). A coarser resolution increases the likelihood that activity related fluorescence from neighboring cells may contaminate the fluorescence observed from imaged neurons. Reducing the FOV and using sparse expression of the indicator lessens this overlap problem.

We do appreciate these recent advances, however, particularly for use in cases where more rapid imaging is desired over a large field of view (CCD acquisition can be much faster than that of standard 2-photon galvo-galvo or even galvo-resonant scanning, as the Thorlabs mesoscope uses). This being said, there are few currently available genetically encoded Ca2+ sensors that are able to measure fluctuations faster than ~10 Hz, which is a speed achievable on the Thorlabs 2-photon mesoscope with our techniques using the “small, multiple FOV” method (Fig. S2d, e).

We have further clarified our discussion of these issues in the main text at ~lines 76-80, pg 2.

The authors' claim of achieving optical clarity for up to 150 days post-surgery with their modified crystal skull approach is significantly longer than the 8 weeks (approximately 56 days) reported in the original study by Kim et al. (2016). Since surgical preparations are an integral part of the manuscript, it may be helpful to provide more details to address the feasibility and reliability of the preparation in chronic studies. A series of images documenting the progression optical quality of the window would offer valuable insight.

Authors’ Response: As you suggest, we now include brief supplementary material demonstrating the changes in the window preparation that we observed over the prolonged time periods of our study, for both the dorsal and side mount preparations. The following link to this material is now referenced at ~line 287, pg 9, and at the end of Fig S1:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/window_preparation_stability.pdf

We have also included brief additional details in the main text that we found were useful for facilitating long term use of these preparations. These are located at ~line 287-290, pg 9.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Sharing raw data and code:

I strongly encourage sharing some of the raw data from your experiments and all the code used for data analysis (e.g. in a github repository). This would help the reader evaluate data quality, and reproduce your results.

Authors’ Response: We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

The data is located here: https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

Our existing GitHub repository, already referenced in the paper, is located here:

https://github.com/vickerse1/mesoscope_spontaneous

We have added an additional reference in the main text to the existence of these publicly available resources, including the appropriate links, located at ~lines 190-200, pg 6.

(2) Use of proprietary software:

The reliance on proprietary tools like LabView and Matlab could be a limitation for some researchers, given the associated costs and accessibility issues. If possible, consider incorporating or suggesting alternatives that are open-source, to make your methodology more accessible to a broader range of researchers, including those with limited resources.

Authors’ Response: We are reluctant to recommend open source software that we have not thoroughly tested ourselves. However, we will mention, when appropriate, possible options for the reader to consider.

Although LabView is proprietary and can be difficult to code, it is particularly useful when used in combination with National Instruments hardware. ScanImage in use with the Thorlabs mesoscope uses National Instruments hardware, and it is convenient to maintain hardware standards across the integrated rig/experimental system. Labview is also useful because it comes with a huge library of device drivers that makes addition of new hardware from basically any source very convenient.

That being said, there are open source alternatives that could conceivably be used to replace parts of our system. One example is AutoPilot (author: Jonny Saunders), for control of behavioral data acquisition: https://open-neuroscience.com/post/autopilot/.

We are not aware of an alternative to Matlab for control of ScanImage, which is the supported control software for the ThorLabs 2-photon mesoscope.

Most of our processing and analysis code (see GitHub page: https://github.com/vickerse1/mesoscope_spontaneous) is in Python, but some of the code that we currently use remains in Matlab form. Certainly, this could be re-written as Python code. However, we feel like this is outside the scope of the current paper. We have provided commenting to all code in an attempt to aid users in translating it to other languages, if they so desire.

(3) Quantifying the effect of tilted head:

To address the potential impact of tilting the mouse's head on your findings, a quantitative analysis of any systematic differences in the behavior (e.g. Bsoid motifs) could be illuminating.

Authors’ Response: We have performed DeepLabCut analysis of all sessions from both preparations, across several iterations with different parameters, to extract pose estimates, and we have also performed BSOiD of these sessions. We did not find any obvious qualitative differences in the number of behavioral motifs identified, the dwell times of these motifs, and similar issues, relating to the issue of tilting of the mouse’s head in the side mount preparation. We also did not find any obvious differences in the relative frequencies of high level qualitative behaviors, such as the ones referred to in Fig. 6, between the two preparations.

Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this configuration (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript. (See ~line 235, pg. 7)

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the Thorlabs mesoscope objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

(4) Clarification in the discussion section:

The paragraph titled "Advantages and disadvantages of our approach" seems to diverge into discussing future directions, rather than focusing on the intended topic. I suggest revisiting this section to ensure that it accurately reflects the strengths and limitations of your approach.

Authors’ Response: We agree with the reviewer that this section included several potential next steps or solutions for each advantage and disadvantage, which the reviewer refers to as “future directions” and are thus arguably beyond the scope of this section. Therefore we have retitled this section as, “Advantages and disadvantages of our approach (with potential solutions):”.

Although we believe this to be a logical organization, and we already include a section focused purely on future directions in the Discussion section, we have refocused each paragraph of the advantages/disadvantages subsection to concentrate on the advantages and disadvantages per se. In addition, we have made minor changes to the “future directions” section to make it more succinct and practical. These changes can be found at lines ~1016-1077, pg 33-34.

Reviewer #2 (Recommendations For The Authors):

Below are some more detailed points that will hopefully help to further improve the quality and scope of the manuscript.

• While it is certainly favorable for many questions to measure large-scale activity from many brain regions, the introduction appears to suggest that this is a prerequisite to understanding multimodal decision-making. This is based on the argument that combining multiple recordings with movement indicators will 'necessarily obscure the true spatial correlation structures'. However, I don't understand why this is the case or what is meant by 'true spatial correlation structures'. Aren't there many earlier studies that provided important insights from individual cortical areas? It would be helpful to improve the writing to make this argument clearer.

Authors’ Response: The reviewer makes an excellent point and we have re-worded the manuscript appropriately, to reflect the following clarifications. These changes can be found at ~lines 58-71, pg. 2.

We believe you are referring to the following passage from the introduction:

“Furthermore, the arousal dependence of membrane potential across cortical areas has been shown to be diverse and predictable by a temporally filtered readout of pupil diameter and walking speed (Shimoaka et al, 2018). This makes simultaneous recording of multiple cortical areas essential for comparison of the dependence of their neural activity on arousal/movement, because combining multiple recording sessions with pupil dilations and walking bouts of different durations will necessarily obscure the true spatial correlation structures.”

Here, we do not mean to imply that earlier studies of individual cortical areas are of no value. This argument is provided as an example, of which there are others, of the idea that, for sequences or distributed encoding schemes that simultaneously span many cortical areas that are too far apart to be simultaneously imaged under conventional 2-photon imaging, or are too sparse to be discovered with 1-photon widefield imaging, there are some advantages of our new methods over conventional imaging methods that will allow for truly novel scientific analyses and insights.

The general idea of the present example, based on the findings of Shimoaka et al, 2018, is that it is not possible to directly combine and/or compare the correlations between behavior and neural activity across regions that were imaged in separate sessions, because the correlations between behavior and neural activity in each region appear to depend on the exact time since the behavior began (Shimoaka et al, 2018), in a manner that differs across regions. So, for example, if one were to record from visual cortex in one session with mostly brief walk bouts, and then from somatosensory cortex in a second session with mostly long walk bouts, any inferred difference between the encoding of walk speed in neural activity between the two areas would run the risk of being contaminated by the “temporal filtering” effect shown in Shimoaka et al, 2018. However, this would not be the case in our recordings, because the distribution of behavior durations corresponding to our recorded neural activity across areas will be exactly the same, because they were recorded simultaneously.

• The text describes different timescales of neural activity but is an imaging rate of 3 Hz fast enough to be seen as operating at the temporal dynamics of the behavior? It appears to me that the sampling rate will impose a hard limit on the speed of correlations that can be observed across regions. While this might be appropriate for relatively slow behaviors and spontaneous fluctuations in arousal, sensory processing and decision formation likely operate on faster time scales below 100ms which would even be problematic at 10 Hz which is proposed as the ideal imaging speed in the manuscript.

Authors’ Response: Imaging rate is always a concern and the limitations of this have been discussed in other manuscripts. We will remind the reader of these limitations, which must always be kept in mind when interpreting fluorescence based neural activity data.

Previous studies imaging on a comparable yet more limited spatial scale (Stringer et al, 2019) used an imaging speed of ~1 Hz. With this in view, our work represents an advance both in spatial extent of imaged cortex and in imaging speed. Specifically, we believe that ~1 Hz imaging may be sufficient to capture flip/flop type transitions between low and high arousal states that persist in general for seconds to tens of seconds, and that ~3-5 Hz imaging likely provides additional information about encoding of spontaneous movements and behavioral syllables/motifs.

Indeed, even 10 Hz imaging would not be fast enough to capture the detailed dynamics of sensory processing and decision formation, although these speeds are likely sufficient to capture “stable” encodings of sensory representations and decisions that must be maintained during a task, for example with delayed match-to-sample tasks.

In general we are further developing our preparations to allow us to perform simultaneous widefield imaging and Neuropixels recordings, and to perform simultaneous 1.2 x 1.2 mm 2-photon imaging and visually guided patch clamp recordings.

Both of these techniques will allow us to combine information across both the slow and fast timescales that you refer to in your question.

We have clarified these points in the Introduction and Discussion sections, at ~lines ~93-105, pg 3, and ~lines 979-983, pg 31 and ~lines 1039-1045, pg 33, respectively.

• The dorsal mount is very close to the crystal skull paper and it was ultimately not clear to me if there are still important differences aside from the headbar design that a reader should be aware of. If they exist, it would be helpful to make these distinctions a bit clearer. Also, the sea shell implants from Ghanbari et al in 2019 would be an important additional reference here.

Authors’ Response: We have added brief references to these issues in our revised manuscript at ~lines 89-97, pg 3:

Although our dorsal mount preparation is based on the “crystal skull paper” (Kim et al, 2016), which we reference, the addition of a novel 3-D printable titanium headpost, support arms, light shields, and modifications to the surgical protocols and CCF alignment represent significant advances that made this preparation useable for pan-cortical imaging using the Thorlabs mesoscope. In fact, we were in direct communication with Cris Niell, a UO professor and co-author on the original Kim et al, 2016 paper, during the initial development of our preparation, and he and members of his lab consulted with us in an ongoing manner to learn from our successful headpost and other hardware developments. Furthermore, all of our innovations for data acquisition, imaging, and analysis apply equally to both our dorsal mount and side mount preparations.

Thank you for mentioning the Ghanbari et al, 2019 paper on the transparent polymer skull method, “See Shells.” We were in fact not aware of this study. However, it should be noted that their preparation seems to, like the crystal skull preparation and our dorsal mount preparation, be limited to bilateral dorsal cortex and not to include, as does our cranial window side mount preparation and the through-the-skull widefield preparation of Esmaeili et al, 2021, a fuller range of lateral cortical areas, including primary auditory cortex.

• When using the lateral mount, rotating the objective, rather than the animal, appears to be preferable to reduce the stress on the animal. I also worry that the rather severe head tilt could be an issue when training animals in more complex behaviors and would introduce an asymmetry between the hemispheres due to the tilted body position. Is there a strong reason why the authors used water instead of an imaging gel to resolve the issue with the meniscus?

Authors’ Response: Our mice readily adapted to the 22.5 degree head tilt and learned to perform 2-alternative forced choice (2-AFC) auditory and visual tasks in this situation (Hulsey et al, 2024; Cell Reports). The advantages and limitations of such a rotation of the mouse, and possible ways to alleviate these limitations, as detailed in the following paragraphs, are now discussed more thoroughly in the revised manuscript. (See ~line 235, pg. 7)

One can look at Supplementary Movie 1 for examples of the relatively similar behavior between the dorsal mount (not rotated) and side mount (rotated) preparations. We do not have behavioral data from mice that were placed in both configurations. Our preliminary comparisons across mice indicates that side and dorsal mount mice show similar behavioral variability. We have added brief additional mention of these considerations on ~lines 235-250, pg 7.

It was in general important to make sure that the distance between the wheel and all four limbs was similar for both preparations. In particular, careful attention must be paid to the positioning of the front limbs in the side mount mice so that they are not too high off the wheel. This can be accomplished by a slight forward angling of the left support arm for side mount mice.

Although it would in principle be nearly possible to image the side mount preparation in the same optical configuration that we do without rotating the mouse, by rotating the objective 20 degrees to the right of vertical, we found that the last 2-3 degrees of missing rotation (our preparation is rotated 22.5 degrees left, which is more than the full available 20 degrees rotation of the objective), along with several other factors, made this undesirable. First, it was very difficult to image auditory areas without the additional flexibility to rotate the objective more laterally. Second, it was difficult or impossible to attach the horizontal light shield and to establish a water meniscus with the objective fully rotated. One could use gel instead (which we found to be optically inferior to water), but without the horizontal light shield, the UV and IR LEDs can reach the PMTs via the objective and contaminate the image or cause tripping of the PMT. Third, imaging the right pupil and face of the mouse is difficult to impossible under these conditions because the camera would need the same optical access angle as the objective, or would need to be moved down toward the air table and rotated up 20 degrees, in which case its view would be blocked by the running wheel and other objects mounted on the air table.

• In parts, the description of the methods is very specific to the Thorlabs mesoscope which makes it harder to understand the general design choices and challenges for readers that are unfamiliar with that system. Since the Mesoscope is very expensive and therefore unavailable to many labs in the field, I think it would increase the reach of the manuscript to adjust the writing to be less specific for that system but instead provide general guidance that could also be helpful for other systems. For example (but not exclusively) lines 231-234 or lines 371 and below are very Thorlabs-specific.

Authors’ Response: We have revised the manuscript so that it is more generally applicable to mesoscopic methods.

We will make revisions as you suggest where possible, although we have limited experience with the other imaging systems that we believe you are referring to. However, please note that we already mentioned at least one other comparable system in the original eLife reviewed pre-print (Diesel 2p, line 209; Yu and Smith, 2021).

Here are a couple of examples of how we have broadened our description:

(1) On lines ~231-234, pg 7, we write:

“However, if needed, the objective of the Thorlabs mesoscope may be rotated laterally up to +20 degrees for direct access to more ventral cortical areas, for example if one wants to use a smaller, flat cortical window that requires the objective to be positioned orthogonally to the target region.”

Here have modified this to indicate that one may in general rotate their objective lens if their system allows it. Some systems, such as the Thorlabs Bergamo microscope and the Sutter MOM system, allow more than 20 degrees of rotation.

(2) On line ~371, pg 11, we write:

“This technique required several modifications of the auxiliary light-paths of the Thorlabs mesoscope”

Here, we have changed the writing to be more general such as “may require…of one’s microscope.”

Thank you for these valuable suggestions.

• Lines 287-299: Could the authors quantify the variation in imaging depth, for example by quantifying to which extent the imaging depth has to be adjusted to obtain the position of the cortical surface across cortical areas? Given that curvature is a significant challenge in this preparation this would be useful information and could either show that this issue is largely resolved or to what extent it might still be a concern for the interpretation of the obtained results. How large were the required nominal corrections across imaging sites?

Authors’ Response: This information was provided previously (lines 297-299):

“In cases where we imaged multiple small ROIs, nominal imaging depth was adjusted in an attempt to maintain a constant relative cortical layer depth (i.e. depth below the pial surface; ~200 micrometer offset due to brain curvature over 2.5 mm of mediolateral distance, symmetric across the center axis of the window).”

This statement is based on a qualitative assessment of cortical depth based on neuron size and shape, the density of neurons in a given volume of cortex, the size and shape of blood vessels, and known cortical layer depths across regions. A ground-truth measurement of this depth error is beyond the scope of the present study. However, we do specify the type of glass, thickness, and curvature that we use, and the field curvature characterization of the Thorlabs mesoscope is given in Fig. 6 of the Sofroniew et al, 2016 eLife paper.

In addition, we have provided some documentation of online fast-z correction parameters on our GitHub page at:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/online_fast_z_correction

,and some additional relevant documentation can be found in our publicly available data repository on FigShare+ at: https://doi.org/10.25452/figshare.plus.c.7052513

• Given the size of the implant and the subsequent work attachments, I wonder to which extent the field of view of the animal is obstructed. Did the authors perform receptive field mapping or some other technique that can estimate the size of the animals' remaining field of view?

Authors’ Response: The left eye is pointed down ~22.5 degrees, but we position the mouse near the left edge of the wheel to minimize the degree to which this limits their field of view. One may view our Fig. 1 and Suppl Movies 1 and 6 to see that the eyes on the left and right sides are unobstructed by the headpost, light shields, and support arms. However, other components of the experimental setup, such as the speaker, cameras, etc. can restrict a few small portions of the visual field, depending on their exact positioning.

The facts that mice responded to left side visual stimuli in preliminary recordings during our multimodal 2-AFC task, and that the unobstructed left and right camera views, along with pupillometry recordings, showed that a significant portion of the mouse’s field of view, from either side, remains intact in our preparation.

We have clarified these points in the text at ~lines 344-346, pg. 11.

• Line 361: What does movie S7 show in this context? The movie seems to emphasize that the observed calcium dynamics are not driven by movement dynamics but it is not clear to me how this relates to the stimulation of PV neurons. The neural dynamics in the example cell are also not very clear. It would be helpful if this paragraph would contain some introduction/motivation for the optogenetic stimulation as it comes a bit out of the blue.

Authors’ Response: This result was presented for two reasons.

First, we showed it as a control for movement artifacts, since inhibition of neural activity enhances the relative prominence of non-activity dependent fluorescence that is used to examine the amplitude of movement-related changes in non-activity dependent fluorescence (e.g. movement artifacts). We have included a reference to this point at ~lines 587-588, pg 18.

Second, we showed it as a demonstration of how one may combine optogenetics with imaging in mesoscopic 2-P imaging. References to this point were already present in the original version of the manuscript (the eLife “ reviewed preprint”).

• Lines 362-370: This paragraph and some of the following text are quite technical and would benefit from a better description and motivation of the general workflow. I have trouble following what exactly is done here. Are the authors using an online method to identify the CCF location of the 2p imaging based on the vessel pattern? Why is it important to do this during the experiment? Wouldn't it be sufficient to identify the areas of interest based on the vessel pattern beforehand and then adjust the 2p acquisition accordingly? Why are they using a dial, shutter, and foot pedal and how does this relate to the working distance of the objective? Does the 'standardized cortical map' refer to the Allen common coordinate framework?

Authors’ Response: We have revised this section to make it more clear.

Currently, the general introduction to this section appears in lines 349-361. Starting in line 362, we currently present the technical considerations needed to implement the overall goals stated in that first paragraph of this section.

In general we use a post-hoc analysis step to confirm the location of neurons recorded with 2-photon imaging. We use “online” juxtaposition of the multimodal map image with overlaid CCF with the 2-photon image by opening these two images next to each other on the ScanImage computer and matching the vasculature patterns “by eye”. We have made this more clear in the text so that the interested reader can more readily implement our methods.

By use of the phrase “standardized cortical map” in this context, we meant to point out that we had not decided a priori to use the Allen CCF v3.0 when we started working on these issues.

• Does Fig. 2c show an example of the online alignment between widefield and 2p data? I was confused here since the use of suite2p suggests that this was done post-recording. I generally didn't understand why the user needed to switch back and forth between the two modes. Doesn't the 2p image show the vessels already? Also, why was an additional motorized dichroic to switch between widefield and 2p view needed? Isn't this the standard in most microscopes (including the Thorlabs scopes)?

Authors’ Response: We have explained this methodology more clearly in the revised manuscript, both at ~lines 485-500, pg 15-16, and ~lines 534-540, pg 17.

The motorized dichroic we used replaced the motorized mirror that comes with the Thorlabs mesoscope. We switched to a dichroic to allow for near-simultaneous optogenetic stimulation with 470 nm blue light and 2-photon imaging, so that we would not have to move the mirror back and forth during live data acquisition (it takes a few seconds and makes an audible noise that we wanted to avoid).

Figure 2c shows an overview of our two step “offline” alignment process. The image at the right in the bottom row labeled “2” is a map of recorded neurons from suite2p, determined post-hoc or after imaging. In Fig. 2d we show what the CCF map looks like when it’s overlaid on the neurons from a single suite2p session, using our alignment techniques. Indeed, this image is created post-hoc and not during imaging. In practice, “online” during imaging, we would have the image at left in the bottom row of Fig. 2c (i.e. the multimodal map image overlaid onto an image of the vasculature also acquired on the widefield rig, with the 22.5 degree rotated CCF map aligned to it based on the location of sensory responses) rotated 90 degrees to the left and flipped over a horizontal mirror plane so that its alignment matches that of the “online” 2-photon acquisition image and is zoomed to the same scale factor. Then, we would navigate based on vasculature patterns “by-eye” to the desired CCF areas, and confirm our successful 2-photon targeting of predetermined regions with our post-hoc analysis.

• Why is the widefield imaging done through the skull under anesthesia? Would it not be easier to image through the final window when mice have recovered? Is the mapping needed for accurate window placement?

Authors’ Response: The headpost and window surgeries are done 3-7 days apart to increase success rate and modularize the workflow. Multimodal mapping by widefield imaging is done through the skull between these two surgeries for two major reasons. First, to make efficient use of the time between surgeries. Second, to allow us to compare the multimodal maps to skull landmarks, such as bregma and lambda, for improved alignment to the CCF.

Anesthesia was applied to prevent state changes and movements of the mouse, which can produce large, undesired effects on neural responses in primary sensory cortices in the context of these mapping experiments. We sometimes re-imaged multimodal maps on the widefield microscope through the window, roughly every 30-60 days or whenever/if significant changes in vasculature pattern became apparent.

We have clarified these points in the main text at ~lines 510-522, pg 20-21, and we added a link to our new supplementary material documenting the changes observed in the window preparation over time:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/window_preparation_stability.pdf

Thank you for these questions.

• Lines 445 and below: Reducing the noise from resonant scanners is also very relevant for many other 2p experiments so it would be helpful to provide more general guidance on how to resolve this problem. Is the provided solution only applicable to the Thorlabs mesoscope? How hard would it be to adjust the authors' noise shield to other microscopes? I generally did not find many additional details on the Github repo and think readers would benefit from a more general explanation here.

Authors’ Response: Our revised Github repository has been modified to include more details, including both diagrams and text descriptions of the sound baffle, respectively:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_for_noise_reduction_on_resonant_scanner_devices.pdf

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_methodology_summary.pdf

However, we can not presently disclose our confidential provisional patent application. Complete design information will likely be available in early 2025 when our full utility patent application is filed.

With respect to your question, yes, this technique is adaptable to any resonant scanner, or, for that matter, any complicated 3D surface that emits sound. We first 3D scan the surface, and then we reverse engineer a solid that fully encapsulates the surface and can be easily assembled in parts with bolts and interior foam that allow for a tight fit, in order to nearly completely block all emitted sound.

It is this adaptability that has prompted us to apply for a full patent, as we believe this technique will be quite valuable as it may apply to a potentially large number of applications, starting with 2-photon resonant scanners but possibly moving on to other devices that emit unwanted sound.

• Does line 458 suggest that the authors had to perform a 3D scan of the components to create the noise reduction shield? If so, how was this done? I don't understand the connection between 3D scanning and printing that is mentioned in lines 464-466.

Authors’ Response: We do not want to release full details of the methodology until the full utility patent application has been submitted. However, we have now included a simplified text description of the process on our GitHub page and included a corresponding link in the main text:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/resonant_scanner_baffle/closed_cell_honeycomb_baffle_methodology_summary.pdf

We also clarified in the main text, at the location that you indicate, why the 3D scanning is a critical part of our novel 3D-design, printing, and assembly protocol.

• Lines 468 and below: Why is it important to align single-cell data to cortical areas 'directly on the 2-photon microscope'? Is this different from the alignment discussed in the paragraph above? Why not focus on data interpretation after data acquisition? I understand the need to align neural data to cortical areas in general, I'm just confused about the 'on the fly' aspect here and why it seems to be broken out into two separate paragraphs. It seems as if the text in line 485 and below could also be placed earlier in the text to improve clarity.

Authors’ Response: Here by “such mapping is not routinely possible directly on the 2-photon mesoscope” what we mean is that it is not possible to do multimodal mapping directly on the mesoscope - it needs to be done on the widefield imaging rig (a separate microscope). Then, the CCF is mapped onto the widefield multimodal map, which is overlaid on an image of the vasculature (and sometimes also the skull) that was also acquired on the widefield imaging rig, and the vasculature is used as a sort of Rosetta Stone to co-align the 2-photon image to the multimodal map and then, by a sort of commutative property of alignment, to the CCF, so that each individual neuron in the 2-photon image can be assigned a unique CCF area name and numerical identifier for subsequent analysis.

We have clarified this in the text, thank you.

The Python code for aligning the widefield and 2-photon vessel images would also be of great value for regular 2p users. It would strongly improve the impact of the paper if the repository were better documented and the code would be equally applicable for alignment of imaging data with smaller cranial windows.

Authors’ Response: All of the code for multimodal map, CCF, and 2-photon image alignment is, in fact, already present on the GitHub page. We have made some minor improvements to the documentation, and readers are more than welcome to contact us for additional help.

Specifically, the alignment you refer to starts in cell #32 of the meso_pre_proc_1.ipynb notebook. In general the notebooks are meant to be run sequentially, starting with cell #1 of meso_pre_proc_1, then going to the next cell etc…, then moving to meso_pre_proc_2, etc… The purpose of each cell is labeled at the top of the cell in a comment.

We now include a cleaned, abridged version of the meso_pre_proc_1.pynb notebook that contains only the steps needed for alignment, and included a direct link to this notebook in the main text:

https://github.com/vickerse1/mesoscope_spontaneous/blob/main/python_code/mesoscope_preprocess_MMM_creation.ipynb

Rotated CCF maps are in the CCF map rotation folder, in subfolders corresponding to the angle of rotation.

Multimodal map creation involves use of the SensoryMapping_Vickers_Jun2520.m script in the Matlab folder.

We updated the main text to clarify these points and included direct links to scripts relevant to each processing step.

• Figure 4a: I found it hard to see much of the structure in the Rastermap projection with the viridis colormap - perhaps also because of a red-green color vision impairment. Correspondingly, I had trouble seeing some of the structure that is described in the text or clearer differences between the neuron sortings to PC1 and PC2. Is the point of these panels to show that both PCs identify movement-aligned dynamics or is the argument that they isolate different movement-related response patterns? Using a grayscale colormap as used by Stringer et al might help to see more of the many fine details in the data.

Authors’ Response: In Fig. 4a the viridis color range is from blue to green to yellow, as indicated in the horizontal scale bar at bottom right. There is no red color in these Rastermap projections, or in any others in this paper. Furthermore, the expanded Rastermap insets in Figs. S4 and S5 provide additional detailed information that may not be clear in Fig 4a and Fig 5a.

We prefer, therefore, not to change these colormaps, which we use throughout the paper.

We have provided grayscale png versions of all figures on our GitHub page:

https://github.com/vickerse1/mesoscope_spontaneous/tree/main/grayscale_figures

In Fig 4a the point of showing both the PC1 and PC2 panels is to demonstrate that they appear to correspond to different aspects of movement (PC1 more to transient walking, both ON and OFF, and PC2 to whisking and sustained ON walk/whisk), and to exhibit differential ability to identify neurons with positive and negative correlations to arousal (PC1 finds both, both PC2 seems to find only the ON neurons).

We now clarify this in the text at ~lines 696-710, pg 22.

• I find panel 6a a bit too hard to read because the identification and interpretation of the different motifs in the different qualitative episodes is challenging. For example, the text mentions flickering into motif 13 during walk but the majority of that sequence appears to be shaped by what I believe to be motif 11. Motif 11 also occurs prominently in the oscillate state and the unnamed sequence on the left. Is this meaningful or is the emphasis here on times of change between behavioral motifs? The concept of motif flickering should be better explained here.

Authors’ Response: Here motif 13 corresponds to a syllable that might best be termed “symmetric and ready stance”. This tends to occur just before and after walking, but also during rhythmic wheel balancing movements that appear during the “oscillate” behavior.

The intent of Fig. 6a is to show that each qualitatively identified behavior (twitch, whisk, walk, and oscillate) corresponds to a period during which a subset of BSOiD motifs flicker back and forth, and that the identity of motifs in this subset differs across the identified qualitative behaviors. This is not to say that a particular motif occurs only during a single identified qualitative behavior. Admittedly, the identification of these qualitative behaviors is a bit arbitrary - future versions of BSOiD (e.g. ASOiD) in fact combine supervised (i.e. arbitrary, top down) and unsupervised (i.e. algorithmic, objective, bottom-up) methods of behavior segmentation in attempt to more reliably identify and label behaviors.

Flickering appears to be a property of motif transitions in raw BSOiD outputs that have not been temporally smoothed. If one watches the raw video, it seems that this may in fact be an accurate reflection of the manner in which behaviors unfold through time. Each behavior could be thought of, to use terminology from MOSEQ (B Datta), as a series of syllables strung together to make a phrase or sentence. Syllables can repeat over either fast or slow timescales, and may be shared across distinct words and sentences although the order and frequency of their recurrence will likely differ.

We have clarified these points in the main text at ~lines 917-923, pg 29, and we added motif 13 to the list of motifs for the qualitative behavior labeled “oscillate” in Fig. 6a.

• Lines 997-998: I don't understand this argument. Why does the existence of different temporal dynamics make imaging multiple areas 'one of the keys to potentially understanding the nature of their neuronal activity'?

Authors’ Response: We believe this may be an important point, that comparisons of neurobehavioral alignment across cortical areas cannot be performed by pooling sessions that contain different distributions of dwell times for different behaviors, if in fact that dependence of neural activity on behavior depends on the exact elapsed time since the beginning of the current behavioral “bout”. Again, other reasons that imaging many areas simultaneously would provide a unique advantage over imaging smaller areas one at a time and attempting to pool data across sessions would include the identification of sequences or neural ensembles that span many areas across large distances, or the understanding of distributed coding of behavior (an issue we explore in an upcoming paper).

We have clarified these points at the location in the Discussion that you have identified. Thank you for your questions and suggestions.

Minor

Line 41: What is the difference between decision, choice, and response periods?

Authors’ Response: This now reads “...temporal separation of periods during which cortical activity is dominated by activity related to stimulus representation, choice/decision, maintenance of choice, and response or implementation of that choice.”

Line 202: What does ambulatory mean in this context?

Authors’ Response: Here we mean that the mice are able to walk freely on the wheel. In fact they do not actually move through space, so we have changed this to read “able to walk freely on a wheel, as shown in Figs. 1a and 1b”.

Is there a reason why 4 mounting posts were used for the dorsal mount but only 1 post was sufficient for the lateral mount?

Authors’ Response: Here, we assume you mean 2 posts for the side mount and 4 posts for the dorsal mount.

In general our idea was to use as many posts as possible to provide maximum stability of the preparations and minimize movement artifacts during 2-photon imaging. However, the design of the side mount headpost precluded the straight-forward or easy addition of a right oriented, second arm to its lateral/ventral rim - this would have blocked access of both the 2-photon objective and the right face camera. In the dorsal mount, the symmetrical headpost arms are positioned further back (i.e. posterior), so that the left and right face cameras are not obscured.

When we created the side mount preparation, we discovered that the 2 vertical 1” support posts were sufficient to provide adequate stability of the preparation and minimize 2-photon imaging movement artifacts. The side mount used two attachment screws on the left side of the headpost, instead of the one screw per side used in the dorsal mount preparation.

We have included these points/clarifications in the main text at ~lines 217-230, pg 7.

Figure S1g appears to be mislabeled.

Authors’ Response: Yes, on the figure itself that panel was mislabeled as “f” in the original eLife reviewed preprint. We have changed this to read “g”.

Line 349 and below: Why is the method called pseudo-widefield imaging?

Authors’ Response: On the mesoscope, broad spectrum fluorescent light is passed through a series of excitation and emission filters that, based on a series of tests that we performed, allow both reflected blue light and epifluorescence emitted (i.e. Stokes-shifted) green light to reach the CCD camera for detection. Furthermore, the CCD camera (Thorlabs) has a much smaller detector chip than that of the other widefield cameras that we use (RedShirt Imaging and PCO), and we use it to image at an acquisition speed of around 10 Hz maximum, instead of ~30-50 Hz, which is our normal widefield imaging acquisition speed (it also has a slower readout than what we would consider to be a standard or “real” 1-photon widefield imaging camera).

For these 3 reasons we refer to this as “pseudo-widefield” imaging. We would not use this for sensory activity mapping on the mesoscope - we primarily use it for mapping cortical vasculature and navigating based on our multimodal map to CCF alignment, although it is actually “contaminated” with some GCaMP6s activity during these uses.

We have briefly clarified this in the text.

Figures 4d & e: Do the colors show mean correlations per area? Please add labels and units to the colorbars as done in panel 4a.

Authors’ Response: For both Figs 4 and 5, we have added the requested labels and units to each scale bar, and have relabeled panels d to say “Rastermap CCF area cell densities”, and panels e to say “mean CCF area corrs w/ neural activity.”

Thank you for catching these omissions/mislabelings.

Line 715: what is superneuron averaging?

Authors’ Response: This refers to the fact that when Rastermap displays more than ~1000 neurons it averages the activity of each group of adjacent 50 neurons in the sorting to create a single display row, to avoid exceeding the pixel limitations of the display. Each single row representing the average activity of 50 neurons is called a “superneuron” (Stringer et al, 2023; bioRxiv).

We have modified the text to clarify this point.

Line 740: it would be good to mention what exactly the CCF density distribution quantifies.

Authors’ Response: In each CCF area, a certain percentage of neurons belongs to each Rastermap group. The CCF density distribution is the set of these percentages, or densities, across all CCF areas in the dorsal or side mount preparation being imaged in a particular session. We have clarified this in the text.

Line 745: what does 'within each CCF' mean? Does this refer to different areas?

Authors’ Response: The corrected version of this sentence now reads: “Next, we compared, across all CCF areas, the proportion of neurons within each CCF area that exhibited large positive correlations with walking speed and whisker motion energy.”

How were different Rastermap groups identified? Were they selected by hand?

Authors’ Response: Yes, in Figs. 4, 5, and 6, we selected the identified Rastermap groups “by hand”, based on qualitative similarity of their activity patterns. At the time, there was no available algorithmic or principled means by which to split the Rastermap sort. The current, newer version of Rastermap (Stringer et al, 2023) seems to allow for algorithmic discretization of embedding groups (we have not tested this yet), but it was not available at the time that we performed these preliminary analyses.

In terms of “correctness” of such discretization or group identification, we intend to address this issue in a more principled manner in upcoming publications. For the purposes of this first paper, we decided that manual identification of groups was sufficient to display the capabilities and outcomes of our methods.

We clarify this point briefly at several locations in the revised manuscript, throughout the latter part of the Results section.

Reviewer #3 (Recommendations For The Authors):

In "supplementary figures, protocols, methods, and materials", Figure S1 g is mislabeled as Figure f.

Authors’ Response: Yes, on the figure itself this panel was mislabeled as “f” in the original reviewed preprint. We have changed this to read “g”.

In S1 g, the success rate of the surgical procedure seems quite low. Less than 50% of the mice could be imaged under two-photon. Can the authors elaborate on the criteria and difficulties related to their preparations?

Authors’ Response: We will elaborate on the difficulties that sometimes hinder success in our preparations in the revised manuscript.

The success rate indicated to the point of “Spontaneous 2-P imaging (window) reads 13/20, which is 65%, not 50%. The drop to 9/20 by the time one gets to the left edge of “Behavioral Training” indicates that some mice do not master the task.

Protocol I contains details of the different ways in which mice either die or become unsuitable or “unsuccessful” at each step. These surgeries are rather challenging - they require proper instruction and experience. With the current protocol, our survival rate for the window surgery alone is as high as 75-100%. Some mice can be lost at headpost implantation, in particular if they are low weight or if too much muscle is removed over the auditory areas. Finally, some mice survive windowing but the imageable area of the window might be too small to perform the desired experiment.

We have added a paragraph detailing this issue in the main text at ~lines 287-320, pg 9.

In both Suppl_Movie_S1_dorsal_mount and Suppl_Movie_S1_side_mount provided (Movie S1), the behaviour video quality seems to be unoptimized which will impact the precision of Deeplabcut. As evident, there were multiple instances of mislabeled key points (paws are switched, large jumps of key points, etc) in the videos.

Many tracked points are in areas of the image that are over-exposed.

Despite using a high-speed camera, motion blur is obvious.

Occlusions of one paw by the other paws moving out of frame.

As Deeplabcut accuracy is key to higher-level motifs generated by BSOi-D, can the authors provide an example of tracking by exclusion/ smoothing of mislabeled points (possibly by the median filtering provided by Deeplabcut), this may help readers address such errors.

Authors’ Response: We agree that we would want to carefully rerun and carefully curate the outputs of DeepLabCut before making any strong claims about behavioral identification. As the aim of this paper was to establish our methods, we did not feel that this degree of rigor was required at this point.

It is inevitable that there will be some motion blur and small areas of over-exposure, respectively, when imaging whiskers, which can contain movement components up to ~150 Hz, and when imaging a large area of the mouse, which has planes facing various aspects. For example, perfect orthogonal illumination of both the center of the eye and the surface of the whisker pad on the snout would require two separate infrared light sources. In this case, use of a single LED results in overexposure of areas orthogonal to the direction of the light and underexposure of other aspects, while use of multiple LEDs would partially fix this problem, but still lead to variability in summated light intensity at different locations on the face. We have done our best to deal with these limitations.

We now briefly point out these limitations in the methods text at ~lines 155-160, pg 5.

In addition, we have provided additional raw and processed movies and data related to DeepLabCut and BSOiD behavioral analysis in our FigShare+ repository, which is located at:

https://doi.org/10.25452/figshare.plus.c.7052513

In lines 153-154, the authors mentioned that the Deeplabcut model was trained for 650k iterations. In our experience (100-400k), this seems excessive and may result in the model overfitting, yielding incorrect results in unseen data. Echoing point 4, can the authors show the accuracy of their Deeplabut model (training set, validation set, errors, etc).

Authors’ Response: Our behavioral analysis is preliminary and is included here as an example of our methods, and not to make claims about any specific result. Therefore we believe that the level of detail that you request in our DeepLabCut analysis is beyond the scope of the current paper. However, we would like to point out that we performed many iterations of DeepLabCut runs, across many mice in both preparations, before converging on these preliminary results. We believe that these results are stable and robust.

We believe that 650k iterations is within the reasonable range suggested by DLC, and that 1 million iterations is given as a reasonable upper bound. This seems to be supported by the literature for example, see Willmore et al, 2022 (“Behavioral and dopaminergic signatures of resilience”, Nature, 124:611, 124-132). Here, in a paper focused squarely on behavioral analysis, DLC training was run with 1.3 million iterations with default parameters.

We now note, on ~lines 153-154, pg 5, that we used 650K iterations, a number significantly less than the default of 1.03 million, to avoid overfitting.

In lines 140-141, the authors mentioned the use of slicing to downsample their data. Have any precautions, such as a low pass filter, been taken to avoid aliasing?

Authors’ Response: Most of the 2-photon data we present was acquired at ~3 Hz and upsampled to 10 Hz. Most of the behavioral data was downsampled from 5000 Hz to 10 Hz by slicing, as stated. We did not apply any low-pass filter to the behavioral data before sampling. The behavioral variables have heterogeneous real sampling/measurement rates - for example, pupil diameter and whisker motion energy are sampled at 30 Hz, and walk speed is sampled at 100 Hz. In addition, the 2-photon acquisition rate varied across sessions.

These facts made principled, standardized low-pass filtering difficult to implement. We chose rather to use a common resampling rate of 10 Hz in an unbiased manner. This downsampled 10 Hz rate is also used by B-SOiD to find transitions between behavioral motifs (Hsu and Yttri, 2021).

We do not think that aliasing is a major factor because the real rate of change of our Ca2+ indicator fluorescence and behavioral variables was, with the possible exception of whisker motion energy, likely at or below 10 Hz.

We now include a brief statement to this effect in the methods text at ~lines 142-146, pg. 4.

Line 288-299, the authors have made considerable effort to compensate for the curvature of the brain which is particularly important when imaging the whole dorsal cortex. Can the authors provide performance metrics and related details on how well the combination of online curvature field correction (ScanImage) and fast-z "sawtooth"/"step" (Sofroniew, 2016)?

Authors’ Response: We did not perform additional “ground-truth” experiments that would allow us to make definitive statements concerning field curvature, as was done in the initial eLife Thorlabs mesoscope paper (Sofroniew et al, 2016).

We estimate that we experience ~200 micrometers of depth offset across 2.5 mm - for example, if the objective is orthogonal to our 10 mm radius bend window and centered at the apex of its convexity, a small ROI located at the lateral edge of the side mount preparation would need to be positioned around 200 micrometers below that of an equivalent ROI placed near the apex in order to image neurons at the same cortical layer/depth, and would be at close to the same depth as an ROI placed at or near the midline, at the medial edge of the window. We determined this by examining the geometry of our cranial windows, and by comparing z-depth information from adjacent sessions in the same mouse, the first of which used a large FOV and the second of which used multiple small FOVs optimized so that they sampled from the same cortical layers across areas.

We have included this brief explanation in the main text at ~lines 300-311, pg 9.

In lines 513-515, the authors mentioned that the vasculature pattern can change over the course of the experiment which then requires to re-perform the realignment procedure. How stable is the vasculature pattern? Would laser speckle contrast yield more reliable results?

Authors’ Response: In general the changes in vasculature we observed were minimal but involved the following: i) sometimes a vessel was displaced or moved during the window surgery, ii) sometimes a vessel, in particular the sagittal sinus, enlarged or increased its apparent diameter over time if it is not properly pressured by the cranial window, and iii) sometimes an area experiencing window pressure that is too low could, over time, show outgrowth of fine vascular endings. The most common of these was (i), and (iii) was perhaps the least common. In general the vasculature was quite stable.

We have added this brief discussion of potential vasculature changes after cranial window surgery to the main text at ~lines 286-293, pg 9.

We already mentioned, in the main text of the original eLife reviewed preprint, that we re-imaged the multimodal map (MMM) every 30-60 days or whenever changes in vasculature are observed, in order to maintain a high accuracy of CCF alignment over time. See ~lines 507-511, pg 16.

We are not very familiar with laser speckle contrast, and it seems like a technique that could conceivably improve the fine-grained accuracy of our MMM-CCF alignment in some instances. We will try this in the future, but for now it seems like our alignments are largely constrained by several large blood vessels present in any given FOV, and so it is unclear how we would incorporate such fine-grained modifications without applying local non-rigid manipulations of our images.

In lines 588-598, the authors mentioned that the occasional use of online fast-z corrections yielded no difference. However, it seems that the combination of the online fast-z correction yielded "cleaner" raster maps (Figure S3)?

Authors’ Response: The Rastermaps in Fig S3a and b are qualitatively similar. We do not believe that any systematic difference exists between their clustering or alignments, and we did not observe any such differences in other sessions that either used or didn’t use online fast-z motion correction.

We now provide raw data and analysis files corresponding to the sessions shown in Fig S3 (and other data-containing figures) on FigShare+ at:

https://doi.org/10.25452/figshare.plus.c.7052513

Ideally, the datasets contained in the paper should be available on an open repository for others to examine. I could not find a clear statement about data availability. Please include a linked repo or state why this is not possible.

Authors’ Response: We have made ~500 GB of raw data and preliminary analysis files publicly available on FigShare+ for the example sessions shown in Figures 2, 3, 4, 5, 6, S3, and S6. We ask to be cited and given due credit for any fair use of this data.

The data is located here:

Vickers, Evan; A. McCormick, David (2024). Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice. Figshare+. Collection:

https://doi.org/10.25452/figshare.plus.c.7052513

We intend to release a complete data set to the public as a Dandiset on the DANDI archive in conjunction with second and third in-depth analysis papers that are currently in preparation.

Author Response

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

Reviewer #1

Public Review

Summary:

(1) This work describes a simple mechanical model of worm locomotion, using a series of rigid segments connected by damped torsional springs and immersed in a viscous fluid.

(2) It uses this model to simulate forward crawling movement, as well as omega turns.

Strengths:

(3) The primary strength is in applying a biomechanical model to omega-turn behaviors.

(4) The biomechanics of nematode turning behaviors are relatively less well described and understood than forward crawling.

(5) The model itself may be a useful implementation to other researchers, particularly owing to its simplicity.

Weaknesses:

(6) The strength of the model presented in this work relative to prior approaches is not well supported, and in general, the paper would be improved with a better description of the broader context of existing modeling literature related to undulatory locomotion.

(7) This paper claims to improve on previous approaches to taking body shapes as inputs.

(8) However, the sole nematode model cited aims to do something different, and arguably more significant, which is to use experimentally derived parameters to model both the neural circuits that induce locomotion as well as the biomechanics and to subsequently compare the model to experimental data.

(9) Other modeling approaches do take experimental body kinematics as inputs and use them to produce force fields, however, they are not cited or discussed.

(10) Finally, the overall novelty of the approach is questionable.

(11) A functionally similar approach was developed in 2012 to describe worm locomotion in lattices (Majmudar, 2012, Roy. Soc. Int.), which is not discussed and would provide an interesting comparison and needed context.

9-11: The paper you recommended and our manuscript have some similarities and differences.

Similarities

Firstly, the components constituting the worm are similar in both models. ElegansBot models the worm as a chain of n rods, while the study by Majmudar et al. (2012) models it as a chain of n beads. Each bead in the Majmudar et al. model has a directional vector, making it very similar to ElegansBot's rod. However, there's a notable difference: in the Majmudar et al. model, each bead has an area for detecting contact between the obstacle and the bead, while in ElegansBot, the rod does not feature such an area.

Secondly, the types of forces and torques acting on the components constituting the worm are similar. Each rod in ElegansBot receives frictional force, muscle force, and joint force. Each bead in the Majmudar et al. model receives a constraint force, viscous force, and a repulsive force from obstacles. Each rod in ElegansBot receives frictional torque, muscle torque, and joint torque. Each bead in the Majmudar et al. model receives elastic torque, constraint torque, drive torque, and viscous torque. The Majmudar et al. model's constraint force and torque are similar to ElegansBot's joint force and torque in that they prevent two connected components of the worm from separating. The Majmudar et al. model's viscous force and torque are similar to ElegansBot's frictional force and torque in that they are forces exchanged between the worm and its surrounding environment (ground surface). The Majmudar et al. model's drive torque is similar to ElegansBot's muscle force and muscle torque as a cause of the worm's motion. However, unlike ElegansBot, the Majmudar et al. model did not consider the force generating the drive torque, and there are differences in how each force and torque is calculated. This will be discussed in more detail below.

Differences

Firstly, the medium in which the worm locomotes is different. ElegansBot is a model describing motion in a homogeneous medium like agar or water without obstacles, while the Majmudar et al. model describes motion in water with circular obstacles fixed at each lattice point. This is because the purposes of the models are different. ElegansBot analyzes locomotion patterns based on the friction coefficient, while the Majmudar et al. model analyzes locomotion patterns based on the characteristics of the obstacle lattice, such as the distance between obstacles. Also, for this reason, the Majmudar et al. model's bead, unlike ElegansBot's rod, receives a repulsive force from obstacles.

Secondly, the specific methods of calculating similar types of forces differ. ElegansBot calculates joint forces by substituting frictional forces, muscle forces, frictional torques, and muscle torques into an equation derived from differentiating a boundary condition equation twice over time, where two neighboring rods always meet at one point. This involves determining the process through which various forces and torques are transmitted across the worm. Specifically, it entails calculating how the frictional forces and torques, as well as the muscle forces and torques acting on each rod, are distributed throughout the entire length of the worm. In contrast, The Majmudar et al. model uses Lagrange multipliers method based on a boundary condition that the curve length determined by each bead's tangential angle does not change, to calculate the constraint force and torque before calculating the drive torque and viscous force. This implies that the Majmudar et al. model did not consider the mechanism by which the drive torque and viscous force received by one bead are distributed throughout the worm. ElegansBot's rod receives an anisotropic Stokes frictional force from the ground surface, while the Majmudar et al. model considered the frictional force according to the Navier-Stokes equation for incompressible fluid, assuming the fluid velocity at the bead's location as the bead's velocity.

Thirdly, unlike the Majmudar et al. model, ElegansBot considers the inertia of the worm components. Therefore, ElegansBot can simulate regardless of how low or high the ground surface's friction coefficient is. the Majmudar et al. model is not like this.

(12) The idea of applying biomechanical models to describe omega turns in C. elegans is a good one, however, the kinematic basis of the model as used in this paper (the authors do note that the control angle could be connected to a neural model, but don't do so in this work) limits the generation of neuromechanical control hypotheses.

8, 12: We do not agree with the claim that ElegansBot could limit other researchers in generating neuromechanical control hypotheses. The term θ_("ctrl" ,i)^((t) ) used in our model is designed to be replaceable with neuromechanical control in the future.

(13) The model may provide insights into the biomechanics of such behaviors, however, the results described are very minimal and are purely qualitative.

(14-1) Overall, direct comparisons to the experiments are lacking or unclear.

14-1: If you look at the text explaining Fig. 2 and 5 (Fig. 2 and 4 in old version), it directly compares the velocity, wave-number, and period as numerical indicators representing the behavior of the worm, between the experiment and ElegansBot.

(14-2) Furthermore, the paper claims the value of the model is to produce the force fields from a given body shape, but the force fields from omega turns are only pictured qualitatively.

13, 14-2: We gratefully accept the point that our analysis of the omega-turn is qualitative. Therefore, we have conducted additional quantitative analysis on the omega-turn and inserted the results into the new Fig. 4. We have considered the term 'Force field' as referring to the force vector received by each rod. We have created numerical indicators representing various behaviors of the worm and included them in the revised manuscript.

(15) No comparison is made to other behaviors (the force experienced during crawling relative to turning for example might be interesting to consider) and the dependence of the behavior on the model parameters is not explored (for example, how does the omega turn change as the drag coefficients are changed).

Thank you for the great idea. To compare behaviors, first, a clear criterion for distinguishing behaviors is needed. Therefore, we have created a new mathematical definition for behavior classification in the revised manuscript (“Defining Behavioral Categories” in Method). After that, we compared the force and power (energy consuming rate) between each forward locomotion, backward locomotion, and omega-turn (Fig. 4). And in the revised manuscript, we newly analyzed how the turning behavior changes with variations in the friction coefficients in Figs. S4-S7.

(16) If the purpose of this paper is to recapitulate the swim-to-crawl transition with a simple model, and then apply the model to new behaviors, a more detailed analysis of the behavior of the model variables and their dependence on the variables would make for a stronger result.

In our revised manuscript, we have quantitatively analyzed the changes occurring in turning behavior from water to agar, and the results are presented in Figs. S9 and S10.

(17) In some sense, because the model takes kinematics as an input and uses previously established techniques to model mechanics, it is unsurprising that it can reproduce experimentally observed kinematics, however, the forces calculated and the variation of parameters could be of interest.

(18) Relatedly, a justification of why the drag coefficients had to be changed by a factor of 100 should be explored.

(19) Plate conditions are difficult to replicate and the rheology of plates likely depends on a number of factors, but is for example, changes in hydration level likely to produce a 100-fold change in drag? or something more interesting/subtle within the model producing the discrepancy?

18, 19: As mentioned in the paper, we do not know if the friction coefficients in the study of Boyle et al. (2012) and the friction coefficients in the experiment of Stephens et al. (2016) are the same. In our revised manuscript, we have explored more in detail the effects of the friction coefficient's scale factor, and explained why we chose a scale factor of 1/100 (“Proper Selection of Friction Coefficients” in Supplementary Information). In summary, we analyzed the changes in trajectory due to scaling of the friction coefficient, and chose the scale factor 1/100 as it allowed ElegansBot to accurately reproduce the worm's trajectory while also being close to the friction coefficients in the Boyle et al. paper.

(20) Finally, the language used to distinguish different modeling approaches was often unclear.

(21) For example, it was unclear in what sense the model presented in Boyle, 2012 was a "kinetic model" and in many situations, it appeared that the term kinematic might have been more appropriate. Thank you for the feedback. As you pointed it out, we have corrected that part to 'kinematic' in the revised manuscript.

(22) Other phrases like "frictional forces caused by the tension of its muscles" were unclear at first glance, and might benefit from revision and more canonical usage of terms.

We agree that the expression may not be immediately clear. This is due to the word limit for the abstract (the abstract of eLife VOR should be under 200 words, and our paper's abstract is 198 words), which forced us to convey the causality in a limited number of words. Therefore, although we will not change the abstract, the expression in question means that the muscle tension, which is the cause of the worm's locomotion, ultimately generates the frictional force between the worm and the ground surface.

Recommendations For The Authors

(23) As I stated in my public review, I think the paper could be made much stronger if a more detailed exploration of turning mechanics was presented.

(24) Relatedly, rather than restricting the analysis to individual videos of turning behaviors, I wonder if a parameterized model of the turning kinematics would be fruitful to study, to try to understand how different turning gaits might be more or less energetically favorable.

We thank the reviewer once again for their suggestion. Thanks to their proposal, we were able to conduct additional quantitative analysis on turning behavior.

Reviewer #2

Public Review

Summary:

(1) Developing a mechanical model of C. elegans is difficult to do from basic principles because it moves at a low (but not very small) Reynolds number, is itself visco-elastic, and often is measured moving at a solid/liquid interface.

(2) The ElegansBot is a good first step at a kinetic model that reproduces a wide range of C. elegans motiliy behavior.

Strengths: (3) The model is general due to its simplicity and likely useful for various undulatory movements.

(4) The model reproduces experimental movement data using realistic physical parameters (e.g. drags, forces, etc).

(5) The model is predictive (semi?) as shown in the liquid-to-solid gait transition.

(6) The model is straightforward in implementation and so likely is adaptable to modification and addition of control circuits.

Weaknesses:

(7) Since the inputs to the model are the actual shape changes in time, parameterized as angles (or curvature), the ability of the model to reproduce a realistic facsimile of C. elegans motion is not really a huge surprise. (8) The authors do not include some important physical parameters in the model and should explain in the text these assumptions.

(9. 1) The cuticle stiffness is significant and has been measured [1].

(10. 2) The body of C. elegans is under high hydrostatic pressure which adds an additional stiffness [2].

(11. 3) The visco-elasticity of C. elegans body has been measured. [3]

Thank you for asking. The stiffness of C. elegans is an important consideration. We took this into account when creating ElegansBot, but did not explain it in the paper. The detailed explanation is as follows. C. elegans indeed has stiffness due to its cuticle and internal pressure. This stiffness is treated as a passive elastic force (elastic force term of lateral passive body force) in the paper of Boyle et al. (2012). However, the maximum spring constant of the passive elastic force is 1/20 of the maximum spring constant of the active elastic force. If we consider this fact in our model, the elastic term of the muscle torque is as follows: ( is the active torque elasticity coefficient, is the passive torque elasticity coefficient)

where

Therefore, there is no need to describe the active and passive terms separately in

Furthermore, since , assuming , then and .

(12) There is only a very brief mention of proprioception.

(13) The lack of inclusion of proprioception in the model should be mentioned and referenced in more detail in my opinion.

As you emphasized, proprioception is an important aspect in the study of C. elegans' locomotion. In our paper, its importance is briefly introduced with a sentence each in the introduction and discussion. However, our research is a model about the process of the creation of body motion originated from muscle forces, and it does not model the sensory system that senses body posture. Therefore, there is no mention of using proprioception in our paper's results section. What is mentioned in the discussion is that ElegansBot can be applied as the kinetic body model part in a combination model of a kinetic body model and a neuronal circuit model that receives proprioception as a sensory signal.

(14) These are just suggested references.

(15) There may be more relevant ones available.

The papers you provided contain specific information about the Young's modulus of the C. elegans body. The first paper (Rahimi et al., 2022) measured the Young's modulus of the cuticle after chemically isolating it from C. elegans, while the second paper (Park et al., 2007) and third paper (Backholm et al., 2013) measured the elasticity and Young's modulus of C. elegans without separating the cuticle. Based on the Young's modulus provided in each paper (although the second and third papers did not measure stiffness in the longitudinal direction), we derived the elastic coefficient (assuming a worm radius of 25 μm, cuticle thickness of 0.5 μm, and 1/25 of longitudinal length of the cuticle of 40 μm). The range was quite broad, from 9.82ⅹ1011 μg/sec2 (from the first paper) to 2.16 ⅹ 108 μg / sec2 (from the third paper). Although the elastic coefficient value in our paper falls within this range, since the range of the elastic coefficient is wide, we think we can modify the elastic coefficient in our paper and will be able to reapply our model if more accurate values become known in the future.

Reviewer #3

Public Review

Summary:

(1) A mechanical model is used with input force patterns to generate output curvature patterns, corresponding to a number of different locomotion behaviors in C. elegans

Strengths:

(2) The use of a mechanical model to study a variety of locomotor sequences and the grounding in empirical data are strengths.

(3) The matching of speeds (though qualitative and shown only on agar) is a strength.

Weaknesses:

(4) What is the relation between input and output data?

ElegansBot takes the worm's body control angle as the input, and produces trajectory and force of each segment of the worm as the output.

(5) How does the input-output relation depend on the parameters of the model?

If 'parameter' is understood as vertical and horizontal friction coefficients, then the explanation for this can be found in Fig. 5 (Fig. 4 in the old version).

(6) What biological questions are addressed and can significant model predictions be made?

Equation of motion deciphering locomotion of C. elegans including turning behaviors which were relatively less well understood.

Recommendations For The Authors

(7) The novelty and significance of the paper should be clarified.

We have added quantitative analyses of turning behavior in the revised manuscript, and we hope this will be helpful to you.

(8) Previously much more detailed models have been published, as compared to this one.

We hope the reviewer can point out any previous model that we may have missed.

(9) The mechanics here are simplified (e.g. no information about dorsal/ventral innervation but only a bending angle) setting limitations on the capacity for model predictiveness.

(10) Such limitations should be discussed.

We view the difference between dorsal/ventral innervation and bending angle not as a matter of simplification, but rather as a reflection of the hierarchy that our model implements. Our model does not consider dorsal/ventral innervation, but it uses the bending angle to reproduce behavior in various input and frictional environments, which signifies the strong predictiveness of ElegansBot (Figure 2, 3, 5 (2, 3, 4 in the old version)). Moreover, if the midline of C. elegans is incompressible, then modeling by dividing into dorsal/ventral, as opposed to modeling solely with the bending angle, does not increase the degree of freedom of the worm model, and therefore does not increase its predictiveness.

(11) The aims of the paper and results need to be supported quantitatively and analyzed through parameter sweeps and intervention.

We have conducted additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

(12) The methods are given only in broad brushstrokes, and need to be much more clear (and ideally sharing all code).

We have thoroughly detailed every aspect of this research, from deriving the physical constants of C. elegans, agar, and water to developing the formulas and proofs necessary for operating ElegansBot and its applications. This comprehensive information is all presented in the Results, Methods, and Supplementary Information sections, as well as in the source code. Moreover, we have already ensured that our research can be easily reproduced by providing detailed explanations and by making ElegansBot accessible through public software databases (PyPI, GitHub). To further aid in its application and understanding, especially for those less familiar with the subject, we have also included minimal code as examples in the database. This code is designed to simplify the process of reproducing the results of the paper, thereby making our research more accessible and understandable. Therefore, we believe that readers will easily gain significant assistance from the extensive information we have provided. Should readers require further help, they can always contact us, and we will be readily available to offer support.

(13) The supporting figures and movies need to include a detailed analysis to evidence the claims.

We have conducted and provided additional quantitative analyses on turning behavior as suggested by Reviewer #1 (Fig. 4, S4-S7, S9, and S10).

Author Response

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

Public Reviews:

Reviewer #1:

Summary:

This manuscript provides some valuable findings concerning the hippocampal circuitry and the potential role of adult-born granule cells in an interesting long-term social memory retrieval. The behavior experiments and strategy employed to understand how adult-born granule cells contribute to long-term social discrimination memory are interesting.

We thank the reviewer for the positive evaluation.

I have a few concerns, however with the strength of the evidence presented for some of the experiments. The data presented and the method described is incomplete in describing the connection between cell types in CA2 and the projections from abGCs. Likewise, I worry about the interpretation of the data in Figures 1 and 2 given the employed methodology. I think that the interpretation should be broadened. This second concern does not impact the interest and significance of the findings.

In response to this concern, we have removed the data concerning abGC projections to PCP4+ and PV-GFP+ cell bodies from Figure 1 and have focused this analysis on dendrites. We now provide high magnification images of dendrites and expand on the methodology, results, and interpretations in the manuscript. We also broaden the interpretation throughout the manuscript to address the reviewer’s concern.

Strengths:

The behavior experiments are beautifully designed and executed. The experimental strategy is interesting.

We appreciate these positive comments.

Weaknesses:

The interpretation of the results may not be justified given the methods and details provided.

We have addressed this concern by providing more methodological details and broadening our interpretation of the results.

Reviewer #2:

Summary:

Laham et al. investigate how the projection from adult-born granule cells into CA2 affects the retrieval of social memories at various developmental points. They use chemogenetic manipulations and electrophysiological recordings to test how this projection affects hippocampal network properties during behavior. I find the study to be very interesting, the results are important for our understanding of how social memories of different natures (remote or immediate) are encoded and supported by the hippocampal circuitry. I have some points that I added below that I think could help clarify the conclusions:

We appreciate the positive assessment and have addressed the more specific points below.

My major concern with the manuscript was that making the transitions between the different experiments for each result section is not very smooth. Maybe they can discuss a bit in a summary conclusion sentence at the end of each result section why the next set of experiments is the most logical step.

In response, we have added summary conclusion sentences at the end of each result section.

In line 113, the authors say that "the DG is known to influence hippocampal theta-gamma coupling and SWRs". Another recent study Fernandez-Ruiz et al. 2021, examined how various gamma frequencies in the dentate gyrus modulate hippocampal dynamics.

We cite this paper in the revised manuscript.

Having no single cells in the electrophysiological recordings makes it difficult to interpret the ephys part. Perhaps having a discussion on this would help interpret the results. If more SWRs are produced from the CA2 region (perhaps aided by projections from abGC), more CA2 cells that respond to social stimuli (Oliva et al. 2020) would reactivate the memories, therefore making them consolidate faster/stronger. On the other hand, the projections from abGC that the authors see, also target a great deal of PV+ interneurons, which have been shown to pace the SWRs frequency (Stark et al 2014, Gan et al 2017), which further suggests that this projection could be involved in SWRs modulation.

We discuss these possibilities and cite Gan et al 2017, Schlingloff et al., 2014, and Stark et al., 2014 in the revised manuscript.

The authors should cite and discuss Shuo et al., 2022 (A hypothalamic novelty signal modulates hippocampal memory).

We mention Chen et al (A hypothalamic novelty signal modulates hippocampal memory.) in the revised manuscript. “Shuo” is the first name of the first author on this paper, so we believe that this is the same paper to which the reviewer refers.

I think the authors forgot to refer to Fig 3a-f, maybe around lines 163-168.

We thank the reviewer for pointing out this error. In the revised manuscript, we refer to all figure panels. Since Fig 3 is now broken into two figures (Fig 3 and 4), the panel lettering has changed in the revised manuscript.

Are the SWRs counted only during interaction time or throughout the whole behavior session for each condition?

The SWRs are counted throughout the whole behavior session for each condition. This is now stated in the revised manuscript.

Figure 3t shows a shift in the preferred gamma phase within theta cycles as a result of abGC projections to CA2 ablation with CNO, especially during Mother CNO condition. I think this result is worth mentioning in the text.

We now mention this finding in the revised manuscript.

Figure 3u in the legend mention "scale bars = 200um", what does this refer to?

The scale bar refers to that shown in Figure 3b, which is now indicated in the legend.

What exactly is calculated as SWR average integral? Is it a cumulative rate? Please clarify.

The integral measure provides information regarding the average total power of SWR events. It sums z-scored amplitude values from beginning to the end of each SWR envelope, and then takes the average across all summed envelopes. SWR integral has been shown to influence SWR propagation (De Filippo and Schmitz, 2023). This is now described in the text.

Alexander et al 2017, "CA2 neuronal activity controls hippocampal oscillations and social behavior", examined some of the CA2 effects in the hippocampal network after CNO silencing, and the authors should cite it.

Alexander et al., 2018, which we believe is the relevant paper, is now cited in the revised manuscript.

Strengths:

Behavioral experiments after abGC projections to CA2 are compelling as they show clearly distinct behavioral readout.

We thank the reviewer for this positive assessment.

Weaknesses:

Electrophysiological experiments are difficult to interpret without additional quantifications (single-cell responses during interactions etc.)

We have addressed this concern by expanding the interpretation of our results.

Reviewer #3:

Laham et al. present a manuscript investigating the function of adult-born granule cells (abGCs) projecting to the CA2 region of the hippocampus during social memory. It should be noted that no function for the general DG to CA2 projection has been proposed yet. The authors use targeted ablation, chemogenetic silencing, and in vivo ephys to demonstrate that the abGCs to CA2 projection is necessary for the retrieval of remote social memories such as the memory of one's mother. They also use in vivo ephys to show that abGCs are necessary for differential CA2 network activity, including theta-gamma coupling and sharp wave-ripples, in response to novel versus familiar social stimuli.

The question investigated is important since the function of DG to CA2 projection remained elusive a decade after its discovery. Overall, the results are interesting but focused on the social memory of the mother, and their description in the manuscript and figures is too cursory. For example, raw interaction times must be shown before their difference. The assumption that mice exhibit social preference between familiar or novel individuals such as mother and non-mother based on social memory formation, consolidation, and retrieval should be better explained throughout the manuscript. Thus, when describing the results, the authors should comment on changes in preference and how this can be interpreted as a change in social memory retrieval. Several critical experimental details such as the total time of presentation to the mother and non-mother stimulus mice are also lacking in the manuscript. The in vivo e-phys results are interesting as well but even more succinct with no proposed mechanism as to how abGCs could regulate SWR and PAC in CA2.

In response to these comments, we provide raw interaction times in a new Figure (Fig. S1). We also provide more information about the experiments and figures in the revision. We explain the rationale for our behavioral interpretations and discuss proposed mechanisms for how abGCs regulate SWR and PAC.

The manuscript is well-written with the appropriate references. The choice of the behavioral test is somewhat debatable, however. It is surprising that the authors chose to use a direct presentation test (presentation of the mother and non-mother in alternation) instead of the classical 3-chamber test which is particularly appropriate to investigate social preference. Since the authors focused exclusively on this preference, the 3-chamber test would have been more adequate in my opinion. It would greatly strengthen the results if the authors could repeat a key experiment from their investigation using such a test. In addition, the authors only impaired the mother's memory. An additional experiment showing that disruption of the abGCs to CA2 circuit impairs social memory retrieval would allow us to generalize the findings to social memories in general. As the manuscript stands, the authors can only conclude the importance of this circuit for the memory of the mother. Developmental memory implies the memory of familiar kin as well.

We selected the direct social interaction test because it allows for more naturalistic social behaviors than measuring investigation times toward social stimuli located inside wire mesh containers. We also decided to focus our studies on the retrieval of mother memories because these are likely the first social memories to be formed. We emphasize that our results cannot be generalized to memories of other social stimuli but given studies on recent social memory formation and retrieval in adults that manipulate abGCs and CA2 separately, we feel that it is likely that this circuit is involved in these functions as well. However, we specify throughout the manuscript that our experiments can only tell us about mother memories. We have also changed the title to reflect this.

The in vivo ephys section (Figure 3) is interesting but even more minimalistic and it is unclear how abGCs projection to CA2 can contribute to SWR and theta-gamma PAC. In Figure 1, the authors suggest that abGCs project preferentially to PV+ neurons in CA2. At a minimum, the authors should discuss how the abGCs to PV+ neurons to CA2 pyramidal neurons circuit can facilitate SWR and theta-gamma PAC.

We have divided Figure 3 into two figures (Figures 3 and 4) and revised the electrophysiology section of the results section. In the revised paper, we now discuss how abGC projections to PV+ interneurons may facilitate SWR and PAC.

Finally, proposing a function for 4-6-week-old abGCs projecting to CA2 begs two questions: What are abGCs doing once they mature further, and more generally, what is the function of the DG to CA2 projection? It would be interesting for the authors to comment on these questions in the discussion.

In response to these comments, we discuss possible answers to these interesting questions.

Recommendations for the authors:

Reviewer #1:

Specifically, in Figure 1, for the analysis of the synapses formed between abGCs and CA2 PNS (as identified by PCP4 expression) and CA2 PV+ cells (as identified by cre-dependent AAV-mCherry expression) in PV-cre line. In panels c and d the soma of a CA2 PN cell is shown, as well as the soma of a PV cell is shown. Why was the soma analyzed? What relevance is there for this? It is my understanding that synapses form on dendrites- this would be much more relevant to show, in my opinion. Also, the methods for panels e and f state that the 3R-Tau+ intensity was analyzed only in stratum lucidum. (There was a normalization for the overall 3R-Tau intensity in SL of CA2 that was obtained by dividing the 3R-Tau intensity of corpus callosum). I don't understand then how a comparison of 3RTau intensity could have been done for CA2 PN soma. There are no CA2 PN soma in stratum lucidum. (This is fairly clearly shown in Figure 1aiii, with the PCP4 staining showing the soma in the somatic layer... not in stratum lucidum). What is being analyzed here?

If the 3R-Tau intensity for dendrites is higher for PV cell dendrites, an example image of dendrites would be very helpful. How was the CA2 PV cell dendrite delimited from the CA2 PN dendrites at 40x magnification for the 3R-Tau intensity? Why were pre-synaptic puncta not examined? Is it possible to determine the post-synaptic target with these methods? This result could be particularly interesting, but I find it very difficult to understand the quantification or the justification behind it. To truly know if a cell is getting a connection, the best method would be to perform whole-cell patch clamp recordings of the post-synpatic target cells and use optogenetics of the abGCs. I understand that perhaps this may be beyond the scope of the paper, but it is a severe limitation for these results.

We have eliminated the cell body measures from Figure 1 and focus instead on the dendrite measures, which we agree are more relevant. We now provide high magnification example images of pyramidal cell (PCP4+) and PV+ interneuron (GFP+) dendrites in Figure 1. We thank the reviewer for pointing out the error about the stratum lucidum as some of the dendrites analyzed are located in the pyramidal cell layer. In addition, neither PCP4 nor GFP label the full extent of dendrites emanating from CA2 pyramidal cells or PV+ interneurons respectively. We mention this in the revised manuscript because abGC projections to more distal dendrites might show a different pattern than that which was observed for proximal dendrites. We also provide more details about how the dendrites were delimited for the analysis, and mention that these results cannot definitively inform us about whether functional synaptic connections have been formed.

Canulation over CA2 is potentially not specific to CA2 terminals. It would be optimal if the authors had some histology demonstrating specific cannula placement, as these surgeries are really tough to get perfectly centered over CA2. Even if it is perfectly centered, how much would the CNO diffuse into CA3? I think that given the methodology, the authors really need to consider that the behavioral results are not only a result of blocking abGC terminals in CA2 alone. Would it really change much if the abGC terminals are also silenced in CA3a/b as well? The McHugh lab has shown that area CA3 is also playing a role in social memory (Chiang, M.-C., Huang, A. J. Y., Wintzer, M. E., Ohshima, T. & McHugh, T. J. A role for CA3 in social recognition memory. Behav Brain Res 354, 2018). It may be that both areas CA2 and CA3 are important for the phenomenon being demonstrated in Figure 2. I think the impact of the study is just as interesting, as this examination of early social memories is very interesting and nicely done. In fact, areas CA2 and CA3 may be acting together (please see Stöber, T. M., Lehr, A. B., Hafting, T., Kumar, A. & Fyhn, M. Selective neuromodulation and mutual inhibition within the CA3-CA2 system can prioritize sequences for replay. Hippocampus 30, 1228-1238, 2020).

We agree that it is possible that CNO infusions targeted at the CA2 would also influence CA3a/b and have revised the paper to include this possible interpretation. We also cite the suggested paper on CA3 involvement in social memory (Chiang et al., 2018) and the paper on CA2-CA3 interactions (Stöber et al, 2020).

Figure 3 is packed with information, but not communicated in a reasonable way. Much more information and a description of the experimental protocol need to be presented. Furthermore, why are there no example traces for the SWRs recorded? There should be more analysis than just a difference score and frequency. How is j, k, and l analyzed and interpreted? Why no example traces there? Also, the n's seem way too small for Figure 3mr. Are there only 32 or three animals used for some of these conditions? This is insufficient in my opinion to conclude much for a 5-minute interaction.

In response to this concern, we have divided Figure 3 into 2 figures – Figure 3 and Figure 4. In Figure 3, we provide example traces for SWRs, with additional SWR data presented in Figures S3 and S4, including data to complement the difference score data in Figure 3. In Figure 4, we include traces of phase amplitude coupling. We also provide more information in the methods about how the phase amplitude coupling data were analyzed. For Figure 4, we used methods described by Tort et al., 2010 to produce a modulation index, which is a measure of the intensity of coupling between theta phase and gamma amplitude. This method additionally allows for visualization of how gamma amplitude is modified across individual theta phase cycles. Regarding the question about n sizes in the 10-12 week abGC group (Fig. 3), the numbers are lower than in the 4-6 week abGC group because by 6 weeks after the first set of recordings, the electrodes in some of the mice were no longer usable. The n sizes for this specific study are 4-5 per group for Nestin-cre mice; 7-8 for Nestin-cre:Gi. This is now clarified in the figure legend.

The discussion section of this paper does not put these results into a broader context with the field. There are other studies examining abGCs and their roles in novelty and memory formation (the work from Juna Song's lab, for example). These should be properly mentioned and discussed.

In response, we have added discussion on the roles of abGCs in nonsocial novelty and memory formation and have cited papers from the Song lab.

In the figure legend for Figure 2, there is no specific explanation for panel h. Perhaps the label is missing in the legend.

We thank the reviewer for noting this error and now include a description in the revised manuscript.

Reviewer #2:

Adding more quantifications (single cells, isolating data during interactions versus noninteraction times) would help understand the results better. In the lack of this, adding a more clear rationale (even if only through the presentation of hypotheses) in between the transitions of the different results sections would make the study easier to read.

In response to this comment, we have added transition sentences between results sections to clarify the rationale and make the manuscript easier to understand.

Reviewer #3:

Line 110: "Hippocampal phase-amplitude coupling (PAC) and generation of sharp waveripples (SWRs) have been linked to novel experience, memory consolidation, and retrieval (Colgin, 2015; Fernandez Ruiz et al., 2019; Meier et al., 2020; Joo and Frank, 2018; Vivekananda et al., 2021). The DG is known to influence hippocampal theta-gamma coupling and SWRs (Bott et al, 2016; Meier et al., 2020), yet no studies have examined the influence of abGCs on these oscillatory patterns." This information comes too early in the result section and is somewhat confusing.

In response to this comment, we have moved this information and provided a better description.

Line 118: "we found that mice with normal levels of abGCs can discriminate between their own mother and a novel mother." Be more descriptive of the results (present the raw interaction times with the statistical test to compare them), this is the conclusion.

In response to this comment, we provide the raw interaction times in a new Figure (Fig. S1) and describe the results in more detail.

Line 121: "These effects were not due to changes in physical activity". Be more specific. Did you subject the mice to a specific test? If not, how did you calculate locomotion? The data presented in the supplementary figure 1a only states the % locomotion.

Locomotion was manually scored whenever an animal moved in the testing apparatus. Speed was not recorded. Total locomotion was divided by trial duration to create a % locomotion measure. We have added these details to the methods.

Line 124: "Coinciding with the recovery of adult neurogenesis, GFAP-TK animals regained the ability to discriminate between their mother and a novel mother". Explain how the difference in interaction time can be interpreted as the ability to discriminate. You could also compute the discrimination index used by several other laboratories (difference of interaction normalized by the total interaction time).

In response to this comment, we describe how the difference in interaction time can be interpreted as the ability to discriminate between novel and familiar mice.

Line 133: "Targeted CNO infusion in Nestin-Cre:Gi mice enabled the inhibition of GiDREADD+ abGC axon terminals present in CA2." Provide data or references to support this claim. Injection of a dye of comparable size to CNO would help. Otherwise, mention that nearby CA3a could be affected as well.

We cannot rule out that nearby CA3a was affected by our cannula infusions of CNO into CA2. Furthermore, since dyes likely diffuse at different rates than CNO, we believe that a dye injection would not eliminate this concern completely. Therefore, we have revised the paper to acknowledge the likelihood that the CNO infusion affected parts of CA3 in addition to CA2. We also changed the title to focus more on the CA2 electrophysiological recordings, which we know were obtained only from the CA2.

Line 150: "When reintroduced to the now familiar adult mouse 6 hours later, after the effects of CNO had largely worn off". Provide data or references supporting this claim.

In response, we cite articles that show behavioral effects of CNO DREADD activation are returned to baseline 6 hrs later.

Line 165: "We found that SWR production is increased during social interaction, with more SWRs produced during novel mouse investigation, presumably during encoding social memories, than during familiar mouse investigation, presumably during retrieval of developmental social memories". How does this compare to the results in Oliva et al, Nature 2021?

The Oliva et al 2021 paper recorded CA2 SWRs during home cage and during post-social stimulus exposure periods of sleep. The timing of the study does not coincide with the measures we made, but we cite the paper.

Line 168: "Inhibition of abGCs in the presence of a social stimulus". How does silencing abGC impact CA2 pyramidal neurons' firing rate?

The direct answer to this question is unknown because we did not measure single units, but based on studies done in the CA3, it is likely that firing rate in CA2 would increase.

Line 203: "abGCs possess a time-sensitive ability to support retrieval of developmental social memories." Can you speculate on the function of the cells later on?

In the revised paper, we speculate about the function of abGCs after they mature and no longer support retrieval of developmental social memories.

Line 229: "GFAP-TK mice were group housed by genotype". Why not housed them with CD1 littermates?

We housed these mice according to genotype to avoid having mice with different levels of abGCs (GFAP-TK + VGCV and CD1 + VGCV) living together in social groups. We did this to avoid potential differences that might emerge in social behavior.

Line 237: "Adult TK, Nestin-cre, and Nestin-cre:Gi offspring underwent a social interaction test in which they directly interacted with the mother". Specify how long was the social interaction time.

In the revised manuscript, we specify that mice interacted with each social stimulus for 5 minutes.

Line 240: "After a 1-hour delay spent in the home cage". Were the mice single-housed or with their littermates during this delay?

In the revised manuscript, we indicate that mice were put back into the home cage with their cagemates during the 1 hr delay period.

Line 241: "The order of stimulus exposure was counterbalanced in all tests." Can you show some data to confirm that the order of presentation did not impair the interaction? Have you considered using your own version of the classical 3-chamber test in order to assess directly the preference for one or the other female mouse?

Our data suggest that the order of testing is not responsible for the observed results. Across all experimental groups without an abGC manipulation (i.e., all direct social interaction assays excluding VGCV+ GFAP-TK trials and CNO+ Nestin-cre:Gi trials), ~84.4% of animals demonstrate a social preference for the novel mother over the mother (CD1 + GFAP-TK VGCV- cohort: 28/33; CD1 VGCV+ cohort: 17/17; CD1 and TK recovery cohort: 24/31; Nestin-cre and Nestin-cre:GI 4-6-week-old abGC cohort: 77/95; 10-12-week-old abGC cohort: 49/55; Total = 195/231 mice with an investigation preference for the novel mother). If stimulus presentation order were to bias social investigation preference toward the first stimulus presented, we would expect the percentage of animals demonstrating a social preference for each stimulus to be around 50%, as roughly half the animals were first exposed to the mother with the other half first exposed to the novel mother. The social novelty preference percentage reported above is comparable to percentages we observe in our lab's novel to familiar social interaction experiments, in which all animals are first exposed to a novel conspecific. We have yet to conduct experiments testing adults using the modified 3-chamber assay described in Laham et al., 2021.

Statistics: The statistical tests used throughout the paper are appropriate but their description is too cursory. Please provide F values and specify the name of the tests used in the figure legends before giving the exact p values.

Author Response

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

eLife assessment

In this valuable study the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3, mediated by the RNA binding protein IGF2BP2. While the study presents interesting and largely solid evidence, part of the work is incomplete, requiring additional controls to more robustly support the major claims. The work would also benefit from further discussion addressing the apparently contradictory effects of circHIPK3 and STAT3 depletion in cancer progression.

Public Reviews:

Reviewer #1 (Public Review):

In this work the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3, by showing that it interacts with an RNA binding protein (IGF2BP2) and, by sequestering it, it regulates the expression of hundreds of genes containing a sequence (11-mer motif) in their untranslated regions (3'-UTR). This sequence is also present in circHIPK3, precisely where IGF2BP2 binds. The study further focuses on one specific case, the STAT3 gene, whose mRNA product is downregulated upon circHIPK3 depletion apparently through sequestering IGF2BP2, which otherwise binds to and stabilizes STAT3 mRNA. The study presents mechanistic insight into the interactions, sequence motifs, and stoichiometries of the molecules involved in this new mode of regulation. Altogether, this new mechanism seems to underlie the effects of circHIPK3 in cancer progression.

Strengths:

The authors show mechanistic insight into a proposed novel "sponging" function of circHIPK3 which is not mediated by sequestering miRNAs but rather by a specific RNA binding protein (IGF2BP2). They address the stoichiometry of the molecules involved in the interaction, which is a critical aspect that is frequently overlooked in this type of study. They provide both genome-wide analysis and a specific case (STAT3) that is relevant for cancer progression.

Weaknesses:

One of the central conclusions of the manuscript, namely that circHIPK3 sequesters IGF2BP2 and thereby regulates target mRNAs, lacks more direct experimental evidence such as rescue experiments where both species are simultaneously knocked down. CircRNA overexpression lacks a demonstration of circularization efficiencies. There seem to be contradictory effects of circHIPK3 and STAT3 depletion in cancer progression, namely that while circHIPK3 is frequently downregulated in cancer, circHIPK3 downregulation in this study leads to downregulation of STAT3. This does not seem to fit the fact that STAT3 is normally activated in a wide diversity of cancers and is positively associated with cell proliferation. The result is neither consistent with the fact that circHIPK3 expression positively correlates with good clinical outcomes. Overall, the authors have achieved some of their aims but additional controls would be advisable to fully support their conclusions.

We thank the reviewer for the important and constructive criticism. All the raised points have now been addressed as described below.

Rescue experiment:

We have now performed the suggested rescue experiment, exploring the potential normalization of target expression upon double knockdown (both circHIPK3 and IGF2BP2). Expression of targets STAT3, NEU and TRAPPC9 were assessed, and all target mRNAs became normalized upon double knockdown, supporting our suggested IGF2BP2 sponging mechanism for circHIPK3. These results have been included in Supplementary Figure 5F.

Circularization efficiency of ectopically expressed circRNAs:

For efficient expression of circRNAs in human cells, we have used a state-of-the-art plasmid construct (Laccase2-circRNA; Kramer et al., 2015, Genes Dev. 2015 Oct 15;29(20):2168-82. doi: 10.1101/gad.270421.115), which has proved superior to many alternatives presented in the literature. To ensure proper circularization efficiency of circHIPK3, we have now subjected purified RNA from transfected HEK293 cells (and from HEK293 Flp-In T-Rex cells with stable integration of cassette) to northern blotting (Supplementary Figure S5H). This demonstrates the production of a single RNase R resistant band of correct size, for both circHIPK3 expression constructs. Due to relatively weak signal to noise ratio (rRNA background), we are unable to calculate an accurate linear-to-circ ratio. Nevertheless, the results suggest efficient production of WT and mutant circHIPK3 using the Laccase2 vector system.

circHIPK3 and STAT3 expression in cancer:

It is correct that STAT3 expression is oden positively correlated with disease progression in many patients suffering from different cancers, and that the observed expression pattern with downregulation of circHIPK3 and STAT3 in BC cells can be perceived as counterintuitive. We note that the STAT3 profile in our time-course knockdown experiments is somewhat dynamic. While downregulation of STAT3 is most pronounced After 24 hrs of circHIPK3 knockdown, the expression tends to be more normalized After 48 and 72 hrs, which could be due to initiating compensatory mechanisms elicited by the cells. Indeed, comparing long-term development of tumors in patients, with numerous primary and accumulating secondary effects, to transient (0-72 hrs) geneexpression analyses has limitations. In addition, despite the oncogenic role of STAT3 having been widely demonstrated, evidence suggest that STAT3 functions are multifaced and not always trivial to classify. Recent evidence has shown that STAT3 can have opposite functions in cancer and act as both a potent tumor promoter and a tumor suppressor (reviewed in Tolomeo and Cascio, 2021, Int J Mol Sci. 2021 Jan; 22(2): 603. doi: 10.3390/ijms22020603). We have now discussed this in more detail (in the discussion section) and stated some of the limitations of our study in terms of the regulation of the STAT3/p53 axis.

Reviewer #2 (Public Review):

The manuscript by Okholm and colleagues identified an interesting new instance of ceRNA involving a circular RNA. The data are clearly presented and support the conclusions. Quantification of the copy number of circRNA and quantification of the protein were performed, and this is important to support the ceRNA mechanism.

We thank the reviewer for the positive feedback.

Reviewer #3 (Public Review):

In Okholm et al., the authors evaluate the functional impact of circHIPK3 in bladder cancer cells. By knocking it down and performing an RNA-seq analysis, the authors found thousands of deregulated genes that look unaffected by miRNAs sponging function and that are, instead, enriched for an 11mer motif. Further investigations showed that the 11-mer motif is shared with the circHIPK3 and able to bind the IGF2BP2 protein. The authors validated the binding of IGF2BP2 and demonstrated that IGF2BP2 KD antagonizes the effect of circHIPK3 KD and leads to the upregulation of genes containing the 11-mer. Among the genes affected by circHIPK3 KD and IGF2BP2 KD (resulting in downregulation and upregulation, respectively) the authors found the STAT3 gene. This was accompanied by consistent concomitant upregulation of one of its targets, TP53. The authors propose a mechanism of competition between circHIPK3 and IGF2BP2 triggered by IGF2BP2 nucleation, potentially via phase separation.

Strengths:

The number of circRNAs continues to drastically grow; however, the field lacks detailed molecular investigations. The presented work critically addresses some of the major pi‘alls in the field of circRNAs and there has been a careful analysis of aspects frequently poorly investigated. The timepoint KD followed by RNA-seq, investigation of the miRNAs-sponge function of circHIPK3, identification of 11-mer motif, identification, and validation of IGF2BP2, and the analysis of copy number ratio between circHIPK3 and IGF2BP2 in assessing the potential ceRNA mode of action have been extensively explored and, comprehensively are convincing.

Weaknesses:

In some parts, the manuscript lacks appropriate internal controls (eg: comparison with normal bladder cells, linear transcript measurements upon the KD, RIP internal controls/ WB analysis, etc), statistical analysis and significance (in some qPCRs), exhaustive description in the methods of microscopy and image analysis, western blot, and a separate section of cell lines used. The use of certain cell lines bladder cancer cells vs non-bladder cells in some experiments for the purpose of the study is also unclear.

Overall, the presented study adds new knowledge in describing circHIPK3 function, its capability to regulate some downstream genes and its interaction and competition for IGF2BP2. However, whereas the experimental part appears technically logical, it remains unclear the overall goal of this study and the final conclusions. The mechanism of condensation proposed, although interesting and encouraging, would need further experimental support and information, especially in the context of cancer.

In summary, this study is a promising step forward in the comprehension of the functional role of circHIPK3. These data could possibly help to better understand the circHIPK3 role in cancer.

We thank the reviewer for the important and constructive criticism. All the raised points have now been addressed as described below.

Internal controls/description of methods:

We have now included suggested internal controls and provided statistical significance measures where needed. We have also described in more detail the usage of different cell lines for different experiments and a comprehensive description of microscopy, image, and western analyses.<br /> The condensation mechanism of circHIPK3 and IGF2BP2 that we propose has been toned down slightly in the discussion, as we agree that these observations are not unequivocal and could potentially be explained by alternative and yet undefined events as discussed in further detail.

Recommendations for the authors:

Major points

(1) In Figure 1B the authors show neither error bars nor statistical analysis. Did they sequence each cell line in single replicates? A clarification on this point would be of help.

All timepoints for J82 and UMUC3 were sequenced in biological triplicates (Figure 1C-G). The data shown in Figure 1B represents prior single RNA-seq runs of all specific cell lines sequenced for selection of appropriate BC cell lines used for further study.

(2) In Figure 1C the quantification of the cognate linear Hipk3 RNA would be desired in order to rule out changes in this species levels that could account for the observed effects upon circHIPK3 KD.

We do not observe a non-specific downregulation of the HIPK3 mRNA upon circHIPK3 knockdown, rather we observe a moderate upregulation at later timepoints. However, western blotting shows that this upregulation is not translated into significantly increased protein levels. This data is now available in Supplementary Figure S1A and S1B.

(3) In Supplementary Figure S1B the authors show the number of differentially expressed genes between time points and baseline upon circHIPK3 KD or scr siRNA transfection. However, in this referee's opinion, the relevant comparison would be the differentially expressed genes between circHIPK3 KD and scr siRNA at different time points. Otherwise, they would be focusing on both circHIPK3-specific and non-specific effects.

The requested comparison is part of the main figures (Figure 1F). The plotted data in Supplementary Figure 1B (Supplementary Figure S1D in the revised version) was included to allow the reviewer to better assess the variability in the data. We therefore believe it provides relevant information and that it should be kept in the final version.

(4) Figure 1E. How many hours of KD do these measurements correspond to? Even if they correspond to 72 h, there seems to be a discrepancy between Fig 1E and 1F in terms of the total number of differentially expressed (DE) genes. Why are there more DE genes in 1E?

The number of differentially expressed genes in Figure 1E represents the total number at all timepoints, while Figure 1F represent single timepoints. We have modified the figure legend to clarify this issue.

(5) In Figure 3B, in order to verify pulldown efficiency, RT-qPCR should be performed instead of endpoint RT-PCR. Otherwise, no robust claim can be made regarding interaction affinities.

We agree that these RIP-PCR results in Figure 3B are only semi-quantitative and therefore do not unequivocally assess binding strength. However, since IGF2BP2 is the RNA binding protein in focus throughout the rest of the study, where additional quantitative RIP-RT-qPCR experiments have been performed, we find this issue negligible. In addition, the semi-quantitative nature of the endpoint PCR experiment has now been mentioned in the main text and figure legend.

(6) The authors claim that IGF2BP2 KD counteracts the effect of circHIPK3 KD on target mRNAs. However, in order to support this claim the authors should perform a rescue experiment where they simultaneously knock down both circHIPK3 and IGF2BP2. Otherwise, the conclusion remains largely supported by a correlation.

Indeed, such an experiment is important. A rescue experiment with double knockdown has now been performed and demonstrates that levels of tested targets; STAT3, NEU and TRAPPC9 become normalized under these conditions, supporting our IGF2BP2/circHIPK3 sponging model. The data is available in Supplementary Figure S5F.

(7) The authors claim that circHIPK3 interacts strongly with IGF2BP2 in bladder cancer cells but not with GRWD1. This is shown in Figure 4A where neither standard errors nor statistical analysis is shown. The authors need to show replicates of this experiment and perform statistics in order to support their claims.

These experiments have been redone with even higher stringency in biological triplicates and fully supports our claims. The data is available in a modified Figure 4A – now including error bars and indications of significance. In addition, we have included western blots demonstrating Input (IN), Flowthrough (FT) and Immunoprecipitation (IP) of correctly sized proteins in Supplementary Figure S4A.

(8) The authors claim that the STAT3 gene, which contains the 11-mer motif in its 3'UTR, becomes downregulated upon circHIPK3 KD in UMUC3 and J82 cells, while it is upregulated upon IGF2BP2 depletion in both cell lines. It is unclear why they show the effect of circHIPK3 KD on STAT3 within a time course while the effect of IGF2BP2 KD in a fixed time point (Figures 5A/S5A and 5B/S5B respectively), and it would be convenient to clarify this point.

The initial time course knockdown experiment for circHIPK3 was conducted to provide a comprehensive dataset for circHIPK3-mediated events and clarify any temporal effects. After identification of IGF2BP2 as an interaction partner of circHIPK3, we chose to harvest cells After knockdown at 48 hrs as knockdown efficiency was prominent at this point. The temporal knockdown efficiency of RNAs (circHIPK3) and proteins (IGF2BP2) differ considerably due to increased stability of proteins compared to target RNA. This is the main reason why only a single timepoint has been assessed.

(9) In Figure 5F the authors show that upon overexpression of wildtype or 11-mer motif-mutant circHIPK3, the binding of IGF2BP2 was reduced while the binding of STAT3 mRNA to IGF2BP2 was increased. In order to rule out differences in circularization efficiencies, it would be convenient to show a northern blot comparing the efficiency of circHIPK3 overexpression relative to its linear cognate RNA for both constructs.

Indeed, circRNA expression constructs may differ considerably in circularization efficiencies. We are using the Laccase2 system developed by the Jeremy Wilusz lab (Kramer et al., 2015), which, at least in our hands, efficiently produces circRNAs from almost any inserted sequence. To address whether the WT and mutant circHIPK3 express similar amounts of circRNA with high efficiency, we performed the suggested northern blot, which displays very similar RNase R resistant circHIPK3 levels. The data is now available in Supplementary Figure S5H. Due to background signal from 18S rRNA in non-RNase R treated samples, we cannot accurately calculate a linear/circular RNA ratio, since no distinct linear RNA species above background is visible on the blot. However, the important part that mutant and WT (RNase R resistant) circRNA are expressed at similar levels, makes us confident about our conclusion that WT circHIPK3 expression interferes with IGF2BP2 binding to STAT3 mRNA.

(10) Figure 1G, several genes were selected as up and downregulated for J82 and UMUc3 cell lines. Were these consistently involved in specific biological processes?

Genes were classified as down or upregulated based on significant (FDR<0.1) fold changes. The most significant genes in both directions were named, disregarding of involvement in any specific biological processes. Initially, we performed a GO-term analysis on these genes and received many hits, but we did not observe a very specific pattern or cluster of genes, suggesting that we are looking at both primary and secondary effects of knocking down circHIPK3. We believe our GSEA of the 50 hallmarks of cancer genes sets, presented in Figure 4D, 4E and Supplementary Figure S4E and S4F is addressing this point in a satisfactory manner.

(11) For differential expression analysis, which data sets were used to group outcomes at different time points. Also, there is an increased number of genes affected after KD - please describe in more detail how you reached that gene number.

As also discussed above (point 3), at each timepoint (Figure 1F) “Scr” was compared to “circHIPK3” knockdown. It makes sense that more and more genes are DE over the course of time as both primary and secondary effects of knockdown will build up over time. We have now clarified which datasets have been used in the figure legend and rewritten the Methods’ section on differential expression analysis.

(12) What happens with the expression of circHIPK3 if STAT3 is KD? What biological processes are modulated by silencing circHIPK3?

(13) What happens in bladder cancer cells if STAT3 and circHIPK3 are KD?

The main goal of our work is to clarify how circRNAs (here circHIPK3) affect gene-expression and cancer pathways. While it would be interesting to explore the consequences of STAT3 knockdown and in combination with circHIPK3, such experiments would require comprehensive additional analyses (RNA-seq), which we believe is beyond the scope of this study at this point.

(14) The rationale of the study and conclusions are unclear. Quote "we extensively evaluate the functional impact of circHIPK3 in bladder cancer cells". As previously published by the authors, as well as mentioned in the manuscript, circHIPK3 is downregulated in cancers and possesses tumor suppressor functions in bladder cancers. Could the authors clarify how the results of the presented study based on the depletion of circHIPK3 fit with the previous discoveries? If the circHIPK3 is generally downregulated compared to normal cells (although higher compared to the linear transcript) why do the authors use a KD approach? Are the bladder cancer cells simply a cell model to study circRNA vs linear? How the condensation model reconciles with circHIPK3 tumor suppressor function based on these results?

We believe that it remains unclear whether circHIPK3 is a direct tumor suppressor, although this is possible judged from the clinical patient data, since STAT3, which has been shown to become activated in many cancers, is also downregulated upon circHIPK3 knockdown. However, differences in immediate effects on gene-expression of circHIPK3 knockdown (0-72 hrs) and long-term development of tumors within patients, may be difficult to compare directly. If STAT3 downregulation contributes to cancer phenotypes in bladder cancer as suggested for several other cancer types (Glioblastoma, prostate cancer, lung cancer etc.) circHIPK3 may indeed still be classified as a tumor suppressor in bladder cancer. It is worth noting that circHIPK3 has been shown to be upregulated and have oncogenic phenotypes in many other cancers, which makes direct correlations between cancers complex and difficult to reconcile. We have revised the discussion to reflect these issues in a more comprehensive fashion. To fully delve into STAT3 regulation in terms of bladder cancer development, progression, cell invasiveness, and survival, we believe are more suitable for future experiments.

At this point, we have identified a novel mechanism of a circRNA deregulated in cancer being able to sponge/regulate the function of an oncogenic RNA binding protein, even though it is severely outnumbered in cells. Importantly, circHIPK3 likely does not function as a miRNA sponge as previously proposed in several previous studies based on circRNA overexpression, reporter constructs and miRNA mimics. We therefore believe that these findings provide new important insights into circHIPK3 function and that the current understanding of circRNAs functioning primarily as miRNA sponges, likely should be revised.

(15) Related to the previous point, if the purpose is to study the role of circHIPK3 in bladder cancer, there is a bit of a lack of consistency and it is sometimes confusing to understand the use of certain cell lines for specific experiments. The initial circHIPK3 KD experiments have been conducted in 2 (out of 11 not malignant/ metastatic) bladder cancer cell lines (J82 and UMUC3). Why this specific selection of exclusively metastatic bladder cell lines? For comparison are the normal bladder cell lines characterized by the same circRNA vs linear ratio?

The selection of bladder cancer cell lines (J82, UMUC3 and FL3) is based on several criteria including expression levels of circHIPK3, cell maintenance characteristics and knockdown/transfection efficiencies. Initially, we included HT1197 cells as well, but batch effects precluded the use of these data.

Furthermore, the subsequent miRNA analysis was conducted exclusively in one bladder cell line (J82 but not in UMUC3), the initial identification of motif again in bladder cells but the initial RBP identification and experimental interaction is conducted in non-bladder cells HepG2 and k562 (reported as main figure 3B) and only subsequently in bladder cell (4A), again in a different cell line (only FL3, but not in J82 and UMUC3). The validation of the interaction of STAT3 by RIP is performed exclusively in FL3. All this also makes someone wonder how specific this mechanism/binding is in bladder cancer cells. There is an attempt to explain this by comparing cell cycle progression analysis upon circHIPK3 KD and IGF2BP2 KD later on but the final conclusions of this analysis remain unclear. The authors should provide more explanation and information in this part of the manuscript.

It is correct that the different bladder cancer cell lines (FL3, J82 and UMUC3) have been used more or less interchangeably between experiments. This is due to the observed common phenotypes, e.g. sharing up to 92% DE genes, and highly significant enrichment of the IGF2BP2 11-mer-motif in downregulated mRNAs upon circHIK3 knockdown in all three cell lines. The ENCODE cell lines HepG2 and K562 were used since the accessible RBP-CLIP data originates from the ENCODE project, where these cells have been used exclusively. Hence, we validated the binding of candidate RBPs (semi-quantitatively) in HepG2 and K562 prior to assessing their RNA binding in the BC cell line FL3. We have used FL3 for RIP and validation of IGF2BP2 binding mainly due to better transfection efficiency and higher expression levels, allowing detection all interrogated components. The fact that we have included three BC cell lines in many experiments instead of only one, and obtained consistent results, solidifies the conclusions that our phenotypes and regulatory mechanisms are likely common for most, if not all, bladder cancer cell lines. We have included a paragraph in the materials and methods section to further clarify the usage of cell lines in the different experiments.

(16) STAT3 gene is used as an example. Where is this gene coming from? How has this gene been selected? Is there any complete list of RNA-seq data of up/down-regulated genes upon circHIPK3 KD? The raw data and gene list should be publicly available to the reviewers.

STAT3 is a major regulator of cancer pathways and therefore an interesting candidate for further analysis as it is differentially expressed between control and circHIPK3 knockdown in all cell lines. We have now included the complete list of DE genes from the time-resolved RNA-seq analyses (DESeq2 output files) in the supplementary material. This data is now available in Supplementary Tables S6 and S7.

(17) In performing the KD of circHIPK3 the authors use a unique siRNA on a splice junction. The authors claim that this is a way to not affect the linear transcript, however, have the authors also ensured experimentally that this doesn't affect in any way the linear RNA? This should be included as an initial internal control.

We do not observe a downregulation of the HIPK3 mRNA upon circHIPK3 downregulation, rather we observe a moderate upregulation at later timepoints. When assessing the HIPK3 protein levels, we observe no significant change After 48 hrs of knockdown. This data is now available in Supplementary Figure S1A and S1B.

(18) Additional controls should be provided for RIP, especially for Fig3B and 4A, Sfig4, 5C such as an internal positive control (es: AGAP2-AS1) of the correct pulldown of IGF2BP2 and/or WB should be shown (in the methods it is told that WB has been used for the analysis of RIP but I couldn't find any)

Indeed, IGF2BP2 likely binds to many mRNAs in the cell. We have now included b-actin mRNA as a low affinity control in the Figure 4A RIP data, showing that circHIPK3 represents a tight binding substrate for IGF2BP2. We have also included a western blot showing the IP of IGF2BP2, IGF2BP2, GRWD1 and GFP. This data is now available in Supplementary Figure S4A.

(19) Additional internal experimental controls should be included to assess the successful transfection and overexpression of circHIPK3 with the laccase-2 driven plasmid and mutated versions before the RIP in 4B and in the 5F. Supportive controls to show equal transfection would be required for Figure 6C-D. Further controls to show that the ASO specifically targets the 11-mer in circHIPK3 but not IGF2BP2 target genes should also be included. Please include this information in the supplementary materials.

We have now included a northern blot showing successful transfection and expression of RNase R resistant circHIPK3 from the Laccase2 vector (WT and mutant) in relation to RIP experiments. This data is now available in Supplementary Figure S5H (see also comments about this above). Equal transfections in cells shown in Figure 6C-D is assessed by comparable levels of GFP expression, which is included as an expression cassette in the modified Laccase2 construct. Pictures were acquired with same exposure time and scaling to ensure that they can be compared directly. The ASO targets circHIPK3 with full complementarity, while STAT3 mRNA has 2 mismatches, leaving the “lesser interaction” with STAT3 theoretical. This has now been clarified in the main text.

(20) Specifically, in 1C and 4A, Sfig4 there is no statistical analysis made and/or significance? This is only reported for the RIP experiment in Fig 5C.

Statistical analyses have now been performed and shown in Figure 4A and we have included binding of ACTB as a low affinity control. In Figure 1C, which displays knockdown efficiency (highly efficient) at the various timepoints, no statistical significance has been displayed, since this is normally not done for such knockdown experiments. In addition, it is also not clear which comparisons would be beneficial. Except for the J82 cell line at 12 hrs compared to 0 hrs, knockdown efficiency is high and statistically significant at all timepoints.

(21) In the assessment of copy number ensuring the same primer efficiency is fundamental, it can't be simply "assumed". Please clarify this point and possibly include this information in the supplementary materials.

It is correct that identical, or at least very similar, primer efficiencies are necessary to make the conclusion that the relationship between GAPDH mRNA and circHIPK3 levels in the cell reflects the quantitatively measured number of molecules. However, since this single comment is only to support the quantitatively measured circHIPK3 molecules by a ballpark estimate, and since we already assume that there are an estimated 10.000-20.000 copies of GAPDH mRNAs in most cells (which we also do not know precisely), we have chosen to remove this statement.

(22) The methodology section is not well organized and looks incomplete. For example, there are two separate sections for circHIPK3 expression conducted in different cell lines, this would be better explained in a single paragraph.

We have now rewritten this section to make it clearer.

The section reporting cell lines and growth conditions is incorporated in "circHIPK3 KD and overexpression" while it should be a separate paragraph and valid for all experiments where these cells have been used. There is no information regarding Western blots, including Antibodies used, and densitometry performed.

This information has now been included.

In "immunofluorescence microscopy" it is not clear what microscope has been used, how many acquisitions have been made, and how acquisition has been performed. Related to this, how the image analysis has been performed? Figures 5I-J "Finally, immunofluorescence staining showed that nuclear and overall STAT3 protein levels are significantly lower upon circHIPK3 KD, while nuclear p53 protein levels are higher" and 6C and D "we observed a significantly higher prevalence of large cytoplasmic condensates in cells expressing high levels of circHIPK3 compared to controls" how this quantification has been made? The conclusive part about the condensation role remains a bit too loose and mostly speculative, largely due to the lack of robust information provided on microscopy and image analysis

We have now included a better description of the acquisition and quantification methods.

Minor

(1) The Van Nostrand et al 2018 citation should refer to the updated publication in Nature and not to the original preprint in Biorxiv.

This reference has now been updated.

(2) In Supplementary Figure S3B, the authors offer no explanation as to why genes that become upregulated upon circHIPK3 knockdown generally contain more circHIPK3-RBP binding sites other than for IGF2BP2. A clarification would be of help.

We do not have any evidence to explain this observation. One possibility is that other RBPs elicit mRNA stabilizing effects on average, whereas abundant IGF2BP2 (~ 120.000-200.000 copies per cell) now able to bind more target mRNAs and elicit destabilization. This remains highly speculative though.

(3) In Supplementary Figure S3D, the authors' claim that the 11-mer motif is found more bound to IGF2BP2 than for other circHIPK3-RBPs should be referred to the corresponding dataset/reference.

This information is stated in the figure legend (K562) and we have now included it in the main text as well: “We evaluated how oden binding sites of circHIPK3-RBPs overlap the 11-mer motif and found that this is more oden the case for IGF2BP2 binding sites than binding sites of the other circHIPK3-RBPs when scrutinizing K562 datasets (Supplementary Figure S3D)”.

(4) In Figure 4C the authors show that, according to previously performed experiments of their group, the 11-mer motif is enriched in upregulated genes compared to downregulated genes upon IGF2BP2 KD in UMUC3. This seems like a confirmation of the results presented in the preceding section (Figure 3H) and it would be clearer if it were presented in the same section.

The data in Figure 3H is based on ENCODE data from IGF2BP2 knockdowns in K562 cells, while in Figure 4C these are from IGF2BP2 knockdown followed by sequencing in UMUC3 cells. We believe the timing of the data is fitting as is, since they relate to non-BC cells and BC cells, respectively.

(5) More in vitro experiments are needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype, and how different cancer hallmarks are modulated by this ceRNA network.

We agree that this study does not fully clarify how these complex molecular interactions relate to bladder cancer progression, including fluctuations of key cancer genes/proteins. Since our focus has been on the mechanisms of circRNA function in relation to bladder cancer, these issues will await further future experimentation.

(6) "apparent" competition (introduction - pag4)? Maybe rephrase more appropriately.

This has been rephrased and “apparent” excluded.´

(7) Fig1C. Relative quantification. Statistical analysis? Is this significant?

See also comment to point 20 above. In Figure 1C we show the knockdown efficiency at the different timepoints. At all timepoints knockdowns are highly significant compared to the control (Scr), which is not significantly changed over time. It seems somewhat redundant to include pvalues for such data. Also, which comparisons should be highlighted? Knockdown is highly efficient, which is what we want to show.

(8) Figure 5H. Western blot. Densitometry quantification performed, how?

This is now described in the Materials and Methods section.

(9) Please specify the concentration of circHIPK3-specific siRNA used.

20 nM. The information is included in the Materials and Methods section.

(10) The control sample refers to scrambled or untreated cells? Instead of using "control samples without siRNA transfection" or "No siRNA" use untreated cells - otherwise, it is a bit confusing.

This has now been modified.

(11) Figure 3 is starting with hepatocellular and leukemia cells; why not with bladder cells?

These experiments were performed based on CLIP-data and RBP knockdown data from the ENCODE project. The cells used are limited to HepG2 and K562.

(12) For Figure 4B, which is the time-point?

This is 24 hrs. Has now been stated.

(13) Figure 5I and J, the expression of STAT3 and circHIPK3 can be also investigated for cellular distribution.

The expression of STAT3 is investigated in Figure 5I. Localization of circRNA by standard RNA-FISH protocols using multiple (>20) probes is inherently difficult due to the cross reaction of probes with the linear mRNA. Certain amplification steps can be included if using a single backsplicing junction probe, but this is oden giving rise to highly ambiguous results as specificity is very limited due to the “one probe“ nature of the design.

(14) Some discussion of the limitations of the study would be of value.

We have included this in the discussion.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors were attempting to determine the extent that CIH altered swallowing motor function; specifically, the timing and probability of the activation of the larygneal and submental motor pools. The paper describes a variety of different motor patterns elicited by optogenetic activation of individual neuronal phenotypes within PiCo in a group of mice exposed to CIH. They show that there are a variety of motor patterns that emerge in CIH mice; this is apparently different than the more consistent motor patterns elicited by PiCo activation in normoxic mice (previously published).

Strengths:

The preparation is technically challenging and gives valuable information related to the role of PiCo in the pattern of motor activation involved in swallowing and its timing with phrenic activity. Genetic manipulations allow for the independent activation of the individual neuronal phenotypes of PiCo (glutamatergic, cholinergic) which is a strength.

We thank the reviewers for acknowledging and summarizing the strengths of this study.

Weaknesses:

(1) The data presented are largely descriptive in terms of the effect of PiCo activation on the probability of swallowing and the pattern of motor activation changes following CIH. Comparisons made between experimental data acquired currently and those obtained in a previous cohort of animals (possibly years before) are extremely problematic, with the potential confounding influence of changing environments, genetics, and litter effects. The statistical analyses (i.e. comparing CIH with normoxic) appear insufficiently robust. Exactly how the data were compared is not described.

Yes, we agree the data are descriptive in terms of characterizing the effect of CIH on PiCo activation. However, we would like to emphasize that the data are also mechanistic because they characterize the effects of specifically, optogenetically manipulating PiCo neurons after being exposed to CIH.

Thank you for this comment and for pointing out our misleading description in the paper. This manuscript is meant to independently characterize the effects of CIH to the response of PiCo stimulation. We are not making direct comparisons between the previously published manuscript where mice were exposed to room air. There has been no statistical analysis made between previously published control and current CIH data, since we are not making a direct comparison, only an observational comparison.

To make this clearer, and to address the reviewers concern, we have removed the room air data from figures 1E, 2C and 3A. However, we believe it is important to keep the data from mice exposed to room air in Figure 2B since we did not include this information in the previously published manuscript. It is important to point out that all mice exposed to CIH have some form of submental activity during laryngeal activation in response to PiCo stimulation. This is not the case when mice are exposed to room air only. In this figure, only descriptive analysis are presented. We adjusted our wording throughout the text, particularly in the discussion, to eliminate any confusion that we are making direct comparisons between the two studies. The following sentence has been added to the discussion “While we do not intend to make direct quantitative comparisons between the previously published PiCo-triggered swallows in control mice exposed to room air (Huff et al 2023) and the data presented here for mice exposed to CIH, we believe it is important to compare the conclusions made in these two studies.” This was the motivation for using the eLife Advance format. Since the present study demonstrates that PiCo affects swallow patterning which was not observed in the control data.

(2) There is limited mechanistic insight into how PiCo manipulation alters the pattern and probability of motor activation. For example, does CIH alter PiCo directly, or some other component of the circuit (NTS)? Techniques that silence or activation projections to/from PiCo should be interrogated. This is required to further delineate and define the swallowing circuit, which remains enigmatic.

We agree with the reviewer that our study raises many more questions than we are able to answer at the moment. This however applies to most scientific studies. Even though swallowing has been studied for many decades, the underlying circuitry remains largely enigmatic. We will continue to investigate the role of PiCo and its interaction with the NTS, in healthy and diseased states. These investigations require many different techniques, and approaches, some of which are still in development. For example, we are currently conducting experiments that silence portions of the NTS related to swallow and PiCo: ChAT/Vglut2 neurons using novel unpublished viral approaches. However, these are separate and ongoing studies beyond the scope of the current one.

To address the reviewer’s comment, we have added to the following to the limitation section: “In addition, this preparation does not allow for recording of PiCo neurons to evaluate the direct effects of CIH in PiCo neuronal activity”. The following has also been added to the discussion: “Rather, our data reveal CIH disrupts the swallow motor sequence which is likely due to changes in the interaction between PiCo and the SPG, presumably located in the cNTS. While it has previously been demonstrated that PiCo is an important region in swallow-breathing coordination (Huff et al., 2023), previous studies did not demonstrate that PiCo is involved in swallow motor patterning itself. Here we show for the first time that CIH leads to disturbances in the generation of the swallow motor pattern that is activated by stimulating PiCo. This suggests that PiCo is not only important for coordinating swallow and breathing, but also modulating swallow motor patterning. Further studies are necessary to directly evaluate the presumed interactions between PiCo and the cNTS.”

(3) The functional significance of the altered (non-classic) patterns is unclear.

Like in our original study, the preparation used to stimulate PiCo does not allow to simultaneously characterize the functional significance of swallowing. Therefore, we have included this as a limitation in the limitation section: “In this preparation we are unable to directly determine the functionality of the variable swallow motor pattern seen after CIH. Different experimental techniques, such as videofluoroscopy would need to be used to directly evaluate functional significance. This technique is beyond the scope of this study and not possible to perform in this preparation. We acknowledge this limits our ability to make direct comparisons between dysphagic swallows in OSA patients.”

Reviewer #1 (Recommendations For The Authors):

(1) A more rigorous experimental approach is required. Littermates should be separated and exposed to either room air or CIH at the same (or close to the same) time.

As stated above, we did not directly compare mice exposed to room air with mice exposed to CIH. Hence, we believe this is not necessary, and it would have meant repeating all the experiments already published in the original eLife paper.

(2) Robust statistical analyses are required to determine whether the effects of CIH on the pattern/probability of motor activation are required.

Since control and CIH group were not compared in this study, statistical hypothesis testing is not appropriate or applicable.

(3) Use a combination of retrograde, Cre- AAVs and Cre-dependent approaches to interrogate the circuitry to/from PiCO that forms the swallowing network. This is what is needed to push this area forward, in my view.

Thank you for this suggestion, we will consider this suggestion as we plan for future experiments. Indeed, we are in the process of developing novel approaches. However, in this context we would like to emphasize that further network investigations are exponentially more complicated given that we need to use a Flpo/Cre approach to specifically characterize the glutamatergic-cholinergic PiCo neurons. Most other laboratories that have studied PiCo have avoided this experimental complication and used only a “cre-dependent” approach. This approach is much simpler, but the data are much less specific and the conclusions sometimes misleading. Stimulating for example cholinergic neurons in the PiCo area will also activate Nucleus ambiguus neurons, stimulating glutamatergic neurons will also activate glutamatergic neurons that are not necessarily the glutamatergic/cholinergic neurons that we use to define PiCo specifically. Readers that are unfamiliar with these different approaches often miss this important difference. Hence, compared to stimulating other areas, stimulating the cholinergic-glutamatergic neurons in PiCo is much more specific than e.g. stimulating preBötzinger complex neurons. There are no markers that will specifically stimulate only preBötzinger complex neurons or neurons in the parafacial Nucleus. Unfortunately, this difference is often overlooked.

(4) It should be made more clear how each of the "non-classic" swallowing patterns could cause dysfunction - especially to the reader who is not completely familiar with the neural control of swallowing.

We agree that it would be helpful to understand the functional implications of these alterations in swallow-related motor activation, however since our approach does not allow us to use any tools to measure or evaluate functional activity it would be inappropriate to make suggestions of this type without any data to back up our conclusion. This is why we have not speculated on the functional implications. We have added the following to the discussion section of this manuscript. “While fine wire EMG studies are an excellent evaluation tool to observe temporal motor pattern of sequential swallow related muscles; it must be combined with tools such as videofluoroscopic swallow study (VFSS) and/or high resolution manometry (HRM) in order to characterize the functional significance of these alterations to the swallow motor pattern shown in this study (Park et al., 2017). Since the preparation in this study utilizes only fine wire EMGs we are not able to evaluate or comment on the functional significance of the variable swallow motor patterns. ”

Minor:

The Results should be written in a way that better conveys the neurophysiological effects of the manipulations. As it stands, it reads like a statistical report on how activation of each neuronal phenotype is statistically different from each other. As such it is difficult to read and understand the salient findings.

Thank you for this insight. We have adjusted the language in the results section.

Reviewer #2 (Public Review):

Summary:

In this study, the authors investigated the role of a medullary region, named Postinspiratory Complex (PiCo), in the mediation of swallow/laryngeal behaviours, their coordination with breathing, and the possible impact on the reflex exerted by chronic intermittent hypoxia (CIH). This region is characterized by the presence of glutamatergic/cholinergic interneurons. Thus, experiments have been performed in single allelic and intersectional allelic recombinase transgenic mice to specifically excite cholinergic/glutamatergic neurons using optogenetic techniques, while recording from relevant muscles involved in swallowing and laryngeal activation. The data indicate that in anaesthetized transgenic mice exposed to CIH, the optogenetic activation of PiCo neurons triggers swallow activity characterized by variable motor patterns. In addition, these animals show an increased probability of triggering a swallow when stimulation is applied during the first part of the respiratory cycle. They conclude that the PiCo region may be involved in the occurrence of swallow and other laryngeal behaviours. These data interestingly improve the ongoing discussion on neural pathways involved in swallow-breathing coordination, with specific attention to factors leading to disruption that may contribute to dysphagia under some pathological conditions.

The Authors' conclusions are partially justified by their data. However, it should be acknowledged that the impact of the study is to a certain extent limited by the lack of knowledge on the source of excitatory inputs to PiCo during swallowing under physiological conditions, i.e. during water-evoked swallowing. Also the connectivity between this region and the swallowing CPG, a structure not well defined, or other brain regions involved in the reflex is not known.

We thank the reviewer for the comments and the strength of the paper. However, with regards to the “lack of knowledge”, we would like to emphasize that PiCo was first described in 2016, while e.g. the preBötzinger complex was described in 1991. Thus, it is not fair to assume the same level of anatomical and physiological understanding for PiCo as we became accustomed to for the preBötzinger complex. We are fairly confident that in 25 years from now, our knowledge of the in- and outputs of PiCo will be much less limited than it currently is.

Strengths:

Major strengths of the manuscript:

• The methodological approach is refined and well-suited for the experimental question. The in vivo mouse preparation developed for this study takes advantage of selective optogenetic stimulation of specific cell types with the simultaneous EMG recordings from upper airway muscles involved in respiration and swallowing to assess their motor patterns. The animal model and the chronic intermittent hypoxia protocol have already been published in previous papers (Huff et al. 2022, 2023).

• The choice of the topic. Swallow disruption may contribute to the dysphagia under some pathological conditions, such as obstructive sleep apnea. Investigations aimed at exploring and clarifying neural structures involved in this behaviour as well as the connectivity underpinning muscle coordination are needed.

• This study fits in with previous works. This work is a logical extension of previous studies from this group on swallowing-breathing coordination with further advances using a mouse model for obstructive sleep apnea.

We thank the reviewers for acknowledging and summarizing the strengths of this study.

Weaknesses:

Major weaknesses of the manuscript:

• The Authors should be more cautious in concluding that the PiCo is critical for the generation of swallowing itself. It remains to demonstrate that PiCo is necessary for swallowing and laryngeal function in a more physiological situation, i.e. swallow of a bolus of water or food. It should be interesting to investigate the effects of silencing PiCo cholinergic/glutamatergic neurons on normal swallowing. In this perspective, the title should be slightly modified to avoid "swallow pattern generation" (e.g. Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production).

Thank you for pointing out that this manuscript suggest PiCo is necessary for swallow generation. We agree further interventions to silence specifically PiCo ChAt/Vglut2 neurons will be necessary to investigate this claim. Which we have begun to evaluate for a future study by developing a novel as yet unpublished approach. We have altered language throughout the text to limit the perception that PiCo is the swallow pattern generator. We have also changed the title to say: Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production

• The duration of swallows evoked by optogenetic stimulation of PiCo is considerably shorter in comparison with the duration of swallows evoked by a physiological stimulus (water). This makes it hard to compare the timing and the pattern of motor response in CIH-exposed mice. In Figure 1, the trace time scale should be the same for water-triggered and PiCo-triggered swallows. In addition, it is not clear if exposure to CIH alters the ongoing respiratory activity. Is the respiratory rhythm altered by hypoxia? If a disturbed or irregular pattern of breathing is already present in CIH-exposed mice, could this alteration interfere with the swallowing behaviour?

Thank you. We have changed the time scale so that all representative traces are on the same time scale.

We explained in the original paper (Huff et al 2023) that the significant decrease in PiCo-evoked swallow duration compared to water evoked is likely due to the absence of oral/upper airway feedback. We are not making comparisons of the effects of CIH on swallow motor pattern between water-evoked and PiCo-evoked. Rather, we are only characterizing the effects of CIH on the swallow motor pattern in PiCo-evoked swallows. The purpose of Figure 1A is to show that the rostocaudal submental-laryngeal sequence in water-evoked swallows is preserved in “canonical” PiCo-evoked swallow like is shown in the original study. While we did not measure the effects of CIH on breathing and the respiratory pattern in this study, it has been established, by others, that CIH causes respiratory muscle weakness, impaired motor control of the upper airway and variable respiratory rhythm and rhythm generation. However, when characterizing the timing of swallow in relation to inspiration (Figure 1 Figure Supplement 1) and the reset of the respiratory rhythm (Figure 3 figure supplement 1) and by observationally comparing these results with mice exposed to room air (Huff et al 2023) we do not observe any obvious differences in swallow-breathing coordination. However, a separate study in wild-type mice focusing on a characterization of swallowing via water after CIH would be better suited to achieve a better understanding of the physiological changes of swallowing after CIH. We would like to point out that this has shown in Huff et al 2022 that altering respiratory rate/pattern via activation of various preBötzinger Complex neurons does not change swallow behavior. Except in the case of Dbx1 PreBötC neuron activation, which was independent of CIH. Increasing or decreasing respiratory rate via activation of PreBötC Vgat and SST neurons did not change the swallow pattern rather it changed the timing of when swallows occurred. It has been reported before by others that swallow has a hierarchical control over breathing and has the ability to shut breathing down. We believe that the swallowing behavior is independent of respiratory pattern and alterations in breathing pattern does not necessarily affect the swallow motor pattern rather could affect the swallow timing.

Reviewer #2 (Recommendations For The Authors):

Abstract

Lines 37-41 "Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly the generation of swallow motor pattern was significantly disturbed."

It should be better:

"Here we show that optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH does not alter swallow-breathing coordination, but unexpectedly triggers variable swallow motor patterns".

Thank you, this has been changed

Lines 41-43 "This suggests, glutamatergic-cholinergic neurons in PiCo are not only critical for the gating of postinspiratory and swallow activity but also play important roles in the generation of swallow motor pattern." I suggest removing any language claiming PiCo is swallow gating and change "generation" in "modulation"

"This suggests that glutamatergic-cholinergic neurons in PiCo are not only critical in regulating swallow-breathing coordination but also play important roles in the modulation of swallow motor pattern."

Thank you, this has been changed

Introduction:

Line 88-90: Actually, in Huff et al. 2023 it is said "PiCo acts as an interface between the swallow pattern generator and the preBötzinger complex to coordinate swallow and breathing". Please, change accordingly. Please, remove Toor et al., 2019 since their conclusions are quite different.

Line 100-101: Please, change the sentence according to the comments reported above.

Thank you, this has been changed

Results:

Lines 104-105: Did you mean: "We confirmed that optogenetic stimulation of PiCo neurons in ChATcre:Vglut2FlpO:ChR2 mice exposed to CIH triggers swallow and laryngeal activation similar to the control mice exposed to room air (Huff et al., 2023)." Otherwise, the sentence is not clear.

Thank you, this has been changed

Lines 129-130: This finding is not surprising since similar results have been reported in Huff et al. 2023.

Thank you, we wanted to confirm that CIH did not alter this characteristic, which it did not. We believe that it is important to include this as it is a criterion for characterizing laryngeal activation.

Lines 219: The number of water swallows is considerably lower than stimulation-evoked swallows. Why?

We inject water into the mouth three times. Typically, there is one swallow in response to each water injection. Pico is stimulated 25 times at each duration. If we were to stimulate swallow with water as many times as optogenetic stimulation there would be an adaptive response to the water stimulation and the mouse would not respond. This does not seem to be the case with PiCo stimulation. Simple answer is, there are many more PiCo stimulations than water stimulation.

Lines 228-232: "PiCo-triggered swallows are characterized by a significant decrease in duration compared to swallows evoked by water in ChATcre:Ai32 mice (265 {plus minus} 132ms vs 144 {plus minus} 101ms; paired t-test: p= 0.0001, t= 5.21, df= 8), Vglut2cre:Ai32 mice (308 {plus minus} 184ms vs 125 {plus minus} 44ms; paired t-test: p= 0.0003, t= 6.46, df= 7), and ChATcre:Vglut2FlpO:ChR2 mice (230 {plus minus} 67ms vs 130 {plus minus} 35ms; paired t-test: p= 0.0005, t= 5.62, df= 8) exposed to CIH (Table S1).".

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Line 252 and 254: remove SEM.

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Discussion

Line 267: ...(Figure 1Bi), while 28% of PiCo-triggered swallows...

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Lines 283-290: "Thus, CIH does not alter PiCo's ability to coordinate the timing for swallowing and breathing. Rather, our data reveals that CIH disrupts the swallow motor sequence likely due to changes in the interaction between PiCo and the SPG, presumably the cNTS.

While it has previously been demonstrated that PiCo is an important region in swallow-breathing coordination (Huff et al., 2023), previous studies did not demonstrate that PiCo is involved in swallow pattern generation itself. Thus, here we show for the first time that CIH resulted in the instability of the swallow motor pattern activated by stimulating PiCo, suggesting PiCo plays a role in its modulation.".

Thank you, this has been changed

Could the observed effects be due to a non-specific effect of hypoxia on neuronal excitability? In addition, it should be considered that PiCo-triggered swallows lack the behavioural setting of water-evoked swallows and do not activate the sensory component of the SPG to the same extent as the water-evoked swallows.

Yes, this is very possible. We stated in our first manuscript that the decrease in PiCo-triggered swallow duration, as compared to water-triggered swallow duration, is likely because oral sensory components are not being activated to the same extent (Huff et al. 2023). Since we do not directly measure neuronal excitability, it is not known (in this study) whether CIH causes changes in the excitability to swallow related areas. However, others have shown increased excitability and activity of Vglut2 neurons after CIH exposure (Kline et al 2007,2010), and we have shown e.g. changes in the excitability of preBötC neurons (Garcia et al. 2016, 2017).

Lines 293-300: The sentence is not clear. Is there any evidence indicating that glutamatergic neurons are differently affected by hypoxia than cholinergic neurons?

Thank you, these sentences have been changed to increase clarity. The section now reads: There was no statistical difference in the probability of triggering a swallow during optogenetic stimulation of ChATcre:Ai32, Vglut2cre:Ai32 and ChATcre:Vglut2FlpO:ChR2 neurons in mice exposed to room air (Huff et al 2023). However, when exposed to CIH, ChATcre:Ai32 and Vglut2:Ai32 mice have a lower probability of triggering a swallow -- in some mice swallow was never triggered via PiCo activation, while water-triggered swallows remained – compared to the ChATcre:Vglut2FlpO:ChR2 mice. While it is possible that portions of the presumed SPG remain less affected by CIH, which could offset these instabilities to produce functional swallows, our data suggest that PiCo targets microcircuits within the SPG that are highly affected by CIH. The NTS is a primary first site for upper airway and swallow-related sensory termination in the brainstem (Jean, 1984). CIH induces changes to the cardio-respiratory Vglut2 neurons, resulting in an increase in cNTS neuronal activity (Kline, 2010; Kline et al., 2007), as well as changes to preBötzinger neurons (Garcia et al., 2017; Garcia et al., 2016) and ChAT neurons in the basal forebrain (Tang et al., 2020). It is reasonable to suggests that CIH has differential effects on neurons that only express ChATcre and Vglut2cre versus the PiCo-specific interneurons that co-express ChATcre and Vglut2FlpO, emphasizing the importance of targeting and manipulating these PiCo-specific interneurons.”

Lines 372-374: "Here we show that PiCo, a neuronal network which is critical for the generation of postinspiratory activity (Andersen et al. 2016) and implicated in the coordination of swallowing and breathing (Huff et al., 2023), is severely affected by CIH.".

Thank you, this has been changed.

Methods

Line 398: Did you mean Slc17a6-IRES2-FlpO-D?

Thank you, this has been changed.

Line 399: were.

Thank you, this has been changed.

Line 403: ... expressing both ChAT and Vglut2 and will be reported as ChATcre:Vglut2FlpO.

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Line 437: Mice of the ChATcre:Ai32, Vglut2cre:Ai32 and ChATcre:Vglut2FlpO:ChR2 lines were kept in collective cages with food and water ad libitum placed inside custom-built chambers.

Thank you, this has been changed.

Line 479: (Figure 6a in Huff et al., 2022).

Line 497: What does Fig 7 refer to?

This should say Figure 1- figure supplement 2, This has been changed

Lines 501-506: "First, swallow was stimulated by injecting 0.1cc of water into the mouth using a 1.0 cc syringe connected to a polyethylene tube. Second, 25 pulses of each 40ms, 80ms, 120ms, 160ms and 200ms continuous TTL laser stimulation at PiCo was repeated, at random, throughout the respiratory cycle. The lasers were each set to 0.75mW and triggered using Spike2 software (Cambridge Electronic Design, Cambridge, UK). These stimulation protocols were performed in all ChATcre:Ai32, Vglut2cre:Ai32, and ChATcre:Vglut2FlpO:ChR2." .

Thank you, this has been changed.

Line 526 and 540: (Fig.6 in Huff et al., 2022) and (Fig.6d in Huff et al., 2022).

Thank you, this has been fixed

Line 594: Figure 5 doesn't exist. Please, change the sentence.

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Line 595 and 609: The reference Kirkcaldie et al. 2012 is referred to the neocortex and doesn't seem appropriate. Please, quote the atlas of Paxinos and Franklin.

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Reference:

Please, correct throughout the text editing of references by removing e.g J.M. or A. or David D. and so on. Only surnames should be mentioned.

Thank you, this has been changed.

Figures:

Figure 1. A and B as well as the purple arrow are lacking. In addition, optogenetic stimulation is applied during different periods of inspiratory activity and this could impact the swallow motor pattern. In Bv, Non-LAR seems very similar to LAR. In panel E, please add the number of animals.

Thank you, this has been fixed.

We used the same optogenetic protocols in the original paper (Huff et al. 2023) and did not observe any changes to the swallow motor patter in relation to the time PiCo was stimulated. The only phase dependent response seen in both control and CIH is when PiCo Is stimulated during inspiration and a swallow is triggered, inspiration will be inhibited. Therefore, we do not believe variability in swallow motor pattern is dependent on the phase of breathing in which PiCo is stimulated.

Biv LAR has a pause in EMG activity before the swallow begins (red arrow pointing to the pause). While Bv Non-LAR does not have this pause, rather the two behaviors converge (red arrow). In order for something to be considered an LAR the pause must be present which is why we separated these two motor patterns.

Figure 1 - Figure Supplement 1. Why do the Authors call the lines "histograms"?

Thank you, this has been fixed. This is a line graph of swallow frequency in relation to inspiration.

Tables:

In tables, data are provided as means and standard deviation. Please, specify this in the Method section.

Thank you, the following is listed in the methods section: “All data are expressed as mean ± standard deviation (SD), unless otherwise noted.”

Reviewer #3 (Public Review):

In the present study, the authors investigated the effects of CIH on the swallowing and breathing responses to PICO stimulation. Their conclusion is that glutamatergic-cholinergic neurons from PICO are not only critical for the gating of post-inspiratory and swallow activity, but also play important roles in the generation of swallow motor patterns. There are several aspects that deserve the authors' attention and comments, mainly related to the study´s conclusions.

• The authors refer to PICO as the generator of post-inspiratory rhythm. However, evidence points to this region as a modulator of post-inspiratory activity rather than a rhythmogenic site (Toor et al., 2019 - 10.1523/JNEUROSCI.0502-19.2019; Oliveira et al., 2021 - 10.1016/j.neuroscience.2021.09.015). For example, sustained activation of PICO for 10 s barely affected the vagus or laryngeal post-inspiratory activity (Huff et al., 2023 - 10.7554/eLife.86103).

Yes, we did refer to PiCo as the postinspiratory rhythm generator as defined as Anderson et al. 2016. We base this statement on the following criteria and experiments: In Anderson et al. 2016, we demonstrate that PiCo can be isolated in vitro, that PiCo neurons are activated in phase with postinspiration, and that they are inhibited during inspiration by preBötC neurons via GABAergic mechanisms and not glycinergic mechanisms. We also demonstrate that optogenetically stimulating cholinergic neurons in the PiCo area resets the inspiratory rhythm both in vivo and in vitro. We also show that PiCo when isolated in transverse slices is autorhythmic and that PiCo, like the preBötC in transverse slices can generate respiratory rhythmic activity in vitro and independent of the preBötC. We also demonstrate that PiCo neurons are an order of magnitude more sensitive to opioids (DAMGO) than the preBötC and that local injections of DAMGO into the PiCo area in vivo abolishes postinspiration, and also abolishes the phase delay of the respiratory rhythm. None of these specific rhythmogenic properties have been studied by the Toor study or the Oliveira et al study. Hence, we do not understand why the reviewer cites these studies as evidence for modulation as opposed to rhythmogenic properties. The fact that PiCo is rhythmogenic should not be considered as an “exclusive property”. Specifically, this does not mean that PiCo is also “modulating” the swallow-breathing coordination as we have demonstrated more specifically in the Huff et al study. In the same sentence we also referred to the PreBӧtzinger complex as the inspiratory rhythm generator as defined by Smith et al 1991, and it seems that the reviewer did not object to this reference. But we would like to point out that the same criteria were used to define the preBötzinger complex as we used for PiCo, except that PiCo neurons are better defined than preBötzinger complex neurons. Dbx1 neurons are often used to characterize the PreBötC, but these neurons form a rostrocaudal and ventrodorsal column which involves also glia cells and transcends the preBötC. Glutamatergic neurons are everywhere, and so are Somatostatin or Neurokinin neurons. Moreover, the 1991 study was only performed in vitro, and did not include a histochemical analysis. We would also like to point out that the present manuscript is investigating the role of PiCo in swallow and laryngeal behaviors, and not specifically postinspiration. Thus, we are not entirely sure how this comment relates to this manuscript.

• The optogenetic activation of glutamatergic and cholinergic neurons from PICO evoked submental and laryngeal responses, and CIH changed these motor responses. Therefore, the authors proposed that PICO is directly involved in swallow pattern generation and that CIH disrupts the connection between PICO and SPG (swallow pattern generator). However, the experiments of the present study did not provide evidence about connections between these two regions nor their possible disruption after CIH, or even whether PICO is part of SPG.

We have edited the text to suggest PiCo modulates swallow motor sequence in addition to the coordination of swallow and breathing. We have also added that further experiments will be necessary to further investigate the connections between PiCo and SPG. But, unfortunately, compared to PiCo, the SPG is much less defined. As already stated above, it cannot be expected that a single study can address all possible open questions. Clearly, more work needs to be done outside of this study to answer all of these questions, which makes this an exciting area of research.

• CIH affects several brainstem regions which might contribute to generating abnormal motor responses to PICO stimulation. For example, Bautista et al. (1995 - 10.1152/japplphysiol.01356.2011) documented that intermittent hypoxia induces changes in the activity of laryngeal motoneurons by neural plasticity mechanisms involving serotonin.

Yes, we thank the reviewer for this comment and we agree that CIH effects multiple brainstem regions. We stated in the manuscript that we are measuring changes in two muscle complexes which spread among three motor neuron pools: hypoglossal nucleus, trigeminal nucleus, and nucleus ambiguus. We have added a discussion on laryngeal activity in the presence of acute bouts of extreme hypoxia, acute intermittent hypoxia, as well as chronic intermittent hypoxia.

• To support the hypothesis that PICO is directly involved in swallow pattern generation the authors should perform the inhibition of Vglut2-ChAT neurons from PICO and then evoke swallow motor responses. If swallow is abolished when the neurons from this region are inhibited, it would indicate that PICO is crucial to generate this behavior.

Thank you. We would like to clarify: “involvement” does not mean “necessary for”. Confusing this difference has caused much confusion and debate in the field. Just as an example: We can argue in great length whether inhibition is necessary for respiratory rhythmogenesis in vivo, but I think there is no question that inhibition is involved in respiratory rhythmogenesis in vivo. But to avoid any confusion, we have changed the text to suggest PiCo is involved in the modulation of swallow motor sequence. We agree various additional inhibition experiments are necessary to explain if PiCo is also a necessary component of the SPG, but this is not the question we have set out to address in this study. To specifically target PiCo we must not only inhibit Vglut2 neurons but neurons that express both ChAT and Vglut2. To our knowledge there are no inhibitory DREADD or opsin techniques for cre/FlpO to specifically target these neurons. As stated above, non-experts in the field do not appreciate this technical nuance. However, we have begun to develop novel techniques necessary to inhibit these specific neurons which will be published in the future.

• In almost all the data presented, the authors observed different patterns of changes in the motor submental and laryngeal responses to PICO activation, including that animals submitted to CIH (6%) presented a "normal" motor response. However, the authors did not discuss the possible explanations and functional implications of this variability.

We agree that it would be helpful to understand the functional implications of these alterations in swallow-related motor activation, however since we are not using any tools to measure or evaluate functional activity it would be inappropriate to make suggestions of this type without any data to back up our conclusion. This is why we have not included any functional implications. We have added the following to the manuscript. “While fine wire EMG studies are an excellent evaluation tool to observe temporal motor pattern of sequential swallow related muscles; it must be combined with tools such as videofluoroscopic swallow study (VFSS) and/or high resolution manometry (HRM) in order to characterize the functional significance of these alterations to the swallow motor pattern shown in this study (Park et al., 2017). Since the preparation in this study utilizes only fine wire EMGs we are not able to evaluate or comment on the functional significance of the variable swallow motor patterns.”

• In Figure 4, the authors need to present low magnification sections showing the PICO transfected neurons as well as the absence of transfection in the ventral respiratory column. The authors could also check the scale since the cAmb seems very small.

Thank you, added different histology images to have a more comparable cAmb. As well as added lower magnification to show absence of transfection in the VRC.

• Finally, the title does not reflect the study. The present study did not demonstrate that PICO is a swallow pattern generator.

We have also changed the title to say: Chronic Intermittent Hypoxia reveals the role of the Postinspiratory Complex in the mediation of normal swallow production

Author Response

eLife assessment

This valuable study examines the activity and function of dorsomedial striatal neurons in estimating time. The authors examine striatal activity as a function of time and the impact of optogenetic striatal manipulation on the animal's ability to estimate a time interval. However, the task's design and methodology present several confounding factors that mean the evidence in support of the authors' claims is incomplete. With these limitations addressed, the work would be of interest to neuroscientists examining how striatum contributes to behavior.

We appreciate the editorial process and are grateful for the thorough, detailed, and constructive reviews. We will respond in detail to every point raised by reviewers in a full revision.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this work, the authors examine the activity and function of D1 and D2 MSNs in dorsomedial striatum (DMS) during an interval timing task. In this task, animals must first nose poke into a cued port on the left or right; if not rewarded after 6 seconds, they must switch to the other port. Critically, this task thus requires animals to estimate if at least 6 seconds have passed after the first nose poke - this is the key aspect of the task focused on here. After verifying that animals reliably estimate the passage of 6 seconds by leaving on average after 9 seconds, the authors examine striatal activity during this interval. They report that D1-MSNs tend to decrease activity, while D2-MSNs increase activity, throughout this interval. They suggest that this activity follows a drift-diffusion model, in which activity increases (or decreases) to a threshold after which a decision (to leave) is made. The authors next report that optogenetically inhibiting D1 or D2 MSNs, or pharmacologically blocking D1 and D2 receptors, increased the average wait time of the animals to 10 seconds on average. This suggests that both D1 and D2 neurons contribute to the estimate of time, with a decrease in their activity corresponding to a decrease in the rate of 'drift' in their drift-diffusion model. Lastly, the authors examine MSN activity while pharmacologically inhibiting D1 or D2 receptors. The authors observe most recorded MSNs neurons decrease their activity over the interval, with the rate decreasing with D1/D2 receptor inhibition.

Major strengths:

The study employs a wide range of techniques - including animal behavioral training, electrophysiology, optogenetic manipulation, pharmacological manipulations, and computational modeling. The behavioral task used by the authors is quite interesting and a nice way to probe interval timing in rodents. The question posed by the authors - how striatal activity contributes to interval timing - is of importance to the field and has been the focus of many studies and labs; thus, this paper can meaningfully contribute to that conversation. The data within the paper is presented very clearly, and the authors have done a nice job presenting the data in a transparent manner (e.g., showing individual cells and animals). Overall, the manuscript is relatively easy to read and clear, with sufficient detail given in most places regarding the experimental paradigm or analyses used.

We are glad our main points came through to the reviewer.

Major weaknesses:

I perceive two major weaknesses. The first is the impact or contextualization of their results in terms of the results of the field more broadly. More specifically, it was not clear to me how the authors are interpreting the striatal activity in the context of what others have observed during interval timing tasks. In other words - what was the hypothesis going into this experiment? Does observing increasing/decreasing activity in D2 versus D1 support one model of interval timing over another, or does it further support a more specific idea of how DMS contributes to interval timing? Or was the main question that we didn't know if D2 or D1 neurons had differential activity during interval timing?

Our hypothesis, based on prior behavioral work from our group describing that blocking striatal D1 and D2 dopamine receptors impaired interval timing (De Corte et al., 2019; Stutt et al., 2023) was D1 and D2 MSNs would have similar patterns of activity during interval timing. We will clarify this in the revision.

In the second, I felt that some of the conclusions suggested by the authors don't seem entirely supported by the data they present, or the data presented suggests a slightly more complicated story. Below I provide additional detail on some of these instances.

Regarding the results presented in Figures 2 and 3:

I am not sure the PC analysis adds much to the interpretation, and potentially unnecessarily complicates things. In particular, running PCA on a matrix of noisy data that is smoothed with a Gaussian will often return PCs similar to what is observed by the authors, with the first PC being a line up/down, the 2nd PC being a parabola that is up/down, etc. Thus, I'm not sure that there is much to be interpreted by the specific shape of the PCs here.

These are insightful points. We will clarify details of our PCA analysis in the revision. We include PCA for comparisons with our past work (Emmons et al., 2017, 2021; Bruce et al., 2021). Second, it is true that these components can be observed in smoothed data; however, when we generated random data using identical parameters, we found that the variance explained by PC1 was not commonly observed in random data. Third, our goal is to compare between D1 and D2 MSNs, not to interpret the PCs. We will make this explicit in our revision.

I think an alternative analysis that might be both easier and more informative is to compute the slope of the activity of each neuron across the 6 seconds. This would allow the authors to quantify how many neurons increase or decrease their activity much like what is shown in Figure 2.

This is exactly the analysis shown in Figure 3D. We will clarify this in the revision.

Relatedly, it seems that the data shown in Figure 2D doesn't support the authors' main claim regarding D2/D1 MSNs increasing/decreasing their activity, as the trial-by-trial slope is near 0 for both cell types.

This likely refers to Figure 3D. In the revision, we will clarify this analysis, add error bars, and note that our goal was to differentiate D2 and D1 MSNs in this analysis. We will also add to this analysis to better make the poin that D2 and D1 MSNs are distinct, contrary to our hypothesis.

Regarding the results in Figure 4:

The authors suggest that their data is consistent with a drift-diffusion model. However, it is unclear how well the output from the model fits the activity from neurons the authors recorded. Relatedly, it is unclear how the parameters were chosen for the D1/D2 versions of this model. I think that an alternate approach that would answer these questions is to fit the model to each cell, and then examine the best-fit parameters, as well as the ability of the model to predict activity on trials held out from the fitting process. This would provide a more rigorous method to identify the best parameters and would directly quantify how well the model captures the data.

This is a great point. Our goal was to fit behavioral activity, not neuronal activity; in our revision, we will do exactly what the reviewer suggests and present data of fits to neuronal activity.

Relatedly, looking at the raw data in Figure 2, it seems that many neurons either fire at the beginning or end of the interval, with more neurons firing at the end, and more firing at the beginning, for D2/D1 neurons respectively. Thus, it's not clear to me whether the drift-diffusion model is a good model of activity. Or, perhaps the model is supposed to be related to the aggregate activity of all D1/D2 neurons? (If so, this should be made more explicit. The comment about fitting the model directly to the data also still stands).

Our model was inspired by the averages in Figure 2G&H; however, we will fit drift-diffusion models to individual neurons exactly as the reviewer suggests.

Further, it's unclear to me how, or why, the authors changed the specific parameters they used to model the optogenetic manipulation. Were these parameters chosen because they fit the manipulation data? This I don't think is in itself an issue, but perhaps should be clearly stated, because otherwise it sounds a bit odd given the parameter changes are so specific. It is also not clear to me why the noise in the diffusion process would be expected to change with increased inhibition.

We will clarify this in our revision, as this is an important point.

Regarding the results in Figure 6:

My comments regarding the interpretation of PCs in Figure 2 apply here as well. In addition, I am not sure that examining PC2 adds much here, given that the authors didn't examine such nonlinear changes earlier in the paper.

We agree – we will remove PC2 in Figure 6 and Figure S9 and add context to the PC analysis noting that we are including for 1) comparisons with past work, 2) our observed variance is much higher than observed in random/smoothed data, and 3) we are primarily interested in comparisons between conditions rather than interpreting the components.

A larger concern though that seems potentially at odds with the authors' interpretation is that there seems to be very little change in the firing pattern after D1 or D2 blockade. I see that in Figure 6F the authors suggest that many cells slope down (and thus, presumably, they are recoding more D1 cells), and that this change in slope is decreased, but this effect is not apparent in Figure 6C, and Figure 6B shows an example of a cell that seems to fire in the opposite direction (increase activity). I think it would help to show some (more) individual examples that demonstrate the summary effect shown by the authors, and perhaps the authors can comment on the robustness (or the variability) of this result.

We agree, although we note D1/D2 blockade changes PC1, which explains the most variance in MSN activity. In the revision, we will show more examples and comment on the robustness of PC1, exactly as the reviewer recommends. The changes in PC1 are rather consistent.

Also, it seems that if the authors want to claim that this manipulation lowers the drift rate. I think to make this claim, they could fit the DDM model and examine whether D is significantly lower.

This is a great idea – we will try to do this.

Regarding the results in Figure 7:

I am overall a bit confused about what the authors are trying to claim here. In Figure 7, they present data suggesting that D1 or D2 blockade disrupts their ability to decode time in the interval of interest (0-6 seconds). However, in the final paragraph of the results, the authors seem to say that by using another technique, they didn't see any significant change in decoding accuracy after D1 or D2 blockade. What do the authors make of this?

We were not clear. The second classifier was predicting response time. This was confusing and we will remove it.

Impact:

The task and data presented by the authors are very intriguing, and there are many groups interested in how striatal activity contributes to the neural perception of time. The authors perform a wide variety of experiments and analysis to examine how DMS activity influences time perception during an interval-timing task, allowing for insight into this process. However, the significance of the key finding - that D2/D1 activity increases/ decreases with time - remains somewhat ambiguous to me. This arises from a lack of clarity regarding the initial hypothesis and the implications of this finding for advancing our understanding of striatal functions.

Again, we are grateful for the constructive and very insightful comments that we look forward to clarifying in a full revision.

Reviewer #2 (Public Review):

Summary:

In the present study, the authors investigated the neural coding mechanisms for D1- and D2-expressing striatal direct and indirect pathway MSNs in interval timing by using multiple strategies. They concluded that D2-MSNs and D1-MSNs have opposing temporal dynamics yet disrupting either type produced similar effects on behavior, indicating the complementary roles of D1- and D2- MSNs in cognitive processing. However, the data was incomplete to fully support this major finding. One major reason is the heterogenetic responses within the D1-or D2-MSN populations. In addition, there are additional concerns about the statistical methods used. For example, the majority of the statistical tests are based on the number of neurons, but not the number of mice. It appears that the statistical difference was due to the large sample size they used (n=32 D2-MSNs and n=41 D1-MSNs), but different neurons recorded in the same mouse cannot be treated as independent samples; they should use independent mouse-based statistical analysis.

Strengths:

The authors used multiple approaches including awake mice behavior training, optogenetic-assistant cell-type specific recording, optogenetic or pharmacological manipulation, neural computation, and modeling to study neuronal coding for interval timing.

We appreciate the reviewer’s careful read recognizing the breadth of our approach.

Weaknesses:

(1) More detailed behavior results should be shown, including the rate of the success switches, and how long it takes to wait in the second nose poke to get a reward. For line 512 and the Figure 1 legend, the reviewer is not clear about the reward delivery. The methods appear to state that the mouse had to wait for 18s, then make nose pokes at the second port to get the reward. What happens if the mouse made the second nose poke before 18 seconds, but then exited? Would the mouse still get the reward at 18 seconds? Similarly, what happens if the mice made the third or more nosepokes within 18 seconds? It is important to clarify because, according to the method described, if the mice made a second nose poke before 18 seconds, this already counted as the mouse making the "switch." Lastly, what if the mice exited before 6s in the first nosepoke?

We agree. These were presented in detail in our prior work (Bruce et al., 2021; Larson et al., 2022; and Weber et al., 2023) and work from others (Balci et al 2008; Tosun et al., 2016. However, we will work on a detailed behavioral schematic in the revision and move supplementary behavioral data in Figure S1 to the main manuscript.

(2) There are a lot of time parameters in this behavior task, the description of those time parameters is mentioned in several parts, in the figure legend, supplementary figure legend, and methods, but was not defined clearly in the main text. It is inconvenient, sometimes, confusing for the readers. The authors should make a schematic diagram to illustrate the major parameters and describe them clearly in the main text.

This is a great suggestion – we will do this – and clarify in the above schematic.

(3) In Line 508, the reviewer suggests the authors pay attention to those trials without "switch". It would be valuable to compare the MSN activity between those trials with or without a "switch".

We analyzed MSN activity on errors in detail Figure 6 of Bruce et al., 2021. These errors are infrequent and inconsistent – we will discuss this in the revision.

(4) The definition of interval is not very clear. It appears that the authors used a 6-second interval in analyzing the data in Figure 2 and Figure 3. But from my understanding, the interval should be the time from time "0" to the "switch", when the mice start to exit from the first nose poke.

We agree. The switch time can be vastly different on some trials, making it challenging to compare different lengths and slopes. However, we will clarify the interval as noted above, and we have a few ideas on how to do the analysis the reviewer suggests.

(5) For Figure 2 C-F, the authors only recorded 32 D2-MSNs in 4 mice, and 41 D1-MSNs in 5 mice. The sample size is too small compared to the sample size usually used in the field. In addition to the small sample size, the single-cell activity exhibited heterogeneity, which created potential issues. For both D1 and D2 MSNs, the authors tried to make conclusions on the "trend" of increasing in D2-MSNs and decreasing in D1-MSNs populations, respectively, during the interval. However, such a conclusion is not sufficiently supported by the data presented. It looks like the single-cell activity patterns can be separated into groups: one is a decreasing activity group, one is an increasing activity group and a small group for on and off response. Because of the small sample size, the author should pay attention to the variance across different mice (which needs to be clearly presented in the manuscript), instead of pooling data together and analyzing the mean activity.

We were not clear – we did this analysis exactly the reviewer suggested. We are not pooling any data – instead – as we state on line 620 – we are using linear-mixed effects models to account for mouse-specific and neuron-specific variance. This approach was developed with our statistics core for exactly the reasons the reviewer suggested. Furthermore, we will add to this analysis demonstrative that it is resistant to outliers. Finally, we will include measures of effect size noting that it is a medium to large effect.

It’s a helpful idea to plot data individually by mice, and we will do so in the revision.

(6) For Figure 2, from the activity in E and F, it seems that the activity already rose before the trial started, the authors should add some longer baseline data before time zero for clarification and comparison, and show the timing of the actual start of the activity with the corresponding behavior. What behavior states are the mice in when initiating the activity?

We can certainly include a longer baseline. We can clarify in the revision that mice initiate trials at the rear nosepoke, and this is what initiates the task cues and the temporal interval.

(7) The authors were focused on the "switch " behavior in the task, but they used an arbitrary 6s time window to analyze the activity, and tried to correlate the decreasing or increasing activities of MSNs to the neural coding for time. A better way to analyze is to sort the activity according to the "switch" time, from short to long intervals. This way, the authors could see and analyze whether the activity of D1 or D2 MSNs really codes for the different length of interval, instead of finding a correlation between average activity trends and the arbitrary 6s time window.

This is a great idea, and we have some ideas on how to adapt the GLM analysis to perform this analysis.

Reviewer #3 (Public Review):

Summary:

The cognitive striatum, also known as the dorsomedial striatum, receives input from brain regions involved in high-level cognition and plays a crucial role in processing cognitive information. However, despite its importance, the extent to which different projection pathways of the striatum contribute to this information processing remains unclear. In this paper, Bruce et al. conducted a study using a range of causal and correlational techniques to investigate how these pathways collectively contribute to interval timing in mice. Their results were consistent with previous research, showing that the direct and indirect striatal pathways perform opposing roles in processing elapsed time. Based on their findings, the authors proposed a revised computational model in which two separate accumulators track evidence for elapsed time in opposing directions. These results have significant implications for understanding the neural mechanisms underlying cognitive impairment in neurological and psychiatric disorders, as disruptions in the balance between direct and indirect pathway activity are commonly observed in such conditions.

Strengths:

The authors employed a well-established approach to study interval timing and employed optogenetic tagging to observe the behavior of specific cell types in the striatum. Additionally, the authors utilized two complementary techniques to assess the impact of manipulating the activity of these pathways on behavior. Finally, the authors utilized their experimental findings to enhance the theoretical comprehension of interval timing using a computational model.

We are grateful for the reviewer’s consideration of our work and recognizing the strengths of our approach.

Weaknesses:

The behavioral task used in this study is best suited for investigating elapsed time perception, rather than interval timing. Timing bisection tasks are often employed to study interval timing in humans and animals.

This is certainly valid, and we will include these points in the revision.

The main results from unit recording (opposing slopes of D1/D2 cell firing rate, as shown in Figure 3D) appear to be very sensitive to a couple of outlier cells, and the predictive power of ensemble recording seems to be only slightly above chance levels.

We are glad that the reviewer raised this. We will add to this analysis demonstrative that it is resistant to outliers. Finally, we will include measures of effect size noting that it is a medium to large effect. Thus, it is significantly above chance, and rather reliable, and supported by our PCA results in Figure 3C.

In the optogenetic experiment, the laser was kept on for too long (18 seconds) at high power (12 mW). This has been shown to cause adverse effects on population activity (for example, through heating the tissue) that are not necessarily related to their function during the task epochs.

Again, this is an important point. We are well aware of heating effects with optogenetics. For the exact reasons noted by the reviewer, we had opsin-negative controls –when the laser was on the exact same time course and parameters – in Figure S5. There were no behavioral effects in controls with identical heating and other effects of the laser. Furthermore, these effects are similar to pharmacological effects in this manuscript and in our prior work (De Corte et al., 2019; Stutt et al., 2023). We will better highlight these issues in the revision.

Given the systemic delivery of pharmacological interventions, it is difficult to conclude that the effects are specific to the dorsomedial striatum. Future studies should use the local infusion of drugs into the dorsomedial striatum.

This is a great point - we did exactly this experiment in De Corte et al, 2019 with local drug infusions. This earlier study was the departure point for this experiment, although it is challenging to combine focal pharmacological inactivation with recordings in mice (we have extensive experience with this in rats in Parker et al., 2015 and Parker et al, 2015). Furthermore, we have similar local optogenetics effects in this paper. We will include these points in the revised manuscript.

Author Response

We would like to thank the three reviewers and the eLife editors for their careful analysis of our work, and for their constructive feedback and positive evaluation. We are especially pleased to see echoed in the reviews and in the editorial assessment that our results underline the importance of taking into account glycosylation in viral evolution, immune surveillance, and in the interpretation of complex epistatic interactions. With this provisional response we would like to communicate to the editors, reviewers and to the eLife readership our intention to integrate in the paper a detailed description of the GM1os and GM2os binding site on the RBD with details on the computational approach we used. We agree that this addition will strengthen the work by making it more self-contained. Also, as suggested by the editorial team, we will provide a comprehensive discussion of published data, as a firmer foundation for our findings.

Author Response

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

eLife assessment

In this study, the authors develop a useful strategy for fluorophore-tagging endogenous proteins in human induced pluripotent stem cells (iPSCs) using a split mNeonGreen approach. Experimentally, the methods are solid, and the data presented support the author's conclusions. Overall, these methodologies should be useful to a wide audience of cell biologists who want to study protein localization and dynamics at endogenous levels in iPSCs.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors have applied an asymmetric split mNeonGreen2 (mNG2) system to human iPSCs. Integrating a constitutively expressed long fragment of mNG2 at the AAVS1 locus, allows other proteins to be tagged through the use of available ssODN donors. This removes the need to generate long AAV donors for tagging, thus greatly facilitating high-throughput tagging efforts. The authors then demonstrate the feasibility of the method by successfully tagging 9 markers expressed in iPSC at various, and one expressed upon endoderm differentiation. Several additional differentiation markers were also successfully tagged but not subsequently tested for expression/visibility. As one might expect for high-throughput tagging, a few proteins, while successfully tagged at the genomic level, failed to be visible. Finally, to demonstrate the utility of the tagged cells, the authors isolated clones with genes relevant to cytokinesis tagged, and together with an AI to enhance signal-to-noise ratios, monitored their localization over cell division.

Strengths:

Characterization of the mNG2 tagged parental iPSC line was well and carefully done including validation of a single integration, the presence of markers for continued pluripotency, selected offtarget analysis, and G-banding-based structural rearrangement detection.

The ability to tag proteins with simple ssODNs in iPSC capable of multi-lineage differentiation will undoubtedly be useful for localization tracking and reporter line generation.

Validation of clone genotypes was carefully performed and highlights the continued need for caution with regard to editing outcomes.

Weaknesses:

IF and flow cytometry figures lack quantification and information on replication. How consistent is the brightness and localization of the markers? How representative are the specific images? Stability is mentioned in the text but data on the stability of expression/brightness is not shown.

To address this comment, we have quantified the mean fluorescence intensity of the tagged cell populations in Fig. S3B-T. This data correlates well with the expected expression levels of each gene relative to the others (Fig. S3A), apart from CDH1 and RACGAP1, which are described in the discussion.

The images in Fig. 2 show tagged populations enriched by FACS so they are non-clonal and are representative of the diversity of the population of tagged cells.

The images shown in Fig. 3 are representative of the clonal tagged populations. The stability of the tag was not quantified directly. However, the fluorescence intensity was very stable across cells in clonal populations. Since these populations were recovered from a single cell and grown for several weeks, this low variability across cells in a population suggests that these tags are stable.

The localization of markers, while consistent with expectations, is not validated by a second technique such as antibody staining, and in many cases not even with Hoechst to show nuclear vs cytoplasmic.

We find that the localization of each protein is distinct and consistent with previous studies. To address this comment, we have added an overlay of the green fluorescence images with brightfield images to better show the location of the tagged protein relative to the nuclei and cytoplasm. We have also added references to other studies that showed the same localization patterns for these proteins in iPSCs and other relevant cell lines.

For the multi-germ layer differentiation validation, NCAM is also expressed by ectoderm, so isn't a good solo marker for mesoderm as it was used. Indeed, the kit used for the differentiation suggests Brachyury combined with either NCAM or CXCR4, not NCAM alone.

Since Brachyury is the most common mesodermal marker, we first tested differentiation using anti-Brachyury antibodies, but they did not work well for flow cytometry. We then switched to anti-NCAM antibodies. Since we used a kit for directed differentiation of iPSCs into the mesodermal lineage, NCAM staining should still report for successful differentiation. In the context of mixed differentiation experiments (embryoid body formation or teratoma assay), NCAM would not differentiate between ectoderm and mesoderm. The parental cells (201B7) have also been edited at the AAVS1 locus in multiple other studies, with no effect on their differentiation potential.

Only a single female parental line has been generated and characterized. It would have been useful to have several lines and both male and female to allow sex differences to be explored.

We agree that it would be interesting (and important) to study differences in protein localization between female and male cell types, and from different individuals with different genetic backgrounds. We see our tool as opening a door for cell biology to move away from randomly collected, transformed, differentiated cell types to more directed comparative studies of distinct normal cell types. Since few studies of cell biological processes have been done in normal cells, a first step is to understand how processes compare in an isogenic background, then future studies can reveal how they compare with other individuals and sexes. We hope that either our group or others will continue to build similar lines so that these studies can be done.

The AI-based signal-to-noise enhancement needs more details and testing. Such models can introduce strong assumptions and thus artefacts into the resolved data. Was the model trained on all markers or were multiple models trained on a single marker each? For example, if trained to enhance a single marker (or co-localized group of markers), it could introduce artefacts where it forces signal localization to those areas even for others. What happens if you feed in images with scrambled pixel locations, does it still say the structures are where the training data says they should be? What about markers with different localization from the training set? If you feed those in, does it force them to the location expected by the training data or does it retain their differential true localization and simply enhance the signal?

The image restoration neural network was used as in Weigert et al., 2018. The model was trained independently for each marker. Each trained model was used only on the corresponding marker and with the same imaging conditions as the training images. From visual inspection, the fluorescent signal in the restored images was consistent with the signal in the raw images, both for interphase and mitotic cells. We found very few artefacts of the restoration (small bright or dark areas) that were discarded. We did not try to restore scrambled images or images of mismatched markers.

Reviewer #2 (Public Review):

Summary:

The authors have generated human iPSC cells constitutively expressing the mNG21-10 and tested them by endogenous tagging multiple genes with mNG211 (several tagged iPS cell lines clones were isolated). With this tool, they have explored several weakly expressed cytokinesis genes and gained insights into how cytokinesis occurs.

Strengths:

Human iPSC cells are used.

Weaknesses:

i) The manuscript is extremely incremental, no improvements are present in the split-fluorescent (split-FP) protein variant used nor in the approach for endogenous tagging with split-FPs (both of them are already very well established and used in literature as well as in different cell types).

Although split fluorescent proteins and the endogenous tagging methodology had been developed previously, their use in human stem cells has not been explored. We argue that human iPSCs are a valuable model for cell biologists to study cellular processes in differentiating cells in an isogenic context for proper comparison. Many normal human cell types have not been studied at the cellular/subcellular level, and this tool will enable those studies. Importantly, other existing cell lines required transformation to persist in culture and represent a single, differentiated cell type that is not normal. Moreover, the protocols that we developed along with this methodology (e.g. workflows for iPSC clonal isolation that include automated colony screening and Nanopore sequencing) will be useful to other groups undertaking gene editing in human cells. Therefore, we argue that our work opens new doors for future cell biology studies.

ii) The fluorescence intensity of the split mNeonGreen appears rather low, for example in Figure 2C the H2BC11, ANLN, SOX2, and TUBB3 signals are very noisy (differences between the structures observed are almost absent). For low-expression targets, this is an important limitation. This is also stated by the authors but image restoration could not be the best solution since a lot of biologically relevant information will be lost anyway.

The split mNeonGreen tag is one of the brighter fluorescent proteins that is available. The low expression that the reviewer refers to for H2BC11, ANLN, TUBB3 and SOX2 is expected based on their predicted expression levels. Further, these images were taken with cells in dishes using lower resolution imaging and were not intended to be used for quantification. As shown in the images in Figures 3H, when using a different microscope with different optical settings and higher magnification, the localization is very clear and quantifiable without needing to use restoration (e.g., compare H2BC11 and ANLN). Using microscopes with high NA objectives, lasers and EMCCD or sCMOS cameras with high sensitivity can sufficiently detect levels of very weakly expressing proteins that can be quantified above background and compared across cells. It is worth noting that each tag may be studied in very different contexts. For example, ANLN will be useful for studies of cytokinesis, while the loss of SOX2 expression and gain of TUBB3 expression may be used to screen for differentiation rather than for localization per se. The reason for endogenous tagging is to study proteins at their native levels rather than using over-expression or fixation with antibodies where artefacts can be introduced. Endogenous tags tag will also enable studies of dynamic changes in localization during differentiation in an isogenic background as described previously.

Importantly, image restoration is not required to image any of these probes! We use it to demonstrate how a researcher can increase the temporal resolution of imaging weakly-expressed proteins for extended periods of time. This data can be used to compare patterns of localization and reveal how patterns change with time and during differentiation. Imaging with fewer timepoints and altered optical settings will still permit researchers to extract quantifiable information from the raw data without requiring image restoration.

iii) There is no comparison with other existing split-FP variants, methods, or imaging and it is unclear what the advantages of the system are.

We are not sure what the reviewer means by this comment. In the future, we plan to incorporate an additional split-FP variant (e.g., split sfCherry) in this iPSC line to enable the imaging of more than one protein in the same cell. However, the split mNeonGreen system is still amenable for use with dyes with different fluorescence spectra that can mark other cellular components, especially for imaging over shorter timespans. In addition to tagging efficiency, the main advantage of split FPs is its scale, as demonstrated by the OpenCell project by tagging 1,310 proteins endogenously (Cho et al., 2022). We developed protocols that facilitate the identification of edited cell lines with high throughput. We also used multiple imaging methods throughout the study that relied on the use of different microscopes and flow cytometry, demonstrating the flexibility of this tagging system. Even for more weakly expressing proteins, the probe could be sufficiently visualized by multiple systems. Such endogenous tags can be used for everything from simply knowing when cells have differentiated (e.g., loss of SOX2 expression, gain of differentiation markers), to studying biological processes over a range of timescales.

Reviewer #3 (Public Review):

The authors report on the engineering of an induced Pluripotent Stem Cell (iPSC) line that harbours a single copy of a split mNeonGreen, mNG2(1-10). This cell line is subsequently used to take endogenous protein with a smaller part of mNeonGreen, mNG2(11), enabling the complementation of mNG into a fluorescent protein that is then used to visualize the protein. The parental cell is validated and used to construct several iPSC lines with endogenously tagged proteins. These are used to visualize and quantify endogenous protein localisation during mitosis.

I see the advantage of tagging endogenous loci with small fragments, but the complementation strategy has disadvantages that deserve some attention. One potential issue is the level of the mNG2(1-10). Is it clear that the current level is saturating? Based on the data in Figure S3, the expression levels and fluorescence intensity levels show a similar dose-dependency which is reassuring, but not definitive proof that all the mNG2(11)-tagged protein is detected.

We have not quantified the levels of mNG21-10 expression directly. However, the increase in fluorescence observed with highly expressed proteins (e.g., ACTB) supports that mNG21-10 levels must be sufficiently high to permit differences among endogenous proteins with vastly different expression levels. To ensure high expression, we used a previously validated expression system comprised of the CAG promoter integrated at the AAVS1 locus, which has previously been used to provide high and stable transgene expression (e.g. Oceguera-Yanez et al., 2016). We acknowledge that it is difficult to confirm that all of the endogenous mNG211-tagged protein is ‘detectable’.

Do the authors see a difference in fluorescence intensity for homo- and heterozygous cell lines that have the same protein tagged with mNG2(11)? One would expect two-fold differences, or not?

To answer this question, we measured the fluorescence intensity of homozygous and heterozygous clones carrying smNG2-anillin and smNG2-RhoA. We found homozygous clones that were approximately twice as bright as the corresponding heterozygous clones (Fig. S4H and I). This suggests that the complementation between mNG21-10 and mNG211 occurs efficiently over a range of mNG211 expression, since anillin is expressed weakly and RhoA is expressed more strongly in iPSCs. However, we also observed some homozygous clones that were not brighter than the corresponding heterozygous clones, which could be due to undetected byproducts of CRISPR or clonal variation in protein expression.

Related to this, would it be favourable to have a homozygous line for expressing mNG2(1-10)?

Our heterozygous cell line leaves the other AAVS1 allele available for integrations of other transgenes for future experiments. While a homozygous line could express more mNG2(1-10), it does not seem to be rate-limiting even with a highly-expressed protein like beta-actin, and we are not sure that it is necessary. The value gained by having the free allele could outweigh the difference in mNG2(1-10) levels.

The complementation seems to work well for the proteins that are tested. Would this also work for secreted (or other organelle-resident) proteins, for which the mNG2(11) tag is localised in a membrane-enclosed compartment?

The interaction between the 1-10 and 11 fragments is strong and should be retained when proteins are secreted. It was recently shown that secreted proteins tagged with GFP11 can be detected when interacting with GFP1-10 in the extracellular space, albeit using over-expression (Minegishi et al., 2023). However, in our work, the mNG21-10 fragment is cytosolic and we have only explored proteins localized to the nucleus or the cytoplasm similar to Cho et al., (2022). By GO annotation, 75% of human proteins are present in the cytoplasm and/or nucleus, which still covers a wide range of proteins of interest. Future versions of our line could include incorporating organelle-targeting peptides to drive the large fragment to specific, non-cytosolic locations.

The authors present a technological advance and it would be great if others could benefit from this as well by having access to the cell lines.

As discussed below, some of the resources are already available, and we are working to make the mNG21-10 cell line available for distribution.

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

The manuscript is methodological, the main achievement is the generation of a stable iPSC with the split Neon system available for the scientific community. Although it is technically solid, the judgement of this reviewer is that the manuscript should be considered for a more specialised/methodological/resource-based journal.

Indeed, we have submitted this article under the “tools and resources” category of eLife, which publishes methodology-centered papers of high technical quality. We felt this was a good venue for the audience that it can reach compared to more specialized journals that may be more limited in scope. For example, our system will be a useful resource for cell biologists and they are more likely to see it in eLife compared to more specialized journals.

Reviewer #3 (Recommendations For The Authors):

(1) The authors present a technological advance and it would be great if others can benefit from this as well. Therefore access to the materials (and data) would be valuable (the authors do a great job by listing all the repair templates and primers).

We have added several pieces of data and information to the supplementary materials, as described below.

For instance:

What is the (complete/plasmid) sequence of the AAVS1-mNG2(1-10) repair plasmid? Will it be deposited at Addgene?

The plasmids used in this paper are now available on Addgene, along with their sequences [ID 206042 for pAAVS1-Puro-CAG-mNG2(1-10) and 206043 for pH2B-mNG2(11)].

The ImageJ code for the detection of colonies is interesting and potentially valuable. Will the code be shared (e.g. at Github, or as supplemental text)?

The ImageJ macro has been uploaded to the CMCI Github page (https://github.com/CMCI/colony_screening). The parameters are optimized to perform segmentation on images obtained using a Cytation5 microscope with our specific settings, but they can be tweaked for any other sets of images. The following text has been added to the methods section: “The code for this macro is available on Github (https://github.com/CMCI/colony_screening)”.

The cell line with the mNG2(1-10) as well as other cell lines can be of interest to others. Will the cell lines be made available? If so, can the authors indicate how?

We are in the process of depositing our cell line in a public repository. This process may take some time for quality control. For now, the cells can be made available by requesting them from the corresponding authors.

(2) How well does the ImageJ macro for detection of the colonies in the well work? Is there any comparison of analysis by a human vs. the macro?

In our most recent experiment, the colony screening macro correctly identified 99.5% of wells compared to manual annotation (83/84 positive wells and 108/108 negative wells). For each 96-well plate, imaging takes 25 minutes, and it takes 7 minutes for analysis. Despite a few false negatives, we expect this macro to be useful for large-scale experiments where multiple 96-well plates need to be screened, which would take hours manually.

(3) The CDH labeling was not readily detected by FACS, but was visible by microscopy. Is the labeling potentially disturbed by the procedure (low extracellular calcium + trypsin?) to prepare the cell for FACS?

It is not clear why the CDH labelling was not detected by FACS. As the reviewer suggests, there could be several reasons: E-cadherin could be broken down by the dissociation reagent (Accutase), or recycled into the cell following the loss of adhesion and the low extracellular calcium in PBS. However, the C-terminal intracellular tail of E-cadherin was tagged, which should not be affected by Accutase. Moreover, recycling into the cell should still result in a detectable fluorescent signal. Notably, the flow cytometry experiments were done as quickly as possible after dissociation to minimize the time that E-cadherin could be degraded or recycled. We also resuspended the cells in MTeSR Plus media instead of PBS, and compared cells grown on iMatrix511 to those grown on Matrigel in case differences in the extracellular matrix affected Ecadherin expression. Another possibility is that the microscopy used for detection of E-cadherin in cells involved using a sweptfield livescan confocal microscope with high NA objective, 100mW 488nm laser and an EMCCD camera with high sensitivity, and perhaps this combination permitted detection better than the detector on the BD FACSMelody used for FACs.

(4) The authors write that the "Tubulin was cytosolic during interphase" which is surprising (and see also figure 3H), as I was expecting it to be incorporated in microtubules. May this be an issue of insufficient resolution (if I'm right this was imaged with 20x, NA=0.35 and so the resolution could be improved by imaging at higher NA)?

Indeed, as the reviewer points out, our terminology (cytosol vs. microtubule) reflects the low resolution of the imaging for the cell populations in dishes and the individual alpha-tubulin monomers being labelled with the mNG211 tag, which are present as cytoplasmic monomers as well as polymers on microtubules. However, even in this image (Fig. 2C), the mitotic spindle microtubules are visible as they are so robust compared to the interphase microtubules. Notably, when we imaged cells from the cloned tagged cell line using a microscope designed for live imaging with a higher NA objective (see above), endogenous tagged TUBA1B was even more clearly visible in spindle microtubules, and was weakly observed in some microtubules in interphase cells, although they are slightly out of focus (Fig. 3H). If we had focused on a lower focal plane where the interphase cells are located and altered the optical settings, we would see more microtubules.

(5) It would be nice to have access to the Timelapse data as supplemental movies (.e.g from the experiments shown in Figure 4).

We have added the movies corresponding to the timeplase images as supplementary movies (Movies S1-6), with the raw and restored movies shown side-by-side.

(6) In Figure 3B, the order of the colors in the bar is reversed relative to the order of the legend. Would it be possible to use the same order? That makes it easier for me (as a colorblind person) to match the colors in the figure with that of the legend.

We have modified the legend in Fig 2B and 3B to be in the same order as the bars.

Author Response

We are deeply grateful for the highly professional analysis of our work by the Journal Editor and Reviewers. Here is our provisional response to some of the reviewer comments. In our response, we would like to address two comments that were common to all Reviewers' responses. We will thoroughly address all of the Reviewers' comments in the final version of the paper.

Incomplete analysis of maturational changes of striato-nigral connections.

In the initial study, we showed that chronic inhibition of striosomal neurons with the DREADD approach during early postnatal development leads to decreased functional innervation of dopaminergic cells by striosomes in adulthood. We have shown that by two approaches: (1) analysis of miniature inhibitory post-synaptic currents (mIPSCs) and (2) analysis of GFP and gephyrin puncta densities around dopaminergic cells. The results from these experiments strongly suggest a decrease in inhibitory drive to dopaminergic neurons of substantia nigra pars compacta, yet we agree that only GFP puncta density can be considered as a direct evidence for weakened striatonigral connections. Reviewers indicated that additional direct measurements of striatonigral synaptic efficacy would be needed to strengthen our conclusions. We completely agree with this statement and will evaluate the possibility of doing the suggested experiments, using optogenetic stimulation of striosomal inputs to dopaminergic neurons.

Inconsistent description of Ca2+ imaging experiments.

Unfortunately, there was a general misunderstanding in interpreting the Ca2+ imaging methods description. All our experiments were done so that baseline Ca2+ oscillations and oscillations in the presence of a drug were recorded in the usual ACSF (containing 3 mM KCl) at the patch-clamp setup chamber. So, conditions were exactly the same as for cell-attached and whole-cell recordings. At the end of each experiment, ACSF containing 8 mM KCl was applied. This high-KCl condition was used to calculate the total number of viable cells reacting to elevated potassium concentrations, and this number was taken as 100 %. Therefore, the percents displayed in the paper represent the actively oscillating cells in common ACSF (3 mM KCl), counted as a percent of the total number of cells that responded to the following high potassium stimulation (8 mM KCl). The formula was: (Number of active cells in 3 mM KCl / number of viable cells active at 8 mM KCl)*100.

Author Response

We appreciate your constructive feedback on our manuscript entitled “Deletion of sulfate transporter SUL1 extends yeast replicative lifespan via reduced PKA signaling instead of decreased sulfate uptake” (ID: eLife-RP-RA-2023-94609). Your comments/suggestions are very helpful for improving our manuscript. In particular, we feel additional experiments and analysis suggested by the reviewers will help strengthen our argument that Sul1 deletion mutant extends lifespan via decreased PKA signaling, instead of via decreased sulfate uptake. Below we outline our response to the reviewer's comments/suggestions and the plans for additional experiments and analysis.

(1) Our current model is that lifespan extension following SUL1 knockout depends on the PKA signaling pathway but not sulfate transport. To further substantiate this, we plan to conduct further transcriptome sequencing and dynamic sulfate uptake experiments using WT, Sul1D and Sul1E427Q strains. If our model is correct, we expect that PKA signaling pathway will be more repressed in Sul1D strain than in Sul1E427Q strain, but the sulfate transport will be similar in both strains. This will add strong evidences supporting the model in addition to the lifespan data.

(2) The reviewer mentioned the disparities observed between the lifespan of WT in Figure 1B and other experimental assays. Although it is known that lifespan for WT varies considerably from experiment to experiment (thus the need for WT control for every lifespan measurement), we agree it is important to make a solid conclusion that Sul1E427Q does not extend lifespan. We plan to measure the lifespan of more cells for the mutant strains illustrated in Figure 1B and update the data and charts.

(3) Other issues, for example, the small images of Msn2/4 in the nucleus, grammar and formatting errors, and the lifespan data of double (Sul1/Msn4) mutants will be addressed in the revised version of the manuscript after we performed the additional experiments/analysis.

Author Response

Reviewer #1 (Public Review):

(1) It is unclear whether the authors took into consideration the contribution of nuclear blebs for nuclear volume measurements. This would be particularly relevant in situations of very strong confinement. Blebs were previously shown to affect volume (Mistriotis et al., JCB 2019). One could argue that the decreased nuclear volume was due to the increased blebbing observed in very strong confinements.

As stated in the main text: “[Nuclear Blebs] had a limited contribution to the increase in nuclearprojected area, as the increase remained significantly different even if protrusions were dismissed to compute the projected area (Fig S3C)”. In addition, a decrease in the nuclear volume was also observed for slight and intermediate confinement (height = 7 and 9 µm), while in these two conditions, no blebs are observed.

(2) From their experimental setup, it is unclear whether the reduced nuclear volume observed after confined cell division arises from a geometrical constraint or is due to an intrinsic nuclear feature. One could argue that cells exiting mitosis under confinement have clustered chromosomes and, therefore, will have decreased volume. This would imply that the nucleus is not "reset" but rather that a geometrical constraint is forcing nuclei to be smaller. One way to test this would be to follow individual cells under confinement, let them enter mitosis, and then release the confinement. If, under these conditions, the daughter nuclei are smaller, then it supports their model. If daughter nuclei recover to their initial value, then it´s simply due to a geometrical constraint that forces the clustering of chromosomes and the reassembly of the NE in a confined space.

We agree with the reviewer. As stated in the discussion, “For now, the mechanisms involved remain elusive”, and “Our results call for an in-depth analysis of the molecular pathways at play”. The experiments suggested by the reviewer are definitely important experiments that we plan to carry out. Indeed, it is important to know if cells that were ‘born’ under confinement will retain smaller nuclei in the next generation if confinement is released, or whether the next generation will recover their initial larger nuclei.

(3) The authors claim that the nucleus adapts to confinement based on evidence that the nucleus no longer shrinks in the second division following the first division. I would argue no further decrease is possible because the DNA is already compacted in the smallest possible volume. If indeed nuclei are in a new homeostatic state as the authors claim, then one would expect nuclei to remain smaller even after confinement is removed. This analysis is missing.

As mentioned above, we agree that “deconfinement experiments” are indeed important. Nevertheless, we respectfully want to point out that the DNA is not compacted to its maximum level during confinement.

First, we observed that the nuclei of the second generation of cells born in confinement no longer shrink for all investigated confinement conditions, including for slight confinement (height of 9 µm, corresponding to an initial nuclear deformation of 41%), where DNA is less confined compared to the very strong confinement condition (height of 3 µm, corresponding to an initial nuclear deformation of 70%).

Second, the total uncompressible volumetric fraction of a cell is smaller than 30% (Roffay et al. PMID: 34785592, Cell Biology by the Numbers ISBN: 9780815345374) this allows a nucleus to be compressed to over 70% of its size, as we observed in the extreme scenario.

(4) Also, if the authors want to claim that this is a mechanism used for cancer cells to adapt to confined situations as the title says, they need to show that normal, near-diploid cells do not behave in the same way. This analysis is missing.

We agree with the reviewer. For the revised version, we have planned to analyze cell response to confinement using the RPE-1 cell line, as a model of a diploid and untransformed cell line. This will be important experiments to know if the nuclear mechanism identified in the HT-29 cell line is also at stake for normal cells.

(5) Authors state that "Loss of nuclear blebs is clearly linked to mitosis, suggesting that nuclear volume and nuclear envelope tension are tightly coupled, and supports the hypothesis that mitosis is a key regulator of nuclear envelope tension". I have a few issues with the way this sentence is written. Firstly, one could say that all nuclear structures (and not only blebs) are lost during mitosis because the nucleus disassembles. Hence, the new homeostatic state could be determined by envelope reassembly after mitosis and not mitosis itself. Thirdly, how can mitosis be a key regulator of nuclear envelope tension when the nucleus is disassembled during the process? These require clarification.

We agree with the reviewer that the formulation used required clarification that will be made in the revised version: for now, we only have evidence that nuclear volume regulation is at stake at mitosis. The most probable hypothesis is that confinement perturbed NE reassembly after mitosis, and that this perturbed reassembly leads to a change in nuclear volume. Complementary experiments are needed to test such a hypothesis, using cell lines stably expressing LAP2/LAP2b-GFP for instance. It is however delicate experiments that will require a dedicated study on its own.

Secondly, I don´t understand why the loss of nuclear blebs suggests that volume and tension are tightly coupled.

Nuclear Blebs appear once nuclei have reached a critical NE tension (Srivastava, et al PMID: 33662810). The fact that cells “born” under confinement have no nuclear blebs means that their nuclei are no longer under tension. This is a direct consequence of the decrease in nuclear volume, implying a coupling between volume and tension.

(6) The authors claim that, unlike previous studies (Lomakin et al), this work shows a "gradual nuclear adaptation". From their results, this is difficult to conclude simply because they do not analyse cPLA2 levels. This is solely based on indirect evidence obtained from cPLA2 inhibition. A gradual adaptation would mean that based on the level of confinement we would expect to have increasingly higher levels of cPLA2 (and therefore nuclear tension).

We thank the reviewer for his/her comment. Indeed, we have no direct evidence of gradual cPLA2 recruitment in our study, as we did not analyze cPLA2 levels.

However, of note, in our study, nuclear volume and tension adaptation occur in the entire range of confinement height (from 3 to 9 µm), with a decrease in nuclear volume inversely correlated with the imposed initial nuclear deformation (fig S2C). On the contrary, in Lomakin et al., for HeLa cells, a threshold of 5 µm confinement is needed to trigger a cell motility response mediated by cPLA2. Such a difference suggests that other parameters are used as a confinement readout by cells during the reassembly of the NE after mitosis.

(7) The authors should refrain from saying that the mechanism behind DNA repair is coupled to the nuclear adaptation they show. There are several points regarding this statement. Firstly, increased DNA damage could be due to nuclear ruptures imposed by confinement at 2h. In fact, the authors show leakage of NLS from the nucleus after confinement (Figure S3A). Secondly, the decrease in DNA damage at 24h could be because these nuclei did not rupture. How can they ensure that cells with low DNA damage at 24h had increased DNA damage at 2h? Finally, one needs to confirm if the nuclei they are analysing at 24h did undergo a round of cell division previously. From the evidence provided, the authors cannot conclude that DNA damage regulation is occurring in confined cells. Moreover, cell cycle arrest is a known effect of DNA damage. Cells with high damage at 2h most likely are arrested or will present with increased mitotic errors (which the authors exclude from their analyses).

We need to clarify our analysis workflow: it was only in live experiments that we excluded cells with abnormal cell division, as cell division was visible in the timelapse. For immuno-staining analysis on fixed samples, all non-apoptotic cells were taken into account in the analysis. The decrease in DNA damage observed at 24h thus applies to all cells under confinement. There is a clear difference between 2h and 24h in the 2AX immunostaining (that is used as a proxy for DNA damage): whereas at 2h almost all cells have several foci (10-15 foci per cells on average fig. 3H), the number of foci in the entire cell population decreases to 1-2 foci per cell at 24h. The population at 24h mainly includes cells that have undergone a round of cell division, with >80 % of normal cells, as quantified in Fig. 3 E. In the revised version, we will include as a supplementary figure, a quantification of the percentage of cells having more than 5 foci at 2h and 24h, as well as large field of views for -2AX immunostaining to illustrate the distribution.

Reviewer #2 (Public Review)

One major limitation is that all experiments are performed in a single cell line, HT-29 human colorectal cancer cells, which has an unusual nuclear envelope composition as it has no lamin B2, low lamin B1 levels, and contains a p53 mutation. Because lamins B1 and B2 play important functions in protecting the nuclear envelope from blebs and confinement-induced rupture, and p53 is crucial in the cellular DNA damage response, it remains unclear whether other cell lines exhibit similar adaptation behavior.

We agree that including other cell lines would help generalize our findings. It would be interesting in the future to analyze if a similar regulation exists for other cell types. In particular, as stated in the discussion, it would be very interesting to investigate whether this nuclear adaptation is universal, or if it is a consequence of a dysregulation in a specific cancer pathway. Our current manuscript is relevant as it uncovers the existence of this highly interesting phenomenon.

Investigating if other cell types have the same capacity to adapt would provide insights into the molecular mechanisms involved. In the revised version, we specifically plan to analyze nuclear response under prolonged confinement in 2 types of cells :(1) normal cells with near diploid characteristics (RPE-1 cell line, as a model of a diploid and untransformed cell line); (2) other colorectal cancer cell lines presenting higher levels of lamin B2 and B1, and no P53 mutation (HCT-116).

Furthermore, although the time-lapse experiments suggest that reduction in nuclear volume occurs primarily during mitosis, the authors do not address whether prolonged confinement, even in the absence of apoptosis, could also result in cells adjusting their nuclear volume, or alternatively normalizing nuclear envelope tension by recruiting additional membrane from the endoplasmic reticulum, which is continuous with the nuclear membranes.

Even if we cannot completely ruin the hypothesis raised by the reviewer, we respectfully want to stress that if additional membrane from the endoplasmic reticulum were recruited, we should observe an increase in nuclear volume at S/G2, which is the case only for the strongest imposed confinment (h=3 µm, corresponding to an initial nuclear deformation of 70 % Figure S2E). It should be however very interesting in the future to directly assess nuclear envelope tension and to follow with high resolution live experiments the eventual recruitment of additional membrane.

Regarding the proposed role of cPLA2, previous studies have shown that cPLA2 recruitment to the nuclear membrane, which is essential to mediate its nuclear mechanotransduction function, requires both an increase in nuclear membrane tension and intracellular calcium. However, the current study does not include any data showing the recruitment of cPLA2 to the nuclear membrane upon confinement, or the disappearance of nuclear membrane-associated cPLA2 during prolonged confinement, leaving unclear the precise function and dynamics of cPLA2 in the process.

We agree with the reviewer that it would be very informative to analyze the recruitment of cPLA2 in live experiments. We plan to do this in future experiments using cPLA2 immunostaining at different time points or the cPLA2-mKate construct. This will be the subject of a dedicated study, together with possible changes in nuclear pores size and organization, as well as nuclear tension analysis. For this article, we plan to add the analysis of the effect of cPLA2 inhibition in live experiments.

Lastly, it remains unclear (1) whether the reduction in nuclear volume is caused by a reduction in nuclear water content, by chromatin compaction, e.g. associated with an increase in heterochromatin, or through other mechanisms, (2) whether the change in nuclear volume is reversible, and if so, how quickly,

We thank the reviewer for his/her comment. This point was also mentioned by Reviewer #1. It is important to know if cells that were ‘born’ under confinement will retain smaller nuclei in the next generation if confinement is released, or whether the next generation will recover their initial larger nuclei. We plan to perform such “deconfinement” experiments and add the results in the revised version. In addition, we also plan to investigate in more detail the DNA compaction state during confinement.

and (3) what functional consequences the substantial reduction in nuclear volume has on nuclear function, as one would expect that this reduction would be associated with a substantial increase in nuclear crowding, affecting numerous nuclear processes.

We agree with the reviewer that such a reduction in nuclear volume would most probably affect numerous nuclear processes that would be highly interesting to decipher in the future. Especially, as pointed out in the discussion, “the regulation of nuclear size identified in this study could have important consequences on resistance to classical chemotherapeutic treatments that target proliferation”. This question merits an entire study and is outside the scope of our current manuscript.

Reviewer #3 (Public Review)

(1) One essential consideration that goes unaddressed is whether the nuclear volume alone is changing under compression (resulting in a higher nuclear to cytoplasmic ratio) or if the cell volume is changing and the nuclear volume is following suit (no change in the N:C ratio). Depending on which of these is the case, the overall model would likely shift. In particular, interpreting the effect of disrupting myosin II activity given its different distribution at the cortex in response to the higher confinement would be influenced by which of these conditions are at play.

We agree with the reviewer. As stated in the discussion, “the nuclear to cytoplasmic volume ratio, which is constant within a given population, is most likely to be impacted by confinement and changes in nuclear envelope tension (24, 45, 46), and might be at play in the regulation we describe herein”.

As mentioned in the results section, “the distance between the cell membrane and the nuclear envelope was significantly reduced with confinement (Fig. 1D, Fig. S1B) and accompanied by the relocalization of the contractility machinery (Phosphorylated Myosin Light Chain (p-MLC) staining) from above the nucleus to the side, indicating a cortex rearrangement (Fig. S1C)”. For the revised version, we plan to investigate if such relocalization is accompanied by a change in the nuclear to cytoplasmic ratio using the p-MLC and nuclei immunostaining performed at 2h and 24h under the entire range of confinement investigated.

(2) -A key approach used and interpreted by the investigators is an assessment of the folding of the "inner lamin envelope", which they derive from an image analysis routine of lamin staining that they developed and argue reflects "nuclear envelope tension". I am not convinced of the robustness of this approach or what it mechanistically reveals. It may or may not reflect the contour of the inner nuclear membrane, which (perhaps) is the most relevant to the authors' interpretation of nuclear envelope tension. Given the major contribution of this data to the model, which is based on the "unfolding" of the nuclear envelope, an orthogonal approach (e.g. electron microscopy - which one needs to truly address the high-frequency undulations of the nuclear envelope) is needed to support the larger conclusions.

We agree with the reviewer that the precise measurement of NE surface area is challenging because of the NE folds, and that our approach is provides semi-quantitative information. Higher-resolution approaches would be necessary to investigate that point in more details, using 3D super-resolution. However, we want to point out that even with our limited resolution, the differences observed in lamin A/C staining are striking (Fig. 3A): while lamin folds are completely absent at 2h under strong confinement, inner lamin folds are massively observed at 24h, showing a pattern very similar to the control condition. In the revised version, we will add more representative images to strengthen that our analysis is representative of our observations.

(3) The authors argue that nuclear tension is lost after mitosis in the confined devices because nuclear volume has decreased. While a smaller nuclear volume might indeed translate to less compressive force from the device on the nucleus, one would imagine that the chromosomes still have to be accommodated and that confining them in a smaller volume could increase the tension. Although arguable, the potential alternative possibilities suggest that actual measurements of nuclear envelope tension are needed to robustly test the model. The authors cite the observation that blebs are less prevalent after mitosis as additional support for this model, but this is expected as nuclear envelope breakdown and reformation will "reset" the nuclear contour while the appearance of blebs at mitotic entry is essential a "memory" of all blebs and ruptures over the entire preceding cell cycle.

We agree with the reviewer that assessing the nuclear envelope tension would enable a better description of the underlying process. It will be the subject of a dedicated study, together with possible changes in nuclear pore size and organization, as well as the analysis of cPLA2 recruitment.

The proposed model in the current study is for the moment simply a geometrical model. Given the simplicity of the model, the fit with our experimental points is striking.

(4) Representative images for the pharmacological perturbations other than blebbistatin are notably absent - only the analyzed data are presented in the manuscript or the supplemental material. How these perturbations (e.g. to cPLA2) also affect the cortex is important to interpret the data given the point raised above. Orthogonal approaches would also strengthen the conclusions (for example, the statement that "nuclear adaptation observed during mitosis requires nuclear tension sensing through cPLA2" requires more evidence to be convincing - it is not sufficiently supported by the data presented). Even if this is the case, the authors acknowledge that cPLA2 is likely not the answer to the adaption observed under the lower degrees of confinement. Thus, the mechanisms underlying the adaptive changes to nuclear volume remain enigmatic.

We thank the reviewer for this insightful comment, and we plan to add representative images for the pharmacological perturbation in the revised version of the manuscript.

(5) One more consideration that seems to go without comment is that the cells under confinement do not appear to successfully complete cytokinesis (Fig. 5b). At a minimum this seems like a major perturbation to cell physiology and needs to be more fully discussed by the authors as playing a role in the observed changes in nuclear volume.

We agree that in the image chosen for Fig. 5b, cytokinesis does not seem to be complete. This is not representative of the entire cell population as 80% of the cell population showed a normal phenotype under very strong confinement with no drug (Fig. 5C and 3E, as well as fig S3D for a representative large field of view). Live experiments using the FUCCI cell lines also show that cells are capable of making several complete divisions under confinement (Fig. 2). Complementary experiments under pharmacological treatments and confinement are planned to extend our analysis of such processes.

Author Response

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

eLife assessment:

This study presents a valuable finding on the possible use of vilazodone in the management of thrombocytopenia through regulating 5-HT1A receptor signaling. The evidence supporting the claims of the authors is solid, with the combined use of computational methods and biochemical assays. The work will be of broad interest to scientists working in the field of thrombocytopenia.

Public Review:

Reviewer #1 (Public Review):

Summary:

This is well-performed research with solid results and thorough controls. The authors did a good job of finding the relationship between the 5-HT1A receptor and megakaryocytopoiesis, which demonstrated the potential of vilazodone in the management of thrombocytopenia. The paper emphasizes the regulatory mechanism of 5-HT1A receptor signaling on hematopoietic lineages, which could further advance the field of thrombocytopenia for therapeutic purposes.

Strengths:

This is comprehensive and detailed research using multiple methods and model systems to determine the pharmacological effects and molecular mechanisms of vilazodone. The authors conducted in vitro experiments using HEL and Meg-01 cells and in vivo experiments using Zebrafish and Kunming-irradiated mice. The experiments and bioinformatics analysis have been performed with a high degree of technical proficiency. The authors demonstrated how vilazodone binds to 5-HTR1A and regulates the SRC/MAPK pathway, which is inhibited by particular 5-HTR1A inhibitors. The authors determined this to be the mechanistic underpinning for the effects of vilazodone in promoting megakaryocyte differentiation and thrombopoiesis.

Weaknesses:

(1) Which database are the drug test sets and training sets for the creation of drug screening models obtained from? What criteria are used to grade the results?

Response: Thank you for your thoughtful comment. The database is built by our laboratory. Firstly, we collected 39 small molecule compounds that can promote MK differentiation or platelet formation and 691 small molecule compounds that have no obvious effect on MK differentiation or platelet formation to buiid the datbase. Then, the data of the remaining 713 types of small molecule compounds were utilized as the Training set, and the Molecular Descriptors of 2 types of active and 15 types of inactive small molecule compounds were randomly picked as the Validation set. With regard to the activity evaluation criteria, the prediction score for each molecule was between 0 and 1, and the model decision was made with a threshold of 0.5. The molecule with a score above the 0.5 threshold was identified as a megakaryopoiesis inducer (1).

Reference：

(1) Mo Q, Zhang T, Wu J, et al. Identification of thrombopoiesis inducer based on a hybrid deep neural network model. Thromb Res. 2023;226:36-50. doi:10.1016/j.thromres.2023.04.011

(2) What is the base of each group in Figure 3b for the survival screening of zebrafish? The positivity rate of GFP-labeled platelets is too low, as indicated by the quantity of eGFP+ cells. What gating technique was used in Figure 3e?

Response: We are deeply grateful for the insightful feedback you have provided regarding Figure 3 and the assessment of zebrafish model. We used 50 zebrafish embryos per group to evaluate VLZ toxicity, and we think this is a suitable and fair baseline. Our gating procedure is clearly depicted in the resulting diagram. Since our goal was to evaluate the fluorescence intensity quantitatively, we isolated the entire zebrafish cell. Since the amount of eGFP+ in various zebrafish tissues found in other literature is likewise quite low and we are unsure of the typical eGFP+ threshold for zebrafish (1, 2), we think this finding should be fair given that each group's activities in the experiment were conducted in parallel.

Reference：

(1) Yang L, Wu L, Meng P, et al. Generation of a thrombopoietin-deficient thrombocytopenia model in zebrafish. J Thromb Haemost. 2022; 20(8): 1900-1909. doi:10.1111/jth.15772

(2) Fallatah W, De Silva IW, Verbeck GF, Jagadeeswaran P. Generation of transgenic zebrafish with 2 populations of RFP- and GFP-labeled thrombocytes: analysis of their lipids. Blood Adv. 2019;3(9):1406-1415. doi:10.1182/bloodadvances.2018023960

(3) In Figure 4C, the MPV values of each group of mice did not show significant downregulation or upregulation. The possible reasons for this should be explained.

Response: Thank you for your thoughtful comment. Megakaryocytes build pseudopodia, which form extensions that release proplatelets into the bone marrow sinusoids. Proplatelets convert into barbell-shaped proplatelets to form platelets in an integrin αIIbβIII mediated process (1-2). Platelet size is established by microtubule and actin-myosin-sceptrin cortical forces which determine platelet size during the vascular formation of barbell proplatelets (3). Conversion is regulated by the diameter and thickness of the peripheral microtubule coil. Proplatelets can also be formed from proplatelets in the circulation (4). Megakaryocyte ploidy correlates with platelet volume following a direct nonlinear relationship to mean platelet volumes (5). Usually there is an equilibrium between platelet generation and clearance from the circulation (normal turnover) controlled by thrombopoietin. When healthy humans receive thrombopoietin, their platelet size decreases (6). Proplatelet formation is dynamic and influenced by platelet turnover (7) which increases upon increased platelet consumption and/or sequestration. In our study, the MPV values of each group of mice did not show significant downregulation or upregulation, from our point of view, there are several possible reasons for these results.

(1) Mice in a radiation-damaged state may result in a decrease in platelet count, but at the same time stimulate the bone marrow to release young and larger platelets, thus keeping the MPV relatively stable.

(2) After radiation injury, bone marrow cells were suppressed, resulting in a decrease in the number of platelets produced, but MPV remained unchanged, possibly because the direct effects of radiation on the bone marrow caused thrombocytopenia, but not necessarily the average platelet size.

Reference：

(1) Thon JN, Italiano JE. Platelet formation. Semin Hematol. 2010(3):220-226. doi: 10.1053/j.seminhematol.2010.03.005.

(2) Larson MK, Watson SP. Regulation of proplatelet formation and platelet release by integrin alpha IIb beta3. Blood. 2006(5):1509-1514. doi: 10.1182/blood-2005-11-011957.

(3) Thon JN, Macleod H, Begonja AJ, et al., Microtubule and cortical forces determine platelet size during vascular platelet production. Nat. Commun. 2012(3):852. doi: 10.1038/ncomms1838.

(4) Machlus KR, Thon JN, Italiano JE Jr. Interpreting the developmental dance of the megakaryocyte: a review of the cellular and molecular processes mediating platelet formation. Br. J. Haematol. 2014(2):227-36. doi: 10.1111/bjh.12758.

(5) Bessman JD. The relation of megakaryocyte ploidy to platelet volume. Am. J. Hematol. 1984(2):161-170. doi: 10.1002/ajh.2830160208.

(6) Harker LA, Roskos LK, Marzec UM, et al., Effects of megakaryocyte growth and development factor on platelet production, platelet life span, and platelet function in healthy human volunteers. Blood. 2000(8):2514-2522. doi: 10.1182/blood.V95.8.2514.

(7) Kowata S, Isogai S, Murai K, et al., Platelet demand modulates the type of intravascular protrusion of megakaryocytes in bone marrow. Thromb. Haemost. 2014(4):743-756. doi: 10.1160/TH14-02-0123.

(4) The PPI diagram and the KEGG diagram in Figure 6 both provide a possible mechanism pathway for the anti-thrombocytopenia effect of vilazodone. How can the authors analyze the differences in their results?

Response: We are appreciated your valuable comments. PPI (Protein-Protein Interaction) refers to the interaction between proteins. Inside cells, proteins interact with each other to perform various biological functions, influencing cell signaling, metabolic pathways, cell cycle, and more. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a database that integrates information on genomes, chemicals, and biological systems. In pharmacoinformatic, KEGG pathways are often used to understand the molecular mechanisms of specific diseases or biological processes. KEGG contains the interrelationships between genes, proteins, and metabolites, helping to reveal key nodes in biological processes. PPI information can be integrated with data from KEGG pathways, such as metabolic and signaling pathways, to gain a more comprehensive understanding of the role of protein-protein interactions in cellular processes and biological functions. For example, by analyzing nodes in the PPI network, proteins associated with a specific disease can be identified, and further examination of these proteins' locations in KEGG pathways can reveal molecular mechanisms underlying the onset and development of the disease. However, this method also has some limitations：

Uncertainty (1): The construction of protein-protein interaction networks and drug interaction networks involves many assumptions and speculations. The edges of these networks may be based on experimental data but can also rely on bioinformatics predictions. Therefore, the accuracy of predictions is limited by the quality and reliability of the data used during network construction.

Insufficient data (2): Despite the availability of a large amount of bioinformatics data for network construction, interactions between some proteins and drugs may still lack sufficient experimental data. This data insufficiency can result in inaccuracies in network predictions.

Dynamics and temporal-spatial changes (3): The dynamics and temporal-spatial changes in biological systems are crucial for drug effects. Pharmacoinformatic may struggle to capture these changes as it often relies on static network representations, overlooking the temporal and dynamic nature of biological systems.

Reference：

(1) Fernando PC, Mabee PM, Zeng E. Integration of anatomy ontology data with protein-protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinformatics. 2020(1):442. doi: 10.1186/s12859-020-03773-2.

(2) Zhang S, Zhao H, Ng MK. Functional module analysis for gene coexpression networks with network integration. IEEE/ACM Trans. Comput. Biol. Bioinform. 2015(5):1146-1160. doi: 10.1109/TCBB.2015.2396073.

(3) Cinaglia P, Cannataro M. A method based on temporal embedding for the pairwise alignment of dynamic networks. Entropy (Basel). 2023(4):665. doi: 10.3390/e25040665.

(5)-HTR1A protein expression is measured only in the Meg-01 cells assay. Similar quantitation through western blot is not shown in other cell models.

Response: Your insightful criticism and recommendation to use different cell models in order to obtain a more accurate depiction of 5-HTR1A protein expression are greatly appreciated. We completely concur that using this strategy would greatly increase the validity of our research. However, establishing a primary megakaryocyte model requires specialized expertise and technical resources, which unfortunately are not readily available to us within the given timeframe. Nevertheless, we acknowledge the limitations of Meg-01 cells, which may exhibit distinct properties compared to true megakaryocytes. To mitigate this concern, we have ensured robust experimental design and rigorous data analysis to interpret our findings within the context of these model cell lines. We believe our results still provide valuable insights into megakaryocyte differentiation and address an important biological question.

Reviewer #2 (Public Review):

Summary:

The authors tried to understand the mechanism of how a drug candidate, VLZ, works on a receptor, 5-HTR1A, by activating the SRC/MAPK pathway to promote the formation of platelets.

Strengths:

The authors used both computational and experimental methods. This definitely saves time and funds to find a useful drug candidate and its therapeutic marker in the subfield of platelets reduction in cancer patients. The authors achieved the aim of explaining the mechanism of VLZ in improving thrombocytopenia by using two cell lines and two animal models.

Weaknesses:

Only two cell lines, HEL and Meg-01 cells, were evaluated in this study. However, using more cell lines is really depending on the workflow and the grant situations of the current research team.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. We fully agree that CD34+ hematopoietic stem/progenitor cells or primary megakaryocytes would provide a more accurate representation of in vitro megakaryopoiesis compared to HEL and Meg-01 cells, which possess limited potential for this process. We acknowledge that our current study did not include experiments with these preferred cell models. This is because our laboratory is still actively developing the technical expertise and resources required for establishing and maintaining primary megakaryocyte and CD34+ cell cultures. Despite the limitations of the current study, we believe the results using HEL and Meg-01 cells provide valuable preliminary insights into the potential effects of VLZ on megakaryocyte differentiation. We are actively working to overcome these limitations and plan to incorporate these more advanced models in our future investigations.

Reviewer #1 (Recommendations For The Authors):

I think the authors can enhance the mechanism study by developing more reliable models and methodologies. The connection to clinical research should be strengthened at the same time.

Response: We deeply appreciate your insightful feedback and valuable suggestions regarding the use of more suitable models for studying the role of VLZ in megakaryocyte differentiation and platelet production. Despite the limitations, we are committed to expanding our research in the future by incorporating your suggestion and establishing a primary megakaryocyte model to further validate our findings and strengthen our conclusions. At the same time, we wholeheartedly concur with your suggestion to combine clinical research. Unfortunately, VLZ is not a first-line treatment for depression in China, and getting blood samples from the matching number of patients for analysis is a challenge. To give additional experimental support for the medication, we have attempted to improve the data in vivo as much as feasible, including by implementing the intervention in normal mice. Our findings should also contribute to the theoretical underpinnings of this medication and aid in its practical application.

Reviewer #2 (Recommendations For The Authors):

Issues the authors need to address:

Figure 7: Why the band intensity of GAPDH in b or e is much greater than that in f, g, or h?

Response: Thank you for your careful observation and insightful comment regarding Figure 7. Because the concentration of each batch of protein samples is different, sometimes the GAPDH band strength is increased by the large loading volume. Other factors that may influence the GAPDH band strength include the instrument's contrast adjustment during exposure and the use of different numbers of holes for electrophoresis. Meanwhile, the original three replicate results of all WB results will be provided in the supplementary materials.

Finally, we sincerely thank you for providing us with this opportunity to make a further revision and modification of our manuscript, and your valuable and scientific comments are useful for the great improvement of our manuscript!

Author Response

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

Response to reviewers

We wish to thank the reviewers for the time taken to appraise the manuscript and the helpful feedback to improve it. We have taken onboard the suggested feedback and incorporated it into the revision. The findings of the revised manuscript are unchanged. Below is a point-by-point response to specific comments.

Public reviews

Reviewer 1

Thank you to reviewer 1 for the thorough and insightful review of our manuscript. We are pleased that the strengths of our research, particularly the use of whole-genome bisulfite sequencing, the combination of animal and human data, and the investigation of a potential dietary intervention were recognized. We are confident that these aspects contribute significantly to the value and originality of our work.

We acknowledge the concerns regarding the statistical rigor of the study, particularly the sample size and data analysis methods. We would like to address these points in more detail:

Sample size: While we agree that a larger sample size would be ideal, the chosen sample size (n=4 per group) is consistent with other murine whole-genome bisulfite sequencing experiments in the field. We have carefully considered the cost-benefit trade-off in selecting this approach. In the revision we discuss the potential limitations of this sample size.

Data analysis: We acknowledge the inconsistencies in the study reporting and have committed to improving the clarity in the revision. We carefully reviewed the concerns regarding the use of causal language and the interpretation of differences in our results. In some cases, the use of causal language is justified by the intervention study design. We also believe other explanations like stochastic variation affecting the same genomic regions in different tissues, are exceedingly unlikely from a statistical viewpoint. In the revision we have adopted a balanced approach to the language.

Confounders: We acknowledge the importance of accounting for potential confounders such as birthweight, alcohol exposure and sex. The pups selected for genome analysis were matched for sex and on litter size as a proxy for in utero alcohol exposure. This careful selection of mice for genome analysis was intentionally guided to mitigate potential confounding.

Statistical rigour: We acknowledge the importance of multiple testing correction in the genome-wide analysis. We used the DSS method of Feng et al (PMID: 2456180) which employs a two-step procedure for assessing significance of a region. Instead of a single p-value for the whole DMR, DSS uses the area statistic to rank candidate regions and control the false discovery rate through shrinkage estimation methods. This approach reduces the risk of reporting false positives due to multiple testing across numerous CpG sites. It is similar in respects to employing local FDR correction at 0.05 level, with an additional minimum effect size threshold applied, and particularly suited to experiments where the number of replicates is low. In the revision we have committed to improving the clarity of the reporting of statistical methods.

Reviewer 2

Thank you to reviewer 2 for the comprehensive and valuable feedback on our manuscript. We take your concerns about the generalizability of our findings and the interpretation of certain results seriously. We would like to address your specific criticisms in detail:

Generalizability and Human Data: We agree that the generalizability of mouse models to human conditions has limitations. However, our study focused on understanding the early molecular alterations caused by moderate PAE, which can be more effectively modelled in a controlled environment like mice. To clarify this, we have strengthened the manuscript by emphasizing the focus on moderate PAE in the title and throughout the paper.

Transcriptome Analysis: We recognize the importance of investigating the functional consequences of PAE-induced DMRs and agree that transcriptome analysis would be highly valuable. We are currently planning to conduct future transcriptomic studies to understand the link between DMRs and gene expression.

Species-Specificity and DMR Enrichment: We acknowledge the likelihood of species-specific PAE effects. Our finding of enrichment of DMRs in non-coding regions was consistent with observations from the Lussier study of FASD. We agree there is further work to do and now highlight this in the discussion.

Tissue Sample Locations: Due to technical restrictions of processing newborn mouse tissue, we are unable to enhance the manuscript with specific tissue regions sampled.

Interpretation of Shared Genomic Regions: We appreciate your point about the alternative explanation for the shared genomic regions between brain and liver. Our interpretation is that regions identified in the alcohol group only affected equally in both tissues are likely established stochastically (as a result of the exposure) in the early embryo and then maintained in the germ layers. We have revised to suggest this is the most likely explanation and we acknowledge a more detailed examination in more tissues would be warranted for proof.

Additional Feedback

Reviewer 1

Introduction

• Line 65 - alcohol consumption is not always preventable and these statements further increase the stigma associated with FASD. A better way to say this would be "a leading cause of neurodevelopmental impairments".

We have implemented this suggestion in revised manuscript.

• The studies cited in lines 87-89 are somewhat outdated, as several more recent studies with better sample sizes have been published in recent years. I would recommend citing more recent publications in addition to these studies. Similarly, the authors should also cite Portales-Casamar et al., 2016 (Epigenetic & Chromatin) for the validation in humans, as it was the original study for those data.

We have added a citation for the study mentioned by Portales-Casamar et al. (2016) in the revised manuscript.

• Lines 95-95 - the authors should elaborate further on the "encouraging results" from choline supplementation studies, as these details may help interpret the findings from their own study.

In the revised manuscript, we replaced “encouraging results” with “results suggesting a high methyl donor diet (HMD) could at least partially mitigate the adverse effects of PAE on various behavioural outcomes”.

• Minor point: DNA methylation is preferable to "methylation" alone when not referring to specific CpGs or sites, as methylation can also refer to protein or RNA methylation.

“Methylation” has been replaced with “DNA methylation” in revised manuscript

Results

• Line 118 - HMD should be defined here.

HMD defined in revised manuscript

• The figures in the main manuscript and supplemental materials are not in the same order as they are presented in the text.

We apologise for this and thank the reviwer for their attendtion to detail. In the revision we have corrected the order of figures to match the text.

• It is concerning that the H20-HMD group had lower baseline weights, which could impact the findings from these analyses. Please discuss how these differences were accounted for in the study design and analyses.

We appreciate the reviewer's concern about the lower baseline weight in the H20-HMD group. We agree that this difference could potentially affect our findings. However, we want to emphasize that total weight gain during pregnancy was statistically similar across all groups by linear mixed effect model. Additionally, all dams were within the healthy weight range for their strain. While we cannot completely rule out any potential influence of baseline weight, we believe the similarity in weight gain and the healthy range of all dams suggest that the in-utero experience of pups regarding weight-related factors was likely comparable across groups.

• I have some concerns regarding the cutoffs used to identify the DMRs, particularly given the small N and number of tests. The authors should report the number of DMRs that meet a multiple testing threshold; if none, they should use a more stringent threshold than p<0.05, as one would expect 950,000 CpGs to meet that threshold by chance (19,000,000 CpGs x 0.05). The authors should also report the number of DMRs tested, as this will be a more appropriate benchmark for their analyses than the number of CpGs (they should also report the specific number here).

We appreciate the reviewer's concerns regarding the DMR cut-offs. We agree that clarifying the methods and justifying our choices is crucial. Our implementation of the DSS method for defining DMRs employs a local FDR p<0.05 cut-off, with additional delta beta threshold of 5%. We have clarified this in the methods section of the revised manuscript . We want to emphasize that the local FDR approach effectively mitigates the concern of chance findings by adjusting for multiple comparisons across the genome. Line 414-420 in the revised methods contains the following amended text

“Differentially methylated regions (DMRs) were identified within each tissue using a Bayesian hierarchical model comparing average DNA methylation ratios in each CpG site between PAE and non-PAE mice using the Wald test with smoothing, implemented in the R package DSS (46). False-discovery rate control was achieved through shrinkage estimation methods. We declared DMRs as those with a local FDR P-value < 0.05 based on the p-values of each individual CpG site in the DMR, and minimum mean effect size (delta) of 5%”

• I also have concerns about the delta cutoff for their DMRs. First, it is not clear if this cutoff is set for a single CpG or across the DMR (even then, it is not clear if this is a mean, median, max, min, etc.) Second, since the authors analyzed CpGs with 10X coverage, they can only reliably detect a delta of 0.1 (1/10 reads).

Thank you for raising this important point. In the revision we have clarified the effect size cutoff reflects the mean effect across CpGs within the DMR as follows (line 418)

“We declared DMRs as those with a local FDR P-value < 0.05 based on the p-values of each individual CpG site in the DMR, and minimum mean effect size (delta) of 5%”

We chose the mean as it provides a comprehensive representation of the overall methylation change within the region, while ensuring all individual CpGs used in the analysis had at least 10x coverage. It is not true that we can only detect a delta of 1/10 reads, the mean effect is the relative difference in means between groups and is not dependent on the underlying sequencing depth.

• Prenatal alcohol exposure is known to impact cell type proportions in the brain, which could lead to differences in DNAm patterns. The authors should address this possibility in the discussion, as well as examine their list of DMRs to determine if they are associated with specific brain cell types. The possibility of cell type differences in the liver should also be discussed.

We agree with the reviewer that PAE-induced alterations in cell type proportions can influence DNA methylation patterns. While isolating specific cell types in our current study's brain and liver samples was not achievable due to tissue limitations, we acknowledge this as a limitation and recognize the need for further investigations incorporating single-cell or cell type-specific approaches in the discussion.

• It is interesting, but maybe not surprising, that more DMRs were identified in the liver compared to the brain. This finding would warrant some additional interpretation in the discussion.

We appreciate and agree that this finding indeed warrants further interpretation. We have added the following sentence into the discussion section of the revised manuscript that provides some potential factors behind this observation.

Lines 263 “Indeed, most of the observed effects were tissue-specific, with more perturbations to the epigenome observable in liver tissue, which may reflect the liver’s specific role in metabolic detoxification of alcohol. Alternatively, cell type composition differences between brain and liver might explain differential sensitivity to alcohols effects”.

• Lines 148-149 - I disagree about the enrichment of decreased DNAm in brain DMRs, as 52.6% is essentially random chance. The authors should also include a statistical test here, such as a chi-squared test, to support this statement.

We agree that a revised interpretation is warranted. The updated manuscript has been amended as follows: “Lower DNA methylation with early moderate PAE in NC mice was more frequently observed in liver DMRs (93.5% of liver DMRs), while brain DMRs were almost equally divided between lower and higher DNA methylation with early moderate PAE (52.6% of brain DMRs had lower DNA methylation with early moderate PAE).”

• Similarly, I would recommend the authors use increased/decreased DNAm, rather than hypermethylated/hypomethylation, as the latter terms are better suited to DNAm values near 100% or 0%.

The use of hyper/hypo methylation is still considered common and well understood even for moderate changes. We agree the use of increased/decreased is more inclusive for a broader audience, so we have amended all references accordingly in the main text.

• Lines 153-155 - please report the statistics to support these enrichment results. A permutation test would be well suited to this analysis.

The reporting of statistics related to the enrichment test has now been amended to read “Overlap permutation tests showed liver DMRs were enriched in inter-CpG regions and non-coding intergenic regions (p < 0.05), while being depleted in all CpG regions and genic regions except 1to5kb, 3UTR and 5UTR regions, where there was no significant difference (Figure 2f).”

• Line 156 - "overwhelming enrichment" is a very strong statement considering the numbers themselves.

Omitted “overwhelming” in revised manuscript. Revised manuscript states: “Using open chromatin assay and histone modification datasets from the ENCODE project, we found enrichment (p < 0.05) of DMRs in open chromatin regions (ATAC-seq), enhancer regions (H3K4me1), and active gene promoter regions (H3K27ac), in mouse fetal forebrain tissue and fetal liver (Table 2).”

• Lines 165-167 - Please describe the analyses and metrics used to determine if the DNAm differences were mitigated in the HMD groups. As it stands, it is not clear if they are simply not significant, or if the delta was decreased. In terms of a figure, a scatter plot of the deltas for these DMRs would be better suited to visualizing these changes.

To determine whether DMRs were mitigated we simply applied the same statistical testing procedure on the subset of PAE DMRs in the group of mice exposed to the HM diet. The sample size is the same, and the burden on multiple testing is reduced as we did not test the entire genome. We believe our interpretation stands although we have urged caution in the discussion as follows (line 319)

“Another key finding from this study was that HMD mitigated some of the effects of PAE on DNA methylation. Although a plausible alternative explanation is that some of the PAE regions were not reproduced in the set of mice given the folate diet, our data are consistent with preclinical studies of choline supplementation in rodent models (34, 35) (36). Moreover, a subset of PAE regions were statistically replicated in subjects with FASD, suggestive or robust associations. Although our findings should be interpreted with caution, they collectively support the notion that alcohol induced perturbation of epigenetic regulation may occur, at least in part, through disruption of the one-carbon metabolism.”

• Given the lenient threshold to identify DMRs, it is possible that PAE-associated DMRs are simply false positives and do not "replicate" in a different subset of animals. One way to check this would be to determine whether there are any differences between mitigated/unmitigated DMRs and the strength of their initial associations. Should the mitigated DMRs skew towards higher p-values and lower deltas, one might consider that these findings could be false positives.

We appreciate the reviewer's concern about potential false positives due to the chosen DMR identification threshold. We reiterate the DMR calling thresholds were adjusted for local FDR; however, we acknowledge the need for further validation. We haven't observed this trend of mitigated DMRs having higher p-values and lower deltas, but we have replicated some PAE DMRs in independent human datasets and found support for their biological plausibility in the context of PAE.

• Related to the HMD analyses, I am concerned that the EtOH-HMD group consumed less alcohol, which could manifest in the PAE-induced DMRs disappearing, unrelated to the HMD exposure. The authors should comment on whether the pups were matched for ethanol exposure and include sensitivity analyses that include ethanol level as a covariate to confirm that their results are not simply due to decreased alcohol exposure.

We appreciate the reviewer's concern regarding the lower alcohol consumption by Dams in the EtOH-HMD group and its potential impact on DMRs. We agree that consistent in utero exposure is crucial for reliable results. Our pup selection for genomic analysis involved matching litter size as a proxy for in utero exposure, so even through the average alcohol consumption was lower for the EtOH-HMD group, we matched pups across treatment groups based on litter size as a proxy for alcohol intake levels, excluding pups with significantly different exposure levels. We agree more robust methods including direct measurement of blood alcohol content would improve the study. We have now incorporated this into the discussion of the revised manuscript on lines 351: “Additionally, we employed an ad-libitum alcohol exposure model rather than direct dosing of dams. Although the trajectories of alcohol consumption were not statistically different between groups, this introduces more variability into alcohol exposure patterns, and might might impact offspring methylation data”

• Lines 172 - please be more specific about the neurocognitive domains tested.

In the revision we have included more detail about the neurocognitive domains tested (originally mentioned in the results) in the methods as follows:

“These tests included the open field test (locomotor activity, anxiety) (38), object recognition test (locomotor activity, spatial recognition) (39), object in place test (locomotor activity, spatial recognition) (40), elevated plus maze test (locomotor activity, anxiety) (41), and two trials of the rotarod test (motor coordination, balance) (42)”

• Line 191 - please report the tissue type used in the human study, as well as the method used to estimate cell type proportions.

We stated in the results section that buccal swabs were used in both human cohorts.

We added to the revised manuscript that cell type proportions were estimated using the EpiDISH R package.

• Related to validation, it is unclear whether the human-identified DMRs were also validated in mice, or if the authors are showing their own DMRs. Please also discuss why DMRs might not have been replicated in AQUA.

We used human data sets to validate observations from our murine model, focusing on regions identified in our early moderate PAE model. This is now explicitly state on line 209 of the revision:

“We undertook validation studies by examining PAE sensitive regions identified in our murine model using existing DNA methylation data from human cohorts to address the generalizability of our findings.”

“In the section entitled ‘Candidate Gene Analysis..’ we used our murine data sets to reproduce previously published associations that included regions identified in both animal and human studies. We posit the lack of replication of our early moderate PAE regions in AQUA is explained in part by species-specific differences and considering the striking differences in effect size seen in regions that did replicate in FASD subjects, the exposure may need to be of sufficient magnitude and duration for the effects seen in brain and liver to survive reprogramming in the blood. The AQUA cohort is largely enriched for low to moderate patterns of alcohol consumption.

• Line 197 - please provide a citation for the ethanol-sensitive regions. There are also several existing DNAm analyses in brain tissues from animal models that should be included as part of these analyses, as several have shown brain-region and sex-specific DMRs related to prenatal alcohol exposure. These contrasts might help the authors further delineate the effects of prenatal alcohol in their model and expand on current literature to explain the deficits caused by alcohol exposure.

Our candidate gene/region selection was informed by a systematic review of previously published human and animal studies reporting associations between in utero exposure to PAE and offspring DNA methylation. We synthesized evidence across several models, tissues and methylation platforms to arrive at a core set of reproducible associations. Line 481 of the methods now includes a citation to our systematic review which details our selection criteria.

Discussion

• Line 211 - This is a strong statement for one hypothesis. It is also possible that different cell types have similar responses to prenatal alcohol exposure. In this scenario, perturbations need not arise before germ layer separation. The authors should soften this causal statement.

We appreciate this point although given the genome size relative to the size of the DMRs we have detected, the chance that different cell types would respond similarly in exactly the same regions seems exceedingly rare. We posit a more likely explanation is early perturbations in the embryo are established stochastically as a result of the exposure (supported by the interventional design) and maintained in the differentiating tissues. We agree further work is needed to prove this, specifically in a wider set of tissues from multiple germ layers so we have amended the discussion as follows:

“These perturbations may have been established stochastically because of alcohol exposure in the early embryo and maintained in the differentiating tissue. Further analysis in different germ layer tissues is required to formally establish this.”

• Lines 222-224 - I completely agree with this statement. However, the authors had the opportunity to examine dosage effects in their model as they measured alcohol-levels from the dams. At the very least, I would recommend sensitivity analyses in their DMRs to assess whether alcohol level/dosage influences their results.

Although a great suggestion to improve the manuscript, we did not have opportunity to examine dosages by design as we selected mice for genome analysis with matched exposure patterns. It would be fascinating to conduct a sensitivity analysis.

Methods:

• Please include the lysis protocol.

Thank you for picking up this error in our reporting. We have now included the following details in the methods which improve the reproducibility of this study: “Ten milligrams of tissue were collected from each liver and brain and lysed in Chemagic RNA Tissue10 Kit special H96 extraction buffer”.

• Please include the total reads for each sample and details of the QC pipeline, including filtering flags, quality metrics, and genome build.

Thank you for suggesting improvements to our reporting which improve the reproducibility of this study. We have included a new supplementary tableTab of sequencing statistics and details of the quality metrics. Please note the genome build is explicitly stated in the methods already.

• Please make your code publicly available to ensure that these analyses can be replicated.

Thank you for this suggestion. A data availability statement has now been included in the revision and code will be made available upon request

• Why were Y chromosome reads included in the dataset?

Y chromosomal reads were not included in the DMR analysis. Amended “We filtered the X chromosomal reads” to “We filtered the sex chromosomal reads” in revised manuscript.

• Please provide the number of total CpGs available for analysis.

Added sentence into results section of revised manuscript: “A total of 21,842,961 CpG sites were initially available for analysis.” We also clarified that the ~19,000,000 CpGs were analysed following coverage filtering.

• Please provide the parameters for the DMR analysis and report how the p-values and deltas were calculated.

We have addressed this in previous comments

• The supplemental materials for the human data are missing.

Thank you for picking up this oversight. The revision now includes an additional data supplement which details the analysis of the human data sets for interested readers.

Tables and figures

• Table 1. It is not clear how the DMRs for this table were selected. The exact p-values and FDR should also be reported in this table. The number of CpGs in these DMRS should also be reported.

Table 1 includes select DMRs that were consistently detected in both brain and liver tissue. These are particularly of interest as they represent regions highly sensitive to alcohol exposure. We agree that exact reporting of p-values would be ideal. Instead of a single p-value for the whole DMR, DSS uses the area statistic to rank candidate regions and control the false discovery rate (FDR) through shrinkage estimation methods. In the revision we have now included region size and number of CpGs in table 1.

• Table 3. Please include p-values for the DMR analyses.

As above we report the area-statistic which is an equivalent measure to assess evidence for differential methylation.

• Figure 2 (Figure 4 in revised manuscript). Please report the N for these analyses. It also seems that the pairwise t-tests were only compared to the H20-NC, which does not provide much insight into the PAE group. The relevance of the sexP analysis to the present manuscript is also unclear.

Figure 2 is now Figure 4 in the revision and the sample size has been included in figure legend. We compared all groups to the control group (H20-NC) as we aimed to determine any differences in intervention groups from the control.

We apologies for lack of clarity around the ‘sex P’ terminology. This refers to the p-value for the main effect of sex on the behavioural outcome. We agree it lacks relevance since the regression models were adjusted for sex. In the revision we have updated the methods as follows (line426) and removed references to sex P

“To examine the effect of alcohol exposure on behavioural outcomes we used linear regression with alcohol group (binary) as the main predictor adjusted for diet and sex.”

• Figure 3ef (Figure 2ef in revised manuscript). It is unclear how the regions random regions were generated. A permutation test would be relevant to determine whether there are any actual enrichment differences.

As stated in methods section: “DMRs were then tested for enrichment within specific genic and CpG regions of the mouse genome, compared to a randomly generated set of regions in the mouse genome generated with resampleRegions in regioneR, with equivalent means and standard deviations.”

• Figure 5. Please include the gene names for these DMRs, as well as their genomic locations. It would also be relevant to annotate these plots with the max, min, and mean delta between groups.

Thank you, we considered this however the DMRs are not in genes so we cannot apply a gene label. The locations are reported on the x-axis and the statistics are shown in Table 3.

• Figure S1b and S2c- It is quite worrisome that the PAE-HMD group drank less throughout pregnancy than their PAE counterparts. Please discuss how this was addressed in the analyses.

We appreciate the reviewer's concern regarding the lower alcohol consumption in the PAE-HMD group and its potential impact on DMRs. We agree that consistent in-utero exposure is crucial for reliable results. Although the total amount of liquid consumed over pregnancy was lower in this group, they started with a lower baseline and the trajectory was not statistically different compared to other groups.

We have now incorporated this into the discussion section of the revised manuscript on lines 336: “Additionally, we employed an ad-libitum alcohol exposure model rather than direct dosing of dams. Although the trajectories of alcohol consumption were not statistically different between groups, this introduces more variability into alcohol exposure patterns, and might might impact offspring methylation data.”

• Figure S1cd. See my comments about Figure 2.

Suggested changes have been incorporated.

• Figure S2d. it is not clear to what the statistics presented in this panel refer. Please clarify and discuss the implications of dietary intake differences on your findings.

Added sentence to caption in revised manuscript: “Statistical analysis involved linear mixed-effects regression comparing trajectories of treatment groups to H2O-NC baseline control group.”

• Figure S3. See my comments about Figure 2.

Suggested changes have been incorporated

• Figure S4. I am confused by the color legend, as it seems both colors are PAE. I also do not see how any regions show increased or decreased DNAm in PAE based on this plot (also no statistics are presented to support these conclusions).

The plot is intended to show there are no gross changes in methylation when averaged across all CpGs within different regulatory genomic contexts. Statistics are not included as it is intuitive from the plot that the means are the same. We have updated the figure legend which now reads

“Figure S4. No evidence for global disruption of methylation by PAE. The figure shows methylation levels averaged across CpGs in different regulatory genomic contexts. Neither brain tissue (A & B), nor liver tissue (C & D) were grossly affected by PAE exposure (blue bars). Bars represent means and standard deviation.”

Author Response

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

We greatly appreciate the editor and reviewers’ careful and professional assessment of this manuscript. We are delighted with the reviewers’ instructive comments and suggestions. We have tried to address the raised points comprehensively. The reviewers’ scrutiny has helped us immensely to discuss and present our work extensively and properly. We are grateful for the reviewers’ efforts and insights. The detailed responses are listed here.

Recommendations for the authors

(1) The intuition behind the model is not properly explained, i.e., the derivation of Eqs. 1-2 and the biological meaning of the AA/OO logic modes. A different notation could be helpful.

We thank the reviewers for this comment, and agree that the interpretation of our model in manuscript was indeed in need of improvement. We have incorporated this suggestion into the manuscript. For clarity, we have substituted AND-AND/OR-OR for original expression of AA/OO, and hope that new notations are helpful for interpreting our work.

In general, considering the diverse audience including those with experimental background, we feel that it is essential to present this manuscript in a more digestible manner. We therefore retain the entire derivation of Eqs. 1-2 in the supplementary method. We have added a qualitative introduction to model derivation and molecular biological significance underlying different logic motifs (AND-AND/OR-OR) in the revised manuscript. Please refer to Page 5 of the revised manuscript, lines 161-167 (see below).

“X and Y are TFs in the CIS network. n1 and n2 are the coefficients of molecular cooperation. k1-k3 in Eq1 and k4-k6 in Ep2 represent the relative probabilities for possible configurations of binding of TFs and CREs. (Fig2.A). d1 and d2 are degradation rates of X and Y, respectively. Here, we considered a total of four CRE’s configurations as shown in Figure 2A (i.e., TFs bind to the corresponding CREs or not, 22=4). Accordingly, depending on the transcription rates (i.e., r0x, r1, r2, r3 in Eq1, similarly in Eq2) of each configuration, we can model the dynamics of TFs in the Shea-Ackers formalism[1, 2].

Thus, the distinct logic operations (AND/OR) of two inputs (e.g., activation by X itself and inhibition by Y) can be further implemented by assigning corresponding profile of transcription rates in four configurations (Fig2.A). From the perspective of molecular biology, the regulatory logics embody the complicated nature of TF regulation that TFs function in a context-dependent manner. Considering the CIS network, when X and Y bind respective CREs concurrently, whether the expression of target gene is turned on or off depends on the different regulatory logics (specifically, off in the AND logic and on in the OR logic; Fig2.A). Notably, instead of exploring the different logics of one certain gene[3, 4], we focus on different combinations of regulatory logics due to dynamics in cell fate decisions is generally orchestrated by GRN with multiple TFs.”

(2) More clearly specify the used parameters and how these are chosen. This would be helpful to get a more quantitative grasp of the conditions that they compare.

We appreciate the reviewers pointing out unspecified parts in the main text. We have now included related discussion in the revised manuscript. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”).

We would like to highlight that the Boolean models with different logic motifs (Fig. 2B) explicitly display the difference of state spaces (i.e., attractor basin). Moreover, as the focus of this work is on the role of regulatory logics in cell fate decisions, we ponder that it is rational to specify the geometry of the landscape based on the hint from Boolean models. Therefore, we reason that it is intuitive and reliable to assign values to used parameters by mapping our ODE models (Eqs. 1-2) to corresponding Boolean models qualitatively (refer to the statement in our original manuscript, Page 5, lines 162-163, “With appropriate parameters, we are able to reproduce the Boolean-like attractor basin in the continuous models”). In producing Figure 2-5, setting of parameters was performed in a heuristic way without particular searching. However, to draw general conclusions, like the "trade-offs between progression and accuracy" and the presence of the fully-connected stage, we sampled a substantial number of sets parameters to ensure statistically robust findings.

(3) Include the explanation of how the nullclines and basins shown in the figures (e.g., Fig. 2C, Fig. 4C, Fig. 4F, etc.) are calculated.

We thank the reviewers for this suggestion. We have incorporated this into the legend of corresponding figures when first mentioned in the main text. Please refer to Page 7 of the revised manuscript, lines 217-223 (see below).

“Fig2.C:

(C) State spaces of the AND-AND (top panel) and OR-OR (bottom panel) motifs in ODE models. Dark and red lines represent nullclines of respectively. Stable steady states (SSS) are denoted as orange dots. Unstable Steady States (USSs) are denoted as white dots. Each axis represents the concentration of each transcription factor, which units are arbitrary. Blue, green and purple areas in state spaces indicate attractor basins representing LX, S and LY, respectively. Color of each point in state space was assigned by the attractors they finally enter according to the deterministic models (Eq1, Eq2). These annotations were used for the following Figure 3-7.”

(4) Clarity on the decisions in the work is needed. For example, the "introduction" of asymmetry of the noise levels (as stated in line 215) appears completely arbitrary. The reason behind it can be guessed in the following paragraph, but the reader shouldn't have to guess.

We agree entirely with the reviewers’ comment. Indeed, this should have been stated more explicitly. The motivation for incorporating asymmetry in the noise levels stems from our endeavor to mimic the inherent biological variability in gene expression within a cell population. We have adjusted the manuscript to better convey the motivation for investigating asymmetric noise level. Please refer to Page 8 of the revised manuscript, lines 237-238 (“In biological systems, it is unlikely that the noise level of different genes is kept perfectly the same.”).

(5) Arbitrary and/or out-of-context jargon is used throughout the manuscript, making it hard to read and follow what the authors mean in some cases. For example, "temporal fully-connected stage" is used for the first time in line 290, and the term is not explained either in the main text or in the manuscript. Similarly, the reference to a Boolean-like and Boolean model (line 163 and Figure 1) without clarifying if this is just an analogy or if a formal model is built, nor the utility and implications of this comparison. Another problem related to jargon occurs on line 291, where the authors talk about "parameter sensibility", but such analysis (as it is normally understood in the field) is never performed; the authors perform a parameter exploration and make some general conclusions about the parameter space, but that is different than a parameter sensitivity analysis.

We thank the reviewers for this comment, as it has prompted us to better clarify our manuscript. We have reviewed the manuscript and made the necessary adjustments to improve its clarity. We do hope that this revision meets the reviewers’ expectations on the clarity and comprehensiveness of our analysis.

Regarding the jargon of "temporal fully-connected stage", we realized that this term was slightly vague and in need of improvement. Instead, we now employ “transitory fully-connected stage” in the revised manuscript to underline the short emergence of this particular stage. Please refer to Page 11 of the revised manuscript, lines 323.

We thank the reviewers for pointing out the lack of clarity concerning the Boolean models. We have now amended the manuscript to make this implicit expression explicit. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B; see Methods), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”). Specifically, we employed the Boolean models (Fig.2B) as the reference to assist us to heuristically evaluate the applicability of used parameters in the ODE models. Therefore, the Boolean models are built formally, and corresponding updated rules are listed in Fig.2A (refer to the middle row in the table called “Logic Function”, now also noted in the legend of Fig.2B, Page 7, lines 213-214). Nevertheless, we do utilize the analogy between the attractor basins from Boolean models and ODE models (refer to Fig.2B-C). Accordingly, we used the term “Boolean-like” to describe the landscape presented by the continuous models (Eqs. 1-2; refer to the statement in our original manuscript, Page 5, lines 162-163, “With appropriate parameters, we are able to reproduce the Boolean-like attractor basin in the continuous models”).

We appreciate the reviewers for this valuable comment, and agree that the usage of “parameter sensibility” was in need of adjustment. We have now amended the manuscript. Please refer to Page 10 of the revised manuscript, lines 318-321 (see below).

“To manifest the generality, we globally screened 6,213 groups of parameter sets under the AND-AND motif, and this logic-dependent intermediated stage can be observed for 82.7% of them (see Methods; Table S1), indicating little dependence on particular parameter setting (1.8% in the OR-OR motif).”

(6) Probably related just to the language clarity (i.e., the abuse of jargon), but we don't understand the conclusion on lines 296-298.

We thank the reviewers for this comment. We have adjusted the manuscript accordingly. Please refer to Page 11 of the revised manuscript, lines 323-327 (see below). And we hope that the reviewers agree with our attempt at mapping into the particular stage in cell fate decisions from the point of landscape.

“Furthermore, this transitory fully-connected stage locates between the fate-undetermined stage (Fig4.C top panel) and fate-determined stage (Fig4.C 3rd panel), comparable to the initiation (or activation) stage before the lineage commitment in experimental observations [5-7]. Therefore, we suspected that the robust fully-connected stage in the AND-AND motif may correspond to a specific period in cell fate decisions.”

(7) The so-called "solution landscape" in Figure 4E needs to be better explained.

We thank the reviewers for this comment. We have introduced the concept of solution landscape, which is a pathway map consisting of all stationary points and their connections, in lines 196-198 of the revised manuscript (see below).

“Furthermore, we introduced the solution landscape method. Solution landscape is a pathway map consisting of all stationary points and their connections, which can describe different cell states and transfer paths of them [82-84].”

In Figure 4E, we added detailed explanation of the solution landscape for the AND-AND motif. Specifically, it describes a hierarchical structure including one 2-saddle (yellow triangle), three 1-saddles (crimson X-cross sign), and three attractors (green dot). The layer of 1-saddles is represented by a blue translucent plane, and the bottom layer is the flow field diagram. The connections from 2-saddle to 1-saddles and from 1-saddles to the attractors are represented by red and blue lines, respectively. The arrow and color of the heatmap correspond to the flow direction and the length of the acceleration at each point in the state space.

(8) Table S1 is not properly annotated, and then it is impossible to interpret how it supports the observations in the paragraph in lines 342-342.

We appreciate the reviewers’ useful feedback. We have refined the annotations of all tables in our manuscript (Table S1-3). Please refer to “Supplementary Table” in resubmitted files.

Specifically, we randomly collected 6,231 sets of parameters for the AND-AND motif and 6,682 sets for the OR-OR motif (k1-k6 in Eq1 and Eq2; refer to Page 6 of the revised supplementary method, see below).

“First, to collect parameter sets with 3 SSSs, we used Latin hypercube sampling (LHS) to screen k-series parameters symmetrically (i.e., k1 = k4, k2 = k5, k3 = k6) ranging from 0.001 to 5 both in the AND-AND and OR-OR motifs. We ultimately collected 6,231 sets for the AND-AND motif and 6,682 sets for the OR-OR motifs (Table S1).”

To analyze the sequence of vanishing SSSs, we further filtered parameter sets with 2 SSSs remained as increasing ux (corresponding to Eq3 in the revised manuscript, Page 10, lines 293). We then got a collection of 6,207 sets for the AND-AND motif and 6,634 sets for the OR-OR motif. Based on these parameter settings, we checked if the observations (refer to Page 13, lines 377-378, “The distinct sequences of attractor basin disappearance as ux increasing can be viewed as a trade-off between progression and accuracy.”) are artifacts of particular parameter choice.

(9) The flow in Section 5 needs to be reorganised. For instance, it is not clear which question the authors are addressing in line 395, or how the proposed approach answers the question stated in lines 381-382.

We greatly thank the reviewers for pointing this out, and acknowledge that the Section 5 was definitely in need of improvement. We have now amended the manuscript to make this implicit understanding explicit. Please refer to Page 15 of the revised manuscript, lines 426-430 (see below).

“In prior sections, we systematically investigated two logic motifs under the noise- and signal-driven modes in silico. With various combinations of logic motifs and driving forces, features about fate-decision behaviors were characterized by computational models. Next, we questioned whether observations in computation can be mapped into real biological systems. And how to discern different logic motifs and driving modes is a prerequisite for answering this question.

To end this, we first evaluated the performance of different models, specifically in simulating the process of stem cells differentiating towards LX (Fig6.A).”

(10) There are two important weak points for the successful classification of the regulatory logic of real gene expression data as presented in the manuscript: (1) the small number of time-points in the datasets and clear peaks in gene expression heterogeneity cannot be identified, and (2) it is not always clear whether cell differentiation really exclusively relies on a CIS network, and which genes constitute it. These limitations should be solved or at least discussed in the manuscript.

We thank the reviewer for this comment. First, we agree entirely that analysis of datasets with more time points will be more amenable to identifying the trends of gene expression variation. We have made a concerted effort towards searching for such datasets, but unfortunately, there are not many such datasets publicly available. Specifically, to apply our computational framework, the datasets of our interest need to fulfill the following three characteristics: (i) sampling at multiple time points (as many as possible); (ii) to illustrate/validate our findings clearly and representatively, we would like the cell fate decisions in the biological systems to follow the classical binary tree-like pattern. i.e., there is one stem cell fate (or progenitor) and two downstream cell fates in the systems; (iii) the core GRN circuits for orchestrating the fate-decision processes have been experimentally confirmed (at least clearly supported). We have also extended the discussion to include above points to explicitly note the limitations regarding the used datasets. Please refer to Page 25 of the revised manuscript, lines 762-766 (see below).

“The gene expression datasets analyzed here are only available for a limited number of time points. Though they meet the need for discerning trends, it is evident that the application to the datasets with more time points will yield clearer and less ambiguous changing trends to support the conclusions of this paper more generally.”

In regards to second point, we do acknowledge that the CIS network may not always be the core module for every fate-decision case (but to our knowledge, this can be assumed in many cases, especially in binary tree-like pattern). For applicability and potential relevance to our intended readership, we developed the models and draw our conclusions primarily based on the CIS topology for its representativeness. We intend to incorporate diverse topologies (like mutual activation with self-activation, Feed-Forward Loop, etc.) in our computational framework presented here in near future. Additionally, we have incorporated this point into the discussion in the revised manuscript. Please refer to Page 25 of the revised manuscript, lines 766-769 (see below).

“Notwithstanding the fact that the CIS network is prevalent in fate-decision programs, there are other topologies of networks that serve important roles in the cell-state transitions, like feed-forward loop, etc. The framework presented in this work should further incorporate diverse network motifs in the future.”

As referred by the reviewers, even if given the CIS network, we may not sure about which genes constitute it in some cases. We agree that further extension of our framework to mining key regulators is an interesting question. We also note that we have become very enthusiastic about recent work that shows how to nominate core factors from high-throughput data[8, 9]. Of note, in the last section of our manuscript titled “The chemical-induced reprogramming of human erythroblasts (EBs) to induced megakaryocytes (iMKs) is the signal-driven fate decisions with an OR-OR-like motif”, we leveraged patterns of temporal expression variance to filter out key regulators (Fig7.F and H). We thus underline the potential of mining genes comprising core GRN circuits through expression variance. Nevertheless, as the focus of the present paper is on the role of regulatory logic in cell fate decisions, we feel it is beyond the scope of the present article to continue the development of our results on this point. Instead, we have included discussion of case that genes comprising the CIS network are not defined. Please refer to Page 23 of the revised manuscript, lines 685-687 (see below).

“Notably, if the genes that constituting the CIS network are not specified, we can conversely leverage the patterns of temporal expression variance to nominate key regulators in a model-guided manner.”

(11) The models used in Figure S5 are never clearly described.

We thank the reviewers for pointing this out. We have now introduced the settings of the models used in Figure S5 more clearly in the legend (see below).

Two logic motifs with the noise-driven mode (FigS5.A, see below):

Author response image 1.

“Initial values were identical with attractor of S fate in Figure 2C (SSSs in green attractor basins). Simulation was preformed 1000 times for each pseudo-time point, with each temporal state (from left to right) recorded as a dot on the plot. Top panel: Noise level of X (σx) is set to 0.21, and σy is 0.09. Bottom panel: Noise level of Y (σy) is set to 0.21, and σx is 0.09. Red arrow represents the direction of fate transitions of S to LX. Other than adding a white noise, parameters were identical with those in Figure 2C.”

Two logic motifs with the signal-driven mode (FigS5.B, see below):

Author response image 2.

“Initial values were identical with attractor of S fate in Figure 2C (SSSs in green attractor basins). Top panel: Noise level of X (σx) and Y (σy) are both set to 0.06. Simulation was preformed 1000 times, with each final state recorded as a dot on the plot. Parameter ux switched from 0 to 0.09 (0, 0.045, 0.09, from left to right). Bottom panel: Noise level of X (σx) and Y (σy) are both set to 0.05. Simulation was preformed 1000 times, with each final state recorded as a dot on the plot. Parameter ux switched from 0 to 0.24 (0, 0.12, 0.24, from left to right). Red arrow represents the direction of fate transitions of S to LX. Other model’s parameters were identical with those in Figure 2C.”

(12) Up until Section 5, "noise levels" have been used to refer to an input/parameter in the model. Here it is assumed as an emergent property. Are the authors talking about the variance in expression (e.g., see line 398)? Is it defined as the coefficient of variation? Clarity is essential to interpret the observations in this section, e.g., "different driving modes change in the patterns of noise rather than expression levels" (lines 399-400).

We greatly appreciate the reviewers pointing this ambiguity out. The term of “noise level” was indeed used to refer the strength of the noise in the models in Section 1-4. For classifying different logic motifs with two driving forces, we needed a practical metric that can be quantified from data, and we found population-level gene expression variance (i.e., “noise level” in line 398) is useful which defined as the coefficient of variation. For clarity, we carefully decide to substitute “expression variance” for “noise level” presented in Section 5-6. We have amended the manuscript accordingly, and hope this revision will be helpful for interpreting our result. Please refer to Page 15 of the revised manuscript.

(13) "Pulse-like behaviour" is used in an arbitrary way, not as it is normally used in the field. Moreover, we consider this jargon expression does not contribute to the understanding of the paper. (The authors probably meant "discrete transitions" vs "gradual transitions".)

We appreciate the reviewers’ valuable feedback regarding our use of the term “Pulse-like behavior”. We agree with the reviewers’ statement, and acknowledge that terminology of noise level’s patterns between different driving modes (noise-driven vs signal-driven; refer to Section 5 in our manuscript) was in need of improvement.

Upon comprehensive consideration, we primarily decided to adopt the terms “monotonic transitions” and “nonmonotonic transitions” to recapitulate the trends of noise level, underlining the distinct temporal noise’s patterns in cell fate decisions brought by two driving forces in a more contrastive way. We anticipate that current jargon expressions will be beneficial for interpreting our work. Please refer to Page 15 of the revised manuscript.

(14) The temporal resolution of the scRNAseq datasets that the authors used is too low to unambiguously distinguish a discrete pattern of gene expression heterogeneity from a rising profile. This limitation needs to be at least acknowledged in the text. Alternatively, the authors might want to identify more recent datasets with higher time resolution.

We appreciate the reviewers’ insightful suggestions. We agree that analysis of datasets with higher time resolution will be more unambiguous to identifying the trends of gene expression variation. We have made a concerted effort towards searching for such datasets, but unfortunately, there are not many such datasets publicly available. Specifically, to apply our computational framework, the datasets of our interest need to fulfill the following three characteristics: (i) sampling at multiple time points (as many as possible); (ii) to illustrate/validate our findings clearly and representatively, we would like the cell fate decisions in the biological systems to follow the classical binary tree-like pattern. i.e., there is one stem cell fate (or progenitor) and two downstream cell fates in the systems; (iii) the core GRN circuits for orchestrating the fate-decision processes have been experimentally confirmed (at least clearly supported). Nevertheless, we recognize this limitation should be mentioned in the paper. So, we have also extended the discussion to include above points. Please refer to Page 25 of the revised manuscript, lines 762-766 (see below).

“The gene expression datasets analyzed here are only available for a limited number of time points. Though they meet the need for discerning trends, it is evident that the application to the datasets with more time points will yield clearer and less ambiguous changing trends to support the conclusions of this paper more generally.”

(15) In the case of embryonic stem cell differentiation, an additional complication is that this protocol yields heterogeneous cell type mixtures, whereas the authors' simulations usually are designed to give differentiation towards a single cell type. This difference makes it difficult to compare measures of gene expression heterogeneity between simulations and the experimental system to infer regulatory logic questionable.

We thank the reviewers for this valuable comment and realize that we were not clear enough in the manuscript regarding the case of embryogenesis. In the biological system devised by Semrau et al[10], mouse embryonic stem cells (mESCs) differentiates into two lineages simultaneously, just as mentioned by the reviewers. We noticed this additional complication and performed other simulations in two logic motifs with increasing noise level of gene X and Y, as presented in Fig.S6E (see below).

Author response image 3.

“(E) Time courses on the coefficient of variation in expression levels of X and Y genes in silico during differentiation under the noise-driven mode. Initial values were set to the attractors of S fate in Figure 2C (SSSs in green attractor basins). Top panel: Noise level of X (σx) and Y (σy) are both set to 0.14. Bottom panel: Noise level of X (σx) and Y (σy) are both set to 0.1. Stochastic simulation was preformed 1000 times for each pseudo-time point.”

Given the noise-driven mode, we further employed the expression pattern of Gbx2-Tbx3 circuit to heuristically infer the logic motif.

(16) In contrast to the hematopoiesis example, the authors do not focus on a specific gene regulatory circuit with the ESC dataset. How their approach is possible on genome-wide data needs to be discussed.

We thank the reviewers for this comment. Indeed, the core GRN orchestrating the fate-decision process reported by Semrau et al[10] is not fully elucidated. We here focus on the Gbx2-Tbx3 circuit (Fig.6H, Fig.S6D). These two TFs were filtered out from 22 candidate TFs and suggested as potential key regulators in the original paper[10]. Accordingly, at this point we followed the original paper’s statement.

In regards to extension into biological systems without specific gene regulatory circuits, we have included discussions about the possibility that genes comprising the CIS network are not defined. Please refer to Page 23 of the revised manuscript, lines 685-687 (see below).

“Notably, if the genes that constituting the CIS network are not specified, we can conversely leverage the patterns of temporal expression variance to nominate key regulators in a model-guided manner.”

(17) [In supplemental material, pp.1] Possible typo: "In our word, we considered a GRN comprised...".

Thanks for spotting this typo. We have amended it in the revised supplemental method (refer to Page 1 of the revised supplementary method).

(18) [In supplemental material, pp.1] In Eqs. (1), the notation for the function HX([X]) implies that HX only depends on X, leaving the combinatorial regulation out. HX([X],[Y]) would be more general and accurate.

Thanks for pointing this out. We have incorporated this suggestion into the manuscript. Please refer to Page 1 of the revised supplementary method.

(19) [In supplemental material, pp.1] There are several works that have shown that the Hill coefficient is rarely representative of the number of binding elements. The model can be more general. See, for example, «Santillán, Moisés. "On the Use of the Hill Functions in Mathematical Models of Gene Regulatory Networks." Mathematical Modelling of Natural Phenomena 3, no. 2 (October 22, 2008): 85-97. https://doi.org/10.1051/mmnp:2008056.» and «Nam, Kee-Myoung, Rosa Martinez-Corral, and Jeremy Gunawardena. "The Linear Framework: Using Graph Theory to Reveal the Algebra and Thermodynamics of Biomolecular Systems." Interface Focus 12, no. 4 (June 10, 2022): 20220013. https://doi.org/10.1098/rsfs.2022.0013.»;

We thank the reviewer for drawing our attention to this and highlighting the above works. Indeed, this is important information to include in the manuscript. We have incorporated this suggestion into the revised supplemental method (refer to Page 1 of the revised supplementary method). These references have now been included in the revised supplemental method (refer to references [2]-[3]).

(20) [Minor] The configuration labels can be confusing, especially the AA, which is rather an AND NOT gate.

We thank the reviewers for this comment. For clarity, we have substituted AND-AND/OR-OR for original expression of AA/OO, and hope that new notations are helpful for interpreting our work.

(21) [Minor] Very low printing quality in Figure 1.

Thanks for the feedback regarding the printing quality of Figure 1. We have made the necessary adjustments to improve its quality. We have also ensured that all other figures in the manuscript meet the required standards.

(22) [Minor] We suggest including a quantitative scale for the bias in Fig. 3E.

Thanks, we have incorporated this suggestion into the manuscript.

(23) [Recommendation] Authors could also evaluate the cell fate decision processes as mutations or other perturbations affect a regulatory network.

We appreciate the reviewers for this valuable recommendation. We agree with the reviewers that further involving new cases would be helpful, especially those mutation-driven disease-related fate-decision processes, such as neutropenia in chemotherapy. However, given the considerable effort towards searching for appropriate datasets, we carefully decide not to make this change.

(24) [Recommendation] The authors could include some discussion of the likely impact of the work on the field and the utility of the methods and data to the community. For example, understanding the fluidity of the epigenetic landscape and the regulatory forces behind cell fate decisions can be of great importance in designing synthetic gene regulatory circuits.

We greatly appreciate the reviewers pointing this out. In the original manuscript, we intentionally limited the length of the discussion to make the whole story more focus. We thank the reviewers for their insightful suggestions regarding the content of discussion. We have incorporated this suggestion into the revised manuscript. Please refer to Page 25, lines 751-757 (see below).

“Recently, synthetic biology has realized the insertion of the CIS network in mammalian cells. One of the prerequisites for recapitulating the complex dynamics of fate transitions in synthetic biology is systematical understanding of the role of GRNs and driving forces in differentiation. And the logic motifs are the essential and indispensable elements in GRNs. Our work also provides a blueprint for designing logic motifs with particular functions. We are also interested in validating the conclusions drawn from our models in a synthetic biology system.”

In addition, a longstanding question of our interest in cell fate decisions is what contributes the distinctive development cross species, like human, mice and so on forth. However, in addition to protein coding sequences, regulatory interactions between genes (i.e., activation and inhibition) also exhibit conservation as reported in recent work of multi-species cell atlas [11], and it is generally acknowledged that gene regulatory networks (GRNs) orchestrate fate-decision procedures. Namely, conserved regulatory programs further bring us a conserved topology of core GRNs. Thus, the logics of regulation, as another vital element in GRNs, is naturally under the spot light (related to the introduction, lines 99-120 of the revised manuscript). Nevertheless, to our knowledge, regulatory logic in cell fate decisions has received only scant attention. We hope that our elucidation of the role of logic motifs in cell fate decisions will attract more inquiries in community into GRN’s regulatory logic.

Public reviews

In this manuscript, Xue and colleagues investigate the fundamental aspects of cellular fate decisions and differentiation, focusing on the dynamic behaviour of gene regulatory networks. It explores the debate between static (noise-driven) and dynamic (signal-driven) perspectives within Waddington's epigenetic landscape, highlighting the essential role of gene regulatory networks in this process. The authors propose an integrated analysis of fate-decision modes and gene regulatory networks, using the Cross-Inhibition with Self-activation (CIS) network as a model. Through mathematical modelling, they differentiate two logic modes and their effect on cell fate decisions: requires both the presence of an activator and absence of a repressor (AA configuration) with one where transcription occurs as long the repressor is not the only species on the promoter (OO configuration).

The authors establish a relationship between noise profiles, logic-motifs, and fate-decision modes, showing that defining any two of these properties allows the inference of the third. They also identify, under the signal-driven mode, two fundamental patterns of cell fate decisions: either prioritising progression or accuracy in the differentiation process. The authors apply this analysis to available high-throughput datasets of cell fate decisions in hematopoiesis and embryogenesis, proposing the underlying driving force in each case and utilising the observed noise patterns to nominate key regulators.

The paper makes a substantial contribution by rigorously evaluating assumptions in gene regulatory network modelling. Notably, it extensively compares two model configurations based on different integration logic, illuminating the consequences of these assumptions in a clear, understandable manner. The practical simulation results effectively bridge theoretical models with real biological systems, adding relevance to the study's insights. With its potential to enhance our understanding of gene regulatory networks across biological processes, the paper holds promise. Its implications extend practically to synthetic circuit design, impacting biotechnology. The conclusions stand out, addressing cell fate decisions and noise's role in gene networks, contributing significantly to our understanding. Moreover, the adaptable approach proposed offers versatility for broader applications in diverse scenarios, solidifying its relevance beyond its current scope.

We thank the reviewers for their enthusiasm for our work, and appreciate the professional, insightful and encouraging assessment.

However, the manuscript in its current form also has some important weaknesses, including the lack of clarity in the text and the questionable generality of specific observations.

We thank the reviewers for this comment. We have reviewed the manuscript and made the necessary adjustments to improve its clarity. We do hope that this revision meets the reviewers’ expectations on the clarity and comprehensiveness of our analysis.

For instance, even when focusing on the CIS network, the effect of alternative model implementations is not discussed. Notably, the input signals are only considered as an additive effect over the differential equations, while signals can potentially affect each of the individual processes.

We agree with the reviewers’ comment that signals may affect at each level of the central dogma, including transcription, translation, etc. Further, we have also included additional section titled “limitation of this study” on this point in the revised manuscript, and explicitly point to the potential limitations of our models. Please refer to Page 25 of the revised manuscript, lines 769-771 (see below).

“In addition, for simplicity and intuition, we here considered signals as uncoupled and additive effects in ODE models, due to feasible mapping in real biological systems, such as ectopic overexpression.”

The proposed model allows for a continuum of interactions/competition between transcription factors, yet only very restrictive scenarios are explored (strict AND/OR logic operations).

We thank the reviewers for this comment, and appreciate them sharing the potential for further generalization of our framework. Indeed, in addition to logic operations, our framework is able to be applied to all two-node circuits (34=81 in total), including mutual activation with self-activation. As the focus of this work is to illustrate the role of logic motifs in cell fate decisions, we mainly concentrated on two classical, intuitive and representative (at least to us) logic operations AND/OR in the context of the CIS network. Nonetheless, we already have four combinations to consider (two logic motifs and two driving forces). And we feel that the currently involved scenarios have properly fulfilled our need to manifest the role of logic motifs. Hence, we carefully decided not to further explore more logic operations in this work. Instead, we have included additional section titled “limitation of this study” in the revised manuscript. Please refer to Page 25 of the revised manuscript, lines 760-762.

“Although our framework enables the investigation of more logic motifs, we chose two classical and symmetrical logic combinations for our analysis. Future work should involve more logic gates like XOR and explore asymmetrical logic motifs like AND-OR.”

Moreover, how the model parameters are chosen throughout the paper is not clear. Similarly, the concentration and times are not clearly specified, making their comparison to experimental data troublesome.

We thank the reviewers for this comment. Regarding how to specify parameters in our model, we have now revised the manuscript. Please refer to Page 5 of the revised manuscript, lines 179-181 (“Benchmarking the Boolean models with different logic motifs (Fig2.B; see Methods), we reproduced the geometry of the attractor basin in the continuous models resembling those represented by corresponding Boolean models (Fig2.C; see Methods).”). In terms of concentration and time, we acknowledge that their units are arbitrary compared to a real experimental system. We now have noted this point in the legend of corresponding figures (Fig2.C, Fig3.B&D, Fig6.B-C, Fig7.E).

We would like to highlight that our entire work is organized in a model-driven fashion (also called top-down). We did not fine-tune the sets of parameters used in our model to specifically match the experimental data. Actually, it is also a longstanding challenge in computational biology since experimental datasets are usually insufficient to specify the parameters in a dynamical model. So, in general, it is inevitable to involve more assumptions such as non-Markov process[12, 13] and may lead to artifacts. Thus, we decided to draw qualitative conclusions (e.g., trends over time) from a quantitative model with sampling of parameter sets. Hence, we did not intentionally tailor our models to fit different datasets (i.e., all models used in our work share same basic setting of parameters), mapping into real biological systems in a top-down manner.

Regarding clarity, how the general model (equations 1-2) transforms into the specific cases evaluated in the paper is not clearly stated in the main text, nor are the positive and negative effects of individual transcription factors adequately explained. Similarly, in the main text and Figure 2, the authors refer to a Boolean model. However, they do not clearly explain how this relates to the differential equation model, nor its relevance to understanding the paper.

We thank the reviewers for this comment, as it has prompted us to better clarify our manuscript. We have adjusted the manuscript accordingly and made the necessary adjustments to improve its clarity.

Additionally, the term "noise levels" is generally used to refer to noise introduced in the "noise-driven" analysis (i.e., as an input or parameter in the models). Nonetheless, it is later claimed to be evaluated as an intrinsic property of the network (likely referring to expression level variability measured by the coefficient of variation).

We greatly appreciate the reviewers pointing this ambiguity out. The term of “noise level” was indeed used to refer the strength of the noise in the models in Section 1-4. For classifying different logic motifs with two driving forces, we needed a practical metric that can be quantified from data, and we found population-level gene expression variance (i.e., “noise level” in line 398) is useful which defined as the coefficient of variation.

For clarity, we carefully decide to substitute “expression variance” for “noise level” presented in Section 5-6. We have amended the manuscript accordingly.

Finally, some jargon is introduced without sufficient context about its meaning (e.g., "temporal fully-connected stage").

Regarding the jargon of "temporal fully-connected stage", we have realized that this term was slightly vague and in need of improvement. Instead, we now employ “transitory fully-connected stage” in the revised manuscript to underline the short emergence of this particular stage. Please refer to Page 10-11 of the revised manuscript, lines 316-327 (see below).

“Notably, in the AND-AND motif we observed a brief intermediated stage before S attractor disappears, where all three fates are directly interconnected (Fig4.C 2nd panel and D 2nd panel, Fig.4E). To manifest the generality, we globally screened 6,213 groups of parameter sets under the AND-AND motif, and this logic-dependent intermediated stage can be observed for 82.7% of them (see Methods; Table S1), indicating little dependence on particular parameter setting (1.8% in the OR-OR motif). Unlike the indirect attractor adjacency structure mediated by S attractor (Fig2.D), the solution landscape with fully-connected structure facilitates transitions between any two pairs of fates. Furthermore, this transitory fully-connected stage locates between the fate-undetermined stage (Fig4.C top panel) and fate-determined stage (Fig4.C 3rd panel), comparable to the initiation (or activation) stage before the lineage commitment in experimental observations [5-7]. Therefore, we suspected that the robust fully-connected stage in the AND-AND motif may correspond to a specific period in cell fate decisions.”

Additionally, proper discussion of previous work is also missing. For instance, the dynamics of the CIS network investigated by the authors have been extensively characterised (see e.g., Huang et al., Dev Biol, 2007), and how the author's results compare to this previous work should be discussed. In particular, the central assumptions behind the derivation of the model proposed in the manuscript must be assessed in the context of previous work.

Thanks for pointing this out. We have extended the discussion to include above points. We have also discussed and cited the work of Huang mentioned above. Please refer to Page 22, lines 644-647 in the revised manuscript (see below).

“One of the most representative work is that Huang et al. [14] modeled the bifurcation in hematopoiesis to reveal the lineage commitment quantitatively. Compared to simply modularizing activation or inhibition effect by employing Hill function in previous work, our models reconsidered the multiple regulations from the level of TF-CRE binding.”

References

(1) Ackers, G.K., A.D. Johnson, and M.A. Shea, Quantitative model for gene regulation by lambda phage repressor. Proc Natl Acad Sci U S A, 1982. 79(4): p. 1129.

(2) Shea, M.A. and G.K. Ackers, The OR control system of bacteriophage lambda: A physical-chemical model for gene regulation. Journal of Molecular Biology, 1985. 181(2): p. 211-230.

(3) Hunziker, A., et al., Genetic flexibility of regulatory networks. Proc Natl Acad Sci U S A, 2010. 107(29): p. 12998-3003.

(4) Kittisopikul, M. and G.M. Suel, Biological role of noise encoded in a genetic network motif. Proc Natl Acad Sci U S A, 2010. 107(30): p. 13300-5.

(5) Brand, M. and E. Morrissey, Single-cell fate decisions of bipotential hematopoietic progenitors. Curr Opin Hematol, 2020. 27(4): p. 232-240.

(6) Zhang, Y., et al., Hematopoietic Hierarchy - An Updated Roadmap. Trends Cell Biol, 2018. 28(12): p. 976-986.

(7) Arinobu, Y., et al., Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell, 2007. 1(4): p. 416-27.

(8)Kamimoto, K., et al., Dissecting cell identity via network inference and in silico gene perturbation. Nature, 2023. 614(7949): p. 742-751.

(9) Hammelman, J., et al., Ranking reprogramming factors for cell differentiation. Nat Methods, 2022. 19(7): p. 812-822.

(10) Semrau, S., et al., Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun, 2017. 8(1): p. 1096.

(11) Li, J., et al., Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nature Genetics, 2022. 54(11): p. 1711-1720.

(12) Stumpf, P.S., F. Arai, and B.D. MacArthur, Modeling Stem Cell Fates using Non-Markov Processes. Cell Stem Cell, 2021. 28(2): p. 187-190.

(13) Stumpf, P.S., et al., Stem Cell Differentiation as a Non-Markov Stochastic Process. Cell Syst, 2017. 5(3): p. 268-282 e7.

(14) Huang, S., et al., Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol, 2007. 305(2): p. 695-713.

Author Response

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

eLife assessment

This work presents some valuable information regarding the molecular mechanisms controlling the regeneration of pancreatic beta cells following induced cell ablation. However, the study lacks the critical lineage tracing result to support the conclusion about the origin of the regenerated beta cells. The results of the pharmacological manipulation of CaN signaling are also incomplete. In particular, these manipulation are not cell-specific, making it difficult to interpret and thus genetic approach is recommended.

Public Reviews:

Reviewer #1 (Public Review):

Induction of beta cell regeneration is a promising approach for the treatment of diabetes. In this study, Massoz et.al., identified calcineurin (CaN) as a new potential modulator of beta cell regeneration by using zebrafish as model. They also showed that calcineurin (CaN) works together with Notch signaling calcineurin (CaN) to promote the beta cell regeneration. Overall, the paper is well organized, and technically sound. However, some evidence seems weak to get the conclusion.

Reviewer #2 (Public Review):

This work started with transcriptomic profiling of ductal cells to identify the upregulation of calcineurin in the zebrafish after beta-cell ablation. By suppressing calcineurin with its chemical inhibitor cyclosporin A and expressing a constitutively active form of calcineurin ubiquitously or specifically in ductal cells, the authors found that inhibited calcineurin activity promoted beta-cell regeneration transiently while ectopic calcineurin activity hindered beta-cell regeneration in the pancreatic tail. They also showed similar effects in the basal state but only when it was within a particular permissive window of Notch activity. To further investigate the roles of calcineurin in the ductal cells, the authors demonstrated that calcineurin inhibition additionally induced the proliferation of the ductal cells in the regenerative context or under a limited level of Notch activity. Interestingly, the enhanced proliferation was followed by a depletion of ductal cells, suggesting that calcineurin inhibition would exhaust the ductal cells. Based on the data, the authors proposed a very attractive and intriguing model of the role of calcineurin in maintaining the balance of the progenitor proliferation and the endocrine differentiation. However, the conclusions of this paper are only partially supported by the data as some evidence from the data remains suggestive.

(1) In the transcriptomic profiling, genes differentially regulated in the ablated adults could be solely due to the chemical effects of metronidazole instead of the beta-cell ablation. A control group without ins:NTR-mCherry but treated with metronidazole is necessary to exclude the side effects of metronidazole.

We believe that it is unlikely that the differential regulation observed is due to metronidazole rather than the beta cell loss. This experimental strategy as proven successful in well-published studies to identify regulators of beta cell regeneration in the zebrafish larvae. Importantly, the candidates identified in these studies were subsequently functionally validated in mammalian models (Lu et al. 2016, Karampelias 2021). Moreover, in our study, we also used another chemical compound, the nifurpirinol (Bergemann et al., 2018), to ablate the beta cells. Regardless of whether we employed metronidazole or nifurpirinol for beta cell ablation, our results consistently indicate a notable involvement of calcineurin. Of note, the nifurpirinol molecule is commonly used in fishkeeping without toxicity reported on the global health of the fish.

(2) Although it has been shown that the pancreatic duct is a major source of the secondary islets in the pancreatic tail in previous studies, there is no direct evidence showing the cyclosporin A-induced cells share the source in this manuscript. Without any proper lineage tracing work, the origin of those cyclosporin A-induced cells cannot be concluded.

Our experimental setting is similar to the one described in Ninov et al. 2013, where lineage tracing experiments demonstrate an increase of beta cell formation in the pancreatic tail that originate from the pancreatic ducts. In our study, we performed the same experiment with the addition of CsA and showed more ductal cell proliferation (Figure 5G) followed by a 19% increase of beta cell regeneration compared to nonregenerative conditions (Figure 2B). It is unlikely that the additional 19% of regenerated beta cells under CaN inhibition come from another source than the 68% first.

On the other hand, the acinar cells cannot be consider as another source of regenerated beta cell as they are not able to form beta cells unless they are artificially reprogrammed (Maddison et al., 2012). Therefore the only other potential source of regenerated beta cell is the endocrine compartment. However at the stage where we performed beta cell ablation, there are no endocrine cell in the pancreatic tail. Moreover, there are no evidence that secondary islets could come from the principal islet, they are tightly associated with the ducts and differentiate form ductal cell (Mi et al., 2023).

Importantly, we demonstrated that overexpression of CaN specifically in the pancreatic ducts prevents beta cell regeneration. CaN effect is therefore intrinsic to the ducts. Moreover, we showed that CsA increase beta cells formation when Notch signalling is repressed. Given that Notch signalling is known to act on the ductal cell population, this strongly suggests again that CsA exacerbate beta cells formation from the ducts.

All of these compelling evidences strongly support the notion that the cyclosporininduced beta cells originate from the ductal cells.

(3) It is interesting to see an increase of beta cells in the primary islet after cyclosporin A treatment (Supplemental Fig 2B). However, it remains unclear if their formation shares the same mechanism with the newly formed beta cells in the pancreatic tail.

There are indeed several source of beta cell regeneration in the primary islet. However, a recent study showed that the contribution of alpha cell to regeneration is minor and the main contributors are ductal and sst1.1 cells (Mi et al., 2023). In our previous publication, we indeed showed that a major source of beta cell in the principal islet is the delta 1.1 cell population. Those sst1.1 cells begin to express insulin and therefore are named ‘bihormonal’ (Carril et al., 2022). We tested if this population is impacted by CsA treatment and we showed below that CsA does not affect bi-hormonal cell formation (Figure 2D supplemental). These new results suggest that the CsA mediated increase of beta cells in the principal islet arise from the ductal cells as observed in the tail. These results were added in the manuscript as Figure 2D supplemental.

Author response image 1.

Tg (sst1.1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with NFP 4µM to induce beta cell ablation. Then larvae were treated with CsA 1µM from 4 to 6 dpf (or ctl with DMSO); prior fixation and analysis of bi-hormonal cells in the principal islet at 6dpf.

(4) The conclusion of the effect of cyclosporin A on the endocrine progenitors (Line 175) is not convincing because the data cannot distinguish the endocrine progenitors from the insulin-expressing cells. Indeed, Figure 2E shows that neurod1+ cells are fewer than ins+ cells (Figure 2D) in the pancreatic tail at 10 dpt, suggesting that all or at least the majority of neurod1+ cells are already ins+.

The neurod1+ cells population indeed included both endocrine progenitor cells and differentiated endocrine cells. However, we would like to point out that the timing of the analysis is essential to reach our conclusion. When we treat with CsA, we show an increase of neurod1+ cells already at 4dpt. At this time point, no hormone- producing cell can yet be detected (Figure 2E). Those additional neurod1+ cell are therefore endocrine progenitors and not beta cells. This result shows that CaN inhibition induces pro-endocrine cell formation in regenerative conditions.

At 10dpt, the neurod1+ cells population includes beta cells as well as endocrine progenitor cell. We agree that the way the data are presented in figure 2D and 2E can be confusing. Those 2 figures come form 2 separated experiments, the number of beta cell in figure 2D can therefore not be compared to the number of Neurod1+ cell in figure 2E. Indeed, from one experiment to another the efficiency and rate of regeneration can vary, independently of calcineurin. To clarify, we added the number of beta cells regenerated in the experiment of figure 2E (see Author response image 2 in red). As you can see in this experiment, regeneration was a bit slower than usual.

Author response image 2.

Tg (neurod1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with NFP 4µM to induce beta cell ablation. Then larvae were treated with CsA 1µM from 4 to 6 dpf (or ctl with DMSO); prior fixation and analysis of GFP+ cells (in grey, pink, dark grey and green), and mCherry+ cells for the condition ablated + CsA in red from 2 to 10 dpf.

(5) Figure 5D shows a significant loss of nkx6.1+ cells in the combined treatment group but there is no direct evidence showing this was a result of differentiation as the authors suggested. This cell loss also outnumbered the increase in ins+ cells (Figure 4D). The cell fates of these lost cells are still undetermined, and the authors did not demonstrate if apoptosis could be a reason of the cell loss.

Firstly, as you can notice on the graphs, we encountered a very high variability between individuals within the same condition. We decided to show this variability by presenting the raw data. This high variability could partially explain the differences that you underline. Moreover, we would like to point out that independently of CaN inhibition the progenitor loss (nkx6.1+ cell) outnumber the gain of beta cells. Indeed, in average there is a loss of 29% (41 GFP+) of the nkx6.1+ cells and a gain of only 6 beta cells after Notch inhibitory treatment. The other progenitors cells being differentiated into other endocrine cell types (pro-endocrine, alpha, delta). In the combined treatment (Notch and CaN inhibitors), we decreased the number of progenitors cell by 50%, i.e 21% (20 cells) more than without CaN inhibitor. However, we increased the number of regenerated beta cells by two fold (6 cell to 12 cells). In brief, the important progenitors cell loss could be explained by precocious differentiation in the pro-endocrine and endocrine cells type. It is therefore normal than the number of beta cells regenerated do not match the progenitors cell number loss and this in presence or absence of CaN inhibition.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Major concerns:

(1) The evidence to indicate the proliferating ductal cell differentiate into beta cell is weak. They should use linkage tracing, or other marker genes immunostaining to confirm that.

The experiment from the Figure 5 A-D is a short term tracing experiment and should have been presented as such in the manuscript. After LY411575 (Notch inhibitor) and CsA treatments at 3dpf, we exposed the larvae to EdU at 4dpf during 8 hours (Figure 5A). We showed that EdU is incorporated in dividing ductal cells at 4dpf (Figure 5C) ant that 2 days later there are newly form beta cells that are EdU+.(see Author response image 3) To reinforce our conclusion, the image below will be added to the manuscript.

Author response image 3.

Tg (nkx6.1:GFP); Tg (ins:NTR*-mCherry) larvae were treated at 3dpf with both CsA 1µM and LY411575 5µM. At 4dpf, the larvae were exposed to EdU 4mM during 8 hours, before analysis at 6 dpf.

(2) To inhibition of CaN and Notch pathway, they just used the pharmacological approaches, genetical approaches should be used to get stronger evidence.

We employed two distinct inhibitors specifically targeting calcineurin (CsA and FK506) for CaN inhibition. While these inhibitors have distinct chemical structures and potential non-specific effects, they both yield the same result of increased beta cell formation under Notch repression (see Figure 4D and Figure 4B in the supplementary data). This convergence of outcomes strongly suggests that the observed effect is primarily attributable to the specific inhibition of calcineurin.

Furthermore, we complemented our inhibitor-based approach with a genetic strategy involving CaN overexpression (see Figure 3). Notably, the overactivation of CaN resulted in a reduction of beta cell regeneration. Given that this genetic approach generated an effect contrary to that achieved with the inhibitors, it provides robust support for our model, which postulates that calcineurin plays a critical role in the regulation of beta cell regeneration (see Figure 3, panels C-E).

As for Notch inhibition, previous published data from our laboratory compared the effects of Notch inhibitor (LY411575) and genetic approaches (mib mutant and transgenic line) on pro-endocrine cell (ascl1b+) and ductal cell (nkx6.1+) formation. This study showed that both Notch inhibitor (LY411575) and Notch repression using genetic approaches recapitulate the same effect: an induction of pro-endocrine cells formation. The specificity of this inhibitor being validated (Ghaye et al., 2015), we did not consider the need of a genetic approach.

(3) The most enriched pathways among the up-regulated genes were DNA replication and cell cycle, which suggested that these genes are more important for the duct cell proliferation, how is Calcineurin related to these pathways, such as regulating the genes important for proliferation?

The transcriptomic data presented in this manuscript suggest that the ductal cells undergo a strong proliferative response after beta cell ablation. This is in accordance with our experimental data showing activation of ductal proliferation after beta cell ablation (Ghaye at al., 2015) and data from this manuscript (Figure 1 I-J).

Calcineurin is a well-known regulator of the cell cycle, and can either promote or repress the cell cycle depending on the cell type. For example, stressing the cell provokes an entry of calcium and subsequently a CaN activation which result in cell cycle arrest (Leech et al. 2020). Nevertheless, depending the cell type, CaN can be either necessary or deleterious to cell proliferation (Goshima et al. 2019; Masaki and Shimada 2022). The intriguing dual role of CaN in cell cycle is well illustrated in β cell regeneration. While CaN should be repressed to enable ductal progenitor amplification and subsequent endocrine differentiation, CaN is then necessary for β cell function and for their replication (Dai et al. 2017; Heit et al. 2006). Moreover, CaN is related to cellular senescence and CaN function is important for proper fin regeneration in zebrafish.

(4) It is hard to understand why they pick up the pathway of cellular senescence signature for the duct cell progenitor neogenesis? Moreover, among these senescence genes, many genes are cell cycle regulators.

In response to beta cell ablation, the ductal cells undergo a strong proliferative response, as shown in our previous data (Ghaye 2015). It was therefore not surprising that many differentially expressed genes are cell cycle regulators. On the other hand, the cellular senescence signature was surprising. Indeed, senescence is usually associated with cell cycle arrest and aging. However, recent studies showed that cellular senescence is required for proper development and regeneration. We therefore wanted to investigate this pathway and more particularly the function of calcineurin, which can either promote or repress the cell cycle in different cell types (see comment above).

(5) The RNA-seq data obtained from adult fish, while the authors use larvae to explore the CaN functions, it may have different conclusion using adult fish. Moreover, it is unclear whether the CaN increased when the beta cell ablated in young larvae.

We decided to first perform functional experiment in the larvae as this model unable the quantification of beta cell regeneration from the ducts in the pancreatic tail. However, to validate our results in non-developmental stages, we perform experiments in juveniles (2 months old) and adults. CsA treatments in juveniles zebrafish recapitulated the same results that in larvae (Figure 2B and Figure 6A-C). Moreover, we showed that CaN overactivation delayed glycemia recovery after ablation adults (Figure 6D-E), which is in accordance with an impaired regeneration. Altogether, these results strongly suggest that CaN act as regulator of beta cell regeneration both in the juvenile/adult and larval stages.

Concerning the expression of CaN in the zebrafish larvae, we tried to detect the level of CaN in the different experimental conditions by in situ hybridization. However, we were not able to detect it using this technique. We also tried immunostaining with antiphospho-nfact3 ser165 polyclonal antibody (Invitrogen) but this antibody does not seem to work in zebrafish. Finally, we tried to sort ductal cell at larval stage to perform a transcriptomic analysis but we were unable to collect enough ductal cells to proceed further. Indeed our staining experiment showed that there are only around 150 ductal cells (nkx6.1+, Figure 5D) at this stage.

(6) The beta cell regeneration in the young larvae usually recovers within ~ 5 days in principle islet. Please also show the beta cell number (PI) during the beta cell recovery after ablation.

We did show beta cell regeneration in the principal islet in Figure 2A-B supplemental. While new beta cells appears quickly in this islet (Carril, Massoz, Dupont et al., 2023), the principal islet has not yet fully recover at 5dpt.

(7) Since the studies did not show the CaN level in Fig.3, it is hard to know that the CaN is exactly expressed.

In the figure 3B, using Tg(hsp70:GFP-CaNCA), it is indeed not possible to see CaN expression at 10 dpt as the heat shocks induce only transiently CaNCA overexpression. However, the transient expression was detected in live shortly after the heat shocks. On the other hand, with the transgenic line Tg(UAS:GFP-CaNCA); Tg(cftr:Gal4), in which GFPCaNCA is continuously expressed allowing us to show CaNCA expression in the pancreatic ducts (Figure 3).

(8) In Fig.6 D and 6E, did these drug treatments change the glucose level in nonablated fish?

As you can see below, the CaN inhibitor, CsA does not affect the glycemia of the fish in non-regenerative conditions.

Author response image 4.

Glycemia of non-ablated fish, 3 days after drug treatment.

(9) The logic of writing in Results is very hard to understand.

We proofed read the paper in an effort to clarify it.

Minor concerns,

(1) Make a scheme for ablation and RNA-seq, and indicate the age of the fish used in Fig. 1.

We added the scheme in Figure 1 supplemental.

(2) In Fig. 1G, two arrows indicated mCherry+ cells is hard to see in the non-ablated fish.

One arrow was indeed mislocated, we moved the arrow and try to improve the intensity of red. However, the only cells are indeed small and can be difficult to see.

(3) In Fig.6, it is hard to know that the arrows indicated islets are small islets (up to 5 cells), how they compared with big islets and defined as small islet. Moreover, some of these islets are almost invisible.

We now show a close up of a portion of the pancreatic tail and show the beta cells with arrows only in this picture, to enhance clarity.

Reviewer #2 (Recommendations For The Authors):

(1) This manuscript needs more proofreading and polishing to increase its readability.

We proofread the manuscript and change some paragraph for more clarity.

(2) The extensive use of words like "modulate" or "regulate" sometimes makes the text ambiguous as the effect is not stated directly and clearly.

We re-wrote some parts of the text and try to avoid using “regulate” as often.

However, as we used both repression and over-activation of CaN, we still use words as regulate to stipulate general conclusions on the function of CaN.

(3) The list of individual differentially regulated genes after the beta-cell ablation in the RNAseq seems missing. This list could be interesting and helpful for other researchers. We added it.

(4) In Figure 1D, "modulated" genes are shown but were they all upregulated like those in Figure 1A? The modulation should be indicated more clearly (e.g. up- or down-regulated) in the figure. The authors can use different colours to illustrate that.

Done.

(5) Is Figure 2D showing the same data extracted from Figure 2B? Does Figure 2D add any information to the data?

No, it does not add data. We actually add the Figure 2D for a better visualisation of the increase at 10dpt.

(6) In the y-axis of Figure 3E, it should be "mCherry".

It already is. We did check all the axis again to be sure it is correct.

(7) Line 219, "Figure 4E supplemental" instead of "Figure 4D supplemental"

Done.

(8) Line 266, "ablated juveniles" instead of "ablated larvae"

Done. Thank you for noticing these mistakes.

(9) In Figure 6A, many mCherry+ cells are hardly visible and there are some greyish white signals in the images that are supposed to show the mCherry channel only. What are those grey signals?

There is no channel showing grey on the picture, I improved the overall quality of this pictures and show close up to improve the figure.

(10) In Figure 6D and 6E, CaNCA overexpression had a significant effect on the glycemia. But did the overexpression affect the beta cell formation or regeneration? We showed that CaNCA overexpression did not affect beta cell formation in absence of regeneration in the larvae (Figure 3E). Moreover, it does not affect the glycemia of the fish in non-regenerative conditions (Author response image 5). As for regenerative conditions, CaN overexpression decreased the regeneration in the larvae (Figure 3E).

Author response image 5.

Glycemia of Tg(UAS:GFP-CaNCA); Tg(cftr:Gal4) fish, overexpressing CaNCA, compared to controls fish, in non-regenerative conditions.

(11) The role of calcineurin seems transient (e.g. Figure 2B and 4E) and does not play a significant role in long term. It would be interesting to see if long-term/repeated treatments of calcineurin inhibitors and overexpression/knockout of important members of calcineurin signaling would affect the pool of progenitors in long term.

We were also interested in the consequences of CaN overexpression on the long term. Our overexpression tool Tg(UAS:CaNCA) allow to address this question, as CaN is overexpress permanently. We assessed the structure of the ducts and the number of beta cells in transgenic larvae and did not see any defects of the ducts whether in regenerative context or not. On the other hand, we showed in this manuscript that CaN effect is specific to regenerative conditions. As a consequence, it is not likely that repeated treatments long after the ablation would continue to affect beta cell formation and the progenitors pool.

Author Response

eLife assessment

We appreciate the assessment carried out by the editorial team at eLife. Therefore, we plan to review the methods section in order to make the statistical analysis more comprehensible for each of the displayed figures.

Public reviews

Reviewer 1

We would like to express our gratitude to Reviewer 1 for providing a thorough summary of our work and highlighting its strengths. With regards to the weaknesses, we are committed to improve the manuscript by performing the necessary changes. First, we will specify the exact p-value in all cases.

Regarding the discussion section, we acknowledge the feedback regarding its potential confusion. In line with the reviewer's suggestion, we will reduce the literature review and highlight our findings.

Finally, for the preprint we did not include cofounders such as HIV infection and ethnicity as our study population did not exhibit viral infections and comprised only Hispanic individuals. We will make a more thorough description of the population of study and address these characteristics explicitly in both the methods section and the initial part of the results.

Reviewer 2

We appreciate and thank reviewer 2 for the commentaries. Although it is true that several papers have described the role of microbiome in COVID-19 severity, we firmly believe that our current work stands out.

There is not much information related to this association in mediterranean countries, especially in the south of Spain. In addition, most of the studies only describe microbiota composition in stool or nasopharyngeal samples separately, without investigating any potential relationships between them as we do.

(1) We agree with the reviewer idea of a limited sample size. We faced the challenge of collecting the samples during the peak of COVID-19 pandemia. Thus, doctors and nurses were overwhelmed and not always available for carrying out patient recruitment following the inclusion criteria. Despite these constraints, we ensured that all included samples met our specified inclusion criteria and were from subjects with confirmed symptomatology.

In addition, our main goal was to identify whether severity of the disease could be assessed through microbiota composition. Therefore we did not include a healthy group. Despite not having a large N, our results should be reproducible as they are supported by statistical analysis.

(2) We thank reviewer commentary, and since our original sentence may have lacked clarity, we intend to modify it to ensure it conveys the intended meaning more effectively.

Nonetheless, we remain confident in the significance of our findings. Not only have we found correlation between microbiota and COVID severity, but we have also described how specific bacteria from each condition is associated with key biochemical parameters of clinical COVID infection.

(3) We appreciate the feedback provided by the reviewer. In this case, we have performed 16S analysis due to its cost-effectiveness compared to metagenomic approaches. Furthermore, 16S analysis has undergone refinements that ensure comprehensive coverage and depth, along with standardized analysis protocols. Unlike 16S, metagenomic approaches lack software tools such as QIIME that facilitate standardization of analysis and, thus, reduce reproducibility of results.

(4) We sincerely appreciate this insightful suggestion. simply listing associations between both microbiomes and COVID-19 severity could not be enough, we intend to discuss how microbiota composition may be linked to the mechanisms underlying COVID-19 pathogenesis in our discussion.

(5) We are grateful for the constructive criticism and intend to rewrite our abstract to enhance clarity. Additionally, we will thoroughly review all figures and their descriptions to ensure accuracy and comprehensibility.

Reviewer 3

We acknowledge the annotations made by reviewer 3 and are committed to addressing all identified weaknesses to enhance the quality of our work. Our idea is to modify the methods section and figures to make them easier to understand.

Specifically, in the case of Figure 1, we recognize an error in the description of the Bray-Curtis test. We appreciate the commentary and we will make the necessary changes. Moreover, there is another observation related to Figure 1 description. We are going to modify it in order to gain accuracy.

For figure 2 we are planning to add a supplementary table showing the abundance of detected genus. Nevermind, we will also update the manuscript text to provide clarification on how we obtained this result.Regarding the clarification about "1% abundance," we want to emphasize that we are referring to relative abundance, where 1 represents 100%. To avoid confusion, we will explicitly state this in both the methods section and figure descriptions. Besides, it is true that the statistical test employed for the analysis is not mentioned in the figure description and we recognize that the image may be difficult to interpret. Therefore, we will modify the text and a supplementary table displaying the abundance and p values is going to be added.

Furthermore, we agree with the reviewer's suggestion to investigate whether the bacteria identified as potential biomarkers for each condition are specific to their respective severity index or if there is a threshold. Thus, we will reanalyze the data and include a supplementary table with the abundance of each biomarker for each condition. We will also place greater emphasis on these results in our discussion.

Finally, in response to the reviewer's suggestion, we are going to go through the nasopharyngeal-fecal axis part in the discussion. It is well described that COVID-19 induces a dysbiosis in both microbiomes.

Consequently, we understand that the ratio we have described could be an interesting tool for assessing COVID severity development as it considers alterations in both environments. However, we acknowledge that there may be room for improvement in clarifying the significance of this intriguing finding and its implications.

Author Response

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

eLife assessment

This comprehensive study provides valuable information on the cooperation of Ikaros with Foxp3 to establish and regulate a major portion of the epigenome and transcriptome of T-regulatory cells. However, the characterization is incomplete in that incontrovertible evidence that these are intrinsic features regulating biological function and not outcomes of the inflammatory micro-environment of the genetically manipulated mice is missing.

Public Reviews:

This study investigates the role of Ikaros, a zinc finger family transcription factor related to Helios and Eos, in T-regulatory (Treg) cell functionality in mice. Through genome-wide association studies and chromatin accessibility studies, the authors find that Ikaros shares similar binding sites to Foxp3. Ikaros cooperates with Foxp3 to establish a major portion of the Treg epigenome and transcriptome. Ikaros-deficient Treg exhibits Th1-like gene expression with abnormal expression of IL-2, IFNg, TNFa, and factors involved in Wnt and Notch signaling. Further, two models of inflammatory/ autoimmune diseases - Inflammatory Bowel Disease (IBD) and organ transplantation - are employed to examine the functional role of Ikaros in Treg-mediated immune suppression. The authors provide a detailed analysis of the epigenome and transcriptome of Ikaros-deficient Treg cells.

These studies establish Ikaros as a factor required in Treg for tolerance and the control of inflammatory immune responses. The data are of high quality. Overall, the study is well organized, and reports new data consolidating mechanistic aspects of Foxp3 mediated gene expression program in Treg cells.

Strengths:

The authors have performed biochemical studies focusing on mechanistic aspects of molecular functions of the Foxp3-mediated gene expression program and complemented these with functional experiments using two models of autoimmune diseases, thereby strengthening the study. The studies are comprehensive at both the cellular and molecular levels. The manuscript is well organized and presents a plethora of data regarding the transcriptomic landscape of these cells.

Response: We thank the reviewers for their careful review and feedback on our manuscript. We appreciate that the reviewers and editors recognize the strength and comprehensive nature of our in vivo, cellular, biochemical, and genome-wide molecular studies, which are well-organized in the manuscript. The acknowledgment of the complementary functional experiments in two models of inflammatory disease is also encouraging.

Weakness:

The authors claim that the mice have no pathologic signs of autoimmune disease even at a relatively old age, yet mice have an increased number of activated CD4+ T cells and T-follicular helper cells (even at the age of 6 weeks) as well as reduced naïve T-cells. Thus, immune homeostasis is perturbed in these mice even at a young age and the eXect of inflammatory microenvironments on cellular functions cannot be ruled out. Further, clear conclusions from the genome-wide studies are lacking.

Response: We agree with the reviewers' comment regarding the absence of overt autoimmune pathologies in Ikzf1-fl/fl-Foxp3-Cre+ mice, despite the increased frequency of activated CD4+ T cells, TFH cells, and apparent perturbation of lymphocyte homeostasis, even at a young age. It is noteworthy that while Ikaros is implicated in various autoimmune diseases, our specific mouse model in which Ikaros expression is lost only in Tregs, may not lead to a strong autoimmune phenotype in part due to the controlled environment of an extra-clean, pathogen-free animal facility. This aligns with a related study by Ana et al (2019, J. Immunol: doi:10.4049/jimmunol.1801270) in Ikzf1-fl/fl-dLck-Cre+ mice with loss of Ikaros expression in all mature CD4+ T cells, including Tregs, that exhibit no overt signs of overt autoimmune disease. Moreover, our transcriptomic studies reveal that increased expression of inflammatory genes in Ikzf1-deficient Treg is coupled with the simultaneous upregulation of genes with positive roles in Treg function. This balance suggests a compensatory mechanism within Ikaros-deficient Tregs that maintains their suppressive function until encountering an inflammatory immune challenge, which eventually leads to loss of Treg suppressive function in Treg-specific Ikaros-deficient mice. Our studies clearly show that Ikaros has cell-intrinsic eXects in Treg that also lead to cell-extrinsic eXects mediated by secreted factors that are likewise regulated by Ikaros. This can be said about the function of any transcription factor in any cell type. Our data clearly support the conclusion from the genome-wide studies that Ikaros plays a major role in establishing the active chromatin landscape, gene expression profile, and function of regulatory T cells in mice.

The following recommendations consolidate the views of the three reviewers of the manuscript.

The experiments suggested and, in some instances, fresh analysis, are thought necessary, so that the evidence of Ikaros-Foxp3 interactions regulating T-regulatory cell biology is comprehensive and solid. We hope the comments are useful to strengthen the comprehensive analysis reported in this submission.

The primary concern is that the indications of inflammation in the mice (see points 1 & 2 below) do not reflect in the experiments or consequent conclusions. The gap in the data should be addressed by testing these interactions in an appropriate context for which suggestions are included.

Please note that the title of the manuscript may be modified to reflect the use of mice as the system of study for this work.

(1) The evidence of inflammation (increased CD4 and T follicular cells) reported in the work requires new experiments to rigorously examine the relationship between Ikaros and Foxp3 to rule out the possible impact of the (inflammatory) microenvironment of the mice (Please see: Zemmour et al., Nat. Immunology 22, 607, 2021). Two possible experimental systems in mice are suggested.

a) The use of heterozygous female mice, which should be phenotypically normal due to the presence of 50% normal Treg. Or,

b) The generation of bone chimeras between wild-type and deficient mice using congenic markers.

Response: We agree that immune dysregulation that develops in the mice with age or during an inflammatory insult due to loss of Ikaros function in the Treg lineage is an important part of the phenotype of the animals. Our studies show that loss of Ikaros function in Treg influences the gene expression program such that Treg now produce inflammatory cytokines and ligands capable of engaging receptors expressed on Treg and other cells. This likely results in autocrine and paracrine signaling that induces further metabolic and gene expression diXerences not observed in wild-type mice. Indeed, we report in the manuscript that a sizable fraction of the diXerentially expressed genes do not appear to be direct Ikaros targets, but rather are downstream of Ikaros target genes such as Il2, Ifng, Notch, and Wnt. The mosaic experiments suggested will be a useful topic of future studies. Importantly, we argue that no gene expression study involving modulation of transcription factor activity in an organism- or cell-based system can be designed to measure only the direct eXects of that transcription factor in a manner isolated from any indirect, downstream eXects on the expression of other genes. We suggest that our current data remain highly valuable, as they reveal real and relevant biology in physiologic in vivo systems that do not depend upon the use of heterologous models. The fact that loss of Ikaros has an eXect not only on its direct targets, but on gene programs driven in turn by the indirect eXects of Ikaros-regulated factors, has been acknowledged in the manuscript.

(2) Figs. 7 and S5 show accumulation of CD4 cells (activated, memory, Tfh, Tfr) in LNs and spleens of the Ikaros KO over time. This is accompanied by elevated Igs but without overt autoimmune disease. KO Tregs had equivalent suppressive activity as WT Tregs against WT TeX in vitro. However, TeX from KO mice were resistant to the suppressive eXects of WT or KO Tregs. The authors interpret this as due to the increased percentage of memory cells within the KO TeXs, although they did not formally prove this point. Figs. 9 and S6 show that Ikaros KO mice are unable to be tolerized for cardiac allograft survival using two diXerent standard tolerogenic regiments. The rejecting allografts are accompanied by increased T-cell infiltration and upregulation of inflammatory genes. The authors suggest there is increased alloantibody, but alloantibody does not seem to have been measured.

Response: We are currently exploring in more detail the dysregulation of humoral immunity in the Ikzf1-deficient Treg model and plan to report these results in a future study.

(3) Linked to the above, a comparison of the chromatin occupancy of Ikaros in resting and activated Tregs would inform on whether and how Ikaros occupancy changes with the activation status of Tregs. Since the authors use in vitro stimulation for RNAseq and ATAC seq, ChIP seq analyses under these matching conditions will greatly add to the quality of the study. Since "Foxp3-dependent", ie. diXerential gene expression in the Foxp3GFPKO cells (PMID: 17220874) gene expression has been shown to be not entirely the same as Treg signature (i.e. gene expression or Tregs compared to Tnv), it will be worth correlating Ikaros, Foxp3 co-occupied genes and the corresponding fate of their expression with Foxp3-dependent and independent Treg signature gene sets.

Response: The prior study by Gavin et al. referred to above used duplicate samples instead of the standard three or more replicates required for a robust diXerential analysis of gene expression. The two samples in this study are variable, and no statistically significant diXerential gene expression was found between the experimental groups when we subjected these data to current analysis methods. For this reason, we have elected not to compare these prior data with our current data, which are robust, reproducible, and analyzed using current statistical methods. Furthermore, the mice used for the prior study develop a fatal inflammatory disease (scurfy) and therefore the Treg examined in this study would be subject to a much stronger extrinsic inflammatory environment than the Treg in our study, as our mice show no overt disease even with age.

Further, the consequence of the cooperation between the two transcription factors that can be inferred from the experiments in the study remains unclear. It is suggested that the authors could first consider the ChIP seq data from Foxp3, Ikaros co- and diXerentially occupied genes, and then correlate with the ATAC seq and gene expression data to comment on the consequence of this cooperation.

Response: We find that Ikaros binding at a given region has a strong eXect on accessibility, as reported in the manuscript, but that Foxp3 occupancy has less consequence, consistent with a prior study suggesting that Foxp3 largely utilizes the open chromatin landscape already present in the conventional CD4 T cell lineage (PMID:23021222). Our data suggest that the dominant eXect of Ikaros on Foxp3 is at the level of chromatin occupancy.

(4) In the comparative analyses of Ikaros and Foxp3 co-occupied regions and gene expression outcome, the authors mention "A total of 4423 Foxp3 binding sites were detected in the open chromatin landscape of wild-type Treg (Supplementary Table 9), and this ChIP-seq signal was enriched at accessible Foxp3 motifs." It is unclear whether the authors focused on the ATAC seq data and only examined the open chromatin regions for this analysis. In that case, it is unclear why. More so because the Ikaros footprint is more apparent in regions where accessibility is reduced upon deletion of Ikaros.

Response: Foxp3 has been shown to bind primarily at open chromatin shared between Tconv and Treg, unlike the pioneer activity of other Fox family members (PMID: 23021222, biorXiv https://www.biorxiv.org/content/10.1101/2023.10.06.561228v2.full.pdf). Consistent with this, we found the majority of peaks were in open chromatin. The motif analysis is quantitative, not binary, and takes into account Foxp3 binding sites at regions considered open in either condition, which is why we can see enrichment of Foxp3 motifs at sites going from more open to less open in the absence of Ikaros.

(5) Comments on figures:

The authors use MFI repeatedly in many of the figures for quantitation of antigen expression. This is misleading as several of the target antigens are normally expressed on a subpopulation of cells, e.g., Eos. Percent positive and MFI would be more relevant. Cytokine production should be presented by intracellular staining (e.g., IL-2, IFNg) as Elisa data does not allow one to determine the percentage of abnormally producing cells.

Response: We show both ICS and ELISA in this paper, preferring ELISA because it is much more quantitative than ICS.

Suppl. Fig. 1c - the panels do not correspond precisely to the legend or the text. At least one panel is missing. In Supp fig 1c, the authors plotted eXector Tregs, which are by definition CD62LloCD44hi, but the Y axis says CD44hiCD62Lhi. Is this a typo? Also on page 4, describing this data the authors mentioned Tfr, but the data is not shown in the Supp fig 1c.

Response: We thank the reviewer for catching these mistakes. We have corrected the typo in the figure panel for Supplementary Figure 1c. Follicular Treg data are indeed presented in Figure 7h, not Supplementary Figure 1, and we have corrected the text.

Fig. 2, which lists the diXerent categories of diXerentially expressed genes, it will be helpful if the authors add two columns indicating fold change and FDR values.

Response: These values are included in Table S1

Fig. 3c, the resolution of the histograms in the inset should be enhanced.

Fig. 3d, a histogram of representative CTV dilution plots, and an explanation of how the quantifications were done may be included.

Fig. 3e - not well labeled. Are these fold changes? Enrichments? Number of gene elements within the GO term that are aXected? Something else?

Fig. 3f - presented out of sequence. The data are a little hard to understand as the color scale is so subtle and the colors so close to one another that it is not entirely clear which gene expressions are increased vs decreased. Other than the simple statement that the Ikaros KO causes numerous changes, there does not seem to be a more consistent message from this data panel.

Fig. 4a, in addition to the bar graphs, it will be better to show the plots in a histogram, gated on Foxp3+ Tregs in WT and KO groups, with representative MFI indicated on top. The resolution of the scatter plots in this figure, as well as some others throughout the manuscript, may be improved. Please increase the resolution wherever necessary.

Fig. 4b should include representative plots for cytokine production gated in Tconv (CD4+Foxp3-) cells.

Figs. 5a-h, S2-3a-d, and Suppl. Tables S4-8 show a comprehensive ATAC-seq and ChIP-seq analysis of genes and chromatin occupied or regulated by Ikaros, comparing Tconv vs Treg, stimulated vs naïve, and WT vs KO cells. It is a comprehensive tour-de-force analysis, again showing the major eXects of Ikaros on the entire Treg landscape of gene regulation.

Fig. S5h-j should be explained or labeled in more detail. The fonts are too small to read, even at 200% magnification; and the cell and gene comparisons are not entirely clear.

Supp. Fig. S3e is not referred to in the text.

Fig. S4a is very diXicult to read; the font and plotted points are too small.

Response: We have improved the clarity of the figures where necessary. We also indicate in the figure legends that full gene lists are to be found in the supplementary tables.

Page 8, "Regions that exhibit reduced accessibility in Ikzf1 cko compared to wild-type Treg are enriched for the binding motif for Ikaros and the motif for TCF1 (Figure 5g).... ". Is this Fig. 5i or 5g?

Response: This statement is correct and is referring to data depicted in Figure 5g.

In Fig 6e, Flag-Ik7 is not visible in any of the inputs. The co-IP between Foxp3 and Runx1 (presumably a positive control) is not eXicient in this experimental condition. Co-IP experiments performed in primary cells upon retroviral transduction of the tagged proteins to confirm observations in cell lines are suggested.

Response: Runx1 is shown to co-precipitate with Foxp3 as expected, although the band is not intense, and the data depicted are representative of 3 experiments. Ik7 was included in this transient transfection experiment as a redundant control, and the referee is correct that Ik7 did not express well in this experiment and cannot be seen in this exposure. We showed these blots intact in the spirit of not digitally altering the data, and because the low Ik7 expression did not impact our ability to demonstrate specific co-precipitation of Foxp3 with full length Ikaros (Ik1). The images include nearly the entire mini-blots, and we have added molecular weight markers for clarity. As indicated in the legend, the cytokine and ChIP data in 6f are from a separate model of retrovirally Foxp3/Ik7transduced T cells that we and others have used in multiple prior studies (e.g. Thomas JI 2007, Thomas JI 2010). The interpretability of these experiments is not impacted by the transient transfection data from figure 6e. It should be noted that a prior study by Rudra et al. that is cited and referred to in the manuscript used a similar approach to also establish that Foxp3 and Ikaros form a complex in cells.

In Fig 6f, the authors state that Foxp3 overexpression in CD4 cells results in promoter occupancy of both IL2 and IFNg, however, data shows only IL2. Also in 6f, Foxp3 overexpression reduces IL2 and IFNg secretion, measured by ELISA, which is recovered by IkDN. However, the eXect of Foxp3 along with WT Ikaros (which should not modulate, and if anything, further repress IL2, IFNg production) is not shown.

Response: The reviewer is correct that ectopic expression of Ikaros leads to repression of cytokine gene expression, which we and others have shown in prior studies. Because the focus of this study was on loss of Ikaros function in Treg, we did not elect to overexpress full-length Ikaros. However, we completely agree that Ikaros GOF in Treg is an important topic for future studies.

Fig. 7e-g, how is %suppression calculated? Can representative CTV dilution plots for the suppression assays be shown?

Response: Cell division was quantified as described previously (see ref 50), and percent suppression represents the reduction in cell division measured by Tconv in the presence of Treg compared to in the absence of Treg. This has been clarified in the methods section.

In Fig 8 and the supplementary figures the representative colon pictures (Fig. S6a-c) do not show convincing diXerences in colon morphology even though all the other histology and clinical parameters are clear. Are the figures mislabeled?

In Fig 8c-e and other histology figures scale bars should be shown.

Fig. 8c-e, the Alcian blue staining among the groups appears similar; perhaps this is due to the low power magnification.

Response: We have edited this figure for clarity

Additional comments:

Fig 10 is explained in the discussion section for the first time. The authors may want to consider including this when introducing Ikzf1 ChIPseq data for the first time in the study.

Response: The reviewer raises a valid point but we have elected to retain the current organizational structure of the manuscript.

A more complete characterization of the activated conventional cells including both CD4+ and CD8+ T cells for cytokine production during aging may be considered, as it is highly likely that abnormalities in cytokine production will be observed.

Response: We agree and are planning additional such experiments in future studies focusing on in vivo models of tolerance.

The failure of suppression of T cell proliferation which the authors claim is due to the presence of activated memory T cells can be better documented by using naive responder cells from the cKO mice.

Response: We agree and are planning additional such experiments in a future study focusing on further aspects of cellular immunobiology impacted by Ikaros, but we will give preference to in vivo models of tolerance in such studies.

Author Response

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

Reviewer #1 (recommendations for the authors):

Additional suggestions for improvement are noted below:

(1) Additional 1. Lns 261-262, as well as abstract: The term 'aerobic fermentation' is not accurate in the context of this manuscript. This terminology should be reserved for conditions where lactate production is observed under optimal aerobic conditions. This is not the case in this study. More lactate was observed in the agr mutant only when cells were grown under microaerobic conditions, where some level of fermentation would be expected to be active (esp. if nitrate is not provided in media).

We modified the text by deleting reference to the “aerobic” fermentation as suggested by the reviewer:

Line 93 (abstract): “Deletion of agr increased both respiration and aerobic fermentation but decreased ATP levels and growth, suggesting that Δagr cells assume a hyperactive metabolic state in response to reduced metabolic efficiency.”

Line 184: “Collectively, these data suggest that Δagr increases respiration and aerobic fermentation to compensate for low metabolic efficiency.”

(2) Additionally, the authors' statement, 'The tendency of Δagr cells to forgo the additional ATP yield from acetate production in favor of NAD+-generating lactate (23, 24) underscores the importance of redox balance in Δagr cells,' appears contradictory to the data presented in Fig 5, where the Δagr mutant demonstrates an approximately threefold increase in acetate production during exponential growth compared to the wild-type strain. A clarification or adjustment in the manuscript may be necessary to ensure consistency and accurate interpretation.

In glucose-fermenting S. aureus, pyruvate can serve as an electron acceptor, generating lactate from lactate dehydrogenases. Acetyl-CoA production proceeds via the pyruvate formate-lyase reaction, which converts pyruvate to formate rather than CO2 and thus does not consume oxidized NAD+. Thus, at a general level, the tendency of fermenting cells to forgo the additional ATP yield from acetate production in favor of NAD+-generating ethanol synthesis underscores the importance of redox balance when respiration is suboptimal. This is especially true for fermenting Δagr strains, as evidenced by increased lactate production compared to their relatively ATP replete wild-type parental strains. However, in the interest of clarity, we removed the sentence in question, because it is not necessary and potentially confusing, and because the additional context it requires would detract from the manuscript by disrupting its sense of narrative and brevity.

(3) Ln 277-285: There still are errors in how this paragraph is worded. What the authors stated in the 'response to the reviewers' (question 13) and the changes they made in the text are different. Here again, the response to question 13 suggested the following, "Collectively, these observations suggest that a surge in NADH production and reductive stress in the Δagr strain induces a burst in respiration, but levels of NADH are saturating, thereby driving fermentation in the presence of oxygen." That bit of it where the authors suggest that fermentation was activated because NADH was saturating is only true under microaerobic conditions and not under oxygen rich conditions.

Reviewer #1 (comment under Review): Data presented in Figure 5 suggest the opposite - a surge in NADH accumulation leading to a decrease in the NAD/NADH ratio, rather than a surge in the 'consumption' of NADH. Clarifying this point in the manuscript would ensure accurate representation of the findings.

Responses to Comments 3 and a comment in the Review have been combined.

Line 280: We thank the Reviewer for their attention to detail in picking up our error in response to question 13 related to the difference in the revised text and “response to reviewers”. We modified the text accordingly.

“Microaerobic conditions and “consumption”: We have modified the wording and fixed the error with respect to “consumption” as pointed out by the reviewer (strikethrough/underlined):

Line 285: “Collectively, these observations suggest that a surge in NADH consumption accumulation and reductive stress in the Δagr strain induces a burst in respiration, but levels of NADH are saturating, thereby driving fermentation under microaerobic conditions in the presence of oxygen.”

Reviewer #2 (recommendations for the authors):

(1) The authors are requested to revise 'we expected a lower NAD+/NADH' in line 280 to 'we expected a higher NAD+/NADH.' Additionally, what was the glucose concentration in TSB media?

NAD+/NADH: We thank the Reviewer for their attention to detail in picking up our error. Our responses to Reviewer 1, Comment 3 above addresses this issue.

Glucose: We modified the Methods as suggested.

Author Response

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

Reviewer #1 (recommendations for the authors):

The following are comments that the authors may wish to address or clarify:

(1) The claim that respiration and fermentation occur concurrently in the agr mutant during aerobic growth is not strongly supported by the evidence presented…. However, since neither lactate production nor a difference in the NAD+/NADH ratio between the wild type and agr mutant was observed, it is challenging to assert that fermentation is occurring. Relying solely on a gene expression signature indicative of fermentation is, in my view, inadequate to conclusively establish that aerobic fermentation is taking place.

Lactate production. The data we provide in Figure 5-E of the original manuscript (Figure 5-C in the revised manuscript) indicates that lactate production is lower in the wild-type compared to the Δagr mutant.

The exact focus of Reviewer 1’s concern is not clearly specified, but may have been referring to how the result was described in the text:

“Although the stimulatory effect of the agr deletion on production of the fermentation product lactate was not observed in optimally aerated broth cultures after growth to late exponential growth phase, it was confirmed for organisms grown in broth under more metabolically demanding, suboptimal aeration conditions (Figure 5E). Overall, these results are consistent with transcription-level up-regulation of respiratory and fermentative pathways in agr-deficient strains.”

The greater sensitivity of suboptimal aeration conditions is unsurprising and relates to a low rate of fermentation during the vigorous aeration (shaking at 250 rpm) conditions commonly used to grow S. aureus. To clarify the point, we modified the text to provide additional context as follows:

Line 271: “Although the stimulatory effect of the agr deletion on production of the fermentation product lactate was not observed in optimally aerated broth cultures after growth to late exponential growth phase, it was confirmed for organisms grown in broth under more metabolically demanding, suboptimal aeration conditions (limitations in the rate of respiration when oxygen is limiting are expected to increase overall levels of fermentation) (Figure 5C). Overall, these results are consistent with transcription-level up-regulation of respiratory and fermentative pathways in agr-deficient strains.” NAD+/NADH ratio. Extended studies of the NAD+/NADH ratio, requested by Reviewer 1 under Comments 12 and 13, document an effect of the Δagr mutant not seen in Figure 5F in the original submission. Our responses to Comments 12 and 13 below address this issue.

(2) The mechanisms through which the ΔagrΔrot double mutant resists H2O2 are not clearly elucidated. While the authors suggest that the ΔagrΔrot double mutant expresses several genes involved in combating oxidative stress, essential genetic studies that would validate this hypothesis have not been conducted.

The data we provide indicate 1) that wild-type strains are tolerant to peroxide and 2) that wild-type strains are able to render inducible several known reactive oxygen species (ROS)-protective genes in the presence of peroxide in a rot-dependent manner. Δagr strains, which do not demonstrate this response, are more readily killed by peroxide. Additional data indicate that increased respiration caused by deletion of agr is associated with increased endogenous ROS. Higher levels of endogenous ROS can modulate tolerance to subsequent challenge by ROS (1). Collectively, these observations support a model of Δagr-induced hyper-susceptibility in which elevation of endogenous ROS results in a suboptimal ROS-defense response that plays a role in increased peroxide lethality.

We prefer to test this model in future studies directed at understanding the complexities of the interaction among agr-mediated tolerance, endogenous ROS levels, and induction of protective responses in S. aureus. Culprit protective genes, alone and in various combinations, will be inactivated in Δagr mutant and wild-type strains, tested in killing assays with and without agents that mitigate endogenous ROS, and subjected to RNAseq, proteomic, and metabolomic analyses, as part of a larger program to identify factors involved in S. aureus tolerance to lethal stress.

To clarify the issue raised by the reviewer we altered the wording in the following sentences as follows:

Line 335: “Elevated expression of protective genes suggests that the double mutant survives damage from H2O2 better because protective genes are rendered inducible (loss of Rot-mediated repression).”

Line 440: “Details of agr-mediated protection are sketched in Figure 10. At low levels of ROS, agr is activated by a redox sensor in AgrA, RNAIII is expressed and represses the Rot repressor, thereby rendering protective genes (e.g., clpB/C, dps) inducible via an unknown mechanism (induction, candidate protective gene(s), and their connection to endogenous ROS levels are being pursued, independent of the current report).

(3) The reason behind the agr mutant's low metabolic efficiency, as evidenced by low levels (Fig 5A) despite enhanced respiration and acetate production, is not clearly explained. Could insights from the modeling shed light on why the ATP levels are low in the agr mutant?

Comparative modeling of central metabolic pathways, in combination with in vitro metabolic analyses of Δagr and wild-type strains, revealed the metabolic inefficiency but cannot explain it. The basis for the metabolic inefficiency conferred by agr inactivation is unknown. The possibility that aberrant sorting of cell wall surface proteins could lead to metabolic inefficiency was raised in the Discussion where we wrote:

“Our work supports this idea by showing that increased respiration caused by deletion of agr is associated with increased ROS-mediated lethality. The basis for the metabolic inefficiency conferred by agr inactivation is unknown. Given that Δagr mutants are unable to downregulate surface proteins during stationary phase (2, 3), it is possible that deletion of agr perturbs the cytoplasmic membrane or the machinery that sorts proteins across the cell wall. In support of this notion, jamming SecY translocation machinery of E. coli results in downstream events shared with antibiotic lethality, including accelerated respiration and accumulation of ROS (4). In this scenario, the formation of a futile macromolecular cycle may accelerate cellular respiration to meet the metabolic demand of unresolvable problems caused by elevated surface sorting.”

For clarification, we modified the text as follows:

Line 461: “Our work supports this idea by showing that increased respiration caused by deletion of agr is associated with increased ROS-mediated lethality. How agr deficiency is connected to the corruption of downstream processes that result in metabolic inefficiency and increased endogenous ROS levels is unknown. Given that Δagr mutants are unable to downregulate surface proteins during stationary phase (2, 3), it is possible that deletion of agr perturbs the cytoplasmic membrane or the machinery that sorts proteins across the cell wall.”

agr has been linked to defects in peptidoglycan autolysis (5). Cho et al. (2019) found that β-lactam treatment can induce a futile cycle of peptidoglycan synthesis and degradation that has been linked to increased production of endogenous ROS (6). Thus, an alternative, nonmutually exclusive route to a futile cycle and elevated endogenous ROS levels in agr-deficient cells other than surface protein dysregulation may be via decreased cell wall cross-linking. We prefer not to include this and other speculations, because they are not necessary or revealing and because they would detract from the manuscript by disrupting its sense of narrative and brevity.

(4) The observation that menadione can protect the agr mutant from H2O2 is perplexing. The authors propose that even though menadione generates superoxide through redox cycling, this superoxide might inhibit the TCA cycle, thereby restricting respiration, which could be advantageous for the agr mutant. To substantiate this hypothesis, it would be imperative to demonstrate that a double mutant ΔagrΔacnA exhibits long-lived protection against H2O2.

Rowe et al. (2020) definitively showed that a burst of menadioneassociated ROS inactivates the TCA cycle in S. aureus, leading to reduced respiration and ATP production (7). Both aconitase activity and ATP levels in menadione-treated cultures were complemented by the antioxidant N-acetyl cysteine. In the present work we demonstrate, using the same experimental conditions as Rowe et al., that menadione protected the Δagr mutant from peroxide killing but had little effect on the wild-type strain. Addition of N-acetyl cysteine in the presence of menadione restored H2O2 susceptibility to the Δagr mutant and had no effect on the wild-type. Collectively, these observations support the idea that menadione inactivates the TCA cycle, leading to reduced respiration, and increased protection of the Δagr mutant from peroxide killing.

As requested, we tested whether the ΔagrΔacnA double mutant exhibits protection against H2O2. The new data we now provide (Figure 8—figure supplement 2A) show that a ΔacnA mutation completely protected the Δagr mutant from peroxide killing after growth to late exponential growth phase, but it had little if any effect on the wild-type strain. To evaluate long-lived protection, we compared survival rates of ΔagrΔacnA mutant and Δagr cells following dilution of overnight cultures and regrowth prior to challenge with H2O2, which revealed partial protection of the Δagr mutant (Figure 8— figure supplement 2B).

We explained these results with the following:

Line 351: “Rowe et al. (2020) showed that menadione exerts its effects on endogenous ROS by inactivating the TCA cycle in S. aureus. To determine whether this mechanism can also induce protection in the Δagr mutant, we inactivated the TCA cycle gene acnA in wild-type and Δagr strains (Figure 8—figure supplement 2). We found that ΔacnA mutation completely protected the Δagr mutant from peroxide killing after growth to late exponential growth phase but had little effect on the wild-type strain. This finding supports the idea that TCA cycle activity contributes to an imbalance in endogenous ROS homeostasis in the Δagr mutant, and that this shift is a critical factor for Δagr hyperlethality. When we evaluated long-lived protection by comparing survival rates of ΔagrΔacnA mutant and Δagr cells following dilution of overnight cultures and regrowth prior to challenge with H2O2, ΔacnA remained protective, but less so (Figure 8—figure supplement 2). These partial effects of an ΔacnA deficiency suggest that Δagr stimulates long-lived lethality for peroxide through both TCA-dependent and TCA-independent pathways.”

(5) Figure 10 presents a model suggesting that Rot-mediated repression of respiration is essential for long-lasting resistance to H2O2 lethality. However, the connection between decreased respiration and long-lived resistance to ROS is not evident, especially considering that the respiration rate varies over the growth phase and does not seem to align with the long-lived and steady protection provided by agr. However, the authors could investigate this by examining whether inactivating qox in the agr mutant restores its resistance to H2O2. The experiments with menadione are not particularly persuasive, as menadione could have additional effects on the cells that are not accounted for.

As requested, we tested whether the ΔagrΔqoxC double mutant exhibits protection against H2O2. qox deficiency was hyperlethal in wild-type and Δagr strains, even with the lowest concentration of H2O2 used in our assay. Indeed, surviving cells were undetectable, precluding comparison of survival differences between wild-type and Δagr mutant strains. This striking finding can be explained by prior work highlighting the profound and pleotropic effects of qox deficiency on metabolism that involve not only control of respiration but also participation in other physiological processes such as cell growth and morphological differences. For example, in Bacillus, qox deficiency decreases TCA cycle flux and increases overflow metabolism (8). Additionally, we confirmed prior work in S. aureus showing that qox deficiency decreases growth rate and yield (9, 10), dramatically increases production of pigment that functions as an oxidation shield, and decreases hemolytic activity (11). Moreover, we found that that qox deficiency results in a striking increase (~150%) in endogenous ROS in both wild-type and agr mutant cells, likely explaining the hyperlethality phenotype. Thus, interpretation of killing assay results must account for the complex and likely reciprocal interactions among Δqox-mediated metabolic changes, agrA-mediated redox sensing, and Δagrmediated changes in metabolism. Since killing data are not necessary or revealing without this information, we prefer to address the role of qox in future studies directed at understanding the complexities of the interaction among agr-mediated tolerance, endogenous ROS levels, and induction of protective responses in S. aureus.

(6) The repeated use of the term 'agr wild type' throughout the text is somewhat distracting. It might be clearer to simply use 'wild type,' as it is implied that this refers to the agr+ genotype.

We modified the text by replacing 'agr wild-type' with “wild-type” as suggested by the Reviewer.

(7) In the text, the authors imply that the extended lag phase of the agr mutant is observed solely in nutrient-limited CDM. However, Figure 1 and Figure Supplement 3A reveal that the strains were actually cultivated in CDM supplemented with glucose and Casamino acids, which makes the medium rich in both carbon and nitrogen, in addition to other nutrients present in CDM. The authors should clarify the composition of the media used and assess whether the term 'nutrient-limited CDM' is accurate in this context.

The extended lag phase of the Δagr mutant is observable in TSB but it is more easily appreciated in CDM, perhaps owing to a larger range of carbohydrates and other nutrient types (TSB a rich and complex medium for which the composition is unknown) and a higher concentration of glucose (2.5 mM versus 2.2 mM).

For clarification, we modified line 135 as follows:

Line 184: “Lag-time differences between strains were more obvious in experiments using less complex, chemically defined medium (CDM)…”

(8) Figure 1 - Figure Supplement 3C represents the growth rate in terms of [OD/min]. However, it would be more accurate to calculate the growth rate (μ) based on the change in the natural logarithm of optical density (OD) relative to the corresponding change in time, using appropriate units (preferably, h⁻¹). Additionally, the method employed for measuring growth rates should be detailed in the Materials and Methods section.

Our responses to Reviewer 2 Minor Comment 1 below address this issue.

(9) The resolution of the inset charts in Figure 4B is poor, and the Y-axis lacks labels. The figure legend should also specify whether the flux distribution (represented by thick black arrows in Fig 4B) is predicted for the wild type or the mutant.

We modified Figure 4B and the legend accordingly.

(10) On Page 9, the term "RT-PCR" should be corrected to "RT-qPCR."

We thank the Reviewer for their attention to detail in picking up our error. We modified text accordingly.

(11) It is ambiguous whether the agr mutant is producing more acetate, based on the information provided in Figure 5B. Since the cells might have entered the post-exponential phase at 5 hours, they could start consuming acetate. Consequently, the elevated acetate concentration in the agr mutant might result from a delay in acetate consumption rather than increased production. To discern between the production and consumption of acetate, it is essential to measure acetate concentrations at earlier time points as well as the corresponding glucose concentrations in the media. This will help ascertain when the agr mutant enters the post-exponential phase. A similar concern also exists in the case of lactate (Fig 5E) since it is not clear when lactate was measured.

As requested, we measured acetate levels at earlier time points (1, 2, 3, 4, h of growth). New Figure 5B shows that the Δagr mutant accumulated more acetate than the wild-type strain during exponential growth at 3 h, well before entry into postexponential phase (see growth curves in Figure 1—figure supplement 1).

In the original report, lactate levels were measured at 4 h for organisms grown under suboptimal aeration conditions (see Reviewer 1, Comment 1). When we measured lactate accumulation at 3 h it remained higher in the Δagr mutant compared to the wildtype. Likewise, acetate levels at 3 h under suboptimal aeration conditions remained elevated in the Δagr mutant compared to the wild-type. These results support the idea that inactivation of agr promotes production rather than decreased consumption of acetate and lactate in the culture medium.

(12) In Figure 5G-H, presenting the actual NAD+ and NADH values side-by-side would facilitate a more straightforward interpretation of the data by the readers.

(13) On Page 9, the text states that respiration and fermentation lower the NAD+/NADH ratio. However, this seems contradictory as these processes would typically increase the NAD+/NADH ratio. Furthermore, it would be beneficial for the authors to provide supporting evidence for the statement made at the beginning of Page 10, which claims that there is greater consumption of NADH in the agr mutant.

Responses to Comments 12 and 13 were grouped together.

We thank the Reviewer for their attention to detail in picking up our error about the NAD+/NADH ratio. The ratio is expected to be elevated by increases in respiration and fermentation, not lowered, owing to increased consumption of NADH.

Figure 5I in the submitted manuscript indicated a small but insignificant decrease in the NAD+/NADH ratio of the Δagr mutant. Thus, the NAD+/NADH ratio remained tightly bounded, but if anything was decreased, not increased.

We explained this finding as follows:

Line 284: “Collectively, these observations suggest that a surge in NADH production and reductive stress in the Δagr strain induces a burst in respiration and fermentation.”

The NAD+/NADH ratio in Figure 5F of the submitted manuscript was calculated from NADH and total (NAD+/NADH) levels. As requested, we measured individual NAD+ and NADH concentrations. We found that the decrease in the NAD+/NADH ratio of the Δagr mutant was now large, significant, and largely due to a relative increase in NADH.

We have included these new data in a revised Figure 5 in the revised version of the manuscript and clarify the relationship among the NAD+/NADH ratio, respiration, and fermentation in the Δagr mutant by modifying the wording of the text as follows:

Line 280: “Since respiration and fermentation generally increase NAD+/NADH ratios and since these activities are increased in Δagr strains (Figure 5C and 5E-F), we expected a higher NAD+/NADH ratio relative to wild-type cells. However, we observed an increase decrease in the NAD+/NADH ratio due to a large surge in NADH accompanied by a modest drop in NAD+ compared to wild-type. Collectively, these observations suggest that a surge in NADH production and reductive stress in the Δagr strain induces a burst in respiration, but levels of NADH are saturating, thereby driving fermentation in the presence of oxygen.

Reviewer #2 (Recommendations For The Authors):

(1) The RNA-seq analysis revealed that the Δagr strain exhibited increased expression of genes involved in respiration and fermentation, suggesting enhanced energy generation. However, metabolic modeling based on transcriptomic data indicated a decrease in tricarboxylic acid (TCA) cycle and lactate flux per unit of glucose uptake in the Δagr mutant. Additionally, intracellular ATP levels were significantly lower in the Δagr mutant compared to the wild-type strain, despite the carbon being directed into an acetate-generating, ATP-yielding carbon "overflow" pathway. Furthermore, growth analysis in nutrient-constrained medium demonstrated a decrease in the growth rate and yield of the Δagr mutant. Given that S. aureus actively utilizes the electron transport chain (ETC) to replenish NAD pools during aerobic growth on glucose, supporting glycolytic flux and pyruvate dehydrogenase complex (PDHC) activity while restricting TCA cycle activity through carbon catabolite repression (CCR), it is suggested that the authors analyze glucose consumption rates in conjunction with the determination of intracellular levels of pyruvate, AcCoA, and TCA cycle intermediates such as citrate and fumarate. These additional experiments will provide valuable insights into the metabolic fate of glucose and pyruvate and their subsequent impact on cellular respiration and fermentation in the Δagr mutant.

(2) The authors highlighted the importance of redox balance in Δagr cells by emphasizing the tendency of these cells to prioritize NAD+-generating lactate production over generating additional ATP from acetate. However, the results regarding acetate and lactate production in Δagr cells during aerobic growth suggest that carbon is directed towards acetate generation rather than lactate.

Responses to Comments 1 and 2 have been combined.

As requested, we measured glucose consumption and intracellular levels of several different metabolites in the wild-type and Δagr mutant strain. The results are consistent with the idea that increased acetogenesis and fermentation in Δagr mutant cells contribute to increased ATP production and NAD+ recycling, respectively. These two processes appear to be relatively favored over the flux of pyruvate carbon into the TCA cycle of the Δagr mutant.

We explained our finding as follows:

Line 288: “To help determine the metabolic fate of glucose, we measured glucose consumption and intracellular levels of pyruvate and TCA-cycle metabolites fumarate and citrate in the wild-type and Δagr mutant strains. At 4 h of growth to late-exponential phase, intracellular pyruvate and acetyl-CoA levels were increased in the Δagr mutant compared to wild-type strain, but levels of fumarate and citrate were similar (Figure 5— figure supplement 1D-E). Glucose was depleted after 4 h of growth, but glucose consumption after 3 h of growth (exponential phase) was increased in the Δagr mutant compared to the wild-type strain (Figure 5—figure supplement 1A). These observations, together with the decrease in the NAD+/NADH ratio and increase in acetate and lactate production described above, are consistent with a model in which respiration in Δagr mutants is inadequate for 1) energy production, resulting in an increase in acetogenesis, and 2) maintenance of redox balance, resulting in an increase in fermentative metabolism, lactate production, and conversion of NADH to NAD+. Increased levels of acetate compared to lactate under optimal aeration conditions suggests that demand for ATP is in excess of demand for NAD+.”

Future work will compare additional extracellular and intracellular (e.g., formate, ethanol, acetoin) metabolites to test these and other models using a combination of approaches (e.g., mass spectrometry, nuclear magnetic resonance, genetic deletion studies, transcriptomics) and will determine the mechanisms underlying metabolic differences in wild-type and Δagr mutant strains.

To maintain a sense of narrative we added a new subheading after the explanation of our findings:

Line 311: “Transcriptional changes due to Δagr mutation are long-lived and result in down-regulation of H2O2-stimulated genes relative to those in an agr wild-type.”

(3) The authors mentioned that respiration and fermentation typically reduce the NAD+/NADH ratios, and since these activities are elevated in Δagr strains (Figure 5F-G), they initially anticipated a lower NAD+/NADH ratio compared to wild-type agr cells. However, the increase in respiration and activation of fermentative pathways leads to a decrease in NADH levels, therefore resulting in an increase in the NAD+/NADH ratio.

We have clarified the issue with new experiments and by modifying the wording as shown in the response to Reviewer 1 Comment 13.

(4) To improve the clarity and completeness of this work, it would be advantageous for the authors to provide specific details regarding the glucose concentration in the TSB media and the aeration conditions during growth, including the flask-tomedium ratio. These additional experimental parameters are essential for ensuring the reproducibility and comprehensiveness of the study, allowing for a more precise understanding and interpretation of the observed metabolic changes in the Δagr strain.

We modified the Methods as suggested.

Minor comments:

(1) The growth rate in Figure 1-figure supplement 3 should not be presented as a simple calculation of OD/min and needs to be recalculated.

We recalculated the growth rate and modified Figure 1 as suggested. The exponential phase was used to determine growth rate (µ) from two datapoints, OD1 and OD2 flanking the linear portion of the curve, following the equation lnOD2-lnOD1/t2-t1, as described (12).

(2) Δrot (BS1301) should be removed from Figure 2 (A) legend as it is not presented in the panel A.

We modified Figure 2 as suggested.

(3) The authors should specify in the Figure 3 (D) legend that the kinetics of killing by H2O2 was performed for ΔrnaIII and ΔagrBD mixtures.

We modified Figure 3 as suggested.

(4) In the Figure 4 legend for (C), the statement "See Supplementary file 2 for supporting information" should be changed to "See Supplementary file 3 for supporting information."

We modified Supplementary file name as suggested.

References cited in responses

(1) Brynildsen MP, Winkler JA, Spina CS, MacDonald IC, Collins JJ. 2013. Potentiating antibacterial activity by predictably enhancing endogenous microbial ROS production. Nature biotechnology 31:160-165.

(2) Morfeldt E, Taylor D, von Gabain A, Arvidson S. 1995. Activation of alphatoxin translation in Staphylococcus aureus by the trans-encoded antisense RNA, RNAIII. EMBO J 14:4569-4577.

(3) Novick RP, Ross HF, Projan SJ, Kornblum J, Kreiswirth B, Moghazeh S. 1993. Synthesis of staphylococcal virulence factors is controlled by a regulatory RNA molecule. EMBO J 12:3967-3975.

(4) Takahashi N, Gruber CC, Yang JH, Liu X, Braff D, Yashaswini CN, Bhubhanil S, Furuta Y, Andreescu S, Collins JJ, Walker GC. 2017. Lethality of MalE-LacZ hybrid protein shares mechanistic attributes with oxidative component of antibiotic lethality. Proc Natl Acad Sci U S A 114:9164-9169.

(5) Fujimoto DF, Bayles KW. 1998. Opposing roles of the Staphylococcus aureus virulence regulators, Agr and Sar, in Triton X-100- and penicillin-induced autolysis. J Bacteriol 180:3724-3726.

(6) Cho H, Uehara T, Bernhardt TG. 2014. Beta-lactam antibiotics induce a lethal malfunctioning of the bacterial cell wall synthesis machinery. Cell 159:13001311.

(7) Rowe SE, Wagner NJ, Li L, Beam JE, Wilkinson AD, Radlinski LC, Zhang Q, Miao EA, Conlon BP. 2020. Reactive oxygen species induce antibiotic tolerance during systemic Staphylococcus aureus infection. Nat Microbiol 5:282-290.

(8) Zamboni N, Sauer U. 2003. Knockout of the high-coupling cytochrome aa3 oxidase reduces TCA cycle fluxes in Bacillus subtilis. FEMS Microbiol Lett 226:121-126.

(9) Halsey CR, Lei S, Wax JK, Lehman MK, Nuxoll AS, Steinke L, Sadykov M, Powers R, Fey PD. 2017. Amino acid catabolism in Staphylococcus aureus and the runction of carbon catabolite repression. mBio 8.

(10) Hammer ND, Reniere ML, Cassat JE, Zhang Y, Hirsch AO, Indriati Hood M, Skaar EP. 2013. Two heme-dependent terminal oxidases power Staphylococcus aureus organ-specific colonization of the vertebrate host. mBio 4.

(11) Lan L, Cheng A, Dunman PM, Missiakas D, He C. 2010. Golden pigment production and virulence gene expression are affected by metabolisms in Staphylococcus aureus. J Bacteriol 192:3068-3077.

(12) Grosser MR, Weiss A, Shaw LN, Richardson AR. 2016. Regulatory requirements for Staphylococcus aureus nitric oxide resistance. J Bacteriol 198:2043-2055.

Author Response

eLife assessment

This study demonstrates mRNA-specific regulation of translation by subunits of the eukaryotic initiation factor complex 3 (eIF3) using convincing methods, data, and analyses. The investigations have generated important information that will be of interest to biologists studying translation regulation. However, the physiological significance of the gene expression changes that were observed is not clear.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Herrmannova et al explore changes in translation upon individual depletion of three subunits of the eIF3 complex (d, e, and f) in mammalian cells. The authors provide a detailed analysis of regulated transcripts, followed by validation by RT-qPCR and/or Western blot of targets of interest, as well as GO and KKEG pathway analysis. The authors confirm prior observations that eIF3, despite being a general translation initiation factor, functions in mRNA-specific regulation, and that eIF3 is important for translation re-initiation. They show that the global effects of eIF3e and eIF3d depletion on translation and cell growth are concordant. Their results support and extend previous reports suggesting that both factors control the translation of 5'TOP mRNAs. Interestingly, they identify MAPK pathway components as a group of targets coordinately regulated by eIF3 d/e. The authors also discuss discrepancies with other reports analyzing eIF3e function.

We would like to note that the first sentence contains a typo; the correct expression is: “…of three subunits of the eIF3 complex (d, e, and h) in mammalian cells”.

Strengths:

Altogether, a solid analysis of eIF3 d/e/h-mediated translation regulation of specific transcripts. The data will be useful for scientists working in the Translation field.

Weaknesses:

The authors could have explored in more detail some of their novel observations, as well as their impact on cell behavior.

Many experiments are on-going in this direction. The original plan was to map all the effects in general and in as much detail as possible to select a few of them for future long-term projects.

Reviewer #2 (Public Review):

Summary:

mRNA translation regulation permits cells to rapidly adapt to diverse stimuli by fine-tuning gene expression. Specifically, the 13-subunit eukaryotic initiation factor 3 (eIF3) complex is critical for translation initiation as it aids in 48S PIC assembly to allow for ribosome scanning. In addition, eIF3 has been shown to drive transcript-specific translation by binding mRNA 5' cap structures through the eIF3d subunit. Dysregulation of eIF3 has been implicated in oncogenesis, however the precise eIF3 subunit contributions are unclear. Here, Herrmannová et al. aim to investigate how eIF3 subcomplexes, generated by knockdown (KD) of either eIF3e, eIF3d, or eIF3h, affect the global translatome. Using Ribo-seq and RNA-seq, the authors identified a large number of genes that exhibit altered translation efficiency upon eIF3d/e KD, while translation defects upon eIF3h KD were mild. eIF3d/e KD share multiple dysregulated transcripts, perhaps due to both subcomplexes lacking eIF3d. Both eIF3d/e KD increase the translation efficiency (TE) of transcripts encoding lysosomal, ER, and ribosomal proteins. This suggests a role of eIF3 in ribosome biogenesis and protein quality control. Many transcripts encoding ribosomal proteins harbor a TOP motif, and eIF3d KD and eIF3e KD cells exhibit a striking induction of these TOP-modified transcripts. On the other hand, eIF3d KD and eIF3e KD lead to a reduction of MAPK/ERK pathway proteins. Despite this downregulation, eIF3d KD and eIF3e KD activate MAPK/ERK signaling as ERK1/2 and c-Jun phosphorylation were induced. Finally, in all three knockdowns, MDM2 and ATF4 protein levels are reduced. This is notable because MDM2 and ATF4 both contain short uORFs upstream of the start codon, and further support a role of eIF3 in reinitiation. Altogether, Herrmannová et al. have gained key insights into precise eIF3-mediated translational control as it relates to key signaling pathways implicated in cancer.

Strengths:

The authors have provided a comprehensive set of data to analyze RNA and ribosome footprinting upon perturbation of eIF3d, eIF3e, and eIF3h. As described above in the summary, these data present many interesting starting points for understanding additional roles of the eIF3 complex and specific subunits in translational control.

Weaknesses:

• The differences between eIF3e and eIF3d knockdown are difficult to reconcile, especially since eIF3e knockdown leads to a reduction in eIF3d levels.

We agree and discuss this problem thoroughly in the corresponding section of our study.

• The paper would be strengthened by experiments directly testing what RNA determinants allow for transcript-specific translation regulation by the eIF3 complex. This would allow the paper to be less descriptive.

We carried out bioinformatic analysis dealing with specific RNA determinants that is presented as the last chapter of our study. A detailed, transcript-specific analysis of these determinants is underway, however, we consider them beyond the scope for this article.

• The paper would have more biological relevance if eIF3 subunits were perturbed to mimic naturally occurring situations where eIF3 is dysregulated. For example, eIF3e is aberrantly upregulated in certain cancers, and therefore an overexpression and profiling experiment would have been more relevant than a knockdown experiment.

This is indeed true and so far we have generated several stable cell lines individually overexpressing selected eIF3 subunits implicated in the observed cancer phenotypes. However, this is a completely different project of one of our PhD students, which will be published as a comprehensive study when completed.

Reviewer #3 (Public Review):

Summary:

In this article, Hermannova et al catalog the changes in ribosome association with mRNAs when the eukaryotic translation initiation factor 3 is disrupted by knocking down subunits of the multisubunit protein. They find that RNAs relying on TOP motifs for translation, such as ribosomal protein RNAs, and RNAs encoding proteins that modify other proteins in the ER or components of the lysosome are upregulated. In contrast, proteins encoding components of MAP kinase cascades are downregulated when subunits of eIF3 are knocked down.

Strengths:

The authors use ribosome profiling of well-characterized mutants lacking subunits of eIF3 and assess the changes in translation that take place. They supplement the ribosome association studies with western blotting to determine protein level changes of affected transcripts. They analyze what is being encoded by the transcripts undergoing translation changes, which is important for understanding more broadly how translation initiation factor levels affect cancer cell translatomes.

Weaknesses:

(1) The data are presented as a catalog of effects, and the paper would be strengthened if there were a clear model tying the various effects together or linking individual subunit knockdown to cancerous phenotypes. It is unclear what the hypothesis is for cells having more MAPK activity with less of the MAPK proteins being translated, so the main findings of the paper become observational without context.

As the signaling pathways are very complex and there is a frequent crosstalk among them (c-Jun can be activated by the ERK pathway as well as the JNK pathway, activated ERKs can phosphorylate many different transcription factors, etc.), we opted not to investigate the reported results any further in this study. As mentioned above, we have several ongoing, long-term projects aiming to elucidate the consequences of the observed changes in protein levels as well as in the phosphorylation status of the MAPK pathway constituents. The take home message of the present study is that eIF3 subunits (d and e) have control over the expression of many proteins involved in the MAPK/ERK pathway and that there is an independent effect (already present in the downregulation of eIF3h, which does not affect the MAPK protein expression) that leads to activation of the ERK pathway, which may be a direct consequence of compromised eIF3 function in general.

(2) The conclusions drawn are presented as very generalized other than in the last paragraph, but the experiments were only done in Hela cells. Since conclusions are being made about how translation changes affect MAP kinase signaling and there is mention in the abstract that dysregulation of these subunits is observed in cancer, at least one other cell line would need to be analyzed to provide evidence that the effects of subunit knockdown aren't cell-line specific.

There are several notes emphasizing that the data presented in this study were obtained only in HeLa cells. We agree that further research in other cell lines will be needed to confirm that what we observed is a general phenomenon. Nonetheless, as noted in the discussion, other reports have already been published strongly indicating that this phenomenon is not unique to HeLa cells (Li et al., 2021, PMID:34520790, HTR-8/SVneo cells). We will review our conclusions and further clarify that our results so far only apply to Hela cells.

(3) It is also unclear how replicates were performed and how many replicates were performed for several experiments. Biological replicates are mentioned, but what the authors did for biological replicates isn't defined and the description of the collection of cells for polysome/ribosome footprint/RNA seq samples makes it unclear whether the "biological replicates" are samples from separate transfections (true biological replicates) or different aliquots or wells from a single transfection (technical replicates) being run over a separate gradient. If using technical replicates, the data comparing the effects of knocking down D vs E vs H subunits are substantially weakened because subunit-specific differences could be the result of non-specific events that occurred in a transfection. It's also notable that while the pooled siRNAs will increase the potency of knockdown, it is possible that one or more of the siRNAs could have off-target effects, and analyzing individual siRNAs would be better for ensuring effects are specific.

We can reassure this reviewer that our Ribo-seq and RNA-Seq libraries were prepared from true biological replicates, grown, and transfected at different times. In fact, for each biological replicate, we used a new aliquot of cells from cryostock from the same batch and transfected the cells with the same passage number only. Multiple biological replicates were grown and all underwent a series of control experiments (polysomes, qPCR, western blot) as described in the article. Based on the results, 3 samples were selected for Ribo-Seq library preparation and 4 for RNA-Seq. We decided to add a fourth replicate for RNA-Seq to increase the data robustness, because RNA-Seq is used to normalize FPs to calculate TE, which was our main metric analyzed in this article.

As for the usage of the siRNA pool from Dharmacon/Horizon – our current article builds on our previous studies (Wagner et al. 2014 PMID: 24912683; Wagner et al. 2016 PMID: 27924037 and Herrmannová et al. 2020 PMID: 31863585), where we thoroughly characterized the effects of downregulation of individual eIF3 subunits on the growth, translation, composition and stability of eIF3 complex and on the 43S preinitiation complex assembly and subsequent mRNA recruitment. In all of these studies, we used the same siRNAs pools, the same cells and the same transfection protocol; therefore, we are convinced that our results are as coherent and reproducible as can possibly be. We have never noticed any off-target effects. Moreover, the ON-TARGETplus siRNA technology we employed uses a patented modification pattern that reduces the incidence of off-targets by up to 90% compared to unmodified siRNA (see the supplier's website for more information).

(4) Many of the changes in protein levels reported by Western are subtle. Data from all western blots making claims of quantitative differences should really be quantified relative to nontreated over-loading control or total protein quantified from the gel, and presented with a degree of error from biological replicates to make conclusions about differences in protein levels between samples.

Generally speaking, we agree with the reviewer’s opinion. In the original version of our study, we felt that it was not necessary to perform a quantification analysis to support our conclusions as it was not important whether a given protein was downregulated to, for example, 60% or 70%, as long as its amount was visibly reduced. The main message resided in the general trend, i.e. that the whole pathway is affected in a similar way. Nevertheless, in order to properly address this criticism, we will provide quantifications in the revised paper.

Author Response

Public Reviews:

Reviewer #1 (Public Review):

The authors observed a decline in autophagy and proteasome activity in the context of Milton knockdown. Through proteomic analysis, they identified an increase in the protein levels of eIF2β, subsequently pinpointing a novel interaction within eIF subunits where eIF2β contributes to the reduction of eIF2α phosphorylation levels. Furthermore, they demonstrated that overexpression of eIF2β suppresses autophagy and leads to diminished motor function. It was also shown that in a heterozygous mutant background of eIF2β, Milton knockdown could be rescued. This work represents a novel and significant contribution to the field, revealing for the first time that the loss of mitochondria from axons can lead to impaired autophagy function via eIF2β, potentially influencing the acceleration of aging. To further support the authors' claims, several improvements are necessary, particularly in the methods of quantification and the points that should be demonstrated quantitatively. It is crucial to investigate the correlation between aging and the proteins eIF2β and eIF2α.

Thank you so much for your comments. We will further investigate the correlation between aging and the proteins eIF2β and eIF2α and include the results in the revised version.

Reviewer #2 (Public Review):

In the manuscript, the authors aimed to elucidate the molecular mechanism that explains neurodegeneration caused by the depletion of axonal mitochondria. In Drosophila, starting with siRNA depletion of Milton and Miro, the authors attempted to demonstrate that the depletion of axonal mitochondria induces the defect in autophagy. From proteome analyses, the authors hypothesized that autophagy is impacted by the abundance of eIF2β and the phosphorylation of eIF2α. The authors followed up the proteome analyses by testing the effects of eIF2β overexpression and depletion on autophagy. With the results from those experiments, the authors proposed a novel role of eIF2β in proteostasis that underlies neurodegeneration derived from the depletion of axonal mitochondria.

The manuscript has several weaknesses. The reader should take extra care while reading this manuscript and when acknowledging the findings and the model in this manuscript.

The defect in autophagy by the depletion of axonal mitochondria is one of the main claims in the paper. The authors should work more on describing their results of LC3-II/LC3-I ratio, as there are multiple ways to interpret the LC3 blotting for the autophagy assessment. Lysosomal defects result in the accumulation of LC3-II thus the LC3-II/LC3-I ratio gets higher. On the other hand, the defect in the early steps of autophagosome formation could result in a lower LC3-II/LC3-I ratio. From the results of the actual blotting, the LC3-I abundance is the source of the major difference for all conditions (Milton RNAi and eIF2β overexpression and depletion). In the text, the authors simply state the observation of their LC3 blotting. The manuscript lacks an explanation of how to evaluate the LC3-II/LC3-I ratio. Also, the manuscript lacks an elaboration on what the results of the LC3 blotting indicate about the state of autophagy by the depletion of axonal mitochondria.

We agree with the reviewer that multiple ways exist to interpret the LC3 blotting for the autophagy assessment. Thus, we analyzed the levels of p62, an autophagy substrate, and found that milton knockdown caused elevated levels of p62 (Figure 2B). Together, these results suggest that autophagic degradation is lowered.

Another main point of the paper is the up-regulation of eIF2β by depleting the axonal mitochondria leads to the proteostasis crisis. This claim is formed by the findings from the proteome analyses. The authors should have presented their proteomic data with much thorough presentation and explanation. As in the experiment scheme shown in Figure 4A, the author did two proteome analyses: one from the 7-day-old sample and the other from the 21-day-old sample. The manuscript only shows a plot of the result from the 7-day-old sample, but that of the result from the 21-day-old sample. For the 21-day-old sample, the authors only provided data in the supplemental table, in which the abundance ratio of eIF2β from the 21-day-old sample is 0.753, meaning eIF2β is depleted in the 21-day-old sample. The authors should have explained the impact of the eIF2β depletion in the 21-day-old sample, so the reader could fully understand the authors' interpretation of the role of eIF2β on proteostasis.

Thank you for your comments. We will include more analyses of the proteomic data in the next version of our manuscript. In this study, we aimed to elucidate the mechanisms by which depletion of axonal mitochondria induces proteostasis disruption prematurely. Thus, we did not investigate the roles of differentially expressed proteins in proteostasis at 21-day-old in milton knockdown. Aging disrupts proteostasis via multiple pathways: eIF2β levels may be lowered by feedback of earlier changes or via interaction with other age-related changes at 21-day-old. We will include more discussion in the next version of our manuscript.

The manuscript consists of several weaknesses in its data and explanation regarding translation.

(1) The authors are likely misunderstanding the effect of phosphorylation of eIF2α on translation. The P-eIF2α is inhibitory for translation initiation. However, the authors seem to be mistaken that the down-regulation of P-eIF2α inhibits translation. Thank you for your comment. We understand that the phosphorylation of eIF2α is inhibitory for translation initiation, as we described in page 9, Line 312-314. We propose a model in which autophagic defects caused by milton knockdown is mediate by upregulation of eIF2β, however, we are not arguing that the translational suppression in milton knockdown is caused by a reduction in p-eIF2α. We found that milton knockdown causes an increase in eIF2β, and overexpression of eIF2β copied phenotypes of milton knockdown such as autophagic defects (Figure 5 and 6). We also found that the increase in eIF2β reduces the level of p-eIF2α (Supplemental Figure 2), thus, eIF2α phosphorylation in milton knockdown may be caused by an increase in eIF2β. However, the effects of upregulation of eIF2β on the function of eIF2 complex is not fully understood. The translational suppression in milton knockdown may be caused by disruption of eIF2 complex, while it is also possible that it is mediated by a function of eIF2β that is yet-to-be-determined, or mediated by the pathways other than eIF2. We will include more details in the revised version.

(2) The result of polysome profiling in Figure 4H is implausible. By 10%-25% sucrose density gradient, polysomes are not expected to be observed. The authors should have used a gradient with much denser sucrose, such as 10-50%. Thank you for pointing it out. We are sorry, it was a mistake. The gradient was actually 10-50%, and we described it wrong. We will correct it in the revised version.

(3) Also on the polysome profiling, as in the method section, the authors seemed to fractionate ultra-centrifuged samples from top to bottom and then measured A260 by a plate reader. In that case, the authors should have provided a line plot with individual data points, not the smoothly connected ones in the manuscript. Thank you for pointing it out. We will replace the graph.

(4) For both the results from polysome profiling and puromycin incorporation (Figure 4H and I), the difference between control siRNA and Milton siRNA are subtle, if not nonexistent. This might arise from the lack of spatial resolution in their experiment as the authors used head lysate for these data but the ratio of Phospho-eIF2α/eIF2α only changes in the axons, based on their results in Figure 4E-G. The authors could have attempted to capture the spatial resolution for the axonal translation to see the difference between control siRNA and Milton siRNA.

Thank you for your comment. A new set of experiments with technical challenges will be required to capture the spatial resolution for the axonal translation. We will work on it and hope to achieve it in the future.

Author Response

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

Reviewer #2 (Recommendations For The Authors):

I would like to thank the authors for their comments. However, my request for additional experiments to consolidate this manuscript and text changes have not been addressed (point 1 and point 2), which I believe are essential for completion of this manuscript.

The reviewer raised the question about the relevant substrates of PARG in S-phase cells (point 1). As we explained in our previous response, the most important substrate of PARG is PARP1, since we observed increased chromatin-associated PARP1 and PARylated PARP1 in cells with PARG depletion. Moreover, PARP1 or PARP1/2 depletion rescued cell lethality caused by PARG depletion. These data strongly suggest that PARP1 is the major substrate of PARG in S phase cells. Of course, PARG may have additional substrates. In the future, we will perform proteomics experiments as suggested by this reviewer to identify additional PARG substrates, which may reveal new roles of PARG in S phase progression.

The reviewer also suggested us to re-organize our manuscript (point 2). However, we prefer to keep the manuscript as it is, since this is how the project evolved. The other reason we would like to share with the readers is the challenge to validate KO cells. This is an important lesson we learned from this study. We hope that this will raise the awareness of hypomorphic mutant cells we often use to draw conclusions about gene functions and/or genetic interactions. We understand that the current flow of our manuscript may bring some confusion. To avoid it, we included additional explanations at the beginning of this manuscript to draw attention to the readers that our initial KO cells may not be complete PARG KO cells, i.e. they may have residual PARG activity. We also included additional discussion of this important point in the Discussion section.

Moreover, WB analysis of PARG KO clones is inconclusive, as the additional prominent band at 50 kDa could be a degradation product. The authors should check PARG levels are localization by IF, which allows detection of intact proteins and their cellular localizations, since the shorter isoform should be localized in the cytosol. WB with PARG isoforms is missing important information regarding Mw of the PARG constructs and Mw labels of western blots, which makes is difficult to evaluate this data and compare to KO. Ideally, KO and PARG isoform samples should be all on one gel for proper comparison with different antibodies.

We appreciate the concerns raised by this reviewer. We agree that the additional prominent band at 50kDa could be a degradation product. As we explained in our previous response, despite using several PARG antibodies, we could not draw a clear conclusion which functional isoforms or truncated forms were expressed in our PARG KO cells.

Immunostaining experiments may not be more conclusive, since IF experiments rely on the same antibodies for recognizing endogenous PARG. Additionally, even a protein mainly localizes in the cytosol, we cannot exclude the possibility that a small fraction of this protein may localize in nuclei and have nuclear functions.

Instead, as we presented in our manuscript, we used a biochemical assay to measure PARG activity in cell lysate and showed that our initial PARG KO cells still have residual PARG activity. However, we could not detect any PARG activity in our complete/conditional PARG KO cells (cKO cells; these cells can only survive in the presence of PARP inhibitor). These data strongly suggest that PARG is essential for cell survival.

Author Response

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

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The author should evaluate the possibility of naturally occurring arrhythmia due to the geometry of the tissues, by using voltage or calcium dye.

Answer: We thank the reviewer for this suggestion. We have performed new experiments using a voltage-sensitive fluorescent dye (i.e. FluoVolt) with data reported in the new Figure 4 + new results section “arrhythmia analysis”. Briefly, we found that our ring-shaped tissues are compatible with live fluorescence imaging. We were then able to show that our cardiac tissues beat regularly, without naturally occurring arrhythmias or extra beats. We could not detect any re-entrant waves in our tissues in the conditions offered by the speed of our camera. A specific paragraph has also been added to the discussion.

(2) There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. We are currently expanding to other cell lines with improvement in survival (see https://insight.jci.org/articles/view/161356). We confirm that the rings do not detach. The pillar was specifically designed to avoid this (See figure 1B).

As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance.

Answer: We propose a system and a measurement method that do not need calibration. Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A). There is thus no influence of the distance of the camera lens.

In order to evaluate the consistency of the mechanical properties of the hydrogel, we reproduced the experiment pictured in Figure1-Supplement 1, and measured the Young’s Modulus of three different gel solutions on different days. In the three experiments performed, we found values of 10.0-12.2 kPa, resulting in a final average value of 11.2 (+/- 0.6) kPa, coherent with the value reported in the article. We are therefore confident that the mechanical properties are consistent across and within wells. More extensive mechanical characterization of the molded gels would require the access to an Atomic Force Microscope (AFM), and is considered in the future.

The author should address the longevity and reproducibility issues, by working on the calibration of camera lens position/distance to tissues and further optimizing the seeding conditions with hydrogels such as collagen or fibrin, and/or making sure the PEG gels have high reproducibility and consistency.

Answer: This paper report seminal data that will serve as a foundation for further use of the platform. This platform (including the design, approach and choice of polymers) allows a fast and reproducible formation of an important number of cardiac tissues (up to 21 per well in a 96-well format, meaning a potential total of about 2,000 tissues) with a limited number of cells.

(3) The evaluation of the arrhythmia should be more extensively explained and demonstrated.

Answer : See answer to comment 1

(4) The results of isoproterenol should be checked as non-paced tissues should have increased beating frequency with increasing dosages. Dofetilide does not typically have a negative inotropic effect on the tissues. Please check on the cell viability before and after dosing

Answer : We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training. We agree that above a concentration of 10nM, dofetilide shows cardiotoxicity in our tissues as tissues completely stop beating.

Reviewer #2 (Recommendations For The Authors):

In addition to the general comments in the public review, I have the following specific suggestions to the authors, that would help improve the manuscript.

(1) Please describe the protocol for preparation of cardiac rings (shown in Figure 1C) in more detail. In particular, please describe how the tissues were transferred from the mold into the 96-well plate and how are they positioned and characterized during the study.

Answer: There is no transfer of the tissues as they directly form in the well, that is pre-equipped with the molded PEG gel (See Figure 1B and methods section). The in situ analysis is a strong asset of this platform.

(2) Please clarify the timepoints in this study. The overall schematic in Figure 1 C shows that the rings were formed on day 22 and then studied for 14 days, while Figure 2B shows data over 20 days following seeding, and Figure 3 shows data 14 days after seeding. It appears that these were separate studies (optimization of myocyte/fibroblast ratio followed by the main study.

Answer: Figure 1C is showing the timeline including the cardiomyocytes differentiation. hiPSC-CMs are indeed seeded in the wells 22 days after starting the differentiation, which represent the Day0 for tissue formation. We apologize for the confusion.

(3) Please explain if the number of rings per well (Figure 2) was used as the only criterion for selecting the myocyte/fibroblast ratio, and if so, why. Were these rings also characterized for their structural and contractile properties?

Answer: Figure 2 supplement 1 report the contractility data according to the different tested ratios, and show no differences. The number for generated ring-shaped tissues was indeed the only criterion retained.

(4) Please provide rationale for using the dermal rather than cardiac fibroblasts.

Answer: We had previous experience generating EHTs using dermal fibroblasts which are easier to obtain commercially. Our approach could in theory also work using cardiac fibroblasts, which we have not tested in the present study.

(5) Figure 2 panels C-E show an interesting segregation of cardiomyocytes into a thin cylindrical layer that does not appear to contain fibroblasts and a shorter and thicker cylinder containing fibroblasts mixed with occasional myocytes. Please specify at which time point this structure forms, and how does it change over time in culture? At which time point were the images taken? It would be helpful to include serial images taken over 1-14 days of study.

Answer: We thank the reviewer for this interesting comment. We have performed additional immunostainings (reported in Figure 2 supplement 3) on tissues at Day 1 and day 7 after seeding. The segregation appears in the 7 first days. It appears that 1 day after seeding the fibroblasts are not yet attached, although the cardiac fiber has already started to be formed. Seven days after seeding, fibroblasts are fully spread and attached, and the contractile ring is formed and well-aligned. Brightfield images are reported in Figure 1E.

(6) In the cardiomyocyte region (Figure 2D) the cells staining for troponin seem to be only at the surfaces. The thickness of the layer is only about 30-40 µµ, so one would assume that cell viability was not an issue. Please specify and discuss the composition of this region.

Answer: We agree but we think this is a technical issue as at the center of the tissue, tissue thickness will limit laser penetration, although at the surface (inner our outer), the laser infiltrates easily between the tissue and the PEG. Moreover, we see on the zoomed view of the tissue in Figure 2 Supplement 2 that we have a staining inside the cardiac fiber, which just appears less strong due to tissue thickness.

(7) Please also discuss segregation in terms of possible causes and the implications of apparently very limited contact between the two cell types, i.e., how representative is this two-region morphology of native heart tissue. Also, it would be interesting to know how the segregation has changed with the change in myocyte/fibroblast ratio.

Answer: We are not sure there is a very limited contact as the use of fibroblasts is critical to ensure the formation of tissues (i.e. no tissues can be formed if we avoid the use of fibroblasts). We agree that these ring-shaped cardiac tissues are not especially representative of a native heart tissue in terms of interactions between several cell types. They were developed as a surrogate for physiopathological and pharmacological experiments (see a recent application in https://insight.jci.org/articles/view/161356)

(8) There is interest and demonstrated ability to culture engineered cardiac tissues over longer periods of time. Please comment what was the rationale for selecting 14-day culture and if the system allows longer culture durations.

Answer: In line with this comment, we have studied the contractile parameters of our rings 28 days after seeding and compared to their contractile parameters at D14. We found a slight increase for all the parameters, which is significant for the maximum contraction speed. Nevertheless, the data is much more variable and the number of tissues is lower (29 for D14 against 17 for D28). Therefore, we demonstrated that long-term culture of our tissues is possible, however not yet optimized. Hence, the following physiological and pharmacological tests have been done at D14.

(9) Figure 3 documents the development of contractile parameters over 14 days of culture. Would it be possible to replace the arbitrary units with the actual values? Also, would it be possible to include the corresponding images of the rings taken at the same time points, to show the associated changes in ring morphologies.

Answer: Contraction amplitude is expressed as a ratio between the contracted / relaxed areas (See figure 3 A): it is a ratio, thus without unit. Corresponding images can be seen in Figure 1 E.

(10) The measured contraction stress, strain, and the speeds of contraction and relaxation improve from day 1 to day 7 and then plateau (Figure 3, Supplemental Figure 3. Please discuss this result.

Answer: The new immunostainings performed on tissues at Day 1 and Day 7 show the progressive alignment of the cardiomyocytes and the muscular fibers, with an almost complete organization at Day 7.

(11) The beating frequency does not appear to markedly change over time, while Figure 3B shows strong statistical significance (***) throughout the 14-day period. Please check/confirm.

Answer: We confirm this result.

(12) Please comment on the lack of effect of isoproterenol on beating frequency.

Answer: We agree with this reviewer on the principle. However, we have repeated the experiments and we confirm our results, i.e. increasing concentrations of isoproterenol induced a trend towards increase in the contraction force and significantly increased contraction and relaxation speeds without change in the beat rate (Figure 5C). We do not have a definitive explanation for this observation. Our hypothesis is that this increase in contraction and relaxation speeds induced by isoproterenol is translated, on average in our study, into an increase in contractile force rather than in an increase in contraction frequency. This may depend on the cell line used, and is very well illustrated in a recent paper from Mannhardt and colleagues (Stem cell reports. 2020; 15(4):983–998). Of the 10 different cell lines tested in engineered heart tissues, all show an increase in contraction and relaxation speeds after isoproterenol administration, but this is translated either into an increase in contractile force (4 cell lines) or into a shortening of the beat (3 cell lines), and only 2 cell lines show an increase in both parameters. Indeed, since iPSC-CMs are immature cardiac cells, it is rare to obtain a positive force-frequency relationship without any maturation medium or mechanical or electrical training.

(13) Please compare the contractile function of cardiac tissues measured in this study with data reported for other iPSC-derived tissue models.

Answer : A specific paragraph tackles this aspect in the discussion

Author Response

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

Public Reviews

We thank the reviewers for their insightful comments and helpful suggestions that allowed us to improve the manuscript.

Reviewer #1:

Thermogenic adipocyte activity associate with cardiometabolic health in humans but decline with age. Identifying the underlying mechanisms of this decline is therefore highly important.

To address this task, Holman and co-authors investigated the effects of two major determinants of thermogenic activity: cold, which induce thermogenic de novo differentiation as well as conversion of dormant thermogenic inguinal adipocytes: and aging, which strongly reduce thermogenic activity. The authors study young and middle-aged mice at thermoneutrality and following cold exposure.

Using linage tracing, the authors conclude that the older group produce less thermogenic adipocytes from progenitor differentiation. However, they found no differences between thermogenic differentiation capacity between the age groups when progenitors are isolated and differentiated in vitro. This finding is consistent with previous findings in humans, demonstrating that progenitor cells derived from dormant perirenal brown fat of humans differentiate into thermogenic adipocytes in vitro. Taken together, this underscores that age-related changes in the microenvironment rather than autonomous alterations in the ASPCs explain the age-related decline in thermogenic capacity. This is an important finding in terms of identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype.

To gain insight into the age-related changes, the authors use single cell and single nuclei RNA sequencing mapping of their two age groups, comparing thermoneutral and cold conditions between the two groups. Interestingly, where the literature previously demonstrated that de novo lipogenesis (DNL) occurs in relation to thermogenic activation, the authors show that DNL in fact is activated in a white adipocyte cell type, whereas the beige thermogenic adipocytes form a separate cluster.

Considering recent findings, that adipose tissue contains several subtypes of ASPCs and adipocytes, mapping the changes at single cell resolution following cold intervention provides an important contribution to the field, in particular as an older group with limited thermogenic adaptation is analyzed in parallel with a younger, more responsive group. This model also allowed for detection of microenvironment as a determining factor of thermogenic response.

The use of only two time points (young and middle-aged) along the aging continuum limits the conclusions that can be made on aging as the only driver of the observed differences between the groups. It should for example be noted that the older mice had higher weights and larger fat depots, thus the phenotype is complex and this should be taken into consideration when interpreting the data.

In conclusion, this study provides an important resource for further studies on how to reactivate dormant thermogenic fat and potentially improve metabolic health.

(1) The authors claim "Aging impairs cold-induced beige adipogenesis and adipocyte metabolic reprogramming". It is previously established in humans that aging strongly associate with a decline in thermogenic capacity. With this in mind, it is easy to accept that the reduced browning observed in the older group is due to age. However, the older group also have larger adipose depots, which also can be a confounding factor. I, therefore, recommend bringing this into the discussion and putting more focus on the complexity of the phenotype. For example, it could be discussed whether the de novo lipogenesis less due to that the adipocytes of older mice is already filled with more lipids. Additional time points along the aging continuum would be needed to make a strong conclusion about age as the determinant, but even so, aging is complex and further definitions and discussion would be needed.

We agree with the reviewer regarding the confounding effect of body weight changes. We have added a paragraph to the discussion (pasted below) to comment on the complexity of the phenotype and the contributing role of linked changes in body weight/composition.

“Aging is a complex process, and unsurprisingly, many pathways have been linked to the aging-related decline in beiging capacity. For example, increased adipose cell senescence, impaired mitochondrial function, elevated PDGF signaling and dysregulated immune cell activity during aging diminish beige fat formation (Benvie et al., 2023; Berry et al., 2017; Goldberg et al., 2021; Nguyen et al., 2021). Of note, older mice exhibit higher body and fat mass, which is associated with metabolic dysfunction and reduced beige fat development. While the effects of aging and altered body composition are difficult to separate, previous studies suggest that the beiging deficit in aged mice is not solely attributable to changes in body weight (Rogers et al., 2012). Further studies, including additional time points across the aging continuum may help clarify the role of aging and ascertain when beiging capacity decreases.”

(2) The study would gain from more comparisons to existing human studies and discussion on the translation potential of the findings. For example, how does the adipocyte subtypes identified in the current study translate to subtypes identified in human adipose tissue (e.g. Emont et al).

We analyzed the human adipose tissue atlas from Emont et al. 2022 (PMID: 35296864). We did not find any obvious homologous human adipocyte subtypes. However, this and other available human single cell studies have not investigated the effects of cold exposure on white adipose tissue depots, which may be necessary to reveal DNL-high and especially beige adipocytes.

(3) The group has contributed multiple studies demonstrating that Prdm16 is a major inducer of a thermogenic phenotype, and the literature shows that Prdm16 promote a thermogenic phenotype in favour of a fibrogenic aging phenotype. It would therefore be interesting to see how Prdm16 is regulated in the current data set, across adipocytes subtypes, age groups and temperature conditions.

We thank the reviewer for this comment. Previous studies showed that PRDM16 protein and not mRNA levels are downregulated during aging (Wang et al., 2019, Cell Metab, PMID: 31155495; Wang et al., 2022, Nature, PMID: 35978186). Consistent with this, we did not observe an agingassociated reduction in Prdm16 mRNA levels in adipocytes in our dataset. We did observe enrichment of Prdm16 mRNA levels in beige adipocytes relative to other adipocyte clusters. We included these data in Fig. 5F.

(4) In Figure 1, it is difficult to understand why the 6 weeks cold exposure is not shown in relation to the thermoneutrality, 3 days and 2-week cold exposure? It would be useful to have this in the same graph relating the levels and showing all four marker genes for all time points.

These experiments were done at different times using separate groups of mice. We have now clarified this in the figure legend.

(5) The older mice had larger inguinal fat depots, suggesting more lipids stored. The morphology of adipose tissue has previously been shown to be modulated by cold acclimation and is also the main similarity between brown adipose tissue in adult humans and young mice beige adipose tissue. Fig S2b suggests smaller adipocytes in the young group. It would also be useful, for comparison to published data, if authors show tissue sections with H&E of their model.

Good point. We added panels showing H&E staining of serial iWAT sections, showing changes in tissue morphology across age and temperature conditions (Figure S1F).

(6) The authors use t-tests to compare the differences induced by e.g. cold or min vs max cell culture media etc, within each age group. However, in my opinion, a two-way Anova with post-tests would be more informative as this would allow for testing the effects of the two age categories on any quantitative variable and allow for addressing whether there is an interaction between the categories.

Following the reviewer’s recommendation, we applied two-way ANOVA with a Tukey correction for multiple comparisons for categorical comparisons with different age groups and conditions. P values from all significant multiple comparison tests are now included within the methods section.

(7) In Figure 5F, please include Adipoq expression between clusters and please add a reference to why Nnat is considered a canonical white adipocyte marker.

We added Adipoq to the violin plot in Figure 5F, showing differential expression across adipocyte clusters. We included a line in the results section to highlight this observation:

“Interestingly, Adiponectin (Adipoq) was differentially expressed across adipocyte clusters, with higher levels in Npr3-high and DNL-high cells.”

We removed “canonical” and added references for Nnat and Lep as white marker genes.

(8) After 14 days of cold exposure, it looks like the DNL high population divides into two populations, did the authors explore if there was any differences between these clusters?

We also noticed this apparent division and explored this question. However, upon increasing the resolution for clustering and splitting the DNL high population, there were no obvious differentially expressed genes that defined the two subclusters. Thus, we opted to keep them together.

(9) As cold treatment transform a subset of cells, can authors perform a data-driven analysis to visualize the directions in their single nuclei data sets by using monocle pseudotime and/or velocity analyses?

This is a good question. We spent a long time trying to address this question using several trajectory and pseudotime analysis methods, including Velocity (scVelo), Slingshot and Dynoverse. Unfortunately, we were unable to obtain concordant results using at least two different methods and felt that the analyses were unreliable.

Reviewer #2:

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice.

Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

(1) It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.

This is a good question. We did not evaluate beige adipogenesis at the 3d timepoint. However, a previous study demonstrates that 3d of cold exposure is sufficient to promote de novo beige adipogenesis (Wang et al., Nat Med. 2013, PMID: 23995282). We observed that beige adipogenesis from Pdgfra+ cells are a relatively minor contributor to beige adipocyte development, even after long term cold exposure in young mice. Based on these data, we presume that beige adipocyte activation (or re-activation) is the dominant mechanism for beige adipocyte development.

To clarify this point, we have included the following lines in the manuscript:

“Previous studies in mice using an adipocyte fate tracking system show that a high proportion of beige adipocytes arise via the de novo differentiation of ASPCs as early as 3 days of cold (Wang et al., 2013).”

“Based on these findings, we presume that mature (dormant beige) adipocytes serve as the major source of beige adipocytes in our cold-exposure paradigm. However, long-term cold exposure also recruits smooth muscle cells to differentiate into beige adipocytes; a process that we did not investigate here (Berry et al., 2016; Long et al., 2014; McDonald et al., 2015; Shamsi et al., 2021).”

(2) Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

We presented FACS quantification of tdTomato+/Pdgfra+ cells in Fig. 2B. We added a graph showing the percentage of Pdgfra+ cells of total live, lin- cells in adipose tissue; this showed no difference between young and aged mice. We did not perform FACS quantification of tdTomato+ beige adipocytes due to the technical challenges with sorting adipocytes. Quantification of total tdTomato+ cells was also unreliable and inconsistent due to the widespread labeling of fibroblasts, blood vessels, along with traced adipocytes. Thus, we did not include this analysis.

(3) Line 112, the sentence seems to be not finished.

This has been corrected.

Author Response

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

Response to Reviewers’ Public Comments

We are grateful for the reviewers’ comments. We have modified the manuscript accordingly and detail our responses to their major comments below.

(1) Reviewer 2 was concerned that transformation of continuous functional data into categorical form could reduce precision in estimating the genetic architecture.

We agree that transforming continuous data into categories may reduce resolution, but it also improves accuracy when the continuous data are affected by measurement noise. In our dataset, many genotypes are at the lower bound of measurement, and the variation in measured fluorescence among these genotypes is largely or entirely caused by measurement noise. By transforming to categorical data, we dramatically reduced the effect of this noise on the estimation of genetic effects. We modified the results and discussion sections to address this point.

(2) Reviewer 2 asked about generalizability of our findings.

Because our paper is the first use of reference-free analysis of a 20-state combinatorial dataset, generalizability is at this point unknown. However, a recent manuscript from our group confirms the generality of the simplicity of genetic architecture: using reference-free methods to analyze 20 published combinatorial deep mutational scans, several of which involve 20-state libraries, we found that main and pairwise effects account for virtually all of the genetic variance across a wide variety of protein families and types of biochemical functions (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057). Concerning the facilitating effect of epistasis on the evolution of new functions, we speculate that this result is likely to be general: we have no reason to think that the underlying cause of this observation – epistasis brings genotypes with different functions closer in sequence space to each other and expands the total number of functional sequences – arises from some peculiarity of the mechanisms of steroid receptor DBD folding or DNA binding. However, we acknowledge that our data involve sequence variation at those sites in the protein that directly mediate specific protein-DNA contact; it is plausible that sites far from the “active site” may have weaker epistatic interactions and therefore have weaker effects on navigability of the landscape. We have addressed these issues in the discussion.

(3) Reviewer 3 asked “in which situation would the authors expect that pairwise epistasis does not play a crucial role for mutational steps, trajectories, or space connectedness, if it is dominant in the genotype-phenotype landscape?”

The question addressed in our paper is not whether epistasis shapes steps, trajectories or connectedness in sequence space but how it does so and what its particular effects are on the evolution of new functions. The dominant view in the field has been that the primary role of epistasis is to block evolutionary paths. We show, however, that in multi-state sequence space, epistasis facilitates rather than impedes the evolution of new functions. It does this by increasing the number of functional genotypes and bringing genotypes with different functions closer together in sequence space. This finding was possible because of the difference in approach between our paper and prior work: most prior work considered only direct paths in a binary sequence space between two particular starting points – and typically only considering optimization of a single function – whereas we studied the evolution of new functions in a multi-state amino acid space, under empirically relevant epistasis informed by complete combinatorial experiments. The result is a clear demonstration that the net effect of real-world levels of epistasis on navigability of the multidimensional sequence landscape is to make the evolution of new functions easier, not harder.

(4) Reviewer 3 asked for “an explanation of how much new biological results this paper delivers as compared with the paper in which the data were originally published.”

Starr 2017 did not use their data to characterize the underlying genetic architecture of function by estimating main and epistatic effects of amino acid states and combinations; it also did not evaluate the importance of epistasis in generating functional variants, determining the transcription factor’s specificity, or shaping evolutionary navigability on the landscape.

(5) Reviewer 3 requested an explanation of how the results would have been (potentially) different if a reference-based approach were used, and how reference-based analysis compares with other reference-free approaches to estimating epistasis.

This topic has been covered in detail in a recent manuscript from our group (Park et al. Biorxiv 2023.09.02.556057). Briefly, reference-free approaches provide the most efficient explanation of an entire genotype-phenotype map, explaining the maximum amount of genetic variance and reducing sensitivity to experimental noise and missing genotypes compared to reference-based approaches. Reference-based approaches tend to infer much more epistasis, especially higher-order epistasis, because measurement error and local idiosyncrasy near the wild-type sequence propagate into spurious high-order terms. Reference-based analyses are appropriate for characterizing only the immediate sequence neighborhood of a particular “wild-type” protein of interest. Reference-free approaches are therefore best suited to understanding genotype-phenotype landscapes as a whole. We have clarified these issues in the revised discussion.

(6) Reviewer 3 suggested that the comparison between the full and main-effects-only model should involve a re-estimation of main effects in the latter case.

This is indeed what we did in our analysis. We have clarified the description in the results and methods sections to make this clear.

(7) Reviewer 3 asked about the applicability of the approach to data beyond those analyzed in the present study and requirements to use it.

Our approach could be used for any combinatorial DMS dataset in which the phenotypic data are categorical (or can be converted to categorical form). Complete sampling is not required: a virtue of reference-free analysis is that by averaging the estimated effects of states and combinations over all variants that contain them, reference-free analysis is highly robust to missing data (except at the highest possible order of epistasis, where only a single variant represents a high-order effect) as long as variant sampling is unbiased with respect to phenotype. All the required code are publicly available at the github link provided in this manuscript. We have also described a general form of reference-free analysis for continuous data and applied it to 20 protein datasets in a recent publication (Park et al. Biorxiv 2023.09.02.556057).

(8)Reviewer 3 suggested that the text could be shortened and made less dense.

We agree and have done a careful edit to streamline the narrative.

Response to Reviewers’ Non-Public Recommendations

(1) Reviewer 1 noted that specific epistatic effects might in some cases produce global nonlinearities in the genotype-phenotype relationship. They then asked how our results might change if we did not impose a nonlinear transformation as part of the genotype-phenotype model. The reviewer’s underlying concern was that the non-specific transformation might capture high-order specific epistatic effects and thus reducing their importance.

Because our data are categorical, we required a model that characterizes the effect of particular amino acid states and combinations on the probability that a variant is in a null, weak, or strong activation class. A logistic model is the classic approach to this kind of analysis. The model structure assumes that amino acid states and combinations have additive effects on the log-odds of being in one functional class versus the lower functional class(es); the only nonlinear transformation is that which arises mathematically when log-odds are transformed into probability through the logistic link function. Thinking through the reviewer’s comment, we have concluded that our model does not make any explicit transformation to account for nonlinearity in the relationship between the effects of specific sequence states/combinations and the measured phenotype (activation class). If additional global nonlinearities are present in the genotype-phenotype relationship – such as could be imposed by limited dynamic range in the production of the fluorescence phenotype or the assay used to measure it – it is possible that the sigmoid shape of the logistic link function may also accommodate these nonlinearities. We have noted this part in the revised manuscript.

(2) Reviewer 1 observed that our model seems to prefer sets of several pairwise interactions among states across sites rather than fewer high-order interactions among those same states.

This finding arises because the pattern of phenotypic variation across genotypes in our dataset is consistent with that which would be produced by pairwise interactions rather than by high-order interactions. In a reference-free framework, these patterns are distinct from each other: a group of second-order terms cannot fit the patterns produced by high-order epistasis, and high-order terms cannot fit the pattern produced by pairwise interactions. Similarly, main-effect terms cannot fit the pattern of phenotypes produced by a pairwise interaction, and a pairwise epistatic term cannot fit the pattern produced by main effects of states at two sites. For example, third-order terms are required when the genotypes possessing a particular triplet of states deviate from that expected given all the main and second-order effects of those states; this deviation cannot be explained by any combination of first- and second-order effects.

We explain this point in detail in our recent manuscript (Park Y, Metzger BPH, Thornton JW. 2023. The simplicity of protein sequence-function relationships. BioRxiv, 2023.09.02.556057) and we summarize it here. Consider the simple example of two sites with two possible states (genotypes 00, 01, 10, and 11). If there are no main effects and no pairwise effects, this architecture will generate the same phenotype for all four variants – the global average (or zero-order effect). If there are pairwise effects but no main effects, this architecture will generate a set of phenotypes on which the average phenotype of genotypes with a 0 at the first site (00 and 01) equals the global average – as does the average of those with 0 at the second site (00 and 10). The epistatic effect causes the individual genotypes to deviate from the global average. This pattern can be fit only by a pairwise epistatic term, not by first-order terms. Conversely, if there are main effects but no pairwise effects, then the average phenotype of genotypes 00 and 01 will deviate from the global average (by an amount equal to the first-order effect), as will the average of (00 and 10): the phenotype of each genotype will be equal to the sum of the relevant first-order effects for the state it contains. This pattern cannot be fit by second-order model terms. The same logic extends to higher orders: a cluster of second-order terms cannot explain variation generated by third-order epistasis, because third-order variation is by definition is the deviation from the best second-order model.

(3) Reviewer 1 suggested several places in the text where citations to prior work would be appropriate.

We appreciate these suggestions and have modified the manuscript to refer to most of these works.

(4) Reviewer 1 pointed to the paper of Gong et al eLife 2013 and asked whether it is known how robust the proteins in our study are to changes in conformation/stability compared to other proteins, and whether this might impact the likelihood of observing higher-order epistasis in this system.

The DBDs that we study here are very stable, and previous work shows that mutations affect DNA specificity primarily by modifying the DBD’s affinity rather than its stability (McKeown et al., Cell 2014). Additionally, Gong et al.’s findings pertain to a globally nonlinear relationship between stability and function, which arises from the Boltzmann relationship between the energy of folding and occupancy of the folded state. Because our data are categorical – based on rank-order of measured phenotype rather than fluorescence as a continuous phenotype – the kind of global nonlinearity observed in Gong’s study are not expected to produce spurious estimates of epistasis in our work. We have modified the discussion to discuss the point.

(5) Reviewer 1 asked a) why the epistatic models produce landscapes on which variants have fewer neighbors on average than main-effects only models and b) why the average distance from all ERE-specific nodes to all SRE-specific nodes is greater with epistasis (but the average distance from ERE to nearest SRE is lower with epistasis).

In the main effects-only landscape, the functional genotypes are relatively similar to each other, because each must contain several of the states that contribute the most to a positive genetic score. Moreover, ERE-specific nodes are similar to each other, and SRE-specific nodes are similar to each other, because each must contain one or more of a relatively small number of specificity-determining states. When epistasis is added to the genetic architecture, two things happen: 1) more genotypes become functional because there are more combinations that can exceed the threshold score to produce a functional activator and 2) these additional functional variants are more different from each other – in general, and within the classes of ERE- or SRE-specific variants – because there are now more diverse combinations of states that can yield either phenotype. As a result, a broader span of sequence space is occupied, but ERE- and SRE-specific variants are more interspersed with each other. This means that the average distance between all pairs of nodes is greater, and this applies to all ERE-SRE pairs, as well. However, the interspersing means that the closest single SRE to any particular ERE is closer than it was without epistasis. We have added this explanation to the main text.

(6) Reviewer 2 asked us to explain why average path length increases with pairwise epistasis as the strength of selection for specificity increases.

This behavior occurs because of the existence of a local peak in the pairwise model. Genotypes on this peak contained few connections to other genotypes, all of which were less SRE specific. Thus, with strong selection, i.e. high population size, the simulations became stuck on the local peak, cycling among the genotypes many times before leaving, resulting in a large increase in the mean step number. As shown in the rest of the figure, when the longest set of paths are removed, there are still differences in the average number of steps with and without epistasis. This issue is described in the methods section.

(7) Reviewers made several suggestions for clarity in the text and figures.

We have modified the paper to address all of these comments.

(8) Reviewer 3 stated that the code should be available.

The code is available at https://github.com/JoeThorntonLab/DBD.GeneticArchitecture.

Author Response

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

Reviewer #1 (Public Review):

Summary:

The authors were trying to understand the relationship between the development of large trunks and longirrostrine mandibles in bunodont proboscideans of Miocene, and how it reflects the variation in diet patterns.

Strengths:

The study is very well supported, written, and illustrated, with plenty of supplementary material. The findings are highly significant for the understanding of the diversification of bunodont proboscideans in Asia during Miocene, as well as explaining the cranial/jaw disparity of fossil lineages. This work elucidates the diversification of paleobiological aspects of fossil proboscideans and their evolutionary response to open environments in the Neogene using several methods. The authors included all Asian bunodont proboscideans with long mandibles and I suggest that they should use the expression "bunodont proboscideans" instead of gomphotheres.

Weaknesses:

I believe that the only weakness is the lack of discussion comparing their results with the development of gigantism and long limbs in proboscideans from the same epoch.

Thank you for your comprehensive review and positive feedback on our study regarding the co-evolution of feeding organs in bunodont proboscideans during the Miocene. We appreciate your suggestion, and have decided to use the term "bunodont elephantiforms" (for more explicit clarification, we use elephantiforms to exclude some early proboscideans, like Moeritherium, ect.) instead of "gomphotheres," and we will make this change in our revised manuscript. We also appreciate the potential weakness you mentioned regarding the lack of discussion comparing our results with the development of gigantism and long limbs in proboscideans from the same epoch. We agree with the reviewer’s suggestion, and we are aware that gigantism and long limbs are potential factors for trunk development. Gigantism resulted in the loss of flexibility in elephantiforms, and long limbs made it more challenging for them to reach the ground. A long trunk serves as compensation for these limitations. limb bones were rare to find in our material, especially those preserved in association with the skull.

Reviewer #2 (Public Review):

This study focuses on the eco-morphology, the feeding behaviors, and the co-evolution of feeding organs of longirostrine gomphotheres (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) which are characterised by their distinctive mandible and mandible tusk morphologies. They also have different evolutionary stages of food acquisition organs which may have co-evolve with extremely elongated mandibular symphysis and tusks. Although these three longirostrine gomphothere families were widely distributed in Northern China in the Early-Middle Miocene, the relative abundances and the distribution of these groups were different through time as a result of the climatic changes and ecosysytems.

These three groups have different feeding behaviors indicated by different mandibular symphysis and tusk morphologies. Additionally, they have different evolutionary stages of trunks which are reflected by the narial region morphology. To be able to construct the feeding behavior and the relation between the mandible and the trunk of early elephantiformes, the authors examined the crania and mandibles of these three groups from the Early and Middle Miocene of northern China from three different museums and also made different analyses.

The analyses made in the study are:

(1) Finite Element (FE) analysis: They conducted two kinds of tests: the distal forces test, and the twig-cutting test. With the distal forces test, advantageous and disadvantageous mechanical performances under distal vertical and horizontal external forces of each group are established. With the twig-cutting test, a cylindrical twig model of orthotropic elastoplasity was posed in three directions to the distal end of the mandibular task to calculate the sum of the equivalent plastic strain (SEPS). It is indicated that all three groups have different mandible specializations for cutting plants.

(2) Phylogenetic reconstruction: These groups have different narial region morphology, and in connection with this, have different stages of trunk evolution. The phylogenetic tree shows the degree of specialization of the narial morphology. And narial region evolutionary level is correlated with that of character-combine in relation to horizontal cutting. In the trilophodont longirostrine gomphotheres, co-evolution between the narial region and horizontal cutting behaviour is strongly suggested.

(3) Enamel isotopes analysis: The results of stable isotope analysis indicate an open environment with a diverse range of habitats and that the niches of these groups overlapped without obvious differentiation.

The analysis shows that different eco-adaptations have led to the diverse mandibular morphology and open-land grazing has driven the development of trunk-specific functions and loss of the long mandible. This conclusion has been achieved with evidence on palaecological reconstruction, the reconstruction of feeding behaviors, and the examination of mandibular and narial region morphology from the detailed analysis during the study.

All of the analyses are explained in detail in the supplementary files. The 3D models and movies in the supplementary files are detailed and understandable and explain the conclusion. The conclusions of the study are well supported by data.

We appreciate your detailed and insightful review of our study. Your summary accurately captures the essence of our research, and we are pleased to note that multiple research methods were used to demonstrate our conclusions. Your recognition of the evidence-based conclusions from paleoecological, feeding behavior reconstruction, and morphological analyses reinforces the validity of our findings. Once again, we appreciate your time and thoughtful reviews.

Reviewer #1 (Recommendations For The Authors):

Thank you very much for the invitation to review this amazing manuscript. It is very well written and supported, and I have only minor suggestions to improve the text:

(1) Some references are not in chronological sequence in the text, and this should be reviewed.

We greatly appreciate the positive comments of the reviewer. We revised the reference of the manuscript as the reviewer’s suggestion.

(2) I suggest the use of the expression "bunodont proboscideans" instead of Gomphotheres because there is no agreement if Amebelodontidae and Choerolophodontidae are within Gomphotheriidae, as well as some brevirrostrine bunodont proboscideans from South America. So I think it is ok to use "Gomphotheriidae", but not gomphotheres to refer to all bunodont proboscideans included in the study.

The reviewer is correct. Using “gomphotheres” to refer to these three groups is inappropriate. We have replaced “gomphotheres” with "bunodont elephantiforms" throughout the entire manuscript. Here, we use “elephantiforms”, not “proboscideans”, to avoid confusion with some early proboscidean members like Moeritherium, ect.

(3) I was expecting some discussion on the development of large trunks related to the gigantism in these bunodont proboscideans, regarding the huge skulls and the columnar limbs.

We appreciate this suggestion, and we are aware that gigantism is a potential factor for trunk development. It is difficult to compare the three groups (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) in terms of their weight and limb bone length, because in our material, limb bones were rarely found, especially those associated with cranial material. Nevertheless, at this stage, all elephantiforms had significantly enlarged cranial sizes and limb bone lengths compared to early members like Phiomia. Gigantism caused the loss of flexibility in elephantiforms, and even the long limbs made it more difficult for an elephantiform to reach the ground. A long trunk compensates for this evolutionary change. Exploring these aspects further is a part of our future work.

(4) The reference to Alejandro et al should be replaced by Kramarz et al (and the correct surname of the authors). The name and surname of this reference need to be corrected. The correct names are Kramarz, A., Garrido, A., Bond, M. 2019. Please correct this in the text too.

We thank the reviewer for catching this error. This reference has been corrected.

Reviewer #2 (Recommendations For The Authors):

I believe your paper will lead to other studies on other Proboscidean groups on the evolution of the mandible and trunk. There are some corrections in the text:

• In line 199 in the text in pdf, "Tassy, 1994" should be "Tassy, 1996".

• In line 241, "studied" should be "studies"

• In line 313, "," after the word "tool" should be "."

We appreciate the reviewer for pointing these errors out and have revised these based on the suggestions.

• In the References, you write "et al." in some references. You should write the names of all of the authors.

• In the References: "Lister AM. 2013" and "Shoshani&Tassy" are not referenced in the text.

• In the References: "Tassy P. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn." should be "Tassy P. 1994. Gaps, parsimony, and early Miocene elephantoids (Mammalia), with a re-evaluation of Gomphotherium annectens (Matsumoto, 1925). Zool. J. Linn. 112, 1-2, 101-117" and replaced before "Tassy P. 1996".

We appreciate the reviewer’s suggestions and have revised these references.

Author Response

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

Reviewer #1

The authors provided experimental data in response to my comments/suggestions in the revision. Overall, most points were appropriate and satisfactory, but some issues remain.

(1) It is not fully addressed how atypical survivors are generated independently of Rad52-mediated homologous recombination.

The newly provided data indicate that the formation of atypical telomeres is independent of the Rad52 homologous recombination pathway.

"The atypical telomeres clones exhibit non-uniform telomere pattern", but the TG-hybridized signals after XhoI digestion are clear and uniform.

"Atypical telomere" clones may carry circular chromosomes embedded with short TG repeats, rather than linear chromosomes. In other words, atypical telomeres may differ from telomeres, the ends of chromosomes. Is atypical telomere formation dependent on NHEJ? Given that "two chromosomes underwent intra-chromosomal fusions" (Line 248), are atypical telomere clones detected frequently in SY13 cells containing two chromosomes?

We thank the reviewer’s questions. Frankly, we have not been able to determine the chromosome structures in these so-called "atypical survivors". As we mentioned in the manuscript, there could be mixed telomere structures, e.g. TG tract amplification, intro-chromosome telomere fusion and inter-chromosome telomere fusion. Worse still, these 'atypical survivors' may not have maintained a stable genome, and their karyotype may have undergone stochastic changes during passages. To avoid misunderstanding, we change the term "atypical" to "uncharacterized" in the revised manuscript.

We have previously shown that deletion of YKU70 does not affect MMEJ-mediated intra-chromosome fusion in single-chromosome SY14 cdc13Δ cells (Wu et al., 2020). In SY12 cells, double knockout of TLC1 and YKU resulted in synthetic lethality, and we were unable to continue our investigation. The result of synthetic lethality of TLC1 and YKU70 double deletion was shown in the Figure 7B in the reviewed preprint version 1, and the result was not included in the reviewed preprint version 2 in accordance with the reviewer's instructions.

"Atypical” survivors could be detected in SY13 cells (Figure 1D), but the frequency of their formation in the SY13 strain appeared to be lower than in SY12. As one can imagine, SY13 contains two chromosomes and its survivors should have a higher frequency of intra-chromosome fusions.

(2) From their data, it is possible that X and Y elements influence homologous recombination, type 1 and type 2 (type X), at telomeres. In particular, the presence of X and Y elements appears to be important for promoting type 1 recombination. In other words, although not essential, subtelomeres have some function in maintaining telomeres. I suggest that the authors include author response image 4 in the text. They could revise their conclusion and the paper title accordingly.

According to this suggestion, we have included author response image 4 in the revised manuscript as Figure 2E, Figure 5D, Figure 6C and Figure 6E. Accordingly, we have changed the title as “Elimination of subtelomeric repeat sequences exerts little effect on telomere essential functions in Saccharomyces cerevisiae”.

(3) Minor points: The newly added data indicate that X survivors are generated in a type 2-dependent manner. The authors could discuss how Y elements were eroded while retaining X elements (line 225, Figure 2A).

Thank this reviewer’s suggestion. We have discussed it in the revised manuscript (p.13 line 244-245). When telomere was deprotected, chromosome end resection took place. Since SY12 only has one Y’-element, it is hard to search homology sequences to repair the Y’-element in XVI-L. When the X-element in XVI-L was exposed by further resection, it is easier to find homology sequences to repair. So, in Type X survivor the Y’-element was eroded while retaining X-element.

Reviewer #2

I would like to congratulate the authors for their work and the efforts they put in improving the manuscript. The major criticism I had previously, ie testing the genetic requirements for the survivor subtypes, has been met. Below are a few minor comments that don't necessarily require a response.

(1) I think the Author response image 6 could have been included in the manuscript. I understand that the authors don't want to overinterpret survivor subtype frequencies, but this figure would have suggested some implication of Rad51 in the emergence of survivors even in the absence of Y' elements. At this stage, however, it is up to the authors, and leaving this figure out is also fine in my opinion.

According to the suggestion, the author response image 6 has been presented as Figure 6—figure supplement 7.

(2) Chromosome circularization seems to rely on microhomologies. Previously, the authors proposed that SY14 circularization depended on SSA (Wu et al. 2020), but here, since circularization appears to be Rad52-independent, it is likely to be based on MMEJ rather than SSA (although there are contradictory results on Rad52's role in MMEJ in the literature).

Yes, we mentioned it in the revised manuscript.

(3) p. 28 lines 511-513: "The erosion sites and fusion sequences differed from those observed in SY12 tlc1Δ-C1 cells (Figure 2D), suggesting the stochastic nature of chromosomal circularization": I don't think they are necessarily stochastic, because the sequences beyond the telomeres are now modified, the available microhomologies have changed as well.

We agreed with your opinion. In different chromosomes, there tend to be some hotspots for chromosome fusion. For example, in Figure 6C and 6F the resection site in Chr1 and Chr2 was the same in SY12XYΔ+Y tlc1Δ-C1 and SY12XYΔ tlc1Δ-C1. So, we speculate that there are some hotspots for chromosome fusion, but which site the cell will choose in one round chromosome fusion event is stochastic.

(4) Typos and other errors:

• p. 3 line 52: "subtelomerice" and "varies" are mispelled.

• p. 5 line 78: "processes" should be "process".

• Supp files are mislabelled (the numbers do not correspond to file name).

• Supp file 2: how come SY12 has only one Y' element and SY13 has two?

• p. 10 line 175: "emerging" should be "emergence".

• p.15 line 276: "counter-selected" should be "being counter-selected" or "counterselection".

• p. 29 line 523: "the formation of them" should be "their formation".

• p. 37 line 653: "could have been an ideal tool": the sentence is grammatically incorrect. Writing "AND could have been an ideal tool" is enough to make it structurally correct.

Thanks for pointing these errors out. We have corrected them in the revised manuscript. For the question “how come SY12 has only one Y' element and SY13 has two?” we were not sure at this moment. We speculated that one of the Y’ might be lost during genetic engineering of the chromosomes by CRISPR–Cas9 system.

Reviewer #3

The authors included statistical analyses of the qPCR data (Fig 4B) as requested, but did not comment on the striking difference in expression of MPH3 and HSP32 in the SY12 strain compared to BY4742. An improvement of the manuscript is the inclusion of rad52 tlc1 strains in their analyses, demonstrating that the "atypical and circular survivors" arose independently of homologous recombination. In addition, by analyzing rad51 and rad50 mutant strain they could demonstrate that the "type X" survivors had similar molecular requirements to type II survivors. Overall, the revised submission improves the article.

We thank the reviewer’s comments and suggestions. The SY12 strain (with three chromosomes) exhibited lower expression levels of both MPH3 and HSP32 compared to the parental strain BY4742 (with 16 chromosomes). We speculated that with the reduced chromosome numbers, the silencing proteins appeared to no longer be titrated by other telomeres that have been deleted. We have added these comments in the revised manuscript.

Wu, Z.J., Liu, J.C., Man, X., Gu, X., Li, T.Y., Cai, C., He, M.H., Shao, Y., Lu, N., Xue, X., et al. (2020). Cdc13 is predominant over Stn1 and Ten1 in preventing chromosome end fusions. Elife 9.

Author Response

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

eLife assessment:

This valuable study describes a new role of epithelial intercellular adhesion molecule 1 (ICAM-1) protein in controlling bile duct size. The effect is mediated via EBP-50 and subapical actomyosin to regulate size of bile canaliculi. These solid findings have theoretical and practical implications in hepatology and human disorders of bile ducts.

Public Reviews:

In this study, Cacho-Navas et al. describe the role of ICAM-1 expressed on the apical membrane of bile canaliculi and its function to control the bile canaliculi (BCs) homeostasis. This is a previously unrecognized function of this protein in hepatocytes. The same authors have previously shown that basolateral ICAM-1 plays a role in controlling lymphocyte adhesion to hepatocytes during inflammation and that this interaction is responsible for the loss of polarity of hepatocytes during disease states.

This new study shows that ICAM-1 is mainly localized in the apical domain of the BC and in association with EBP-50, communicates with the subapical acto-myosin ring to regulate the size and morphology of the BC. They used the well-known immortal cell line of liver cells (HepG2) in which they deleted ICAM-1 gene by CRISPR-Cas9 editing and hepatic organoids derived from WT and ICAM-1-KO mice. alternating KO as well as rescue experiments. They show that in the absence of apical ICAM-1, the BC become dilated.

The data sufficiently support the conclusions of the study.

Recommendations for the authors:

We would like to thank the editor and reviewer for recognizing the manuscript's value and the solid nature of the data. We are also thankful to them for acknowledging that the manuscript supports the conclusions. Below, we have addressed their commentaries and questions in a point-by-point rebuttal document:

We have a few suggestions to improve the manuscript:

(1) HepG2 cells form canaliculi-like structures but are not the ideal system to study the apical basal polarity. On the other hand, hepatic organoids can assume a hepatocyte-like phenotype, when cultured under specific conditions but are not functionally comparable to hepatocytes organized in a 3D structure with a hollow lumen that does not recapitulate the BC physiological structure. Therefore, primary hepatocyte in collagen sandwich would be the best model to study the polarization of BCs and could be isolated from WT and ICAM-1-KO mice, that are available. Some of the major findings should be confirmed in this system.

We adopted the culture of hepatic organoids as an experimental strategy motivated by the difficulties to culture primary hepatocytes experienced in previous analyses (RegleroReal, Cell Rep, 2014). The generation of organoids or mature hepatocytes from various sources of stem cells is a commonly employed strategy in hepatocyte cell biology (Meyer et al. EMBO Rep, 2023), due to the difficulties in maintaining mature hepatic epithelial cell cultures for longer than a few hours.

The hepatic organoids we have used in the manuscript are being accepted as advanced cellular strategies for a broad range of fields (Belenguer, Nat Commun, 2022; de Crignis, eLife, 2021; Huch, Cell, 2015). Despite they have some morphological differences with real hepatocytes, we conducted a thorough characterization of their organization identifying canalicular-like structures with functional (CFDA) and molecular (HA-4) markers, which we believe adds value to the manuscript. In addition, the organoid technology has allowed us to import the bipotent precursors to get an permanent source of hepatic cells without the need to import and use the ICAM-1_KO mice, in line with the current guides to reduce animal experimentation.

Taking this into account and to further validate data obtained with our cellular systems, we carried out a quantification of the canalicular diameter in livers from WT and ICAM1_KO cells (New Figure 8B), which validates our data on human cell lines and organoids. We acknowledge that the data obtained from hepatic tissues cannot rule out the contribution of immune cell adhesion to changes in the hepatocyte architecture. However, these experiments, together with the aforementioned organoids and human cell lines, strongly suggest a role for hepatic ICAM-1 in regulating canalicular size.

(2) Overexpression of proteins was used in the study. While this approach is an easier means to visualize, without the use of specific antibodies, it is known to alter the distribution of the protein compared to the endogenous one.

Most of our characterization has been done with antibodies or other fluorescent tools against endogenous proteins localized at BCs: CD59, F-actin, EBP50, MHC, MLC…. In addition, we have included MDR1-GFP and GFP-Rab11, the latter to analyze the subapical compartment (SAC) surrounding BCs. As requested by the reviewer, we now include in a new Supplementary Figure 1C the confocal analyses of endogenous canalicular markers, radixin and MRP2, as well as a new Supplementary Figure 1D containing the staining of an endogenous marker of the SAC, plasmolipin/PLLP (Fraticelli et al, Nat Cell Biol, 2015; Cacho-Navas, Cell Mol Life Sci, 2022), which is consistent with the previous analyses performed with GFP-Rab11.

(3) In the absence of ICAM-1, BCs change shape and dimension but still show the presence of microvilli. What happens to the distribution of polarized transporters like Mrp2, or the transport of bile acids (CFDA clearance) in vivo in the KO animal?

Thank you for this comment. We have analyzed this transporter in murine livers and human hepatic cells. MRP2 distribution does not significantly change and is concentrated in BCs also in ICAM-1_KO livers (New Figure 8C). Likewise, ICAM-1 gene edition does not affect MRP2 localization in the polarized human hepatic epithelial cell line in vitro (Supplementary Figure 1C). We cannot rule out changes for this transporter in other murine liver cell types in vivo, such as sinusoidal endothelial cells, which we believe should be further addressed in a different piece of work.

(4) Does the lack of ICAM-1 affect the cell viability, proliferation or cell size?

ICAM-1_KO cells proliferate slightly more slowly than their WT counterparts, with no detected changes in cell size and death. We present these data in Supplementary Figure 1, A and B.

(5) Are the findings recapitulated in the livers of ICAM-1 KO animals?

ICAM-1 KO animals present enlarged BCs, which is consistent with the main findings of the manuscript (Figure 8B).

The text needs to be more concise. Some of the concepts, in particular those already published, should be condensed. There is a large amount of experiments that are difficult to connect logically. Possibly, cartoons summarizing the approach of the figure could help the reader.

The text of Results and Discussion sections has been shortened by almost 100 words, despite the additional panels and experiments are now described and discussed. New cartoons have been added in Figure 5G and Figure 8F, in addition to those previously included in Figure 1 and Supplementary Figure 6, the latter containing a graphical descriptions of the main conclusions.

Also, more detailed information about statistical analysis (what post-test was used?), concentration of cytokines, and description of the mouse model should be included in the methods.

Cytokine concentrations have been included in the legend of Figure 3 and in the Cell and Culture section of Methods. A brief description of the ICAM-1_KO mouse and the corresponding reference for further information is also provided in the Organoid Culture section of Methods. A statistical analysis section describing the post-test used is also included at the end of Methods. The references of anti-plasmolipin, anti-radixin and antiMRP2 antibodies, as well as the new fixation methods used for immunofluorescence are also included in the corresponding Antibody List and in the Confocal Microscopy section of Methods, respectively . .

Figure 3D. Sample names should be added as in the rest of the figures.

The arrangement of sample names in Figure 3D has been revised and is now similar to that of Figure 3A.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this paper, Song, Shi, and Lin use an existing deep learning-based sequence model to derive a score for each haplotype within a genomic region, and then perform association tests between these scores and phenotypes of interest. The authors then perform some downstream analyses (fine-mapping, various enrichment analyses, and building polygenic scores) to ensure that these associations are meaningful. The authors find that their approach allows them to find additional associations, the associations have biologically interpretable enrichments in terms of tissues and pathways, and can slightly improve polygenic scores when combined with standard SNP-based PRS.

Strengths:

• I found the central idea of the paper to be conceptually straightforward and an appealing way to use the power of sequence models in an association testing framework.

• The findings are largely biologically interpretable, and it seems like this could be a promising approach to boost power for some downstream applications.

Weaknesses:

• The methods used to generate polygenic scores were difficult to follow. In particular, a fully connected neural network with linear activations predicting a single output should be equivalent to linear regression (all intermediate layers of the network can be collapsed using matrix-multiplication, so the output is just the inner product of the input with some vector). Using the last hidden layer of such a network for downstream tasks should also be equivalent to projecting the input down to a lower dimensional space with some essentially randomly chosen projection. As such, I am surprised that the neural network approach performs so well, and it would be nice if the authors could compare it to other linear approaches (e.g., LASSO or ridge regression for prediction; PCA or an auto-encoder for converting the input to a lower dimensional representation).

Response: We thank the reviewer for the recognition and valuable suggestion on our work. Just as the reviewer suggested, our polygenic prediction procedure is equivalent to linear transformation and in this revision, we indeed found that it was unnecessary to use neural network framework to replace linear model. Indeed, both our result and previous work indicated that linear model fitted polygenic traits better than non-linear one, which was also the reason we chose linear activation for neural network in the original manuscript.

In this revision, we followed the reviewer’s suggestion to apply a more straightforward linear framework for polygenic prediction. We first calculated weighted sum of HFS for each block (1,361 independent blocks in total), then, in each target ancestry, we used LASSO regression to integrate them with SNP PRS into one final score. We also conducted comparative analysis in British European test set and found that LASSO, ridge and elastic net gave similar result, and LASSO performed slightly better. By applying this straightforward framework and sliding window strategy, we moderately improved the prediction performance.

Line 349: “Using height as a representative trait, we first estimated the proportion of variance captured by top loci, and found that HFS of loci with PIP>0.4 (n=5,101) captured roughly 80% of variance explained by all genome-wide loci (n=1,200,024 corresponded to sling-window strategy; Figure 5A). We then calculated HFS+LDAK in non-British European (NBE), South Asian (SAS), East Asian (EAS) and African (AFR) population in UK Biobank, and observed 17.5%, 16.1%, 17.2% and 39.8% improvement over LDAK alone (p=3.21×10-16, 0.0001, 0.002 and 0.001, respectively. Figure 5C).”

Author response image 1.

• A very interesting point of the paper was the low R^2 between the HFS scores in adjacent windows, but the explanation of this was unclear to me. Since the HFS scores are just deterministic functions of the SNPs, it feels like if the SNPs are in LD then the HFS scores should be and vice versa. It would be nice to compare the LD between adjacent windows to the average LD of pairs of SNPs from the two windows to see if this is driven by the fact that SNPs are being separated into windows, or if sei is somehow upweighting the importance of SNPs that are less linked to other SNPs (e.g., rare variants).

Response: We thank the reviewer for the suggestion on understanding LD mechanism. In this revision, we used chromosome 1 as an example and calculate the pairwise LD among all SNPs within two adjacent loci. As shown in Figure S1 (below), although HFS-based LD is still significantly lower than median SNP-based LD (paired Wilcoxon test p=1.76e-5), we found that median SNP LD between loci was still lower than what typically observed between adjacent SNPs in GWAS (histogram of x axis; median =0.06). We reasoned that dividing SNPs into block is one of the reasons that HFS suffer less LD than standard GWAS, but not the whole story.

Author response image 2.

We agree with the reviewer that the effect of rare variants could also play an important role. In fact, sei author has also found that rare variants tended to have larger sei-predicted effects. We conducted an approximate analysis that remove all rare variants and repeated HFS calculation. Indeed, here HFS LD has profoundly raised to median=0.14, indicating that involving rare variants was vital for low LD.

Author response image 3.

Line 123: “Further evaluation indicated that this low LD was led by two factors: integration of rare variant impacts and segmentation. Firstly, excluding rare variants from HFS caused the LD raised to median=0.14 (Method; Figure S2C). Secondly, median LD of SNPs from adjacent loci was 0.06, which was significantly higher than HFS LD (paired Wilcoxon p=1.76×10-5) but significantly lower than HFS LD without rare variants (paired Wilcoxon p<2.2×10-16).”

• There were also a number of robustness checks that would have been good to include in the paper. For instance, do the findings change if the windows are shifted? Do the findings change if the sequence is reverse-complemented?

Response: Following the reviewer’s suggestion, we conducted a sliding window analysis where all loci were shifted 2048 bp, thereby doubling the total number of loci. In fine-mapping analysis, more than 90% of the causal loci were reproduced in sliding window analysis, either by themselves or by a overlapping locus:

Line 207: “29.4% of causal loci (PIP>0.95) in the original analysis were still causal in sliding window analysis. 31.1% and 29.3% of causal loci whose 5’ and 3’ overlapping locus had PIP>0.95 in sliding window analysis, respectively, while themselves were no longer causal.”

In polygenic prediction analysis, sliding window strategy significantly improved prediction accuracy, as we discussed in question 1.

As for the issue of reverse complement, the nature of sei input layer is to encode both strand in a symmetric manner, such that the output for both strands would be the same. We have also run sei on the reverse complement (generated by seqkit seq -r -p) to verify that original sequence and reverse complement give the same output.

• It was also difficult to contextualize the present work in terms of recent results showing that sequence models tend to not do very well at predicting cross-individual expression changes (and such results presumably hold for predicting cross-individual chromatin changes). In particular, it would be good for the authors to contrast their findings with the work of Alex Sasse and colleagues (https://www.biorxiv.org/content/10.1101/2023.03.16.532969.abstract) and Connie Huang and colleagues (https://www.biorxiv.org/content/10.1101/2023.06.30.547100.abstract).

Response: Following the reviewer’s suggestion, we added a new discussion paragraph on the issue of sequence model performance on interindividual variations. In brief, we suggest that although the drawback of lack of cross-individual training sets exists and future improvement is necessary, chromatin changes could be better predicted than gene expression. This is because the latter task requires information on long range interaction, which varies among genes and are difficult to be captured by using reference genome as training set. We made a schematic to clarify this:

Author response image 4.

We also noticed a few recent studies that directly validated sei predictions by experiments and showed significant accuracy, such as https://doi.org/10.1016/j.neuron.2022.12.026. Taken together, while we agreed that it is necessary to improve sequence model by adding more cross-individual training samples, the current SOTA model sei could still provide unique value to our study.

Line 423: “The challenge of using sequence-based deep learning (DL) models in HFS applications is further compounded by their difficulty in predicting variations between individuals. Recent studies(Huang et al., 2023; Sasse et al., 2023) indicate that DL models, trained on the reference human genome, demonstrate limited accuracy in predicting gene expression levels across different individuals. This limitation is likely due to the models' inability to account for long-range regulatory patterns, which are crucial for understanding the impact of variants on gene expression and vary across genes. In contrast, our study leveraged sequence-determined functional genomic profiles in association studies, which mitigates this issue to an extent. For instance, although sei cannot identify the specific gene regulated by a given input sequence, it can predict changes in the sequence's functional activity. Future improvements in DL models' ability to predict interindividual differences could be achieved by incorporating cross-individual data in the training process. An example of such data is the EN-TEX(Rozowsky et al., 2023) dataset, which aligns functional genomic peaks with the specific individuals and haplotypes they correspond to.”

Reviewer #2 (Public Review):

Summary:

In this work, Song et al. propose a locus-based framework for performing GWAS and related downstream analyses including finemapping and polygenic risk score (PRS) estimation. GWAS are not sufficiently powered to detect phenotype associations with low-frequency variants. To overcome this limitation, the manuscript proposes a method to aggregate variant impacts on chromatin and transcription across a 4096 base pair (bp) loci in the form of a haplotype function score (HFS). At each locus, an association is computed between the HFS and trait. Computing associations at the level of imputed functional genomic scores should enable the integration of information across variants spanning the allele frequency spectrum and bolster the power of GWAS.

The HFS for each locus is derived from a sequence-based predictive model. Sei. Sei predicts 21,907 chromatin and TF binding tracks, which can be projected onto 40 pre-defined sequence classes ( representing promoters, enhancers, etc.). For each 4096 bp haplotype in their UKB cohort, the proposed method uses the Sei sequence class scores to derive the haplotype function score (HFS). The authors apply their method to 14 polygenic traits, identifying ~16,500 HFS-trait associations. They finemap these trait-associated loci with SuSie, as well as perform target gene/pathway discovery and PRS estimation.

Strengths:

Sequence-based deep learning predictors of chromatin status and TF binding have become increasingly accurate over the past few years. Imputing aggregated variant impact using Sei, and then performing an HFS-trait association is, therefore, an interesting approach to bolster power in GWAS discovery. The manuscript demonstrates that associations can be identified at the level of an aggregated functional score. The finemapping and pathway identification analyses suggest that HFS-based associations identify relevant causal pathways and genes from an association study. Identifying associations at the level of functional genomics increases the portability of PRSs across populations. Imputing functional genomic predictions using a sequence-based deep learning model does not suffer from the limitation of TWAS where gene expression is imputed from a limited-size reference panel such as GTEx.

However, there are several major limitations that need to be addressed.

Major concerns/weaknesses:

(1) There is limited characterization of the locus-level associations to SNP-level associations. How does the set of HFS-based associations differ from SNP-level associations?

Response: We thank the reviewer for the recognition and the valuable suggestion on our manuscript. Following the reviewer’s suggestion, in this revision we added a paragraph to compare the basic characteristics between HFS-based and SNP-based association study. These comparisons suggested that HFS had no advantage in testing marginal association, but performed better in detecting causal associations.

Line 144: “When comparing HFS association with the standard SNP-based GWAS on the same data, we found that 98% of significant HFS loci also harbored a significant SNP. There were a few cases (n=0~5) where significant HFS loci did not harbored even marginal SNP association (GWAS p>0.01), which were due to the lack of common SNP in these loci. HFS association p value was higher than GWAS p value in 95 % of significant loci, suggested that HFS did not improve power to detect marginal effect. The genomic control inflation factor (λGC) for the HFS association test varied between 0.99 for asthma and 1.50 for height, closely resembling the SNP GWAS (Pearson Correlation Coefficient [PCC]=0.91, paired t-test p=0.16; Method and Figure S3). We concluded that HFS-based association tests had adequate power and do not introduce additional p-value inflation.”

(2) A clear advantage of performing HFS-trait associations is that the HFS score is imputed by considering variants across the allele frequency spectrum. However, no evidence is provided demonstrating that rare variants contribute to associations derived by the model. Similarly, do the authors find evidence that allelic heterogeneity is leveraged by the HFS-based association model? It would be useful to do simulations here to characterize the model behavior in the presence of trait-associated rare variants.

Response: Following the reviewer’s suggestion, we conducted a sensitivity analysis that removed all rare (MAF<0.01) variants and repeated the HFS analysis (HFScommon) on chromosome 1. In linear association analysis, we found that 10.6% of HFS signals (p<5×10-8) were missed by HFScommon. In fine-mapping, 55.3% of HFS causal signals (PIP>0.95) were missed by HFScommon. We concluded that rare variants played an important role in the performance of HFS, especially its advantages in fine-mapping.

Line 175: “We also found that rare variants played an important role in the good find-mapping performance of HFS: when variants with MAF<0.01 were removed, 55.3% of the causal signals would be missed in HFS+SUSIE analysis.”

We then attempted to conduct a simulation analysis where rare variants were causal to the phenotype, and the association statistics were the same as real GWAS of height. However, such simulation seemed not to properly reflect real scenario: no matter how we changed the association between rare variants and the phenotype, HFS association p-value could hardly reached the significance level of SNP association. We proposed that this is because simulation could not properly reflect how variants impact functional genomics: in fact, when randomly selected a rare variant as causal variant, there is high possibility that it had no impact on functional genomics, therefore its HFS would be close to zero. When such a variant was set as causal (which is unlikely in real scenario), HFS would not properly capture the association. We reasoned that it might be difficult to evaluate HFS by simulation, since the nonlinear relation between SNP and HFS as well as among SNPs were difficult to be properly simulated.

Author response image 5.

(3) Sei predicts chromatin status / ChIP-seq peaks in the center of a 4kb region. It would therefore be more relevant to predict HFS using overlapping sequence windows that tile the genome as opposed to using non-overlapping windows for computing HFS scores. Specifically, in line 482, the authors state that "the HFS score represents overall activity of the entire sequence, not only the few bp at the center", but this would not hold given that Sei is predicting activity at the center for any sequence.

Response: We thank the reviewer for the suggestion on sliding window design. In this revision, we shifted all loci 2,048 bp to double the number of loci and repeated the fine-mapping and polygenic prediction analysis. For fine-mapping, we found that the result was generally robust with regard to sliding window procedure, and the majority of the causal associations were retained:

Line 207: “29.4% of causal loci (PIP>0.95) in the original analysis were still causal in sliding window analysis. 31.1% and 29.3% of causal loci whose 5’ and 3’ overlapping locus had PIP>0.95 in sliding window analysis, respectively, while themselves were no longer causal.”

In polygenic prediction, sliding window analysis provided a significantly improved performance compared with previous analysis on non-overlapping loci:

However, since in this revision we have several updates on the polygenic prediction procedure, it was difficult to quantify how much improvement was led by sliding window design. Thus, we directly showed the new result in figure 5 but did not compare it with the original result.

We also modified the previously imprecise statement to:

Line 490: “…it integrated information of the entire sequence, not only the few bp at the center.”

(4) Is the HFS-based association going to miss coding variation and several regulatory variants such as splicing variants? There are also going to be cases where there's an association driven by a variant that is correlated with a Sei prediction in a neighboring window. These would represent false positives for the method, it would be useful to identify or characterize these cases.

Response: As the reviewer suggested, sei captured only functional genomic features and is by nature prone not to perform well when the causal variants impact protein sequences. In this revision, we characterized this by focusing on causal exonic variants (SNP PIP>0.95):

Line 322: “On the other hand, HFS perform worse than SNP-based fine-mapping on exonic regions. Taking height as an example, PolyFun detected 125 causal SNPs (PIP>0.95) in the exonic regions, but only 16% (20) of loci that harbored them also reached PIP>0. 5 (11 reached PIP>0.95) in HFS+SUSIE analysis. Among the 105 loci that missed such signals (HFS PIP<0.5), 12 had a nearby locus (within 10kb) showing HFS PIP>0.95, which likely reflected false positive led by LD. Thus, SNP-based analysis should be prioritized over HFS in coding regions.”

Additional minor concerns:

(1) It's not clear whether SuSie-based finemapping is appropriate at the locus level, when there is limited LD between neighboring HFS bins. How does the choice of the number of causal loci and the size of the segment being finemapped affect the results and is SuSie a good fit in this scenario?

Response: Following the reviewer’s suggestion, we reran SUSIE under different predefined causal loci number (from 2 to 10), and found that the identified causal loci were consistent.

Author response image 6.

Line 211: “Besides, HFS+SUSIE was also robust when the predefined number of causal loci (L=2 to 10) was changed, and the number of detected loci were not changed.”

As for the size of segmentation, we divided the predefined segmentations (independent blocks detected by LDetect) into two half and reran SUSIE, and found that three additional causal loci emerged in one half. This suggested that using too small segmentation might increase the false positive rate. However, since there is no LD between independent blocks (which was guaranteed by LDetect), it is not necessary to use even longer blocks.

Author response image 7.

Line 133: “Simulation analysis revealed that when a non-reference sequence class score was associated the trait, reference class score could still capture median 70% of HFS-trait association R2.”

(2) It is not clear how a single score is chosen from the 117 values predicted by Sei for each locus. SuSie is run assuming a single causal signal per locus, an assumption which may not hold at ~4kb resolution (several classes could be associated with the trait of interest). It's not clear whether SuSie, run in this parameter setting, is a good choice for variable selection here.

Response: As we discussed below (question 3), in this revision we no longer applied SUSIE to find one sequence class score for each locus due to the impact of overfitting, and use the reference sequence class uniformly for all loci. As reviewer suggested, we applied simulation to evaluate how this procedure influence HFS performance, especially when multiple sequence class of the same locus is causal to the phenotype. We found that reference sequence class score could capture median 69.1% of phenotypic R2 when the causal sequence class is not the reference, and captured median 59.2% of R2 when there was 2~5 non-reference causal class. We concluded that the loss led by skipping sequence class selection is mild, and it is necessary to do so in consideration of the risk of overfitting.

Author response image 8.

(3) A single HFS score is being chosen from amongst multiple tracks at each locus independently. Does this require additional multiple-hypothesis correction?

Response: We agree with the reviewer that choosing the sequence class for each locus represented multiple testing, and with additional experiments we indeed observed some evidences of overfitting of this procedure. Thus, in this revision, we no longer applied the per-locus feature selection procedure, but instead used the sequence class corresponded to the reference (hg38) sequence. Consequently, additional multiple-testing correction is avoided with this procedure. We admitted that such simplification missed certain information, but as mentioned above, such lost is moderate, and is necessary to ensure statistical robustness and reduce false positive. In fact, with such simplification we better controlled the inflation factor of HFS GWAS and got better portability in polygenic prediction.

(4) The results show that a larger number of loci are identified with HFS-based finemapping & that causal loci are enriched for causal SNPs. However, it is not clear how the number of causal loci should relate to the number of SNPs. It would be really nice to see examples of cases where a previously unresolved association is resolved when using HFS-based GWAS + finemapping.

Response: In this revision, we did not observe a clear relation between causal loci number and causal gene number. The only trend is that SNP-based fine-mapping seemed to perform better at coding regions, in accordance with the fact that HFS capture functional genomic signals. We also added new interpretations to highlight some examples where HFS resolve previously unresolved association signals. For example,

Line 287: “Specifically, in 1q32.1 region, HFS+SUSIE identified two loci with PIP>0.9 (Figure 4B). SNP-based association also found significant association in this region, but SNP fine-mapping(Weissbrod et al., 2020) could not resolve this signal and only found seven signals between PIP=0.1 to 0.5.”

(5) Sequence-based deep learning model predictions can be miscalibrated for insertions and deletions (INDELs) as compared to SNPs. Scaling INDEL predictions would likely improve the downstream modeling.

Response: Following the reviewer’s suggestion, we conducted a sensitivity analysis that removed all indel on chromosome 1 and repeated HFS analysis. Removing indel has indeed increased the number of significant (p<5e-8) association by 9%, but also slightly increased inflation factor (paired wilcox test p=0.0001). In fine mapping analysis, removing indel caused a 4.7% decrement in the number of detected causal association (PIP>0.95). We reasoned that the potential miscalibration on indel has indeed impacted the statistical power of HFS, but the proper approach to control this impact might not be direct and is still await optimizing. In this revision, we still kept all indels in the analysis, since we proposed that the power of fine-mapping is more important than the power of marginal association.

Line 213: “Lastly, removing insertion and deletion would reveal 9% more significant association (p<5×10-8) but 4.7% less causal association (PIP>0.95), and slightly increased inflation factor (Wilcoxon p=0.0001, Figure S4).”

Author response image 9.

Reviewer #1 (Recommendations For The Authors):

It was unclear to me why the sei output was rounded to two decimal places to "avoid influence of sei prediction noise". Wouldn't rounding introduce additional noise?

Response: We thank the reviewer for pointing out our inadequate description. The rounding procedure is used to mask the low value that likely did not reflect any real change. The idea is that, even if a variant actually does not bring about any functional changes, sei would still output a very low HFS value that is not equal to, but close to, zero. By rounding procedure, such low values would be set to zero, which could avoid noise. We have added this rationale to the method section:

Line 529: “This is due to the fact that even if a variant actually makes no impact on functional genomics, sei would still output a value that are close to but not equal to reference sequence class score. Rounding procedure would set such HFS to zero and remove the random value from sei.”

Minor comments / typos:

• There are many typos in the abstract.

Response: We have revised the typo and grammar issues in the abstract in this revision.

• I believe "Arachnoid acid-intelligence" should be "Arachidonic acid-intelligence".

• Consistently there is no space between text and parenthetical citations. For example, "sei(Chen et al., 2022)" should be "sei (Chen et al., 2022)".

• Line 110: "at least one non-reference haplotypes" --> "at least one non-reference haplotype".

• Line 155: "data-based method" --> "data-based methods".

• Lines 165-166: "functionally importance" --> "functional importance".

Response: We have made these revisions accordingly.

• Line 210: the sentence containing "this annotation on conditioned of a set of baseline annotations" is unclear.

Response: We have revised this sentence as “…regressed the PIP against this annotation, with a set of baseline annotations included as covariates, similar to the LDSC framework.”

• Line 213: "association" --> "associations".

• Line 219: "association" --> "associations".

• Line 251: "result" --> "results".

• Line 269: "result" --> "results".

• Line 289: "known to involved" --> "known to be involved".

• Line 356: "LDAK along" --> "LDAK alone".

• Line 362: "BOLT-LMM along" --> "BOLT-LMM alone".

• Supplement: "Hihglighted" --> "Highlighted".

Response: We have made these revisions accordingly.

• Line 444: Were "British ancestry Caucasians" defined as individuals that self-identified as "white British"? If so, then they should be described as "self-identified "white British"".

Response: As the reviewer pointed out, we have changed the description as self-identified British ancestry Caucasians.

Reviewer #2 (Recommendations For The Authors):

(1) A 2022 cistrome-wide association study (CWAS) computed associations between genetically-predicted chromatin activity and phenotypes. Adding a reference to this paper would be helpful. https://pubmed.ncbi.nlm.nih.gov/36071171/

Response: Following the reviewer’s suggestion, we discussed the similarity between CWAS and our study:

Line 89: “In line with this notion, a recent similar strategy called cistrome-wide association study (CWAS) integrated variant-chromatin activity and variant-phenotype association to boost power of genetic study of cancer. (Baca et al., 2022).”

(2) Line 487 states: "We applied sei to predict 21,906 functional genomic tracks for each sequence, without normalizing for histone mark." It's not clear what normalization is being referred to here.

Response: We have revised the sentence to:

Line 495: “We applied sei to predict 21,906 functional genomic tracks for each sequence, without normalizing for histone mark (divided each track score by the sum of histone mark score) as suggested by the sei author.”

(3) The figures are extremely low resolution, they need to be updated.

Response: In this revision, we uploaded separate pdf file for each figure to provide high resolution graphs.

(4). The results section was difficult to follow and would benefit from being written more clearly.

Response: In this revision, we re-arranged some of the result section to better clarify the main idea. We moved all statistical results to the bracket and focused our main text on the interpretation. For example,

Line 123: “Further evaluation indicated that this low LD was led by two factors: integration of rare variant impacts and segmentation. Firstly, excluding rare variants from HFS caused the LD raised to median=0.14 (Method; Figure S2C). Secondly, median LD of SNPs from adjacent loci was 0.06, which was significantly higher than HFS LD (paired Wilcoxon p=1.76×10-5) but significantly lower than HFS LD without rare variants (paired Wilcoxon p<2.2×10-16).”

(5) "Along" is used several times in the final results section (PRS estimation), this should be "alone".

Response: We have modified all misused “along” by “alone” in this revision.

(6) Instead of using notation identifying genomic location, it might be clearer to provide gene names when illustrating examples of trait-associated promoters.

Response: In this revision, we added gene name of the corresponding promoters to the main text to better clarify the findings.

Author Response

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

Public Comments

Reviewer 1

(1) Despite the well-established role of Netrin-1 and UNC5C axon guidance during embryonic commissural axons, it remains unclear which cell type(s) express Netrin-1 or UNC5C in the dopaminergic axons and their targets. For instance, the data in Figure 1F-G and Figure 2 are quite confusing. Does Netrin-1 or UNC5C express in all cell types or only dopamine-positive neurons in these two mouse models? It will also be important to provide quantitative assessments of UNC5C expression in dopaminergic axons at different ages.

Netrin-1 is a secreted protein and in this manuscript we did not examine what cell types express Netrin-1. This question is not the focus of the study and we consider it irrelevant to the main issue we are addressing, which is where in the forebrain regions we examined Netrin-1+ cells are present. As per the reviewer’s request we include below images showing Netrin-1 protein and Netrin-1 mRNA expression in the forebrain. In Figure 1 below, we show a high magnification immunofluorescent image of a coronal forebrain section showing Netrin-1 protein expression.

Author response image 1.

This confocal microscope image shows immunofluorescent staining for Netrin-1 (green) localized around cell nuclei (stained by DAPI in blue). This image was taken from a coronal section of the lateral septum of an adult male mouse. Scale bar = 20µm

In Figures 2 and 3 below we show low and high magnification images from an RNAscope experiment confirming that cells in the forebrain regions examined express Netrin-1 mRNA.

Author response image 2.

This confocal microscope image of a coronal brain section of the medial prefrontal cortex of an adult male mouse shows Netrin-1 mRNA expression (green) and cell nuclei (DAPI, blue). Brain regions are as follows: Cg1: Anterior cingulate cortex 1, DP: dorsopeduncular cortex, fmi: forceps minor of the corpus callosum, IL: Infralimbic Cortex, PrL: Prelimbic Cortex

Author response image 3.

A higher resolution image from the same sample as in Figure 2 shows Netrin-1 mRNA (green) and cell nuclei (DAPI; blue). DP = dorsopeduncular cortex

Regarding UNC5c, this receptor homologue is expressed by dopamine neurons in the rodent ventral tegmental area (Daubaras et al., 2014; Manitt et al., 2010; Phillips et al., 2022). This does not preclude UNC5c expression in other cell types. UNC5c receptors are ubiquitously expressed in the brain throughout development, performing many different developmental functions (Kim and Ackerman, 2011; Murcia-Belmonte et al., 2019; Srivatsa et al., 2014). In this study we are interested in UNC5c expression by dopamine neurons, and particularly by their axons projecting to the nucleus accumbens. We therefore used immunofluorescent staining in the nucleus accumbens, showing UNC5 expression in TH+ axons. This work adds to the study by Manitt et al., 2010, which examined UNC5 expression in the VTA. Manitt et al. used Western blotting to demonstrate that UNC5 expression in VTA dopamine neurons increases during adolescence, as can be seen in the following figure:

 References:

Daubaras M, Bo GD, Flores C. 2014. Target-dependent expression of the netrin-1 receptor, UNC5C, in projection neurons of the ventral tegmental area. Neuroscience 260:36–46. doi:10.1016/j.neuroscience.2013.12.007

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254-10.20110.2011

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Phillips RA, Tuscher JJ, Black SL, Andraka E, Fitzgerald ND, Ianov L, Day JJ. 2022. An atlas of transcriptionally defined cell populations in the rat ventral tegmental area. Cell Reports 39:110616. doi:10.1016/j.celrep.2022.110616

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

(2) Figure 1 used shRNA to knockdown Netrin-1 in the Septum and these mice were subjected to behavioral testing. These results, again, are not supported by any valid data that the knockdown approach actually worked in dopaminergic axons. It is also unclear whether knocking down Netrin-1 in the septum will re-route dopaminergic axons or lead to cell death in the dopaminergic neurons in the substantia nigra pars compacta?

First we want to clarify and emphasize, that our knockdown approach was not designed to knock down Netrin-1 in dopamine neurons or their axons. Our goal was to knock down Netrin-1 expression in cells expressing this guidance cue gene in the dorsal peduncular cortex.

We have previously established the efficacy of the shRNA Netrin-1 knockdown virus used in this experiment for reducing the expression of Netrin-1 (Cuesta et al., 2020). The shRNA reduces Netrin-1 levels in vitro and in vivo.

We agree that our experiments do not address the fate of the dopamine axons that are misrouted away from the medial prefrontal cortex. This research is ongoing, and we have now added a note regarding this to our manuscript.

Our current hypothesis, based on experiments being conducted as part of another line of research in the lab, is that these axons are rerouted to a different brain region which they then ectopically innervate. In these experiments we are finding that male mice exposed to tetrahydrocannabinol in adolescence show reduced dopamine innervation in the medial prefrontal cortex in adulthood but increased dopamine input in the orbitofrontal cortex. In addition, these mice show increased action impulsivity in the Go/No-Go task in adulthood (Capolicchio et al., Society for Neuroscience 2023 Abstracts)

References:

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(3) Another issue with Figure1J. It is unclear whether the viruses were injected into a WT mouse model or into a Cre-mouse model driven by a promoter specifically expresses in dorsal peduncular cortex? The authors should provide evidence that Netrin-1 mRNA and proteins are indeed significantly reduced. The authors should address the anatomic results of the area of virus diffusion to confirm the virus specifically infected the cells in dorsal peduncular cortex.

All the virus knockdown experiments were conducted in wild type mice, we added this information to Figure 1k.

The efficacy of the shRNA in knocking down Netrin-1 was demonstrated by Cuesta et al. (2020) both in vitro and in vivo, as we show in our response to the reviewer’s previous comment above.

We also now provide anatomical images demonstrating the localization of the injection and area of virus diffusion in the mouse forebrain. In Author response image 4 below the area of virus diffusion is visible as green fluorescent signal.

Author response image 4.

Fluorescent microscopy image of a mouse forebrain demonstrating the localization of the injection of a virus to knock down Netrin-1. The location of the virus is in green, while cell nuclei are in blue (DAPI). Abbreviations: DP: dorsopeduncular cortex IL: infralimbic cortex

References:

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(4) The authors need to provide information regarding the efficiency and duration of knocking down. For instance, in Figure 1K, the mice were tested after 53 days post injection, can the virus activity in the brain last for such a long time?

In our study we are interested in the role of Netrin-1 expression in the guidance of dopamine axons from the nucleus accumbens to the medial prefrontal cortex. The critical window for these axons leaving the nucleus accumbens and growing to the cortex is early adolescence (Reynolds et al., 2018b). This is why we injected the virus at the onset of adolescence, at postnatal day 21. As dopamine axons grow from the nucleus accumbens to the prefrontal cortex, they pass through the dorsal peduncular cortex. We disrupted Netrin-1 expression at this point along their route to determine whether it is the Netrin-1 present along their route that guides these axons to the prefrontal cortex. We hypothesized that the shRNA Netrin-1 virus would disrupt the growth of the dopamine axons, reducing the number of axons that reach the prefrontal cortex and therefore the number of axons that innervate this region in adulthood.

We conducted our behavioural tests during adulthood, after the critical window during which dopamine axon growth occurs, so as to observe the enduring behavioral consequences of this misrouting. This experimental approach is designed for the shRNa Netrin-1 virus to be expressed in cells in the dorsopeduncular cortex when the dopamine axons are growing, during adolescence.

 References:

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018b. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

(5) In Figure 1N-Q, silencing Netrin-1 results in less DA axons targeting to infralimbic cortex, but why the Netrin-1 knocking down mice revealed the improved behavior?

This is indeed an intriguing finding, and we have now added a mention of it to our manuscript. We have demonstrated that misrouting dopamine axons away from the medial prefrontal cortex during adolescence alters behaviour, but why this improves their action impulsivity ability is something currently unknown to us. One potential answer is that the dopamine axons are misrouted to a different brain region that is also involved in controlling impulsive behaviour, perhaps the dorsal striatum (Kim and Im, 2019) or the orbital prefrontal cortex (Jonker et al., 2015).

We would also like to note that we are finding that other manipulations that appear to reroute dopamine axons to unintended targets can lead to reduced action impulsivity as measured using the Go No Go task. As we mentioned above, current experiments in the lab, which are part of a different line of research, are showing that male mice exposed to tetrahydrocannabinol in adolescence show reduced dopamine innervation in the medial prefrontal cortex in adulthood, but increased dopamine input in the orbitofrontal cortex. In addition, these mice show increased action impulsivity in the Go/No-Go task in adulthood (Capolicchio et al., Society for Neuroscience 2023 Abstracts)

References

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Jonker FA, Jonker C, Scheltens P, Scherder EJA. 2015. The role of the orbitofrontal cortex in cognition and behavior. Rev Neurosci 26:1–11. doi:10.1515/revneuro2014-0043 Kim B, Im H. 2019. The role of the dorsal striatum in choice impulsivity. Ann N York Acad Sci 1451:92–111. doi:10.1111/nyas.13961

(6) What is the effect of knocking down UNC5C on dopamine axons guidance to the cortex?

We have found that mice that are heterozygous for a nonsense Unc5c mutation, and as a result have reduced levels of UNC5c protein, show reduced amphetamine-induced locomotion and stereotypy (Auger et al., 2013). In the same manuscript we show that this effect only emerges during adolescence, in concert with the growth of dopamine axons to the prefrontal cortex. This is indirect but strong evidence that UNC5c receptors are necessary for correct adolescent dopamine axon development.

References

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

(7) In Figures 2-4, the authors only showed the amount of DA axons and UNC5C in NAcc. However, it remains unclear whether these experiments also impact the projections of dopaminergic axons to other brain regions, critical for the behavioral phenotypes. What about other brain regions such as prefrontal cortex? Do the projection of DA axons and UNC5c level in cortex have similar pattern to those in NAcc?

UNC5c receptors are expressed throughout development and are involved in many developmental processes (Kim and Ackerman, 2011; Murcia-Belmonte et al., 2019; Srivatsa et al., 2014). We cannot say whether the pattern we observe here is unique to the nucleus accumbens, but it is certainly not universal throughout the brain.

The brain region we focus on in our manuscript, in addition to the nucleus accumbens, is the medial prefrontal cortex. Close and thorough examination of the prefrontal cortices of adult mice revealed practically no UNC5c expression by dopamine axons. However, we did observe very rare cases of dopamine axons expressing UNC5c. It is not clear whether these rare cases are present before or during adolescence.

Below is a representative set of images of this observation, which is now also included as Supplementary Figure 4:

Author response image 5.

Expression of UNC5c protein in the medial prefrontal cortex of an adult male mouse. Low (A) and high (B) magnification images demonstrate that there is little UNC5c expression in dopamine axons in the medial prefrontal cortex. Here we identify dopamine axons by immunofluorescent staining for tyrosine hydroxylase (TH, see our response to comment #9 regarding the specificity of the TH antibody for dopamine axons in the prefrontal cortex). This figure is also included as Supplementary Figure 4 in the manuscript. Abbreviations: fmi: forceps minor of the corpus callosum, mPFC: medial prefrontal cortex.

References:

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254- 10.20110.2011

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

(8) Can overexpression of UNC5c or Netrin-1 in male winter hamsters mimic the observations in summer hamsters? Or overexpression of UNC5c in female summer hamsters to mimic the winter hamster? This would be helpful to confirm the causal role of UNC5C in guiding DA axons during adolescence.

This is an excellent question. We are very interested in both increasing and decreasing UNC5c expression in hamster dopamine axons to see if we can directly manipulate summer hamsters into winter hamsters and vice versa. We are currently exploring virus-based approaches to design these experiments and are excited for results in this area.

(9) The entire study relied on using tyrosine hydroxylase (TH) as a marker for dopaminergic axons. However, the expression of TH (either by IHC or IF) can be influenced by other environmental factors, that could alter the expression of TH at the cellular level.

This is an excellent point that we now carefully address in our methods by adding the following:

In this study we pay great attention to the morphology and localization of the fibres from which we quantify varicosities to avoid counting any fibres stained with TH antibodies that are not dopamine fibres. The fibres that we examine and that are labelled by the TH antibody show features indistinguishable from the classic features of cortical dopamine axons in rodents (Berger et al., 1974; 1983; Van Eden et al., 1987; Manitt et al., 2011), namely they are thin fibres with irregularly-spaced varicosities, are densely packed in the nucleus accumbens, sparsely present only in the deep layers of the prefrontal cortex, and are not regularly oriented in relation to the pial surface. This is in contrast to rodent norepinephrine fibres, which are smooth or beaded in appearance, relatively thick with regularly spaced varicosities, increase in density towards the shallow cortical layers, and are in large part oriented either parallel or perpendicular to the pial surface (Berger et al., 1974; Levitt and Moore, 1979; Berger et al., 1983; Miner et al., 2003). Furthermore, previous studies in rodents have noted that only norepinephrine cell bodies are detectable using immunofluorescence for TH, not norepinephrine processes (Pickel et al., 1975; Verney et al., 1982; Miner et al., 2003), and we did not observe any norepinephrine-like fibres.

Furthermore, we are not aware of any other processes in the forebrain that are known to be immunopositive for TH under any environmental conditions.

To reduce confusion, we have replaced the abbreviation for dopamine – DA – with TH in the relevant panels in Figures 1, 2, 3, and 4 to clarify exactly what is represented in these images. As can be seen in these images, fluorescent green labelling is present only in axons, which is to be expected of dopamine labelling in these forebrain regions.

References:

Berger B, Tassin JP, Blanc G, Moyne MA, Thierry AM (1974) Histochemical confirmation for dopaminergic innervation of the rat cerebral cortex after destruction of the noradrenergic ascending pathways. Brain Res 81:332–337.

Berger B, Verney C, Gay M, Vigny A (1983) Immunocytochemical Characterization of the Dopaminergic and Noradrenergic Innervation of the Rat Neocortex During Early Ontogeny. In: Proceedings of the 9th Meeting of the International Neurobiology Society, pp 263–267 Progress in Brain Research. Elsevier.

Levitt P, Moore RY (1979) Development of the noradrenergic innervation of neocortex. Brain Res 162:243–259.

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C (2011) The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394.

Miner LH, Schroeter S, Blakely RD, Sesack SR (2003) Ultrastructural localization of the norepinephrine transporter in superficial and deep layers of the rat prelimbic prefrontal cortex and its spatial relationship to probable dopamine terminals. J Comp Neurol 466:478–494.

Pickel VM, Joh TH, Field PM, Becker CG, Reis DJ (1975) Cellular localization of tyrosine hydroxylase by immunohistochemistry. J Histochem Cytochem 23:1–12.

Van Eden CG, Hoorneman EM, Buijs RM, Matthijssen MA, Geffard M, Uylings HBM (1987) Immunocytochemical localization of dopamine in the prefrontal cortex of the rat at the light and electron microscopical level. Neurosci 22:849–862.

Verney C, Berger B, Adrien J, Vigny A, Gay M (1982) Development of the dopaminergic innervation of the rat cerebral cortex. A light microscopic immunocytochemical study using anti-tyrosine hydroxylase antibodies. Dev Brain Res 5:41–52.

(10) Are Netrin-1/UNC5C the only signal guiding dopamine axon during adolescence? Are there other neuronal circuits involved in this process?

Our intention for this study was to examine the role of Netrin-1 and its receptor UNC5C specifically, but we do not suggest that they are the only molecules to play a role. The process of guiding growing dopamine axons during adolescence is likely complex and we expect other guidance mechanisms to also be involved. From our previous work we know that the Netrin-1 receptor DCC is critical in this process (Hoops and Flores, 2017; Reynolds et al., 2023). Several other molecules have been identified in Netrin-1/DCC signaling processes that control corpus callosum development and there is every possibility that the same or similar molecules may be important in guiding dopamine axons (Schlienger et al., 2023).

References:

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Schlienger S, Yam PT, Balekoglu N, Ducuing H, Michaud J-F, Makihara S, Kramer DK, Chen B, Fasano A, Berardelli A, Hamdan FF, Rouleau GA, Srour M, Charron F. 2023. Genetics of mirror movements identifies a multifunctional complex required for Netrin-1 guidance and lateralization of motor control. Sci Adv 9:eadd5501. doi:10.1126/sciadv.add5501

(11) Finally, despite the authors' claim that the dopaminergic axon project is sensitive to the duration of daylight in the hamster, they never provided definitive evidence to support this hypothesis.

By “definitive evidence” we think that the reviewer is requesting a single statistical model including measures from both the summer and winter groups. Such a model would provide a probability estimate of whether dopamine axon growth is sensitive to daylight duration. Therefore, we ran these models, one for male hamsters and one for female hamsters.

In both sexes we find a significant effect of daylength on dopamine innervation, interacting with age. Male age by daylength interaction: F = 6.383, p = 0.00242. Female age by daylength interaction: F = 21.872, p = 1.97 x 10-9. The full statistical analysis is available as a supplement to this letter (Response_Letter_Stats_Details.docx).

Reviewer 3

(1) Fig 1 A and B don't appear to be the same section level.

The reviewer is correct that Fig 1B is anterior to Fig 1A. We have changed Figure 1A to match the section level of Figure 1B.

(2) Fig 1C. It is not clear that these axons are crossing from the shell of the NAC.

We have added a dashed line to Figure 1C to highlight the boundary of the nucleus accumbens, which hopefully emphasizes that there are fibres crossing the boundary. We also include here an enlarged image of this panel:

Author response image 6.

An enlarged image of Figure1c in the manuscript. The nucleus accumbens (left of the dotted line) is densely packed with TH+ axons (in green). Some of these TH+ axons can be observed extending from the nucleus accumbens medially towards a region containing dorsally oriented TH+ fibres (white arrows).

(3) Fig 1. Measuring width of the bundle is an odd way to measure DA axon numbers. First the width could be changing during adult for various reasons including change in brain size. Second, I wouldn't consider these axons in a traditional bundle. Third, could DA axon counts be provided, rather than these proxy measures.

With regards to potential changes in brain size, we agree that this could have potentially explained the increased width of the dopamine axon pathway. That is why it was important for us to use stereology to measure the density of dopamine axons within the pathway. If the width increased but no new axons grew along the pathway, we would have seen a decrease in axon density from adolescence to adulthood. Instead, our results show that the density of axons remained constant.

We agree with the reviewer that the dopamine axons do not form a traditional “bundle”. Therefore, throughout the manuscript we now avoid using the term bundle.

Although we cannot count every single axon, an accurate estimate of this number can be obtained using stereology, an unbiassed method for efficiently quantifying large, irregularly distributed objects. We used stereology to count TH+ axons in an unbiased subset of the total area occupied by these axons. Unbiased stereology is the gold-standard technique for estimating populations of anatomical objects, such as axons, that are so numerous that it would be impractical or impossible to measure every single one. Here and elsewhere we generally provide results as densities and areas of occupancy (Reynolds et al., 2022). To avoid confusion, we now clarify that we are counting the width of the area that dopamine axons occupy (rather than the dopamine axon “bundle”).

References:

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

(4) TH in the cortex could also be of noradrenergic origin. This needs to be ruled out to score DA axons

This is the same comment as Reviewer 1 #9. Please see our response below, which we have also added to our methods:

In this study we pay great attention to the morphology and localization of the fibres from which we quantify varicosities to avoid counting any fibres stained with TH antibodies that are not dopamine fibres. The fibres that we examine and that are labelled by the TH antibody show features indistinguishable from the classic features of cortical dopamine axons in rodents (Berger et al., 1974; 1983; Van Eden et al., 1987; Manitt et al., 2011), namely they are thin fibres with irregularly-spaced varicosities, are densely packed in the nucleus accumbens, sparsely present only in the deep layers of the prefrontal cortex, and are not regularly oriented in relation to the pial surface. This is in contrast to rodent norepinephrine fibres, which are smooth or beaded in appearance, relatively thick with regularly spaced varicosities, increase in density towards the shallow cortical layers, and are in large part oriented either parallel or perpendicular to the pial surface (Berger et al., 1974; Levitt and Moore, 1979; Berger et al., 1983; Miner et al., 2003). Furthermore, previous studies in rodents have noted that only norepinephrine cell bodies are detectable using immunofluorescence for TH, not norepinephrine processes (Pickel et al., 1975; Verney et al., 1982; Miner et al., 2003), and we did not observe any norepinephrine-like fibres.

References:

Berger B, Tassin JP, Blanc G, Moyne MA, Thierry AM (1974) Histochemical confirmation for dopaminergic innervation of the rat cerebral cortex after destruction of the noradrenergic ascending pathways. Brain Res 81:332–337.

Berger B, Verney C, Gay M, Vigny A (1983) Immunocytochemical Characterization of the Dopaminergic and Noradrenergic Innervation of the Rat Neocortex During Early Ontogeny. In: Proceedings of the 9th Meeting of the International Neurobiology Society, pp 263–267 Progress in Brain Research. Elsevier.

Levitt P, Moore RY (1979) Development of the noradrenergic innervation of neocortex. Brain Res 162:243–259.

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C (2011) The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394.

Miner LH, Schroeter S, Blakely RD, Sesack SR (2003) Ultrastructural localization of the norepinephrine transporter in superficial and deep layers of the rat prelimbic prefrontal cortex and its spatial relationship to probable dopamine terminals. J Comp Neurol 466:478–494.

Pickel VM, Joh TH, Field PM, Becker CG, Reis DJ (1975) Cellular localization of tyrosine hydroxylase by immunohistochemistry. J Histochem Cytochem 23:1–12.

Van Eden CG, Hoorneman EM, Buijs RM, Matthijssen MA, Geffard M, Uylings HBM (1987) Immunocytochemical localization of dopamine in the prefrontal cortex of the rat at the light and electron microscopical level. Neurosci 22:849–862.

Verney C, Berger B, Adrien J, Vigny A, Gay M (1982) Development of the dopaminergic innervation of the rat cerebral cortex. A light microscopic immunocytochemical study using anti-tyrosine hydroxylase antibodies. Dev Brain Res 5:41–52.

(5) Netrin staining should be provided with NeuN + DAPI; its not clear these are all cell bodies. An in situ of Netrin would help as well.

A similar comment was raised by Reviewer 1 in point #1. Please see below the immunofluorescent and RNA scope images showing expression of Netrin-1 protein and mRNA in the forebrain.

Author response image 7.

This confocal microscope image shows immunofluorescent staining for Netrin-1 (green) localized around cell nuclei (stained by DAPI in blue). This image was taken from a coronal section of the lateral septum of an adult male mouse. Scale bar = 20µm

Author response image 8.

This confocal microscope image of a coronal brain section of the medial prefrontal cortex of an adult male mouse shows Netrin-1 mRNA expression (green) and cell nuclei (DAPI, blue). RNAscope was used to generate this image. Brain regions are as follows: Cg1: Anterior cingulate cortex 1, DP: dorsopeduncular cortex, IL: Infralimbic Cortex, PrL: Prelimbic Cortex, fmi: forceps minor of the corpus callosum

Author response image 9.

A higher resolution image from the same sample as in Figure 2 shows Netrin-1 mRNA (green) and cell nuclei (DAPI; blue). DP = dorsopeduncular cortex

(6) The Netrin knockdown needs validation. How strong was the knockdown etc?

This comment was also raised by Reviewer 1 #1.

We have previously established the efficacy of the shRNA Netrin-1 knockdown virus used in this experiment for reducing the expression of Netrin-1 (Cuesta et al., 2020). The shRNA reduces Netrin-1 levels in vitro and in vivo.

References:

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(7) If the conclusion that knocking down Netrin in cortex decreases DA innervation of the IL, how can that be reconciled with Netrin-Unc repulsion.

This is an intriguing question and one that we are in the planning stages of addressing with new experiments.

Although we do not have a mechanistic answered for how a repulsive receptor helps guide these axons, we would like to note that previous indirect evidence from a study by our group also suggests that reducing UNC5c signaling in dopamine axons in adolescence increases dopamine innervation to the prefrontal cortex (Auger et al, 2013).

References

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

(8) The behavioral phenotype in Fig 1 is interesting, but its not clear if its related to DA axons/signaling. IN general, no evidence in this paper is provided for the role of DA in the adolescent behaviors described.

We agree with the reviewer that the behaviours we describe in adult mice are complex and are likely to involve several neurotransmitter systems. However, there is ample evidence for the role of dopamine signaling in cognitive control behaviours (Bari and Robbins, 2013; Eagle et al., 2008; Ott et al., 2023) and our published work has shown that alterations in the growth of dopamine axons to the prefrontal cortex leads to changes in impulse control as measured via the Go/No-Go task in adulthood (Reynolds et al., 2023, 2018a; Vassilev et al., 2021).

The other adolescent behaviour we examined was risk-like taking behaviour in male and female hamsters (Figures 4 and 5), as a means of characterizing maturation in this behavior over time. We decided not to use the Go/No-Go task because as far as we know, this has never been employed in Siberian Hamsters and it will be difficult to implement. Instead, we chose the light/dark box paradigm, which requires no training and is ideal for charting behavioural changes over short time periods. Indeed, risk-like taking behavior in rodents and in humans changes from adolescence to adulthood paralleling changes in prefrontal cortex development, including the gradual input of dopamine axons to this region.

References:

Bari A, Robbins TW. 2013. Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in neurobiology 108:44–79. doi:10.1016/j.pneurobio.2013.06.005

Eagle DM, Bari A, Robbins TW. 2008. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology 199:439–456. doi:10.1007/s00213-008-1127-6

Ott T, Stein AM, Nieder A. 2023. Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons. Nat Commun 14:7537. doi:10.1038/s41467-023-43271-6

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

Vassilev P, Pantoja-Urban AH, Giroux M, Nouel D, Hernandez G, Orsini T, Flores C. 2021. Unique effects of social defeat stress in adolescent male mice on the Netrin-1/DCC pathway, prefrontal cortex dopamine and cognition (Social stress in adolescent vs. adult male mice). Eneuro ENEURO.0045-21.2021. doi:10.1523/eneuro.0045-21.2021

(9) Fig2 - boxes should be drawn on the NAc diagram to indicate sampled regions. Some quantification of Unc5c would be useful. Also, some validation of the Unc5c antibody would be nice.

The images presented were taken medial to the anterior commissure and we have edited Figure 2 to show this. However, we did not notice any intra-accumbens variation, including between the core and the shell. Therefore, the images are representative of what was observed throughout the entire nucleus accumbens.

To quantify UNC5c in the accumbens we conducted a Western blot experiment in male mice at different ages. A one-way ANOVA analyzing band intensity (relative to the 15-day-old average band intensity) as the response variable and age as the predictor variable showed a significant effect of age (F=5.615, p=0.01). Posthoc analysis revealed that 15-day-old mice have less UNC5c in the nucleus accumbens compared to 21- and 35-day-old mice.

Author response image 10.

The graph depicts the results of a Western blot experiment of UNC5c protein levels in the nucleus accumbens of male mice at postnatal days 15, 21 or 35 and reveals a significant increase in protein levels at the onset adolescence.

Our methods for this Western blot were as follows: Samples were prepared as previously (Torres-Berrío et al., 2017). Briefly, mice were sacrificed by live decapitation and brains were flash frozen in heptane on dry ice for 10 seconds. Frozen brains were mounted in a cryomicrotome and two 500um sections were collected for the nucleus accumbens, corresponding to plates 14 and 18 of the Paxinos mouse brain atlas. Two tissue core samples were collected per section, one for each side of the brain, using a 15-gauge tissue corer (Fine surgical tools Cat no. NC9128328) and ejected in a microtube on dry ice. The tissue samples were homogenized in 100ul of standard radioimmunoprecipitation assay buffer using a handheld electric tissue homogenizer. The samples were clarified by centrifugation at 4C at a speed of 15000g for 30 minutes. Protein concentration was quantified using a bicinchoninic acid assay kit (Pierce BCA protein assay kit, Cat no.PI23225) and denatured with standard Laemmli buffer for 5 minutes at 70C. 10ug of protein per sample was loaded and run by SDS-PAGE gel electrophoresis in a Mini-PROTEAN system (Bio-Rad) on an 8% acrylamide gel by stacking for 30 minutes at 60V and resolving for 1.5 hours at 130V. The proteins were transferred to a nitrocellulose membrane for 1 hour at 100V in standard transfer buffer on ice. The membranes were blocked using 5% bovine serum albumin dissolved in tris-buffered saline with Tween 20 and probed with primary (UNC5c, Abcam Cat. no ab302924) and HRP-conjugated secondary antibodies for 1 hour. a-tubulin was probed and used as loading control. The probed membranes were resolved using SuperSignal West Pico PLUS chemiluminescent substrate (ThermoFisher Cat no.34579) in a ChemiDoc MP Imaging system (Bio-Rad). Band intensity was quantified using the ChemiDoc software and all ages were normalized to the P15 age group average.

Validation of the UNC5c antibody was performed in the lab of Dr. Liu, from whom it was kindly provided. Briefly, in the validation study the authors showed that the anti-UNC5C antibody can detect endogenous UNC5C expression and the level of UNC5C is dramatically reduced after UNC5C knockdown. The antibody can also detect the tagged-UNC5C protein in several cell lines, which was confirmed by a tag antibody (Purohit et al., 2012; Shao et al., 2017).

References:

Purohit AA, Li W, Qu C, Dwyer T, Shao Q, Guan K-L, Liu G. 2012. Down Syndrome Cell Adhesion Molecule (DSCAM) Associates with Uncoordinated-5C (UNC5C) in Netrin-1mediated Growth Cone Collapse. The Journal of biological chemistry 287:27126–27138. doi:10.1074/jbc.m112.340174

Shao Q, Yang T, Huang H, Alarmanazi F, Liu G. 2017. Uncoupling of UNC5C with Polymerized TUBB3 in Microtubules Mediates Netrin-1 Repulsion. J Neurosci 37:5620–5633. doi:10.1523/jneurosci.2617-16.2017

(10) "In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, and reduction in UNC5C expression appears to cause growth of mesolimbic dopamine axons to the prefrontal cortex".....This is confusing. Figure 2 shows a developmental increase in UNc5c not a decrease. So when is the "reduction in Unc5c expression" occurring?

We apologize for the mistake in this sentence. We have corrected the relevant passage in our manuscript as follows:

In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, particularly when mesolimbic and mesocortical dopamine projections segregate in the nucleus accumbens (Manitt et al., 2010; Reynolds et al., 2018a). In contrast, dopamine axons in the prefrontal cortex do not express UNC5c except in very rare cases (Supplementary Figure 4). In adult male mice with Unc5c haploinsufficiency, there appears to be ectopic growth of mesolimbic dopamine axons to the prefrontal cortex (Auger et al., 2013). This miswiring is associated with alterations in prefrontal cortex-dependent behaviours (Auger et al., 2013).

References:

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

(11) In Fig 3, a statistical comparison should be made between summer male and winter male, to justify the conclusions that the winter males have delayed DA innervation.

This analysis was also suggested by Reviewer 1, #11. Here is our response:

We analyzed the summer and winter data together in ANOVAs separately for males and females. In both sexes we find a significant effect of daylength on dopamine innervation, interacting with age. Male age by daylength interaction: F = 6.383, p = 0.00242. Female age by daylength interaction: F = 21.872, p = 1.97 x 10-9. The full statistical analysis is available as a supplement to this letter (Response_Letter_Stats_Details.docx).

(12) Should axon length also be measured here (Fig 3)? It is not clear why the authors have switched to varicosity density. Also, a box should be drawn in the NAC cartoon to indicate the region that was sampled.

It is untenable to quantify axon length in the prefrontal cortex as we cannot distinguish independent axons. Rather, they are “tangled”; they twist and turn in a multitude of directions as they make contact with various dendrites. Furthermore, they branch extensively. It would therefore be impossible to accurately quantify the number of axons. Using unbiased stereology to quantify varicosities is a valid, well-characterized and straightforward alternative (Reynolds et al., 2022).

References:

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

(13) In Fig 3, Unc5c should be quantified to bolster the interesting finding that Unc5c expression dynamics are different between summer and winter hamsters. Unc5c mRNA experiments would also be important to see if similar changes are observed at the transcript level.

We agree that it would be very interesting to see how UNC5c mRNA and protein levels change over time in summer and winter hamsters, both in males, as the reviewer suggests here, and in females. We are working on conducting these experiments in hamsters as part of a broader expansion of our research in this area. These experiments will require a lengthy amount of time and at this point we feel that they are beyond the scope of this manuscript.

(14) Fig 4. The peak in exploratory behavior in winter females is counterintuitive and needs to be better discussed. IN general, the light dark behavior seems quite variable.

This is indeed a very interesting finding, which we have expanded upon in our manuscript as follows:

When raised under a winter-mimicking daylength, hamsters of either sex show a protracted peak in risk taking. In males, it is delayed beyond 80 days old, but the delay is substantially less in females. This is a counterintuitive finding considering that dopamine development in winter females appears to be accelerated. Our interpretation of this finding is that the timing of the risk-taking peak in females may reflect a balance between different adolescent developmental processes. The fact that dopamine axon growth is accelerated does not imply that all adolescent maturational processes are accelerated. Some may be delayed, for example those that induce axon pruning in the cortex. The timing of the risk-taking peak in winter female hamsters may therefore reflect the amalgamation of developmental processes that are advanced with those that are delayed – producing a behavioural effect that is timed somewhere in the middle. Disentangling the effects of different developmental processes on behaviour will require further experiments in hamsters, including the direct manipulation of dopamine activity in the nucleus accumbens and prefrontal cortex.

Full Reference List

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

Bari A, Robbins TW. 2013. Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in neurobiology 108:44–79. doi:10.1016/j.pneurobio.2013.06.005

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

Daubaras M, Bo GD, Flores C. 2014. Target-dependent expression of the netrin-1 receptor, UNC5C, in projection neurons of the ventral tegmental area. Neuroscience 260:36–46. doi:10.1016/j.neuroscience.2013.12.007

Eagle DM, Bari A, Robbins TW. 2008. The neuropsychopharmacology of action inhibition: crossspecies translation of the stop-signal and go/no-go tasks. Psychopharmacology 199:439– 456. doi:10.1007/s00213-008-1127-6

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Jonker FA, Jonker C, Scheltens P, Scherder EJA. 2015. The role of the orbitofrontal cortex in cognition and behavior. Rev Neurosci 26:1–11. doi:10.1515/revneuro-2014-0043

Kim B, Im H. 2019. The role of the dorsal striatum in choice impulsivity. Ann N York Acad Sci 1451:92–111. doi:10.1111/nyas.13961

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254-10.2011

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Ott T, Stein AM, Nieder A. 2023. Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons. Nat Commun 14:7537. doi:10.1038/s41467-023-43271-6

Phillips RA, Tuscher JJ, Black SL, Andraka E, Fitzgerald ND, Ianov L, Day JJ. 2022. An atlas of transcriptionally defined cell populations in the rat ventral tegmental area. Cell Reports 39:110616. doi:10.1016/j.celrep.2022.110616

Purohit AA, Li W, Qu C, Dwyer T, Shao Q, Guan K-L, Liu G. 2012. Down Syndrome Cell Adhesion Molecule (DSCAM) Associates with Uncoordinated-5C (UNC5C) in Netrin-1-mediated Growth Cone Collapse. The Journal of biological chemistry 287:27126–27138. doi:10.1074/jbc.m112.340174

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018b. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schlienger S, Yam PT, Balekoglu N, Ducuing H, Michaud J-F, Makihara S, Kramer DK, Chen B, Fasano A, Berardelli A, Hamdan FF, Rouleau GA, Srour M, Charron F. 2023. Genetics of mirror movements identifies a multifunctional complex required for Netrin-1 guidance and lateralization of motor control. Sci Adv 9:eadd5501. doi:10.1126/sciadv.add5501

Shao Q, Yang T, Huang H, Alarmanazi F, Liu G. 2017. Uncoupling of UNC5C with Polymerized TUBB3 in Microtubules Mediates Netrin-1 Repulsion. J Neurosci 37:5620–5633. doi:10.1523/jneurosci.2617-16.2017

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

Torres-Berrío A, Lopez JP, Bagot RC, Nouel D, Dal-Bo G, Cuesta S, Zhu L, Manitt C, Eng C, Cooper HM, Storch K-F, Turecki G, Nestler EJ, Flores C. 2017. DCC Confers Susceptibility to Depression-like Behaviors in Humans and Mice and Is Regulated by miR-218. Biological psychiatry 81:306–315. doi:10.1016/j.biopsych.2016.08.017

Vassilev P, Pantoja-Urban AH, Giroux M, Nouel D, Hernandez G, Orsini T, Flores C. 2021. Unique effects of social defeat stress in adolescent male mice on the Netrin-1/DCC pathway, prefrontal cortex dopamine and cognition (Social stress in adolescent vs. adult male mice). Eneuro ENEURO.0045-21.2021. doi:10.1523/eneuro.0045-21.2021

Private Comments

Reviewer #1

(12) The language should be improved. Some expression is confusing (line178-179). Also some spelling errors (eg. Figure 1M).

We have removed the word “Already” to make the sentence in lines 178-179 clearer, however we cannot find a spelling error in Figure 1M or its caption. We have further edited the manuscript for clarity and flow.

Reviewer #2

(1) The authors claim to have revealed how the 'timing of adolescence is programmed in the brain'. While their findings certainly shed light on molecular, circuit and behavioral processes that are unique to adolescence, their claim may be an overstatement. I suggest they refine this statement to discuss more specifically the processes they observed in the brain and animal behavior, rather than adolescence itself.

We agree with the reviewer and have revised the manuscript to specify that we are referring to the timing of specific developmental processes that occur in the adolescent brain, not adolescence overall.

(2) Along the same lines, the authors should also include a more substantiative discussion of how they selected their ages for investigation (for both mice and hamsters), For mice, their definition of adolescence (P21) is earlier than some (e.g. Spear L.P., Neurosci. and Beh. Reviews, 2000).

There are certainly differences of opinion between researchers as to the precise definition of adolescence and the period it encompasses. Spear, 2000, provides one excellent discussion of the challenges related to identifying adolescence across species. This work gives specific ages only for rats, not mice (as we use here), and characterizes post-natal days 28-42 as being the conservative age range of “peak” adolescence (page 419, paragraph 1). Immediately thereafter the review states that the full adolescent period is longer than this, and it could encompass post-natal days 20-55 (page 419, paragraph 2).

We have added the following statement to our methods:

There is no universally accepted way to define the precise onset of adolescence. Therefore, there is no clear-cut boundary to define adolescent onset in rodents (Spear, 2000). Puberty can be more sharply defined, and puberty and adolescence overlap in time, but the terms are not interchangeable. Puberty is the onset of sexual maturation, while adolescence is a more diffuse period marked by the gradual transition from a juvenile state to independence. We, and others, suggest that adolescence in rodents spans from weaning (postnatal day 21) until adulthood, which we take to start on postnatal day 60 (Reynolds and Flores, 2021). We refer to “early adolescence” as the first two weeks postweaning (postnatal days 21-34). These ranges encompass discrete DA developmental periods (Kalsbeek et al., 1988; Manitt et al., 2011; Reynolds et al., 2018a), vulnerability to drug effects on DA circuitry (Hammerslag and Gulley, 2014; Reynolds et al., 2018a), and distinct behavioral characteristics (Adriani and Laviola, 2004; Makinodan et al., 2012; Schneider, 2013; Wheeler et al., 2013).

References:

Adriani W, Laviola G. 2004. Windows of vulnerability to psychopathology and therapeutic strategy in the adolescent rodent model. Behav Pharmacol 15:341–352. doi:10.1097/00008877-200409000-00005

Hammerslag LR, Gulley JM. 2014. Age and sex differences in reward behavior in adolescent and adult rats. Dev Psychobiol 56:611–621. doi:10.1002/dev.21127

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Kalsbeek A, Voorn P, Buijs RM, Pool CW, Uylings HBM. 1988. Development of the Dopaminergic Innervation in the Prefrontal Cortex of the Rat. The Journal of Comparative Neurology 269:58–72. doi:10.1002/cne.902690105

Makinodan M, Rosen KM, Ito S, Corfas G. 2012. A critical period for social experiencedependent oligodendrocyte maturation and myelination. Science 337:1357–1360. doi:10.1126/science.1220845

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

Reynolds LM, Flores C. 2021. Mesocorticolimbic Dopamine Pathways Across Adolescence: Diversity in Development. Front Neural Circuit 15:735625. doi:10.3389/fncir.2021.735625

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette MP, Arvanitogiannis A, Flores C. 2018. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schneider M. 2013. Adolescence as a vulnerable period to alter rodent behavior. Cell and tissue research 354:99–106. Doi:10.1007/s00441-013-1581-2

Spear LP. 2000. Neurobehavioral Changes in Adolescence. Current directions in psychological science 9:111–114. doi:10.1111/1467-8721.00072

Wheeler AL, Lerch JP, Chakravarty MM, Friedel M, Sled JG, Fletcher PJ, Josselyn SA, Frankland PW. 2013. Adolescent Cocaine Exposure Causes Enduring Macroscale Changes in Mouse Brain Structure. J Neurosci 33:1797–1803. doi:10.1523/jneurosci.3830-12.2013

(3) Figure 1 - the conclusions hinge on the Netrin-1 staining, as shown in panel G, but the cells are difficult to see. It would be helpful to provide clearer, more zoomed images so readers can better assess the staining. Since Netrin-1 expression reduces dramatically after P4 and they had to use antigen retrieval to see signal, it would be helpful to show some images from additional brain regions and ages to see if expression levels follow predicted patterns. For instance, based on the allen brain atlas, it seems that around P21, there should be high levels of Netrin-1 in the cerebellum, but low levels in the cortex. These would be nice controls to demonstrate the specificity and sensitivity of the antibody in older tissue.

We do not study the cerebellum and have never stained this region; doing so now would require generating additional tissue and we’re not sure it would add enough to the information provided to be worthwhile. Note that we have stained the forebrain for Netrin-1 previously, providing broad staining of many brain regions (Manitt et al., 2011)

References:

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

(4) Figure 3 - Because mice tend to avoid brightly-lit spaces, the light/dark box is more commonly used as a measure of anxiety-like behavior than purely exploratory behavior (including in the paper they cited). It is important to address this possibility in their discussion of their findings. To bolster their conclusions about the coincidence of circuit and behavioral changes in adolescent hamsters, it would be useful to add an additional measure of exploratory behaviors (e.g. hole board).

Regarding the light/dark box test, this is an excellent point. We prefer the term “risk taking” to “anxiety-like” and now use the former term in our manuscript. Furthermore, our interest in the behaviour is purely to chart the development of adolescent behaviour across our treatment groups, not to study a particular emotional state. Regardless of the specific emotion or emotions governing the light/dark box behaviour, it is an ideal test for charting adolescent shifts in behaviour as it is well-characterized in this respect, as we discuss in our manuscript.

(5) Supplementary Figure 4,5 The authors defined puberty onset using uterine and testes weights in hamsters. While the weights appear to be different for summer and winter hamsters, there were no statistical comparison. Please add statistical analyses to bolster claims about puberty start times. Also, as many studies use vaginal opening to define puberty onset, it would be helpful to discuss how these measurements typically align and cite relevant literature that described use of uterine weights. Also, Supplementary Figures 4 and 5 were mis-cited as Supp. Fig. 2 in the text (e.g. line 317 and others).

These are great suggestions. We have added statistical analyses to Supplementary Figures 5 and 6 and provided Vaginal Opening data as Supplementary Figure 7. The statistical analyses confirm that all three characters are delayed in winter hamsters compared to summer hamsters.

We have also added the following references to the manuscript:

Darrow JM, Davis FC, Elliott JA, Stetson MH, Turek FW, Menaker M. 1980. Influence of Photoperiod on Reproductive Development in the Golden Hamster. Biol Reprod 22:443–450. doi:10.1095/biolreprod22.3.443

Ebling FJP. 1994. Photoperiodic Differences during Development in the Dwarf Hamsters Phodopus sungorus and Phodopus campbelli. Gen Comp Endocrinol 95:475–482. doi:10.1006/gcen.1994.1147

Timonin ME, Place NJ, Wanderi E, Wynne-Edwards KE. 2006. Phodopus campbelli detect reduced photoperiod during development but, unlike Phodopus sungorus, retain functional reproductive physiology. Reproduction 132:661–670. doi:10.1530/rep.1.00019

(6) The font in many figure panels is small and hard to read (e.g. 1A,D,E,H,I,L...). Please increase the size for legibility.

We have increased the font size of our figure text throughout the manuscript.

Reviewer #3

(15) Fig 1 C,D. Clarify the units of the y axis

We have now fixed this.

Full Reference List

Adriani W, Laviola G. 2004. Windows of vulnerability to psychopathology and therapeutic strategy in the adolescent rodent model. Behav Pharmacol 15:341–352. doi:10.1097/00008877-200409000-00005

Hammerslag LR, Gulley JM. 2014. Age and sex differences in reward behavior in adolescent and adult rats. Dev Psychobiol 56:611–621. doi:10.1002/dev.21127

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Kalsbeek A, Voorn P, Buijs RM, Pool CW, Uylings HBM. 1988. Development of the Dopaminergic Innervation in the Prefrontal Cortex of the Rat. The Journal of Comparative Neurology 269:58–72. doi:10.1002/cne.902690105

Makinodan M, Rosen KM, Ito S, Corfas G. 2012. A critical period for social experiencedependent oligodendrocyte maturation and myelination. Science 337:1357–1360. doi:10.1126/science.1220845

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

Reynolds LM, Flores C. 2021. Mesocorticolimbic Dopamine Pathways Across Adolescence: Diversity in Development. Front Neural Circuit 15:735625. doi:10.3389/fncir.2021.735625 Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schneider M. 2013. Adolescence as a vulnerable period to alter rodent behavior. Cell and tissue research 354:99–106. doi:10.1007/s00441-013-1581-2

Spear LP. 2000. Neurobehavioral Changes in Adolescence. Current directions in psychological science 9:111–114. doi:10.1111/1467-8721.00072

Wheeler AL, Lerch JP, Chakravarty MM, Friedel M, Sled JG, Fletcher PJ, Josselyn SA, Frankland PW. 2013. Adolescent Cocaine Exposure Causes Enduring Macroscale Changes in Mouse Brain Structure. J Neurosci 33:1797–1803. doi:10.1523/jneurosci.3830-12.2013