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Reply to the reviewers

Thank you very much for your editorial handling of our manuscript entitled 'A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis'. We have taken on board the reviewers' comments and thank them for their diligence and time in improving our manuscript.

Please find our responses to each of the comments below.

Reviewer(s)' comments

Reviewer #1

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Major comments:

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__1.1. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). __

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__Response: __The figure order has been revised according to the reviewer's suggestion, while still following eLife's formatting guidelines for naming supplementals. Thank you.

1.2. I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.

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Response: Thank you for your insightful suggestion regarding the inclusion of more CWI-related genes in the wheat module linked to the FgKnr4 fungal module F16, or vice versa. We did observe a co-regulated response between the wheat module W05 which is correlated to the FgKnr4 module F16. Namely, we observed an enrichment of oxidative stress genes including respiratory burst oxidases and two catalases (lines 304 - 313) in the correlated wheat module (W05). Early expression of these oxidative stress inducing genes likely induces the CWI pathway in the fungus, which is regulated by FgKnr4. Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Scaffolding protein-encoding genes are typically expressed earlier than the genes they regulate to enable pre-assembly with their interacting partners, ensuring that signaling pathways are ready to activate when needed. In this context, the CWI integrity MAPKs Bck1 and Mkk1 are part of module F05, which includes two chitin synthases and a glucan synthase. This module is highly expressed during the late symptomless phase. The MAPK Mgv1, found in module F13, is expressed consistently throughout the infection process, which aligns with the expectation that MAPKs are mainly post-transcriptionally regulated. Thank you for bringing our attention to this, this is now included in the discussion (lines 427 - 443) along with eigengene expression plots of all modules added to the supplementary (Figure 3 - figure supplement 1).

To explore potential shared functions of FgKnr4 with other genes in its module, we re-analyzed the high module membership genes within module F16, which includes FgKnr4, using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ). This analysis revealed that 8 out of 15 of these genes are associated with cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence of Knr4 results in cell division dysfunction (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Accordingly, we tested sensitivity of ΔFgknr4 to microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added Figure 7, and referred to in lines 338-348.

__Specific issues: __

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1.3. In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard.

__Response: __Thank you for your suggestion. We have amended the manuscript to include an additional panel that shows the dissected spikelet without its outer glumes, making the eye shaped diseased regions more visible in Figure 5.

__1.4. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. __

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Response: __Thank you for your insight. We have revised our conclusions based on this image to state that while ΔFgknr4 can colonise host tissue, it does so less effectively compared to the wild-type strain as we are unable to quantitatively evaluate fungal burden using image-colour thresholding due to the overlapping colours of the fungal cells and wheat tissues. Decreased host colonisation is evidenced by (i) reduced fungal hyphae proliferation, particularly in the thicker adaxial cell layer, (ii) collapsed air spaces in wheat cells, and (iii) increased polymer deposition at the wheat cell walls, indicating an enhanced defence response. __Figure 5 has been amended to include these observations in the corresponding figure legend and the resin images now include insets with detailed annotation.

__1.5. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. __

Response: __We have amended this to now include the data in __Figure 5 - figure supplement 2B, thank you.

Reviewer #2

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__Major issues: __

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2.1 If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?

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Response: __Thank you for raising this point regarding the role of FgKnr4 in the CWI pathway and the expectations for genes of shared function within the FgKnr4 module F16. We did observe that the module containing FgKnr4 (F16) was also correlated to a wheat module (W05) which was significantly enriched for oxidative stress genes. This pathogen-host correlated pattern led us to study module F16, which otherwise lacks significant gene ontology term enrichment, unique gene set enrichments, and contains few characterised genes. This is now highlighted in __lines 233-246. This underscores the strength of the WGCNA. By using high-resolution RNA-seq data to map modules to specific infection stages, we identified an important gene that would have otherwise been overlooked. This approach contrasts with other network analyses that often rely on the guilt-by-association principle to identify novel virulence-related genes within modules containing known virulence factors, potentially overlooking significant pathways outside the scope of prior studies. Therefore, our analysis has already benefited from several advantages of WGCNA, including the identification of key genes with high module membership that may be critical for biological processes, as well as generating a high-resolution, stage-specific co-expression map of the F. graminearum infection process in wheat. This point is now emphasised in lines 233-252. As discussed in response to reviewer 1, Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ) which would explain its clustering separate from the CWI pathway genes. The high module membership genes within module F16 containing FgKnr4 were re-analysed using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ), which found that 8/15 of these genes were related to cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence Knr4 leads to dysfunction in cell division. Accordingly, we tested sensitivity of ΔFgknr4 to the microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added as Figure 7 and referred to in lines 338-348.

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2.2. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.

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__Response: __We are in complete agreement with the reviewer and are not suggesting that FgKnr4 is an effector or virulence factor, we have been careful with our wording to indicate that FgKnr4 is simply necessary for full virulence and its disruption results in reduced virulence and have outlined how we believe FgKnr4 participates in a fungal signaling pathway required for infection of wheat.

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2.3. What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below) ____https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y____ DOI: 10.1371/journal.pone.0013021. The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis.

Response: __The 2016 New Phytologist gene regulatory network (GRN) by Guo et al. is large and comprehensive. However, only three of the eleven datasets are in planta, with just one dataset focusing on F. graminearum infection on wheat spikes. The other two in planta datasets involve barley infection and Fusarium crown rot. By combining numerous in planta and in vitro datasets, the previous GRNs lack the fine resolution needed to identify genetic relationships under specific conditions, such as the various stages of symptomatic and symptomless F. graminearum infection of mature flowering wheat plants. This limitation is highlighted in the 2016 paper itself. This network is expanded in the Guo et al., 2020 BMC genomics paper where it includes one additional in planta and nine in vitro datasets. However, the in planta dataset involves juvenile wheat coleoptile infection, which serves as an artificial model for wheat infection but is not on mature flowering wheat plants reminiscent of Fusarium Head Blight of cereals in the field. This model differs significantly in the mode of action of F. graminearum, notably DON mycotoxin is not essential for virulence in this context (Armer et al. 2024, https://pubmed.ncbi.nlm.nih.gov/38877764/ ). The Guo et al., 2020 paper still faces the same issues in terms of resolution and the inability to draw conclusions specific to the different stages of F. graminearum infection. Additionally, these GRNs use Affymetrix data, which miss over 400 genes (~ 3 % of the genome) from newer gene models. In contrast, our study addresses these limitations by analysing a meticulously sampled, stage- and tissue-specific in planta RNA-seq dataset using the latest reference annotation. Our approach provides higher resolution and insights into host transcriptomic responses during the infection process. The importance of our study in the context of these GRNs is now addressed in the introduction (__lines 85-92).

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2.4. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN. Many bioinformatic tools are available to identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?

__Response: __Thank you for your suggestions. In this study we have shown the association between the main fungal virulence factor of F. graminearum, DON mycotoxin, with wheat detoxification responses. Through this we have identified a set of tri5 responsive genes and validated this correlation in two genes belonging to the phenylalanine pathway and one transmembrane detoxification gene. Although we could validate more genes in this tri5 responsive wheat module, our paper aimed to investigate previously unstudied aspects of the F. graminearum infection process and how the fungus responded to changing conditions within the host environment. We accomplished this by characterising a gene within a fungal module that had limited annotation enrichment and few characterised genes. Tri5 on the other hand is the most extensively studied gene in F. graminearum and while the network we generated may offer new insights into tri5 responsive genes, this is beyond the scope of our current study. In addition to the tri5 co-regulated response, we have also demonstrated the coordinated response between the fungal module F16, which contains FgKnr4 that is necessary for tolerance to oxidative stress, and the wheat module W05, which is enriched for oxidative stress genes.

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While our co-expression network approach can be used to explore and validate other early downstream signaling and defense components in wheat cells, several challenges must be considered: (a) the poor quality of wheat gene calls, (b) genetic redundancy due to both homoeologous genes and large gene families, and (c) the presence of DON, which can inhibit translation and prevent many transcriptional changes from being realised within the host responses. Additionally, most plant host receptors are not transcriptionally upregulated in response to pathogen infection (most R gene studies for the NBS-LRR and exLRR-kinase classes), making their discovery through a transcriptomics approach unlikely. These points will be included in our discussion (lines 408-413), thank you.

Specific issues

• *

2.5. Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058____) impact the wheat module genes.

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Response: __Our goal was to show that wheat genes respond to the whole TRI cluster, not just individual TRI genes. Therefore, the tri5 mutant serves as a solid proof-of-concept, because TRI5 is essential for DON biosynthesis, the primary function of the TRI gene cluster, thereby representing the function of the cluster as a whole. This is now clarified in __lines 217-219. Additionally, the uncertainties surrounding other TRI mutants would complicate the question we were addressing-namely, whether a wheat module enriched in detoxification genes is responding to DON mycotoxin, as implied by shared co-expression patterns with the TRI cluster. For instance, the referenced TRI14 paper indicates that DON is produced in the same amount in vitro in a single media. Although the difference is not significant, the average DON produced is lower for the two Δtri14 transformants tested. Therefore, we cannot definitively rule out that TRI14 is involved in DON biosynthesis and extrapolate this to DON production in planta. Despite this, the suggestion is interesting, and would make a nice experiment but we believe it does not contribute to the overall aim of this study.

2.6. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?

__Response: __We agree that this would be an interesting comparison to make but unfortunately no dataset comparing in planta expression of the tri5 mutant within wheat spikes exists.

2.7. Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module. The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples.

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Response: __The 15 genes with the highest module membership were selected as initial candidates for further shortlisting from the 74 genes within module F16. In WGCNA, genes with high module membership (MM) (i.e. intramodular connectivity) are predicted to be central to the biological functions of the module (Langfelder and Horvath, 2008; https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559 ) and continues to be a metric to identify biologically significant genes within WGCN analyses (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-024-05366-0 Tominello-Ramirez et al., 2024; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151341/ ;Zheng et al., 2022; https://www.nature.com/articles/s41598-020-80945-3 Panahi and Hejazi et al 2021). Following methods by Mateus et al. (2019) (https://academic.oup.com/ismej/article/13/5/1226/7475138 ) key genes were defined as those exhibiting elevated MM within the module, which were also strongly correlated (R > |0.70|) with modules of the partner organism (wheat). We have clarified this point in the manuscript. Thank you for the suggestion. (__Lines 253-263).

2.____8. A list from every module that pass this criteria will be useful resource for functional characterization studies.

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__Response: __A supplementary spreadsheet has been generated which includes full lists of the top 15 genes with the highest module membership within the five fungal modules correlated to wheat modules and a summary of shared attributes among them. Thank you for this suggestion.

2.9. Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File?

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Response: For clarity, the TRI genes in module F12 are TRI3, TRI4, TRI11, TRI12, and TRI14 which was stated in Table 1. TRI5 clusters with its neighboring regulatory gene TRI6 in module F11, which exhibits a similar but reduced expression pattern compared to module F12. To improve clarity on this the TRI genes in module F12 are also listed in-text in line 168 and added to Figure 4. The enrichment and correlated relationship of W12 to a cluster's expression still imply a correlated response of the wheat gene to the TRI cluster's biosynthetic product (DON), which is absent in the Δtri5 mutant.

TRI14 and TRI12 are listed in PHI-base. TRI12 was mistakenly excluded due to an unmapped Uniprot ID, which were added separately in the spreadsheet. We will recheck all unmapped ID lists to ensure all PHI-base entries are included in the final output. Thank you for pointing out this error.

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2.10. What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.

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__Response: __This is a consequence of each entry having a separate PHI ID, which represents different interactions including inoculations on different cultivar. Cultivar and various experimental details were omitted from the spreadsheet to reduce information density, however the multiple PHI base ID's will be kept separate to make the data more user friendly when working with the PHI-base database. An explanation for this is now provided in the file's explanatory worksheet, thank you.

Reviewer #3:

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3.1. Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.

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__Response: __ In the wheat genome, only high-confidence gene calls are used by the global community (Choulet et al., 2023; https://link.springer.com/chapter/10.1007/978-3-031-38294-9_4 ) until a suitable and stable wheat pan-genome becomes available.

3.2. The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?

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Response: FPKM was calculated using the GenomicFeatures package and included on GitHub to enhance accessibility for other users. However, the input for WGCNA and this study as a whole was normalised counts rather than FPKM. The FPKM analysis was done to improve interoperability of the data for future users and made available on Github. To complement this, the information regarding FPKM calculation is now included in the methods section of the revised manuscript (line 491).

3.3. Do the authors have a Southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?

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__Response: __No, but the phenotype is attributed to the presence or absence of ZtKnr4, as the mutant was successfully complemented in multiple phenotypic aspects. This satisfies Koch's postulates which is the gold standard for reverse genetics experimentation (Falkow 1988; https://www.jstor.org/stable/4454582 ).

__3.4. Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs. __

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__Response: __Graphs have been modified to display the distribution of all samples, thank you.

3.5. Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707

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__Response: __Thank you this has now been amended.

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Referee #3

Evidence, reproducibility and clarity

The authors of the manuscript entitled "A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis" used a weighted gene co-expression network to identify Fusarium graminearum genes highly expressed during early symptomless infection of wheat. Based on its sequence and previous studies, authors selected FgKnr4 from the early symptomless Fusarium modules. The characterization of knockout strains revealed a role in morphogenesis, growth, cell wall stress tolerance, and virulence in F. graminearum and the phylogenetically distant fungus Zymoseptoria tritici.

The methods are properly described and statistical analysis are reasonable so reproducibility is possible. The RNA-seq dataset is already published and the authors provided a repository with the code used to create the co-expression network. However, I have the following questions:

• Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.
• The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?
• Do the authors have a southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?
• Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs.
• Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707

Referees cross-commenting

I agree with reviewer 1, the order in which the figures are called in the text is confusing. Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited.

I agree with reviewer 1, data of DON mycotoxin production in infected issues is need it to support statement in line 272-273.

I agree with Reviewer 2, the criteria to exclude genes from the final selection list should be explained.

Significance

The study showed, once again, that a weighted gene co-expression network is a great method to identify new genes of interest regardless of the organism or condition even if not very popular in the fungal pathogen field yet. The study proved that functions identified in a WGCN module from a pathogen have their opposite in the host module. The authors go beyond the theory and demonstrate the effect of the highest expressed gene during the early symptomless stage of infection in maize and wheat fungal pathogens.

Fungal pathogen, RNA-seq, metabolic models, metabolism, comparative genomics

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Referee #2

Evidence, reproducibility and clarity

Summary: The authors in this manuscript use "dual weighting" to identify clusters or modules of genes from the fungus F. graminearum (Fg) with coordinated expression patterns with genes in wheat modules - potentially uncover key regulators or pathways linking Fg genes with plant traits, including plant pathogenesis. As proof of concept, the authors use one of the fungal genes FgKnr4 identified in a fungal module that has strong link with the wheat module. They were able to show that this gene is likely involved in CWI pathway and affects virulence properties of the fungus

Major comments:

Does the WGCN provide useful framework to link fungal genes affecting plant traits? If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? This is not forthcoming. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.

Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058) impact the wheat module genes. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?

Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module.

The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples. Did similar exclusion criteria used for other modules and if so, how many genes in each module pass the criteria? For example, Did TRI5 in module F12 pass this criteria. A list from every module that pass this criteria will be useful resource for functional characterization studies.

Minor comments:

Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File? What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.

Referees cross-commenting

agree with both reviewers regarding clarification of Figures.

one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?

Significance

What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below)

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y DOI: 10.1371/journal.pone.0013021

The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN.

Many bioinformatic tools are available to Identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

A public mRNA-seq dataset from Dilks et al. (2019) for wheat spikelets infected by Fusarium graminearum was used to generate a dual weighted gene co-expression network (WGCN). Since colonization of the spike by F. graminearum progresses from spikelet to spikelet, thereby forming an infection-gradient from early to late stages, quasi spatio-temporal resolution for the transcriptomic dataset can be achieved by cutting the spike into equal pieces along this gradient (in this case cuts were done at rachis internodes 1-2, 3-4, 5-6, and 7-8. The authors created co-expression networks for both, fungal and plant genes, and cross-correlated them. They identify several modules specific for each infection stage. For further analysis, the authors focus on two module pairs. (1) the wheat module 12 (W12), which correlates to Fusarium module 12 (F12), and (2) the Fusarium module 16 (F16) and the correlated wheat modules 1 and 5 (W01/W05). The W12/F12 modules were deemed of interest because they were specific to the transition from symptomless to symptomatic infection stage. Here, the authors find genes related to mycotoxin production to be upregulated in the F12 module, while the W12 is enriched in genes involved in detoxification. F16 and W01/W05 are specific to the earliest stages of infection, and thus most likely involved in fungal virulence. Here, one of the key genes identified is FgKnr4, which the authors show to be important for fungal virulence, as gene knockout leads to a premature stop of disease progression. As the authors show that FgKnr4 is involved in activating cell wall-integrity mechanisms, and may function in oxidative stress-resistance, this reduced virulence may be the result a reduced ability of the fungus to withstand plant defense mechanisms. Interestingly, knocking out an orthologue of FgKnr4 in Zymoseptoria tritici led to similarly reduced virulence of this pathogenic fungus on wheat plant.

Comments:

Overall, I find the WGCN analysis to be very interesting and informative, especially because of the different stages of infection. As the dataset is made public (I believe), I think that this will be a really important resource for the community. The exemplary functional analysis of the F16/W01/W05 modules via FgKnr4 is very interesting and demonstrates that novel genes involved in virulence can be identified via this approach. A similar more detailed analysis of the W12/F12 modules with a focus on detoxification mechanisms in the plant (i.e. the W12 module) would be a very interesting bonus, but as much as I would be interested in reading about it, functional gene analyses in wheat are obviously time-consuming, and it is not essential to this manuscript. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. In contrast to the sometimes confusing data presentation, I find the table of correlated modules (table 1) very helpful, and obviously am happy to see that all data is available in the first author's GitHub account.

Referees cross-commenting

just to clarify in regards to my comment on Figures 5C-D, and Reviewer #3's comment "Regardless of figures 5C-D I am no expert in the field therefore I can only say they look like they have not been edited." - I didn't want to insinuate that the images have been edited. Based on the images provided, I just can't see what the authors state is shown. So this is not about editing/manipulation - just about image quality/choice. The phenotypic descriptions by the authors are quite detailed ("eye-shaped lesions", 'visibly reduced fungal burden'...), but at least for me, the images aren't good enough to illustrate and underpin their statements. Maybe better images are needed, maybe magnifications of the exact regions showing the phenotypes? But this is simply a matter of presentation, not of editing/manipulation.

Second, I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.

Significance

In summary, I think that the presented WGCN analysis of mRNA-seq data with quasi-spatio-temporal resolution is a very helpful approach to identify novel fungal virulence and plant immunity genes, and with the created datasets made public, this will be an interesting and valuable resource for the community. The identification and functional analysis of FgKnr4 works as proof-of-principle. If the data presentation is improved, I believe that this will be an interesting publication.

Transparent Peer Review

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Referee #3

Evidence, reproducibility and clarity

This study identified Cpn1, a fission yeast ortholog of human Caprin1, to be involved in heterochromatin re-establishment in S. pompe by potentially regulating heterochromatic transcript stability and localization. Moreover, Cpn1 was shown to be important for stress granule formation. Although the role of Cpn1 for heterochromatin establishment and granule formation is strong, the point that there is a crosstalk between the two is weaker and could be explained through multiple independent effects. Overall, the manuscript provides interesting new observations with regards to Cpn1 function that are adequate for publication, however, a few additional validations need to be performed to strengthen the crosstalk conclusion, which could be a new significant connection.

The following comments could be addressed before publication to improve the manuscript.

Specific Comments

1. The point that there is a "Crosstalk between heterochromatin integrity and cytoplasmic RNP granule formation" could be strengthened before publication, or the tone of the manuscript revised to reflect the issues below.

A) There is no data in this section showing a direct crosstalk between heterochromatin integrity and granules. The results show that certain mutants alter heterochromatin integrity the formation of PABP-containing granules. However, those proteins could have different functions in the nucleus, cytoplasm and upon stress. Thus, granule formation could be independent of heterochromatin perturbation. E.g. Loss of Ago1 could impact cytoplasmic RNA abundance/ stability and this therefore influence granule assembly. Similarly, Cpn1/Caprin could be a multifunctional protein in S. pombe and affect various aspects of RNA metabolism. The possibility, that these proteins have multiple different functions in the cell and heterochromatin integrity and cytoplasmic RNP granule formation could be due to different functions of those proteins and not due to a crosstalk should be discussed as well.

B) Figure 5 title says that "disruption of heterochromatin alters the formation of PABP-containing RNP granules". There is no data in this figure that shows that "disruption of heterochromatin" directly causes granules. Its rather that heterochromatin mutants show altered PABP-containing RNP granules. While this is fine, it should be pointed out that this is a correlation, with a direct connection being inferred.

C) The strongest connection of heterochromatin integrity to RNP granules is the accumulation of heterochromatic transcripts in those granules. Therefore, the manuscript could be strengthened by rearranging the sections/figures.

In addition, Fig. 5A shows PABP containing granules in unstressed conditions for rik1, clr4, ago1 loss. This suggests that those granules contain lots of polyadenylated RNA. Evidence is needed that the mutations studied are not affecting global cytoplasmic translation or mRNA decay (e.g. by puromycin staining and smRNA-FISH staining or qPCR (e.g. GAPDH)). Moreover, it needs to be shown that endogenous expression of Cpn1 is unchanged. If those perturbations affect Cpn1 (or Nxt3) levels, the granule phenotype could be solely due to changes in stress granule promoting proteins. 2. The work would be strengthened by adding some additional experiments. Specifically:

A) "Previous studies by ourselves and others have provided evidence that accumulation of transcripts on chromatin can impair heterochromatin assembly, possibly through increased formation of RNA-DNA hybrids". Increased RNA-DNA hybrids/ R-loop structures were shown to lead to genomic instability, which could lead to micronuclei formation and nuclei leakage in the cytoplasm during stress. It would be nice to provide close-up view images of those stress induced cytoplasmic granules, that contain cenRNA. Do they contain weaker DAPI signal in those granules, which would be indicative of micronuclei? Moreover, providing an additional stain using either an R-loop antibody, cGAS, or a nuclei membrane marker such as LAMIN B1 could be used to rule out micronuclei/ membranous assemblies.

B) RNA FISH and quantification for PABP foci in unstressed and stressed clr4Δ dhp1-2 cells is key, which should show the highest changes in foci and RNA accumulation in foci. Are those cells showing an even stronger effect on heterochromatin establishment?

C) Compared to clr4Δ cpn1Δ, do clr4Δ dhp1-2 cpn1Δ (or cpn1Δ and dhp1-2 cpn1Δ) form more stress induced granules? And do those granules contain more heterochromatic transcripts?

D) Is heterochromatic transcripts localization in -glucose induced granules also seen with heat shock? 3. Additional tests and quantification is needed to support that conclusion: "...whereas heterochromatin mutants show increased Pabp granule formation in absence of stress, they show a significant reduction in the average number of Pabp foci formed in the presence of stress,...". Different proteins could regulate/loss of proteins impact granule assembly in different manners, for example RNA localization, assembly, disassembly, foci number, foci size, % cells with foci etc. It looks like rik1Δ shows larger foci. Therefore, upon stress, there could be indeed fewer foci because they are larger. A quantification of foci area and total foci area per cell should support or reject that conclusion. Moreover, is the same trend also observed with starvation stress, or only heat shock? 4. The field generally believes G3BP1 is not an endonuclease, and therefore the authors might want to edit the section: " ...these could include, for example, Cpn1 binding partner Nxt3, the human ortholog of which, G3BP1, has been shown to function as an endoribonuclease for degradation of selected RNAs (63)." Although, it was shown that G3BP1 has endoribonuclease activity in that reference, this was never reproduced and is generally now accepted in the field that G3BP1 does not function as a endoribonuclease to regulate RNA homeostasis.

Minor comment:

1. Fig. 2 is a bit confusing. Maybe it could help if the controls - to rule out heterochromatin maintenance (B, C,D) - could be better grouped together or put into supplementary.
2. Fig 4 A, figures for Cpn1R6A-GFP upon glucose starvation is missing.
3. Fig 4 D, intensity is weaker in mkt1Δ, its difficult to see the granules. It looks like based on the image that there are less granules, but the quantification shows unchanged granules.
4. Fig 5 D/ Supl S5A, images with stress are missing for rik1 loss (the ones leading to qualifications in 5D)
5. Fig 5 C, rik1Δ, ago1Δ, co-colocalised with Cpn1-GFP are missing
6. Fig.6D, one arrow showing cytoplasmic foci is shifted. PABP stain needs to be added to highlight these are cytoplasmic PABP foci.
7. cen(dg) transcripts show lack of localization in granules in unstressed cells. The explanation why is a bit unclear. "It remained possible that these foci might rather be associated with RNA degradation, ...". Or RNA degradation happens in the nucleoplasm or cytoplasm. Moreover, in the discussion: "Although we were not able to detect stable accumulation of heterochromatic RNAs in these granules by RNA-FISH, we suspect that this may be because these granules are associated with RNA turnover, although it is also possible that they arise as a result of altered RNP homeostasis more broadly." This is confusing. If those granules form due to accumulation of heterochromatic RNAs, then they cannot be the sites for their degradation, because then they would disassemble, if heterochromatic RNAs are degraded.
8. Clr4Δ and other conditions shows accumulation of cen(dg) RNA-FISH at the centromeres. It would be informative to see if Cpn1-GFP shows colocalization, which could provide additional evidence for the two models in Fig. 7.
9. Fig. 6D/E: Its difficult to see changes of cen(dg) RNA-FISH intensity at the centromeres in the images. A close-up view should be provided without overexposure to indicate differences.

Significance

This manuscript begins to address a possible relationship between stress granule formation and the regulation of heterochromatin. This is an interesting connection, although the mechanism by which such these two processes are connected is unclear at this time.

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Referee #2

Evidence, reproducibility and clarity

In their manuscript titled "Fission yeast Caprin protein is required for efficient heterochromatin establishment", Zhang and colleagues describe the role of Caprin1 (Cpn1), the fission yeast ortholog of mammalian CAPRIN1, in the de novo establishment of heterochromatin. Using reporter assays for heterochromatin formation, they found that deletion of Cpn1 reduces H3K9 methylation, thereby impairing the de novo establishment of silencing, while the maintenance of silencing at centromeric repeats remains unaffected.

The authors demonstrate that Cpn1 interacts with the fission yeast orthologs of known human CAPRIN1 interaction partners, Nxt3 and Ubp3. These factors are then tested for their influence on the establishment of silencing.

Using microscopy to observe tagged Cpn1 and known stress granule markers, the authors show that Cpn1 localizes to stress granules. They also quantitatively assess the impact of Cpn1 interactors on stress granule formation. Additionally, the authors note that different RNAi mutants have stress granules even in the absence of envirmental stresses.

Finally, they investigate the subcellular localization of the cen(dg) transcript using single-molecule RNA FISH. They find that in heterochromatin mutant cells, cen(dg) transcripts localize to the nucleus and exhibit both nuclear and cytoplasmic localization under glucose starvation conditions. The authors also show, through quantification of RNA FISH and RT-qPCR, that cen(dg) transcripts accumulate in Cpn1 mutants.

Major comments:

The authors suggest that Cpn1 is requried for efficient degradation of RNA which in-turn helps the establishemnt of heterochromatin potentially by preventing the formation of R-loops. Yet the localization of Cpn1 under non-stressed conditions is cytoplasmic. Additionally the authors show that Cpn1 helps to limit the accumulation of heterochromatic trancripts on chromatin, yet there is no evidence for a nuclear pool of Cpn1 in these condtions.

In order to strengthen the link between these nuclear processes it is important that the authors follow up on some of the aspects detailed below.

Is Cpn1 also present in the nucleus in non-stressed conditions? This would be a pre-requesit for a direct mechanisic link for the model that the authors suggest. This could be, for example, adressed by LeptomycinB treatment followed by imaging of Cpn1. Such an experiment could reveal if the protein is shutteling between the nucleus and the cytoplasm.

In Fig 6 C the authors show co-localization of dh-transcripts with Papb1 under glucose starvation conditions. To strengthen their hypothesis of cen(dg) RNA binding/regulation by Cpn1 show and quantify co-localization of Cpn1 and cen(dg) transcripts.

The authors observe cytoplasmic cenRNA in Clr4-delta dhp1-2 cells. To substantiate the hypothesis that Cpn1 binds such transcript co-localization of Cpn1-GFP with cen(dg) transcripts should be examined.

Can the authors rule out that deletion of Cpn1 affects the RNA levels of proteins important for heterochromatin establishment? As Cpn1 could regulate the stability of mRNAs in the cytoplasm it might be worthwhile to consider also an indirect effect of Cpn1 deletion on the process of heterochromatin establishment. The study would benefit from a genomic characterization of Cpn1-delta cells using RNAseq or an extended discussion of this potential caveat.

Minor comments:

RNA-FISH images are labeled cen RNA, please provide consistent lables for the transcript throught the manuscript (cen(dg)).

Please display individual data points for replicate experiments when displaying qPCR results. This would give the reader more opportunities to judge the distribution of the data points. (Fig2 C,D,F,G,H, Fig6 F)

Fig 2 G: it is not immediately obvious that the two bar plots display different HOOD amplicons. The presentation of the data could be improved.

Significance

In the presented manuscript Zhang and colleauges place Cpn1 as a novel factor into the fission yeast RNAi pathway. This study suggests a link between RNAi and stress granule biology which would provide a novel connection of these fields. The manuscript will be of interest to a specialized audience, both in RNAi/heterochromation formation and stress granule biology and additionally would provide a novel function of a CAPRIN ortholog.

The manuscript is well written and overall the data is presented well. Furthermore the authors provide a solid genetic characterization of Cpn1's effect on heterochromatin establishment. While the manuscript provides several interesting observations their mechanistic link remains unclear and I belive that it would be very important to substaniate their observations with experiments supporting a direct mechanistic link between heterochromatin establishemnt and Cpn1.

Field of expertise: small RNA mediated heterochromatin formation, RNA biology, chromatin biology

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study, Zhang et al. identified a novel factor named Cpn1 promoting de novo heterochromatin establishment in the fission yeast Schizosaccharomyces pombe. The authors established an elegant genetic approach to identify mutants impaired in the de novo heterochromatin assembly, allowing the quantitative assessment of heterochromatin establishment in a highly reproducible manner. This approach allowed them to revisit potential candidates for heterochromatin establishment from a previous study, leading to the identification of Cpn1. Cells lacking Cpn1 display primarily defects in heterochromatin establishment but not maintenance at constitutive heterochromatin. While the function of Cpn1 was unknown, the authors established a functional link to stress granule formation, demonstrating that Cpn1 is the ortholog of the human RNA-binding protein CAPRIN1, likewise forming a complex with two other factors, Nxt3 and Ubp3. Mutating a putative RNA-binding RRG motif in Cpn1 largely phenocopied the establishment defect seen in the deletion mutant. Moreover, Cpn1 and its complex members co-localize with the stress granule marker poly(A)-binding protein, Pabp. Conversely, deleting Cpn1 or mutating its RRG motif resulted in a reduced number of stress granule formation.

Providing a further link between heterochromatin and stress granules, the authors showed that heterochromatin-deficient mutants accumulate Papb foci in the absence of stress cells, which was largely dependent on Cpn1. Conversely, the number of stress granules was reduced under stress conditions in these mutants, suggesting that heterochromatic transcripts compete with canonical RNPs that form stress granules. The molecular mechanism by which Cpn1 contributes to heterochromatin establishment remains unclear, though. In contrast to Mkt1, another establishment factor previously studied by the authors, no heterochromatic transcripts were found to be associated with Cpn1 when performing RNA-IP (RIP) experiments. The authors then analyzed pericentromeric transcripts (cen RNA) by smRNA-FISH. Deleting the H3K9 methyltransferase Clr4 resulted in the formation of nuclear foci that co-localized with the centromeric histone variant CENP-A. Glucose starvation increased the number of foci, which were also found in the cytoplasm under this stress condition and partially co-localized with Pabp. Notably, inactivating the exoribonuclease Dph1/Xrn2 also resulted in increased nuclear foci formation and accumulation in the cytosol, which was prevented when Cnp1 was absent. Hence, the authors proposed a model, by which Cpn1 limits accumulation of heterochromatic transcripts on chromatin by facilitating their export and cytoplasmic degradation by Dhp1.

Major comments

1. The authors suggest that Cnp1 contributes to heterochromatin establishment by facilitating the removal of excessive heterochromatic transcripts from chromatin. Nevertheless, despite the accumulation of pericentromeric transcripts inside the nucleus in clr4∆, direct evidence for their accumulation on chromatin remains elusive. While the authors cautiously avoid assigning Cnp1 a definite role in heterochromatic transcripts removal, investigating RNA:DNA hybrid accumulation through DNA-RNA immunoprecipitation (DRIP) could strengthen their conclusions. Given the successful application of DRIP by the authors in their Mkt1 study (Taglini et al., 2020) and its prior used by others (PMID: 28404620), this approach appears feasible and judicious when appropriate controls are implemented.
2. While the data support the hypothesis that Cpn1 binds RNA, the authors could not detect Cpn1 association with heterochromatin transcripts. This could be due to transient interactions and their fast turnover, as the authors suggested. The authors could repeat the RIP experiments in mutants that prevents turnover of transcripts, for instance in dhp1-2 and rrp6∆ mutants.

Minor comments:

1. How was the quantification of Pabp and Cpn1 foci performed? Little information is provided ("images were [...] exported to ImageJ analysis"). Given the presence of additional (diffuse) signals even under stress conditions, I'm wondering how foci were distinguished from background? Was there a threshold for signals considered to be 'foci' versus background? The authors should give a more detailed description in the figure legends or Materials & Methods section.
2. How was the relative sm-FISH intensity in Figure 6D and E determined? Have there been internal controls to ensure that hybridization efficiency was comparable for different strains/samples?

Significance

The is an intriguing study that provides several functional links between heterochromatin establishment and stress responses, using a combination of elegant yeast genetics, imaging, biochemical approaches and proteomics. While it was previously shown that heterochromatic transcripts can accumulate on chromatin interfering with heterochromatin assembly (Broenner et al., 2017), this study conceptionally advances our understanding of this process by describing a potential role for Cpn1 in facilitating nuclear export of heterochromatic transcripts. This study further describes the conservation of the human CAPRIN1 complex and its role in cytosolic stress granule assembly in yeast and therefore will be of broad interest for researchers interested in heterochromatin assembly and RNP homeostasis. A limitation of this study is the lack of a distinct molecular mechanism by which Cpn1 promotes heterochromatin establishment. Performing additional experiments could strengthen the authors' arguments and contribute to a better understanding of the underlying mechanism(s).

I have a longstanding expertise in heterochromatin assembly, transcriptional silencing and yeast genetics using S. pombe and S. cerevisiae as model systems.

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Reply to the reviewers

Manuscript number: RC-2024-02546

Corresponding author: Woo Jae, Kim

1. General Statements

The goal of this study is to provide the insights of one specific neuron ‘SIFa’ controls interval timing behavior by its receptor ‘SIFaR’ through neuropeptide relay. Interval timing, or the sense of time in the seconds to hours range, is important in foraging, decision making, and learning in humans via activation of cortico-striatal circuits. Interval timing requires completely distinct brain processes from millisecond or circadian timing. In summary, interval timing allows us to subjectively sense the passage of physical time, allowing us to integrate action sequences, thoughts, and behavior, detect developing trends, and predict future consequences.

Many researchers have tried to figure out how animals, including humans, can estimate time intervals with such precision. However, most investigations have been conducted in the realm of psychology rather than biology thus far. Because the study of interval timing was limited in its ability to intervene in the human brain, many psychologists concentrated on developing convincing theoretical models to explain the known occurrence of interval timing.

To overcome the limits of studying interval timing in terms of genetic control, we have reported that the time investment strategy for mating in Drosophila males can be a suitable behavioral platform to genetically dissect the principle of brain circuit mechanism for interval timing. For example, we previously reported that males prolong their mating when they have previously been exposed to rivals (Kim, Jan & Jan, "Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals" Nature Neuroscience, 2012) (Kim et al, 2012), and this behavior is regulated by visual stimuli, clock genes, and neuropeptide signaling in a subset of neurons (Kim, Jan & Jan, “A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating” Neuron, 2013) (Kim et al, 2013). And we also reported that the sensory inputs are required for sexual experienced males to shorten their mating time (Lee, Sun, et al, “Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster” PLOS genetics, 2023) (Lee et al, 2023).

Throughout their lives, all animals must make decisions in order to optimize their utility function. Male reproductive success is determined by how many sperms successfully fertilize an egg with a restricted number of investment resources. To optimize male reproductive fitness, a time investment strategy has been devised. As a consequence, we believe that the flexible responses of mating duration to different environmental contexts in Drosophila males might be an excellent model to investigate neural circuits for interval timing.

The most well-known features of mammalian modulating energy homeostasis between the gut and the brain is one of the most intensively studied neuro-modulatory circuits via the neuronal relay of neuropeptides. In this article, we report that SIFa controls two alternate interval timing behaviors through neuropeptide relay signaling by SIFaR and other important neuropeptides and transmits the internal states of the male brain into decision making. According to our findings, male Drosophila utilize SIFa-SIFaR signaling modulating LMD and SMD behaviors. During our investigation in this regulation, we found a subset of cells that express SIFaR in SOG and AG region are important for the modulation of interval timing behaviors. Furthermore, we discovered a neuropeptide named Corazonin (Crz) which expressed in SIFaR is important for both LMD and SMD behaviors.

Our discovery of neuropeptide relay of SIFa-SIFaR-Crz-CrzR in male Drosophila in modulating interval timing behaviors will be a huge step forward in our knowledge of interval timing behavior.

2. Point-by-point description of the revisions

Reviewer #1

Comment 1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.

__ Answer:__ We are grateful for the insightful feedback regarding the structure of our data presentation. In response to your valuable suggestion, we have made adjustments in this revised version by downsizing the diagram and ensuring the spacing between the panels.

Comment 2. The use of three independent RNAi lines to knock down SIFaR expression is experimentally solid, as the common phenotype observed with all 3 lines supports the conclusion that the SIFaR is important for mating duration choice. However, the authors have not tested whether these lines effectively reduce SIFaR expression, nor whether the GAL80 constructs used to delimit knockdown are able to effectively do so. This makes it hard to make definitive conclusions with these manipulations, especially in the face of negative results. A lack of complete knockdown is suggested by the fact that the F24F06 driver rescues lethality when used to express SIFaR in the B322 mutant background, but does not itself produce lethality when used to express SIFaR RNAi. The authors should either conduct experiments to determine knockdown efficiency or explicitly acknowledge this limitation in drawing conclusions from their experiments. A similar concern relates to the CrzR knockdown experiments (eg Figure 7).

   __Answer:__ We appreciate the reviewer's attention to the details of our experimental design. Indeed, the validation of SIFaR-RNAi efficiency is crucial for interpreting our results accurately. In our initial experiments, we focused on the consistent phenotypic outcomes across the three independent RNAi lines, which collectively suggest the importance of SIFaR in LMD and SMD behaviors. However, we recognize the importance of confirming the effectiveness of our RNAi constructs in reducing SIFaR expression. Initially, we incorporated experiments utilizing *elav-GAL80* to demonstrate that the SIFaR knockdown mediated by the *elavc155* driver is sufficient to eliminate LMD and SMD behaviors. The corresponding results are presented in Figure 1C-D, with a detailed description provided in the manuscript as detailed below.

"The inclusion of elav-GAL80, which suppresses GAL4 activity in a pan-neuronal context, was found to restore both LMD and SMD behaviors when SIFaR was knocked down by a pan-neuronal elavc155 driver (Fig. 1C-D). This observation suggests that the reduction in SIFaR expression mediated by the elavc155 driver is sufficient to significantly impair LMD and SMD behaviors."

   In response to the comments, we have conducted a thorough reevaluation in our revised manuscript. Specifically, we have confirmed the efficiency of the SIFaR-RNAi line HMS00299, which exhibited the most pronounced phenotype when co-expressed with the tub-GAL4 and nSyb-GAL4 drivers, using quantitative real-time PCR (qRT-PCR). It has come to our attention that we omitted mentioning the embryonic lethality induced by the HMS00299 line when combined with either tub-GAL4 or nSyb-GAL4 drivers, which is consistent with the homozygous lethality observed in the *SIFaRB322* mutant. To address this, we have performed qRT-PCR experiments by crossing the HMS00299 line with tub-GAL4; tub-GAL80ts, allowing for the temporary knockdown of SIFaR specifically during the adult stage. We utilized w-/SIFaR-RNAis as a control in these experiments. The outcomes are illustrated in Figure 1E, and we have made the necessary modifications and additions to the manuscript to accurately reflect the efficiency of the SIFaR-RNAi line as detailed below.

"To ensure that RNAi did not have an off-target effect, we tested three independent RNAi strains and found that all three RNAi successfully disrupted LMD/SMD when expressed in neuronal populations. (Fig. S1E-J). We chose to use the HMS00299 line as SIFaR-RNAi for all our experiments because it efficiently disrupts LMD/SMD without UAS-dicer expression. Employment of broad drivers, including the tub-GAL4 and the strong neuronal driver nSyb-GAL4, with HMS00299 line consistently results in 100% embryonic lethality (data not shown). This phenotype mirrors the homozygous lethality observed in the SIFaRB322 mutant. The efficiency of HMS00299 SIFaR-RNAi lines was also validated through quantitative PCR analysis (Fig. 1E). Consequently, we infer that the knockdown of SIFaR using the HMS00299 line nearly completely diminishes the levels of the SIFaR protein."

We also examined the knockdown efficiency of CrzR in the experiments related to Figure 8 (revised version), following a similar approach (Fig. S7K).

Comment 3. Most of the behavioral experiments lack traditional controls, for example flies that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.

• *Answer: We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. Consequently, in accordance with the reviewer's recommendation, we conducted genetic validation experiments on novel genetic crosses, including SIFaR-RNAi/+, CrzR-RNAi/+, and GAL4NP5270/+, and incorporated the results in the supplementary figures (Supplementary information 1). We have made the necessary modifications and additions to the manuscript as below.

"Given those genetic controls, as evidenced by consistent exhibition of normal LMD and SMD behaviors (Supplemental information 1), the observed reduction in SIFaR expression, driven by elavc155, is deemed sufficient to induce significant disruptions in LMD and SMD behaviors."

However, we understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"__Mating Duration Assays for Successful Copulation__The mating duration assay in this study has been reported (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023). To enhance the efficiency of the mating duration assay, we utilized the Df(1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR) (Parks et al. 2004; Yapici et al. 2008). Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males (Yapici et al. 2008). We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For group reared (naïve) males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female into the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023; Huang et al. 2024; Zhang et al. 2024). Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test."

   We appreciate the reviewer's inquiry regarding the genetic background of our experimental lines. In response to the comments, we would like to clarify the following. All of our GAL4, UAS, or RNAi lines, which were utilized as the virgin female stock for outcrosses, have been backcrossed to the Canton-S (CS) genetic background for over ten generations. The majority of these lines, particularly those employed in LMD assays, have been maintained in a CS backcrossed status for several years, ensuring a consistent genetic background across multiple generations. Our experience has indicated that the genetic background, particularly that of the X chromosome inherited from the female parent, plays a pivotal role in the expression of certain behavioral traits. Therefore, we have consistently employed these fully outcrossed females as virgins for conducting experiments related to LMD and SMD behaviors. It is noteworthy that, in contrast to the significance of genetic background for LMD behaviors, we have previously established in our work (Lee *et al*, 2023) that the genetic background does not significantly influence SMD behaviors. This distinction is important for the interpretation of our findings. To provide a comprehensive understanding of our experimental design, we have detailed the genetic background considerations in the __"Materials and Methods"__ section, specifically in the subsection __"Fly Stocks and Husbandry"__ as outlined below.

"To reduce the variation from genetic background, all flies were backcrossed for at least 3 generations to CS strain. For the generation of outcrosses, all GAL4, UAS, and RNAi lines employed as the virgin female stock were backcrossed to the CS genetic background for a minimum of ten generations. Notably, the majority of these lines, which were utilized for LMD assays, have been maintained in a CS backcrossed state for long-term generations subsequent to the initial outcrossing process, exceeding ten backcrosses. Based on our experimental observations, the genetic background of primary significance is that of the X chromosome inherited from the female parent. Consequently, we consistently utilized these fully outcrossed females as virgins for the execution of experiments pertaining to LMD and SMD behaviors. Contrary to the influence on LMD behaviors, we have previously demonstrated that the genetic background exerts negligible influence on SMD behaviors, as reported in our prior publication (Lee et al, 2023). All mutants and transgenic lines used here have been described previously."

Comment 4. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".

* * Answer: We are grateful to the reviewer for highlighting the potential for confusion among readers regarding the visualization methods used in our figures. In response to this valuable feedback, we have now included a more detailed explanation of the graph visualization techniques in the legends of Figure 1, as detailed below. This additional information should enhance the clarity and understanding of the figure for all readers.

"In the mating duration (MD) assays, light grey data points denote males that were group-reared (or sexually naïve), whereas blue (or pink) data points signify males that were singly reared (or sexually experienced). The dot plots represent the MD of each male fly. The mean value and standard error are labeled within the dot plot (black lines). Asterisks represent significant differences, as revealed by the unpaired Student’s t test, and ns represents non-significant differences (*p* *

Comment 5.* The authors have consistently conflated overlap of neuronal processes with synaptic connections. Claims of synaptic connectivity deriving solely from overlap of processes should be tempered and qualified.

• For example, they say (Lines 201-202) "These findings suggest that SIFa neurons and GAL424F06-positive neurons form more synapses in the VNC than in the brain." This is misleading. Overlap of 24F06-LexA>CD8GFP and SIFa-GAL4>CD8RFP tells us nothing about synapse number, or even whether actual synapses are being formed.*

• *Answer: We sincerely thank the reviewer for their insightful and constructive feedback regarding the interpretation of our data. We acknowledge the important point raised about the limitations of inferring synapse numbers from the overlap of membrane GFP and RFP signals. We fully concur that more specific techniques, such as the GRASP method, are necessary to accurately quantify synapse numbers, as we have demonstrated in subsequent sections of our manuscript. In the section where we describe the SIFa-SIFaR neuronal architecture labeled with membrane GFP and RFP, we recognize the need for caution in not overstating the implications of these findings as indicative of synapse formation. In light of the reviewer's comments, we have revised our discussion to more accurately reflect the nature of the SIFa-SIFaR neuronal arborizing patterns, as detailed below. This revision aims to provide a more nuanced interpretation of our observations and to align with the current scientific understanding of synaptic quantification.

"As previously reported, SIFa neurons arborize extensively throughout the CNS, but the neuronal processes of GAL424F06-positive neurons are enriched in the optic lobe (OL), sub-esophageal ganglion (SOG), and abdominal ganglion (AG) (GFP signal in Fig. 2F). Neuronal processes that are positive for SIFa and SIFaR strongly overlap in the prow (PRW), prothoracic and metathoracic neuromere (ProNm and MesoNm), and AG regions (yellow signals in Fig. S3A). We quantified these overlapping neuronal processes between SIFa- and SIFaR-positive neurons and found that approximately 18% of SIFa neurons and 52% of GAL424F06-positive neurons overlap in brain (Fig. S3B, C), whereas approximately 48% of SIFa and 54% of GAL424F06-positive neurons overlap in VNC (Fig. S3D, E). These findings suggest that SIFa neurons and GAL424F06-positive neurons form more neuronal processes in the VNC than in the brain."

• * Lines 210-211: "The overlap of DenMark and syt.EGFP signals was highly enriched in both SOG and ProNm regions, indicating that these regions are where GAL424F06 neurons form interconnected networks". This is misleading. Overlap of DenMark and syt.EGFP does not indicate synapses (especially since these molecules can be expressed outside the expected neuronal compartment if driven at high enough levels).*

• *Answer: We are grateful for the reviewer's critical insights regarding our interpretation of the DenMark and syt.eGFP experiments. We acknowledge the reviewer's point that the overlap of DenMark and syt.eGFP signals does not conclusively indicate synapses and that some of these signals can be expressed outside the expected neuronal compartments, particularly at high levels.

  It is important to note that DenMark and syt.eGFP are markers of synaptic polarity. In the original publication of DenMark, the authors demonstrated that while these two markers are closely apposed, they do not necessarily overlap, as seen in the labeled yellow areas. They concluded that these areas could represent closely apposed regions where "LNv neurons establish presynaptic contacts within the aMe, suggesting that these contacts are on the postsynaptic sites of the LNv neurons themselves. (Nicolaï et al, 2010)" The authors also observed that DenMark-enriched structures appear juxtaposed to, rather than coexpressed with, syt.eGFP, indicating a potential for synapse formation between R neurons within the eb. In contrast, projections to the suboesophageal ganglion, which show strong Syt–GFP expression, are devoid of DenMark, suggesting a different interpretation of the signals (Nicolaï et al, 2010).

  Building on these findings, we have reanalyzed our data with caution (as shown in Figure S3). In the SOG region, where we observed strong yellow signals, these were not limited to cell bodies but also extended to the middle region filled with neural processes. Upon close examination of the DenMark and syt.eGFP signals, we confirmed that these yellow signals are closely juxtaposed, suggesting the possibility of synapse formation between SIFaR24F06 neurons within the SOG. We emphasize that this interpretation is based on the original findings from the DenMark study. To provide clarity for general readers, we have added further explanations regarding the interpretation of these signals, as detailed below. We believe that our revised analysis and the additional explanations will help to clarify the potential implications of our findings, while also acknowledging the limitations and the need for further investigation.

"DenMark-enriched structures, localized within the SOG, are observed in close apposition to syt.eGFP signals, as indicated by the white-dashed circles (Fig. S3Fa). This spatial relationship suggests that SIFaR-expressing neurons, identified by GAL424F06 labeling, may form synapses with one another within the SOG. The colocalization of yellow signals resulting from the interaction between DenMark and syt.eGFP has been previously interpreted and validated by other researchers, supporting our observation (Nicolaï 2010,Kennedy 2018). In contrast to the yellow signals observed in the SOG, which are indicative of neural processes, the yellow signals detected in the ProNm appear to be associated with cell bodies rather than neural processes, as DenMark signals are often observed to leak out (as shown in Fig. S3Fb) (Nicolaï 2010,Kennedy 2018). Despite the presence of juxtaposed DenMark and syt.GFP signals in the ProNm, the interpretation of the yellow signals as potential synapses between SIFaR neurons remains an open question (indicated by the question mark in Fig. S3K).

• • Lines 320-322: "Neurons expressing Crz exhibit robust synaptic connections with SIFaR24F06 neurons located in the PRW region of the SOG in the brain (panels of Brain and SOG in Fig. 5A)". This is again misleading. They are not actually measuring synapses here, but instead looking at area of overlap between neuronal processes of Crz and SIFaR cells.*

  Answer: We sincerely appreciate the reviewer's critical feedback regarding our initial data interpretation. We acknowledge the important distinction that overlapping membrane markers do not provide a direct measure of synapse formation. In line with the reviewer's suggestion, we have revised the relevant sentence to more accurately reflect this understanding, as detailed below.

"Neurons expressing Crz were observed in close proximity to SIFaR24F06-expressing neurons within the PRW-SOG of the brain (panels of Brain and SOG in Fig. 6A)."

• * In Figs 3B and S4A, they are claiming that all neuronal processes within a given delineated brain area are synapses. The virtual fly brain and hemibrain resource have a way to actually identify synapses. This should be used in addition to the neuron skeleton. Otherwise, it is misleading to label these as synapses.*

  Answer: We are grateful for the reviewer's insightful comments that highlighted the potential for misleading information in our previous submission. Upon careful reexamination of the virtual fly brain model, we have made the necessary corrections and updated the figures in our revised manuscript (Figure 3B and S4B). This reanalysis has allowed us to further substantiate our findings, confirming that SIFa neurons indeed establish dense synaptic connections with multiple regions of the central brain.

• * Furthermore, measuring the area of GRASP signal is not the same as quantifying synapses. We don't know if synapse number changes (eg in lines 240-242).*

• Answer: We sincerely appreciate the reviewer's valuable suggestion regarding our quantification methods for assessing synaptic changes using GRASP signals. We acknowledge the reviewer's accurate observation that GRASP signals alone cannot provide an exact quantification of synapse number changes. In response to this feedback, we have employed the 'Particle analysis' function of ImageJ to infer the number of synapses from GRASP signals, clearly labeling them as 'number of particles' (as exemplified in Figures S4G and S4J). Additionally, we have compared the average size of each particle to enable a more precise comparison of synapse number changes (as shown in Figures S4H and S4K). While it is true that GRASP signals should not be directly equated with synapse counts, the quantification of GRASP signal intensity can still provide insights into the underlying synaptic connectivity, as described in the original GRASP paper (Feinberg et al, 2008a). Following this approach, previous studies have used signal intensity quantifications to draw conclusions about changes in synaptic specificity in various mutants. Since our methods for measuring GRASP intensity are consistent with the original techniques, we have updated our Y-axis labeling to reflect 'normalized GFP intensity (Norm. GFP Int.)', as exemplified in Figure 4. This change aims to provide a clearer and more accurate representation of our data.

Comment 6.* In general, the first part of the manuscript (implicating SIFaR in mating duration) is much stronger than the second part, which attempts to demonstrate that SIFa acts through Crz-expressing neurons to induce its effects. The proof that SIFa acts through Crz-expressing neurons to modify mating duration is tenuous. The most direct evidence of this, achieved via knockdown on Crz in SIFaR-expressing cells, is relegated to supplemental figures. The calcium response of the Crz neurons to SIFa neuron activation (Fig. 6) is more of a lack of a decrease that is observed in controls. Also, this is only done in the VNC. Why not look in the brain, because the authors previously stated a hypothesis that the "transmission of signals through SIFaR in Crz-expressing neurons is limited to the brain" (lines 381-382)?

Furthermore, the authors suggest that Crz acts on cells in the heart to regulate mating duration. It would be useful to add a discussion/speculation as to possible mechanisms for heart cells to regulate mating decisions. Is there evidence of CrzR in the heart? The SCope data presented in Fig. 7I-L and S7G-H is hard to read.*

• Answer: We sincerely appreciate the reviewer's constructive feedback on the section of our manuscript that discusses the role of the SIFa-SIFaR connection in regulating mating duration. We understand that the initial presentation may not have been sufficiently convincing. As we detailed in our previous biorXiv preprint (Wong et al, 2019), we conducted a comprehensive screen of numerous neuropeptides and their receptors that mediate SIFa signals through SIFaR and added those data in Supplementary Table S1 and S2. Among these, Crz was identified as a key neuropeptide in this pathway and is also well-documented for its role in mating duration (Tayler et al, 2012). Our data clearly demonstrate that Crz neurons are responsive to the activity of SIFa neurons, supporting the validity of this connection. Additionally, in another manuscript focusing on the input signals for SIFa (Kim et al*, 2024), we established that CrzR does not function in SIFa neurons, confirming the bidirectional nature of SIFa-to-Crz signaling.

  Inadvertently, we had relegated the Crz knockdown results to supplementary figures, under the assumption that our screening results regarding the relationship between SIFaR and neuropeptides were already well-covered (Wong et al, 2019). In light of the reviewer's comments, we have now relocated the Crz knockdown results, particularly those involving SIFaR-expressing cells, to the main figures (Figure 6F-G). We have also included a more detailed description of our previous screening results within the manuscript, as outlined below, to provide a more comprehensive understanding of our findings.

"Furthermore, the Crz peptide and Crz-expressing neurons have been characterized as pivotal relay signals in the SIFa-to-SIFaR pathway, which is essential for modulating interval timing behaviors (Wong 2019)."

   We greatly appreciate the reviewer's critical and constructive feedback regarding the detection of SIFa-to-Crz long-distance signaling, particularly their observation that this signaling is detectable from the brain to the VNC but not between brain regions. In response to the reviewer's suggestions, we have made the following adjustments to our manuscript:

1. We have relocated our SIFa-Crz GCaMP data pertaining to the VNC region to the Figures 6L-O) to maintain focus on the primary findings within the main text.
2. Our deeper analysis has led to the identification of two cells in the Super Intermediate Protocerebrum (SIP) regions that coexpress both Crz and SIFaR24F06, as well as OL cells (Figure 6D-E).
3. We have included GCaMP data from the brain region in the main figure to provide a comprehensive view of the signaling dynamics (Fig. 6P-R and Fig. S6N-P).
4. Upon examining the SIFa-to-Crz signaling through GCaMP calcium imaging, we observed that the calcium levels in Crz+/SIFaR+ SIP neurons consistently decreased upon SIFa activation (Figure 6P-R). In contrast, the calcium signals in Crz+/SIFaR+ OL neurons increased upon SIFa activation, similar to the pattern observed in Crz+ AG neurons in the VNC (Figure 6M-O and Figure S6N-P).

5. We have summarized these findings in Figure 6S and provided a detailed description of the results in the manuscript, as outlined below. "To elucidate the direct response of Crz neurons to the activity of SIFa neurons, we conducted live calcium (Ca2+) imaging in the Super Intermediate Protocererbrum (SIP), OL and AG region of the VNC, where Crz neurons are situated (Fig. 6D, Fig. S6M). Upon optogenetic stimulation of SIFa neurons, we observed a significant increase in the activity of Crz in OL and AG region (Fig. 6L-O, Fig. S6N-P), evidenced by a sustained elevation in intracellular Ca2+ levels that persisted in a high level before gradually declining to baseline levels, where the cells in top region of the SIP exhibit consistently drop down after stimulated the SIFa neurons (Fig. 6P-R). These calcium level changes were in contrast to the control group (without all-trans retinal, ATR) (Fig. 6L-R, Fig. S6N-P). These findings confirm that Crz neurons in OL and AG are activated in response to SIFa neuronal activity, but the activity of Crz neurons in SIP are inhibited by the activition of SIFa neuron, reinforcing their role as postsynaptic effectors in the neural circuitry governed by SIFa neurons. Moreover, these results provide empirical support for the hypothesis that SIFa-SIFaR/Crz-CrzR long-range neuropeptide relay underlies the neuronal activity-based measurement of interval timing."

   We are grateful for the reviewer's opportunity to elaborate on the intriguing findings concerning the expression of CrzR in the heart and its potential link to mating duration. In the context of traditional interval timing models (Meck et al, 2012; Matell, 2014; Buhusi & Meck, 2005), the role of a pacemaker in generating a temporal flow for measuring time is considered essential. The heart, being a well-known pacemaker organ in animals, provides a compelling framework for our discussion. In response to the reviewer's insightful comments, we have expanded upon our hypotheses in the DISCUSSION section, exploring the possible connections between cardiac function and the regulation of mating duration. Our reflections on this topic are detailed as follows:

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"It has been reported that the interaction between the brain and the heart can influence time perception in humans (Khoshnoud et al, 2024). Heart rate is governed by intrinsic mechanisms, such as the muscle pacemaker, as well as extrinsic factors including neural and hormonal inputs (Andersen et al, 2015). Moreover, the pacemaker function is essential for the generation of interval timing capabilities (Meck et al, 2012; Matell, 2014; Buhusi & Meck, 2005), with the heart being recognized as the primary pacemaker organ within the animal body. Consequently, the CrzR in the fly heart may respond to the Crz signal sent by SIFaR+/Crz+ cells and modulate the heart rate, thereby impacting the perception of time in male flies."

   We appreciate the reviewer's interest in the expression of CrzR in the heart and its potential implications for our study. In response to the reviewer's comments, we have conducted a thorough examination of the fly SCope RNAseq dataset. Our analysis revealed that CrzR is indeed broadly expressed in heart tissue, particularly in areas where the Hand gene is also expressed. This significant finding has been incorporated into our manuscript and is depicted in Figure 8L. As illustrated in Figures 8I-L, which present the SCope tSNE plot for various cell types including neurons, glial cells, muscle systems, and heart, the heart tissue exhibits the most robust expression of CrzR. This observation suggests that the Hand-GAL4 mediated CrzR knockdown experiments may provide insights into the role of CrzR expression in the heart and its influence on the interval timing behavior of male fruit flies. We have expanded upon this interpretation in the relevant sections of our manuscript to ensure a clear and comprehensive understanding of our results.

• *

Comment 7. In several cases, the effects of being raised single are opposite the effects of sexual experience. For example, in Fig. 4T, calcium activity is increased in the AG following sexual experience, but decreased in flies raised singly. Likewise, Crz-neurons in the OL have increased CaLexA signal in singly-raised flies but reduced signals in flies with previous sexual experience. In some cases, manipulations selectively affect LMD or SMD. It would be useful to discuss these differences and consider the mechanistic implications of these differential changes, when they all result in decreased mating duration. This could help to clarify the big picture of the manuscript.

   __Answer:__ We sincerely appreciate the reviewer's insightful suggestions regarding the potential mechanistic underpinnings of how differential calcium activities may modulate LMD and SMD behaviors. In response to this valuable input, we have expanded our discussion to include a hypothesis on how neuropeptide relays could potentially induce context-dependent modulation of synaptic changes and calcium activities within distinct neuronal subsets. This addition aims to provide a more comprehensive understanding of the complex interactions at play, as detailed in the revised manuscript.

"Employing two distinct yet comparable models of interval timing behavior, LMD and SMD, we demonstrated that differential SIFa to SIFaR signaling is capable of modulating context-dependent behavioral responses. Synaptic strengths between SIFa and SIFaR neurons was notably enhanced in group-reared naive males. However, these synaptic strengths specifically diminished in the OL, CB, and AG when males were singly reared, with a particular decrease in the AG region when males were sexually experienced (Fig. 4A-J). Intriguingly, overall calcium signaling within SIFaR24F06 neurons was significantly reduced in group-reared naive males, yet these signals surged dramatically in the OL with social isolation and in the AG with sexual experience (Fig. 4K-T). These calcium signals, as reported by the transcriptional calcium reporter CaLexA, were corroborated by GCaMP live imaging in both the AG and OL regions (Fig. 6L-O and Fig. S6N-P), indicating a close association between elevated calcium levels and LMD and SMD behaviors. The modulation of context-dependent synaptic plasticity and calcium dynamics by the SIFa neuropeptide through a single SIFaR receptor raises the question of how a single receptor can elicit such diverse responses. Recent neuroscientific studies in Drosophila have shown that individual neurons can produce multiple neurotransmitters and that neuropeptides are often colocalized with small molecule neurotransmitters (Nässel 2018,Deng 2019,Croset 2018,Kondo 2020). Consistent with this, we have previously reported that SIFa neurons utilize a variety of neurotransmitters, including glutamate, dopamine, and tyramine (Kim 2024). Therefore, we propose that the SIFa-SIFaR-Crz-CrzR neuropeptidergic relay circuitry may interact with different neurotransmitters in distinct neuronal subpopulations to regulate context-dependent behaviors. Supporting this hypothesis, glutamate, known to function as an inhibitory neurotransmitter in the olfactory pathway of Drosophila (Liu 2013), may be one such candidate. We speculate that neuropeptide cotransmission could underlie the mechanisms facilitating these complex, context-dependent behavioral patterns. Further research is warranted to elucidate how such cotransmission contributes to the intricate behavioral repertoire of the fly."

Minor Comments: Comment 8. For CaLexA experiments (eg Fig 7A-D), signal intensity should be quantified in addition to area covered. Increased intensity would indicate greater calcium activity within a particular set of neurons.

• *Answer: We appreciate the reviewer's insightful comments and acknowledge the importance of using intensity measurements in our analysis of CaLexA signals. We concur that the intensity of these signals is indeed correlated with the area measurements, which is a critical factor to consider. In response to the reviewer's valuable suggestion, we have revised our approach and now present our data based on intensity measurements. These have been incorporated as a primary dataset in all our CaLexA results to provide a more accurate representation of our findings. Additionally, we have updated the labeling of our Y-axis to "Norm. GFP Int.", which stands for "normalized GFP intensity". This change ensures clarity and consistency in the presentation of our data, aligning with the reviewer's recommendations and enhancing the overall quality of our manuscript.

Comment 9. In Figure 5K: quantification of cell overlap is missing. In the text they state that there are ~100 neurons that co-express SIFaR24F06 and Crz. How was this determined? Is there a graph or numerical summary of this assertion?

    __Answer:__ We sincerely thank the reviewer for pointing out the oversight in our initial submission regarding the quantification data. In response to this valuable feedback, we have now included the quantification of neurons co-expressing SIFaR24F06 and Crz in the optic lobe (OL) within Figure 6E. This addition ensures that the figure is complete and provides the necessary numerical support for our observations.

Comment 10. In lines 709-711: "Our experience suggests that the relative mating duration differences between naïve and experienced condition and singly reared are always consistent; however, both absolute values and the magnitude of the difference in each strain can vary. So, we always include internal controls for each treatment as suggested by previous studies." I had trouble understanding this section of methods. What is done with the data from the internal controls?

   __Answer:__ We appreciate the reviewer's attention to the methodology of our study, particularly regarding the use of internal controls in our mating duration assays. As referenced in our cited work by Bretman et al. (2011) (Bretman *et al*, 2011), our internal control strategy involves a comparison of mating durations between males that have been presented with specific sensory cues and those that have not. This approach includes assessing both males that have been exposed to signals and those that have not, which serves as an internal control for each experimental setup. The purpose of this design is to isolate the effects of our manipulations from other potential confounding factors. In response to the reviewer's comments, we have provided a more detailed description of our mating duration assay in the Methods section. We have also expanded our explanation to clarify how this internal control mechanism ensures that any observed differences in mating duration are attributable to the experimental manipulations and not to extraneous variables. This additional information should provide a clearer understanding of our methodology and the rationale behind our experimental design.

Comment 11. Could the authors comment on why the brain GRASP signal is so different in Figures 3A and 4A? I realize that different versions of GRASP were used in these experiments, but I would expect broad agreement between the different approaches.

   __Answer: __We appreciate the reviewer’s insight. GRASP and t-GRASP are similar technologies that can clearly show the synaptic connection between neurons. GRASP technology was first generated and performed in *C. elegens* (Feinberg *et al*, 2008b). In 2018, the researchers developed a targeted GFP Reconstitution Across Synaptic Partners method, t-GRASP, which resulted in a strong preferential GRASP signal in synaptic regions.

   In our study, we utilized both techniques because of the limitations of the chromosomes where GAL4 and lexA lines located. We also found that during data processing, our method could clearly distinguish the changes in GRASP and t-GRASP signals across three different conditions (naïve, single, and exp.). Therefore, we do not have a particular preference for one technique over the other; both methods are applicable to our experiment.

   The genotype we used in Figure 3A is *SIFa2A-lexA, GAL424F06; lexAop-nSyb-spGFP1-10, UAS-CD4-spGFP11*, where the synaptic transmission occurs from *SIFa2A-lexA *to* GAL424F06*. In Figure 4A, the genotype we used is *GAL4SIFa.PT, lexASIFaR-2A; lexAop-2-post-t-GRASP, UAS-pre-t-GRASP*, where the synaptic transmission occurs from SIFa. PT to SIFaR-2A.

   In our back-to-back submission paper, “Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male *Drosophila melanogaster,*” (Kim *et al*, 2024) we identified that *SIFa2A* can label posterior-ventral SIFa neurons (SIFaVP), which can only project to ellipsoid body and fan-shaped body. Combining the GRASP technique, Figure 3A cannot show a strong signal as in Figure 4A. We’ve shown in Figure 1G that *SIFaR-2A *covers almost the whole CNS in *Drosophila*. Thus, the synaptic transmission from SIFa. PT (label 4 SIFa neurons) to SIFaR-2A shows a strong signal under the use of the t-GRASP technique. In this case, the GRASP signals in Figure 3A and Figure 4A are so different because of the usage of different GRASP techniques and different fly lines. We appreciate the reviewer's attention to the clarity of our presentation. In response to the comments, we have taken the opportunity to meticulously revise the figure legends to ensure that the differences are explicitly highlighted and easily understood by the readers.

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Reviewer #2

Major concerns: Comment 1.* It is highly interesting that the duration of mating behavior is dependent on external and motivational factors. In fact, that provides an elegant way to study which neuronal mechanisms orchestrate these factors. However, it remains elusive why the authors link the differentially motivated durations of mating behavior to the psychological concept of interval timing. This distracts from the actually interesting neurobiology, and is not necessary to make the study interesting. *

* * Answer: We are grateful for the opportunity provided by the reviewer to elaborate on our rationale for utilizing the mating duration of male fruit flies as an exemplary genetic model for studying interval timing. At the outset, we would like to acknowledge that mating duration has gained recognition as a valuable genetic model for interval timing, as evidenced by the NIH-NIGMS R01 grant awarded to Michael Crickmore. This grant, which can be reviewed at the provided link (https://grantome.com/grant/NIH/R01-GM134222-01), underscores the significance of this model. Crickmore and colleagues have described in the grant's abstract that "mating duration in Drosophila offers a powerful system for exploring changes in motivation over time as behavioral goals are achieved," and it has the potential to provide "the first mechanistic description of a neuronal interval timing system."

   In light of this, we have incorporated our rationale into the INTRODUCTION section of our manuscript, as detailed below. We believe that our argumentation, supported by the grant's emphasis on the topic, will not only address the reviewer's concerns but also demonstrate to the broader scientific community the significance of the fruit fly's mating duration as a model for interval timing. This concept has been a cornerstone in the historical development of neuroscientific understanding of time perception. We hope that our expanded discussion will effectively convey the potential of the fruit fly mating duration as a genetic model to offer profound insights into the neural mechanisms underlying interval timing, a concept of enduring importance in the field of neuroscience.

"The dimension of time is the fundamental basis for an animal's survival. Being able to estimate and control the time between events is crucial for all everyday activities (RICHELLE & LEJEUNE, 1980). The perception of time in the seconds-to-hours range, referred to as ‘interval timing’, is involved in foraging, decision making, and learning via activation of cortico-striatal circuits in mammals (Golombek et al, 2014). Interval timing requires entirely different neural mechanisms from millisecond or circadian timing (Meck et al, 2012; Merchant et al, 2012; Buhusi & Meck, 2005). There is abundant psychological research on time perception because it is a universal cognitive dimension of experience and behavioral plasticity. Despite decades of research, the genetic and neural substrates of temporal information processing have not been well established except for the molecular bases of circadian timing (Buhusi et al, 2009; Tucci et al, 2014). Thus, a simple genetic model system to study interval timing is required. Considering that the mating duration in fruit flies, which averages approximately 20 minutes, is well within the range addressed by interval timing mechanisms, this behavioral parameter provides a relevant context for examining the neural circuits that modulate the Drosophila's perception of time intervals. Such an investigation necessitates an understanding of the extensive neural and behavioral plasticity underlying interval timing (Thornquist et al, 2020; Gautham et al, 2024; Crickmore & Vosshall, 2013)."

Comment 2.* In figure 4 A and 4K, fluorescence microscopy images of brains and ventral nerve chords are shown, one illustrating GRASP experiments, and one showing CaLexA experiments. The extreme difference between the differentially treated flies (bright fluorescence versus almost no fluorescence) is - in its drastic form- surprising. Online access to the original confocal microscopy images (raw data) might help to convince the reader that these illustrations do not reflect the most drastic "representative" examples out of a series of brain stainings. *

* * Answer: We sincerely appreciate the reviewer's thoughtful suggestion to enhance the accessibility of our microscopy images for readers who may be interested. In response to this valuable feedback, we have compiled all of our quantified image files into zip format and included them as Supplementary Information 2 and 3. We believe that this additional material will be beneficial for readers seeking a more in-depth view of our data.

Comment 3. In particular for behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important.

   __Answer:__ We sincerely appreciate the reviewer's critical feedback regarding the genetic controls in our study. We acknowledge the importance of this aspect and wish to clarify that we have indeed conducted a substantial number of genetic control experiments for both LMD and SMD behaviors. It is worth noting that much of this data has been previously published in other works. Recognizing the interest from another reviewer on the same topic, we have chosen to reiterate our response here for clarity and convenience. Our comprehensive approach to genetic controls ensures the robustness of our findings, and we believe that the published data further substantiates the reliability of our experimental procedures.

   We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. However, in accordance with the reviewer's recommendation, we conducted genetic validation experiments on novel genetic crosses, including SIFaR-RNAi/+, CrzR-RNAi/+, and GAL4NP5270/+, and incorporated the results in the supplementary figures (Supplementary information 1). We have made the necessary modifications and additions to the manuscript as below.

"Given those genetic controls, as evidenced by consistent exhibition of normal LMD and SMD behaviors (Supplemental information. 1), the observed reduction in SIFaR expression, driven by elavc155, is deemed sufficient to induce significant disruptions in LMD and SMD behaviors."

We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"__Mating Duration Assays for Successful Copulation__The mating duration assay in this study has been reported (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023). To enhance the efficiency of the mating duration assay, we utilized the Df(1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR) (Parks et al. 2004; Yapici et al. 2008). Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males (Yapici et al. 2008). We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For group reared (naïve) males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female into the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023; Huang et al. 2024; Zhang et al. 2024). Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test."

Minor comments: Comment 4.* Line 75: word missing ("...including FEEDING-RELATED BEHAVIOR, courtship, ..."). *

   __Answer:__ We appreciate your vigilance in identifying this error. We have made the necessary correction to ensure the accuracy of our manuscript.*

*

Comment 5.* Line 120: word missing ("SIFaR expression in adult neurons BUT not glia..."). *

   __Answer:__ We appreciate your careful review and attention to detail. Thank you for bringing this to our notice. We have made the necessary corrections to address the error.*

*

Comment 6.* I find the figures often to be quite overloaded, and anatomical details often very small (e.g., figure 7A). *

* * Answer: We appreciate the constructive critique on the layout of our data presentation. Following your insightful recommendation, we have revised the manuscript to enhance clarity. Specifically, we have resized the diagram to be more compact and have also increased the spacing between the panels for better readability.

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Reviewer #3

Major Comments Comment 1.* Are the key conclusions convincing? The key conclusions are intriguing but require more robust data to be fully convincing. While the study presents compelling evidence for the involvement of SIFa and SIFaR in mating behaviors, additional experiments are needed to firmly establish the proposed mechanisms. *

* * Answer: We are deeply grateful for the insightful and constructive feedback provided by the reviewer on the SIFa-to-SIFaR signaling pathway. We are particularly encouraged by the reviewer's agreement with our findings that support the role of SIFa and SIFaR in regulating mating duration. We concur with the reviewer's suggestion that additional experiments and mechanistic insights are essential to substantiate our conclusions. To this end, we have conducted and included several new experiments, particularly GCaMP data, in the main figures (Figure 6 and S6). Our focus has been intensified on the SIFa-to-Crz signaling, given Crz's established role in controlling mating duration behavior. Below is a summary of the additional experiments we have incorporated:

1. We have repositioned the SIFa-Crz GCaMP data related to the VNC to Figures 6L-O to ensure that the main text highlights our primary findings.
2. Our more detailed analysis has identified two cells in the Super Intermediate Protocerebrum (SIP) regions that co-express Crz and SIFaR24F06, along with OL cells (Figure 6D-E).
3. To provide a complete view of the signaling dynamics, we have included GCaMP data from the brain region in the main figure (Figure 6P-R and Supplementary Figure S6N-P).
4. Through GCaMP calcium imaging to assess SIFa-to-Crz signaling, we found that calcium levels in Crz+/SIFaR+ SIP neurons consistently decreased with SIFa activation (Figure 6P-R). Conversely, calcium signals in Crz+/SIFaR+ OL neurons increased with SIFa activation, mirroring the pattern seen in Crz+ AG neurons in the VNC (Figure 6M-O and Supplementary Figure S6N-P).

5. A synthesis of these results is presented in Figure 6S, and we have elaborated on these findings in the manuscript with a detailed description, as detailed below. "To elucidate the direct response of Crz neurons to the activity of SIFa neurons, we conducted live calcium (Ca2+) imaging in the Super Intermediate Protocererbrum (SIP), OL and AG region of the VNC, where Crz neurons are situated (Fig. 6D, Fig. S6M). Upon optogenetic stimulation of SIFa neurons, we observed a significant increase in the activity of Crz in OL and AG region (Fig. 6L-O, Fig. S6N-P), evidenced by a sustained elevation in intracellular Ca2+ levels that persisted in a high level before gradually declining to baseline levels, where the cells in top region of the SIP exhibit consistently drop down after stimulated the SIFa neurons (Fig. 6P-R). These calcium level changes were in contrast to the control group (without all-trans retinal, ATR) (Fig. 6L-R, Fig. S6N-P). These findings confirm that Crz neurons in OL and AG are activated in response to SIFa neuronal activity, but the activity of Crz neurons in SIP are inhibited by the activition of SIFa neuron, reinforcing their role as postsynaptic effectors in the neural circuitry governed by SIFa neurons. Moreover, these results provide empirical support for the hypothesis that SIFa-SIFaR/Crz-CrzR long-range neuropeptide relay underlies the neuronal activity-based measurement of interval timing."

   We are truly grateful for the reviewer's perceptive recommendations concerning the possible mechanisms of LMD and SMD behaviors. In light of this constructive feedback, we have enhanced our discussion to encompass a theoretical framework on the potential role of neuropeptide relays in mediating context-dependent adjustments of synaptic plasticity and calcium signaling within specific neuronal populations. This supplementary perspective is designed to elucidate the intricate dynamics involved, as further elaborated in the updated manuscript.

"Employing two distinct yet comparable models of interval timing behavior, LMD and SMD, we demonstrated that differential SIFa to SIFaR signaling is capable of modulating context-dependent behavioral responses. Synaptic strengths between SIFa and SIFaR neurons was notably enhanced in group-reared naive males. However, these synaptic strengths specifically diminished in the OL, CB, and AG when males were singly reared, with a particular decrease in the AG region when males were sexually experienced (Fig. 4A-J). Intriguingly, overall calcium signaling within SIFaR24F06 neurons was significantly reduced in group-reared naive males, yet these signals surged dramatically in the OL with social isolation and in the AG with sexual experience (Fig. 4K-T). These calcium signals, as reported by the transcriptional calcium reporter CaLexA, were corroborated by GCaMP live imaging in both the AG and OL regions (Fig. 6L-O and Fig. S6N-P), indicating a close association between elevated calcium levels and LMD and SMD behaviors. The modulation of context-dependent synaptic plasticity and calcium dynamics by the SIFa neuropeptide through a single SIFaR receptor raises the question of how a single receptor can elicit such diverse responses. Recent neuroscientific studies in Drosophila have shown that individual neurons can produce multiple neurotransmitters and that neuropeptides are often colocalized with small molecule neurotransmitters (Nässel 2018,Deng 2019,Croset 2018,Kondo 2020). Consistent with this, we have previously reported that SIFa neurons utilize a variety of neurotransmitters, including glutamate, dopamine, and tyramine (Kim 2024). Therefore, we propose that the SIFa-SIFaR-Crz-CrzR neuropeptidergic relay circuitry may interact with different neurotransmitters in distinct neuronal subpopulations to regulate context-dependent behaviors. Supporting this hypothesis, glutamate, known to function as an inhibitory neurotransmitter in the olfactory pathway of Drosophila (Liu 2013), may be one such candidate. We speculate that neuropeptide cotransmission could underlie the mechanisms facilitating these complex, context-dependent behavioral patterns. Further research is warranted to elucidate how such cotransmission contributes to the intricate behavioral repertoire of the fly."

• *

Comment 2. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify certain claims as preliminary or speculative. Specifically, the proposed SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is only investigated via imaging approach. More experiments using behavioral tests are needed to confirm that Crz relays the SIFa signaling pathway. For example, Crz-Gal4>UAS-SIFaR RNAi should be done to show that SIFaR+ Crz+ cells are necessary for LMD and SMD.

   __Answer:__ We are grateful for the reviewer's constructive suggestion regarding the need to provide additional behavioral assays using RNAi knockdown to substantiate the SIFa-SIFaR/Crz-CrzR neuropeptide relay. Following the reviewer's advice, we have conducted experiments involving SIFaR24F06/Crz-RNAi and Crz-GAL4/SIFaR-RNAi. The outcomes of these experiments have been detailed and are now presented in a clear and comprehensive manner.

   To further aid in the understanding of our results, we have also included a summary diagram in Figure 6S, which illustrates the key findings from these assays. This visual representation is intended to provide a concise overview of the data and to highlight the significance of the SIFa-SIFaR/Crz-CrzR neuropeptide relay in the context of our study.

Comment 3.* Would additional experiments be essential to support the claims of the paper? Yes, additional experiments are essential. Detailed molecular and imaging studies are needed to support claims about synaptic reorganization. For example: ○ More controls are needed for RNAi and Gal80ts experiments, such as Gal4-only control, RNAi-only control, etc. *

   __Answer:__ We sincerely appreciate the reviewer's critical feedback regarding the genetic controls in our study. We acknowledge the importance of this aspect and wish to clarify that we have indeed conducted a substantial number of genetic control experiments for both LMD and SMD behaviors. It is worth noting that much of this data has been previously published in other works. Recognizing the interest from another reviewer on the same topic, we have chosen to reiterate our response here for clarity and convenience. Our comprehensive approach to genetic controls ensures the robustness of our findings, and we believe that the published data further substantiates the reliability of our experimental procedures.

   We sincerely thank the reviewer for insightful comments regarding the absence of traditional genetic controls in our study of LMD and SMD behaviors. We acknowledge the importance of such controls and wish to clarify our rationale for not including them in the current investigation. The primary reason for not incorporating all genetic control lines is that we have previously assessed the LMD and SMD behaviors of GAL4/+ and UAS/+ strains in our earlier studies. Our past experiences have consistently shown that 100% of the genetic control flies for both GAL4 and UAS exhibit normal LMD and SMD behaviors. Given these findings, we deemed the inclusion of additional genetic controls to be non-essential for the present study, particularly in the context of extensive screening efforts. However, in accordance with the reviewer's recommendation, we conducted genetic validation experiments on novel genetic crosses, including SIFaR-RNAi/+, CrzR-RNAi/+, and GAL4NP5270/+, and incorporated the results in the supplementary figures (Supplementary information 1). We have made the necessary modifications and additions to the manuscript as below.

"Given those genetic controls, as evidenced by consistent exhibition of normal LMD and SMD behaviors (Supplemental information 1), the observed reduction in SIFaR expression, driven by elavc155, is deemed sufficient to induce significant disruptions in LMD and SMD behaviors."

We understand the value of providing a clear rationale for our methodology choices. To this end, we have added a detailed explanation in the "MATERIALS AND METHODS" section and the figure legends of Figure 1. This clarification aims to assist readers in understanding our decision to omit traditional controls, as outlined below.

"__Mating Duration Assays for Successful Copulation__The mating duration assay in this study has been reported (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023). To enhance the efficiency of the mating duration assay, we utilized the Df(1)Exel6234 (DF here after) genetic modified fly line in this study, which harbors a deletion of a specific genomic region that includes the sex peptide receptor (SPR) (Parks et al. 2004; Yapici et al. 2008). Previous studies have demonstrated that virgin females of this line exhibit increased receptivity to males (Yapici et al. 2008). We conducted a comparative analysis between the virgin females of this line and the CS virgin females and found that both groups induced SMD. Consequently, we have elected to employ virgin females from this modified line in all subsequent studies. For group reared (naïve) males, 40 males from the same strain were placed into a vial with food for 5 days. For single reared males, males of the same strain were collected individually and placed into vials with food for 5 days. For experienced males, 40 males from the same strain were placed into a vial with food for 4 days then 80 DF virgin females were introduced into vials for last 1 day before assay. 40 DF virgin females were collected from bottles and placed into a vial for 5 days. These females provide both sexually experienced partners and mating partners for mating duration assays. At the fifth day after eclosion, males of the appropriate strain and DF virgin females were mildly anaesthetized by CO2. After placing a single female into the mating chamber, we inserted a transparent film then placed a single male to the other side of the film in each chamber. After allowing for 1 h of recovery in the mating chamber in 25℃ incubators, we removed the transparent film and recorded the mating activities. Only those males that succeeded to mate within 1 h were included for analyses. Initiation and completion of copulation were recorded with an accuracy of 10 sec, and total mating duration was calculated for each couple. Genetic controls with GAL4/+ or UAS/+ lines were omitted from supplementary figures, as prior data confirm their consistent exhibition of normal LMD and SMD behaviors (Kim et al. 2012; Kim et al. 2013; Lee et al. 2023; Huang et al. 2024; Zhang et al. 2024). Hence, genetic controls for LMD and SMD behaviors were incorporated exclusively when assessing novel fly strains that had not previously been examined. In essence, internal controls were predominantly employed in the experiments, as LMD and SMD behaviors exhibit enhanced statistical significance when internally controlled. Within the LMD assay, both group and single conditions function reciprocally as internal controls. A significant distinction between the naïve and single conditions implies that the experimental manipulation does not affect LMD. Conversely, the lack of a significant discrepancy suggests that the manipulation does influence LMD. In the context of SMD experiments, the naïve condition (equivalent to the group condition in the LMD assay) and sexually experienced males act as mutual internal controls for one another. A statistically significant divergence between naïve and experienced males indicates that the experimental procedure does not alter SMD. Conversely, the absence of a statistically significant difference suggests that the manipulation does impact SMD. Hence, we incorporated supplementary genetic control experiments solely if they deemed indispensable for testing. All assays were performed from noon to 4 PM. We conducted blinded studies for every test."

*○ Using synaptic markers and high-resolution imaging to observe synaptic changes directly. *

   __Answer:__ We sincerely appreciate the reviewer's constructive suggestion to provide high-resolution imaging for a more direct observation of synaptic changes. While we have already included high-resolution imaging data showcasing postsynaptic and presynaptic alterations using Denmark and syt.eGFP (Figure S3), GRASP (Figure 3A-D), and tGRASP (Figure 4A-J), we recognize the value of further elucidation. Consequently, we have conducted additional experiments to examine the presynaptic changes in SIFaR24F06 neurons under varying social contexts, as presented in Figure 5A-G. We are confident that the comprehensive dataset we have now provided, which includes these new findings, will not only address the reviewer's concerns but also effectively convey to the readers the dynamic and critical nature of SIFa-SIFaR synaptic changes in modulating interval timing behaviors.

*○ Electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze their functional connectivity and activity patterns. *

   __Answer:__ We sincerely appreciate the reviewer's constructive suggestions regarding the inclusion of electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze functional connectivity and activity patterns. In response to this valuable feedback, we have conducted *in vivo* calcium imaging using the GCaMP indicator. The results have been incorporated into our manuscript, demonstrating SIFa-SIFaR connectivity and alterations in activity patterns (Figure 5H-L), as well as SIFa-Crz connectivity and changes in activity patterns (Figure 6 and Figure S6). We are confident that these additional data provide compelling evidence supporting the notion that the SIFa-SIFaR/Crz-CrzR neuropeptide relay circuits are robustly interconnected and exhibit activity changes in concert with the observed neuronal modifications.

• *

Comment 4.* Are the suggested experiments realistic in terms of time and resources? The suggested experiments are realistic but will require considerable time and resources. Detailed molecular interaction studies, imaging synaptic plasticity, and electrophysiological recordings could take several months to over a year, depending on the complexity and availability of necessary equipment and expertise. The cost would be moderate to high, involving expenses for reagents, imaging equipment, and animal husbandry for maintaining Drosophila stocks. *

* * Answer: We are grateful for the reviewers' understanding and support for our additional analysis in the revision experiments. While we have already conducted a multitude of experiments pertinent to this manuscript, we are well-positioned to provide a comprehensive revision of the data within a relatively short timeframe.

Comment 5. Are the data and the methods presented in such a way that they can be reproduced? The methods are generally described in detail, allowing for potential reproducibility. However, more precise documentation of certain experimental conditions, such as the timing and conditions of RNAi induction and temperature controls, is necessary. The methods about imaging analysis are too detailed. The exact steps about how to use ImageJ should be removed.

* * Answer: We sincerely appreciate the reviewer's meticulous comments regarding the omission of certain methodological details in our manuscript. In response, we have now included a detailed description of the temperature control procedures for conditional RNAi induction in the "Fly Stocks and Husbandry" section, as detailed below.

"For temperature-controlled experiments, including those utilizing the temperature-sensitive tub-GAL80ts driver, the flies were initially crossed and maintained at a constant temperature of 22℃ within an incubator. The temperature shift was initiated post-eclosion. Once the flies had emerged, they were transferred to an incubator set at an elevated temperature of 29℃ for a defined period, after which the experimental protocols were carried out. Wild-type flies were Canton-S (CS)."

   We appreciate the reviewer's guidance on refining our manuscript. In response to the suggestion, we have streamlined the image analysis methods section, removing excessive details to present the information in a more concise and clear manner as below.

"Quantitative analysis of fluorescence intensity

To ascertain calcium levels and synaptic intensity from microscopic images, we dissected and imaged five-day-old flies of various social conditions and genotypes under uniform conditions. The GFP signal in the brains and VNCs was amplified through immunostaining with chicken anti-GFP primary antibody. Image analysis was conducted using ImageJ software. For the quantification of fluorescence intensities, an investigator, blinded to the fly's genotype, thresholded the sum of all pixel intensities within a sub-stack to optimize the signal-to-noise ratio, following established methods (Feinberg 2008). The total fluorescent area or region of interest (ROI) was then quantified using ImageJ, as previously reported. For CaLexA signal quantification, we adhered to protocols detailed by Kayser et al. (Kayser et al, 2014), which involve measuring the ROI's GFP-labeled area by summing pixel values across the image stack. This method assumes that changes in the GFP-labeled area are indicative of alterations in the CaLexA signal, reflecting synaptic activity. ROI intensities were background-corrected by measuring and subtracting the fluorescent intensity from a non-specific adjacent area, as per Kayser et al. (Kayser et al, 2014). For the analysis of GRASP or tGRASP signals, a sub-stack encompassing all synaptic puncta was thresholded by a genotype-blinded investigator to achieve the optimal signal-to-noise ratio. The fluorescence area or ROI for each region was quantified using ImageJ, employing a similar approach to that used for CaLexA quantification (Feinberg 2008)."

Comment 6. Are the experiments adequately replicated and statistical analysis adequate? Most figures in the manuscript need to be re-plotted. The right y-axis "Difference between means" is not necessary, if not confusing. The image panels are too small to see, while the quantification of overlapping cells are unnecessarily large. The figures are too crowded with labels and texts, which makes it extremely difficult to comprehend the data.

   __Answer:__ We appreciate the reviewer's suggestion to refine our figures, and we have indeed reformatted them to provide clearer presentation and improved readability. Regarding the removal of dot blot membranes (DBMs), we have given this considerable thought. While we understand the recommendation, we have chosen to retain the DBMs in our manuscript. Our decision is based on the fact that our analysis encompasses not only traditional t-tests but also incorporates estimation statistics, which have been demonstrated to be effective for biological data analysis (Claridge-Chang & Assam, 2016). The inclusion of DBMs is essential for the accurate interpretation of these estimation statistics, ensuring a comprehensive representation of our findings.

Minor Comments Comment 7. Specific experimental issues that are easily addressable. Clarify the timing of RNAi induction and provide more detailed figure legends for better understanding and reproducibility.

   __Answer:__ We sincerely appreciate the reviewer's suggestion aimed at enhancing our manuscript. As previously addressed in our response to __*Comment 5*__, we have incorporated additional details regarding the timing of RNAi induction within the Methods section. Furthermore, we have expanded upon the figure legends to provide a clearer understanding of our findings, ensuring that the content is accessible to a broader readership.

* Comment 8. Are prior studies referenced appropriately? Yes. *

__ Answer:__ We are grateful for the reviewer's acknowledgment that our references have been appropriately included and integrated into the manuscript.

* Comment 9. Are the text and figures clear and accurate? The text is generally clear, but the figures need re-work. See comment above. *

• *Answer: We appreciate the feedback from the reviewers regarding the clarity of our figures. In response to other reviewers' concerns about the figures appearing too crowded, we have carefully revised the layout of all figures to ensure they are more spacious and aesthetically improved for better readability and visual appeal.

* Comment 10. Suggestions to improve the presentation of data and conclusions. Use smaller fonts in the bar plots and make the plots smaller. Enlarge the imaging panels and let the pictures tell the story. *

* * Answer: We sincerely appreciate the reviewer's constructive suggestion. In response, we have revised the figures by enlarging the images and adjusting the font sizes in the bar plots to enhance readability and clarity.

REFERENCES

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Bretman A, Westmancoat JD, Gage MJG & Chapman T (2011) Males Use Multiple, Redundant Cues to Detect Mating Rivals. Curr Biol 21: 617–622

Buhusi CV, Aziz D, Winslow D, Carter RE, Swearingen JE & Buhusi MC (2009) Interval Timing Accuracy and Scalar Timing in C57BL/6 Mice. Behav Neurosci 123: 1102–1113

Buhusi CV & Meck WH (2005) What makes us tick? Functional and neural mechanisms of interval timing. Nat Rev Neurosci 6: 755–765

Claridge-Chang A & Assam PN (2016) Estimation statistics should replace significance testing. Nat Methods 13: 108–109

Crickmore MA & Vosshall LB (2013) Opposing Dopaminergic and GABAergic Neurons Control the Duration and Persistence of Copulation in Drosophila. Cell 155: 881–893

Feinberg EH, VanHoven MK, Bendesky A, Wang G, Fetter RD, Shen K & Bargmann CI (2008a) GFP Reconstitution Across Synaptic Partners (GRASP) Defines Cell Contacts and Synapses in Living Nervous Systems. Neuron 57: 353–363

Feinberg EH, VanHoven MK, Bendesky A, Wang G, Fetter RD, Shen K & Bargmann CI (2008b) GFP Reconstitution Across Synaptic Partners (GRASP) Defines Cell Contacts and Synapses in Living Nervous Systems. Neuron 57: 353–363

Gautham AK, Miner LE, Franco MN, Thornquist SC & Crickmore MA (2024) Molecular control of temporal integration matches decision-making to motivational state. bioRxiv: 2024.03.01.582988

Golombek DA, Bussi IL & Agostino PV (2014) Minutes, days and years: molecular interactions among different scales of biological timing. Philosophical Transactions Royal Soc B Biological Sci 369: 20120465

Khoshnoud S, Leitritz D, Bozdağ MÇ, Igarzábal FA, Noreika V & Wittmann M (2024) When the heart meets the mind: Exploring the brain-heart interaction during time perception. J Neurosci: e2039232024

Kim WJ, Jan LY & Jan YN (2012) Contribution of visual and circadian neural circuits to memory for prolonged mating induced by rivals. Nat Neurosci 15: 876–883

Kim WJ, Jan LY & Jan YN (2013) A PDF/NPF Neuropeptide Signaling Circuitry of Male Drosophila melanogaster Controls Rival-Induced Prolonged Mating. Neuron 80: 1190–1205

Kim WJ, Song Y, Zhang T, Zhang X, Ryu TH, Wong KC, Wu Z, Wei Y, Schweizer J, Nguyen K-NH, et al (2024) Peptidergic neurons with extensive branching orchestrate the internal states and energy balance of male Drosophila melanogaster. bioRxiv: 2024.06.04.597277

Lee SG, Sun D, Miao H, Wu Z, Kang C, Saad B, Nguyen K-NH, Guerra-Phalen A, Bui D, Abbas A-H, et al (2023) Taste and pheromonal inputs govern the regulation of time investment for mating by sexual experience in male Drosophila melanogaster. PLOS Genet 19: e1010753

Matell MS (2014) Neurobiology of Interval Timing. Adv Exp Med Biol: 209–234

Meck WH, Doyère V & Gruart A (2012) Interval Timing and Time-Based Decision Making. Frontiers Integr Neurosci 6: 13

Merchant H, Harrington DL & Meck WH (2012) Neural Basis of the Perception and Estimation of Time. Annu Rev Neurosci 36: 313–336

Nicolaï LJJ, Ramaekers A, Raemaekers T, Drozdzecki A, Mauss AS, Yan J, Landgraf M, Annaert W & Hassan BA (2010) Genetically encoded dendritic marker sheds light on neuronal connectivity in Drosophila. Proc National Acad Sci 107: 20553–20558

RICHELLE M & LEJEUNE H (1980) Time in Animal Behaviour. Part III: Mech: 188–199

Tayler TD, Pacheco DA, Hergarden AC, Murthy M & Anderson DJ (2012) A neuropeptide circuit that coordinates sperm transfer and copulation duration in Drosophila. Proc National Acad Sci 109: 20697–20702

Thornquist SC, Langer K, Zhang SX, Rogulja D & Crickmore MA (2020) CaMKII Measures the Passage of Time to Coordinate Behavior and Motivational State. Neuron 105: 334-345.e9

Tucci V, Buhusi CV, Gallistel R & Meck WH (2014) Towards an integrated understanding of the biology of timing. Philosophical Transactions Royal Soc B Biological Sci 369: 20120470

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Referee #3

Evidence, reproducibility and clarity

Summary

The article investigates the role of the neuropeptide SIFa and its receptor SIFaR in regulating two distinct mating duration behaviors in male Drosophila melanogaster, Longer-Mating-Duration (LMD) and Shorter-Mating-Duration (SMD). The study reveals that SIFaR expression in specific neurons is required for both behaviors. It shows that social context and sexual experience lead to synaptic reorganization between SIFa and SIFaR neurons, altering internal brain states. The SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is essential for generating these behaviors, with Crz neurons responding to SIFa neuron activity. Furthermore, CrzR expression in non-neuronal cells is critical for regulating LMD and SMD behaviors. The study utilizes neuropeptide RNAi screening, chemoconnectome (CCT) knock-in, and genetic intersectional methods to elucidate these findings.

Major Comments

• Are the key conclusions convincing? The key conclusions are intriguing but require more robust data to be fully convincing. While the study presents compelling evidence for the involvement of SIFa and SIFaR in mating behaviors, additional experiments are needed to firmly establish the proposed mechanisms.
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify certain claims as preliminary or speculative. Specifically, the proposed SIFa-SIFaR/Crz-CrzR neuropeptide relay pathway is only investigated via imaging approach. More experiments using behavioral tests are needed to confirm that Crz relays the SIFa signaling pathway. For example, Crz-Gal4>UAS-SIFaR RNAi should be done to show that SIFaR+ Crz+ cells are necessary for LMD and SMD.
• Would additional experiments be essential to support the claims of the paper? Yes, additional experiments are essential. Detailed molecular and imaging studies are needed to support claims about synaptic reorganization. For example:
  • More controls are needed for RNAi and Gal80ts experiments, such as Gal4-only control, RNAi-only control, etc.
  • Using synaptic markers and high-resolution imaging to observe synaptic changes directly.
  • Electrophysiological recordings from neurons expressing SIFa and SIFaR to analyze their functional connectivity and activity patterns.
• Are the suggested experiments realistic in terms of time and resources? The suggested experiments are realistic but will require considerable time and resources. Detailed molecular interaction studies, imaging synaptic plasticity, and electrophysiological recordings could take several months to over a year, depending on the complexity and availability of necessary equipment and expertise. The cost would be moderate to high, involving expenses for reagents, imaging equipment, and animal husbandry for maintaining Drosophila stocks.
• Are the data and the methods presented in such a way that they can be reproduced? The methods are generally described in detail, allowing for potential reproducibility. However, more precise documentation of certain experimental conditions, such as the timing and conditions of RNAi induction and temperature controls, is necessary. The methods about imaging analysis are too detailed. The exact steps about how to use ImageJ should be removed.
• Are the experiments adequately replicated and statistical analysis adequate? Most figures in the manuscript need to be re-plotted. The right y-axis "Difference between means" is not necessary, if not confusing. The image panels are too small to see, while the quantification of overlapping cells are unnecessarily large. The figures are too crowded with labels and texts, which makes it extremely difficult to comprehend the data.

Minor Comments

• Specific experimental issues that are easily addressable. Clarify the timing of RNAi induction and provide more detailed figure legends for better understanding and reproducibility.
• Are prior studies referenced appropriately? Yes.
• Are the text and figures clear and accurate? The text is generally clear, but the figures need re-work. See comment above.
• Suggestions to improve the presentation of data and conclusions. Use smaller fonts in the bar plots and make the plots smaller. Enlarge the imaging panels and let the pictures tell the story.

Significance

Nature and Significance of the Advance

This study aims to advance understanding of how neuropeptides modulate context-dependent behaviors in Drosophila. It provides novel insights into the role of SIFa and SIFaR in interval timing behaviors, contributing to the broader field of neuropeptide research and behavioral neuroscience. However, the significance of the findings is limited by the preliminary nature of some claims and the need for additional supporting data.

Context in Existing Literature

The work builds on previous studies that identified various roles of neuropeptides in behavior modulation but lacked detailed mechanistic insights. By elucidating the SIFa-SIFaR/Crz-CrzR pathway, this study attempts to fill a gap in the literature, but more robust evidence is required to solidify its contributions.

Interested Audience

The findings will interest neuroscientists, behavioral biologists, and researchers studying neuropeptides and their roles in behavior and neural circuitry. Additionally, this research may have implications for understanding neuropeptidergic systems in other organisms, making it relevant to a broader audience in the fields of neurobiology and physiology.

Field of Expertise

Keywords: Neuropeptides, Drosophila melanogaster, Behavioral Neuroscience. Areas without sufficient expertise: courtship behavior.

Recommendation

I recommend a major revision of this manuscript. The study presents intriguing findings, but several key claims are preliminary and require additional experiments for support. The data is poorly presented and the figures can be significantly improved. Detailed molecular and imaging studies, as well as more rigorous statistical analyses, are necessary to strengthen the conclusions. Addressing these concerns will significantly improve the robustness and impact of the paper.

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Referee #2

Evidence, reproducibility and clarity

Zhang et al., "Long-range neuropeptide relay as a central-peripheral communication mechanism for the context-dependent modulation of interval timing behaviors".

The authors investigate mating behavior in male fruit flies, Drosophila melanogaster, and test for a role of the SIFamide receptor (SIFaR) in this type of behavior, in particular mating duration in dependence of social isolation and prior mating experience. The anatomy of SIFamide-releasing neurons in comparison with SIFamide receptor-expressing neurons is characterized in a detail-rich manner. Isolating males or exposing them to mating experience modifies the anatomical organization of SIFamidergic axon termini projecting onto SIFamide receptor-expressing neurons. This structural synaptic plasticity is accompanied by changes in calcium influx. Lastly, it is shown that corazonin-releasing neurons are modulated by SIFamide releasing neurons and impact the duration of mating behavior.

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior such as mating is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point. The manuscript has only some points that are less convincing, and these should be addressed.

Major concerns:

• It is highly interesting that the duration of mating behavior is dependent on external and motivational factors. In fact, that provides an elegant way to study which neuronal mechanisms orchestrate these factors. However, it remains elusive why the authors link the differentially motivated durations of mating behavior to the psychological concept of interval timing. This distracts from the actually interesting neurobiology, and is not necessary to make the study interesting.
• In figure 4 A and 4K, fluorescence microscopy images of brains and ventral nerve chords are shown, one illustrating GRASP experiments, and one showing CaLexA experiments. The extreme difference between the differentially treated flies (bright fluorescence versus almost no fluorescence) is - in its drastic form- surprising. Online access to the original confocal microscopy images (raw data) might help to convince the reader that these illustrations do not reflect the most drastic "representative" examples out of a series of brain stainings.
• In particular for behavioral experiments, genetic controls should always be conducted. That is, both the heterozygous Gal4-line as well as the heterozygous UAS-line should be used as controls. This is laborious, but important.

Minor comments:

• Line 75: word missing ("...including FEEDING-RELATED BEHAVIOR, courtship, ...").
• Line 120: word missing ("SIFaR expression in adult neurons BUT not glia...").
• I find the figures often to be quite overloaded, and anatomical details often very small (e.g., figure 7A).

Significance

Overall, this highly interesting study advances our knowledge about the behavioral roles of SIFamide, and contributes to an understanding how motivated behavior is orchestrated by modulatory peptides. The approach to take the entire organism, including peripheral tissue, into consideration, is very good and a rather unique point.

Since decades it has been investigated how sensory stimuli are processed and encoded by the brain, and how behavioral actions are executed. Likewise, principles underlying learning and memory, sleep, orentation, circadian rhythms, etc. are subject to intense investigation. However, how motivational factors (sleep pressure, hunger, sexual drive) are actually "encoded", signaled and finally used to orchstrate behavior and guide decision-making is, to a very large degree, unknown - in any species. The model use here (Drosophila and its peptidergic system wit SIFamide as a central hub) represents actually a ideal entry point to study just this question. In this sense, the manuscript is at the forefront of modern, state-of-the-art neurobiology.

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Referee #1

Evidence, reproducibility and clarity

This manuscript from Zhang et al. primarily investigates the contribution of the SIFa neuropeptide receptor (SIFaR) to mating duration in male fruit flies. Through RNAi-mediated downregulation, they show that SIFaR receptor is necessary for previous experience to alter mating duration. Using cell-specific knockdown and rescue of the SIFaR receptor, they identify a population of ~400 neurons that could underlie this effect. This is still a large number of cells but is narrowed from the ~1,200 total SIFaR-expressing neurons. They then use the GRASP synaptic labeling technique to show that SIFa+ neurons form synapses onto the relevant SIFaR-expressing population, and that the area of synaptic contact is systematically altered depending on the fly's past mating history. Finally, they provide evidence to argue that SIFa neurons act through SIFaR neurons that release the neuropeptide corazonin to regulate mating duration. Overall, the authors have used an impressive array of techniques in their attempt to define the neural circuits and molecules involved in changing internal state to modify the duration of mating.

Major Comments:

1. The authors are to be commended for the sheer quantity of data they have generated, but I was often overwhelmed by the figures, which try to pack too much into the space provided. As a result, it is often unclear what components belong to each panel. Providing more space between each panel would really help.
2. The use of three independent RNAi lines to knock down SIFaR expression is experimentally solid, as the common phenotype observed with all 3 lines supports the conclusion that the SIFaR is important for mating duration choice. However, the authors have not tested whether these lines effectively reduce SIFaR expression, nor whether the GAL80 constructs used to delimit knockdown are able to effectively do so. This makes it hard to make definitive conclusions with these manipulations, especially in the face of negative results. A lack of complete knockdown is suggested by the fact that the F24F06 driver rescues lethality when used to express SIFaR in the B322 mutant background, but does not itself produce lethality when used to express SIFaR RNAi. The authors should either conduct experiments to determine knockdown efficiency or explicitly acknowledge this limitation in drawing conclusions from their experiments. A similar concern relates to the CrzR knockdown experiments (eg Figure 7).
3. Most of the behavioral experiments lack traditional controls, for example flies that contain either the GAL4 or UAS elements alone. The authors should explain their decision to omit these control experiments and provide an argument for why they are not necessary to correctly interpret the data. In this vein, the authors have stated in the methods that stocks were outcrossed at least 3x to Canton-S background, but 3 outcrosses is insufficient to fully control for genetic background.
4. Throughout the manuscript, the authors appear to use a single control condition (sexually naïve flies raised in groups) to compare to both males raised singly and males with previous sexual experience. These control conditions are duplicated in two separate graphs, one for long mating duration and one for short mating duration, but they are given different names (group vs naïve) depending on the graph. If these are actually the same flies, then this should be made clear, and they should be given a consistent name across the different "experiments".
5. The authors have consistently conflated overlap of neuronal processes with synaptic connections. Claims of synaptic connectivity deriving solely from overlap of processes should be tempered and qualified.
   • For example, they say (Lines 201-202) "These findings suggest that SIFa neurons and GAL424F06-positive neurons form more synapses in the VNC than in the brain." This is misleading. Overlap of24F06-LexA>CD8GFP and SIFa-GAL4>CD8RFP tells us nothing about synapse number, or even whether actual synapses are being formed.
   • Lines 210-211: "The overlap of DenMark and syt.EGFP signals was highly enriched in both SOG and ProNm regions, indicating that these regions are where GAL424F06 neurons form interconnected networks". This is misleading. Overlap of DenMark and syt.EGFP does not indicate synapses (especially since these molecules can be expressed outside the expected neuronal compartment if driven at high enough levels).
   • Lines 320-322: "Neurons expressing Crz exhibit robust synaptic connections with SIFaR24F06 neurons located in the PRW region of the SOG in the brain (panels of Brain and SOG in Fig. 5A)". This is again misleading. They are not actually measuring synapses here, but instead looking at area of overlap between neuronal processes of Crz and SIFaR cells.
   • In Figs 3B and S4A, they are claiming that all neuronal processes within a given delineated brain area are synapses. The virtual fly brain and hemibrain resource have a way to actually identify synapses. This should be used in addition to the neuron skeleton. Otherwise, it is misleading to label these as synapses.
   • Furthermore, measuring the area of GRASP signal is not the same as quantifying synapses. We don't know if synapse number changes (eg in lines 240-242).
6. In general, the first part of the manuscript (implicating SIFaR in mating duration) is much stronger than the second part, which attempts to demonstrate that SIFa acts through Crz-expressing neurons to induce its effects. The proof that SIFa acts through Crz-expressing neurons to modify mating duration is tenuous. The most direct evidence of this, achieved via knockdown on Crz in SIFaR-expressing cells, is relegated to supplemental figures. The calcium response of the Crz neurons to SIFa neuron activation (Fig. 6) is more of a lack of a decrease that is observed in controls. Also, this is only done in the VNC. Why not look in the brain, because the authors previously stated a hypothesis that the "transmission of signals through SIFaR in Crz-expressing neurons is limited to the brain" (lines 381-382)?

Furthermore, the authors suggest that Crz acts on cells in the heart to regulate mating duration. It would be useful to add a discussion/speculation as to possible mechanisms for heart cells to regulate mating decisions. Is there evidence of CrzR in the heart? The SCope data presented in Fig. 7I-L and S7G-H is hard to read. 7. In several cases, the effects of being raised single are opposite the effects of sexual experience. For example, in Fig. 4T, calcium activity is increased in the AG following sexual experience, but decreased in flies raised singly. Likewise, Crz-neurons in the OL have increased CaLexA signal in singly-raised flies but reduced signals in flies with previous sexual experience. In some cases, manipulations selectively affect LMD or SMD. It would be useful to discuss these differences and consider the mechanistic implications of these differential changes, when they all result in decreased mating duration. This could help to clarify the big picture of the manuscript.

Minor Comments:

1. For CaLexA experiments (eg Fig 7A-D), signal intensity should be quantified in addition to area covered. Increased intensity would indicate greater calcium activity within a particular set of neurons.
2. In Figure 5K: quantification of cell overlap is missing. In the text they state that there are ~100 neurons that co-express SIFaR24F06 and Crz. How was this determined? Is there a graph or numerical summary of this assertion?
3. In lines 709-711: "Our experience suggests that the relative mating duration differences between naïve and experienced condition and singly reared are always consistent; however, both absolute values and the magnitude of the difference in each strain can vary. So, we always include internal controls for each treatment as suggested by previous studies." I had trouble understanding this section of methods. What is done with the data from the internal controls?
4. Could the authors comment on why the brain GRASP signal is so different in Figures 3A and 4A? I realize that different versions of GRASP were used in these experiments, but I would expect broad agreement between the different approaches.

Significance

This study will be most relevant to researchers interested in understanding neuronal control of behavior. The manuscript offers a conceptual advance in identifying cell types and molecules that influence mating duration decisions. The strength of the manuscript is the number of different assays used; however, there is a sense that this has occurred at the cost of providing a cohesive narrative. The first part of the manuscript (detailing the role of SIFaR in LMD and SMD) is relatively stronger and more conclusive.

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Reply to the reviewers

1. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Reviewer #1 (Evidence, reproducibility, and clarity (Required)): This is an interesting manuscript from the Jagannathan laboratory, which addresses the interaction proteome of two satellite DNA-binding proteins, D1 and Prod. To prevent a bias by different antibody affinities they use GFP-fusion proteins of D1 and Prod as baits and purified them using anti GFP nanobodies. They performed the purifications in three different tissues: embryo, ovary and GSC enriched testes. Across all experiments, they identified 500 proteins with surprisingly little overlap among tissues and between the two different baits. Based on the observed interaction of prod and D1 with members of the canonical piRNA pathway the authors hypothesized that both proteins might influence the expression of transposable elements. However, neither by specific RNAi alleles or mutants that lead to a down regulation of D1 and Prod in the gonadal soma nor in the germline did they find an effect on the repression of transposable elements. They also did not detect an effect of a removal of piRNA pathway proteins on satellite DNA clustering, which is mediated by Prod and D1. However, they do observe a mis-localisation of the piRNA biogenesis complex to an expanded satellite DNA in absence of D1, which presumably is the cause of a mis-regulation of transposable elements in the F2 generation.This is an interesting finding linking satellite DNA and transposable element regulation in the germline. However, I find the title profoundly misleading as the link between satellite DNA organization and transgenerational transposon repression in Drosophila has not been identified by multi-tissue proteomics but by a finding of the Brennecke lab that the piRNA biogenesis complex has a tendency to localise to satellite DNA when the localisation to the piRNA locus is impaired. Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner.

We thank the reviewer for the overall positive comments on our manuscript. As noted above, we have performed a substantial number of revision experiments and improved our text. We believe that our revised manuscript demonstrates a clear link between our proteomics data and the transposon repression. We would like to make three main points,

1. Our proteomics data identified that D1 and Prod co-purified transposon repression proteins in embryos. To test the functional significance of this association, we have used Drosophila genetics to generate flies lacking embryonic D1. In adult ovaries from these flies, we observe a striking elevation in transposon expression and Chk2-dependent gonadal atrophy. Along with the results from the control genotypes (F1 D1 mutant, F2 D1 het), our data clearly indicate that embryogenesis (and potentially early larval development) are a period when D1 establishes heritable TE silencing that can persist throughout development.
2. Based on the newly acquired RNA-seq and small RNA seq data, we have edited our title to more accurately reflect our data. Specifically, we have substituted the word 'transgenerational' with 'heritable', meaning that the presence of D1 during early development alone is sufficient to heritably repress TEs at later stages of development.
3. In addition, our RNA seq and small RNA seq experiments demonstrate that D1 makes a negligible contribution to piRNA biogenesis and TE repression in adults, despite the significant mislocalization of the RDC complex. In this regard, our results are substantially different from the recent Kipferl study from the Brennecke lab (Baumgartner et al. 2022).

   Major comments Unfortunately, the proteomic data sets are not very convincing. Not even the corresponding baits are detected in all assays. I wonder whether the extraction procedure really allows the authors to analyze all functionally relevant interactions of Prod and D1. It would be good to see a western blot or an MS analysis of the soluble nuclear extract they use for purification compared to the insoluble chromatin. It may well be that a large portion of Prod or D1 is lost in this early step. I also find the description of the proteomic results hard to follow as the authors mostly list which proteins the find as interactors of Prod and D1 without stating in which tissue or with what bait they could purify them (i.e. p7: Importantly, our hits included known chromocenter-associated or pericentromeric heterochromatin-associated proteins, such as Su(var)3-9[52], ADD1[23,24], HP5[23,24],mei-S332[53], Mes-4[23], Hmr[24,39,54], Lhr[24,39], and members of the chromosome passenger complex, such as borr and Incenp[55]). It would be interesting to at least discuss tissue specific interactions.

Out of six total AP-MS experiments in this manuscript (D1 x 3, Prod x 2 and Piwi), we observe a strong enrichment of the bait in 5/6 attempts (log2FC between 4-12). In the initial submission, the lack of a third high-quality biological replicate for the D1 testis sample meant that only the p-value (0.07) was not meeting the cutoff. To address this, we have repeated this experiment with an additional biological replicate (Fig. S2A), and our data now clearly show that D1 is significantly enriched in the testis sample.

As suggested by the reviewer, we have also assessed our lysis conditions (450mM NaCl and benzonase) and the solubilization of our baits post-lysis. In Fig. S1D, we have blotted equivalent fractions of the soluble supernatant and insoluble pellet from GFP-Piwi embryos and show that both GFP-Piwi and D1 are largely solubilized following lysis. In Fig. S1E, we also show that our IP protocol works efficiently.

GFP-Prod pulldown in embryos is the only instance in which we do not detect the bait by mass spectrometry. Here, one reason could be relatively low expression of GFP-Prod in comparison to GFP-D1 (Fig. S1E), which may lead to technical difficulties in detecting peptides corresponding to Prod. However, we note that Prod IP co-purified proteins such as Bocks that were previously suggested as Prod interactors from high-throughput studies (Giot et al. 2003; Guruharsha et al. 2011). In addition, Prod IP from embryos also co-purified proteins known to associate with chromocenters such as Hcs and Saf-B. Finally, the concordance between D1 and Prod co-purified proteins from embryo lysates (Fig. 2A, C) suggest that the Prod IP from embryos is of reasonable quality.

We also acknowledge the reviewer's comment that the description of the proteomic data was hard to follow. Therefore, we have revised our text to clearly indicate which bait pulled down which protein in which tissue (lines 148-156). We have also highlighted and discussed bait-specific and tissue-specific interactions in the text (lines 162-173).

Minor comments The authors may also want to provide a bit more information on the quantitation of the proteomic data such as how many values were derive from the match-between runs function and how many were imputed as this can severely distort the quantification.

Figure 1: Distribution of data after imputation in embryo (left), ovary (middle) and testis (right) datasets. Imputation is performed with random sampling from the 1% least intense values generated by a normal distribution.

To ensure the robustness of our data analysis, we considered only those proteins that were consistently identified in all replicates for at least one bait (GFP-D1, GFP-Prod or NLS-GFP). This approach resulted in a relative low number of missing values. However, it is also important to bear in mind that in an AP-MS experiment, the number of missing values is higher, as interactors are not identified in the control pulldown. Importantly, the imputation of missing values during the data analysis did not alter the normal distribution of the dataset (Fig. 1, this document). Detailed information regarding the imputed values is also provided (Table 1, this document). The coding script used for the data analysis is available in the PRIDE submission of the dataset (Table 2, this document). This information has been added to our methods section under data availability.

Table 1: ____Number of match-between-runs and imputations for embryo, ovary and testis datasets

Dataset

#match-between-runs

%match-between-runs

%imputation

Embryo

5541/27543

20.11%

8.36%

Ovary

1936/9530

20.30%

8.18%

Testis

1748/7168

24.39%

3.12%

Table 2: ____Access to the PRIDE submission of the datasets

Name

ID PRIDE

UN reviewer

PW reviewer

IP-MS of D1 from Testis tissue

PXD044026

reviewer_pxd044026@ebi.ac.uk

ydswDQVW

IP-MS of Piwi from Embryo tissue

PXD043237

reviewer_pxd043237@ebi.ac.uk

TMCoDsdx

IP-MS of Prod and D1 proteins from Ovary tissue

PXD043236

reviewer_pxd043236@ebi.ac.uk

VOHqPmaS

IP-MS of Prod and D1 proteins from Embryo tissue

PXD043234

reviewer_pxd043234@ebi.ac.uk

L77VXdvA

**Referee Cross-Commenting** I agree with the two other reviewers that the connection between the interactome and the transgenerational phenotype is unclear. This is also what I meant i my comment that the title is somewhat misleading. A systematic analysis of the D1 and Prod knock down effect on mRNAs and small Rnas would indeed be helpful to better understand the interesting effect.

As suggested by the reviewer, we have performed RNA seq and small RNA seq in control and D1 mutant ovaries (Fig. 4) to fully understand the contribution of D1 in piRNA biogenesis and TE repression. Briefly, the mislocalization of RDC complex in D1 mutant ovaries does not significantly affect TE-mapping piRNA biogenesis (Fig. 4C, E). In addition, loss of D1 does not substantially alter TE expression in the ovaries (Fig. 4B) or alter the expression of genes involved in TE repression (Fig. 4F). Along with the results presented in Fig. 5 and Fig. 6, our data clearly indicate that embryogenesis (and potentially early larval development) is a critical period during which D1 makes an important contribution to TE repression.

Reviewer #1 (Significance (Required)): Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner. It may be mostly interesting for the Drosophila community but could also be exiting for a broader audience interested in the connection of heterochromatin and its indirect effect on the regulation of transposable elements.

We thank the reviewer again for the helpful and constructive comments, which have enabled us to significantly improve our study. We are excited by the results from our study, which illuminate unappreciated aspects of transcriptional silencing in constitutive heterochromatin.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Chavan et al. set out to enrich our compendium of pericentric heterochromatin-associated proteins - and to learn some new biology along the way. An ambitious AP-Mass baited with two DNA satellite-binding proteins (D1 and Prod), conducted across embryos, ovaries, and testes, yielded hundreds of candidate proteins putatively engaged at chromocenters. These proteins are enriched for a restricted number of biological pathways, including DNA repair and transposon regulation. To investigate the latter in greater depth, the authors examine D1 and prod mutants for transposon activity changes using reporter constructs for multiple elements. These reporter constructs revealed no transposon activation in the adult ovary, where many proteins identified in the mass spec experiments restrict transposons. However, the authors suggest that the D1 mutant ovaries do show disrupted localization of a key member of a transposon restriction pathway (Cuff), and infer that this mislocalization triggers a striking, transposon derepression phenotype in the F2 ovaries.

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

We appreciate the reviewer taking the time to provide thoughtful comments and constructive suggestions to improve the manuscript. We believe that we have addressed all the comments raised to the significant advantage of our paper.

Major comments 1. The introduction requires quite a radical restructure to better highlight the A) importance of the work and B) limit information whose relevance is not clear early in the manuscript. A. Delineating who makes up heterochromatin is a long-standing problem in chromosome biology. This paper has huge potential to contribute to this field; however, it is not the first. Others are working on this problem in other systems, for example PMID:29272703. Moreover, we have some understanding of the distinct pathways that may impact heterochromatin in different tissues (e.g., piRNA biology in ovaries vs the soma). Also, the mutant phenotypes of prod and D1 are different. Fleshing out these three distinct points could help the reader understand what we know and what we don't know about heterochromatin composition and its special biology. Understanding where we are as a field will offer clear predictions about who the interactors might be that we expect to find. For example, given the dramatically different D1 and prod mutant phenotypes (and allele swap phenotypes), how might the interactors with these proteins differ? What do we know about heterochromatin formation differences in different tissues? And how might these differences impact heterochromatin composition?

The reviewer brings up a fair point and we have significantly reworked our introduction. We share the reviewer's opinion that improved knowledge of the constitutive heterochromatin proteome will reveal novel biology.

1. The attempt to offer background on the piRNA pathway and hybrid dysgenesis in the Introduction does not work. As a naïve reader, it was not clear why I was reading about these pathways - it is only explicable once the reader gets to the final third of the Results. Moreover, the reader will not retain this information until the TE results are presented many pages later. I strongly urge the authors to shunt the two TE restriction paragraphs to later in the manuscript. They are currently a major impediment to understanding the power of the experiment - which is to identify new proteins, pathways, and ultimately, biology that are currently obscure because we have so little handle on who makes up heterochromatin.

We agree with this suggestion. We have introduced the piRNA pathway in the results section (lines 205 - 216), right before this information is needed. We've also removed the details on hybrid dysgenesis, since our new data argues against a maternal effect from the D1 mutant.

The implications of the failure to rescue female fertility by the tagged versions of both D1 and Prod are not discussed. Consequently, the reader is left uneasy about how to interpret the data.

We understand this point raised by the reviewer. However, in our proteomics experiments, we have used GFP-D1 and GFP-Prod ovaries from ~1 day old females (line 579, methods). These ovaries are morphologically similar to the wild type (Fig. S1C) and their early germ cell development appears to be intact. Moreover, chromocenter formation in female GSCs is comparable to the wildtype (Fig. 1C-D). These data, along with the rescue of the viability of the Prod mutant (Fig. S1A-B), suggest that the presence of a GFP tag is not compromising D1 or Prod function in the early stages of germline development and is consistent with published and unpublished data from our lab. In addition, D1 and Prod from ovaries co-purify proteins such as Rfc38 (D1), Smn (D1), CG15107 (Prod), which have been identified in previous high-throughput screens (Guruharsha et al. 2011; Tang et al. 2023). For these reasons, we believe that GFP-D1 and GFP-Prod ovaries are a good starting point for the AP-MS experiment. We speculate that the failure to completely rescue female fertility may be due to improper expression levels of GFP-D1 or GFP-Prod flies at later stages of oogenesis, which are not present in ovaries from newly eclosed females and therefore unlikely to affect our proteomic data.

1. How were the significance cut-offs determined? Is the p-value reported the adjusted p-value? As a non-expert in AP-MS, I was surprised to find that the p-value, at least according to the Methods, was not adjusted based on the number of tests. This is particularly relevant given the large/unwieldy(?) number of proteins that were identified as signficant in this study. Moreover, the D1 hit in Piwi pull down is actually not significant according to their criteria of p We used a standard cutoff of log2FC>1 and p2FC and low p-value) since these are more likely to be bona fide interactors. Third, we have used string-DB for functional analyses where we can place our hits in existing protein-protein interaction networks. Using this approach, we detect that Prod (but not D1) pulls down multiple members of the RPA complex in the embryo (RPA2 and RpA-70, Fig. S2B) while embryonic D1 (but not Prod) pulls down multiple components of the origin recognition complex (Orc1, lat, Orc5, Orc6, Fig. S2C) and the condensin I complex (Cap-G, Cap-D2, barr, Fig. S2D). Altogether, these filtering strategies allow us to eliminate as many false positives as possible while making sure to minimize the loss of true hits through multiple testing correction.

How do we know if the lack of overlap across tissues is indeed germline- or soma-specialization rather than noise?

To address this part of the comment, we have amended our text (lines 162-173) as follows,

'We also observed a substantial overlap between D1- and Prod-associated proteins (yellow points in Fig. 2A, B, Table S1-3), with 61 hits pulled down by both baits (blue arrowheads, Fig. 2C) in embryos and ovaries. This observation is consistent with the fact that both D1 and Prod occupy sub-domains within the larger constitutive heterochromatin domain in nuclei. Surprisingly, only 12 proteins were co-purified by the same bait (D1 or Prod) across different tissues (magenta arrowheads, Fig. 2C, Table S1-3). In addition, only a few proteins such as an uncharacterized DnaJ-like chaperone, CG5504, were associated with both D1 and Prod in embryos and ovaries (Fig. 2D). One interpretation of these results is that the protein composition of chromocenters may be tailored to cell- and tissue-specific functions in Drosophila. However, we also note that the large number of unidentified peptides in AP-MS experiments means that more targeted experiments are required to validate whether certain proteins are indeed tissue-specific interactors of D1 and Prod.'

To make this inference, conducting some validation would be required. More generally, I was surprised to see no single interactor validated by reciprocal IP-Westerns to validate the Mass-Spec results, though I am admittedly only adjacent to this technique. Note that colocalization, to my mind, does not validate the AP-MS data - in fact, we would a priori predict that piRNA pathway members would co-localize with PCH given the enrichment of piRNA clusters there.

Here, we would point out that we have conducted multiple validation experiments with a specific focus on the functional significance of the associations between D1/Prod and TE repression proteins in embryos. While the reviewer suggests that piRNA pathway proteins may be expected to enrich at the pericentromeric heterochromatin, this is not always the case. For example, Piwi and Mael are present across the nucleus (pulled down by D1/Prod in embryos) while Cutoff, which is present adjacent to chromocenters in nurse cells, was not observed to interact with either D1 or Prod in the ovary samples.

Therefore, for Piwi, we performed a reciprocal AP-MS experiment in embryos due to the higher sensitivity of this method (GFP-Piwi AP-MS, Fig. 3B). Excitingly, this experiment revealed that four largely uncharacterized proteins (CG14715, CG10208, Ugt35D1 and Fit) were highly enriched in the D1, Prod and Piwi pulldowns and these proteins will be an interesting cohort for future studies on transposon repression in Drosophila (Fig. 3C).

Furthermore, we believe that determining the localization of proteins co-purified by D1/Prod is an important and orthogonal validation approach. For Sov, which is implicated in piRNA-dependent heterochromatin formation, we observe foci that are in close proximity to D1- and Prod-containing chromocenters (Fig. 3A).

As for suggestion to validate by IP-WBs, we would point out that chromocenters exhibit properties associated with phase separated biomolecular condensates. Based on the literature, these condensates tend to associate with other proteins/condensates through low affinity or transient interactions that are rarely preserved in IP-WBs, even if there are strong functional relationships. One example is the association between D1 and Prod, which do not pull each other down in an IP-WB (Jagannathan et al. 2019), even though D1 and Prod foci dynamically associate in the nucleus and mutually regulate each other's ability to cluster satellite DNA repeats (Jagannathan et al. 2019). Similarly, IP-WB using GFP-Piwi embryos did not reveal an interaction with D1 (Fig. S4B). However, our extensive functional validations (Figures 4-6) have revealed a critical role for D1 in heritable TE repression.

The AlphaFold2 data are very interesting but seem to lack of negative control. Is it possible to incorporate a dataset of proteins that are not predicted to interact physically to elevate the impact of the ones that you have focused on? Moreover, the structural modeling might suggest a competitive interaction between D1 and piRNAs for Piwi. Is this true? And even if not, how does the structural model contribute to your understanding for how D1 engages with the piRNA pathway? The Cuff mislocalization?

In the revised manuscript, we have generated more structural models using AlphaFold Multimer (AFM) for proteins (log2FC>2, p0.5 and ipTM>0.8), now elaborated in lines 175-177. Despite the extensive disorder in D1 and Prod, we identified 22 proteins, including Piwi, that yield interfaces with ipTM scores >0.5 (Table S4, Table S8). These hits are promising candidates to further understand D1 and Prod function in the future.

For the predicted model between Prod/D1 and Piwi (Fig. S4A), one conclusion could indeed be competition between D1/Prod and piRNAs for Piwi. Another possibility is that a transient interaction mediated by disordered regions on D1/Prod could recruit Piwi to satellite DNA-embedded TE loci in the pericentromeric heterochromatin. These types of interactions may be especially important in the early embryonic cycles, where repressive histone modifications such as H3K9me2/3 must be deposited at the correct loci for the first time. We suggest that mutating the disordered regions on D1 and Prod to potentially abrogate the interaction with Piwi will be important for future studies.

The absence of a TE signal in D1 and Prod mutant ovaries would be much more compelling if investigated more agnostically. The observation that not all TE reporter constructs show a striking signal in the F2 embryos makes me wonder if Burdock and gypsy are not regulated by these two proteins but possibly other TEs are. Alternatively, small RNA-seq would more directly address the question of whether D1 and Prod regulate TEs through the piRNA pathway.

We completely agree with this comment from the reviewer. We have performed RNA seq on D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background. Since Chk2 arrests germ cell development in response to TE de-repression and DNA damage(Ghabrial and Schüpbach 1999; Moon et al. 2018), we reasoned that the chk2 mutant background would prevent developmental arrest of potential TE-expressing germ cells and we observed that both genotypes exhibited similar gonad morphology (Fig. 4A). From our analysis, we do not observe a significant effect on TE expression in the absence of D1, except for the LTR retrotransposon tirant (Fig. 4B). We also do not observe differential expression of TE repression genes (Fig. 4F).

We have complemented our RNA seq experiment with small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Here, overall piRNA production and antisense piRNAs mapping to TEs were largely unperturbed (Fig. 4C-E).

Overall, our data is consistent with the TE reporter data (Fig. S7) and suggests that zygotic depletion of D1 does not have a prominent role in TE repression. However, we have uncovered that the presence of D1 during embryogenesis is critical for TE repression in adult gonads (Fig. 6, Fig. S9).

I had trouble understanding the significance of the Cuff mis-localization when D1 is depleted. Given Cuff's role in the piRNA pathway and close association with chromatin, what would the null hypothesis be for Cuff localization when a chromocenter is disrupted? What is the null expectation of % Cuff at chromocenter given that the chromocenter itself expands massively in size (Figure 4D). The relationship between these two factors seems rather indirect and indeed, the absence of Cuff in the AP would suggest this. The biggest surprise is the absence of TE phenotype in the ovary, given the Cuff mutant phenotype - but we can't rule out given the absence of a genome-wide analysis. I think that these data leave the reader unconvinced that the F2 phenotype is causally linked to Cuff mislocalization.

We apologize that this data was not more clearly represented. In a wild-type context, Cuff is distributed in a punctate manner across the nurse cell nuclei, with the puncta likely representing piRNA clusters (Fig. 5A-B). We find that a small fraction of Cuff (~5%) is present adjacent to the nurse cell chromocenter (inset, Fig. 5A and Fig. 5D). In the absence of D1, the nurse cell chromocenters increase ~3-4 fold in size. However, the null expectation is still that ~5% of total Cuff would be adjacent to the chromocenter, since the piRNA clusters are not expected to expand in size. In reality, we observe ~27% of total Cuff is mislocalized to chromocenters. Our data indicate that the satellite DNA repeats at the larger chromocenters must be more accessible to Cuff in the D1 mutant nurse cells. This observation is corroborated by the significant increase in piRNAs corresponding to the 1.688 satellite DNA repeat family (Fig. 4E).

The lack of TE expression in the F1 D1 mutant was indeed surprising based on the Cuff mislocalization but as the reviewers pointed out, we only analyzed two TE reporter constructs in the initial submission. In the revised manuscript, we have performed both RNA seq and small RNA seq. Surprisingly, our data reveal that the Cuff mislocalization does not significantly affect piRNA biogenesis (Fig. 4C, D) and piRNAs mapping to TEs. As a result, both TE repression (Fig. 4B) and fertility (Fig. 6D) are largely preserved in the absence of D1 in adult ovaries.

Finally, we thank the reviewer for their excellent suggestion to incorporate the F2 D1 heterozygote (Fig. S9) in our analysis! This important control has revealed that the maternal effect of the D1 mutant is negligible for gonad development and fertility (Fig. 6B-D). Rather, our data clearly emphasize embryogenesis (or early larval development) as a key period during which D1 can promote heritable TE repression. Essentially, D1 is not required during adulthood for TE repression if it was present in the early stages of development.

Apologies if I missed this, but Figure 5 shows the F2 D1 mutant ovaries only. Did you look at the TM6 ovaries as well? These ovaries should lack the maternally provisioned D1 (assuming that females are on the right side) but have the zygotic transcription.

As mentioned above, this was a great suggestion and we've now characterized this important control in the context of germline development and fertility, to the significant advantage of our paper.

Minor comments 9. Add line numbers for ease of reference

We apologize for this. Line numbers have been added in the full revision.

1. The function of satellite DNA itself is still quite controversial - I would recommend being a bit more careful here - the authors could refer instead to genomic regions enriched for satellite DNA are linked to xyz function (see Abstract line 2 and 7, for example.)

The abstract has been rewritten and does not directly implicate satellite DNA in a specific cellular function. However, we have taken the reviewer's suggestion in the introduction (line 57-58).

"Genetic conflicts" in the introduction needs more explanation.

This sentence has been removed from the introduction in the revised manuscript.

"In contrast" is not quite the right word. Maybe "However" instead (1st line second paragraph of Intro)

Done. Line 57 of the revised manuscript.

Results: what is the motivation for using GSC-enriched testis?

We use GSC-enriched testes for practical reasons. First, they contain a relatively uniform population of mitotically dividing germ cells unlike regular testes which contain a multitude of post-mitotic germ cells such as spermatocytes, spermatids and sperm. Second, GSC-enriched testes are much larger than normal testes and reduced the number of dissections that were needed for each replicate.

1. Clarify sentence about the 500 proteins in the Results section - it's not clear from context that this is the union of all experiments.

Done. Lines 145-149 in the revised manuscript.

The data reported are not the first to suggest that satellite DNA may have special DNA repair requirements. e.g., PMID: 25340780

We apologize if we gave the impression that we were making a novel claim. Specialized DNA repair requirements at repetitive sequences have indeed been previously hypothesized(Charlesworth et al. 1994) and studied and we have altered the text to better reflect this (lines 193-195). We have cited the study suggested by the reviewer as well as studies from the Chiolo(Chiolo et al. 2011; Ryu et al. 2015; Caridi et al. 2018) and Soutoglou(Mitrentsi et al. 2022) labs, which have also addressed this fascinating question.

Page 10: indicate-> indicates.

Done.

1. Page 14: revise for clarity: "investigate a context whether these interactions could not take place"

We've incorporated this suggestion in the revised text (lines 383-386).

1. Might be important to highlight the 500 interactions are both direct and indirect. "Interacting proteins" alone suggests direct interactions only.

Done. Lines 145-149.

The effect of the aub mutant on chromocenter foci did not seem modest to me - however, the bar graphs obscure the raw data - consider plotting all the data not just the mean and error?

Done. This data is now represented by a box-and-whisker plot (Fig. S5), which shows the distribution of the data.

Reviewer #2 (Significance (Required)):

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

This manuscript represents a significant contribution to the field of chromosome biology.

We thank the reviewer for the positive evaluation of our manuscript, and we really appreciate the great suggestion for the F2 D1 heterozygote control! Overall, we believe that our manuscript is substantially improved with the addition of RNA seq, small RNA seq and important genetic controls. Moreover, we are excited by the potential of our study to open new doors in the study of pericentromeric heterochromatin.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): In the manuscript entitled "Multi-tissue proteomics identifies a link between satellite DNA organization and transgenerational transposon repression", the authors used two satellite DNA-binding proteins, D1 and Prod, as baits to identify chromocenter-associated proteins through quantitative mass spectrometry. The proteomic analysis identified ~500 proteins across embryos, ovaries, and testes, including several piRNA pathways proteins. Depletion of D1 or Prod did not directly contribute to transposon repression in ovary. However, in the absence of maternal and zygotic D1, there was a dramatic increase of agametic ovaries and transgenerational transposon de-repression. Although the study provides a wealth of proteomic data, it lacks mechanistic insights into how satellite DNA organization influence the interactions with other proteins and their functional consequences.

We thank the reviewer for highlighting that this study will be a valuable resource for future studies on the composition and function of pericentromeric heterochromatin. Based on the reviewer's request for more mechanistic knowledge into how satellite DNA organization affects transposon repression, we have performed RNA seq and small RNA seq, added important genetic controls and significantly improved our text. In the revised manuscript, our data clearly demonstrate that embryogenesis (and potentially early larval development) is a critical time period when D1 contributes to heritable TE repression. Flies lacking D1 during embryogenesis exhibit TE expression in germ cells as adults, which is associated with Chk2-dependent gonadal atrophy. We are particularly excited by these data since TE loci are embedded in the satellite DNA-rich pericentromeric heterochromatin and our study demonstrates an important role for a satellite DNA-binding protein in TE repression.

Major____ comments 1. While the identification of numerous interactions is significant, it would be helpful to acknowledge that lots of these proteins were known to bind DNA or heterochromatin regions. To strengthen the study, I recommend conducting functional validation of the identified interactions, in addition to the predictions made by Alphfold 2.

We are happy to take this comment on board. In fact, we believe that the large number of DNA-binding and heterochromatin-associated proteins identified in this study are a sign of quality for the proteomic datasets. In the revised manuscript, we have highlighted known heterochromatin proteins co-purified by D1/Prod in lines 148-151 as well as proteins previously suggested to interact with D1/Prod from high-throughput studies in lines 153-156 (Guruharsha et al. 2011; Tang et al. 2023). In this study, we have focused on the previously unknown associations between D1/Prod and TE repression proteins and functionally validated these interactions as presented in Figures 3-6.

The observation of transgenerational de-repression is intriguing. However, to better support this finding, it would be better to provide a mechanistic explanation based on the data presented.

We appreciate this comment from the reviewer, which is similar to major comment #6 raised by reviewer #2. To generate mechanistic insight into the underlying cause of gonad atrophy in the F2 D1 mutant, we have performed RNA seq, small RNA seq and analyzed germline development and fertility in the F2 D1 heterozygous control (Fig. S9).

For the RNA seq, we used D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background. Since Chk2 arrests germ cell development in response to TE de-repression and DNA damage(Ghabrial and Schüpbach 1999; Moon et al. 2018), we reasoned that the chk2 mutant background would prevent developmental arrest of potential TE-expressing germ cells and we observed that both genotypes exhibited similar gonad morphology (Fig. 4A). From our analysis, we do not observe a significant effect on TE expression in the absence of D1, except for the LTR retrotransposon tirant (Fig. 4B). We also do not observe differential expression of TE repression genes (Fig. 4F).

We have complemented our RNA seq experiment with small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Here, overall piRNA production and antisense piRNAs mapping to TEs were largely unperturbed (Fig. 4C-E). Together, these data are consistent with the TE reporter data (Fig. S7) and suggests that zygotic depletion of D1 does not have a prominent role in TE repression.

However, we have uncovered that the presence of D1 during embryogenesis is critical for TE repression in adult gonads (Fig. 6, Fig. S9). Essentially, either only maternal deposited D1 (F1 D1 mutant) or only zygotically expressed D1 (F2 D1 het) were sufficient to ensure TE repression and fertility. In contrast, a lack of D1 during embryogenesis (F2 D1 mutant) led to elevated TE expression and Chk2-mediated gonadal atrophy.

Our results also explain why previous studies have not implicated either D1 or Prod in TE repression, since D1 likely persists during embryogenesis when using depletion approaches such as RNAi-mediated knockdown or F1 generation mutants.

Minor____ comments 3. Given the maternal effect of the D1 mutant, in Figure 4, I suggest analyzing not only nurse cells but also oocytes to gain a more comprehensive understanding.

We agree with the reviewer that this experiment can be informative. In the F2 D1 mutant ovaries, germ cell development does not proceed to a stage where oocytes are specified, thus limiting microscopy-based approaches. Nevertheless, we have gauged oocyte quality in all the genotypes using a fertility assay (Fig. 6D) since even ovaries that have a wild-type appearance can produce dysfunctional gametes. In this experiment, we observe that fertility is largely intact in the F1 D1 mutant and F2 D1 heterozygote strains, suggesting that it is the presence of D1 during embryogenesis (or early larval development) that is critical for heritable TE repression and proper oocyte development.

The conclusion that D1 and Prod do not directly contribute to the repression of transposons needs further analysis from RNA-seq data. Alternatively, the wording could be adjusted to indicate that D1 and Prod are not required for specific transposon silencing, such as Burdock and gypsy.

Agreed. We have performed RNA-seq in D1 heterozygous (control) and D1 mutant ovaries in a chk26006 background (Fig. 4A, B) as described above.

As D1 mutation affects Cuff nuclear localization, it would be insightful to sequence the piRNA in the ovaries.

Agreed. We have performed small RNA profiling from D1 heterozygous (control) and D1 mutant ovaries. Despite the significant mislocalization of the RDC complex, overall piRNA production and antisense piRNAs mapping to TEs were largely unaffected (Fig. 4C-E). However, we observed significant changes in piRNAs mapping to complex satellite DNA repeats (Fig. 4D), but these changes were not associated with a maternal effect on germline development or fertility (F2 D1 heterozygote, Fig. 6B-D).

**Referee Cross-Commenting**

I appreciate the valuable insights provided by the other two reviewers regarding this manuscript. I concur with their assessment that substantial improvements are needed before considering this manuscript for publication.

1. The proteomics data has the potential to be a valuable resource for other scientific community. However, I share the concerns raised by reviewer 1 about the current quality of the data sets. Addressing this, it will augment the manuscript with additional data to show the success of the precipitation. Moreover, as reviewer 2 and I suggested, additional co-IP validations of the IP-MS results are needed to enhance the reliability.

In the revised manuscript, we have performed multiple experiments to address the quality of the MS datasets based on comments raised by reviewer #1. They are as follows,

Out of six total AP-MS experiments in this manuscript (D1 x 3, Prod x 2 and Piwi), we observe a strong enrichment of the bait in 5/6 attempts (log2FC between 4-12, Fig. 2A, B, Fig. S2A, Table S1-S3, Table S7). In the D1 testis sample from the initial submission, the lack of a third biological replicate meant that only the p-value (0.07) was not meeting the cutoff. To address this, we have repeated this experiment with an additional biological replicate (Fig. S2A), and our data now clearly show that D1 is also significantly enriched in the testis sample.

As suggested by the reviewer #1, we have assessed our lysis conditions (450mM NaCl and benzonase) and the solubilization of our baits post-lysis. In Fig. S1D, we have blotted equivalent fractions of the soluble supernatant and insoluble pellet from GFP-Piwi embryos and show that both GFP-Piwi and D1 are largely solubilized following lysis. In Fig. S1E, we also show that our IP protocol works efficiently.

The only instance in which we do not detect the bait by mass spectrometry is for GFP-Prod pulldown in embryos. Here, one reason could be relatively low expression of GFP-Prod in comparison to GFP-D1 (Fig. S1E), which may lead to technical difficulties in detecting peptides corresponding to Prod. However, we note that Prod IP from embryos co-purified proteins such as Bocks that were previously suggested as Prod interactors from high-throughput studies (Giot et al. 2003; Guruharsha et al. 2011). In addition, Prod IP from embryos also co-purified proteins known to associate with chromocenters such as Hcs(Reyes-Carmona et al. 2011) and Saf-B(Huo et al. 2020). Finally, the concordance between D1 and Prod co-purified proteins from embryo lysates (Fig. 2A, C) suggest that the Prod IP from embryos is of reasonable quality.

As for the IP-WB validations, we would point out that chromocenters exhibit properties associated with phase separated biomolecular condensates. In our experience, these condensates tend to associate with other proteins/condensates through low affinity or transient interactions that are rarely preserved in IP-WBs, even if there are strong functional relationships. One example is the association between D1 and Prod, which do not pull each other down in an IP-WB (Jagannathan et al. 2019), even though D1 and Prod foci dynamically associate in the nucleus and mutually regulate each other's ability to cluster satellite DNA repeats (Jagannathan et al. 2019). Similarly, IP-WB using GFP-Piwi embryos did not reveal an interaction with D1 (Fig. S4B). However, our extensive functional validations (Figures 4-6) have revealed a critical role for D1 in heritable TE repression.

I agree with reviewer 2 that the present conclusion is not appropriate regarding D1 and Prod do not contribute to transposon silencing. As reviewer 2 and I suggested, the systematical analysis by using both mRNA-seq and small RNA-seq is required to draw the conclusion.

Agreed. We have performed RNA seq and small RNA seq as elaborated above. Our conclusions on the role of D1 in TE repression are now much stronger.

1. The transgenerational phenotype is an intriguing aspect of the study. I agree with reviewer 2 that the inclusion of TM6 ovaries would be a nice control. Further, it is hard to connect this phenotype with the interactions identified in this manuscript. It would be appreciated if the author could provide a mechanistic explanation.

We have significantly improved these aspects of our study in the revised manuscript. Through the analysis of germline development in the F2 D1 heterozygotes as suggested by reviewer #2, in addition to the recommended RNA seq and small RNA seq, we have now identified embryogenesis (and potentially early larval development) as a time period when D1 makes an important contribution to TE repression. Loss of D1 during embryogenesis leads to TE expression in adult germline cells, which is associated with Chk2-dependent gonadal atrophy. Taken together, we have pinpointed the specific contribution of a satellite DNA-binding protein to transposon repression.

Reviewer #3 (Significance (Required)):

Although this study successfully identified several interactions, the authors did not fully elucidate how these interactions contribute to the transgenerational silencing of transposons or ovary development.

We thank the reviewer for the thoughtful comments and overall positive evaluation of our study, especially the proteomic dataset. We are confident that the revised manuscript has improved our mechanistic understanding of the contribution made by a satellite DNA-binding protein in TE repression.

References

Baumgartner L, Handler D, Platzer SW, Yu C, Duchek P, Brennecke J. 2022. The Drosophila ZAD zinc finger protein Kipferl guides Rhino to piRNA clusters eds. D. Bourc'his, K. Struhl, and Z. Zhang. eLife 11: e80067.

Caridi CP, D'Agostino C, Ryu T, Zapotoczny G, Delabaere L, Li X, Khodaverdian VY, Amaral N, Lin E, Rau AR, et al. 2018. Nuclear F-actin and myosins drive relocalization of heterochromatic breaks. Nature 559: 54-60.

Charlesworth B, Sniegowski P, Stephan W. 1994. The evolutionary dynamics of repetitive DNA in eukaryotes. Nature 371: 215-220.

Chiolo I, Minoda A, Colmenares SU, Polyzos A, Costes SV, Karpen GH. 2011. Double-strand breaks in heterochromatin move outside of a dynamic HP1a domain to complete recombinational repair. Cell 144: 732-744.

Ghabrial A, Schüpbach T. 1999. Activation of a meiotic checkpoint regulates translation of Gurken during Drosophila oogenesis. Nat Cell Biol 1: 354-357.

Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, et al. 2003. A protein interaction map of Drosophila melanogaster. Science 302: 1727-1736.

Guruharsha KG, Rual JF, Zhai B, Mintseris J, Vaidya P, Vaidya N, Beekman C, Wong C, Rhee DY, Cenaj O, et al. 2011. A protein complex network of Drosophila melanogaster. Cell 147: 690-703.

Huo X, Ji L, Zhang Y, Lv P, Cao X, Wang Q, Yan Z, Dong S, Du D, Zhang F, et al. 2020. The Nuclear Matrix Protein SAFB Cooperates with Major Satellite RNAs to Stabilize Heterochromatin Architecture Partially through Phase Separation. Molecular Cell 77: 368-383.e7.

Jagannathan M, Cummings R, Yamashita YM. 2019. The modular mechanism of chromocenter formation in Drosophila eds. K. VijayRaghavan and S.A. Gerbi. eLife 8: e43938.

Mitrentsi I, Lou J, Kerjouan A, Verigos J, Reina-San-Martin B, Hinde E, Soutoglou E. 2022. Heterochromatic repeat clustering imposes a physical barrier on homologous recombination to prevent chromosomal translocations. Molecular Cell 82: 2132-2147.e6.

Moon S, Cassani M, Lin YA, Wang L, Dou K, Zhang ZZ. 2018. A Robust Transposon-Endogenizing Response from Germline Stem Cells. Dev Cell 47: 660-671 e3.

Pascovici D, Handler DCL, Wu JX, Haynes PA. 2016. Multiple testing corrections in quantitative proteomics: A useful but blunt tool. PROTEOMICS 16: 2448-2453.

Reyes-Carmona S, Valadéz-Graham V, Aguilar-Fuentes J, Zurita M, León-Del-Río A. 2011. Trafficking and chromatin dynamics of holocarboxylase synthetase during development of Drosophila melanogaster. Molecular Genetics and Metabolism 103: 240-248.

Ryu T, Spatola B, Delabaere L, Bowlin K, Hopp H, Kunitake R, Karpen GH, Chiolo I. 2015. Heterochromatic breaks move to the nuclear periphery to continue recombinational repair. Nat Cell Biol 17: 1401-1411.

Tang H-W, Spirohn K, Hu Y, Hao T, Kovács IA, Gao Y, Binari R, Yang-Zhou D, Wan KH, Bader JS, et al. 2023. Next-generation large-scale binary protein interaction network for Drosophila melanogaster. Nat Commun 14: 2162.

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Referee #3

Evidence, reproducibility and clarity

In the manuscript entitled "Multi-tissue proteomics identifies a link between satellite DNA organization and transgenerational transposon repression", the authors used two satellite DNA-binding proteins, D1 and Prod, as baits to identify chromocenter-associated proteins through quantitative mass spectrometry. The proteomic analysis identified ~500 proteins across embryos, ovaries, and testes, including several piRNA pathways proteins. Depletion of D1 or Prod did not directly contribute to transposon repression in ovary. However, in the absence of maternal and zygotic D1, there was a dramatic increase of agametic ovaries and transgenerational transposon de-repression. Although the study provides a wealth of proteomic data, it lacks mechanistic insights into how satellite DNA organization influence the interactions with other proteins and their functional consequences.

Major

1. While the identification of numerous interactions is significant, it would be helpful to acknowledge that lots of these proteins were known to bind DNA or heterochromatin regions. To strengthen the study, I recommend conducting functional validation of the identified interactions, in addition to the predictions made by Alphfold 2.
2. The observation of transgenerational de-repression is intriguing. However, to better support this finding, it would be better to provide a mechanistic explanation based on the data presented. Minor
3. Given the maternal effect of the D1 mutant, in Figure 4, I suggest analyzing not only nurse cells but also oocytes to gain a more comprehensive understanding.
4. The conclusion that D1 and Prod do not directly contribute to the repression of transposons needs further analysis from RNA-seq data. Alternatively, the wording could be adjusted to indicate that D1 and Prod are not required for specific transposon silencing, such as Burdock and gypsy.
5. As D1 mutation affects Cuff nuclear localization, it would be insightful to sequence the piRNA in the ovaries.

Referee Cross-Commenting

I appreciate the valuable insights provided by the other two reviewers regarding this manuscript. I concur with their assessment that substantial improvements are needed before considering this manuscript for publication.

1. The proteomics data has the potential to be a valuable resource for other scientific community. However, I share the concerns raised by reviewer 1 about the current quality of the data sets. Addressing this, it will augment the manuscript with additional data to show the success of the precipitation. Moreover, as reviewer 2 and I suggested, additional co-IP validations of the IP-MS results are needed to enhance the reliability.
2. I agree with reviewer 2 that the present conclusion is not appropriate regarding D1 and Prod do not contribute to transposon silencing. As reviewer 2 and I suggested, the systematical analysis by using both mRNA-seq and small RNA-seq is required to draw the conclusion.
3. The transgenerational phenotype is an intriguing aspect of the study. I agree with reviewer 2 that the inclusion of TM6 ovaries would be a nice control. Further, it is hard to connect this phenotype with the interactions identified in this manuscript. It would be appreciated if the author could provide a mechanistic explanation.

Significance

Although this study successfully identified several interactions, the authors did not fully elucidate how these interactions contribute to the transgenerational silencing of transposons or ovary development.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Chavan et al. set out to enrich our compendium of pericentric heterochromatin-associated proteins - and to learn some new biology along the way. An ambitious AP-Mass baited with two DNA satellite-binding proteins (D1 and Prod), conducted across embryos, ovaries, and testes, yielded hundreds of candidate proteins putatively engaged at chromocenters. These proteins are enriched for a restricted number of biological pathways, including DNA repair and transposon regulation. To investigate the latter in greater depth, the authors examine D1 and prod mutants for transposon activity changes using reporter constructs for multiple elements. These reporter constructs revealed no transposon activation in the adult ovary, where many proteins identified in the mass spec experiments restrict transposons. However, the authors suggest that the D1 mutant ovaries do show disrupted localization of a key member of a transposon restriction pathway (Cuff), and infer that this mislocalization triggers a striking, transposon derepression phenotype in the F2 ovaries.

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

Major

1. The introduction requires quite a radical restructure to better highlight the A) importance of the work and B) limit information whose relevance is not clear early in the manuscript. A. Delineating who makes up heterochromatin is a long-standing problem in chromosome biology. This paper has huge potential to contribute to this field; however, it is not the first. Others are working on this problem in other systems, for example PMID:29272703. Moreover, we have some understanding of the distinct pathways that may impact heterochromatin in different tissues (e.g., piRNA biology in ovaries vs the soma). Also, the mutant phenotypes of prod and D1 are different. Fleshing out these three distinct points could help the reader understand what we know and what we don't know about heterochromatin composition and its special biology. Understanding where we are as a field will offer clear predictions about who the interactors might be that we expect to find. For example, given the dramatically different D1 and prod mutant phenotypes (and allele swap phenotypes), how might the interactors with these proteins differ? What do we know about heterochromatin formation differences in different tissues? And how might these differences impact heterochromatin composition? B. The attempt to offer background on the piRNA pathway and hybrid dysgenesis in the Introduction does not work. As a naïve reader, it was not clear why I was reading about these pathways - it is only explicable once the reader gets to the final third of the Results. Moreover, the reader will not retain this information until the TE results are presented many pages later. I strongly urge the authors to shunt the two TE restriction paragraphs to later in the manuscript. They are currently a major impediment to understanding the power of the experiment - which is to identify new proteins, pathways, and ultimately, biology that are currently obscure because we have so little handle on who makes up heterochromatin.
2. The implications of the failure to rescue female fertility by the tagged versions of both D1 and Prod are not discussed. Consequently, the reader is left uneasy about how to interpret the data.
3. How were the significance cut-offs determined? Is the p-value reported the adjusted p-value? As a non-expert in AP-MS, I was surprised to find that the p-value, at least according to the Methods, was not adjusted based on the number of tests. This is particularly relevant given the large/unwieldy(?) number of proteins that were identified as signficant in this study. Moreover, the D1 hit in Piwi pull down is actually not significant according to their criteria of p <0.05 (D1 is p=0.05).
4. How do we know if the lack of overlap across tissues is indeed germline- or soma-specialization rather than noise? To make this inference, conducting some validation would be required. More generally, I was surprised to see no single interactor validated by reciprocal IP-Westerns to validate the Mass-Spec results, though I am admittedly only adjacent to this technique. Note that colocalization, to my mind, does not validate the AP-MS data - in fact, we would a priori predict that piRNA pathway members would co-localize with PCH given the enrichment of piRNA clusters there.
5. The AlphaFold2 data are very interesting but seem to lack of negative control. Is it possible to incorporate a dataset of proteins that are not predicted to interact physically to elevate the impact of the ones that you have focused on? Moreover, the structural modeling might suggest a competitive interaction between D1 and piRNAs for Piwi. Is this true? And even if not, how does the structural model contribute to your understanding for how D1 engages with the piRNA pathway? The Cuff mislocalization?
6. The absence of a TE signal in D1 and Prod mutant ovaries would be much more compelling if investigated more agnostically. The observation that not all TE reporter constructs show a striking signal in the F2 embryos makes me wonder if Burdock and gypsy are not regulated by these two proteins but possibly other TEs are. Alternatively, small RNA-seq would more directly address the question of whether D1 and Prod regulate TEs through the piRNA pathway.
7. I had trouble understanding the significance of the Cuff mis-localization when D1 is depleted. Given Cuff's role in the piRNA pathway and close association with chromatin, what would the null hypothesis be for Cuff localization when a chromocenter is disrupted? What is the null expectation of % Cuff at chromocenter given that the chromocenter itself expands massively in size (Figure 4D). The relationship between these two factors seems rather indirect and indeed, the absence of Cuff in the AP would suggest this. The biggest surprise is the absence of TE phenotype in the ovary, given the Cuff mutant phenotype - but we can't rule out given the absence of a genome-wide analysis. I think that these data leave the reader unconvinced that the F2 phenotype is causally linked to Cuff mislocalization.
8. Apologies if I missed this, but Figure 5 shows the F2 D1 mutant ovaries only. Did you look at the TM6 ovaries as well? These ovaries should lack the maternally provisioned D1 (assuming that females are on the right side) but have the zygotic transcription.

Minor

1. Add line numbers for ease of reference
2. The function of satellite DNA itself is still quite controversial - I would recommend being a bit more careful here - the authors could refer instead to genomic regions enriched for satellite DNA are linked to xyz function (see Abstract line 2 and 7, for example.)
3. "Genetic conflicts" in the introduction needs more explanation.
4. "In contrast" is not quite the right word. Maybe "However" instead (1st line second paragraph of Intro)
5. Results: what is the motivation for using GSC-enriched testis?
6. Clarify sentence about the 500 proteins in the Results section - it's not clear from context that this is the union of all experiments.
7. The data reported are not the first to suggest that satellite DNA may have special DNA repair requirements. e.g., PMID: 25340780
8. Page 10: indicate-> indicates.
9. Page 14: revise for clarity: "investigate a context whether these interactions could not take place"
10. Might be important to highlight the 500 interactions are both direct and indirect. "Interacting proteins" alone suggests direct interactions only.
11. The effect of the aub mutant on chromocenter foci did not seem modest to me - however, the bar graphs obscure the raw data - consider plotting all the data not just the mean and error?

Significance

The dataset produced by the AP-Mass Spec offers chromosome biologists an unprecedented resource. The PCH is long-ignored chromosomal region that has historically received minimal attention; consequently, the pathways that regulate heterochromatin are understudied. Moreover, attempting to connect genome organization to transposon regulation is a new and fascinating area. I can easily envision this manuscript triggering a flurry of discovery; however, there is quite a lot of work to do before the data can fully support the claims.

This manuscript represents a significant contribution to the field of chromosome biology.

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Referee #1

Evidence, reproducibility and clarity

This is an interesting manuscript from the Jagannathan laboratory, which addresses the interaction proteome of two satellite DNA-binding proteins, D1 and Prod. To prevent a bias by different antibody affinities they use GFP-fusion proteins of D1 and Prod as baits and purified them using anti GFP nanobodies. They performed the purifications in three different tissues: embryo, ovary and GSC enriched testes. Across all experiments, they identified 500 proteins with surprisingly little overlap among tissues and between the two different baits. Based on the observed interaction of prod and D1 with members of the canonical piRNA pathway the authors hypothesized that both proteins might influence the expression of transposable elements. However, neither by specific RNAi alleles or mutants that lead to a down regulation of D1 and Prod in the gonadal soma nor in the germline did they find an effect on the repression of transposable elements. They also did not detect an effect of a removal of piRNA pathway proteins on satellite DNA clustering, which is mediated by Prod and D1. However, they do observe a mis-localisation of the piRNA biogenesis complex to an expanded satellite DNA in absence of D1, which presumably is the cause of a mis-regulation of transposable elements in the F2 generation.

This is an interesting finding linking satellite DNA and transposable element regulation in the germline. However, I find the title profoundly misleading as the link between satellite DNA organization and transgenerational transposon repression in Drosophila has not been identified by multi-tissue proteomics but by a finding of the Brennecke lab that the piRNA biogenesis complex has a tendency to localise to satellite DNA when the localisation to the piRNA locus is impaired.

Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner.

Major comments

Unfortunately, the proteomic data sets are not very convincing. Not even the corresponding baits are detected in all assays. I wonder whether the extraction procedure really allows the authors to analyze all functionally relevant interactions of Prod and D1. It would be good to see a western blot or an MS analysis of the soluble nuclear extract they use for purification compared to the insoluble chromatin. It may well be that a large portion of Prod or D1 is lost in this early step. I also find the description of the proteomic results hard to follow as the authors mostly list which proteins the find as interactors of Prod and D1 without stating in which tissue or with what bait they could purify them (i.e. p7: Importantly, our hits included known chromocenter-associated or pericentromeric heterochromatin-associated proteins, such as Su(var)3-9[52], ADD1[23,24], HP5[23,24],mei-S332[53], Mes-4[23], Hmr[24,39,54], Lhr[24,39], and members of the chromosome passenger complex, such as borr and Incenp[55]). It would be interesting to at least discuss tissue specific interactions.

Minor comments

The authors may also want to provide a bit more information on the quantitation of the proteomic data such as how many values were derive from the match-between runs function and how many were imputed as this can severely distort the quantification.

Referee Cross-Commenting

I agree with the two other reviewers that the connection between the interactome and the transgenerational phenotype is unclear. This is also what I meant i my comment that the title is somewhat misleading. A systematic analysis of the D1 and Prod knock down effect on mRNAs and small Rnas would indeed be helpful to better understand the interesting effect.

Significance

Nevertheless, the investigation of the D1 and Prod interactome is interesting and might reveal insights into the pathways that drive the formation of centromeres in a tissue specific manner. It may be mostly interesting for the Drosophila community but could also be exiting for a broader audience interested in the connection of heterochromatin and its indirect effect on the regulation of transposable elements.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript by Kehrer et al., use an elegant Apex2 BioID method to identify novel putative microneme proteins by mass-spectrometry and pick one candidate for further characterization. They identify a novel putative microneme protein they name Akratin which they characterize through targeted gene deletion and a series of complementation experiments. This reveals first that akratin appears to be functioning in male gamete egress, and though complementation using a putative trafficking mutant, also in midgut traversal.

Overall the study is thoroughly performed but some of the conclusions are not fully supported.

1)The newly identified microneme protein is still putative in my mind as the authors have not co-localized it with another marker. This is crucial for conclusions about its putative function and crucial for the trafficking experiment as explained below. It is also important given the high number of putative false positives in the BioID experiment.

2)I would consider it essential to also localise the Apex2 tagged SOAP protein as the authors cannot be sure that there is a partial mislocalisation of the protein leading to false positives.

3)I am not convinced by the trafficking defect. This could be because the localsation in the images are not easy to distinguish and it may be much clearer looking down the microscope. I think co-localisation with another microneme marker would go a long way and demonstrating that akratin upon mutation actually localises elsewhere is important. It is even more important since there is no phenotype in male egress, but then later in ookinetes, which is a bit surprising if this is a proper conserved trafficking motif.

4)The candidate selection section is poorly described. A flow chart or clearer inclusion/ exclusion criteria would be useful.

5)I understand the approach to focus on more abundant biotinylated proteins, however, I think it may not be the best approach to use peptide counting. Apex2 labelling as the authors rightfully say, is mainly based on tyrosine labelling of surface exposed areas, so the abundance of proteins in the IP will depend on accessible tyrosines, protein abundance, distance from the bait, size of the protein and how many tryptic peptides can be generated. Reproducible results between 2 conditions are more likely to show true positives and may be the best way to prioritize, or assign confidence. Also: cOuld the authors use mean intensity values for the peptides covering proteins as a metric for abundance using label free quantification? This is not a requirement but may allow quantification in a slightly better way. I am not sure about the Table S1 colour scheme (the legend does not explain green, purple and blue shading). Are all green ones confirmed microneme proteins? Please add a proper descripton of the table and columns.

6)Figure 2C and D are from PlasmoDB and should ideally not be included as figure panels. This is misleading and could either be mentioned in the text, or put into supplementary data with a clear note that the authors have not aquired these data. I would also suggest to move figures 3A-C into figure 2 and present the KO with the complementation data for a direct comparison.

Minor:

1)When the authors say "numbers of peptides identified": is this unique peptides or does it include non-unique peptides?

2)Figures 1 I-K could move into supplementary as they are somewhat non-informative given the nature of BioID described in the main points.

3)Line 253: Whether akratin is involved in membrane lysis directly, or important for microneme secretion so this is a knock-on effect is not known. This could be added to the discussion, but there is no evidence for this statement in the results section.

4)Line 274: Refers to Figure 3F, which does not exist.

5)Line 333: Overall I think this is a bit of an overstatement. The use of Apex2 in these conditions is definitely nice to see but for now the authors have validated none of the microneme proteins by co-localization. So we are still a bit in the dark how well the method works in terms of false positives. The targeting motif in my mind is not yet confirmed in the absence of co-localisations with other markers. An alternative explanation could be that the c-terminus of the protein is important for its function in one stage, but not another but that trafficking is not- or only marginally affected.

Significance

The significance of the manuscript in my mind lies in the application of Apex2 in Plasmodium parasites, which will be an advance for the field. However, we do not learn about labeling times, how short it can be so its potential is not fully looked at.

The list of the putative micronemes will of course be of high interest for the community, but because of the limited validation in this study will require further validation by others.

The identification of the dual function of this protein in transmission in egress and ookinete traversal is interesting and surely leads to further studies. The identification of a putative differential trafficking motif is intruiging, if, as stated in the major concerns, this can be validated.

My expertise lies in Plasmodium biology with good knowledge of mass-spectrometry approaches.

Referees Cross-commenting

I agree with the assessment of the other reviewer, a slightly more detailed discussion of the hits would be desireable (exported proteins, why are they there). This could be a drawback of the system used, and mentioned.

Western blot of the GFP is a very good idea to clarify whether the localization is maybe, in parts, GFP that is not fused to the full lenght protein, either by cleavage, or a breakdown product.

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Referee #1

Evidence, reproducibility and clarity

Proximity labelling using BirA has emerged as a highly successful approach to identify interaction partners and proteins in compartments of a protein of interest in the living cell. A number of recent studies applied this approach with malaria parasites and demonstrated its usefulness. However, a drawback of using BirA is the time required to obtain good yields of biotinylated proteins (usually many hours). APEX is a much faster alternative to BirA that however has so far not been used in apicomplexan parasites. Here Kehrer and colleagues use APEX2 to identify proteins of the micronemes of mature Ookinetes, a task that due to the short time available for labelling, would have been difficult with BirA. From the obtained hits the authors chose a protein they named akratin and carried out a detailed functional analysis. They found that akratin is needed for microgametogenesis and for ookinete migration. Complementation with either the Pb or Pf GFP-tagged version of akratin rescued all defects of the mutant, despite the low sequence similarity and differing number of predicted transmembrane segments in the P. falciparum protein. Interestingly a mutant form of this protein with an ablated putative trafficking motif in its C-terminus also caused an ookinete migration phenotype but microgametogenesis was unaffected, hinting at intricacies in its function.

This is a clearly written and interesting paper reporting the first use of APEX-based BioID in malaria parasites and demonstrates that it is possible to take advantage of BioID in short lived stages. The ookinete microneme proteome reported here compares favourably with that derived from organelle purifications and will provide a resource to identify the proteins and understand their involvement in migration and adhesion of this and possibly other parasite stages. The example protein chosen for functional analysis in this work nicely illustrates this. A validation of some more of the unknown hits to be true microneme proteins would have been beneficial, but given the high number of known true positives in the hit list this is not absolutely essential for the paper. Overall this manuscript therefore provides a nice piece of work adding a list of proteins for future study and a new player important for mosquito infection.

Minor points:

1.several proteins taken as true hits in TableS1 are not obvious to me, such as plasmepsin V and several exported proteins. Are they expected to be trafficked via the micronemes in ookinetes?

2.Fig. 1C: '+ Streptavidin', this should be '+ streptavidin beads'

3please insert a referral to Figure S2 (similarity of the Plasmodium akratin homologs) somewhere around line 180, the only reference to this figure I could find in the text was in line 214. Figure S3 (line 184) is mentioned in a context to support the absence of homologues outside Plasmodium spp but shows the generation of the akratin KO. Maybe this should have been a citation to Figure S2, but then something is missing from that figure.

4.line 192, it might be useful to clarify in the text what 10 and 7 blood stage growth means (multiplication rate?).

5.the observation that akratin as a multi TM protein with signal peptide seems to be soluble in the parasite is rather unlikely. Is there a precedent for this? My best guess would be that this is GFP alone due to degradation or processing of the akratin-GFP fusion. A Western blot, if sufficient material is available, would clarify this. In regards to the localisation of akratin in the different stages it should also be taken into account that calling anything 'vesicular' based on fluorescent microscopy is rather speculative.

6.line 199, how can the low number of ookinetes affect their speed? Could this remark have been intended for the next sentence (199-201/Fig. 2I) as the lack of transmission to mosquitoes may have been due to the reduced number of ookinetes rather than a deficiency of individual ookinetes? To exclude the latter, were parasite number used matched to exclude that this was the case in Fig. 2I?

7.line 272-4, is there a way to quantify the phenotype in ookinetes, e.g. using intensity profile plots showing the distribution across the cell? Many cells still seem to show peripheral staining in the trafficking mutant. Could it again be that some of the protein is processed and the increased cytoplasmic staining represents GFP alone (see also comment 5)? In that case the level of processing rather than trafficking (alone) might be affected in the mutant.

8.lines 396 and 397, there is a minuscule -1 before the chemicals in my pdf.

9.line 58. remove 'not' and 'or': neither been identified nor been characterised

10.lines 88 to 94: English could be polished some more in this part. Line 91, replace 'ones', e.g. with 'proteins'

11.lines 245 and 253: add commas after (Figure 4B) and (Figure 4D), respectively

12.line 254; change possibly into possible

13.line 296-71: it might be helpful to mention in the text that this was again the complementation strategy (used for the Pb and Pf akratin)

14.while going beyond the scope of this manuscript, would it be possible to use the APEX introduced here to label structures in EM? A comment on this might be useful for readers interested in using this domain.

Significance

This is the first use of APEX2 in apicomplexans and this will be very useful for the field. The ookinete microneme proteome will provide a resource to study key aspects of this stage. The unknown proteins in the hit list still have to be confirmed to be true positives, but given the high number of true positives among the known proteins, the success rate should be acceptable. Akratin is a new protein among several known to be important for transmission to the mosquito host. As illustrated by this and other studies, many important questions remain how microgametes egress from the cell and how in vitro gliding and mosquito gut wall penetration relate. Akratin, together with the previously known proteins, will be instrumental in solving these questions.

My expertise (to put this assessment into perspective): I am a cell biologist working with P. falciparum blood stages and have no or limited experience with mosquito stages and P. berghei. My grasp of the technical difficulties to work with ookinetes and other mosquito stages as presented in this manuscript are therefore limited. Nevertheless, it appears to me that the work is well done and that the overall outcome from this paper is significant and will be of great interest to the field.

Referees Cross-Commenting

I think the other reviewer's request to carry out a co-loc experiment with a microneme marker to show that akratin and SOAP-APEX are in the correct location is very reasonable and should be done. I also share the view about the trafficking domain. This is reflected in my comment (points 5 and 7) on quantifying the phenotype of the 'trafficking mutant' and the possbility of not looking at the full length protein as it may be a processed/degraded version of the protein that still contains GFP. Western blots, if possible with these parasite stages, might clear this up.

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Reply to the reviewers

Manuscript number: RC-2024-02378

Corresponding author(s): Angelika Böttger

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• *

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

After we have carefully studied the four reviews we have received, we made some major revisions to the manuscript. These included the following main points:

• Concerns regarding clarity of the manuscript: we have substantially edited the abstract, introduction and discussion part of the manuscript and added many more references to previous work by other authors, especially Cazet 2021, Tursch 2022 and Gahan 2017. We focused our introduction and discussion on organizer function and on the Gierer-Meinhardt-Model for pattern formation. We think that the conclusions are of great general interest because they suggest a function of the Hydra head organizer according to the original definition by Hans Spemann, that is “harmonious interlocking of separate processes which makes up development”. Notch signaling, in our interpretation, is an instrument for this function of the organizer. Comparison with Craspedacusta compellingly illustrates this idea.
• Concerns regarding Craspedacusta experiments: we have isolated four Craspedacusta transcripts (CsSp5, CsWnt3, CsAlx and CsNOWA) and analyzed their response to DAPT during head regeneration in Craspedacusta. This revealed that DAPT did not inhibit CsWnt3 expression, in accordance with it not having an effect on head regeneration in Craspedacusta However, DAPT inhibited expression of the other potential CsNotch target genes, confirming that DAPT generally works in Craspedacusta polyps as Notch-inhibitor.
• Concerns regarding HyKayak function: we have conducted a rescue experiment to show the function of Hykayak as a target for Notch-regulated repressor genes and a local inhibitor of Wnt-3 expression, which revealed that the expected up-regulation of HyWnt3 in DAPT-treated animals was very weak and did not rescue the DAPT-regeneration phenotype-this was discussed, but data were not included.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Major: • The introduction is lacking a full description of what is known about transcriptional changes during Hydra regeneration and in particular the role of Wnt signalling in this process. Of note the authors do not cite several important studies from recent years including (but not limited to):

*https://doi.org/10.1073/pnas.2204122119 *

*https://doi.org/10.1186/s13072-020-00364-6 *

*https://doi.org/10.1101/587147 *

*https://doi.org/10.7554/eLife.60562 This problem is further compounded later when the authors do not cite/discuss work which has performed the same or similar analyses to their own. The authors should endeavor to give a more comprehensive background knowledge. *

Answer:

Our work focuses on the role of Notch-signalling during Hydra head regeneration, specifically when the head is removed at an apical position. We therefore now have included additional information about transcriptional changes during this process in the introduction. In addition, we have included the suggested citations in the introduction to give a more general background knowledge.

e.g. .Following decapitation, the expression of Hyβ-catenin and HyTcf was upregulated earliest, followed by local activation of Wnt genes. Among these, HyWnt3 and HyWnt11 appeared within 1.5 h of head removal, followed by HyWnt1, HyWnt9/10c, HyWnt16, and HyWnt7, indicating their role in the formation of the Hydra head organizer (Hobmayer et al., 2001; Lengfeld et al., 2009; Philipp et al., 2009; Tursch et al., 2022).

• The authors do not cite or reference at all the study by Cazet et al. which used iCRT14 along with RNAseq and ATACseq to probe the role of Wnt signaling during early regeneration. This is a major issue. Although I appreciate that the authors have done much longer time courses and that their data therefore add something to our understanding it will still be important to discuss here. For example, the authors show that Wnt3 is activated normally in iCRT14 animals. Is this also seen in the RNAseq from Cazet et al.*
• *

Answer:

iCRT14 was used in Hydra regeneration experiments by Gufler et al (which we did cite) and Cazet et al, but the specific aspects of hypostome and tentacle regeneration were not addressed. Cazet et al. have analyzed HyWnt3expression after iCRT treatment during the first 12 hrs of regeneration. Our data show, in addition that HyWnt3 is not controlled by TCF-dependent transcription during Hydra head regeneration after apical cuts throughout the whole regeneration process including the morphogenesis state. Nevertheless, the other Wnt-genes, which are indicated in canonical Wnt-signalling are affected by iCRT14 also in our study.

We have now included comparison of Cazet- and our data, we wrote:

“HyWnt3 and Wnt9/10c expression are swiftly induced by injuries. When HyWnt3 and HyWnt9/10 activities are sustained, organizers can be formed, which induce ectopic heads when the original organizer tissue (the head) is removed (Cazet et al., 2021; Tursch et al., 2022).”

The effect of iCRT14 had been analyzed in previous studies (Cazet et al., 2021; Gufler et al., 2018; Tursch et al., 2022). All showed b-catenin-dependency for down-regulation of head specific genes in foot regenerates at time points up to 12 hrs after head removal, including HyWnt3. They also stated a failure of head regeneration in the presence of iCRT14 but, in accordance with our study, did not reveal that HyWnt3 expression at future heads depended on b-catenin. None of these studies analyzed the regeneration of tentacles and hypostomes separately and they did not report whether* the regeneration of hypostomes 48 hrs after head removal occurred normally upon iCRT14 treatment. *

• The visualizations used in Figure 3 are quite difficult to interpret and do to in all cases match descriptions in the text. The way the same type data is displayed in figure 5 so much nicer. It is also better to treat the same types of data in the same manner consistently throughout the paper. For Hes, for example, the authors state that there is a reduction although the data shows that this is very small and taking into account the 95% confidence interval does not seem to be significant. If this is the case then the positive control is not working in this experiment. This would be much clear if individual time points were compared like in figure 5 and statistical tests shown. The authors then state that Alx is not affected but there is actually a larger effect than what they deemed significant for Hes (the axes are notably different between these two and I think a more consistent axis would make the genes more comparable). Similarly, Gsc is described as being not affected at 8 hours but it appears again to change more that the positive control Hes. Given this I would call into question the validity of this dataset and/or the interpretation by the authors. A more thorough analysis including taking better into account statistical significance would go a long way to increasing confidence in this data. • The same issues in interpretation described for Figure 3 also apply to figure 4. The authors state that Wnt7 is affected less than Wnt1 and 3 but this is not evident in the figure and no comparative analysis is performed to confirm this. The same for Wnt 11 and 9/10c where what the authors description is very difficult to see in the figure. Sp5 is apparently upregulated, but this is not discussed. Again the axes are notably different making it even more difficult to compare between samples. __Answer*____:__

We have now presented the data by simple scatter blots with significance information for every data point. This allows comparison between samples as requested by the reviewer. The GAMs were moved to the supplement. We believe that some readers may appreciate GAM-representation of the data because of the accessibility of the confidence interval over time.

Concerning DAPT:

“We now performed RT-qPCR analysis to compare gene expression dynamics of these genes during head regeneration 0, 8, 24, 36 and 48 hrs after head removal. Animals were either treated with 30 µM DAPT in 1% DMSO, or 1% DMSO as control for the respective time frames. Timepoint 0 was measured immediately after head removal. The results of these analyses revealed that HyHes expression was clearly inhibited by DAPT during the first 36 hrs after head removal (Fig. 3B), confirming previously published data which had indicated HyHes as a direct target for NICD (Münder et al., 2010). HyAlx expression levels were slightly up-regulated after 24 hrs, but later partially inhibited by DAPT (Fig. 3C). CnGsc expression under DAPT treatment initially (8hrs) was comparable to control levels, but then it was strongly inhibited (Fig. 3D). This goes along with the observed absence of organizer activity in regenerating Hydra tips (Münder et al., 2013). Interestingly, a similar result was seen for HySp5 expression, which was also normal at 8 hrs but was then inhibited by DAPT at later time points (Fig. 3E). HyKayak, while expression is normal after 8 hrs, was strongly overexpressed between 24 and 36 hrs of regeneration in DAPT-treated polyps in comparison to control regenerates (Fig. 3F).

Concerning iCRT14

Next, following the same procedure as described for DAPT, we compared the gene expression dynamics of iCRT14-treated regenerates with control regenerates. We found that the expression of HyWnt3 was not inhibited by iCRT14. In fact, it even appeared slightly up-regulated at the 8 hrs time point (Fig. 4A). Normal HyWnt3-expression at the end of the regeneration period was confirmed by in-situ hybridization for HyWnt3 as shown in Fig. 1D, indicating that HyWnt3 expression patterns and expression levels in ecto- and endodermal cells of the hypostome were faithfully regenerated (Fig. 4A). In contrast, HyAlx expression was completely abolished by iCRT14 (Fig. 4B), consistent with the observation that iCRT14-treated head regenerates did not regenerate any tentacles (Fig. 1A). HySp5 expression was not significantly affected by iCRT14 treatment at any time point (Fig. 4C).

Furthermore, we found that CnGsc levels in iCRT14 remained similar to control regenerates up to 24 hrs, but were attenuated at later time points (Fig. 4D), very similar to the expression dynamics of the Notch-target gene HyHes (Fig. 4E). The expression of HyKayak was decreased at 8 hrs after head removal in the presence of iCRT14, but then increased above control levels after 48 hrs (Fig. 4F). There were no significant changes in the expression dynamics of HyBMP2/4 and HyBMP5/8b between iCRT14-treated regenerates and controls (Fig. 4G, H).”

The precise number of biological replicates can be seen in the individual diagrams, they included for most genes three biological replicates, with always three technical replicates for each data point. Biological replicates were obtained over several years by different researchers. For some genes, we obtained very consistent data with high confidence in every experiment (e.g. HyWnt3, HyBMP4). We illustrate this in table 1, where three arrows indicate all such cases. With some genes we observed greater variation, which we interpret as no effect or a minor effect in table 1. Some of these variations may be explained by our observation of wave-like patterns in the expression dynamics. Therefore, we have included the following statement:

“In addition, the gene expression dynamics for many of the analyzed genes appears in wave-like patterns in some experiments (see Figs S3 and S4). As we have only four time points measured, we cannot draw strong conclusions from these observations, except that some of the deviations in our data points (e.g. 48 hrs HyHes)”

• In their description of figure 4 the authors completely omit to discuss the Cazet et al dataset which has the exact same early timepoints for iCRT14 treatment. This must be discussed and compared and any difference noted. * Answer:

We included the iCRT14 results from Cazet et al., in our revised manuscript (see above).

• End of page 11: The authors propose a model thereby the role of Notch in Wnt3 expression may be due to the presence of a repressor. However, I don't see any putative evidence at that stage. The authors also do not cite relevant work from both Cazet et al. and Tursch et al which show that Wnt3 is likely upregulated by bZIP TFs. In both these cases the authors show evidence of bZIP TF binding sites in the Wnt3 promoter along with other analyses. This is very relevantto the model presented by the authors here and must be discussed and compared. - * In particular the authors put forward HyKayak as an inhibitor of Wnt3 and this should be discussed along with the previous work.

Answer:

Tursch et al. 2022 did not claim that HyWnt3 is upregulated by bZiP TFs. They showed that HyWnt3 was strongly upregulated in a position-independent manner upon inhibition of the p38 or JNK (c-Jun N-terminal kinase) pathways (i.e., stress-induced MAPK pathways). This would rather support our hypothesis that HyKayak (AP-1 protein) might be a repressor of Wnt3-expression.

Cazet et al have indicated that injury-responsive bZIP TFs are the most plausible regulators of canonical Wnt-signalling components during the early generic wound response. They identified CRE-elements, which can be bound by bZIP TFs, in the putative regulatory sequences of HyWnt3. However, they focused on the early stage of regeneration (0-12hpa), and showed that bZIP TFs, including jun, fos and creb are transiently upregulated at 3hpa and hypothesise that they could induce the upregulation of HyWnt3 at this stage as an injury response. We have to point out that the Hydra fos-homolog Hykayak, which our work is concerned with, is not identical with the fos-gene described in Cazet’s paper. In addition, the Hykayak gene was downregulated by Notch signalling during the morphogenesis state of regeneration (24-36 hrs), which is not the same stage investigated by Cazet et al. To avoid confusion, we have now included the Cazet-fos-sequence in our sequence comparison in Fig. S1 (fos_Cazet_HYDVU). Moreover, we have included more information about fos_Cazet in the context of a comparison with HyKayak.

• *

Different bZiP transcriptional factors (TFs) may have different effects on the expression of Wnt genes, and these effects are context-dependent. In previous research, Cazet et al. identified another Hydra fos gene (referred to as fos_cazet) and bZiP TF binding sites in the putative regulatory sequences of HyWnt3 and HyWnt9/10c. They showed that bZiP TF-genes, including Jun and fos, were transiently upregulated 3 hrs after amputation, therefore they hypothesized that bZiP TFs could induce TCF-independent upregulation of HyWnt3 during the early generic wound response (Cazet et al., 2021). However, in our study HyKayak expression continuously increased throughout the entire head regeneration process (Fig. 3E and 4E) including the morphogenesis stages (24-48 hrs post-amputation). Another study reported that inhibition of the JNK pathway (which disrupts formation of the AP-1 complex) resulted in upregulation of HyWnt3 expression in both, head and foot regenerates (Tursch et al., 2022). This result might support our hypothesis, but it only included the first 6 hours after amputation, similar to Cazet’s research. Therefore, it appears that HyKayak and fos_Cazet may have opposing roles in the regulation of Wnt-gene expression and are possibly activated by different signaling pathways depending on the stages of regeneration.

• On page 12 the authors conclude based on gene expression in inhibitor treatment that there is a “change in complex composition of the two transcription factors.” This is something which would require biochemical evidence and I therefore suggest they remove this entirely. * Answer:

we have removed this sentence

• The authors use experiments in Craspedacusta to test their hypothesis of the role of Wnt and Notch signaling in Hydra. There is, in my opinion, an incorrect logic here. Regardless of the outcome, the roles of Wnt and Notch could conceivably be different in the two species and therefore testing hypothesis from one is not possible in the other. The authors should reframe their discussion of this to be more of a comparative framework. Moreover, the results do not necessarily indicate what the authors say. In Hydra Notch signaling is required to form the hypostome/mouth and this is not the case in Craspedacusta while Wnt signaling is required. The authors do not cite an important study from another Hydrozoan Hydractinia (Gahan at al.,2017). In that study the authors show that DAPT inhibits tentacles during regeneration but that the hypostome (or at least the arrangement of neurons and cnidocytes around the mouth) forms normally. This would indicate that in Hydractinia the process of head formation is different to in Hydra and would be congruent with what is shown here in Craspedacusta. This should be more thoroughly discussed, and all relevant literature cited.* Answer:

We have concentrated our Craspedacusta work on Notch-signalling now. We only show that DAPT does not inhibit the regeneration of Craspedacusta heads. We have included new data showing that nevertheless it has an effect on the expression of hypothetical Notch target genes, but not on CsWnt3 (new Fig. 7). We have re-written our discussion accordingly and included the Hydractinia-work about Notch (Gahan2017). Although the Hydractinia paper lacks gene expression studies making it difficult to directly compare with the Hydra data, it supports our claim that Notch is required for regeneration of polyps with head and tentacles. We indeed do not know anything about Wnt-signalling in Craspedacusta. Our new results show that it is probably expressed in the head, because we observe very low levels of expression in the polyps after head removal, which increases considerably during regeneration of the head. This was included in the results:

Results:

“Finally, we investigated the expression of the Craspedacusta Wnt3-gene and its response to DAPT treatment during head regeneration. We observed low expression level of CsWnt3 after head removal (t=0), which dramatically increased as the head regenerated, suggesting that Wnt3 is expressed in the head of Craspedacusta polyps as it is in the head of other cnidarians including Hydra, Hydractinia and Nematostella (Hobmayer et al., 2000; Kusserow et al., 2005; Plickert et al., 2006). In accordance with having no effect on head regeneration, DAPT also did not inhibit CsWnt3 expression during this process in Craspedacusta. This is opposite to the situation in Hydra. If CsWnt3 would be involved in the Craspedacusta head regeneration, this could explain the failure of DAPT to interfere with this process”.

Discussion part

“Head regeneration also occurs in the colonial sea water hydrozoan Hydractinia. Colonies consist of stolons covering the substrate and connecting polyps, including feeding polyps, which have hypostomes and tentacles, and are capable of head regeneration, similar to Hydra polyps. Wnt3 is expressed at the tip of the head and by RNAi mediated knockdown it was shown that this gene is required for head regeneration (Duffy et al., 2010). In the presence of DAPT, Gahan et al observed that proper heads did not regenerate, similar to Hydra. However, they observed regeneration of the nerve ring around the hypostome indicating the possibility that hypostomes had been regenerated. Unfortunately, this study did not include gene expression data and therefore it is not clear whether Wnt3 expression was affected or not (Gahan et al., 2017).

…..

An interesting question was whether regeneration of cnidarian body parts, which are only composed of one module, also requires Notch-signalling. This is certainly true for the Hydra foot, which regenerates fine in the presence of DAPT (Käsbauer et al. 2007). Moreover, we tested head regeneration in Craspedacusta polyps, which do not have tentacles, and show that DAPT does not have an effect on this regeneration process. This corroborates our idea that Notch is required for regeneration in cnidarians, when this process involves two pattern forming processes to produce two independent structures, which are controlled by different signalling modules. This would be the case for the Hydra and for the Hydractinia heads, but not for Craspedacusta. ”

…

Moreover, we indicate at the end of our discussion that further studies about head regeneration in Craspedacusta and the genes involved would be desirable. We believe this would be beyond the scope of the current paper and we are working on a new Craspedacusta study.

“Future studies on expression patterns of the genes that control formation of the Hydra head, including Sp5 and Alx in Craspedacusta could provide insights into the evolution of cnidarian body patterns. Sp5 and Alx appear to be conserved targets of Notch-signalling in the two cnidarians we have investigated. Wnt-3, while being inhibited by Notch-inhibition in Hydra head regenerates, is not a general target of Notch signalling. It was not affected by DAPT in our comparative transcriptome analysis (Moneer et al. 2021b) on uncut Hydra polyps, and it was also not affected by DAPT in regenerating heads of Craspedacusta.”

• From reading the manuscript I do not fully understand the model the authors put forward. It is unclear what "coordinating two independent pattern forming systems" really means. It might be beneficial to make a schematic illustration of the model and how it goes wrong in both sets of inhibitor treatments. * Answer:

We have edited the manuscript considerably and explained what we mean with the two pattern forming systems. It starts with the abstract:

“Hydra head regeneration consists of two parts, hypostome/organizer and tentacle development.”

…

Thus, in accordance with regeneration of two head structures we find two signaling and gene expression modules with HyWnt3 and HyBMP4 part of a hypostome/organizer module, and BMP5/8, HyAlx and b-catenin part of a tentacle module. We conclude that Notch functions as an inhibitor of tentacle production in order to allow regeneration of hypostome/head organizer.

…

“Polyps of Craspedacusta do not have tentacles and thus, after head removal only regenerate a hypostome with a crescent of nematocytes around the mouth opening. This corroborates the idea that Notch-signaling mediates between two pattern forming processes during Hydra head regeneration”

We have included the description of the organizer concept in the introduction, because we consider this relevant for our model:

“The “organizer effect” entails a “harmonious interlocking of separate processes which makes up development”, or a side-by-side development of structures independently of each other (Spemann, 1935). In addition to inducing the formation of such structures, the organizer must ensure their patterning (Anderson and Stern, 2016). With reference to Hydra’s hydranth formation after head removal or transplantation, this involves the side-by side induction of hypostome tissue and tentacle tissue. Moreover, it includes the establishment of a regularly organized ring of tentacles with the hypostome doming up in the middle. The function of the Hydra“center of organization” would then be to pattern hypostome and tentacles and to allow for their harmonious re-formation after head removal”.

In the discussion we integrate the organizer concept with the Gierer-Meinhardt reaction-diffusion models which still explain many aspects of Hydra development.

“Is Notch part of the organizer? The organizer is defined as a piece of tissue with inductive and structuring capacity. Notch is expressed in all cells of Hydra polyps (Prexl et al., 2011) and overexpression of NICD does not induce second axes all over the Hydra body column (Pan et al., 2024), as seen with overexpression of stabilized b-catenin (Gee et al., 2010). Moreover, Notch functions differently during regeneration after apical and basal cuts. Phenotypically during head regeneration in DAPT, we clearly recognize a missing inhibition of tentacle tissue after apical cuts and missing inhibition of head induction after basal cuts (Pan et al., 2024). We would thus rather suggest that the organizer activity of Hydra tissue utilizes Notch-signaling as a mediator of inhibition. As our study of transgenic NICD overexpressing and knockdown polyps had suggested, the localization of Notch signaling cells depends on relative concentrations of Notch- and Notch-ligand proteins, which are established by gradients of signaling molecules that define the Hydra body axis (Pan et al., 2024; Sprinzak et al., 2010) . This is in very good agreement with a ”reaction-diffusion-model” provided by Alfred Gierer and Hans Meinhardt (Gierer and Meinhardt, 1972; Meinhardt and Gierer, 1974) suggesting a gradient of positional values across the Hydra body column. This gradient may determine the activities of two activation/inhibition systems, one for tentacles and one for the head. When the polyps regenerate new heads, Notch could provide inhibition for either system, depending on the position of the cut.

We provide a new Fig. 8., which clearly illustrates the effects of DAPT and iCRT14 on hypostome and tentacle regeneration.

Minor: • The abstract could be rewritten to have more of an introduction to the problem rather than jumping directly into results. It would also be beneficial if the abstract followed the logic of the paper.

Answer: We agree and have re-written the abstract.

• In Figure 3 and 4 it is not clear why they are divided into A and B. It appears that the categorization of genes into different groups lacks a clear rationale. This seems totally unnecessary. In addition, the order in which the genes are described in the text does not match what is seen in the figure making it confusing to follow. • In Figure 5 the authors use two different types of charts and I would stick with one. B is much better as it shows the individual data points as well as other information. I would use this throughout including in Figure 3 and 4. *

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__Answer: __

We changed Fig. 3, 4 and 5 according to these comments and now present the data in one format over all three figures, in scatterplots (more detailed answer above).

We are now describing the results in the order of the figures, with A and B omitted.

Figure S3 is missing a description of panel C.

In figure S3 it is not clear why the inhibitor was removed and not kept on throughout the experiment. Please discuss. __Answer: __

Fig. S3 was removed.

Figure S4 has no A or B in the figure, only in the legend. __Answer: __

We have included A and B…

*Reviewer #1 (Significance (Required)):

Although some of the authors data appear to be novel I find the study makes only minor progress on the questions. In particular the authors do not properly cite the relevant literature and to put their manuscript into the correct context. The new model proposed by the authors is based entirely on qPCR data which is not thoroughly analyzed and are not strong enough in the absence of information about the spatial expression the genes they discuss. The proposal of HyKayak as a negative regulator of Wnt3 is interesting but the authors do not provide any solid direct evidence for this (ChIP, EMSA etc) and it is somewhat in disagreement with other models of bZIP function in the literature (which again are not discussed).*

The manuscript is of limited general interest. It has a number of interesting observations which would be of interest to the Hydra community and the broader cnidarian community. The study lacks contextualization within a broader framework, whether it be in the context of regeneration or Wnt/Notch signaling. This limitation may narrow the overall interest in it.

Answer:

Our previous analysis of the effect of Notch on head regeneration in Hydra (Münder 2013) had suggested the inhibition model, which is part of Fig. 8. We show now that during head regeneration in Hydra formation of two structures is guided by different signaling/transcription modules, one using Wnt3 and BMP4, but not b-catenin; and one using BMP5/8 and b-catenin. We suggest that Notch functions as an inhibitor “of use” to the organizer when the “two-part” head structure is regenerated.

We agree that our original manuscript was not well enough written to clearly put it into developmental context. We now focus the discussion of our work sharply on the organizer problem and think that the conclusions are of great general interest. In a simple view they suggest that the function of the Hydra head organizer is to allow harmonious development of head and tentacles, which we consider separate, and on a molecular basis independently regulated parts of the Hydra head. Notch signaling, in our interpretation, is an instrument of the organizer. Our comparison with Craspedacusta illustrates this idea. Craspedacusta only regenerates one head structure, which is possible in the absence of this instrument (also see reviewers 3 and 4).

Concerning HyKayak, there is no disagreement with other authors as we analyze a fos-gene different from the one discussed by Cazet et al (see above). We have conducted a rescue experiments as suggested by reviewer 3 with the Kayak-inhibitor and with HyKayak shRNAi knockdown, however, rescue of the phenotype was not achieved although HyWnt3 was upregulated after DAPT treatment in the knockdown group. We attribute this to the very strong effect of DAPT. We have adjusted our hypothesis and only suggest that HyKayak could be a target for the Notch-induced repressor genes (e.g.HyHes). We mentioned this failed rescue in the manuscript (answer for see reviewer 3). Further experiments, e.g Chip/EMSA constitute a new project on the basis of these ideas and should be reserved for further studies of the Kayak-function in Hydra.

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

*The study investigates the role of Notch and beta-catenin signaling in coordinating head regeneration in Hydra. It combines gene expression dynamics, inhibitor treatments, and comparisons with Craspedacusta polyps to propose a lateral inhibition model for Notch function during Hydra head regeneration, mediating between two pattern-forming systems.

Three main concerns arise from this work:*

• Lack of spatial expression data: The study proposes a model based on pattern-forming systems but falls short of providing direct spatial expression data for the genes under consideration in both control and treated scenarios. This gap weakens the empirical support for the proposed model. __Answer:*__

The expression patterns for most of the presented genes including HyAlx and HyWnt3 in the presence and absence of DAPT have been published before (Münder 2013). Expression patterns for all other genes during regeneration (except Hykayak) are already known from literature. For Hykayak we have included expression data from Siebert et al (single cell transcriptome analysis) in the supplementary material. For iCRT14 treatment, we carried out a FISH-experiment and showed that HyWnt3 is expressed in the normal pattern at the hypostome. For further genes after DAPT and iCRT-treatment in situ hybridisation data are indeed lacking (e.g. BMP5/8). However, we have analyzed some very strongly downregulated regulated genes (e.g. HyAlx completely downregulated by iCRT14, all HyWnts and BMP2-4completely downregulated by DAPT) and for those in situ hybridisation could (1) be difficult due to low expression in treated samples and (2) may not be informative.

• Clarity and relevance of Craspedacusta comparisons: The section discussing the regeneration in Craspedacusta polyps appears somewhat disjointed from the main narrative, with its contribution to the overarching story of Hydra regeneration remaining unclear. *

Answer:

We had not intended to explain gene expression during Craspedacusta head regeneration but wanted to prove our hypothesis that Notch is needed to allow side-by-side development of two newly arising structures, which use different signalling modules during head regeneration. That Notch is __not __needed for the regeneration of Craspedacusta, a polyp without tentacles, appears to strengthen our main hypothesis. In order to connect this point more clearly to the narrative we have included new data. We show that CsWnt3 expression lowers after head removal and rises when the head regenerates, indicating CsWnt3-expression in the head of Craspedacusta polyps. Moreover, we show now that Notch in Craspedacusta may have similar target genes as in Hydra (e.g. Sp5 and Alx), might also affect nematocyte differentiation as in Hydra, but does not inhibit Wnt3 expression. We also acknowledge that a precise understanding of the molecular pathways for head regeneration in Craspedacusta requires further work and have removed the results of iCRT14 treatment because of our lack of knowledge about the role of b-catenin in Craspedacusta patterning. Citations from our changed text are found in the answer to reviewer 1.

• Accessibility of the text: The study's presentation, including its title, abstract, and main text, presents challenges in terms of clarity and accessibility, making it difficult for readers to follow and understand the research's scope, methodologies, and conclusions.*

• *

Answer:

We agree and have completely re-written the abstract, and large parts of the introduction and discussion (also see above answer for reviewer 1).

Reviewer #2 (Significance (Required)):

In conclusion, while the study aims to advance our understanding of the complex signaling pathways governing Hydra head regeneration, it necessitates significant revisions. Enhancing the empirical evidence through detailed spatial patterning data, clarifying the comparative analysis with Craspedacusta polyps, and __refining the narrative __to improve accessibility are critical steps needed to solidify the study's contributions to the field.

Answer:

By including Kayak-expression data from Siebert et al and indicating the places of major expression of all analysed genes schematically in the Figs describing the qPCR data we revised our manuscript. We have added new data about Craspedacusta and believe that our re-written manuscript refines the narrative by focusing on the organizer (see answer to reviewer 1).

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Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Major comments:

- In the abstract, the authors assert that their findings "indicate competing pathways for hypostome and regeneration." However, the nature of this competition and its resolution is not adequately elucidated within the manuscript. The term "competition" lacks context and clarity, leaving the reader without a clear understanding of what pathways are competing, for what, and how this competition is resolved during regeneration. Furthermore, this concept is not further explored or referenced throughout the remainder of the manuscript, leaving it somewhat disconnected from the main body of the research. It is recommended that the authors either revise the statement in the abstract to provide more clarity on the competing pathways and their implications for regeneration, or alternatively, if the authors believe there is sufficient evidence to support the claim of competing pathways, they should expand upon this point within the main body of the manuscript. Additional argumentation and evidence would be necessary to substantiate such a claim and provide a deeper understanding of the mechanisms underlying regeneration in Hydra.

Answer:

We agree and have removed any reference to “competing” pathways from the abstract and the main text.

- The abstract makes a significant assertion regarding the mechanism by which Notch signaling impacts the expression of HyWnt3, suggesting that it operates by inhibiting HyKayak-mediated repression of HyWnt3 rather than directly activating transcription at the HyWnt3 promoter. This claim is central to the goals outlined in the study, which aim to elucidate the functioning of Notch signaling in HyWnt3 expression. To bolster this assertion, it would be prudent for the authors to conduct experiments demonstrating the mediating role of Kayak. Specifically, demonstrating that downregulation of Kayak through RNAi can rescue the DAPT-mediated downregulation of Wnt3 would provide strong support for the authors' claim. Additionally, while not strictly necessary, it would be beneficial to investigate whether chemical inhibition of Wnt can rescue the phenotype resulting from Kayak RNAi. Conducting and analyzing such experiments within a 2-3-month revision period should be feasible given that the authors already possess all necessary materials and have developed the required methods. These additional experiments would not only strengthen the evidence supporting the authors' claim but also provide further insights into the regulatory mechanisms at play in Notch signaling and HyWnt3 expression.

• *

Answer:

We have conducted the suggested rescue experiments with the kayak-inhibitor, however, rescue was not achieved. We also tried rescue experiments by combining DAPT treatment and Kayak shRNA knockdown. HyWnt3 was slightly upregulated after DAPT treatment in the Kayak knockdown group but the phenotype could not be rescued. We are therefore now only state that HyKayak could be a target for the Notch-induced repressor genes (e.g.HyHes). We mentioned the failed rescue experiments in the manuscript:

Results:

*The up-regulation of HyKayak by DAPT suggests that HyKayak may serve as a potential target gene for Notch-regulated repressors including HyHes and CnGsc, potentially acting as a repressor of HyWnt3 gene transcription. *

Discussion:

We therefore suggest that the Hydra Fos-homolog HyKayak inhibits HyWnt3 expression and can be a target for a Notch-induced transcriptional repressor (like HyHes) in the regenerating Hydra head. Nevertheless, we were not able to rescue the DAPT-phenotype by inhibiting HyKayak, neither by using the inhibitor nor by shRNA-treatment, probably due to the strength of the DAPT effect. Therefore, we cannot exclude that Notch activates HyWnt3 directly, or that it represses unidentified Wnt-inhibitors through HyHes or CnGsc.

- The usage of the term "lateral inhibition" in the title and abstract of the manuscript carries specific implications, as it is commonly associated with distinct mechanisms in the context of Notch signaling and reaction-diffusion systems. Notably, in the Notch signaling context, lateral inhibition typically refers to the amplification of small differences between neighboring cells through direct interactions, facilitated by the limitations of Notch signaling to immediate neighbors. Conversely, in reaction-diffusion systems, such as the Gierer-Meinhardt model, lateral inhibition describes long-range inhibition associated with pattern formation.

Given this discrepancy, it is crucial for the authors to clarify their interpretation of "lateral inhibition" to avoid ambiguity and ensure accurate understanding. If they are referring to Notch-specific lateral inhibition, they should provide evidence of adjacent localization of Notch and Delta cells to support their argument. Alternatively, if they are invoking the concept of long-range inhibition described by the Gierer-Meinhardt model, they must explain how a membrane-tethered ligand like Notch can exert effects beyond one cell diameter from the signaling center.

* Regardless of the interpretation chosen by the authors, addressing this clarification will have significant implications for the subsequent treatment of their arguments. Depending on their chosen interpretation, experimental demonstrations may be necessary to substantiate their claims, which could be laborious and time-consuming. However, such demonstrations are essential for establishing the validity of the term "lateral inhibition" as used in the title and abstract of the manuscript.*

Answer:

We agree with the reviewer concerning the term “lateral inhibition” and have now removed it. Instead, we have emphasized that our data clearly show during apical regeneration a Notch-mediated inhibition of tentacle tissue formation. We also discuss data from our most recent publication (Pan 2024) showing that this is the opposite at basal cuts, where the loss of Notch function leads to the regeneration of two heads. We then discuss this in the context of the Gierer-Meinhardt Model and in the context of the organizer (also see above in answer to reviewer 1):

It is true that it is difficult to reconcile the long-range signaling processes, on which the Gierer-Meinhardt model is based with the cell-cell interactions mediated by Notch-signaling. We have now published a mathematical model to explain our understanding of this for the role of Notch during budding and in steady state animals (Pan2024), which is based on work by Sprinzak et al 2010. For head regeneration, we do not have such a model yet. Here we are looking at expression patterns changing over time. Therefore, we assume waves of gene expression, relying on the autoinhibitory function of the HyHes-repressor. This is included in the discussion:

In addition, the gene expression dynamics for many of the analyzed genes appears in wave-like patterns in some experiments (see Figs S3 and S4). As we have only four time points measured, we cannot draw strong conclusions from these observations, except that some of the deviations in our data points (e.g. 48 hrs HyHes) might be caused by oscillations. Nevertheless, we propose that the dynamic development of gene expression patterns over the time course of regeneration hint at a wave like expression of Notch-target genes (e.g. HyAlx, (Münder et al., 2013; Smith et al., 2000)). Hes-genes have been implicated in mediating waves of gene expression, e.g. during segmentation and as part of the circadian clock (Kageyama et al., 2007). This property is due to the capability of Hes-proteins to inhibit their own promoter. Future models for head regeneration in Hydra should consider the function of Notch to inhibit either module of the regeneration process and the potential for the Notch/Hes system to cause waves of gene expression. Such waves intuitively seem necessary to change the gene expression patterns underlying morphogenesis during the time course of head regeneration.

- The utilization of Craspedacusta as a comparative model in the argumentation of the manuscript appears somewhat unclear. The authors posit that Notch is essential for organizer emergence in Hydra, while Wnt is not necessary, as indicated by the observed effects of iCRT14 beta-catenin/TCF inhibition. However, in Craspedacusta, which lacks tentacles but possesses an organizer, one might anticipate a conserved requirement for organizer formation but not tentacle development. Therefore, it would be reasonable to expect that Craspedacusta would still form an organizer under iCRT14 treatment but would not depend on Notch signaling, as the necessity to separate tentacle formation from organizer formation is absent. The authors' observation that Craspedacusta fails to form an organizer under iCRT14 treatment partially aligns with these expectations. However, the complexity of the results suggests a need for a deeper understanding of the involvement of different pathways in Craspedacusta. Before applying inhibitors, it would be crucial to elucidate the spatiotemporal differences in the expression of relevant Wnt and Notch pathway components between Hydra and Craspedacusta. This knowledge would provide valuable insights into the specific roles of these pathways in organizer formation and tentacle development in both species, helping to clarify the observed differences in response to iCRT14 treatment. Additionally, considering the possibility of differential sensitivity to iCRT14 (see comment below) between Hydra and Craspedacusta would be essential for accurately interpreting the results and drawing meaningful conclusions regarding the involvement of Notch and Wnt signaling pathways in these processes.

Answer:

We have clarified in our re-written manuscript that the organizer functions in Hydra heads and head regeneration to harmonize the development of two independent structures (see answer for reviewer 1) and that Notch-signalling is an instrument to achieve this. Craspedacusta polyps do not have tentacles, thus we do not see two independent structures. Correspondingly, we see that they do not need Notch-signaling. We do not know whether they have organizer tissue, because they are too small to perform transplantation experiments. Similarly, in situ hybridisation experiments to look for CsWnt expression are hard to envisage. What we have now done during the revision of this paper are RT-qPCR experiments to follow the expression of CsWnt3 after head removal until a new head is formed. This indicated the localization of CsWnt3 expression in the head (citations in response to reviewer 1).

We agree that the role of Wnt/b-catenin for Craspedacusta cannot be sufficiently described with our iCRT14 experiment and therefore removed it. To strengthen the DAPT data, we also examined Craspedacusta homologs of the Hydra Notch-target genes that we had previously identified (Moneer2021). We found that expression of CsSp5 and CsAlx were inhibited by DAPT. This was also true for the nematocyte gene NOWA (see new Fig. 7). In Hydra, DAPT blocks one important differentiation step of nematocytes and therefore the expression of all genes expressed in differentiating capsule precursors, including NOWA is inhibited, while the number of mature capsules does not change. To see the same DAPT effect on NOWA-expression in Craspedacusta reassured us that DAPT had entered the animals and might also have a similar effect on nematocytes as in Hydra.

Minor comments - The concentration-dependent effects of iCRT14 on beta-catenin signaling, as demonstrated by Gufler et al. 2018, suggest that the efficacy of inhibition may vary depending on the concentration used. Specifically, Gufler et al. found that a concentration of 10µM was sufficient for efficient inhibition of beta-catenin signaling. However, in the current study, the authors utilized a concentration of 5µM of iCRT14. Given the central role of the observed effects, particularly the persistence of Wnt3 expression, in the argumentation of the manuscript, it is plausible that these effects could be attributed to partial inhibition resulting from the lower concentration of iCRT14 used in the study. To address this potential limitation, the authors could consider conducting a quick examination of the effects of 10µM iCRT14 or utilizing other inhibitors of beta-catenin/TCF interaction, such as iCRT3. By comparing the effects of different concentrations or alternative inhibitors, the authors could ascertain whether the observed effects are indeed attributable to partial inhibition from 5µM iCRT14, or if they persist despite higher concentrations or alternative inhibitors. This additional experimentation would provide valuable insights into the specificity and efficacy of the inhibition and strengthen the validity of the conclusions drawn regarding the role of beta-catenin signaling in the observed phenomena.

Answer:

The iCRT14 concentration was adjusted to 5 µM because the initial 10µM proved to be too toxic. 5µM also produced the same phenotypes and results as seen before. Cazet et al. also used 5 µM iCRT14 in their study.

- The use of Generalized Additive Models (GAMs) in Figures 3 and 4 to present the time series qPCR results may introduce some challenges in interpretation due to the potential for distortion of values at specific time points based on neighboring ones. Given the relatively low time resolution of the data, this approach could lead to a distorted depiction of the temporal dynamics. For instance, in Figure 3B, where Wnt3 peaks at 10 hours, the absence of measurements between 8 and 24 hours introduces uncertainty regarding the accuracy and reliability of this peak at 10 hours.

* To address these concerns and enhance clarity, it may be advisable for the authors to consider presenting the data using simple boxplots instead of GAMs. Boxplots provide a more straightforward visualization of the distribution of data at each time point, allowing for a clearer interpretation of trends and fluctuations over time. This approach would mitigate the potential for distortion introduced by GAMs and provide a more accurate representation of the temporal dynamics observed in the qPCR results*

• *

Answer:

We agree and have changed the data representation to simple scatterplots (see answers for reviewer 1).

- The comparison of the effects of iCRT14 versus DAPT treatments would benefit from having consistent gene expression data across both treatments. However, in Figure 4A, there are fewer genes tested compared to Figure 3A, with Hes and Kayak omitted. While the authors interpretation suggests that these genes may not change after iCRT14 treatment due to their upstream position in the signaling pathways, it is essential to empirically demonstrate this relationship, as it is central to the conclusions drawn. To address this gap in the analysis, it would be valuable for the authors to provide a time series of differential expression for Hes and Kayak following iCRT14 treatment.

Answer:

We have provided a time series for expression of HyHes and HyKayak in responses to iCRT14 treatment during regeneration (see Fig.4).

“We found that the expression the Notch-target gene HyHes remained similar to control regenerates up to 24 hrs, but then was attenuated (Fig. 4A), possibly due to failure of tentacle boundary formation, the tissue where HyHes is strongly expressed…The expression of HyKayak was decreased at 8 hrs after head removal in the presence of iCRT14, came back to normal up to 36 hrs and was suddenly increased after 48 hrs (Fig. 4E), correlating with inhibition of the HyHes repressor. There were no significant changes in the expression dynamics of HyBMP2/4 and HyBMP5/8b between iCRT14-treated regenerates and controls (Fig. 4F, G).”

- The analysis of the impact of chemical inhibition of Notch and Wnt signaling in Figure 7 schematic highlights changes in spatial expression patterns of the target genes. However, the interpretation of their impact primarily relies on qPCR data. As evident from Figure 7, when Notch is inhibited, it is anticipated that Kayak expression will shift from the area of the tentacles to the tip. This spatial shift in expression patterns is a critical aspect of the authors' arguments, especially considering the centrality of Kayak in their findings. Notably, similar spatial expression patterns have been demonstrated for Alx using FISH in Pan et al., available on BioRxiv. Given the importance of Kayak in the presented arguments, it is advisable to also investigate its spatial expression patterns using techniques such as FISH.

• *

Answer:

We have, instead of FISH-experiments, included expression data for HyKayak from Siebert et al 2019 (single cell transcriptome data) in Fig. S1D, which show its expression in head- and battery cells (tentacle cells). This is similar to HyAlx. Therefore, Kayak-FISH would be expected to reveal expression of the gene at the tip of the regenerate the whole time, similar to HyAlx, because tentacle gene inhibition or patterning does not occur (see Münder 2013). Due to the failure of our rescue experiment to demonstrate the function of kayak we have omitted kayak from Fig. 8 and only mention in the discussion that it could be a target for Notch activated transcriptional repressors, like HyHes or CnGsc.

Reviewer #3 (Significance (Required)):

*The paper introduces novelties to the field of regeneration and developmental biology by leveraging Craspedacusta polyp as a novel model system for investigating the evolutionary and developmental dynamics of tentacles. In doing so, it sheds new light on the intricate mechanisms underlying tentacle formation and patterning. Furthermore, the study implicates Kayak in the regulation of Wnt3, adding a fresh perspective to our understanding of the molecular pathways governing Hydra regeneration. Notably, the results of the research challenge the prevailing notion of autoregulation of Wnt3, which has long been considered fundamental to organizer formation in Hydra. While these findings offer intriguing insights, further investigation will be crucial to conclusively ascertain the validity of this assertion. *

• *

Despite the clarity of the data presented, the interpretation and integration of these findings in the manuscript are lacking. The narrative at times feels disjointed, with different storylines loosely connected. While the findings are intriguing and merit publication, a substantial revision of the manuscript is necessary to provide a more coherent and illuminating interpretation of the results. *The implications of this research extend beyond the specific confines of Craspedacusta polyp and Hydra biology. It holds significant relevance for both the Hydra biology community and the broader field of Notch signaling research. *

By highlighting the pivotal role of Notch signaling in regeneration and patterning within Hydra, the study enriches our comprehension of this model organism and its evolutionary adaptations. Moreover, it provides a valuable lens through which the evolution of Notch signalling cascades can be examined. This interdisciplinary approach underscores the interconnectedness of diverse biological systems and underscores the importance of exploring novel model organisms to unravel the complexities of evolution and development.

• *

Answer:

We have edited the manuscript considerably and re-written the introduction and the discussion parts. We are focusing on integrating this work with the organizer concept in developmental biology, and on the Gierer-Meinhardt-model, and point out that Notch-signaling is required for the development of two head structures by inhibiting the development of either one during head regeneration, which is necessary to enable the development of the other one. Which one is inhibited depends on the positional value of the tissue where the cut occurs. Craspedacusta polyps do only have one structure. We suggest that this is why head regeneration does not require Notch-signalling in Craspedacusta. In contrast, as we have included in our discussion now, Hydractinia polyps, again with head/mouth and tentacles, require Notch-signaling for head regeneration (according to Gahan 2019), see also answers for reviewers 1 and 2.

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Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Major comments:

The conclusions from the experiments are drawn accurately, not overstating the results. The main conclusion, that in Hydra Notch pathway mediates between two patterning modules, hypostome and tentacle forming modules, is supported by in situ hybridization and qPCR analyses of hypostome and tentacle specific genes.

OPTIONAL. Authors hypothesize, that Notch maintains expression of Wnt3 vie its targets, transcriptional repressors Goosecoid or Hes, which halt the expression of Wnt3 repressor HyKayak. Epistatic relationships between Notch, Goosecoid or Hes and HyKayak could be tested, first, by combining pharmacological inhibition of Notch by DAPT with shRNA-mediated knockdown and second, in double knockdowns generated by electroporating shRNAs for two genes simultaneously. If the proposed in the pathway relationships are correct the repressive effect of DAPT treatment on an organizer regeneration should be rescued in HyKayak shRNA-mediated knockdown. Regeneration of an organizer also should occur in Notch/HyKayak and Goosecoid (Hes)/HyKayak shRNA-mediated double knockdowns. Electroporation of shRNAs for multiple genes is an effective and quick way to generate double and triple knockdowns. The proposed experiments will much strengthen the conclusions drawn from this study. Given that the authors have successfully used shRNA-mediated technique to generate HyKayak knockdown animals, they should be able to complete the proposed experiments within in a couple of months. Answer:

We very much like the suggested strategy to probe the regeneration pathways by shRNA-mediated knockdown experiments- this will be a basis for future investigations.

We conducted the suggested rescue experiment by combining the DAPT treatment and Kayak shRNA knockdown. HyWnt3 was slightly upregulation after DAPT treatment in the Kayak knockdown group. However, this upregulation did not rescue the organizer’s regeneration. We think that the effect of DAPT is too strong. We have included this in the discussion of our results (see answer for reviewer 2).

• The data are presented in a logical and clear manner. The paper is easy to read, and the conclusions are explicit for each experimental section. The methodology is described in detail and should be easy to reproduce.*

• All experiments are done with multiple biological and technical replicates. However, the description of statistical analysis used in each case is missing, p values and error bars are missing in Fig. 2B and Fig. S4. Author should add this information in the main text or in the figure legends.*

Answer:

The statistical information was now added in the methods section.

Minor comments:

• Fig. 1E. It would be more convincing to present tentacle and hypostome regeneration data separately, comparing hypostome regeneration in treated animals with DMSO control, and in a separate analysis comparing tentacle regeneration with control. Provide the description of statistical method, p values and error bars. If authors prefer to stick to the current way of presenting they should also provide description of statistical analysis used and statistical data.*
• *

Answer:

We changed the representation in Fig. 1E. We now use scatter plots in the main text with p-values added, and explained the statistics of the GAM representation in the supplementary material.

• Results, section 4 Kayak. Authors use T5424 inhibitor to block the potential interactions between HyKayak with HyJun. The resulted increase in Wnt3 expression measured by qPCR clearly supports the idea of HyKayak being a repressor of Wnt3. However, authors are going further and present the phenotype of T5424 treatment, shortening of the tentacles. Many factors can influence the length of the tentacles. For example, shortening of tentacles is a strong indication of poisoning or animal being in general unwell. At a concentration double of the one used in the experiment T5424 causes a disintegration of the animals (Fig. 3S). It would be more convincing if the authors could provide an in situ hybridization image showing an expansion of Wnt3 expression domain down the hypostome. This is the result one would expect from the inhibition of HyKayak which, according to the proposed mechanism, restricts Wnt3 spatial expression to the most apical portion of the regenerating tip. Alternatively, authors could try to see if T5424 rescues the inhibition of an organizer formation resulted by DAPT treatment. The latter experiment might be difficult to perform due to a possible toxic effect of multidrug treatment. I suggest that authors either include the proposed experiments or leave the results of the Fig S3 out.*

Answer:

According to this suggestion we have removed the phenotypes of polyps after treatment with T5424.

• Results, section 3.2, paragraph 4. 'This also applies for the suggested Hydra organizer gene CnGsc, and BMP2/4 (Broun, Sokol et al. 1999). Please, insert the citation for BMP2/4.*

• *

Answer:

We inserted the citation for BMP2-4 (Watanabe 2014).

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Reviewer #4 (Significance (Required)):

*Significance:

The current study is a continuation of the author's previous work where they have characterized Notch pathway in Hydra and showed its role in the regeneration of an organizer and patterning of Hydra head. Here, they present the study of Notch pathway in the context of b-catenin pathway, a pathway that has been shown to be essential for the axis induction and patterning in Hydra. The authors challenge this dogma and show, that during head regeneration b-catenin transcriptional activity is not required either to maintain the expression of wnt3 nor to acquire an inductive activity of the regenerating organizer. Second, they show, that transcriptional fos-related factor Kayak is negatively regulated by Notch-signaling and, in turn, represses transcription of Wnt3. Based on those findings authors propose a function of the Notch pathway in Hydra head regeneration, particularly in spatial separation of the hypostome/organizer module from the tentacle module. The role of Notch pathway in lateral inhibition is well documented in bilaterians. However, in Cnidaria, a sister group to Bilateria, the function of Notch was so far restricted to neurogenesis. This study is very important for our understanding of the evolution of morphogenesis as it shows the ancient role that the Notch pathway is playing in axial patterning, possibly, through lateral inhibition.

This study can be of a great interest to both researchers specializing in cnidarian development and to a broader audience interested in the evolution of morphogenesis.*

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Referee #4

Evidence, reproducibility and clarity

This is a very nice study exploring the function of Notch pathway in Hydra, a member of early Metazoa Cnidaria. The main conclusions of the study are:

1. -catenin pathway is required not for regeneration of Hydra hypostome/ the organizer, as previously thought, but rather for regeneration of tentacles and correct patterning of Hydra head.
2. During head regeneration Notch pathway, possibly, through lateral inhibition, blocks tentacle fate at the most apical region of the regenerating tip, allowing a hypostome/an organizer to develop. This might occur via the targets of Notch pathway, transcriptional repressors Goosecoid and Hes.
3. During head regeneration Notch pathway is required to maintain the expression of Hydra organizer gene Wnt3 as well as other canonical wnt genes. This occurs, possibly, through repression of HyKayak, Hydra homologue of a transcriptional fos-related factor Kayak, that, in turn, represses Wnt3.

Major comments:

1. The conclusions from the experiments are drawn accurately, not overstating the results. The main conclusion, that in Hydra Notch pathway mediates between two patterning modules, hypostome and tentacle forming modules, is supported by in situ hybridization and qPCR analyses of hypostome and tentacle specific genes.
2. OPTIONAL. Authors hypothesize, that Notch maintains expression of Wnt3 vie its targets, transcriptional repressors Goosecoid or Hes, which halt the expression of Wnt3 repressor HyKayak. Epistatic relationships between Notch, Goosecoid or Hes and HyKayak could be tested, first, by combining pharmacological inhibition of Notch by DAPT with shRNA-mediated knockdown and, second, in double knockdowns generated by electroporating shRNAs for two genes simultaneously. If the proposed in the pathway relationships are correct the repressive effect of DAPT treatment on an organizer regeneration should be rescued in HyKayak shRNA-mediated knockdown. Regeneration of an organizer also should occur in Notch/HyKayak and Goosecoid(Hes)/HyKayak shRNA-mediated double knockdowns. Electroporation of shRNAs for multiple genes is an effective and quick way to generate double and triple knockdowns. The proposed experiments will much strengthen the conclusions drawn from this study. Given that the authors have successfully used shRNA-mediated technique to generate HyKayak knockdown animals, they should be able to complete the proposed experiments within in a couple of months.
3. The data are presented in a logical and clear manner. The paper is easy to read, and the conclusions are explicit for each experimental section. The methodology is described in detail and should be easy to reproduce.
4. All experiments are done with multiple biological and technical replicates. However, the description of statistical analysis used in each case is missing, p values and error bars are missing in Fig. 2B and Fig. S4. Author should add this information in the main text or in the figure legends.

Minor comments:

1. Fig. 1E. It would be more convincing to present tentacle and hypostome regeneration data separately, comparing hypostome regeneration in treated animals with DMSO control, and in a separate analysis comparing tentacle regeneration with control. Provide the description of statistical method, p values and error bars. If authors prefer to stick to the current way of presenting they should also provide description of statistical analysis used and statistical data.
2. Results, section 4 Kayak. Authors use T5424 inhibitor to block the potential interactions between HyKayak with HyJun. The resulted increase in Wnt3 expression measured by qPCR clearly supports the idea of HyKayak being a repressor of Wnt3. However, authors are going further and present the phenotype of T5424 treatment, shortening of the tentacles. Many factors can influence the length of the tentacles. For example, shortening of tentacles is a strong indication of poisoning or animal being in general unwell. At a concentration double of the one used in the experiment T5424 causes a disintegration of the animals (Fig. 3S). It would be more convincing if the authors could provide an in situ hybridization image showing an expansion of Wnt3 expression domain down the hypostome. This is the result one would expect from the inhibition of HyKayak which, according to the proposed mechanism, restricts Wnt3 spatial expression to the most apical portion of the regenerating tip. Alternatively, authors could try to see if T5424 rescues the inhibition of an organizer formation resulted by DAPT treatment. The latter experiment might be difficult to perform due to a possible toxic effect of multidrug treatment. I suggest that authors either include the proposed experiments or leave the results of the Fig S3 out.
3. Results, section 3.2, paragraph 4. 'This also applies for the suggested Hydra organizer gene CnGsc, and BMP2/4 (Broun, Sokol et al. 1999). Please, insert the citation for BMP2/4.

Significance

The current study is a continuation of the author's previous work where they have characterized Notch pathway in Hydra and showed its role in the regeneration of an organizer and patterning of Hydra head. Here, they present the study of Notch pathway in the context of -catenin pathway, a pathway that has been shown to be essential for the axis induction and patterning in Hydra. The authors challenge this dogma and show, that during head regeneration -catenin transcriptional activity is not required either to maintain the expression of wnt3 nor to acquire an inductive activity of the regenerating organizer. Second, they show, that transcriptional fos-related factor Kayak is negatively regulated by Notch signaling and, in turn, represses transcription of Wnt3. Based on those findings authors propose a function of the Notch pathway in Hydra head regeneration, particularly in spatial separation of the hypostome/organizer module from the tentacle module. The role of Notch pathway in lateral inhibition is well documented in bilaterians. However, in Cnidaria, a sister group to Bilateria, the function of Notch was so far restricted to neurogenesis. This study is very important for our understanding of the evolution of morphogenesis as it shows the ancient role that the Notch pathway is playing in axial patterning, possibly, through lateral inhibition.

This study can be of a great interest to both researchers specializing in cnidarian development and to a broader audience interested in the evolution of morphogenesis.

The reviewer's field of expertise includes cnidarian development, axial patterning and morphogenesis in Hydra, biochemical pathways in Hydra axial patterning, Hippo pathway regulation and tissue patterning in multiple organisms

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Referee #3

Evidence, reproducibility and clarity

Summary:

In the reviewed study by Steichele et al., the authors investigate the roles of Notch and Wnt signaling pathways in the regeneration process of Hydra hypostome and tentacles. They observe that treatment with DAPT prevents the emergence of an organizer and hypostome, whereas inhibition of beta-catenin/TCF interaction does not affect organizer formation but hinders tentacle development. The authors delve into the molecular mechanisms underlying these observations, examining the impact of different treatments on the expression of genes crucial for organizer and tentacle emergence. Notably, they identify Kayak as a target of Notch signaling, which is upregulated following DAPT treatment. Furthermore, RNAi against Kayak results in the overexpression of Wnt3, suggesting an inhibitory role of Kayak on Wnt3 expression. This inhibitory effect is corroborated using the Fos/Jun inhibitor T5224. In their investigation, the authors compare the effects of Notch and Wnt signaling inhibition using a polyp species, Craspedacusta, which possesses an organizer but lacks tentacles. They find that while Notch inhibition does not affect this polyp, inhibition of canonical Wnt signaling does.

Major comments:

• In the abstract, the authors assert that their findings "indicate competing pathways for hypostome and regeneration." However, the nature of this competition and its resolution is not adequately elucidated within the manuscript. The term "competition" lacks context and clarity, leaving the reader without a clear understanding of what pathways are competing, for what, and how this competition is resolved during regeneration. Furthermore, this concept is not further explored or referenced throughout the remainder of the manuscript, leaving it somewhat disconnected from the main body of the research.

It is recommended that the authors either revise the statement in the abstract to provide more clarity on the competing pathways and their implications for regeneration, or alternatively, if the authors believe there is sufficient evidence to support the claim of competing pathways, they should expand upon this point within the main body of the manuscript. Additional argumentation and evidence would be necessary to substantiate such a claim and provide a deeper understanding of the mechanisms underlying regeneration in Hydra.<br /> - The abstract makes a significant assertion regarding the mechanism by which Notch signaling impacts the expression of HyWnt3, suggesting that it operates by inhibiting HyKayak-mediated repression of HyWnt3 rather than directly activating transcription at the HyWnt3 promoter. This claim is central to the goals outlined in the study, which aim to elucidate the functioning of Notch signaling in HyWnt3 expression. To bolster this assertion, it would be prudent for the authors to conduct experiments demonstrating the mediating role of Kayak. Specifically, demonstrating that downregulation of Kayak through RNAi can rescue the DAPT-mediated downregulation of Wnt3 would provide strong support for the authors' claim. Additionally, while not strictly necessary, it would be beneficial to investigate whether chemical inhibition of Wnt can rescue the phenotype resulting from Kayak RNAi. Conducting and analyzing such experiments within a 2-3-month revision period should be feasible given that the authors already possess all necessary materials and have developed the required methods. These additional experiments would not only strengthen the evidence supporting the authors' claim but also provide further insights into the regulatory mechanisms at play in Notch signaling and HyWnt3 expression. - The usage of the term "lateral inhibition" in the title and abstract of the manuscript carries specific implications, as it is commonly associated with distinct mechanisms in the context of Notch signaling and reaction-diffusion systems. Notably, in the Notch signaling context, lateral inhibition typically refers to the amplification of small differences between neighboring cells through direct interactions, facilitated by the limitations of Notch signaling to immediate neighbors. Conversely, in reaction-diffusion systems, such as the Gierer-Meinhardt model, lateral inhibition describes long-range inhibition associated with pattern formation.

Given this discrepancy, it is crucial for the authors to clarify their interpretation of "lateral inhibition" to avoid ambiguity and ensure accurate understanding. If they are referring to Notch-specific lateral inhibition, they should provide evidence of adjacent localization of Notch and Delta cells to support their argument. Alternatively, if they are invoking the concept of long-range inhibition described by the Gierer-Meinhardt model, they must explain how a membrane-tethered ligand like Notch can exert effects beyond one cell diameter from the signaling center.

Regardless of the interpretation chosen by the authors, addressing this clarification will have significant implications for the subsequent treatment of their arguments. Depending on their chosen interpretation, experimental demonstrations may be necessary to substantiate their claims, which could be laborious and time-consuming. However, such demonstrations are essential for establishing the validity of the term "lateral inhibition" as used in the title and abstract of the manuscript. - The utilization of Craspedacusta as a comparative model in the argumentation of the manuscript appears somewhat unclear. The authors posit that Notch is essential for organizer emergence in Hydra, while Wnt is not necessary, as indicated by the observed effects of iCRT14 beta-catenin/TCF inhibition. However, in Craspedacusta, which lacks tentacles but possesses an organizer, one might anticipate a conserved requirement for organizer formation but not tentacle development. Therefore, it would be reasonable to expect that Craspedacusta would still form an organizer under iCRT14 treatment but would not depend on Notch signaling, as the necessity to separate tentacle formation from organizer formation is absent.

The authors' observation that Craspedacusta fails to form an organizer under iCRT14 treatment partially aligns with these expectations. However, the complexity of the results suggests a need for a deeper understanding of the involvement of different pathways in Craspedacusta. Before applying inhibitors, it would be crucial to elucidate the spatiotemporal differences in the expression of relevant Wnt and Notch pathway components between Hydra and Craspedacusta. This knowledge would provide valuable insights into the specific roles of these pathways in organizer formation and tentacle development in both species, helping to clarify the observed differences in response to iCRT14 treatment. Additionally, considering the possibility of differential sensitivity to iCRT14 (see comment below) between Hydra and Craspedacusta would be essential for accurately interpreting the results and drawing meaningful conclusions regarding the involvement of Notch and Wnt signaling pathways in these processes.

Minor comments

• The concentration-dependent effects of iCRT14 on beta-catenin signaling, as demonstrated by Gufler et al. 2018, suggest that the efficacy of inhibition may vary depending on the concentration used. Specifically, Gufler et al. found that a concentration of 10µM was sufficient for efficient inhibition of beta-catenin signaling. However, in the current study, the authors utilized a concentration of 5µM of iCRT14. Given the central role of the observed effects, particularly the persistence of Wnt3 expression, in the argumentation of the manuscript, it is plausible that these effects could be attributed to partial inhibition resulting from the lower concentration of iCRT14 used in the study. To address this potential limitation, the authors could consider conducting a quick examination of the effects of 10µM iCRT14 or utilizing other inhibitors of beta-catenin/TCF interaction, such as iCRT3. By comparing the effects of different concentrations or alternative inhibitors, the authors could ascertain whether the observed effects are indeed attributable to partial inhibition from 5µM iCRT14, or if they persist despite higher concentrations or alternative inhibitors. This additional experimentation would provide valuable insights into the specificity and efficacy of the inhibition and strengthen the validity of the conclusions drawn regarding the role of beta-catenin signaling in the observed phenomena.
• The use of Generalized Additive Models (GAMs) in Figures 3 and 4 to present the time series qPCR results may introduce some challenges in interpretation due to the potential for distortion of values at specific time points based on neighboring ones. Given the relatively low time resolution of the data, this approach could lead to a distorted depiction of the temporal dynamics. For instance, in Figure 3B, where Wnt3 peaks at 10 hours, the absence of measurements between 8 and 24 hours introduces uncertainty regarding the accuracy and reliability of this peak at 10 hours.

To address these concerns and enhance clarity, it may be advisable for the authors to consider presenting the data using simple boxplots instead of GAMs. Boxplots provide a more straightforward visualization of the distribution of data at each time point, allowing for a clearer interpretation of trends and fluctuations over time. This approach would mitigate the potential for distortion introduced by GAMs and provide a more accurate representation of the temporal dynamics observed in the qPCR results - The comparison of the effects of iCRT14 versus DAPT treatments would benefit from having consistent gene expression data across both treatments. However, in Figure 4A, there are fewer genes tested compared to Figure 3A, with Hes and Kayak omitted. While the authors interpretation suggests that these genes may not change after iCRT14 treatment due to their upstream position in the signaling pathways, it is essential to empirically demonstrate this relationship, as it is central to the conclusions drawn. To address this gap in the analysis, it would be valuable for the authors to provide a time series of differential expression for Hes and Kayak following iCRT14 treatment. - The analysis of the impact of chemical inhibition of Notch and Wnt signaling in Figure 7 schematic highlights changes in spatial expression patterns of the target genes. However, the interpretation of their impact primarily relies on qPCR data. As evident from Figure 7, when Notch is inhibited, it is anticipated that Kayak expression will shift from the area of the tentacles to the tip. This spatial shift in expression patterns is a critical aspect of the authors' arguments, especially considering the centrality of Kayak in their findings. Notably, similar spatial expression patterns have been demonstrated for Alx using FISH in Pan et al., available on BioRxiv. Given the importance of Kayak in the presented arguments, it is advisable to also investigate its spatial expression patterns using techniques such as FISH.

Significance

The paper introduces novelties to the field of regeneration and developmental biology by leveraging Craspedacusta polyp as a novel model system for investigating the evolutionary and developmental dynamics of tentacles. In doing so, it sheds new light on the intricate mechanisms underlying tentacle formation and patterning. Furthermore, the study implicates Kayak in the regulation of Wnt3, adding a fresh perspective to our understanding of the molecular pathways governing Hydra regeneration. Notably, the results of the research challenge the prevailing notion of autoregulation of Wnt3, which has long been considered fundamental to organizer formation in Hydra. While these findings offer intriguing insights, further investigation will be crucial to conclusively ascertain the validity of this assertion.

Despite the clarity of the data presented, the interpretation and integration of these findings in the manuscript are lacking. The narrative at times feels disjointed, with different storylines loosely connected. While the findings are intriguing and merit publication, a substantial revision of the manuscript is necessary to provide a more coherent and illuminating interpretation of the results.

The implications of this research extend beyond the specific confines of Craspedacusta polyp and Hydra biology. It holds significant relevance for both the Hydra biology community and the broader field of Notch signaling research. By highlighting the pivotal role of Notch signaling in regeneration and patterning within Hydra, the study enriches our comprehension of this model organism and its evolutionary adaptations. Moreover, it provides a valuable lens through which the evolution of Notch signaling cascades can be examined. This interdisciplinary approach underscores the interconnectedness of diverse biological systems and underscores the importance of exploring novel model organisms to unravel the complexities of evolution and development.

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Referee #2

Evidence, reproducibility and clarity

The study investigates the role of Notch and beta-catenin signaling in coordinating head regeneration in Hydra. It combines gene expression dynamics, inhibitor treatments, and comparisons with Craspedacusta polyps to propose a lateral inhibition model for Notch function during Hydra head regeneration, mediating between two pattern-forming systems.

Three main concerns arise from this work:

1. Lack of spatial expression data: The study proposes a model based on pattern-forming systems but falls short of providing direct spatial expression data for the genes under consideration in both control and treated scenarios. This gap weakens the empirical support for the proposed model.
2. Clarity and relevance of Craspedacusta comparisons: The section discussing the regeneration in Craspedacusta polyps appears somewhat disjointed from the main narrative, with its contribution to the overarching story of Hydra regeneration remaining unclear.
3. Accessibility of the text: The study's presentation, including its title, abstract, and main text, presents challenges in terms of clarity and accessibility, making it difficult for readers to follow and understand the research's scope, methodologies, and conclusions.

Significance

In conclusion, while the study aims to advance our understanding of the complex signaling pathways governing Hydra head regeneration, it necessitates significant revisions. Enhancing the empirical evidence through detailed spatial patterning data, clarifying the comparative analysis with Craspedacusta polyps, and refining the narrative to improve accessibility are critical steps needed to solidify the study's contributions to the field.

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Referee #1

Evidence, reproducibility and clarity

Steichele et al tackle a long standing question about the precise role of Notch signalling during Hydra head regeneration. They compare the inhibition of Wnt signalling and Notch signalling by pharmacological inhibition. The authors show that inhibition of Wnt signalling blocks tentacle formation but not formation of the hypostome, wnt3 expression or organizer activity. The authors further attempt to understand this in a comparative sense using Craspedacusta. In addition, the authors propose HyKayak as a potential repressor of Wnt3 expression downstream of Notch signalling. there are howerver a number of major and minor problems with the study which must be addressed before it is suitable for publication as outlined below:

Major:

• The introduction is lacking a full description of what is known about transcriptional changes during Hydra regeneration and in particular the role of Wnt signalling in this process. Of note the authors do not cite several important studies from recent years including (but not limited to):

https://doi.org/10.1073/pnas.2204122119

https://doi.org/10.1186/s13072-020-00364-6

https://doi.org/10.1101/587147

https://doi.org/10.7554/eLife.60562

This problem is further compounded later when the authors do not cite/discuss work which has performed the same or similar analyses to their own. The authors should endeavor to give a more comprehensive background knowledge. - The authors do not cite or reference at all the study by Cazet et al. which used iCRT14 along with RNAseq and ATACseq to probe the role of Wnt signaling during early regeneration. This is a major issue. Although I appreciate that the authors have done much longer time courses and that their data therefore add something to our understanding it will still be important to discuss here. For example, the authors show that Wnt3 is activated normally in iCRT14 animals. Is this also seen in the RNAseq from Cazet et al., - The visualizations used in Figure 3 are quite difficult to interpret and do to in all cases match descriptions in the text. The way the same type data is displayed in figure 5 si much nices.It is also better to treat the same types of data in the same manner consistently throught the paper. For Hes, for example, the authors state that there is a reduction although the data shows that this is very small and taking into account the 95% confidence interval does not seem to be significant. If this is the case then the positive control is not working in this experiment. This would be much clear if individual time points were compared like in figure 5 and statistical tests shown. The authors then state that Alx is not affected but there is actually a larger effect than what they deemed significant for Hes ( the axes are notably different between these two and I think a more consistent axis would make the genes more comparable). Similarly, Gsc is described as being not affected at 8 hours but it appears again to change more that the positive control Hes. Given this I would call into question the validity of this dataset and/or the interpretation by the authors. A more thorough analysis including taking better into account statistical significance would go along way to increasing confidence in this data. - The same issues in interpretation described for Figure 3 also apply to figure 4. The authors state that Wnt7 is affected less than Wnt1 and 3 but this is not evident in the figure and no comparative analysis is performed to confirm this. The same for Wnt 11 and 9/10c where what the authors description is very difficult to see in the figure. Spt5 is apparently upregulated, but this is not discussed. Again the axes are ntably different making it even more difficult to compare between samples. - In their description of figure 4 the authors completely omit to discuss the Cazet et al dataset which has the exact same early timepoints for iCRT14 treatment. This must be discussed and compared and any difference noted. - End of page 11: The authors propose a model thereby the role of Notch in Wnt3 expression may be due to the presence of a repressor. However, I don't see any putative evidence at that stage. The authors also do not cite relevant work from both Cazet et al. and Tursch et al which show that Wnt3 is likely upregulated by bZIP TFs. In both these cases the authors show evidence of bZIP TF binding sites in the Wnt3 promoter along with other analyses. This is very relevant to the model presented by the authors here and must be discussed and compared. In particular the authors put forward HyKayak as an inhibitor of Wnt3 and this should be discussed along with the previous work. - On page 12 the authors conclude based on gene expression in inhibitor treatment that there is a change in complex composition of the two transcription factors. This is something which would require biochemical evidence and I therefore suggest they remove this entirely. - The authors use experiments in Craspedacusta to test their hypothesis of the role of Wnt and Notch signaling in Hydra. There is, in my opinion, an incorrect logic here. Regardless of the outcome, the roles of Wnt and Notch could conceivably be different in the two species and therefore testing hypothesis from one is not possible in the other. The authors should reframe their discussion of this to be more of a comparative framework. Moreover, the results do not necessarily indicate what the authors say. In Hydra Notch signaling is required to form the hypostome/mouth and this is not the case in Craspedacusta while Wnt signaling is required. The authors do not cite an important study from another Hydrozoan Hydractinia (Gahan at al.,2017). In that study the authors show that DAPT inhibits tentacles during regeneration but that the hypostome (or at least the arrangement of neurons and cnidocytes around the mouth) forms normally. This would indicate that in Hydractinia the process of head formation is different to in Hydra and would be congruent with what is shown here in Craspedacusta. This should be more thoroughly discussed, and all relevant literature cited. - From reading the manuscript I do not fully understand the model the authors put forward. It is unclear what "coordinating two independent pattern forming systems" really means. It might be beneficial to make a schematic illustration of the model and how it goes wrong in both sets of inhibitor treatments.

Minor:

• The abstract could be rewritten to have more of an introduction to the problem rather than jumping directly into results. It would also be beneficial if the abstract followed the logic of the paper.
• In Figure 3 and 4 it is not clear why they are divided into A and B. It appears that the categorization of genes into different groups lacks a clear rationale .This seems totally unnecessary. In addition, the order in which the genes are described in the text does not match what is seen in the figure making it confusing to follow.
• In Figure 5 the authors use two different types of charts and I would stick with one. B is much better as it shows the individual data points as well as other information. I would use this throughout including in Figure 3 and 4.
• Figure S3 is missing a description of panel C.
• In figure S3 it is not clear why the inhibitor was removed and not kept on throughout the experiment. Please discuss.
• Figure S4 has no A or B in the figure, only in the legend.

Significance

Although some of the authors data appear to be novel I find the study makes only minor progress on the questions. In particular the authors do not properly cite the relevant literature and to put their manuscript into the correct context. The new model proposed by the authors is based entirely on qPCR data which is not thoroughly analyzed and are not strong enough in the absence of information about the spatial expression the genes they discuss. The proposal of HyKayak as a negative regulator of Wnt3 is interesting but the authors do not provide any solid direct evidence for this (ChIP, EMSA etc) and it is somewhat in disagreement with other models of bZIP function in the literature (which again are not discussed).

The manuscript is of limited general interest. It has a number of interesting observations which would be of interest to the Hydra community and the broader cnidarian community. The study lacks contextualization within a broader framework, whether it be in the context of regeneration or Wnt/Notch signaling. This limitation may narrow the overall interest in it.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

I have mixed feelings regarding this manuscript. On the one hand, the authors did an impressive amount of work. On the other hand, the manuscript seems overly descriptive (writing should be more concise) without a clear message or hypothesis that is cohesive to all the presented evidence. Below, I will outline my concerns.

We appreciate the comment about missing a cohesive presentation. We worked extensively to improve that in the revised manuscript.

Reviewer #1- first part

1. I am not an expert in the field of viral biology and immunology. I wonder how well the IFN treatment mimics the cellular response to infection (yet without the virus). Also, how good is ruxolitinib at blocking the IFN response ? I would appreciate it if you could explain both with one or two sentences and provide the necessary references.

The reviewer is correct that we cannot claim that interferon treatment mimics exactly the cellular response. However, the expression of interferon-stimulated genes (ISGs) is a major arm of the antiviral response to HCMV (c.f. doi:10.3390/v10090447, doi:10.2217/fvl-2018-0189). In addition, Ruxolitinib is a potent and selective Janus kinase 1 and 2 inhibitor (doi:10.1021/ol900350k), and we have shown in the past that it very effectively reduces the expression of many ISGs (doi: 10.1038/s41590-018-0275-z). Since ISGs constitute a major part of the host response to HCMV infection, the fact that their expression leads to minor changes in the tRNA pool strongly suggests that it is mainly the virus (as opposed to the host cell) that mediates the changes seen in the tRNA pools during HCMV infection. In the revised version, these claims were amended, and relevant references were added (pages 5, lines 132-136).

(MAJOR) Can these two treatments really allow the effects of host response and viral infection to be separated? OR in other words, are these two effects really orthogonal? In my opinion, they are NOT. Fig. 1E seems to support my opinion, as the changes seen for the "IFN" sample relative to the "uninfected" sample (referred to as "changes-A" below), are parallel to the changes seen for the "24hpi + ruxo" sample relative to the "24hpi" sample ("changes-B"). More specifically, changes-A represent the host response, as argued by the author, whereas changes-B represent the elimination of the host response (due to ruxo, conditioned on the virus-driven effect). If the virus-driven effect and the host response could really be separated, one would expect changes-A and changes-B are more or less opposite. However, they appeared to be parallel, suggesting that uninfected versus infected conditions can have totally different (even opposite) host responses. More importantly, if one cannot separate the host response from virus-driven effects, the conclusion of "tRNA changes are driven by virus, not host response" is then unfounded.

This is an important point to clarify. Changes-A indeed represent the effect of the host antiviral response on the tRNA pool. Changes-B, however, represent a mix of two effects. 1: counteracting the effect of the host antiviral response on the tRNA pool, which we show is a minor effect, and 2: The enhanced effect of the virus, since ruxolitinib, by inhibiting the host antiviral response, enhances the viral infection. It may indeed be that both the virus and the host antiviral effects are in the same direction. However, it is clear that the antiviral effect is minor. Thus, it is likely that the second effect of ruxolitinib (i.e., allowing enhanced viral infection) is the more substantial one. Therefore, it seems as though the viral effect and the elimination of the host effect are in the same direction. This point was clarified in the revised version (page 6, lines 145-146).

Even if we let go of this previous point and accept that these results indeed offer some support for the notion that the virus-driven effect are the main contributor to the shifts in tRNA pool, the support is at best moderate. A big gap here is "how?" I suggest the authors should at least give some insight on how virus can do that in Discussion (and mention it with one sentence in Results).

We certainly welcome the challenge, which we now meet in the revision. In short, here, transcription regulation of tRNAs, mainly upon viral infection, is poorly studied. Unlike other herpesviruses, HCMV does not cause a host shut-off of the host transcripts. Upon HCMV infection, the tRNA transcription machinery is upregulated significantly, which probably contributes to the upregulation in pre-tRNA (doi.org/10.1016/j.semcdb.2023.01.011). However, it is still unknown what the viral factors are that promote upregulation in the tRNA transcription machinery. We now relate to this point in the results (page 6, lines 147-148) and discuss the known effects of viral infection of tRNA expression in the discussion section (page 15, lines 447-451).

The authors compared the HCMV codon usage to the proliferation and differentiation signatures of human cells. But these two signatures are not compared with measured tRNA expression. It might shed some light on the general characteristics of tRNA pool shifts due to infection (towards a proliferation-like or differentiation-like signature). This fits in the general topic of virus-host interaction and might give more evidence for the point that HMCV is adapted to a differentiation signature (as it drives the host into that state).

We performed the analysis suggested by the reviewer. We found that the tRNA pool of uninfected HFF cells correlated to the same extent with proliferation codon usage (r=0.29, p-value=0.029) and differentiation codon usage (r=0.26, p-value=0.05). Similar correlations to the proliferation and differentiation signature were found when analyzing the tRNA pool 72h post-infection (proliferation r=0.33, p-value=0.011, differentiation r=0.28, p-value=0.034). This result suggests no general shift in the tRNA pool towards a specific codon usage signature.

How is the dashed box in Fig3A/B chosen?

We determined the dashed lines based on the most prominent groups of transcripts best adapted to proliferation or differentiation codon usage signatures. Figure S3A clearly shows the two groups without viral genes. We emphasize this point in the legend of Figure S3A (page 36, lines 1157).

The tAI values shown in Fig3C-E are extremely low (compared to other reports I am aware of). Does this mean that the adaptation of viral codon usage to human cell supply is actually very weak? This is in opposition to the major claims made in this section.

We acknowledge that the tAI values presented here are lower than typically presented. However, this is due to how tAI was calculated rather than the potential weak adaptation between viral genes and tRNA supply. Specifically, unlike previous works that estimate tRNA availability based on tRNA gene copy number, here we calculated tAI using tRNA sequencing (in order to capture the dynamics in the tRNA pool during infection). Indeed, the value of tAI calculated by tRNA read counts is lower than tAI calculated by tRNA copy number. This is due to the skewed distribution of tRNA read counts (some tRNAs are highly expressed, and others are lowly expressed), while tRNA copy number is distributed more evenly. Thus, due to the mathematical nature of the tAI (computing geometric rather than arithmetic average of tRNA availability), the skewed distribution observed in the data results in lower tAI values. When computing tAI based on gene copy number, we get higher tAI values (0.3 on average). Nevertheless, as all tAI calculations here were done similarly, the comparisons between gene groups or genes are valid.

I believe that the part about SARS-CoV-2 could be made more concise. It is sufficient to mention that results may differ from those obtained with HCMV in one paragraph.

The section on SARS-Cov-2 is now made rather succinct. This virus is mainly given as a comparison to the primary virus studied in this paper - HCMV.

Line 299 on page 11 - I do not believe codon usage between different viruses can be directly compared, let alone reaching such a conclusion. Some viruses have low CAI or tAI to humans, but they have co-evolved with humans for a long time. Furthermore, there are viruses that infect multiple hosts, but their CAI for a host with which they have long co-evolved is higher while their CAI for a host that is relatively new is lower.

We agree with the reviewer that a direct link between co-evolution time and tAI may not always exist. Indeed, other factors might explain the observation that SARS-CoV-2 genes are less adapted than HCMV genes. These may include effective population sizes and mutation rates that vary substantially. We, therefore, removed this conclusion from the manuscript.

(MAJOR) A more general comment is that there is a difference between tRNA expression and the abundance of translation-ready tRNA. The process of charging tRNA with amino acids may take a long time. It is the abundance of the charged-tRNA (the ternary complex of aminoacylated tRNA and EF-Tu-GTP) that is of biological importance. In this regard, the use of tRNA expression falls short.

The reviewer raises a valid point. Indeed, our tRNA sequencing protocol measures both charged and uncharged tRNAs that constitute the cell's mature tRNA pool. Compared to previous studies that focus on the transcription process of tRNAs in viral-infection models by sequencing the pre-tRNAs, here we look at the mature tRNA pool that accounts for both transcription and post-transcription processes. Therefore, we changed the use of "tRNA expression" to "mature-tRNA levels" and "highly" or "lowly-abundant tRNAs" rather than “highly” or “lowly expressed tRNAs” in the manuscript. We note, however, that although limited in the ability to differentiate between charged and uncharged tRNAs, the tRNA sequencing protocol used here is commonly used and validated as a state-of-the-art protocol in tRNA sequencing (10.1016/j.molcel.2021.01.028, 10.1038/s41467-020-17879-x, etc.), mainly because it addresses the level of "ready-to-use" tRNA.

Reviewer #1- second part

1. (MAJOR) Prior to the actual competition assay in the first high-throughput screen (cell competition assay), the authors applied two days of antibiotic selection and two days of recovery. This could result in a serious problem of false negatives or drop outs. Specifically, an sgRNA targeting an essential gene with high efficiency would kill the cells, leaving no (or a small number of) cells in the ancestor population at the beginning of the competition process. A sgRNA's enrichment in competing populations cannot be reliably estimated in such situations. I am not certain that the FDR used in Figure 5B is sufficient to address this issue. Please clarify whether it could. Providing raw counts for competing and ancestor populations would also be helpful.

As customary in CRISPR screens, the step of lentiviral transduction and antibiotic selection is necessary to ensure that only CRISPR-edited cells are left in the population. Indeed, essential genes like housekeeping genes are probably removed from the competing population relatively quickly, which might result in their dropouts. We could have lost some tRNA hits in the cell growth CRISPR screen (Figure 5B-C) because of their overall essentiality for cell growth. The MAGcK tool we used, the state-of-the-art in the field, filters out sgRNAs with low read counts to be able to calculate false discovery rates. Indeed, we identified 15 tRNAs that were depleted from the competing cells. We believe that our procedure minimizes the concern of dropouts. tRNA dropout in the HCMV infection CRISPR screen (Figure 6B-C) can also happen, which means our screen underestimates the essentiality of tRNAs to HCMV infection. However, this concern does not affect the significance of the hits we did find. We acknowledge this inherent difficulty in CRISPR screens and will provide the raw read counts of all samples upon full submission. We emphasize, though, that while valid, this concern applies to essentially any CRISPR screen that is commonplace in genomics these days.

It is also highly questionable to me the nearly negligible effects of tRNA modification enzymes. This may be explained by the point above. Indeed, the dots of tRNA modification enzymes in general appear to have higher FDR (lower y values) when compared to red dots with similar enrichment levels.

This is a valid point. We found a lack of essentiality of tRNA modification enzymes in both screens. We analyzed additional CRISPR screens and compared the effect of tRNA modification enzyme knockouts relative to the restriction and dependency factors we used in the library. The tested screens included 34 knockout CRISPR screens we downloaded from the BioGRID ORCS database that have similar parameters to our screen. Namely, they all test cell proliferation in a time-course manner, using a pooled sgRNA library and using the MAGeCK tool for data analysis. Overall, the screens use different human cell lines and diverse sgRNA libraries. Although potentially surprising, we found that the lack of essentiality of tRNA modification enzymes was also observed in the analyzed CRISPR screens (Figure S5B and on page 11, lines 322-330, and on page 18, lines 539-541). One potential reason is if some of these enzymes were "backed up" by others, which we mentioned. Another explanation is that most tRNA modification enzymes are indeed not essential for growth and for viral infection (now described in the Discussion, page 18, lines 544-545). Alternatively, dropouts can explain this result, as suggested by the reviewer. To examine the likelihood of the dropout option, we examined the average raw read count of the tRNA modification enzyme in the ancestor samples. We compared it to that of other sub-groups. We found that raw read counts of the tRNA modification enzymes are not different than other sub-groups in the CRISPR library. Thus, the dropout issue cannot explain our screens' lack of essentiality of tRNA modification enzymes.

The screen based on IE2-GFP labeled HCMV measures a phenotype that is very difficult to interpret. Particularly, I am not sure if GFP2 and GFP3 are good controls for comparing GFP4 (GFP1 might be better). Various factors can affect GFP levels, including, but not limited to, dilution caused by a rapidly dividing host cell, unhealthy translational machinery resulting from infection or microenvironment. My point is supported by some observations in Fig6B. For example, SEC61B, a restriction factor for HCMV infection, is enriched in the GFP2 group, contrary to expectations. It is necessary for the authors to prove with firm evidence that their choice of GFP signal thresholds is appropriate.

We acknowledge the concern. Specifically, the translation of the GFP gene itself could be affected by the tRNA manipulation done. To account for this potential concern, we tested the codon usage of the eGFP gene (which is the GFP version we used in the system) and compared it with tRNA essentiality, as determined by the cell growth CRISPR screen. We report this in the revised manuscript (page 13, lines 390-392, and added Figure S6D). We found that GFP does not tend to significantly use codons that correspond to essential or less essential tRNAs. The same lack of correlation was also found for the tRNA essentiality upon HCMV infection (not shown).

More generally, we show that GFP intensity does correlate with viral genome copies (Figure S6A). Also, from mRNA-seq data of temporal HCMV infection (10.1016/j.celrep.2022.110653), IE2 (UL122) shows a dynamic expression- high expression pick in early infection, then a decline in expression level followed by a gradual increase.

Altogether, we believe that the IE2-GFP level provides a good estimation for viral load.

Regarding SEC61B, which served as a control in our screen – the referee is rightly asking why it behaves oppositely from what's expected, given that this was supposed to be a restriction factor of HCMV infection. We returned to the literature on the essentiality of this gene upon HCMV infection. In Weissman's paper (10.1038/384432a0), which was the reference for choosing control genes in our system, this gene was targeted through two different CRISPR technologies, once with CRISPR knockout and once with CRISPRi. Interestingly, only upon CRISPRi did this gene prove to be a restriction factor (i.e., improved infection upon reduction of the gene). We comment on this peculiar fact in the revised manuscript (page 13, lines 370-374). However, we note that the rest of our positive and negative controls deliver the expected results – increasing or reducing infection as expected from their role, thus lending considerable support to our experimental system. It is possible, especially in light of our screen, and since other positive and negative controls behave as expected, that the status of the SEC61B gene as a "restriction factor" of viral infection needs to be reconsidered, as we now suggest.

I would appreciate more information regarding why restriction factors of cell growth have a high GFP2/GFP4. Intuitively, a KO of restriction factors of cell growth should result in better growth and higher GFP, thus leading to enrichment in GFP4, not GFP2.

The reviewer raises an interesting question (although not at the heart of this work, as sgRNAs for the cell growth restriction factor mainly aim to serve as controls for the CRISPR screen). HCMV has a complex interaction with the cellular cell cycle. Specifically, it establishes a unique G1/S arrest that is both stimulatory and inhibitory since, on the one hand, it serves the virus to arrest the cell cycle, a critical step for viral genome replication. On the other hand, the virus needs many of the resources that serve cell growth. Both p53 and CDKN1A are important regulators at this stage; therefore, their interaction with the virus may indeed be complex. For example, p53 is upregulated by a viral infection. However, it is sequestered in the viral replication compartments, and its transcriptional are down-regulated, but its absence harms viral propagation (doi: 10.1128/mBio.02934-21, doi: 10.1128/jvi.72.3.2033-2039.1998, doi: 10.1128/jvi.00505-06). Therefore, it is not surprising that genes related to cell growth and cell cycle have complex effects on HCMV infection. We mention the essentiality of p53 for HCMV infection in the results (page 14, line 404).

Line 404 "nonetheless"

We appreciate the reviewer for noticing the typo. We corrected it.

Reviewer #1 (Significance (Required)):

The relation between human tRNA supply and viral translation is a topic of profound biological and biomedical importance. In this study, the authors used HCMV infection as the primary model to investigate this question. Results fall into two major parts: (i) changes in the tRNA pool during viral infection, and (ii) the impact of tRNA-related gene KO on viral infection.

We appreciate the detailed report. We addressed the major points raised in the revised manuscript.

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

In this study by Aharon-Hefetz et al., the researchers examined changes in tRNA pools during virus infections. The translation machinery plays a crucial role in virus replication. Consequently, host cells have developed sensors and effectors within this compartment to counteract viral mechanisms. The translation apparatus serves as a pivotal point in the virus-host conflict. Therefore, investigating alterations in the translation machinery during infections is vital for gaining a comprehensive understanding of the infection process. This study offers a thorough and high-quality analysis of data in a relevant cell culture system involving two different viruses. By conducting tRNA sequencing, the researchers studied the human tRNA pool following infections with human Cytomegalovirus (HCMV) and SARS-CoV-2. Changes in tRNA expression induced by HCMV were mainly driven by the virus infection itself, with minimal impact from the cellular immune response. Interestingly, specific tRNA post-transcriptional modifications seemed to influence stability and were subject to manipulation by HCMV. Conversely, SARS-CoV-2 did not lead to significant alterations in tRNA expression or post-transcriptional modifications. Moreover, a systematic CRISPR screen targeting human tRNA genes and modification enzymes allowed the identification of specific tRNAs and enzymes that either enhanced or reduced HCMV infectivity and cellular growth. This information enabled them to control the development of HCMV-specific tRNA modifications, highlighting the importance of these tRNA epitranscriptome modifications in virus replication. The authors concluded that the observed differences between the viruses are consistent with HCMV genes aligning with differentiation codon usage and SARS-CoV-2 genes reflecting proliferation codon usage. This observation's connection to the biology of HCMV and SARS-CoV-2 lies in the codon usage of structural and gene expression-related viral genes, showing a significant adaptation to host cell tRNA pools. Notably, these genes from both viruses demonstrated the highest adaptation to the tRNA pool of infected cells. The reason behind this phenomenon remains unclear. One hypothesis suggests that a high level of structural gene expression is necessary during activation. Testing this hypothesis could involve examining if hindering tRNA modifications affects virus morphogenesis. In summary, this study presents an interesting and innovative perspective on how viruses modify the translation machinery. The meticulous analysis sheds light on a central interaction point between viruses and their host cells.

Reviewer #2 (Significance (Required)):

In summary, this study presents an interesting and innovative perspective on how viruses modify the translation machinery. The meticulous analysis sheds light on a central interaction point between viruses and their host cells.

We thank the reviewer for finding our work interesting, innovative, and well analyzed

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary

Aharon-Hefetz et al. present the expression dynamics and modification signatures of tRNAs using DM-tRNA-seq in human foreskin fibroblasts or Calu3 cells during infections with two diverse viruses, HCMV and SARS-CoV2, respectively. They also use a newly designed tRNA-centric CRISPR library to screen the essentiality of tRNA and tRNA factors during HCMV-GFP infection. They find several tRNAs that are differentially expressed during HCMV infection, and most closely resemble the set of tRNAs shown to be used during cellular differentiation. Additionally, tRNA differential expression does not resemble that following interferon treatment, implying that virus modulation of tRNAs is unique to the general interferon response. They compare codon usage signatures during infection to their prior-defined sets of proliferation/differentiation tRNA genes. In their CRISPR screen, they find that different tRNAs can promote or restrict HCMV infection levels, as measured by the intensity of GFP fluorescence marker in their virus. Surprisingly, there were few tRNA modification factor hits that contributed to growth or infection.

Reviewer #3- major comments

1. The topic of this work is important, and the analysis performed here is assumed to be top quality, based on the previous work by the last author. The weakness with this body of work is a lack of rigor, specifically regarding validation and follow-up studies. Without these experiments, the reader lacks confidence in stated conclusions. For example: There is no validation or clue to how penetrant CRISPR is against tRNA genes. Given how duplicated some tRNA families are, it is possible that CRISPR is more effective against certain families compared to others. While this is likely an inherent caveat in all CRISPR screens, it would lend confidence in this approach to see some validation of tRNA KO by northern blot or RT-qPCR or sequencing.

We thank the reviewer for raising this important issue. Indeed, many tRNA genes appear in multiple copies in the human genome. Yet, based on our previous work, we expect parallel editing of multiple copies using the same sgRNA. In our previous work (doi.org/10.7554/eLife.58461), we validated, based on several tRNA families, the ability of our tRNA CRISPR system to successfully target and affect tRNA expression levels. This included sequencing of the edited tRNA genes (i.e., DNA sequencing), in which we observed diverse INDEL mutations that predicted full disruption of the tRNA structure. Furthermore, we sequenced the tRNA pool of CRISPR-edited cells and found the downregulation of the targeted tRNAs to be up to 2-4-fold. This previous work provides foundations and confidence in this tRNA-CRISPR approach.

Nevertheless, to further mitigate the reviewer's concern, we also plan to perform additional experiments in the current settings. We will choose individual tRNAs from our CRISPR screen as representatives to validate CRISPR editing. We will target each tRNA independently and test expression reduction by sequencing. We shall share the results in the full revision if granted.

1. There is no validation that tRNA modification factor knockouts alter tRNA modification levels. Without this knowledge, the lack of essentiality cannot be confidently and fully interpreted. If the group does not validate whether individual tRNA modification factor knockouts alter modification profiles, then all possible explanations should be posited. For example, it is possible that 1) there could be major redundancy among tRNA modification enzymes, as the authors posit in the Discussion 2) tRNA modification enzymes are not essential for growth bc their activity/the modification they place is non-essential for growth, OR 3) the knockouts are not fully penetrant. I think this Discussion should be expanded to make caveats clearer. Perhaps referencing whether tRNA modification factors have been shown to be essential in other CRISPR screens would be helpful.

Regarding the possible explanations for the lack of essentiality of tRNA modification enzymes, we agree with all three possibilities the reviewer raised. Reviewer #1 raised an additional option, in which tRNA modification enzymes are essential for HCMV infection and cell growth; thus, we cannot detect them in the screens because they drop out early in the process (before collecting the ancestor samples). We checked this possibility and found comparable read counts of sgRNAs targeting tRNA modification enzymes to that of other sub-libraries. This result suggests the drop-outs of sgRNA targeting are unlikely to happen on our screens.

Furthermore, as the reviewer asked, we analyzed additional CRISPR screens and compared the effect of tRNA modification enzyme knockouts relative to the restriction and dependency factors we used in the library. The tested screens included 34 knockout CRISPR screens we downloaded from the BioGRID ORCS database that have similar parameters to our screen. Namely, they all test cell proliferation in a time-course manner, using a pooled sgRNA library and using the MAGeCK tool for data analysis. Overall, the screens use different human cell lines and diverse sgRNA libraries. Although potentially surprising, we found that the lack of essentiality of tRNA modification enzymes was also observed in the analyzed CRISPR screens (Figure S5B and on page 11, lines 322-330, and on page 18, lines 539-541).

1. There is no validation that factors modulating GFP intensity in the HCMV screen actually impact virus replication. This is the point most important to this body of work. While GFP intensity does correlate to genome copies as shown by the authors, GFP read-out on a case-by-case basis could be simply due to factors required for expression/translation of GFP. Are any of the tRNA hits enriched or not represented in GFP reporter sequence? Either way, this information is informative.

We acknowledge the concern. Specifically, the translation of the GFP gene itself could be affected by the tRNA manipulation done. To account for this potential concern, we tested the codon usage of the eGFP gene (which is the GFP version we used in the system) and compared it with tRNA essentiality, as determined by the cell growth CRISPR screen. We report this in the revised manuscript (page 13, lines 390-392, and added Figure S6D). We found that GFP does not tend to significantly use codons that correspond to essential or less essential tRNAs. The same lack of correlation was also found for the tRNA essentiality upon HCMV infection (not shown).

Additionally, given that the hits are cross-compared ONLY to other infected (low intensitiy "GFP+") cells, and not to an uninfected population, there is no guarantee that these primarily drive HCMV infection. The top hits should be validated in HFFs, infected with HCMV, with resulting titers/viral gene expression/genome copies measured. Additionally, the reasons for not using a GFP- population as a control should be clarified.

We agree that additional experiments on some hits may be warranted. We plan to examine for such an effect on infection using an individual gene version of the assay. In particular, we will target individually candidate tRNA genes following validation (as described previously in point 1). We will then infect the tRNA-targeted cells with HCMV and measure the effectiveness of HCMV infection using a standard titer assay.

The reviewer also suggest comparing GFP1/2/3 to an ancestor in addition to comparing them to GFP4. Towards that we now show a GFP2 vs ancestor comparison (shown below). The results look very similar and are now added to the supplemental material of the revised manuscript (page 13, lines 385-387, Figure S6B).

Though careful codon usage analysis for HCMV versus the human host was analyzed, it seems pertinent to analyze whether the differentially expressed tRNAs during infection correlate to either codon usage profiles. Figure 3C and S3C intend to address this point for viral gene groups; however, I would encourage the authors to expand the description of these results to make them easier to interpret, especially for those not in the tRNA field. For example, "tRNA adaptation index (tAI)" is not defined in the text, but simply referenced. For clarity, you should include a brief explanation of what this measure describes. Following, when reporting results from Figure 3, the results can then be delivered with more specific and interpretable language. These steps will ensure maximal scientific communication to the audience.

We appreciate the reviewer's comment regarding the importance of scientific communication and making this manuscript easier to interpret, especially for readers unfamiliar with the world of tRNAs and translation efficiency. We added a description of our motivation to use tAI and the meaning of the measurement (page 9, lines 241-243). We also elaborated on the results part and made the results more interpretable (page 9, lines 245 and 249-250).

Finally, given that changes are most visible at 72 hpi, the analysis should include expression based on this time point for comparison.

Regarding the time point used for tAI calculation (Figure 3), we tested the tAI measured by the tRNA pool at 72hpi and got very similar results to that obtained using the tRNA pool measured at 24hpi. As 24hpi represents the pick of HCMV infection, we decided to present this analysis. In the current revised version, we also added the analysis done using the tRNA pool measured 72hpi as suggested by the reviewer (Figure S3D).

Reviewer #3- minor comments

1. I would recommend more care in terminology used for the CRISPR screen (Figures 5 and 6) to make the manuscript easier to digest. Labeling sgRNAs-containing cells as " Reduced Growth/Infection" or "Increased Growth/Infection" is not immediately easy to understand. For example, saying this sgRNA "increased growth" could refer to the knockdown increasing growth OR could mean that this sgRNA was enriched in cells with increased growth, which are opposing. It might be more clear to state to use depleted/enriched terminology in these figure labels. This also applies to the text, be sure to plainly describe the terminology and what it means each time you refer to the CRISPR results.

This is a good point. Indeed, focusing on the significant enrichment of the sgRNAs, rather than their effect on growth or infection, is more straightforward. We changed the terminology in Figures 5C and 6C and the text in the current version.

Is there actual evidence that the new tRNA sgRNA library is more effective than that used previously? State if so.

We assume the referee refers to our previous paper on the smaller-scale library (doi.org/10.7554/eLife.58461). The addition here is that the library is much more comprehensive (the previous one targeted only 20 tRNAs). We point it out in the revised manuscript (page 17, lines 499-501).

Fig 1A-C: The cutoff for "red" symbol distinction is not stringent enough. 1.05 would be red, but that is not convincingly upregulated. The cutoff should be at least FC>1.2.

We thank the reviewer for bringing our intention to this point. In the current version, we changed the cutoff of absolute fold change higher than 1.2 in Figures 1A-C and S1A (also in legend).

Need thorough description of tRNA bioinformatics and modification analysis (citing past work is not appropriate here-need to make accessible to your audience).

Further thorough descriptions of tRNA bioinformatics and modification analysis are added in the revised version (page 6, lines 149-151, page 7, lines 178-183).

Line 182- Result headings could be more informative, even with small adjustments. For example "Specific tRNA modifications are modulated in response to HCMV infection" is more clear and accurate, as there are only a few measurable changes in tRNA modification. Limitations of using sequencing techniques to analyze modifications (versus MS) should also be discussed.

We changed that heading accordingly.

We also mentioned the advantages and disadvantages of using sequencing to assess tRNA modification levels (page 7, lines 184-187).

It is not immediately clear why the viral plot looks different in Fig S3B compared to Fig 3B.

We thank the referee for spotting this. We employed different length cutoffs on the genes in each panel and have now fixed that in the revised manuscript.

Line 254-255. This point is not immediately clear-please include more specific language detailing the logic leading to this conclusion.

Indeed, the logic here was missing. The idea was that longer genes are associated with gene conservation, hence functionality. Thus, non-canonical HCMV genes that are both long and codon-optimized might have a function during HCMV infection. We added this explanation to the text (pages 8-9, lines 235-238).

Line 408- "may be essential"-I would modify the language here. Especially given there is no true comparison with uninfected cells.

We improved the language throughout the revised manuscript.

There are a number of recent publications profiling tRNA expression in herpesviruses. These should be mentioned and discussed in the context of this work. I know some were included in the reference list, but the body of work as a whole, and how this work fits in and pushes the horizon, could be further emphasized. It is quite impressive that this is a conserved feature of herpesvirus infection. a. PMID: 36752632 b. PMID: 35110532 c. PMID: 34535641 d. PMID: 33986151 e. PMID: 33323507 f. PMID: 35458509

We thank the reviewer for highlighting these works. We added a discussion item regarding tRNA expression in HCMV and other herpesviruses with the references (pages 15-16, lines 447-458)

CoV2 Discussion point-The lack of tRNA expression regulation might have more to do with the length of the infection (6 hpi cov2- also didn't see much a change at 5hpi with hcmv). This should be proposed as a possibility.

It is a possibility that due to the high stability of tRNAs, expression regulation of tRNAs will not affect the tRNA pool in short infection such as of SARS-CoV-2. We added this explanation in the discussion part, page 15, lines 441-442.

Line 582. Misspelled schlafen in Discussion. (SLFN, not SFLN)

The point is fixed in the revised manuscript.

Reviewer #3 (Significance (Required)):

General assessment: I found this paper exciting to read, given the dearth of knowledge regarding viral modulation of tRNA expression.

We appreciate the reviewer's comment

However, the work is highly descriptive, with a complete absence of follow-up or validation studies. At the very least, I would have hoped that the authors validated that viral titer (and not just GFP intensity) was impacted by some of the hits. The lack of confirmation and quality control overall diminishes confidence in the stated conclusions.

However, I think the topic is timely, important, and that this manuscript offers tools to the community at large to learn more about viral manipulation or other drivers of tRNA regulation. Once follow-up/validation experiments are added to the work, as detailed below, this manuscript will be of broad importance and highly impactful.

As mentioned above, we plan to add such validations to the fully revised manuscript.

Advance: While there have been many studies suggesting tRNA regulation occurs during viral infection (these pubs should be referenced as mentioned above), this is an advance due to the fact that it begins to address whether tRNA expression changes functionally impact viral replication. This will be much more solid with follow-up experiments confirming that hits alter HCMV replication (rather than GFP intensity).

Audience: This will be of broad interest to those with interest in virology and gene expression. The new sub-libraries of tRNA-related factors might be useful to be tested in other cell types and settings. Again, as the work stands, it is descriptive and hypothesis-stimulating, but the conclusions need validation and further support.

We thank the referee for the encouraging words and the suggested analyses. We already implemented most of the suggestions in the current revised version and hope to add further experiments in a fully revised manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary

Aharon-Hefetz et al. present the expression dynamics and modification signatures of tRNAs using DM-tRNA-seq in human foreskin fibroblasts or Calu3 cells during infections with two diverse viruses, HCMV and SARS-CoV2, respectively. They also use a newly designed tRNA-centric CRISPR library to screen the essentiality of tRNA and tRNA factors during HCMV-GFP infection. They find several tRNAs that are differentially expressed during HCMV infection, and most closely resemble the set of tRNAs shown to be used during cellular differentiation. Additionally, tRNA differential expression does not resemble that following interferon treatment, implying that virus modulation of tRNAs is unique to the general interferon response. They compare codon usage signatures during infection to their prior-defined sets of proliferation/differentiation tRNA genes. In their CRISPR screen, they find that different tRNAs can promote or restrict HCMV infection levels, as measured by the intensity of GFP fluorescence marker in their virus. Surprisingly, there were few tRNA modification factor hits that contributed to growth or infection.

Major Comments

1. The topic of this work is important, and the analysis performed here is assumed to be top quality, based on the previous work by the last author. The weakness with this body of work is a lack of rigor, specifically regarding validation and follow-up studies. Without these experiments, the reader lacks confidence in stated conclusions. For example:
   • a. There is no validation or clue to how penetrant CRISPR is against tRNA genes. Given how duplicated some tRNA families are, it is possible that CRISPR is more effective against certain families compared to others. While this is likely an inherent caveat in all CRISPR screens, it would lend confidence in this approach to see some validation of tRNA KO by northern blot or RT-qPCR or sequencing.
   • b. There is no validation that tRNA modification factor knockouts alter tRNA modification levels. Without this knowledge, the lack of essentiality cannot be confidently and fully interpreted. If the group does not validate whether individual tRNA modification factor knockouts alter modification profiles, then all possible explanations should be posited. For example, it is possible that 1) there could be major redundancy among tRNA modification enzymes, as the authors posit in the discussion 2) tRNA modification enzymes are not essential for growth bc their activity/the modification they place is non-essential for growth, OR 3) the knockouts are not fully penetrant. I think this discussion should be expanded to make caveats clearer. Perhaps referencing whether tRNA modification factors have been shown to be essential in other CRISPR screens would be helpful.
   • c. There is no validation that factors modulating GFP intensity in the HCMV screen actually impact virus replication. This is the point most important to this body of work. While GFP intensity does correlate to genome copies as shown by the authors, GFP read-out on a case-by-case basis could be simply due to factors required for expression/translation of GFP. Are any of the tRNA hits enriched or not represented in GFP reporter sequence? Either way, this information is informative. Additionally, given that the hits are cross-compared ONLY to other infected (low intensitiy "GFP+") cells, and not to an uninfected population, there is no guarantee that these primarily drive HCMV infection. The top hits should be validated in HFFs, infected with HCMV, with resulting titers/viral gene expression/genome copies measured. Additionally, the reasons for not using a GFP- population as a control should be clarified.
2. Though careful codon usage analysis for HCMV versus the human host was analyzed, it seems pertinent to analyze whether the differentially expressed tRNAs during infection correlate to either codon usage profiles. Figure 3C and S3C intend to address this point for viral gene groups; however, I would encourage the authors to expand the description of these results to make them easier to interpret, especially for those not in the tRNA field. For example, "tRNA adaptation index (tAI)" is not defined in the text, but simply referenced. For clarity, you should include a brief explanation of what this measure describes. Following, when reporting results from Figure 3, the results can then be delivered with more specific and interpretable language. These steps will ensure maximal scientific communication to the audience. Finally, given that changes are most visible at 72 hpi, the analysis should include expression based on this time point for comparison.

Minor Comments

1. I would recommend more care in terminology used for the CRISPR screen (Figures 5 and 6) to make the manuscript easier to digest. Labeling sgRNAs-containing cells as " Reduced Growth/Infection" or "Increased Growth/Infection" is not immediately easy to understand. For example, saying this sgRNA "increased growth" could refer to the knockdown increasing growth OR could mean that this sgRNA was enriched in cells with increased growth, which are opposing. It might be more clear to state to use depleted/enriched terminology in these figure labels. This also applies to the text, be sure to plainly describe the terminology and what it means each time you refer to the CRISPR results.
2. Is there actual evidence that the new tRNA sgRNA library is more effective than that used previously? State if so.
3. Fig 1A-C: The cutoff for "red" symbol distinction is not stringent enough. 1.05 would be red, but that is not convincingly upregulated. The cutoff should be at least FC>1.2.
4. Need thorough description of tRNA bioinformatics and modification analysis (citing past work is not appropriate here-need to make accessible to your audience)
5. Line 182- Result headings could be more informative, even with small adjustments. For example "Specific tRNA modifications are modulated in response to HCMV infection" is more clear and accurate, as there are only a few measurable changes in tRNA modification. Limitations of using sequencing techniques to analyze modifications (versus MS) should also be discussed.
6. It is not immediately clear why the viral plot looks different in Fig S3B compared to Fig 3B.
7. Line 254-255. This point is not immediately clear-please include more specific language detailing the logic leading to this conclusion.
8. Line 408- "may be essential"-I would modify the language here. Especially given there is no true comparison with uninfected cells.
9. There are a number of recent publications profiling tRNA expression in herpesviruses. These should be mentioned and discussed in the context of this work. I know some were included in the reference list, but the body of work as a whole, and how this work fits in and pushes the horizon, could be further emphasized. It is quite impressive that this is a conserved feature of herpesvirus infection.
   • a. PMID: 36752632
   • b. PMID: 35110532
   • c. PMID: 34535641
   • d. PMID: 33986151
   • e. PMID: 33323507
   • f. PMID: 35458509
10. CoV2 discussion point-The lack of tRNA expression regulation might have more to do with the length of the infection (6 hpi cov2- also didn't see much a change at 5hpi with hcmv). This should be proposed as a possibility.
11. Line 582. Misspelled schlafen in discussion. (SLFN, not SFLN)

Significance

General assessment: I found this paper exciting to read, given the dearth of knowledge regarding viral modulation of tRNA expression. However, the work is highly descriptive, with a complete absence of follow-up or validation studies. At the very least, I would have hoped that the authors validated that viral titer (and not just GFP intensity) was impacted by some of the hits. The lack of confirmation and quality control overall diminishes confidence in the stated conclusions. However, I think the topic is timely, important, and that this manuscript offers tools to the community at large to learn more about viral manipulation or other drivers of tRNA regulation. Once follow-up/validation experiments are added to the work, as detailed below, this manuscript will be of broad importance and highly impactful.

Advance: While there have been many studies suggesting tRNA regulation occurs during viral infection (these pubs should be referenced as mentioned above), this is an advance due to the fact that it begins to address whether tRNA expression changes functionally impact viral replication. This will be much more solid with follow-up experiments confirming that hits alter HCMV replication (rather than GFP intensity).

Audience: This will be of broad interest to those with interest in virology and gene expression. The new sub-libraries of tRNA-related factors might be useful to be tested in other cell types and settings. Again, as the work stands, it is descriptive and hypothesis-stimulating, but the conclusions need validation and further support.

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Referee #2

Evidence, reproducibility and clarity

In this study by Aharon-Hefetz et al., the researchers examined changes in tRNA pools during virus infections. The translation machinery plays a crucial role in virus replication. Consequently, host cells have developed sensors and effectors within this compartment to counteract viral mechanisms. The translation apparatus serves as a pivotal point in the virus-host conflict. Therefore, investigating alterations in the translation machinery during infections is vital for gaining a comprehensive understanding of the infection process.

This study offers a thorough and high-quality analysis of data in a relevant cell culture system involving two different viruses. By conducting tRNA sequencing, the researchers studied the human tRNA pool following infections with human Cytomegalovirus (HCMV) and SARS-CoV-2. Changes in tRNA expression induced by HCMV were mainly driven by the virus infection itself, with minimal impact from the cellular immune response. Interestingly, specific tRNA post-transcriptional modifications seemed to influence stability and were subject to manipulation by HCMV. Conversely, SARS-CoV-2 did not lead to significant alterations in tRNA expression or post-transcriptional modifications.

Moreover, a systematic CRISPR screen targeting human tRNA genes and modification enzymes allowed the identification of specific tRNAs and enzymes that either enhanced or reduced HCMV infectivity and cellular growth. This information enabled them to control the development of HCMV-specific tRNA modifications, highlighting the importance of these tRNA epitranscriptome modifications in virus replication. The authors concluded that the observed differences between the viruses are consistent with HCMV genes aligning with differentiation codon usage and SARS-CoV-2 genes reflecting proliferation codon usage. This observation's connection to the biology of HCMV and SARS-CoV-2 lies in the codon usage of structural and gene expression-related viral genes, showing a significant adaptation to host cell tRNA pools. Notably, these genes from both viruses demonstrated the highest adaptation to the tRNA pool of infected cells. The reason behind this phenomenon remains unclear. One hypothesis suggests that a high level of structural gene expression is necessary during activation. Testing this hypothesis could involve examining if hindering tRNA modifications affects virus morphogenesis. In summary, this study presents an interesting and innovative perspective on how viruses modify the translation machinery. The meticulous analysis sheds light on a central interaction point between viruses and their host cells.

Significance

In summary, this study presents an interesting and innovative perspective on how viruses modify the translation machinery. The meticulous analysis sheds light on a central interaction point between viruses and their host cells.

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Referee #1

Evidence, reproducibility and clarity

I have mixed feelings regarding this manuscript. On the one hand, the authors did an impressive amount of work. On the other hand, the manuscript seems overly descriptive (writing should be more concise) without a clear message or hypothesis that is cohesive to all the presented evidence. Below, I will outline my concerns.

Regarding the first part.

1. I am not an expert in the field of viral biology and immunology. I wonder how well the IFN treatment mimics the cellular response to infection (yet without the virus). Also, how good is ruxolitinib at blocking the IFN response ? I would appreciate it if you could explain both with one or two sentences and provide the necessary references.
2. (MAJOR) Can these two treatments really allow the effects of host response and viral infection to be separated? OR in other words, are these two effects really orthogonal? In my opinion, they are NOT. Fig. 1E seems to support my opinion, as the changes seen for the "IFN" sample relative to the "uninfected" sample (referred to as "changes-A" below), are parallel to the changes seen for the "24hpi + ruxo" sample relative to the "24hpi" sample ("changes-B"). More specifically, changes-A represent the host response, as argued by the author, whereas changes-B represent the elimination of the host response (due to ruxo, conditioned on the virus-driven effect). If the virus-driven effect and the host response could really be separated, one would expect changes-A and changes-B are more or less opposite. However, they appeared to be parallel, suggesting that uninfected versus infected conditions can have totally different (even opposite) host responses. More importantly, if one cannot separate the host response from virus-driven effects, the conclusion of "tRNA changes are driven by virus, not host response" is then unfounded.3. Even if we let go of this previous point and accept that these results indeed offer some support for the notion that the virus-driven effect are the main contributor to the shifts in tRNA pool, the support is at best moderate. A big gap here is "how?" I suggest the authors should at least give some insight on how virus can do that in Discussion (and mention it with one sentence in Results).
3. The authors compared the HCMV codon usage to the proliferation and differentiation signatures of human cells. But these two signatures are not compared with measured tRNA expression. It might shed some light on the general characteristics of tRNA pool shifts due to infection (towards a proliferation-like or differentiation-like signature). This fits in the general topic of virus-host interaction and might give more evidence for the point that HMCV is adapted to a differentiation signature (as it drives the host into that state).
4. How is the dashed box in Fig3A/B chosen?
5. The tAI values shown in Fig3C-E are extremely low (compared to other reports I am aware of). Does this mean that the adaptation of viral codon usage to human cell supply is actually very weak? This is in opposition to the major claims made in this section.
6. I believe that the part about SARS-CoV-2 could be made more concise. It is sufficient to mention that results may differ from those obtained with HCMV in one paragraph.
7. Line 299 on page 11 - I do not believe codon usage between different viruses can be directly compared, let alone reaching such a conclusion. Some viruses have low CAI or tAI to humans, but they have co-evolved with humans for a long time. Furthermore, there are viruses that infect multiple hosts, but their CAI for a host with which they have long co-evolved is higher while their CAI for a host that is relatively new is lower.
8. (MAJOR) A more general comment is that there is a difference between tRNA expression and the abundance of translation-ready tRNA. The process of charging tRNA with amino acids may take a long time. It is the abundance of the charged-tRNA (the ternary complex of aminoacylated tRNA and EF-Tu-GTP) that is of biological importance. In this regard, the use of tRNA expression falls short.

Regarding the second part,

1. (MAJOR) Prior to the actual competition assay in the first high-throughput screen (cell competition assay), the authors applied two days of antibiotic selection and two days of recovery. This could result in a serious problem of false negatives or drop outs. Specifically, an sgRNA targeting an essential gene with high efficiency would kill the cells, leaving no (or a small number of) cells in the ancestor population at the beginning of the competition process. A sgRNA's enrichment in competing populations cannot be reliably estimated in such situations. I am not certain that the FDR used in Figure 5B is sufficient to address this issue. Please clarify whether it could. Providing raw counts for competing and ancestor populations would also be helpful.
2. It is also highly questionable to me the nearly negligible effects of tRNA modification enzymes. This may be explained by the point above. Indeed, the dots of tRNA modification enzymes in general appear to have higher FDR (lower y values) when compared to red dots with similar enrichment levels.
3. The screen based on IE2-GFP labeled HCMV measures a phenotype that is very difficult to interpret. Particularly, I am not sure if GFP2 and GFP3 are good controls for comparing GFP4 (GFP1 might be better). Various factors can affect GFP levels, including, but not limited to, dilution caused by a rapidly dividing host cell, unhealthy translational machinery resulting from infection or microenvironment. My point is supported by some observations in Fig6B. For example, SEC61B, a restriction factor for HCMV infection, is enriched in the GFP2 group, contrary to expectations. It is necessary for the authors to prove with firm evidence that their choice of GFP signal thresholds is appropriate.
4. I would appreciate more information regarding why restriction factors of cell growth have a high GFP2/GFP4. Intuitively, a KO of restriction factors of cell growth should result in better growth and higher GFP, thus leading to enrichment in GFP4, not GFP2.
5. Line 404 "nonetheless"

Significance

The relation between human tRNA supply and viral translation is a topic of profound biological and biomedical importance. In this study, the authors used HCMV infection as the primary model to investigate this question. Results fall into two major parts: (i) changes in the tRNA pool during viral infection, and (ii) the impact of tRNA-related gene KO on viral infection.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The manuscript by Jeong et al. describes effects of neuronal signalling on collective behavior by measuring social distance (SD) which is used as a measure for social network behavior. Authors screened for a panel of inbred DGRP lines and compared the SD due to prior experience of group or single culturing when flies are recorded in a 55 mm diameter petri-dish. The screen uncovered 3 short Sd and three long-SD lines, and subsequent experiments showed differences in various behaviors such as recovery from injury, search for food and SD. Using RNA-seq from heads of flies they implicate Dsk signalling and show neuronal architecture and activity differences between grouped and isolated male flies. They implicate Dsk signalling in recovery from injury affecting SD but it was dispensable for grouped vs. isolated flies. I have suggestions to support the claims made, analysis and interpretation of the data and improve the clarity of writing. See my specific comments below.

Major comments:

1. For recording social behaviour in flies, arenas with sloped walls have been extensively used called 'fly bowl' (Simon et al. 2010 doi:10.1371/journal.pone.0008793; Robie et al., 2017, doi: 10.1016/j.cell.2017.06.032), or 'flyworld' (Liu et al., 2018 doi:10.1371/journal.pcbi.1006410). Such geometry ensures that flies don't walk on the side of the arena, and don't occlude each other. However, in the screen carried out in this manuscript, a petri-dish of 5.5 cm diameter filled with agar was used to record social network formation. Given the propensity of flies to walk on the walls of such circular arena, it will be difficult to know if the long and short SD behavior resulting from propensity to form clusters is an artefact of the assay condition used. It would be important to test the SNB and SD of at least the 6 selected short and long SD lines in arena with sloped walls to rule out this possibility.

To minimize complications from side-wall walking or z-stacking of individual flies, the circular arena used in our study was filled with agar, making a space of 5.5 cm in diameter x ~0.15 cm in height (see below). In fact, the surface tension of the agar solution led to a rounded interface between the agar bed and the side wall that could prevent side-wall walking and partially mimic the sloped wall effects. We included representative video clips for SNB recording in the revised manuscript to further justify our experimental conditions. We also revised our method text accordingly and cited the relevant reference.

Methods section would require additional details about the SNB assay for instance, the height of the agar bed and the effective height in which interactions was recorded is not mentioned.

As described in our response to reviewer #1, major comment #1 above, we revised our method text accordingly.

Figure 2C and 2D results in larvae seems to contradict previous studies that have shown that isolated flies eat more as adults (Li et al. Nature, 2021) and Dsk-RNAi increases feeding in larvae and adults (Soderberg et al., 2012). It might be due to unique characteristic of DGRP lines used and would be helpful to discuss this.

We reason that high-digging activity in a group of individual larvae can increase the accessibility to "solid" food, thereby promoting their food intake over 12-h during development. However, food consumption rates and their regulation can vary depending on developmental stages or feeding conditions (e.g., larvae vs. adults; liquid vs. solid food; long-term vs. short-term) (https://pubmed.ncbi.nlm.nih.gov/30914005/; https://pubmed.ncbi.nlm.nih.gov/24937262/). It is thus not fair to align our results directly to those observed under very different biological/experimental contexts. For instance, Li et al. measured the amount of liquid food consumption in individually isolated adults from group culture vs. transient social isolation (i.e., 1-week isolation after eclosion). Soderberg et al. assessed the 15-minute feeding activities of larvae or adults on solid food but did not compare the feeding activity between grouped vs. isolated individuals. Therefore, it is not conclusive whether the DSK-depletion phenotypes are relevant to social experience or whether DSK signaling controls short-term feeding per se. Accordingly, we believe our observations do not necessarily contradict previous studies or indicate characteristics unique to the DGRP lines used in our study.

Rutabaga mutants for Fig S3 are directly compared with CS flies in the maze assay and it appears from methods that these lines were not isogenized, this can significantly impact the results. Similarly for some of the subsequent Dsk experiments it appears that lines were not isogenized (see below). These experiments would either need to be repeated of this caveat needs to be explicitly mentioned to avoid misinterpretation of the data.

Since genetic backgrounds could substantially contribute to mutant phenotypes, we mentioned the caveat in our revised manuscript. As the reviewer suggested above, testing isogenized lines could be one option to confirm the genetic effects. Our study took an alternative approach where the observed phenotypes were validated by independent genetic models. For instance, the importance of DSK signaling in injury-induced SNB plasticity was validated by genomic deletions of DSK and DSK receptor genes, as well as by transgenic RNAi (Fig. 7B and 7C). In the revised manuscript, we examined additional mutant alleles of rutabaga (Fig. S6, rut[1] and rut[2080]), CCKLR-17D1 (Fig. S15, CCKLR-17D1[delta1] and CCKLR-17D1[delta2]), and CCKLR-17D3 genes (Fig. S15, CCKLR-17D3[delta1] and CCKLR-17D3[delta2]) to substantiate our original findings.

For Figure 3 describing RNA-seq data additional analysis would be helpful. Gene expression from isolated and grouped flies have been studied earlier by microarray and RNA-seq methods (Wang et al., PNAS 2008; Agrawal et al., JEB 2020; Li et al. Nature, 2021). Data from these studies should be compared with to see if there are common patterns of gene expression between long and short SD flies vs. group and isolated flies.

According to the reviewer's suggestion, we compared our DEG analysis with those reported in the previous studies. Genes upregulated in socially deprived flies overlapped substantially between our data and the published ones. However, the number of genes commonly upregulated in grouped cultures was very limited in the pairwise comparisons, and Dsk was the only gene upregulated across DEG analyses. Also, DEGs between the short vs. long SD lines barely overlapped with those between grouped vs. isolated flies across independent studies. We speculate that Drosophila has evolved a genetic reprogram where social isolation robustly induces the expression of select genes regardless of genetic backgrounds (i.e., DGRP lines in our study vs. Canton-S in the previous studies), whereas diverse genetic pathways shape the baseline SD traits. We revised our text accordingly and included these new analyses in the revised manuscript (Fig. S10 and Dataset S3).

GEO accession number and the analyzed list of DEGs should be provided as supplementary information.

As described in our original manuscript, we submitted our raw data to the European Nucleotide Archive (ENA accession number PRJEB61423). Since ENA and GEO share their data, uploading our data redundantly onto the GEO should not be necessary. Our original manuscript also included all the DEG lists as supplementary tables (Datasets S1-S3).

Figure 3E & F are not referred to in the main text, also there is no description of how the data was generated. Is this based on published data from Mackay lab about DGRP lines, if so, aggression experiments were not convincing in those studies and have been shown to not recapitulate 'real' aggression by other labs for several of the DGRP lines tested (Chowdhury et al., 2021, doi: 10.1038/s42003-020-01617-6).

The main text of our original manuscript actually referred to Fig. 3E and 3F. We revised our figure legend to indicate the resources of raw DGRP data and clarified the method for the correlation comparisons. Since we employed the published aggression data from the Mackay lab study (https://pubmed.ncbi.nlm.nih.gov/26100892), we experimentally validated that the long-SD lines indeed show more lunges (i.e., a well-established indicator of aggression behaviors) than the short-SD lines in our revised manuscript (Fig. S11).

Dsk was shown to be reduced in isolated flies by RNA-seq and play a role in aggression by an earlier study (Agrawal et al., 2020) and should be cited appropriately (line 180-181) and elsewhere.

The paper was appropriately cited in our original/revised manuscript.

For Fig. 4A-B, source images for other two DGRP lines should be included at least in supplementary information, if not as main figure.

Representative confocal images for the other DGRP lines were included in the revised manuscript (Fig. 6A).

For Fig. 5, what is the reason that uninjured flies don't show any SD phenotype? Are there any changes in their velocity? This is mentioned in passing on line 228-29 but should be properly discussed.

Genomic deletions or transgenic manipulations of the DSK-CCKLR-17D1 pathway gave consistent effects on the injury-induced clustering but not on baseline SD or walking speed. We reason that the DSK-CCKLR-17D1 pathway is dedicated to encoding early-life social experience by enforcing DSK neuron activity and their male-specific postsynaptic signaling. We clarified our text including the genetic background issue in the revised manuscript. Please also see our response to reviewer #1, major comment #4 above.

Trans-Tango and UAS-Denmark, SytGFP experiments were performed previously by Wu et al., 2020 and Wang et al., 2021 for Dsk, these two studies observed that P1 neurons are presynaptic and Dsk neurons are post synaptic but in Figure 4 it's not clear what are the presynaptic and post synaptic neurons. Also these studies are not cited appropriately in this section.

The two studies expressed trans-Tango in P1 neurons (P1>trans-Tango) to demonstrate that DSK-expressing neurons are postsynaptic to P1 neurons. They further visualized some overlaps between axon terminals of P1 neurons (P1>sytGFP) and dendrites of DSK neurons (Dsk>DenMark). On the other hand, we expressed the trans-Tango in DSK neurons (Dsk>trans-Tango) to visualize their male-specific/social experience-dependent postsynaptic targets. We also visualized brain regions positive for both synaptic signals from Dsk>sytGFP and the postsynaptic signals from Dsk>trans-Tango. The two studies were cited in our original manuscript to discuss presynaptic partners of DSK neurons and their distinct roles in animal behaviors. We further cited the two studies in this result section of our revised manuscript.

Minor comments:

1. Line no. 286: please mention about the relative humidity and light & dark cycle conditions and when experiments were conducted (ZT).

Flies were reared at 40-50% humidity under 12-h light: 12-h dark cycles. Behavioral experiments were primarily conducted between ZT4 and ZT8. We revised the method text accordingly.

Line no. 311: How many days old flies were used (isolated and group housed) for the behavior and transcriptomic studies?

We revised the method text to better describe our experimental conditions.

Line no. 349: for RNA extraction please mention how many fly heads were used and ZT for collection.

Flies were harvested at ZT4-6, and total RNAs were extracted from 35 fly heads. We revised the method text accordingly.

Line no. 358: Italicize "Drosophila melanogaster".

Corrected.

Reviewer #1 (Significance (Required)):

This manuscript will be of interest to neuroscientists studying Drosophila social behaviors. The manuscript asks interesting questions and authors have done extensive set of experiments but the progress appears incremental given the current state of the field, especially for the later part of the manuscript. Some of the interpretation would also require additional data to bolster the claims made. Finally, the findings from this study could be better discussed in the context of what it is already known.

We believe our revised text and additional data in the revised manuscript clarify the reviewer concerns and better support our original findings.

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

The article explores the social network behavior (SNB) of Drosophila, focusing on individual social distance (SD) within groups over time. A systemic analysis revealed that short SD is associated with long developmental time, low food intake, and hypoactivity. Group culturing compensates for developmental inferiority in short social distance individuals. Social interactions during early development positively impact adult physiology and adaptive social plasticity. Transcriptome analyses show genetic diversity for SD traits. The neuropeptide Drosulfakinin (DSK) signaling mediates social network behavior plasticity via receptor CCKLR-17D1, particularly in males, suggesting a dedicated neural mechanism encoding early-life experiences to adaptively transform group properties. The research suggests that animals have developed neural mechanisms to encode early-life experiences. It offers insights into the genetic foundation and adaptability of social behavior in Drosophila, shedding light on the neural processes involved in social memory and the adaptive behaviors of groups. These findings have broader implications for understanding similar neural mechanisms governing social memory and group behaviors in other species.

Major concerns:

Major 1. In Figure 2H, the latency to 75% arrival of short-SD isolated fruit flies (no matter with or without pioneers) is close to that of group fruit flies with pioneers while the latency to 75% arrival of long-SD isolated fruit flies (no matter with or without pioneers) is close to that of group fruit flies without pioneers; How to explain the difference in latency to 75% array between long-SD and short-SD isolated fruit flies? It seems that only in the long-SD fruit flies from the grouped experience, the absence of pioneers will increase the time it takes to reach the target food in the maze.

Social deprivation effects on pioneer-free group foraging somewhat varied across the long SD lines (Fig. 4B and S5, blue). We reason that the hyperactivity of individual long-SD flies facilitates their food-seeking behaviors in the maze, weakening the pioneer effects or even overriding the group property. Nonetheless, our statistical analyses validated that 1) prior social experience did not significantly affect the group performance of 16 naive flies in the maze assay (the only exception was DGRP707, a long-SD line that showed longer latency in group-cultured naive flies than in socially isolated ones), 2) the presence of pioneers significantly shortened the latency in both short- and long-SD lines, and 3) the pioneer effects were evident only in group-cultured flies. We accordingly revised our result text to better elucidate our conclusion.

Major 2. In Figure 3C, there are two up-regulated genes in the Drosophila group that overlap in short-SD and long-SD strains. Apart from Dsk, what is the other gene? In addition, for isolated fruit flies, both short-SD and long-SD lines have more gene expression upregulated. How to explain this phenomenon? Can you briefly explore the reasons for their upregulation and instead of involvement in SNB plasticity, what kind of physiological functions may they have?

The other gene commonly upregulated in group-cultured DGRP flies was Arc1 (Activity-regulated cytoskeleton-associated protein), implicated in synaptic plasticity and fat metabolism (https://flybase.org/reports/FBgn0033926.htm). Arc1 downregulation upon social isolation could be relevant to the weak postsynaptic signaling of DSK neurons (Fig. 6D and 6E) or be a part of the metabolic reprogramming (Fig. 5D; also see below). Nonetheless, we focused on the neuropeptide DSK, given its unique expression in the brain and relevance to other social behaviors (e.g., mating, aggression). In fact, Dsk was the only overlapping gene that was downregulated upon social isolation across independent studies (Fig. S10A).

As the reviewer pointed out, social isolation upregulated many genes, including those involved in metabolism. Our revised manuscript additionally showed that upregulated but not downregulated genes upon social isolation were substantially conserved across genetic backgrounds or independent DEG studies (Fig. 5C and S10A). We speculate that Drosophila has evolved a genetic reprogram where social isolation elevates metabolic gene expression to adaptively induce a metabolic shift for energy storage and fitness. We revised our text accordingly.

Major 3. In lines 219-223, the genomic deletion by mutant or depletion by RNA interference emphasizes the role of neuropeptides DSK and its receptor CCKLR-17D1 in injury-induced clustering behaviors. How about the effect of neuropeptides overexpression? Do they confer injury-induced social interactions to isolated male flies. Meanwhile, in line 238, the transgenic excitation of CCKLR-17D1 neurons emphasizes the function of neuronal synaptic transmission in the pathway. Indeed, both neuropeptide expression and neuronal synaptic connections may be involved in the regulation of injury-induced clustering behaviors. It is recommended to separate the discussion of protein expression and the respective regulatory modes at the neuronal circuit level.

We could not test DSK overexpression effects on injury-induced clustering in socially isolated males since we failed to validate DSK overexpression from a relevant transgenic line (https://flybase.org/reports/FBal0184043.htm). Instead, we provided additional data in the revised manuscript that independent genomic deletions of the CCKLR-17D1 locus (Fig. S15) or transgenic silencing of the synaptic transmission in CCKLR-17D1 neurons (Fig. S16) suppressed the injury-induced clustering in group-cultured male flies. According to the reviewer's suggestion, we modified our text to better distinguish between the effects of gene/protein expression vs. relevant neuron activities on social behavior plasticity.

Major 4. Since a significant portion of the work in the first half of this paper is focused on elucidating two types of social distance in SNB, is there any difference in the regulation of social network plasticity by Dsk signaling pathway in the short-SD and long-SD lines?

As the reviewer suggested, it will be informative to determine if Dsk signaling for social behavior plasticity is differentially regulated in short- vs. long-SD lines. One technical issue is that genetic factors shaping their SD traits still need to be defined. So, we are limited to performing standard genetic/transgenic experiments using the DGRP lines while retaining their SD phenotypes. Accordingly, our current approach was to compare DSK expression in short- vs. long-SD lines under grouped- vs. isolated-culture conditions. Future studies should address the review comment above.

Minor ones:

Minor 1. There is a color difference between the data spots and the figure legends in Figure 2H.

Corrected.

Minor 2. The anatomical sample images in Figure 4 and Figure 5 require scale bars.

We added scale bars to Fig. 6 and 7 in the revised manuscript.

Minor 3. The "grouped" and "grp" in Figures 3B-3F can be unified as "grp", while the "isolated" and "iso" can be unified as "iso". So that the male and female symbols in Figure 3F will not have any deviation in the mark.

We unified the labels throughout the revised manuscript according to the reviewer's suggestion.

Minor 4. The difference in Denmark signals of each group of neurons under the condition of injury should also be compared in Figure 4C.

The DenMark signals were also compared between control and injury conditions (Fig. S12).

Minor 5. What is the effect of inactivating CCKLR-17D1 or CCKLR-17D3 by shibire on injury-induced clustering in group-cultured adults in Figure 5E? (This relates to major comment 3)

We actually employed a tetanus toxin light chain (TNT) to block synaptic transmission in CCKLR-17D1 neurons and found that the transgenic manipulation of CCKLR-17D1 neuron activity suppressed injury-induced clustering in group-cultured males (Fig. S16). Since 1) our additional data using independent deletion alleles further excluded the possible implication of CCKLR-17D3 in the SNB plasticity (Fig. S15) and 2) a transgenic Gal4 knock-in for the CCKLR-17D-3 locus is not available, we focused on the CCKLR-17D1 experiments in our current study and wished to leave more detailed circuit analyses for future studies.

Reviewer #2 (Significance (Required)):

General assessment:

The strengths of this work is that the authors have identified specific lines with short social distance or long social distance by conducting extensive screening experiments. By transcriptome analyses and gene ontology (GO) analyses they revealed genes up or down regulation in the social experience. They have also narrowed down to the DSK signaling involved in the social experience encoding process. However, the study's limitation lies in the lack of clarity regarding the DSK signaling pathway. The mechanisms through which social experiences affect neuronal activity and synaptic connections remain unclear. Further research on upstream and downstream pathways could enhance understanding. Although the article proposes injury-induced clustering behaviors, the key sensory pathways involved in social network behavior plasticity during early social experiences are not well-defined. Conducting sensory deprivation experiments could elucidate sensory involvement. Overall, the study's strengths lie in its comprehensive approach, large sample size, and translational potential. To enhance future research, investigating the complexity of neural mechanisms and expanding the exploration of regulating pathways could be beneficial. Additionally, exploring the ecological relevance of the findings could deepen our understanding of social behavior in natural environments.

Our current work provides a neuroanatomical basis for early-life social memory and experience-dependent plasticity of social-interaction behaviors. We believe future studies will build up the mechanical details for social experience-dependent DSK expression, DSK neuron activity, and behavioral outputs. Regarding the key sensory pathways, we examined injury-induced SNB plasticity of distinct sensory mutants (e.g., olfactory, visual, auditory, etc.) and our revised manuscript provided additional data that norpA-dependent visual sensing might play a crucial role in this process (Fig. S9), consistent with the previous finding that vision is required for larval clustering behaviors in Drosophila (https://pubmed.ncbi.nlm.nih.gov/28918946/).

Advance:

Compared to previous studies such as Heiko Dankert et al.'s publication in 2009 in Nature Methods and Assa Bentzur et al.'s publication in 2020 in Current Biology, which also investigated the impact of early life experiences on male social behavior and examined various aspects of social network construction, this study employs a systematic analysis of social network behavior (SNB) in Drosophila, integrating genetic, physiological, and behavioral assessments. The authors conducted detailed and systematic analyses through transcriptome and gene ontology (GO) analyses, including the visualization of gene expression heatmaps, volcano plots, and overlapping analysis of differentially expressed genes (DEGs) between grouped and isolated conditions. Additionally, this research delved into the regulatory pathway of DSK signaling in male-specific SNB plasticity, with a particular focus on the DSK to CCKLR-17D1 signaling, which encodes early social experiences. The research provides valuable insights into the genetic basis and adaptability of social behavior in Drosophila. Moreover, it illuminates the neural mechanisms that underlie social memory and the ability of groups to adapt across different species.

Audience:

Researchers conducting basic research in genetics, neuroscience, behavioral biology, and evolutionary biology, particularly those focused on understanding social behavior and its underlying genetic and neural mechanisms, will find this study highly relevant. Additionally, researchers studying social cognition, social memory, and group dynamics in various species may also be interested in these findings.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Jeong et al. investigate the influence of genetic factors and early-life social experience on social network behaviors in adult Drosophila. Utilizing isogenic DGRP lines, the study correlates social distances with key developmental and physiological traits-developmental time, digging activity, and food intake. The findings suggest that adult flies with shorter social distance -indicating closer proximity to each other-face developmental disadvantages that are offset by the benefits of social grouping. The authors argue for an evolutionary advantage in such social behaviors, suggesting they help compensate for individual developmental deficits. The study further identifies the Dsk signaling pathway as a key mediator of social network behavior plasticity in male flies, particularly under challenging conditions like mechanical injury.

The study undertakes a broad range of behavioral and neurogenetic approaches, demonstrating an extensive scope of research efforts. Despite its ambitious scope, the manuscript lacks a clear rationale and cohesive flow among its sections. The numerous experiments do not merge into a unified narrative, leaving the reader questioning the reasoning and progression behind the experimental choices. The manuscript needs a clearer structure, well-defined hypotheses, and more detailed methodological descriptions. Greater emphasis on novelty and better integration with existing literature are also needed. The lack of control experiments and adequate statistical analysis weakens some conclusions.

Major comments

1. The authors show that flies in short SD lines reduce their activity over time, leading to the formation of social clusters (Fig. 1B). This clustering could potentially be attributed to reduced activity rather than active social preferences. It would be informative to test whether these SD flies exhibit similar social behaviors when placed in a larger arena, to test if the clustering persists under varied environmental conditions.

Short-SD flies did not reduce their moving speed over time when we placed a single fly in the original arena and assessed its locomotor behavior individually (Fig. S2). Thus, it is likely that the reduced activity in a group of short-SD flies is an effect of clustering over time but not necessarily the cause. We also confirmed that short and long SD lines retain their clustering property even in a larger arena (8.5 cm in diameter) (Fig. S3). We included these new data in our revised manuscript to better demonstrate active social preferences in the DGRP lines.

In lines 78-79, the authors claim that "short-SD flies gradually reduced SD over time and stayed in the cluster." However, the study established SD clustering by only analyzing behavior during the last quarter of a 10-minute window, assigning a single data point to each fly and taking the average for group SD. Yet, a single value cannot demonstrate whether initially formed clusters remained stable-unchanged-over time. To strengthen this point, the authors could investigate dynamic changes in SD over a longer period to demonstrate stability, or alternatively, adjust the language to better convey the findings. Additionally, including a time scale in Fig. 1B would enhance the clarity of these findings.

We traced dynamic changes in SD and walking speed of representative DGRP lines over the 10-minute window (Fig. 2A and 2B) and modified our text accordingly in the revised manuscript. We also included a time scale in Fig. 1B and relevant figures.

The statistical analysis presented in Figure 2C-D raises concerns. It appears that feeding and digging efficiency in both SD type lines benefit from socialization, suggesting that the effects attributed to SD might stem from the overall digging and feeding activity of each line. Therefore, it is crucial to integrate both social distance (short vs. long) and socialization (grouped vs. isolated) into the analysis using methods that allow for the assessment of confounding effects (interaction), such as two-way ANOVA or regression, depending on the data. This would help authors to clarify whether isolation reduces feeding overall (both line types) and determine if this reduction is more pronounced in short-SD lines. Additionally, it is counterintuitive that lines with more larvae per cluster show worse digging efficiency when previous studies, such as Dombrovski et al. (2017), have shown that larger groups of larvae typically exhibit better digging efficiency. This discrepancy highlights the need for a thorough re-evaluation of the data and assumptions regarding group dynamics and their impact on resource access.

As the reviewer suggested, we employed ordinary or aligned ranks transformation 2-way ANOVA (depending on the normality and equal variance of a given dataset) to determine if social distance and socialization cooperatively contribute to developmental phenotypes. Our new analyses confirmed significant interaction effects of social distance and socialization on most developmental phenotypes tested (i.e., larval digging activity, developmental time, %male progeny, and %eclosion success). These results convincingly support that short-SD larvae benefit more from socialization than long-SD larvae to compensate for the inferior phenotypes in isolated individuals. We speculate that the feeding amount of isolated long-SD individuals may be saturating for normal development (i.e., developmental time, %male progeny), possibly explaining the lack of interaction effects on food intake while displaying developmental inferiorities only in isolated short-SD individuals. We reason that grouped long-SD flies should not necessarily display poorer digging activity than grouped short-SD flies since isolated long-SD individuals displayed much higher digging activity than isolated short-SD individuals. Consistent with the previous finding, both SD lines showed better digging efficiency when grouped than isolated. We included these new analyses in the revised manuscript and modified our text accordingly. To clarify any statistical issues, we included a summary of all our statistical analyses performed in the revised manuscript (Dataset S5).

The choice to use the percentage of male progeny as a measure of developmental success is confusing, especially without an explanation for why it is favored over measures like overall progeny survival rates. As with digging and feeding, the statistical analysis should include an examination of potential interaction effects to fully assess how social conditions impact developmental outcomes.

The percentage of male progeny was one of the most evident developmental phenotypes on which social distance and socialization showed significant interaction effects. In the revised manuscript, we further included the percentage of eclosed flies as a measure for the overall progeny survival rate (Fig. 3F) and performed 2-way analyses to validate the significant interaction effects of social distance and socialization on various larval/developmental phenotypes. Please see our response to reviewer #3, major comment #3 above.

The rationale for using physical injury to induce SNB in the study is not clearly explained, raising concerns about the potential impact of injury on overall locomotion. Before employing such a method in sociality experiments, it is crucial to demonstrate that the injury does not affect locomotion. Additionally, the study's methodologies for transitioning between grouped and isolated cultures (present only in Fig. 2I and not in the methods section), as well as the specific methods used to measure social distance (SD) in isolated flies, are not sufficiently detailed. This lack of clarity complicates the evaluation of the study's conclusions.

To determine if the long-SD lines express their social behaviors selectively (e.g., upon physiological challenges), we introduced physical injury to the SNB analysis. There was a positive correlation between locomotion activity and SD trait among DGRP lines (i.e., DGRP lines with low walking speed and centroid velocity exhibited short-SD phenotypes in general) (Fig. 1D). This observation thus prompted us to hypothesize that modest injury may reduce locomotor activity in individuals, facilitate their interactions in a group, and shorten the overall SD. The mechanical injury actually shortened SD in both the short- and long-SD lines (Fig. 4D and S7A). Under the same experimental condition, mechanical injury reduced walking speed and centroid velocity only in the long-SD lines (Fig. S8), whereas social isolation blunted the injury effects (Fig. 4D, S7A, and S8). We reason that our injury condition does not severely impair general locomotion per se to abolish or overestimate SNB, but low activity in grouped long-SD flies is likely a consequence of their injury-induced clustering. We clarified our original text for the rationale and included the new data in the revised manuscript (Fig. S8). We also revised the method text for the transitions between grouped and isolated cultures, as well as for measuring SD in isolated flies.

Lines 104-106 "The clustering property of short-SD lines may have evolved as a compensation mechanism for their developmental inferiority in individuals". To support this claim, the authors should assess the significance of interactions terms as stated earlier.

Please see our responses to the reviewer's relevant comments above (reviewer #3, major comments #3 and #4).

In Fig. 4, the authors conclude that Drosulfakinin (DSK) signaling encodes early-life experiences for SNB plasticity. It is crucial for the authors to differentiate whether changes in feeding behavior are directly due to DSK or if they are secondary effects resulting from altered social interactions mediated by DSK.

Previous studies demonstrated that DSK is a satiety-signaling molecule whose expression is elevated upon feeding to suppress food intake (https://pubmed.ncbi.nlm.nih.gov/34398892/; https://pubmed.ncbi.nlm.nih.gov/32314736/; https://pubmed.ncbi.nlm.nih.gov/25187989/; https://pubmed.ncbi.nlm.nih.gov/22969751/). Under our experimental context, social isolation downregulated DSK expression and DSK neuron activity, whereas isolated larvae rather reduced their food intake. It is thus unlikely that changes in the feeding behavior of isolated larvae directly implicate DSK-dependent satiety signaling. We discussed this issue in our revised manuscript.

In Fig 4, the data show that DSK peptide is significantly increased in cell bodies in grouped long DS lines when compared with grouped short DS lines (Fig. 4B). However, no changes are reported at the level of DSK projection levels when comparing these groups. Can the authors clarify this?

The SD-trait effects on DSK levels were evident in cell bodies but not in DSK neuron projections. We reason that axonal transport or processing of the neuropeptide was limiting under the group-culture condition. These observations might also be relevant to our conclusion that Dsk is not crucial for shaping the SD traits per se. We revised our text accordingly.

Additionally, the data show that DSK activity is reduced by isolation in both types of SD. To clarify if this effect is driven by isolation only, and not type of line (short vs long SD line), the interaction term should be tested. Furthermore, it is not clear what lines are used in live imaging (e.g., Fig 4C-F).

We detected no significant interaction effects of SD type and social isolation on DSK expression (Fig. 6B). Live-brain imaging of the GCaMP-expressing DSK neurons was performed using a transgenic line (i.e., Dsk-Gal4>UAS-GCaMP) in a wild-type background. Since genetic factors shaping the SD traits were not defined in each DGRP line, we could not combine the transgenes with DGRP backgrounds while retaining their respective SD phenotypes (please also see our response to reviewer #2 major comment #4 above). Nonetheless, the GCaMP imaging demonstrates that 1) either injury or social isolation alone significantly affects DSK neuron activity, but 2) the two conditions act independently on the GCaMP signals (i.e., no significant interaction effects). We clarified it in our revised manuscript and displayed each genotype used in our imaging experiments.

In the 'Male-specific DSK-CCKLR-17D1 signalling mediates SNB plasticity' section (line 217), the analysis should include an interaction term to account for the possible confounding effects of isolation and injury on SD. This would aid in determining whether the impacts of social isolation and injury on DSK signalling and SNB plasticity are independent of each other or if they interact in significant ways, as stated by the authors.

Throughout our revised manuscript, we performed 2-way analyses to validate the interaction effects of isolation and injury on SD and support our conclusion. We also included a summary of all our statistical analyses performed in the revised manuscript (Dataset S5).

Minor comments

1. The introduction section would also benefit from major rewriting to clearly indicate the research gap and hypothesis tested. The introduction section would also benefit from major rewriting to clearly indicate the research gap and hypothesis tested. The manuscript would benefit from a more thorough integration of previous studies related to Drosophila social behavior (e.g., Blumstein, D. T. et al., 2010; Schneider, J., Dickinson, M. H., & Levine, J. D., 2012; Simon, A. F. et al., 2012; Ramdya, P. et al., 2015). While the current references are adequate, a more detailed discussion of how this study builds upon and diverges from existing literature would be beneficial.

The introduction of our original manuscript starts from previous findings on Drosophila social behaviors and clearly indicates what remains elusive, thereby defining our biological questions. We further explain why we focus on SD among other social network measures published previously and outline our approaches for new findings in this study (i.e., the principles of social network behavior and its plasticity). Since our original text was written in a concise manner, we revised the introduction to give a more detailed description of what earlier studies have discovered according to the reviewer suggestion.

A better description of methods, especially behavioral approaches, could vastly help in understanding the results. Clarifying the methodologiy, particularly the behavioral approaches, would greatly enhance the understanding of the results. Also, the method for quantifying the total number of larvae per vial is unclear, particularly whether variations in larval density were considered. This is crucial, as different densities could affect the available sensory cues necessary for larval aggregation, such as vision (e.g. Dombrovski et al. Curr Biol. 2019). Better descriptions of the results and inclusion of exact statistical analyses used in support of the claims are also needed.

We revised our method text to better describe our experimental conditions. We further described how we controlled larval density to prepare group-cultured larvae and adults for analyzing larval behaviors and developmental phenotypes. Finally, we included a summary of all our statistical analyses performed in the revised manuscript (Dataset S5).

Some terms and descriptions in the manuscript are somewhat ambiguous, such as "social memory" and "adaptive social plasticity" and should be better defined.

We better defined the two key short terms in the introduction of our revised manuscript.

Line 86-89: "Social interactions compensate for developmental inferiority in short-SD larvae Why do flies display SNB? One clue comes from the previous observation that Drosophila larvae collectively dig culture media and improve food accessibility, possibly facilitating their constitutive feeding during early development..." - This paragraph could be moved to the introduction section.

As we reorganized the introduction in our revised manuscript, we feel it should be fine to leave the paragraph above in the original context.

In lines 77-78, the manuscript mentions that the locomotion trajectories of individual flies confirm certain characteristics but fails to provide an analysis of individual locomotion metrics, e.g., tortuosity, distance walked, etc. The authors should add quantitative analysis to support claims about trajectories or alternatively rephrase the sentence to remove any claims about the trajectories of flies.

As the reviewer suggested, we added quantitative analyses of cumulative walking distances over time and total walking distances in individual flies to our revised manuscript (Fig. 2D and S1C).

After screening 175 strains, three short and long SD lines were selected. It would be good if justification for the authors' choice were included, as the selected lines were not the ones with the longest or shortest SD as seen in Fig. 1C.

We ranked individual DGRP lines for each of the two group properties (i.e., SD and centroid velocity) and then selected the top and bottom three lines based on their average ranking. We included this rationale in our revised manuscript.

Other comments:

• Line 72-73: What correlation was performed? This should be included in the results/methods section.

As described in the figure legend of our original manuscript, the significance of the correlation was determined by Spearman correlation analysis. We further included the method description in the results and methods section of our revised manuscript.

• Line 113: Change "pre-trained colleagues" to "pre-trained flies".

Changed.

• Lines 321 and 325: Use "3D" instead of "2D" as three dimensions are given?

Corrected.

• Ensure all figures are correctly scaled and aligned.

We revised our figures to avoid any of these issues.

• Video: Including short videos for each behavioral test (e.g., feeding) would help in understanding it.

We included representative video files for SNB and aggression assays in the revised manuscript (Video S1-S12).

• Fig. 4 should include control neurons that do not change with social grouping; authors should also show ROI.

We included new data for control neurons (Fig. S13, Pdf-Gal4>UAS-GCaMP7f) and also showed ROI for quantification in our revised manuscript (Fig. 6C and S13).

• Line 27, 86: Change "inferiority" to "disadvantage".

We feel inferiority fits better in the context of our overall study.

Reviewer #3 (Significance (Required)):

This study extends existing knowledge by linking specific genetic pathways to behavioral outcomes in a well-established model system, providing new insights into the genetic and neural basis of social behavior. The use of DGRP lines to dissect the impact of genetic variation on behavior is particularly valuable. The identification of the Dsk signaling pathway as a mediator of these behaviors under stress is interesting. However, the study would benefit from more in-depth statistical analysis and expanded experimental designs to solidify the conclusions. It should also more clearly highlight the novelty of its findings and better integrate them with the current literature on Dsk signaling and social behaviors.

My expertise is in behavioral neuroscience. The insights from this study promise to deepen our understanding of the genetic and neural mechanisms behind social behaviors. The potential implications of this research are likely to extend well beyond Drosophila, influencing studies across various species.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Summary:

The manuscript investigates the role of Drosulfakinin (Dsk) signaling in Drosophila social network behavior (SNB) and its plasticity based on early-life experiences. The study employs a systematic analysis using 175 inbred strains to link short social distance (SD) with developmental time, food intake, and activity levels. Key findings suggest that social interactions during development compensate for individual developmental inferiority and that early-life social experience is necessary for adaptive social behaviors in adults. The genetic basis of SNB is further explored through transcriptome analyses, implicating Dsk and one of its receptors in mediating these behaviors.

Major comments:

Are the key conclusions convincing?

The key conclusions are well-supported by the data presented. The association between early-life social interactions and adult social behaviors is convincingly demonstrated through multiple experimental setups.

We appreciate the reviewer's positive feedback on our rigorous approaches and key conclusions.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

Some claims, particularly those regarding the evolutionary implications of Dsk signaling and its conservation across species, might benefit from being presented as hypotheses or speculations rather than definitive conclusions. This would align with the current evidence while acknowledging the need for further investigation.

As the reviewer suggested, we toned down our claims on the evolutionary implications of Dsk signaling.

Would additional experiments be essential to support the claims of the paper?

Measure aggression and male-male courtship behavior in the 6 DGRP lines to examine whether SD correlates with these behaviors.

Aggression but not male-male courtship behaviors correlated with SD phenotypes in the 6 DGRP lines. We included the new data in our revised manuscript (Fig. S4 and S11). Please also see our response to a relevant reviewer comment above (reviewer #1, major comment #7).

Include behavior results of flies tested in Figures 4C and D.

We included the behavior data in our revised manuscript (Fig. S12A).

Repeat the CCKLR-17D1 experiments shown in Figures 5 F and G for CCKLR-17D3 to provide extra evidence that CCKLR-17D1 mediates DSK's effects on SNB.

We employed a transgenic Gal4 knock-in for the CCKLR-17D1 locus to specifically manipulate the activity of CCKLR-17D1-expressing neurons. However, a Gal4 knock-in line for the CCKLR-17D3 locus was not available for pairwise comparison. We instead provide extra evidence for CCKLR-17D1 function in SNB plasticity by showing that 1) independent genomic deletions of CCKLR-17D1 but not CCKLR-17D3 suppressed injury-induced clustering in group-cultured males (Fig. S15) and 2) blocking of synaptic transmission in CCKLR-17D1 neurons phenocopied CCKLR-17D1 deletion (Fig. S16). Please also see our response to a relevant reviewer comment above (reviewer #2, minor comment #5)

Are the suggested experiments realistic in terms of time and resources?

These experiments are realistic and feasible within typical research timelines. These might require a few months and moderate funding.

According to the reviewer suggestions, we included new pieces of data in our revised manuscript to address the reviewer concerns and further support our conclusions.

Are the data and the methods presented in such a way that they can be reproduced?

The methods section is detailed, providing sufficient information for replication.

We appreciate the reviewer's positive feedback on our method description.

Are the experiments adequately replicated and statistical analysis adequate?

The experiments appear to be adequately replicated, and the statistical analyses are generally appropriate. However, ensuring consistent selection of statistical methods can further support the evidence presented (Figures 2C and D).

We performed more appropriate statistical analyses in the revised manuscript (e.g., 2-way ANOVA of the data presented in our original Fig. 2C and 2D) and included a summary of all the statistical analyses in the revised manuscript (Dataset S5). Please also see our responses to the reviewer comments above (reviewer #3, major comments #3 and #10; reviewer #3, minor comment #2).

Minor comments:

Specific experimental issues that are easily addressable:

Ensure clarity in the presentation of figures and legends. Some figures could benefit from more detailed legends explaining all aspects of the data shown.

We revised our figures and figure legends to address this issue and improve clarity.

Are prior studies referenced appropriately?

The manuscript references prior studies appropriately, providing a solid context for the current research.

We appreciate the reviewer comment.

Are the text and figures clear and accurate?

The text is clear, but some figures, particularly those with complex data, could be more informative with additional annotations.

We revised our figures and figure legends to address this issue.

The larvae pictures in Figure 2A should be replaced with ones with higher resolution with drawn larval contours.

We replaced the larval pictures with higher resolution and indicated individual larvae with arrows in the revised manuscript (Fig. 3A).

Scale bars are missing in most of the images shown.

We added scale bars to our revised figures.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Consider providing a graphical abstract summarizing the key findings. This would aid readers in quickly grasping the main conclusions. Additionally, breaking down complex figures into simpler, more focused panels might improve readability.

As the reviewer suggested, we split complex figures into simpler ones to improve the readability of our revised manuscript and data. We also provided a graphical abstract summarizing our findings (Fig. 8).

Reviewer #4 (Significance (Required)):

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This study provides significant conceptual advances in understanding the genetic and neurobiological basis of social behavior in Drosophila. By linking early-life social experiences to adult social behaviors, it highlights the importance of developmental context in shaping adult phenotypes.

Place the work in the context of the existing literature (provide references, where appropriate).

The work builds on previous studies on Drosophila social behavior and neurogenetics. It extends the current understanding by integrating developmental and adult behaviors with genetic and molecular analyses. References to foundational works in Drosophila social behavior and recent studies on neuropeptide signaling are well-placed.

State what audience might be interested in and influenced by the reported findings.

Researchers in the fields of neurogenetics, behavioral ecology, developmental biology, and evolutionary biology will find this work particularly relevant. It also has implications for those studying social behavior across species, including mammals.

Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Expertise: Neurogenetics, Behavioral Neuroscience, Drosophila Genetics, Social Behavior, Bioinformatics. I have sufficient expertise to evaluate the genetic, behavioral, and transcriptomics aspects of the study. Specific details on the imaging studies might require additional expert evaluation.

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

Learn more at Review Commons

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Referee #4

Evidence, reproducibility and clarity

Summary:

The manuscript investigates the role of Drosulfakinin (Dsk) signaling in Drosophila social network behavior (SNB) and its plasticity based on early-life experiences. The study employs a systematic analysis using 175 inbred strains to link short social distance (SD) with developmental time, food intake, and activity levels. Key findings suggest that social interactions during development compensate for individual developmental inferiority and that early-life social experience is necessary for adaptive social behaviors in adults. The genetic basis of SNB is further explored through transcriptome analyses, implicating Dsk and one of its receptorS in mediating these behaviors.

Major comments:

Are the key conclusions convincing?

The key conclusions are well-supported by the data presented. The association between early-life social interactions and adult social behaviors is convincingly demonstrated through multiple experimental setups. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Some claims, particularly those regarding the evolutionary implications of Dsk signaling and its conservation across species, might benefit from being presented as hypotheses or speculations rather than definitive conclusions. This would align with the current evidence while acknowledging the need for further investigation.

Would additional experiments be essential to support the claims of the paper?

Measure aggression and male-male courtship behavior in the 6 DGRP lines to examine whether SD correlates with these behaviors. Include behavior results of flies tested in Figures 4C and D. Repeat the CCKLR-17D1 experiments shown in Figures 5 F and G for CCKLR-17D3 to provide extra evidence that CCKLR-17D1 mediates DSK's effects on SNB.

Are the suggested experiments realistic in terms of time and resources?

These experiments are realistic and feasible within typical research timelines. These might require a few months and moderate funding.

Are the data and the methods presented in such a way that they can be reproduced?

The methods section is detailed, providing sufficient information for replication.

Are the experiments adequately replicated and statistical analysis adequate?

The experiments appear to be adequately replicated, and the statistical analyses are generally appropriate. However, ensuring consistent selection of statistical methods can further support the evidence presented (Figures 2C and D).

Minor comments:

Specific experimental issues that are easily addressable:

Ensure clarity in the presentation of figures and legends. Some figures could benefit from more detailed legends explaining all aspects of the data shown.

Are prior studies referenced appropriately?

The manuscript references prior studies appropriately, providing a solid context for the current research.

Are the text and figures clear and accurate?

The text is clear, but some figures, particularly those with complex data, could be more informative with additional annotations. The larvae pictures in Figure 2A should be replaced with ones with higher resolution with drawn larval contours. Scale bars are missing in most of the images shown.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Consider providing a graphical abstract summarizing the key findings. This would aid readers in quickly grasping the main conclusions. Additionally, breaking down complex figures into simpler, more focused panels might improve readability.

Significance

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This study provides significant conceptual advances in understanding the genetic and neurobiological basis of social behavior in Drosophila. By linking early-life social experiences to adult social behaviors, it highlights the importance of developmental context in shaping adult phenotypes.

Place the work in the context of the existing literature (provide references, where appropriate).

The work builds on previous studies on Drosophila social behavior and neurogenetics. It extends the current understanding by integrating developmental and adult behaviors with genetic and molecular analyses. References to foundational works in Drosophila social behavior and recent studies on neuropeptide signaling are well-placed.

State what audience might be interested in and influenced by the reported findings.

Researchers in the fields of neurogenetics, behavioral ecology, developmental biology, and evolutionary biology will find this work particularly relevant. It also has implications for those studying social behavior across species, including mammals.

Define your field of expertise with a few keywords to help the authors contextualize your point of view.

Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Expertise: Neurogenetics, Behavioral Neuroscience, Drosophila Genetics, Social Behavior, Bioinformatics. I have sufficient expertise to evaluate the genetic, behavioral, and transcriptomics aspects of the study. Specific details on the imaging studies might require additional expert evaluation.

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

Learn more at Review Commons

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Referee #3

Evidence, reproducibility and clarity

Jeong et al. investigate the influence of genetic factors and early-life social experience on social network behaviors in adult Drosophila. Utilizing isogenic DGRP lines, the study correlates social distances with key developmental and physiological traits-developmental time, digging activity, and food intake. The findings suggest that adult flies with shorter social distance -indicating closer proximity to each other-face developmental disadvantages that are offset by the benefits of social grouping. The authors argue for an evolutionary advantage in such social behaviors, suggesting they help compensate for individual developmental deficits. The study further identifies the Dsk signaling pathway as a key mediator of social network behavior plasticity in male flies, particularly under challenging conditions like mechanical injury.

The study undertakes a broad range of behavioral and neurogenetic approaches, demonstrating an extensive scope of research efforts. Despite its ambitious scope, the manuscript lacks a clear rationale and cohesive flow among its sections. The numerous experiments do not merge into a unified narrative, leaving the reader questioning the reasoning and progression behind the experimental choices. The manuscript needs a clearer structure, well-defined hypotheses, and more detailed methodological descriptions. Greater emphasis on novelty and better integration with existing literature are also needed. The lack of control experiments and adequate statistical analysis weakens some conclusions.

Major comments

1. The authors show that flies in short SD lines reduce their activity over time, leading to the formation of social clusters (Fig. 1B). This clustering could potentially be attributed to reduced activity rather than active social preferences. It would be informative to test whether these SD flies exhibit similar social behaviors when placed in a larger arena, to test if the clustering persists under varied environmental conditions.
2. In lines 78-79, the authors claim that "short-SD flies gradually reduced SD over time and stayed in the cluster." However, the study established SD clustering by only analyzing behavior during the last quarter of a 10-minute window, assigning a single data point to each fly and taking the average for group SD. Yet, a single value cannot demonstrate whether initially formed clusters remained stable-unchanged-over time. To strengthen this point, the authors could investigate dynamic changes in SD over a longer period to demonstrate stability, or alternatively, adjust the language to better convey the findings. Additionally, including a time scale in Fig. 1B would enhance the clarity of these findings.
3. The statistical analysis presented in Figure 2C-D raises concerns. It appears that feeding and digging efficiency in both SD type lines benefit from socialization, suggesting that the effects attributed to SD might stem from the overall digging and feeding activity of each line. Therefore, it is crucial to integrate both social distance (short vs. long) and socialization (grouped vs. isolated) into the analysis using methods that allow for the assessment of confounding effects (interaction), such as two-way ANOVA or regression, depending on the data. This would help authors to clarify whether isolation reduces feeding overall (both line types) and determine if this reduction is more pronounced in short-SD lines. Additionally, it is counterintuitive that lines with more larvae per cluster show worse digging efficiency when previous studies, such as Dombrovski et al. (2017), have shown that larger groups of larvae typically exhibit better digging efficiency. This discrepancy highlights the need for a thorough re-evaluation of the data and assumptions regarding group dynamics and their impact on resource access.
4. The choice to use the percentage of male progeny as a measure of developmental success is confusing, especially without an explanation for why it is favored over measures like overall progeny survival rates. As with digging and feeding, the statistical analysis should include an examination of potential interaction effects to fully assess how social conditions impact developmental outcomes.
5. The rationale for using physical injury to induce SNB in the study is not clearly explained, raising concerns about the potential impact of injury on overall locomotion. Before employing such a method in sociality experiments, it is crucial to demonstrate that the injury does not affect locomotion. Additionally, the study's methodologies for transitioning between grouped and isolated cultures (present only in Fig. 2I and not in the methods section), as well as the specific methods used to measure social distance (SD) in isolated flies, are not sufficiently detailed. This lack of clarity complicates the evaluation of the study's conclusions.
6. Lines 104-106 "The clustering property of short-SD lines may have evolved as a compensation mechanism for their developmental inferiority in individuals". To support this claim, the authors should assess the significance of interactions terms as stated earlier.
7. In Fig. 4, the authors conclude that Drosulfakinin (DSK) signaling encodes early-life experiences for SNB plasticity. It is crucial for the authors to differentiate whether changes in feeding behavior are directly due to DSK or if they are secondary effects resulting from altered social interactions mediated by DSK.
8. In Fig 4, the data show that DSK peptide is significantly increased in cell bodies in grouped long DS lines when compared with grouped short DS lines (Fig. 4B). However, no changes are reported at the level of DSK projection levels when comparing these groups. Can the authors clarify this?
9. Additionally, the data show that DSK activity is reduced by isolation in both types of SD. To clarify if this effect is driven by isolation only, and not type of line (short vs long SD line), the interaction term should be tested. Furthermore, it is not clear what lines are used in live imaging (e.g., Fig 4C-F).
10. In the 'Male-specific DSK-CCKLR-17D1 signalling mediates SNB plasticity' section (line 217), the analysis should include an interaction term to account for the possible confounding effects of isolation and injury on SD. This would aid in determining whether the impacts of social isolation and injury on DSK signalling and SNB plasticity are independent of each other or if they interact in significant ways, as stated by the authors.

Minor comments

1. The introduction section would also benefit from major rewriting to clearly indicate the research gap and hypothesis tested. The introduction section would also benefit from major rewriting to clearly indicate the research gap and hypothesis tested. The manuscript would benefit from a more thorough integration of previous studies related to Drosophila social behavior (e.g., Blumstein, D. T. et al., 2010; Schneider, J., Dickinson, M. H., & Levine, J. D., 2012; Simon, A. F. et al., 2012; Ramdya, P. et al., 2015). While the current references are adequate, a more detailed discussion of how this study builds upon and diverges from existing literature would be beneficial.
2. A better description of methods, especially behavioral approaches, could vastly help in understanding the results. Clarifying the methodologiy, particularly the behavioral approaches, would greatly enhance the understanding of the results. Also, the method for quantifying the total number of larvae per vial is unclear, particularly whether variations in larval density were considered. This is crucial, as different densities could affect the available sensory cues necessary for larval aggregation, such as vision (e.g. Dombrovski et al. Curr Biol. 2019). Better descriptions of the results and inclusion of exact statistical analyses used in support of the claims are also needed.
3. Some terms and descriptions in the manuscript are somewhat ambiguous, such as "social memory" and "adaptive social plasticity" and should be better defined.
4. Line 86-89: "Social interactions compensate for developmental inferiority in short-SD larvae Why do flies display SNB? One clue comes from the previous observation that Drosophila larvae collectively dig culture media and improve food accessibility, possibly facilitating their constitutive feeding during early development..." - This paragraph could be moved to the introduction section.
5. In lines 77-78, the manuscript mentions that the locomotion trajectories of individual flies confirm certain characteristics but fails to provide an analysis of individual locomotion metrics, e.g., tortuosity, distance walked, etc. The authors should add quantitative analysis to support claims about trajectories or alternatively rephrase the sentence to remove any claims about the trajectories of flies.
6. After screening 175 strains, three short and long SD lines were selected. It would be good if justification for the authors' choice were included, as the selected lines were not the ones with the longest or shortest SD as seen in Fig. 1C.

Other comments:

• Line 72-73: What correlation was performed? This should be included in the results/methods section.
• Line 113: Change "pre-trained colleagues" to "pre-trained flies".
• Lines 321 and 325: Use "3D" instead of "2D" as three dimensions are given?
• Ensure all figures are correctly scaled and aligned.
• Video: Including short videos for each behavioral test (e.g., feeding) would help in understanding it.
• Fig. 4 should include control neurons that do not change with social grouping; authors should also show ROI.
• Line 27, 86: Change "inferiority" to "disadvantage".

Significance

This study extends existing knowledge by linking specific genetic pathways to behavioral outcomes in a well-established model system, providing new insights into the genetic and neural basis of social behavior. The use of DGRP lines to dissect the impact of genetic variation on behavior is particularly valuable. The identification of the Dsk signaling pathway as a mediator of these behaviors under stress is interesting. However, the study would benefit from more in-depth statistical analysis and expanded experimental designs to solidify the conclusions. It should also more clearly highlight the novelty of its findings and better integrate them with the current literature on Dsk signaling and social behaviors.

My expertise is in behavioral neuroscience. The insights from this study promise to deepen our understanding of the genetic and neural mechanisms behind social behaviors. The potential implications of this research are likely to extend well beyond Drosophila, influencing studies across various species.

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Referee #2

Evidence, reproducibility and clarity

The article explores the social network behavior (SNB) of Drosophila, focusing on individual social distance (SD) within groups over time. A systemic analysis revealed that short SD is associated with long developmental time, low food intake, and hypoactivity. Group culturing compensates for developmental inferiority in short social distance individuals. Social interactions during early development positively impact adult physiology and adaptive social plasticity. Transcriptome analyses show genetic diversity for SD traits. The neuropeptide Drosulfakinin (DSK) signaling mediates social network behavior plasticity via receptor CCKLR-17D1, particularly in males, suggesting a dedicated neural mechanism encoding early-life experiences to adaptively transform group properties. The research suggests that animals have developed neural mechanisms to encode early-life experiences. It offers insights into the genetic foundation and adaptability of social behavior in Drosophila, shedding light on the neural processes involved in social memory and the adaptive behaviors of groups. These findings have broader implications for understanding similar neural mechanisms governing social memory and group behaviors in other species.

Major concerns:

Major 1. In Figure 2H, the latency to 75% arrival of short-SD isolated fruit flies (no matter with or without pioneers) is close to that of group fruit flies with pioneers while the latency to 75% arrival of long-SD isolated fruit flies (no matter with or without pioneers) is close to that of group fruit flies without pioneers; How to explain the difference in latency to 75% array between long-SD and short-SD isolated fruit flies? It seems that only in the long-SD fruit flies from the grouped experience, the absence of pioneers will increase the time it takes to reach the target food in the maze.

Major 2. In Figure 3C, there are two up-regulated genes in the Drosophila group that overlap in short-SD and long-SD strains. Apart from Dsk, what is the other gene? In addition, for isolated fruit flies, both short-SD and long-SD lines have more gene expression upregulated. How to explain this phenomenon? Can you briefly explore the reasons for their upregulation and instead of involvement in SNB plasticity, what kind of physiological functions may they have?

Major 3. In lines 219-223, the genomic deletion by mutant or depletion by RNA interference emphasizes the role of neuropeptides DSK and its receptor CCKLR-17D1 in injury-induced clustering behaviors. How about the effect of neuropeptides overexpression? Do they confer injury-induced social interactions to isolated male flies. Meanwhile, in line 238, the transgenic excitation of CCKLR-17D1 neurons emphasizes the function of neuronal synaptic transmission in the pathway. Indeed, both neuropeptide expression and neuronal synaptic connections may be involved in the regulation of injury-induced clustering behaviors. It is recommended to separate the discussion of protein expression and the respective regulatory modes at the neuronal circuit level.

Major 4. Since a significant portion of the work in the first half of this paper is focused on elucidating two types of social distance in SNB, is there any difference in the regulation of social network plasticity by Dsk signaling pathway in the short-SD and long-SD lines?

Minor ones:

Minor 1. There is a color difference between the data spots and the figure legends in Figure 2H.

Minor 2. The anatomical sample images in Figure 4 and Figure 5 require scale bars.

Minor 3. The "grouped" and "grp" in Figures 3B-3F can be unified as "grp", while the "isolated" and "iso" can be unified as "iso". So that the male and female symbols in Figure 3F will not have any deviation in the mark.

Minor 4. The difference in Denmark signals of each group of neurons under the condition of injury should also be compared in Figure 4C.

Minor 5. What is the effect of inactivating CCKLR-17D1 or CCKLR-17D3 by shibire on injury-induced clustering in group-cultured adults in Figure 5E? (This relates to major comment 3)

Significance

General assessment:

The strengths of this work is that the authors have identified specific lines with short social distance or long social distance by conducting extensive screening experiments. By transcriptome analyses and gene ontology (GO) analyses they revealed genes up or down regulation in the social experience. They have also narrowed down to the DSK signaling involved in the social experience encoding process. However, the study's limitation lies in the lack of clarity regarding the DSK signaling pathway. The mechanisms through which social experiences affect neuronal activity and synaptic connections remain unclear. Further research on upstream and downstream pathways could enhance understanding. Although the article proposes injury-induced clustering behaviors, the key sensory pathways involved in social network behavior plasticity during early social experiences are not well-defined. Conducting sensory deprivation experiments could elucidate sensory involvement. Overall, the study's strengths lie in its comprehensive approach, large sample size, and translational potential. To enhance future research, investigating the complexity of neural mechanisms and expanding the exploration of regulating pathways could be beneficial. Additionally, exploring the ecological relevance of the findings could deepen our understanding of social behavior in natural environments.

Advance:

Compared to previous studies such as Heiko Dankert et al.'s publication in 2009 in Nature Methods and Assa Bentzur et al.'s publication in 2020 in Current Biology, which also investigated the impact of early life experiences on male social behavior and examined various aspects of social network construction, this study employs a systematic analysis of social network behavior (SNB) in Drosophila, integrating genetic, physiological, and behavioral assessments. The authors conducted detailed and systematic analyses through transcriptome and gene ontology (GO) analyses, including the visualization of gene expression heatmaps, volcano plots, and overlapping analysis of differentially expressed genes (DEGs) between grouped and isolated conditions. Additionally, this research delved into the regulatory pathway of DSK signaling in male-specific SNB plasticity, with a particular focus on the DSK to CCKLR-17D1 signaling, which encodes early social experiences. The research provides valuable insights into the genetic basis and adaptability of social behavior in Drosophila. Moreover, it illuminates the neural mechanisms that underlie social memory and the ability of groups to adapt across different species.

Audience:

Researchers conducting basic research in genetics, neuroscience, behavioral biology, and evolutionary biology, particularly those focused on understanding social behavior and its underlying genetic and neural mechanisms, will find this study highly relevant. Additionally, researchers studying social cognition, social memory, and group dynamics in various species may also be interested in these findings.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Jeong et al. describes effects of neuronal signalling on collective behavior by measuring social distance (SD) which is used as a measure for social network behavior. Authors screened for a panel of inbred DGRP lines and compared the SD due to prior experience of group or single culturing when flies are recorded in a 55 mm diameter petri-dish. The screen uncovered 3 short Sd and three long-SD lines, and subsequent experiments showed differences in various behaviors such as recovery from injury, search for food and SD. Using RNA-seq from heads of flies they implicate Dsk signalling and show neuronal architecture and activity differences between grouped and isolated male flies. They implicate Dsk signalling in recovery from injury affecting SD but it was dispensable for grouped vs. isolated flies. I have suggestions to support the claims made, analysis and interpretation of the data and improve the clarity of writing. See my specific comments below.

Major comments:

1. For recording social behaviour in flies, arenas with sloped walls have been extensively used called 'fly bowl' (Simon et al. 2010 doi:10.1371/journal.pone.0008793; Robie et al., 2017, doi: 10.1016/j.cell.2017.06.032), or 'flyworld' (Liu et al., 2018 doi:10.1371/journal.pcbi.1006410). Such geometry ensures that flies don't walk on the side of the arena, and don't occlude each other. However, in the screen carried out in this manuscript, a petri-dish of 5.5 cm diameter filled with agar was used to record social network formation. Given the propensity of flies to walk on the walls of such circular arena, it will be difficult to know if the long and short SD behavior resulting from propensity to form clusters is an artefact of the assay condition used. It would be important to test the SNB and SD of at least the 6 selected short and long SD lines in arena with sloped walls to rule out this possibility.
2. Methods section would require additional details about the SNB assay for instance, the height of the agar bed and the effective height in which interactions was recorded is not mentioned.
3. Figure 2C and 2D results in larvae seems to contradict previous studies that have shown that isolated flies eat more as adults (Li et al. Nature, 2021) and Dsk-RNAi increases feeding in larvae and adults (Soderberg et al., 2012). It might be due to unique characteristic of DGRP lines used and would be helpful to discuss this.
4. Rutabaga mutants for Fig S3 are directly compared with CS flies in the maze assay and it appears from methods that these lines were not isogenized, this can significantly impact the results. Similarly for some of the subsequent Dsk experiments it appears that lines were not isogenized (see below). These experiments would either need to be repeated of this caveat needs to be explicitly mentioned to avoid misinterpretation of the data.
5. For Figure 3 describing RNA-seq data additional analysis would be helpful. Gene expression from isolated and grouped flies have been studied earlier by microarray and RNA-seq methods (Wang et al., PNAS 2008; Agrawal et al., JEB 2020; Li et al. Nature, 2021). Data from these studies should be compared with to see if there are common patterns of gene expression between long and short SD flies vs. group and isolated flies.
6. GEO accession number and the analyzed list of DEGs should be provided as supplementary information.
7. Figure 3E & F are not referred to in the main text, also there is no description of how the data was generated. Is this based on published data from Mackay lab about DGRP lines, if so, aggression experiments were not convincing in those studies and have been shown to not recapitulate 'real' aggression by other labs for several of the DGRP lines tested (Chowdhury et al., 2021, doi: 10.1038/s42003-020-01617-6).
8. Dsk was shown to be reduced in isolated flies by RNA-seq and play a role in aggression by an earlier study (Agrawal et al., 2020) and should be cited appropriately (line 180-181) and elsewhere.
9. For Fig. 4A-B, source images for other two DGRP lines should be included at least in supplementary information, if not as main figure.
10. For Fig. 5, what is the reason that uninjured flies don't show any SD phenotype? Are there any changes in their velocity? This is mentioned in passing on line 228-29 but should be properly discussed.
11. Trans-Tango and UAS-Denmark, SytGFP experiments were performed previously by Wu et al., 2020 and Wang et al., 2021 for Dsk, these two studies observed that P1 neurons are presynaptic and Dsk neurons are post synaptic but in Figure 4 it's not clear what are the presynaptic and post synaptic neurons. Also these studies are not cited appropriately in this section.

Minor comments:

1. Line no. 286: please mention about the relative humidity and light & dark cycle conditions and when experiments were conducted (ZT).
2. Line no. 311: How many days old flies were used (isolated and group housed) for the behavior and transcriptomic studies?
3. Line no. 349: for RNA extraction please mention how many fly heads were used and ZT for collection.
4. Line no. 358: Italicize "Drosophila melanogaster".

Significance

This manuscript will be of interest to neuroscientists studying Drosophila social behaviors. The manuscript asks interesting questions and authors have done extensive set of experiments but the progress appears incremental given the current state of the field, especially for the later part of the manuscript. Some of the interpretation would also require additional data to bolster the claims made. Finally, the findings from this study could be better discussed in the context of what it is already known.

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Reply to the reviewers

We thank all reviewers for their constructive criticism and suggestions. We have addressed all the points as detailed below. We also added an experiment that strengthens the connection between replication stress and GSF2 and suggests a role of GSF2 in recovery from the DNA replication checkpoint arrest (Fig. 4g).

Reviewer #1 (Evidence, reproducibility and clarity)

Summary

The manuscript by the Khmelinskii group reports that they have successfully constructed two conditional degron libraries of budding yeast for almost all proteins. For this purpose, the authors employed an improved auxin-inducible degron (AID2). Initially, they constructed yeast libraries by fusing HaloTag to the N- or C-terminus of proteins and found that C-terminal tagging is less likely to affect the location and function of proteins (Fig. 1). Based on this finding, the authors fused mNG-AID*-3Myc or AID*-3Myc (AID-v1 or AID-v2 library, respectively) to more than 5600 proteins and found that 4079 proteins were significantly depleted when cells were treated with 5-Ph-IAA (Fig. 2). A fitness defect was observed for over 60% of essential proteins, indicating the target depletion showed the expected phenotype in many cases (Fig. 3). Finally, the authors screened proteins required for maintaining viability in the presence of MMS, CPT and HU, and identified common proteins involved in DNA repair (such as RAD52 epistasis proteins) and other proteins specific for MMS, CPT or HU resistance (Fig. 4). Furthermore, the authors revealed that an ER membrane protein, Gsf2, is required for HU resistance, which was not found in previous studies with the YKO library because gsf2∆ cells in the YKP library had acquired a suppressor mutation (Fig. 4e).

Major comments

1 - In Figure S2a, the authors initially checked the growth of yeast cells expressing OsTIR1(F74G) under the GAL1 promoter, saying that "expression of OsTir1(F74G) from the strong galactose-inducible GAL1 promoter had a negligible impact on yeast fitness (page 3)". To me, the OsTIR1(G74G) expressing cells showed slightly slower growth compared to the control cells. Moreover, the cells expressing it under the very strong GPD promoter showed apparent slow growth, suggesting that OsTIR1(F74G) overexpression caused a side effect. The authors should carefully evaluate the cells with GAL1-OsTIR1(F74G).

Indeed high levels of OsTir1(F74G) impaired growth, at least in the strain background used in our experiments. Expression from the strongest promoter we tested (GPD) resulted in an obvious fitness defect, whereas conditional expression from the strong GAL1 promoter had a small impact on fitness and expression from the weaker CYC1 and ADH1 promoters did not affect fitness (Fig. S2a). Despite the small fitness impairment, we decided to use the GAL1-OsTIR1(F74G) construct for the AID libraries for two reasons: the conditional nature of this promoter is likely to limit adaptation to expression of OsTir1(F74G) and the high expression levels of OsTir1(F74G) are less likely to limit degradation of AID-tagged proteins. We added this explanation to the Results section.

As suggested by the reviewer, we quantitatively evaluated the fitness impact of the GAL1-OsTIR1(F74G) construct. Using the colony size data of the AID-v1 library (grown on galactose medium with 1 µM 5-Ph-IAA, Fig. 2c), we compared colony sizes of OsTIR1– and OsTIR1+ strains for non-essential ORFs. As degradation of non-essential proteins is not expected to affect fitness, the difference in colony size between OsTIR1– and OsTIR1+ strains can be attributed to OsTir1 expression. On average, the presence of the OsTIR1 construct reduced colony size by 7% (median fitness of OsTIR1+ strains relative to OsTIR1– strains of 0.93 ± 0.06, n = 4698 non-essential ORFs). We performed the same comparison for strains that did not exhibited OsTIR1-dependent protein degradation. In this set of strains, the presence of the OsTIR1 construct also reduced colony size by 7% (median fitness of OsTIR1+ strains relative to OsTIR1– strains of 0.93 ± 0.05, n = 624 ORFs in the “not affected” group in Fig. 2d). We added this information to Fig. S3a.

2 - Given the possibility that OsTIR1(F74G) overexpression might cause a growth problem, it is not appropriate to compare OsTIR1+ and OsTIR1- conditions for evaluating growth fitness (Fig. 2). As shown in Fig. S4b, it is more appropriate to compare the +/- 5-Ph-IAA conditions. Additionally, the 5-Ph-IAA concentration used in this study was not clearly mentioned in the method section and figure legends.

The two approaches, comparison of OsTIR1– and OsTIR1+ strains grown on galactose with 5-Ph-IAA (as was done for the AID-v1 library) and comparison of galactose ± 5-Ph-IAA conditions (as was done for the AID-v2 library), have advantages and disadvantages but should yield similar results. The technical noise (due to spatial effects on the screen plates) is lower for the comparison of OsTIR1– and OsTIR1+ strains, as the two strains for each ORF can be grown next to each other on the same plate (Fig. 2c). Furthermore, corrections of spatial effects are more precise with this layout as the frequency of fitness defects per plate is lower. On the other hand, comparison of galactose ± 5-Ph-IAA conditions implicitly corrects for the fitness impact of the GAL1-OsTIR1(F74G) construct, as the fitness distribution of each condition is normalized to the median of that condition, but this fitness impact of OsTir1 cannot be determine from the screen results.

We now explicitly corrected the colony size data of the AID-v1 library for the fitness impact of OsTir1 expression (quantified in the previous point) and updated all the analyses and results shown in Fig. 3, Fig. S3b-e and Fig. S4a. The correction was performed using the multiplicative model, whereby the fitness impacts of OsTir1 expression and degradation of the AID-tagged protein are independent. Overall, our observations and conclusions stand unchanged with the corrected data.

Finally, the 5-Ph-IAA concentration (1 µM) used in all experiments is now indicated in the figure legends and the Methods section.

3 - The authors found that fitness defects were observed for over 60% of essential proteins (Fig. 3). In other words, depletion of the remaining 40% was not enough to induce growth defects. The authors should discuss how the current AID library can be improved to achieve better target depletion. Previous literature reported various possibilities, such as using a tandem degron tag and combining AID with the Tet promoter system (PMID 25181302, 26081484). Although optional, it would be wonderful if the authors would generate an improved library.

Following the reviewer’s suggestion, we added the following statement to the discussion:

“In the future, the libraries could be potentially improved with N-terminal tagging of ORFs that currently exhibit incomplete or no degradation of AID-tagged proteins or using multiple copies of the AID* tag to enhance protein degradation (Kubota et al, 2013; Nishimura & Kanemaki, 2014).”

Minor comments

4 - 5-Ph-IAA is not auxin because it does not induce the auxin responses in plants (PMID 29355850). Therefore, the authors should be careful when they refer to 5-Ph-IAA and should not call it auxin.

We corrected this and now refer to 5-Ph-IAA explicitly throughout the manuscript.

5 - The availability of the HaloTag and AID libraries should be indicated.

We added the following statement to the Methods section: “All strains, plasmids and libraries are available upon request.”

6 - Page 3: "Finally, the extent of AID-dependent degradation varied with protein abundance, in that highly expressed proteins were more likely to be only partially degraded compared to lowly expressed ones (Fig. 2e, Fig. S2e)". Fig. S2e should be Fig. S2d, shouldn't it?

We corrected this mistake.

Reviewer #1 (Significance):

This paper is technically robust and well-conducted. It presents a comprehensive study showcasing the effectiveness of the conditional degron library. The HaloTag libraries will also be useful. The yeast libraries presented in this study will be invaluable for future screenings and studies across all aspects of yeast biology.

Reviewer #2 (Evidence, reproducibility and clarity):

In this study, the authors reported the development and characterization of two AID-tagged strain libraries for the model organism S. cerevisiae. The libraries are based on the latest AID technology, AID2. One library contains a fluorescent protein fused to the AID, whereas the other library does not have the fluorescent protein, thus offering better compatibility with imaging-based screens. The authors show that AID-dependent protein degradation can be achieved for most of the library strains, and a growth phenotype was induced for a high fraction of essential genes. Genetic screens for DNA damage-sensitive mutants showcased the applicability of the libraries.

I only have the following minor comments and suggestions for the authors to consider.

Point 1, Page 3

"Optimized tagging of proteins with these N-terminal localization signals likely also contributes to the lack of correlation between differential fitness defects and occurrence of terminal localization signals (Fig. S1f, Table S2)."

Is this because the genes that cannot tolerate C-terminal addition are already depleted in the C-SWAT library? In the C-SWAT library, a 15-amino-acid linker L3 is added to the C-terminus.

That is certainly a possibility. During construction of SWAT library, tagging with N-SWAT and C-SWAT acceptor modules failed for 251 and 353 ORFs, respectively (Weill et al. 2018, Meurer et al. 2018). However, these ORFs are not enriched in N- or C-terminal localization signals, respectively (4.6% ORFs with C-terminal signals in C-SWAT library vs 3.3% among failed C-SWAT strains; 12.3% ORFs with N-terminal signals in N-SWAT library vs 2.0% among failed N-SWAT strains).

The most significant trend in the data is enrichment of ribosomal subunits in both sets of failed strains: 3.9% and 16.3% of the genes mapped to the GO term “ribosome” in the N-SWAT library and the set of failed N-SWAT strains, respectively; 3.6% and 15.9% of the genes in the C-SWAT library and the set of failed C-SWAT strains, respectively. This is consistent with what was reported by Weill et al. for failed N-SWAT strains.

Point 2, Page 3

"Expression of OsTir1(F74G) from the strong galactose-inducible GAL1 promoter had a negligible impact on yeast fitness (Fig. S2a)."

I wonder why the authors chose to use an inducible promoter to express OsTir1(F74G). In other studies, for example Snyder et al. 2019, OsTir1 has been expressed from a constitutive promoter.

Despite the small fitness impairment, we decided to use the GAL1-OsTIR1(F74G) construct for the AID libraries for two reasons: the conditional nature of this promoter is likely to limit adaptation to expression of OsTir1(F74G) and the high expression levels of OsTir1(F74G) are less likely to limit degradation of AID-tagged proteins. We added this explanation to the Results section.

Please see our response to reviewer 1, points 1 and 2.

Point 3, Page 3

"A similar frequency was previously observed with a set of AID alleles constructed for 758 essential ORFs using the original AID system (Snyder et al, 2019). However, over a third of these alleles exhibited fitness defects even in the absence of auxin, which were further compounded by off-target effects of auxin, highlighting the advantages of the AID2 system."

Snyder et al. 2019 used a TAP-AID-6FLAG tag. The fitness defect in the absence of auxin may not necessarily be due to the AID part of the tag, as TAP tagging is known to compromise the functions of some genes.

We corrected our statement as follows:

“A similar frequency was previously observed with a set of AID alleles constructed for 758 essential ORFs using the original AID system (Snyder et al, 2019). However, over a third of these alleles exhibited fitness defects even in the absence of auxin, which were further compounded by off-target effects of auxin.”

Point 4, Page 3

"Interestingly, complete degradation of 33% of essential proteins did not result in a fitness defect. It is possible that in some cases partial degradation results in low protein levels that are below the detection limit of our assay but are sufficient for viability."

Are these "33% of essential proteins" enriched with genes with low expression levels? I guess genes with low expression levels are more likely to fall below the detection limit even when partially depleted. Are there extreme examples where a highly expressed essential gene does not exhibit a fitness defect when the protein product is no longer detectable?

We performed the analysis suggest by the reviewer, and observed no difference in pre-degradation protein levels between essential & degraded proteins with and without a fitness defect (now shown in Fig. S3b). This also showed that there are indeed several essential proteins with high pre-degradation proteins levels and without a fitness defects upon degradation to below our detection limit: Pgi1, Nhp2, Smt3, Gus1, Dys1, Sis1, Fas2 and Rpo26 (in the abundance bin 4 in Fig. S2f).

In addition, we considered the nature of the essential genes in these two groups. Namely, we compared the frequency of core essential genes, which are always required for viability, and conditional essential genes, which vary in essentiality depending on the genetic background or environment (Bosch-Guiteras & van Leeuwen, 2022). Interestingly, the set of essential and degraded proteins without an accompanying fitness defect was enriched in conditional essential genes defined by two independent measures: essentiality across S. cerevisiae natural isolates (Peter et al, 2018) or with bypass suppression interactions in a laboratory strain (van Leeuwen et al, 2020) (Fig. S3c, odds ratio = 1.6, p-value = 0.04 in a Fisher’s exact test and odds ratio = 1.7, p-value = 0.02, respectively). This suggests that conditional essentiality could explain the observed lack of fitness defects upon degradation of some essential proteins.

We added this analysis to the Results section.

Reviewer #2 (Significance):

This study generated highly valuable resources for functional genomic studies.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary: In this manuscript, the authors construct and analyze a genome-wide collection of AID-tagged S. cerevisiae strains. The manuscript is clearly written and the analysis appears to be thorough. This collection will be quite useful to the yeast community. There are some issues to address, listed below.

1. page 1, abstract - "...with protein abundance and tag accessibility as limiting factors." It's not clear what the authors mean by protein abundance as a limiting factor. Are they referring to the protein level pre-depletion? Please clarify.

That is correct. We clarified this statement as follows:

“Almost 90% of AID-tagged proteins were degraded in the presence of the auxin analog 5-Ph-IAA, with initial protein abundance and tag accessibility as limiting factors.”

1. page 1, second paragraph of the Intro, end of the paragraph - There are publications prior to Van Leeuwen et al. 2016 that describe suppressors lurking in the deletion set. Here are two that should be cited: Hughes et al. 2000, DOI: 10.1038/77116 and Teng et al. 2013, DOI: 10.1016/j.molcel.2013.09.026.

We added the references pointed out by the review.

1. The goal of this work, stated in the first sentence of results, is to construct genome-wide AID libraries. Yet, to test whether N-terminal or C-terminal tagging is better, the authors used a Halo tag. Those results showed that, for the Halo tag, C-terminal tagging was less likely to impair function. Why weren't these tests done with the same AID tag used to build the libraries in the next section? What is the evidence that the results for a Halo tag will be the same as for an AID tag? While hard to find it documented in publications, there is a lot of anecdotal evidence that the type of tag can make a big difference, as well as its location. While this section will be of interest to those using Halo tags, it's not clear how it relates to the rest of the paper, especially given the careful characterization of the AID library in the next section.

We chose the Halo tag due its size (33 kDa), similar to many commonly used fluorescent protein tags and to the mNG-AID*-3myc tag in the AID-v1 library, and lack of evidence for a dominant negative effect on the tagged proteins. This is now stated in the Results section.

We agree that further work is needed to understand how the type of tag, its size and biophysical properties, and the linker between the tag and the protein of interest affect protein localization and function across the proteome. This is now stated in the Results section.

1. Throughout this manuscript, including the Tables, in cases where the protein is no longer detected, please do not describe this as "complete degradation." Instead, please use "not detectable." This is clearly the case for essential proteins that are no longer detected but that still grow, so it is very likely the case for many or all of the others. If the authors have any understanding of the sensitivity of their fluorescence assay, then that would be helpful to know. For example, they could add a control, taking a known amount of a fluorescent protein and analyzing known dilutions to assay the level of detection.

We appreciate the reviewer’s suggestion. We decided against “not detectable” instead of “complete degradation” to avoid confusion with proteins that are not detectable pre-degradation. Nevertheless, we replaced “complete degradation” with “degradation” and added the following explanation to the Results section:

“Out of 5079 proteins detected in OsTIR1– strains, 4455 (~88%) were significantly depleted in OsTIR1+ strains (Fig. 2d, Table S3). 3981 proteins could not be detected specifically in the OsTIR1+ background. Hereafter, we will refer to these proteins as degraded, although it is likely that at least in some cases degradation is not complete but the remainder is below the detection limit of our plate reader assay. Nevertheless, 474 proteins were unequivocally degraded only partially, as they were detectable in the OsTIR1+ background but at reduced levels compared to the OsTIR1– background (Fig. 2d).”

To estimate the detection limit of the colony fluorescence assay, we correlated the background-corrected mNG intensities in OsTIR1– strains with absolute levels (in molecules per cell) of 1167 proteins determined by Lawless et al. (PMID 26750110). Based on a linear fit, the threshold above which proteins are considered “detected” in our analysis, mNG/bkg(OsTIR1–) > 1.2, corresponds to 200 molecules per cell (95% confidence interval 18 to 2187 molecules per cell). We added this information to the Results section and Fig. S2c.

This detection limit is in line with our results, where low abundance proteins such as the centromeric histone Cse4/CENP-A (with two Cse4 molecules per centromere adding to 64 molecules per cell, Aravamudhan et al. PMID: 23623551 and several times that amount elsewhere in the cell, Collins et al. PMID: 15530401) can be detected in the colony assay (Table S3).

1. Fig. S2a and page 3, first full paragraph - The authors wrote that expression of OsTir1(F74G) from the strong GAL1 promoter had a negligible impact on growth. However, the figure shows that there is an obvious effect on growth after 48 hours of incubation, with much smaller colonies. This defect is much less obvious after 72 hours. This difference suggests that the growth effect would have been even more obvious at 24 hours. I think that the text should be modified to indicate this effect.

We now quantified the fitness impact of the GAL1-OsTIR1(F74G) construct and rephrased this part of the manuscript. In addition, we corrected the AID-v1 library screen results for the fitness impact of the GAL1-OsTIR1(F74G) construct and updated all figures and tables. Please see our response to reviewer 1, points 1 and 2.

1. One of the main justifications for the construction of the AID library is to allow assays for essential genes. Yet that was not a feature of the screen for DNA damage response factors. Were any essential genes identified in those screens? It would be of great interest to identify lower levels of 5-Ph-IAA that only mildly affect growth of essential genes and then to repeat the screens.

58 out of the combined 165 potential resistance factors identified in the three screens are essential genes. We added this information to the Results section and essential genes are now indicated in Fig. S5c.

We now show that chemical-genetic interactions for both essential and non-essential genes can be reproduced in spot tests using the MMS screen as an example (Fig. S5d). We also show that additional essential hits can be identified at lower concentrations of 5-Ph-IAA, which allow determining chemical-genetic interactions for strains that otherwise exhibit no growth in 1 μM 5-Ph-IAA (Fig. S5e). As the screens serve as a demonstration of possible uses of the AID libraries, we consider additional exhaustive screening for DNA damage response factors beyond the scope of this manuscript.

1. A big advantage of AID depletion over deletions is the ability to look at strains very shortly after loss of the protein of interest. In many cases in the literature, experiments are done after one or two hours after depletion. Yet in this work, there are no data presented on how effective depletion is in the short term versus after a long period of growth (24 hours). It would be a strong addition to the manuscript to include a time course for at least a subset of the proteins to look at the loss of signal over time, either by fluorescence or by Western blots.

We performed time courses of protein depletion with immunoblotting for 12 strains (4 proteins from the “degraded”, “partially degraded” and “not affected” groups each). The results in Fig. S2e show that “degraded” proteins are depleted to below the detection limit within 60min of 5-Ph-IAA addition, “partially degraded” proteins are depleted less or exhibit a degradation-resistant pool, and the levels of “not affected” proteins remain stable over time, consistent with their classification based on mNG fluorescence in the colony assay. We added this information to the Results section.

Reviewer #3 (Significance):

The library will be of use to the yeast community.

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Referee #3

Evidence, reproducibility and clarity

Summary: In this manuscript, the authors construct and analyze a genome-wide collection of AID-tagged S. cerevisiae strains. The manuscript is clearly written and the analysis appears to be thorough. This collection will be quite useful to the yeast community. There are some issues to address, listed below.

1. page 1, abstract - "...with protein abundance and tag accessibility as limiting factors." It's not clear what the authors mean by protein abundance as a limiting factor. Are they referring to the protein level pre-depletion? Please clarify.
2. page 1, second paragraph of the Intro, end of the paragraph - There are publications prior to Van Leeuwen et al. 2016 that describe suppressors lurking in the deletion set. Here are two that should be cited: Hughes et al. 2000, DOI: 10.1038/77116 and Teng et al. 2013, DOI: 10.1038/77116 .
3. The goal of this work, stated in the first sentence of results, is to construct genome-wide AID libraries. Yet, to test whether N-terminal or C-terminal tagging is better, the authors used a Halo tag. Those results showed that, for the Halo tag, C-terminal tagging was less likely to impair function. Why weren't these tests done with the same AID tag used to build the libraries in the next section? What is the evidence that the results for a Halo tag will be the same as for an AID tag? While hard to find it documented in publications, there is a lot of anecdotal evidence that the type of tag can make a big difference, as well as its location. While this section will be of interest to those using Halo tags, it's not clear how it relates to the rest of the paper, especially given the careful characterization of the AID library in the next section.
4. Throughout this manuscript, including the Tables, in cases where the protein is no longer detected, please do not describe this as "complete degradation." Instead, please use "not detectable." This is clearly the case for essential proteins that are no longer detected but that still grow, so it is very likely the case for many or all of the others. If the authors have any understanding of the sensitivity of their fluorescence assay, then that would be helpful to know. For example, they could add a control, taking a known amount of a fluorescent protein and analyzing known dilutions to assay the level of detection.
5. Fig. S2a and page 3, first full paragraph - The authors wrote that expression of OsTir1(F74G) from the strong GAL1 promoter had a negligible impact on growth. However, the figure shows that there is an obvious effect on growth after 48 hours of incubation, with much smaller colonies. This defect is much less obvious after 72 hours. This difference suggests that the growth effect would have been even more obvious at 24 hours. I think that the text should be modified to indicate this effect.
6. One of the main justifications for the construction of the AID library is to allow assays for essential genes. Yet that was not a feature of the screen for DNA damage response factors. Were any essential genes identified in those screens? It would be of great interest to identify lower levels of 5-Ph-IAA that only mildly affect growth of essential genes and then to repeat the screens.
7. A big advantage of AID depletion over deletions is the ability to look at strains very shortly after loss of the protein of interest. In many cases in the literature, experiments are done after one or two hours after depletion. Yet in this work, there are no data presented on how effective depletion is in the short term versus after a long period of growth (24 hours). It would be a strong addition to the manuscript to include a time course for at least a subset of the proteins to look at the loss of signal over time, either by fluorescence or by Western blots.

Significance

The library will be of use to the yeast community.

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Referee #2

Evidence, reproducibility and clarity

In this study, the authors reported the development and characterization of two AID-tagged strain libraries for the model organism S. cerevisiae. The libraries are based on the latest AID technology, AID2. One library contains a fluorescent protein fused to the AID, whereas the other library does not have the fluorescent protein, thus offering better compatibility with imaging-based screens. The authors show that AID-dependent protein degradation can be achieved for most of the library strains, and a growth phenotype was induced for a high fraction of essential genes. Genetic screens for DNA damage-sensitive mutants showcased the applicability of the libraries.

I only have the following minor comments and suggestions for the authors to consider.

Point 1

Page 3

"Optimized tagging of proteins with these N-terminal localization signals likely also contributes to the lack of correlation between differential fitness defects and occurrence of terminal localization signals (Fig. S1f, Table S2). " Is this because the genes that cannot tolerate C-terminal addition are already depleted in the C-SWAT library? In the C-SWAT library, a 15-amino-acid linker L3 is added to the C-terminus.

Point 2

Page 3

"Expression of OsTir1(F74G) from the strong galactose-inducible GAL1 promoter had a negligible impact on yeast fitness (Fig. S2a)." I wonder why the authors chose to use an inducible promoter to express OsTir1(F74G). In other studies, for example Snyder et al. 2019, OsTir1 has been expressed from a constitutive promoter.

Point 3

Page 3

"A similar frequency was previously observed with a set of AID alleles constructed for 758 essential ORFs using the original AID system (Snyder et al, 2019). However, over a third of these alleles exhibited fitness defects even in the absence of auxin, which were further compounded by off-target effects of auxin, highlighting the advantages of the AID2 system." Snyder et al. 2019 used a TAP-AID-6FLAG tag. The fitness defect in the absence of auxin may not necessarily be due to the AID part of the tag, as TAP tagging is known to compromise the functions of some genes.

Point 4

Page 3

"Interestingly, complete degradation of 33% of essential proteins did not result in a fitness defect. It is possible that in some cases partial degradation results in low protein levels that are below the detection limit of our assay but are sufficient for viability." Are these "33% of essential proteins" enriched with genes with low expression levels? I guess genes with low expression levels are more likely to fall below the detection limit even when partially depleted. Are there extreme examples where a highly expressed essential gene does not exhibit a fitness defect when the protein product is no longer detectable?

Significance

This study generated highly valuable resources for functional genomic studies.

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Referee #1

Evidence, reproducibility and clarity

Summary

The manuscript by the Khmelinskii group reports that they have successfully constructed two conditional degron libraries of budding yeast for almost all proteins. For this purpose, the authors employed an improved auxin-inducible degron (AID2). Initially, they constructed yeast libraries by fusing HaloTag to the N- or C-terminus of proteins and found that C-terminal tagging is less likely to affect the location and function of proteins (Fig. 1). Based on this finding, the authors fused mNG-AID-3Myc or AID-3Myc (AID-v1 or AID-v2 library, respectively) to more than 5600 proteins and found that 4079 proteins were significantly depleted when cells were treated with 5-Ph-IAA (Fig. 2). A fitness defect was observed for over 60% of essential proteins, indicating the target depletion showed the expected phenotype in many cases (Fig. 3). Finally, the authors screened proteins required for maintaining viability in the presence of MMS, CPT and HU, and identified common proteins involved in DNA repair (such as RAD52 epistasis proteins) and other proteins specific for MMS, CPT or HU resistance (Fig. 4). Furthermore, the authors revealed that an ER membrane protein, Gsf2, is required for HU resistance, which was not found in previous studies with the YKO library because gsf2∆ cells in the YKP library had aquired a suppressor mutation (Fig. 4e).

Major comments

• In Figure S2a, the authors initially checked the growth of yeast cells expressing OsTIR1(F74G) under the GAL1 promoter, saying that "expression of OsTir1(F74G) from the strong galactose-inducible GAL1 promoter had a negligible impact on yeast fitness (page 3)". To me, the OsTIR1(G74G) expressing cells showed slightly slower growth compared to the control cells. Moreover, the cells expressing it under the very strong GPD promoter showed apparent slow growth, suggesting that OstIR1(F74G) overexpression caused a side effect. The authors should carefully evaluate the cells with GAL1-OsTIR1(F74G).
• Given the possibility that OsTIR1(F74G) overexpression might cause a growth problem, it is not appropriate to compare OsTIR1+ and OsTIR1- conditions for evaluating growth fitness (Fig. 2). As shown in Fig. S4b, it is more appropriate to compare the +/- 5-Ph-IAA conditions. Additionally, the 5-Ph-IAA concentration used in this study was not clearly mentioned in the method section and figure legends.
• The authors found that fitness defects were observed for over 60% of essential proteins (Fig. 3). In other words, depletion of the remaining 40% was not enough to induce growth defects. The authors should discuss how the current AID library can be improved to achieve better target depletion. Previous literature reported various possibilities, such as using a tandem degron tag and combining AID with the Tet promoter system (PMID 25181302, 26081484). Although optional, it would be wonderful if the authors would generate an improved library.

Minor comments

• 5-Ph-IAA is not auxin because it does not induce the auxin responses in plants (PMID 29355850). Therefore, the authors should be careful when they refer to 5-Ph-IAA and should not call it auxin.
• The availability of the HaloTag and AID libraries should be indicated.
• Page 3: "Finally, the extent of AID-dependent degradation varied with protein abundance, in that highly expressed proteins were more likely to be only partially degraded compared to lowly expressed ones (Fig. 2e, Fig. S2e)". Fig. S2e should be Fig. S2d, shouldn't it?

Significance

This paper is technically robust and well-conducted. It presents a comprehensive study showcasing the effectiveness of the conditional degron library. The HaloTag libraries will also be useful. The yeast libraries presented in this study will be invaluable for future screenings and studies across all aspects of yeast biology.

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Reply to the reviewers

Response to Reviewer #1

Major comments:

1. * The connection between vB12 and MMA is weak, and the attempt to connect these pieces to PPI seems somehow forced. For instance, the authors do not convincingly demonstrate that MMA causes the PPI deficit. Furthermore, vB12 may rescue PPI independently of MMA. The authors should be more transparent about the lack of connection or causality between changes in metabolism and behavior.* We appreciate the reviewer's comment and acknowledge that we have not demonstrated causal relationships between increased MMA, PPI deficits in Tbx1+/- mice and their rescue by vB12. They are associations.

In the revised manuscript, we have clarified this in the Discussion, para.4, by adding the following phrase. “The results of our study do not prove a causal relationship between elevated brain MMA and PPI impairment, nor do they tell us whether rescue of the PPI impairment by vB12 occurs by reducing MMA”.

Regarding the comment of a weak connection between vitamin B12 and MMA, we respectfully disagree.

The biochemistry underlying the connection is outlined clearly in the Introduction, page 4, para. 2.

Patients with vitamin B12 deficiency typically exhibit elevated levels of MMA and administration of vitamin B12 (cobalamin) helps to normalize MMA values (Robert & Brown, 2003). Furthermore, several animal models with genetic alterations in the vitamin B12 pathway exhibit high levels of MMA. For instance, mice lacking the cobalamin transporter have increased MMA (Bernard et al., 2018). Additionally, mice lacking the mutase (Mut), which requires vitamin B12 as a cofactor for the conversion of methylmalonyl CoA to succinyl-CoA for entry into the Krebs cycle, demonstrate elevated levels of MMA and are unresponsive to vitamin B12 treatment (Peters et al. 2006). In the revised manuscript, we have cited these references (Introduction, page 4, para. 2).

Throughout the manuscript, an important control is missing: WT+ vB12 group. Data from this group should be added to Figures 1, 3, 4, 6, and Supplemental Figures 3 and 4 to show the effect of vB12 on WT mice.

All of the experiments reported in the original manuscript included this control group although it was not always included in the data analysis and therefore in the figures, as observed by the reviewer, In the revised manuscript all of the relevant figures and tables now include these data.

In Figures 1C and 3, data from the respective WT controls in the Df1 and Tbx1 cohorts should be shown.

• *

The wild type (WT) animals serve as the control group for both Tbx1+/- and Df/+ mice because they were littermates, obtained from matings between Df1/+ and Tbx1+/- mice. This has been clarified in the Materials and Methods, and in a new cartoon which has been added to Supplementary Figure 1 (1C) showing all of the animal groups used for the various studies (NMR, transcriptomics, behavior).

The Supplemental Table S1 includes 17 WT controls for Tbx1+/- and 6 WT controls for Df1/+, but Figure 1C includes only one group of 11 WT controls. For which group were those 11 WT controls?

Here are several examples of inconsistency in the data: "For this, we first performed a preliminary metabolome analysis using isolated whole brains of male and female Tbx1+/- (n= 18) and WT (n= 10) mice between one and two months of age. A set of metabolites was quantified in brain extracts by liquid chromatography tandem mass spectrometry (LC-MS/MS) (Supplementary Table 1)." Again, the number of mice the authors note in the manuscript does not match that shown in Supplementary Table 1 (Tbx1+/- (n=14) and WT (N=17). "We analyzed whole brain tissue isolated from Df1/+ and WT (control) mice (n = 5 per genotype)." Again, the numbers of mice do not match those in Supplementary Table 1, which notes 6 Df1/+ mice and 6 WT mice.

We apologize for these errors and inconsistencies in the text and tables, all of which have been corrected in the revised manuscript. In addition, we have added the aforementioned cartoon (Supplementary Figure 1C) and we have improved the presentation of the data (genotypes and treatments) in Supplementary Tables 1 and 2. We hope that these changes provide the expected clarity to the data.

MMA is the only metabolite that is similarly changed between Tbx1+/- and Df1/+ brains. This is an interesting observation. However, there is no other overlap in metabolic changes between these two mutants. This is a concern that requires clarification

We appreciate the reviewer's comment. The observation is not altogether surprising considering that the Df1 deletion includes at least 9 genes involved in metabolic pathways (cited refs (Maynard, 2008;Meechan, 2011; Devaraju and Zakharenko, 2017)) any of which might counteract or compensate for changes caused by Tbx1 mutation alone. In addition, heterozygosity for other genes in the deleted region (Df1 encompasses over 20 genes) might affect metabolic processes indirectly. In the revised manuscript we have added the following phrase to the Discussion, para.1 “Thus, even though the two mutants are genetically and metabolically very different, in Df1/+ mice, the MMA phenotype is not affected by heterozygosity of other genes in the deletion.

The authors mention that MMA is not changed in pre-term Tbx1+/- embryos, but no data are provided. What about MMA levels in Df1/+ embryos?

In pre-term Tbx1+/- and WT embryos MMA was undetectable. This is now stated in the final paragraph of the first section of the Results.

We did not measure MMA in Df1/+ embryos. It was not a goal of the study to compare the metabolome of these two genetically very different mutants. The MMA data on Df1/+ mice are presented because they show the potential relevance of this phenotype for the human disease, and they justify the use of the single gene (Tbx1+/-) mutants for studies into metabolism-related disease mechanisms. See also response to point 12

* * In some cases, the differences in metabolites (e.g., glutamine, glutamate, phosphoethanolamine, taurine, leucine, myo-inositol, and niacinamide) between the WT and Tbx1+/- mice is very minimal (Supplemental Figure 2). The y-axis scale should start at 0.

We have changed the y-axis settings where necessary

The vB12 is administered via two different regimens: 1) every 3 days for 28 days, at 4-8 weeks of age for metabolic measurements, and 2) twice a week for 2 months for PPI behavioral testing. Is there any reason the authors chose different protocols?

We apologize for the confusion, which was due to an oversight in the Materials and Methods section that has been corrected. The weekly injection regimen was the same for mice used in the behavioral and metabolic studies, but the treatment time was shorter for the metabolic studies, for practical reasons beyond our control; mice received vB12 injections twice a week, beginning at 4 wks of age and continuing until 8 wks or 12 wks of age for metabolic and behavioral studies respectively.

* * The authors should add the following references to the study: Long et al., Neurogenetics (2006), which shows no change in PPI in Tbx1+/- mice. This discrepancy compared with the current study results and those of Paylor et al., Proc Natl Acad Sci U S A (2006) should be discussed.

We have not cited the study by Long et al. because there are no obvious reasons for the discrepancy (age, mouse strain, sex) that could be discussed. Beyond this of course we cannot comment on data generated by another research group. Nevertheless, the presence of the PPI deficit in Tbx1+/- mice has been confirmed in two different Tbx1 alleles Tbx1 lacZ/+ and Tbx1ΔE5/+, by two different investigators, Dr. Richard Paylor using Tbx1 lacZ/+ mice (Paylor et al. 2001) and Dr. Elvira De Leonibus (co-author of this manuscript) using Tbx1ΔE5/+ mice, in two different countries (USA and Italy) in a rederived colony of mice.

• Figure 6B is a concern. The PPI decrease in the Tbx1+/- group appears to be driven by results from 3-4 mice. First, are those data statistical outliers? *

With all due respect, this is not the case. Eight Tbx1+/- mice, i.e., >50% of those tested had PPI values below the minimum observed in WT mice. The behavioural data were checked for the presence of outliers in each group using the Grubbs test, which yielded negative results. Our finding of PPI deficits in Tbx1+/- mice are in line with previously published data in Tbx1+/- and other animal models of 22q11.2 microdeletion (Paylor et al., 2006; Paylor and Lindsay, 2006; Stark et al., 2008), as well as in humans (Sobin et al., 2005).

Second, experiments in the same mice would be more informative. Do PBS-treated mutants recover PPI if they are treated with vB12 and vice versa? If the authors are concerned about the age difference, they also should include age-dependent effects on PPI.

We decline to perform the proposed experiment for the reasons described in section 4 of the Revision Plan

• *

Because vB12 treatment completely rescued the MMA level in Df1/+ mice (Figure 3), the authors should include a figure showing PPI test results in Df1/+ mice.

Vitamin B12 treatment fully rescued the MMA phenotype in both mutants (Figure 3). Whether it rescues the PPI defect in Df1/+ mice is not important for this study. We used Df1/+ mice as an entry point, in order to give validity to the pursuit of the MMA phenotype in the single gene mutant (Tbx1+/-), in which we expect that it will be easier to find disease mechanisms. For this reason, we focused our attention on identifying metabolic alterations in adult Tbx1+/- mice.

See also response to point 7.

*Figure 1A and B table: Did the authors mean Log2FC instead of FC? The authors also should present the *

*source data by adding supplemental tables that include raw data and normalized conversion, etc., as described in the multivariate statistical data analysis of the LC-MS/MS data. *

• *

The new Figures 1A, Figure 3 and the accompanying tables now state Log2FC. New Supplementary Table 1 presents the raw data that were normalized on the basis of the amount of protein in the samples, described and referenced in the Materials and Methods

"...we identified a new metabolic phenotype that was associated with reduced sensorimotor gating deficits in Tbx1+/- mice". Although the authors showed the PPI rescue by treating Tbx1+/- mice with vB12, that result alone does not prove the association of metabolic phenotype with sensorimotor-gating deficit; other supporting data are needed.

• *

This is perhaps a question of semantics; by associated we mean that the two phenotypes, metabolic alterations and reduced PPI were observed together

The authors stated, "Results showed that there were very few differentially expressed genes in Tbx1+/- vs WT brains, (n=22 out of 14535 expressed genes (Fig. 5 and Supplementary Tab. 2)". However, they described how 3 differentially expressed genes are involved in mitochondrial activity in the Discussion. The authors should describe those 3 genes and their relation to the metabolic change.

The results that the reviewer refers to have changed in the revised manuscript due to the inclusion of the control group WT +vB12 in the data analysis. The transcriptome analysis revealed that vB12 had a stronger impact than genotype, and as a consequence, the statistical analysis of all groups did not highlight minor differences between the two genotypes.

Figure 5B: The authors claimed that they detected similar transcription profiles between WT+vB12 vs. Tbx1+/-+vB12, comparing Tbx1+/-+PBS vs. Tbx1+/-+vB12. This is based on 947 genes being downregulated and 834 being upregulated, which is not appropriate. The authors should normalize those data to the numbers of genes upregulated and downregulated in WT+PBS vs. WT+vB12 respective groups.

We said that we detected similar transcription profiles in PBS-treated WT and Tbx1+/- brains; a WT+vB12 group was not present. The latter group is included in revised manuscript and the data reanalyzed comparing all groups.

See also response to points 2 and 15.

Minor comments

1. Supplementary Table S1 shows the identical MMA concentration "0.2" for 6 controls. Is this correct? This was an error that has been corrected; the value is 0.00 (not detectable).

* Remove the callout for Figure 1C at the end of the second paragraph in Results*.

This figure is no longer present

*There are multiple typos throughout the manuscript. *

Here are several examples:

1. * Fig1B graph- Df/+ => Df1/+* Figure changed in revised manuscript

2. "Together, the hydrophilic and lipophilic results revealed a group of 6 compounds that characterized the brain metabolic differences between Tbx1+/- and WT mice (Figure 2B, 2C)". Figure 2A should be included also. Corrected

3. "In support of this notion, is the finding that...(remove) Removed

4. Remove double periods: "The pathways found are depicted in Figure 2C' which reports the impact of each pathway versus p values.." Corrected

5. Panel labels in all figures are misplaced. Panel labels are aligned correctly

We have performed a spelling and grammar check on the text

"In support of this notion... at least nine orthologs are involved in mitochondrial metabolism". What are those 9 mitochondrial genes? Kolar et al., Schizophr Bull (2023) indicates that there are 8 mitochondrial genes within the 22q11.2 locus. The authors need to list these genes.

This reference, which is a review, has been cited in the Introduction, para.3 along with the genes.

The review presents nine mitochondrial genes which the authors divide into two groups, 1) Genes expressed in mitochondria (SLC25A1, TXNRD2, MRPL40, PRODH, and COMT) and 2) Genes that have been shown to have an impact on mitochondrial function (TANGO2, ZDHHC8, UFD1L, and DGCR8). In the abstract they mention only eight genes, the ninth gene COMT is mentioned in the text.

Reviewer #2 (Significance (Required)):

*The manuscript titled "Tbx1 haploinsufficiency causes brain metabolic and behavioral anomalies in adult mice which are corrected by vitamin B12 treatment" by Caterini et al. presents a comparative metabolomics study in the brains of mouse models carrying a heterozygous mutation in the transcription factor Tbx1. This mutation is contrasted with a chromosomal deficiency encompassing Tbx1, among other gene loci, known as Df1/+, which serves as a mouse model for 22q11 microdeletion syndrome. The primary and most significant finding of the study is that Tbx1 heterozygosity alone induces broad metabolomic alterations in the entire brain parenchyma, despite Tbx1 expression being confined to vascular endothelial cells. The authors leverage this observation to investigate the effects of dietary supplementation with vitamin B12, which alters the metabolome in a manner interpreted by the authors as rescuing or reversing the Tbx1 heterozygosity phenotype. This study holds promise for understanding the individual gene contributions to the penetrant behavioral phenotypes observed in Df1/+ and 22q11 affected subjects. This potential arises from the clear and consequential metabolic phenotypes described, notably the accumulation of methylmalonic acid.

However, despite the intriguing metabolic phenotypes, there are significant issues hindering incontrovertible conclusions. *

Response to Reviewer #2

Major comments

1. Despite the intriguing metabolic phenotypes, there are significant issues hindering incontrovertible conclusions. Chief among these problems is the experimental design's nature, where the effects of genotype and a pharmacological intervention, vitamin B12, are assessed. The current design overlooks the effects of vitamin B12 on wild-type animals in metabolic and behavioral measures, thus precluding the attribution of the effects of vitamin B12 to a rescue. See response to Reviewer 1 (point 2) who made the same criticism. This group is now included in the data analysis of the relevant experiments.

*An alternative explanation, consistent with the measurements, is that vitamin B12 modifies metabolites and transcripts irrespective of genotype. A suggestion of this possibility is the observed effect of B12 lowering glutamate levels in Tbx1 mutant tissue below those in wild-type brain tissue (Fig. 4C). *

This might be true for some metabolites. Indeed, we found 5 metabolites that responded similarly to vB12 in both WT and Tbx1 +/- mice. In contrast, three metabolites responded to vB12 in both WT and Tbx1+/- mice, but the response was more pronounced in Tbx1+/- mice. Finally, a group of eight metabolites was altered exclusively in Tbx1+/- mice after vB12 treatment, including inosine, glutamate and short-chain fatty acids (SCFAs), Figure 4 and Supplementary Figure 6. Thus, overall, our data suggest that with only a few exceptions, the metabolic response to vB12 treatment is genotype-dependent.

• *

This experimental design issue is exacerbated by the multitude of analytes measured by metabolomics, all collectively assumed to change as part of a common genotype-B12 interaction mechanism. This interpretation is feasible only if none of the analytes were to respond to B12 in wild-type animals.

• *

As specified above, the response to vB12 was genotype-dependent. The inclusion of the vB12-treated WT dataset should address this point.

A second major issue arises from the assertion that Tbx1 is exclusively expressed in mouse brain endothelial cells and not in brain parenchyma. A significant unresolved question is how a gene expressed solely in endothelial cells can alter the brain parenchyma metabolome and transcriptome. This issue remains unaddressed and is not sufficiently discussed. If this assertion holds true, then the observations bear great importance in understanding how Df1/+ causes brain phenotypes and, by extension, in human 22q11.

There are quite a lot of published data from the mouse demonstrating the brain endothelial-specific expression of Tbx1 and the lack of expression in other brain cell types. These include studies using reporter genes (Paylor, 2006; Cioffi, 2014), Tbx1Cre based cell fate mapping (Cioffi, 2014, Cioffi, 2022) and single cell whole genome transcriptions (Ximerakis et al., 2019); (https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain). All are cited in the manuscript.

HOW the mutation of Tbx1 alters the brain metabolome and transcriptome will be the object of future studies, Currently, we do not have any data. At the reviewer’s request, we have extended the discussion of this point in the revised manuscript (Discussion, para. 4).

In this vein, the authors should consider that Tbx1 is not expressed in brain endothelial cells in humans and is minimally expressed in fetal astrocytes (see https://brainrnaseq.org/).

https://brainrnaseq.org provides a tool to evaluate the gene expression in the fetal brain. The sequencing was performed on fetal human brain tissue after elective pregnancy termination (4wks-9wks, it is not clear). Our analysis focuses on adult mice, which may contribute to observed differences.

Moreover, Yi et al. (2010) generated a gene expression atlas of human embryogenesis spanning from 4 to 9 weeks of gestational age, revealing downregulation of TBX1 during this timeframe. Conversely, in the normal adult human brain, TBX1 expression is identified in endothelial cells, as indicated in "The Human Brain Cell Atlas v1.0" presented for visualization and data mining through the Chan Zuckerberg Initiative’s CellxGene application, referring to the atlas ontology in Ding et al. (2016). * 3. A third major concern pertains to the general poor quality of the figures. Many figures appear to be directly exported from the software used for data analysis without proper curation. They are inadequately labeled, lack color codes to clarify differences (e.g., volcano plots), feature lettering fonts that are difficult to discern, and have lettering panels placed in awkward positions. Fig. 1 would benefit by the addition of a pathway diagram showing which metabolites are changing. Figure tables/spreadsheets either have sheets labeled in Italian or are empty. Collectively, the manuscript needs more careful data curation and presentation*.

Many of the figures and tables have been modified with respect to the original manuscript and issues of clarity and quality have been improved where necessary.

Other points for consideration are listed below. • The abstract results section does not mention the Df1 mutants at all, and overall the description of the results should be improved

Corrected • The abstract would benefit from defining vB12 before using the abbreviation

Corrected • The section of the Results describing MMA accumulation in the brain would benefit from

• *explaining the choice of 1 month of age for terminal experiments and the choice to use whole brains (are there particularly brain regions suspected to be affected?), * The majority of animals were 2 months of age at sacrifice (age and sex of individual animals are indicated in Supplementary Table 1). Young adult mice were the object of the study for the reason described in the first paragraph of the Results section, namely “Human studies of brain metabolism have mainly been conducted on children and adolescent patients. Therefore, in order to determine whether similar anomalies were present in the mouse models, we performed our studies on young mice between 1 and 2 months of age (Dutta et al., 2016)”.

This is also the age at which the behavioural phenotype has been demonstrated (Paylor et al., 2006), and therefore could, potentially be rescued by vB12 treatment. We do not have regional information pertaining to the adult brain.

2) describing any sex effects for Tbx1 mutants (and clarifying what data points for Tbx1 animals correspond to which sex), and 3) including what sex was used for Df1 experiments.

In preliminary experiment we analyzed males and females’ mice, before electing to use only males. To obtained reliable information about the impact of gender on metabolism and transcription we would have to use much larger numbers of animals. In Supplementary Table 1 pertaining to males and females are now indicated.

• The authors demonstrate that vB12 rescues PPI but use no other behavioral paradigms. It is possible that these mutations and/or vB12 could be impacting anxiety-like behaviors or other behavioral phenotypes. By only including PPI, the authors limit the interpretation of the "rescue" of this phenotype by vB12. * Reduced PPI was the only behavioral anomaly identified in Tbx1+/- mice that were subjected to a standard battery of behavioral tests (Paylor et al., 2006). *

* *Reduced PPI was the only behavioral anomaly identified in Tbx1+/- mice that were subjected to a standard battery of behavioral tests (Paylor et al., 2006).

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This is a terrific paper looking at influences of Tbx1 heterozygosity on metabolic phenotypes in mice. A weakness is that the locus on effects of B12 is totally unclear--could be neurovascular or even peripheral, but correcting this weakness might include study of Tbx1 conditional mutants, beyond the scope of this study.

• *

Reviewer #3 (Significance (Required)):* good significance

Only two minor suggestions. 'We selected to study primarily Tbx1 single gene mutants because it is the primary candidate disease gene". What is the basis for this statement? Mouse +/- mutants in Mrpl40, Txnrd2, ProhD, and probably others have shown brain phenotypes.*

• *

The basis for TBX1 being considered as the primary candidate disease gene is the finding of TBX1 point mutations in patients who have the full spectrum of clinical phenotypes associated with 22q11.2 deletion syndrome without the chromosomal deletion, namely, congenital heart defects, immune defects, facial dysmorphism, learning defects and developmental delay. Similarly, in the mouse, Tbx1 mutation recapitulates the phenotype observed in multigene deletion mutants, such as Df1/+ mice.

We do not say (or think) that heterozygosity of other genes from del22q11.2 does not contribute to the disease, but mutations of other genes have not been found in individuals with a 22q11.2DS phenotype but without the chromosomal deletion.

In the discussion, the authors could close the loop on low glutamine could result in lower gaba in inhibitory interneurons, and its correction with B12 could restore gaba levels.

Discussion, para. 3. We thank the reviewer for comment. However, the GABA concentration is not altered in Tbx1 haploinsufficient brains; it is only upregulated by Vitamin B12. Therefore, this assumption may be very speculative. Due to differences in the release and reabsorption rates of the three compounds (glutamine, glutamate, and GABA), correctly evaluating the glutamine-glutamate cycle requires separating astrocytes from neurons. We have only discussed the upregulation of glutamate and the GABA response to Vitamin B12, which may counteract the excess of glutamate.

1. __4__Description of analyses that authors prefer not to carry out Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

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Reviewer 1, point 11. The PPI decrease in the Tbx1+/- group appears to be driven by results from 3-4 mice. First, are those data statistical outliers?

Second, experiments in the same mice would be more informative. Do PBS-treated mutants recover PPI if they are treated with vB12 and vice versa? If the authors are concerned about the age difference, they also should include age-dependent effects on PPI.

We are unable to perform this experiment because, as stated in the manuscript, the mice were sacrificed at the end of the experiment and the brains preserved for histological analysis (not part of this study). The generation of mice for new experiments would take about one year. With all due respect, we do not believe that the data that would be obtained are sufficiently important to justify, ethically and economically, this work.

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Referee #3

Evidence, reproducibility and clarity

This is a terrific paper looking at influences of Tbx1 heterozygosity on metabolic phenotypes in mice. A weakness is that the locus on effects of B12 is totally unclear--could be neurovascular or even peripheral, but correcting this weakness might include study of Tbx1 conditional mutants, beyond the scope of this study.

Only two minor suggestions.

• 'We selected to study primarily Tbx1 single gene mutants because it is the primary candidate disease gene". What is the basis for this statement? Mouse +/- mutants in Mrpl40, Txnrd2, ProhD, and probably others have shown brain phenotypes.

• In the discussion, the authors could close the loop on low glutamine could result in lower gaba in inhibitory interneurons, and its correction with B12 could restore gaba levels.

Significance

good significance

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Referee #2

Evidence, reproducibility and clarity

The manuscript titled "Tbx1 haploinsufficiency causes brain metabolic and behavioral anomalies in adult mice which are corrected by vitamin B12 treatment" by Caterini et al. presents a comparative metabolomics study in the brains of mouse models carrying a heterozygous mutation in the transcription factor Tbx1. This mutation is contrasted with a chromosomal deficiency encompassing Tbx1, among other gene loci, known as Df1/+, which serves as a mouse model for 22q11 microdeletion syndrome. The primary and most significant finding of the study is that Tbx1 heterozygosity alone induces broad metabolomic alterations in the entire brain parenchyma, despite Tbx1 expression being confined to vascular endothelial cells. The authors leverage this observation to investigate the effects of dietary supplementation with vitamin B12, which alters the metabolome in a manner interpreted by the authors as rescuing or reversing the Tbx1 heterozygosity phenotype. This study holds promise for understanding the individual gene contributions to the penetrant behavioral phenotypes observed in Df1/+ and 22q11 affected subjects. This potential arises from the clear and consequential metabolic phenotypes described, notably the accumulation of methylmalonic acid.

• However, despite the intriguing metabolic phenotypes, there are significant issues hindering incontrovertible conclusions. Chief among these problems is the experimental design's nature, where the effects of genotype and a pharmacological intervention, vitamin B12, are assessed. The current design overlooks the effects of vitamin B12 on wild-type animals in metabolic and behavioral measures, thus precluding the attribution of the effects of vitamin B12 to a rescue. An alternative explanation, consistent with the measurements, is that vitamin B12 modifies metabolites and transcripts irrespective of genotype. A suggestion of this possibility is the observed effect of B12 lowering glutamate levels in Tbx1 mutant tissue below those in wild-type brain tissue (Fig. 4C). This experimental design issue is exacerbated by the multitude of analytes measured by metabolomics, all collectively assumed to change as part of a common genotype-B12 interaction mechanism. This interpretation is feasible only if none of the analytes were to respond to B12 in wild-type animals. Given that this experimental design recurs across a substantial portion of the paper, it may constitute a factor that undermines the strength of the conclusions.

• A second major issue arises from the assertion that Tbx1 is exclusively expressed in mouse brain endothelial cells and not in brain parenchyma. A significant unresolved question is how a gene expressed solely in endothelial cells can alter the brain parenchyma metabolome and transcriptome. This issue remains unaddressed and is not sufficiently discussed. If this assertion holds true, then the observations bear great importance in understanding how Df1/+ causes brain phenotypes and, by extension, in human 22q11. In this vein, the authors should consider that Tbx1 is not expressed in brain endothelial cells in humans and is minimally expressed in fetal astrocytes (see https://brainrnaseq.org/).

• A third major concern pertains to the general poor quality of the figures. Many figures appear to be directly exported from the software used for data analysis without proper curation. They are inadequately labeled, lack color codes to clarify differences (e.g., volcano plots), feature lettering fonts that are difficult to discern, and have lettering panels placed in awkward positions. Fig. 1 would benefit by the addition of a pathway diagram showing which metabolites are changing. Figure tables/spreadsheets either have sheets labeled in Italian or are empty. Collectively, the manuscript needs more careful data curation and presentation.

Other points for consideration are listed below:

1) The abstract results section does not mention the Df1 mutants at all, and overall the description of the results should be improved

2) The abstract would benefit from defining vB12 before using the abbreviation

3) The section of the Results describing MMA accumulation in the brain would benefit from 1) explaining the choice of 1 month of age for terminal experiments and the choice to use whole brains (are there particularly brain regions suspected to be affected?), 2) describing any sex effects for Tbx1 mutants (and clarifying what data points for Tbx1 animals correspond to which sex), and 3) including what sex was used for Df1 experiments.

4) The authors demonstrate that vB12 rescues PPI but use no other behavioral paradigms. It is possible that these mutations and/or vB12 could be impacting anxiety-like behaviors or other behavioral phenotypes. By only including PPI, the authors limit the interpretation of the "rescue" of this phenotype by vB12.

Significance

The manuscript titled "Tbx1 haploinsufficiency causes brain metabolic and behavioral anomalies in adult mice which are corrected by vitamin B12 treatment" by Caterini et al. presents a comparative metabolomics study in the brains of mouse models carrying a heterozygous mutation in the transcription factor Tbx1. This mutation is contrasted with a chromosomal deficiency encompassing Tbx1, among other gene loci, known as Df1/+, which serves as a mouse model for 22q11 microdeletion syndrome. The primary and most significant finding of the study is that Tbx1 heterozygosity alone induces broad metabolomic alterations in the entire brain parenchyma, despite Tbx1 expression being confined to vascular endothelial cells. The authors leverage this observation to investigate the effects of dietary supplementation with vitamin B12, which alters the metabolome in a manner interpreted by the authors as rescuing or reversing the Tbx1 heterozygosity phenotype. This study holds promise for understanding the individual gene contributions to the penetrant behavioral phenotypes observed in Df1/+ and 22q11 affected subjects. This potential arises from the clear and consequential metabolic phenotypes described, notably the accumulation of methylmalonic acid.

However, despite the intriguing metabolic phenotypes, there are significant issues hindering incontrovertible conclusions.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Using mass spectrometry, nuclear magnetic resonance spectroscopy, and transcriptomics analyses, Caterino et al. identified metabolic changes in the brains of young adult Df1/+ mouse models of 22q11.2 deletion syndrome (22q11DS). They observed similar changes in Tbx1+/- mice, as Tbx1 is one of the genes encoded inside the 22q11.2 microdeletion. Among the metabolites identified, methylmalonic acid (MMA) showed the most prominent increase. Systemic vitamin B12 (vB12) administration rescued the MMA increase and had strong effects on the brain metabolomes and transcriptomes in Tbx1+/- and Df1/+ mice. Finally, the pre-pulse inhibition (PPI) deficit observed in Tbx1+/- mice was partially rescued by vB12 treatment. As such, this study provides a description of metabolic changes caused by Tbx1 haploinsufficiency and suggests vB12 as a therapeutic avenue in 22q11DS. Although intriguing, this study suffers from several shortcomings that should be corrected or clarified before publication.

Major comments:

1. The connection between vB12 and MMA is weak, and the attempt to connect these pieces to PPI seems somehow forced. For instance, the authors do not convincingly demonstrate that MMA causes the PPI deficit. Furthermore, vB12 may rescue PPI independently of MMA. The authors should be more transparent about the lack of connection or causality between changes in metabolism and behavior.

2. Throughout the manuscript, an important control is missing: WT+ vB12 group. Data from this group should be added to Figures 1, 3, 4, 6, and Supplemental Figures 3 and 4 to show the effect of vB12 on WT mice.

3. In Figures 1C and 3, data from the respective WT controls in the Df1 and Tbx1 cohorts should be shown.

4. The Supplemental Table S1 includes 17 WT controls for Tbx1+/- and 6 WT controls for Df1/+, but Figure 1C includes only one group of 11 WT controls. For which group were those 11 WT controls?

5. Here are several examples of inconsistency in the data: "For this, we first performed a preliminary metabolome analysis using isolated whole brains of male and female Tbx1+/- (n= 18) and WT (n= 10) mice between one and two months of age. A set of metabolites was quantified in brain extracts by liquid chromatography tandem mass spectrometry (LC-MS/MS) (Supplementary Table 1)." Again, the number of mice the authors note in the manuscript does not match that shown in Supplementary Table 1 (Tbx1+/- (n=14) and WT (N=17). "We analyzed whole brain tissue isolated from Df1/+ and WT (control) mice (n = 5 per genotype)." Again, the numbers of mice do not match those in Supplementary Table 1, which notes 6 Df1/+ mice and 6 WT mice.

6. MMA is the only metabolite that is similarly changed between Tbx1+/- and Df1/+ brains. This is an interesting observation. However, there is no other overlap in metabolic changes between these two mutants. This is a concern that requires clarification.

7. The authors mention that MMA is not changed in pre-term Tbx1+/- embryos, but no data are provided. What about MMA levels in Df1/+ embryos?

8. In some cases, the differences in metabolites (e.g., glutamine, glutamate, phosphoethanolamine, taurine, leucine, myo-inositol, and niacinamide) between the WT and Tbx1+/- mice is very minimal (Supplemental Figure 2). The y-axis scale should start at 0.

9. The vB12 is administered via two different regimens: 1) every 3 days for 28 days, at 4-8 weeks of age for metabolic measurements, and 2) twice a week for 2 months for PPI behavioral testing. Is there any reason the authors chose different protocols?

10. The authors should add the following references to the study: Long et al., Neurogenetics (2006), which shows no change in PPI in Tbx1+/- mice. This discrepancy compared with the current study results and those of Paylor et al., Proc Natl Acad Sci U S A (2006) should be discussed.

11. Figure 6B is a concern. The PPI decrease in the Tbx1+/- group appears to be driven by results from 3-4 mice. First, are those data statistical outliers? Second, experiments in the same mice would be more informative. Do PBS-treated mutants recover PPI if they are treated with vB12 and vice versa? If the authors are concerned about the age difference, they also should include age-dependent effects on PPI.

12. Because vB12 treatment completely rescued the MMA level in Df1/+ mice (Figure 3), the authors should include a figure showing PPI test results in Df1/+ mice.

13. Figure 1A and B table: Did the authors mean Log2FC instead of FC? The authors also should present the source data by adding supplemental tables that include raw data and normalized conversion, etc., as described in the multivariate statistical data analysis of the LC-MS/MS data.

14. "...we identified a new metabolic phenotype that was associated with reduced sensorimotor gating deficits in Tbx1+/- mice". Although the authors showed the PPI rescue by treating Tbx1+/- mice with vB12, that result alone does not prove the association of metabolic phenotype with sensorimotor-gating deficit; other supporting data are needed.

15. The authors stated, "Results showed that there were very few differentially expressed genes in Tbx1+/- vs WT brains, (n=22 out of 14535 expressed genes (Fig. 5 and Supplementary Tab. 2)". However, they described how 3 differentially expressed genes are involved in mitochondrial activity in the Discussion. The authors should describe those 3 genes and their relation to the metabolic change.

16. Figure 5B: The authors claimed that they detected similar transcription profiles between WT+vB12 vs. Tbx1+/-+vB12, comparing Tbx1+/-+PBS vs. Tbx1+/-+vB12. This is based on 947 genes being downregulated and 834 being upregulated, which is not appropriate. The authors should normalize those data to the numbers of genes upregulated and downregulated in WT+PBS vs. WT+vB12 respective groups.

Minor comments:

1. Supplementary Table S1 shows the identical MMA concentration "0.2" for 6 controls. Is this correct?

2. Remove the callout for Figure 1C at the end of the second paragraph in Results.

3. There are multiple typos throughout the manuscript. Here are several examples:

a) Fig1B graph- Df/+ => Df1/+

b) "Together, the hydrophilic and lipophilic results revealed a group of 6 compounds that characterized the brain metabolic differences between Tbx1+/- and WT mice (Figure 2B, 2C)". Figure 2A should be included also.

c) "In support of this notion, is the finding that...(remove)

d) Remove double periods: "The pathways found are depicted in Figure 2C' which reports the impact of each pathway versus p values.."

e) Panel labels in all figures are misplaced.

f) "In support of this notion... at least nine orthologs are involved in mitochondrial metabolism". What are those 9 mitochondrial genes? Kolar et al., Schizophr Bull (2023) indicates that there are 8 mitochondrial genes within the 22q11.2 locus. The authors need to list these genes.

Significance

Caterino et al. provide the evidence of a change in brain metabolism associated with 22q11.2 deletion syndrome (22q11DS), which is a major risk factor for neuropsychiatric disease, especially schizophrenia. The authors attributed these changes to haploinsufficiency of Tbx1, which is located inside the 22q11.2 genomic locus, as Tbx1+/- mice and 22q11DS models (Df1/+ mice) demonstrated similar metabolic changes. Among these changes, the authors detected an abnormally high accumulation of methylmalonic acid (MMA), a toxic metabolite that negatively affects mitochondrial activity and glutamate uptake. The authors also showed that systemic vitamin B12 (vB12) administration in Tbx1+/- mice and 22q11DS mice returned MMA to its normal levels in the brains of both strains. Finally, administration of vB12 partially rescued a behavioral phenotype in Tbx1+/- mice. Specifically, vB12 improved pre-pulse inhibition (PPI) of the acoustic startle in these mutants. This study may potentially motivate a new direction of research toward elucidating the Tbx1-dependent metabolic mechanism and its connection to abnormal behavioral phenotypes in 22q11DS.

• This is the first thorough characterization of metabolic changes in the brain of mouse models of 22q11DS. This work may advance our understanding of the mechanism of this disease and schizophrenia in general. Previous studies mostly focused on cell-autonomous mechanisms in 22q11DS, but because Tbx1 is not expressed in the adult brain, this study strongly argues that non-cell-autonomous mechanisms are involved in the pathogenicity of 22q11DS. This description is the strongest part of the manuscript. However, as it stands, this study does not causally connect the metabolite abnormalities with aberrant behavior in 22q11DS models. The rescue of metabolic changes by vB12 is interesting, but the interpretation of the results is a bit speculative. There are also several missing controls and other limitations that preclude this reviewer from a more enthusiastic assessment of this manuscript. The manuscript has the potential to be much stronger if the authors add more experiments and analyze their data in a more appropriate manner.

• This manuscript describes metabolic abnormalities in 22q11DS models that are driven by the haploinsufficiency of the Tbx1 gene, which is located within the 22q11DS-related genomic region. This is an incremental advance in the 22q11DS field. The authors identified multiple metabolic abnormalities that would be of interest to researchers in the fields of psychiatry and translational and basic neuroscience. They also showed that some metabolic abnormalities are improved by vB12 treatment. They argue that vB12 improves PPI, the abnormality of which is associated with many psychiatric diseases, including schizophrenia. If confirmed, this will be a conceptual advance in the translational neuroscience field. However, making such a statement, exciting as it may be, would be speculative if based solely on the data presented in this manuscript..

• This reviewer has expertise in basic and translational neuroscience with focus on neurodevelopmental psychiatric diseases.

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Reply to the reviewers

Reviewer1

I recommend combining Figures 1 and 3 either before or after the results shown in Figure 2, so the reader's expectation for quantification is immediately satisfied.

Response：

Thank you for your suggestion. In the revised manuscript, images of GFP::POP-1 in compound mutants are moved to Figure 3. The schematic diagram of the gonad (previously Fig. 1A) and GFP::POP-1 images in wild type are kept in Figure 1, as they are described in Introduction.

Major comments:

Delving into the figure legend of Fig. 3 and the normalization procedure described in the Methods "Quantification of POP-1 asymmetry in the Z1 and Z4 division" raised concerns. The method therein described is overly complicated but also neglects background subtraction. My first question about this method: what range of distances between daughters is measured in Z? These distances are not discussed in absolute terms, and this is important for our understanding of how much correction for tissue depth might be necessary, as L1s are very thin.

To check my understanding, the authors use as a control a nuclear-localized GFP driven in the somatic gonad precursors in otherwise wild-type worms by the sys-1 promoter. They observe that the regression on a log scale of anterior:posterior (and vice versa) Z1 and Z4 daughter fluorescence over the distance between the daughters in the Z plane is fit by y = −0.034x + 0.0148, which is practically a slope of 0 and an intercept of 0. This means that they observed an ~1:1 ratio (as log(1)=0) of fluorescence in the anterior and posterior daughters of otherwise wild-type worms, at least across the range of very small X values of relevant distances between daughters (again, the relevant range of distances really matters and should be presented), making the normalization seem unnecessary.

Response:

Normalization is essential to compare POP-1 signals between daughter cells since the signal intensities depend on the depth of cells. Depth differences between SGP daughter cells range from 0 to 7.5 micrometers. For example, when we input the maximum difference (7.5) into our correction equation y = −0.034x + 0.0148 (the logarithmically transformed linear regression equation), we get:

y = −0.034 * 7.5 + 0.0148 = -0.2402

To interpret this on the original scale, we apply the inverse logarithmic transformation:

10^(-0.2402) ≈0.575

This result indicates that even if GFP::POP-1 expression is the same in both cells, the depth difference alone can cause approximately a 1.74-fold (1/0. 575) difference in fluorescence intensity.

Similarly, if we use a median value of 3.5 micrometers as the depth difference, we get: y = -0.1042. After the inverse logarithmic transformation, this corresponds to a 0.787 or 1.27 (1/0.787) fold difference in fluorescence intensity.

Without normalization, we risk misinterpreting such differences in expression levels when in reality the expression is the same. Conversely, actual differences in GFP::POP-1 signal could be masked or overestimated due to the depth effect.

In the revised manuscript, examples of depth differences between SGP daughters are shown in Fig. 2S which is added in response to the comment of reviewer 2, asking images of lin-17 mom-5 animals.

In the revised manuscript, we explained the depth effects in the legend of Fig. 3 as follows.

"Since SGP daughter cells are often present at distinct focal planes, we normalized the depth effects on fluorescence intensities (see Materials and Methods for details) for the quantification shown in (B). The images in (A) and (C) are from animals with SGP daughters at similar depths."

Then based on this regression and 95% CI, the authors predict values that reflect true equivalence of fluorescence of POP-1::GFP in the two SGP daughters, compare the observed values to these predictions, and ultimately display in violin plots these differences of observed and expected. Correct?

Response:

Yes, your understanding is correct.

Is this complicated treatment the only way to detect differences in polarity of anterior and posterior daughters of Z1 and Z4? What happens if the authors measure GFP::POP-1 and calculate the following?

Z1p(MGV - background control from same focal plane)

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Z1a(MGV - background control from same focal plane)

If this straightforward analysis shows asymmetric signal in the control that is made symmetrical or reversed in the mutants, the hypothesis would seem to be supported with a much more straightforward method. Samples could be analyzed separately in two bins by worm body position, which affects which cell is superficial in the sample. As it is, the Figure 3 Y axis label is hard to interpret without reading the methods at length, diminishing its impact.

Response:

Thank you for the suggestion. Your suggested calculation would be simple if we could assume that control signals (sys-1p::GFP::NLS or sys-1p::GFP::POP-1 in the same wild-type cell) on the same focal plane are the same among animals. However, since there are apparent variations in expression levels among individuals, your suggested method is not appropriate for evaluating differences in sys-1p::GFP::POP-1 intensities between the SGP daughter cells of the same animal.

Missing control: The sys-1 promoter-driven NLS-tagged fluorescent protein as a control to compare to the GFP::POP-1 is analyzed only in the wild-type, and apparently not in the mutants under consideration. Phillips et al. (2007) show that sys-1p transcriptional activity is equivalent between the SGP daughters in wild-type worms, but neither those results nor the method of normalizing to a sys-1p::GFP::NLS signal in this paper address the question of whether sys-1 promoter activity is equivalent in these cells in mutants upstream in the Wnt pathway. If the current method of normalization is to be used, it seems important to normalize to the sys-1p::GFP::NLS regression in each mutant background.

Response:

Thank you for your suggestion. We used sys-1p::GFP::NLS as a control to normalize depth effects, which should be the same across all genotypes because the GFP molecules in SGPs should be equally distributed between SGP daughter cells, not because sys-1 promoter activities are similar among them. Since SGP daughters divide within a short time (about 2 hours), it is likely that the fluorescence of newly synthesized GFP (maturation time of about 1 hour) in SGP daughters is neglectable compared to GFP inherited from the SGP cells. Similarly, sys-1p::GFP::POP-1 signals in SGP daughters reflect the distribution of GFP::POP-1 from SGPs rather than the transcriptional activities of the sys-1 promoter in the daughter cells. sys-1p::GFP::POP-1 or sys-1p::GFP::SYS-1 has been widely used to evaluate polarity of asymmetric divisions in a number of studies, none of which consider transcriptional differences of the sys-1 promoter in the daughter cells.

1. How was lin-17(mn589) generated? if this is the first report of this allele, full information on what the lesion is and how it was derived should to be reported.

Response:

Thank you for your question regarding the lin-17(mn589) allele. We would like to point out that the information about this allele is provided in the Methods section of the original manuscript as follows.

"lin-17(mn589) (gifted by Mike Herman) carries a mutation in the seventh cysteine residue of the CRD domain (C104Y). mn589 exhibits 47% Psa phenotype (indicating T cell polarity defects)."

The methods section lacks a description of how the mes-1 experiments were done, in terms of timing, duration, and temperature; mes-1(bn7) is a temperature sensitive allele.

Response:

Thank you for pointing out the lack of detailed methodology for the mes-1 experiments. The germless phenotype of mes-1 mutants is partial even at high temperatures. We have not performed temperature shifts to observe the phenotype. As per your suggestion, we added the following text to the Strains section:

"mes-1(bn7) is a temperature-sensitive allele with higher penetrance of the germless phenotype at 25{degree sign}C than at 15{degree sign}C, and was grown at 22.5{degree sign}C. The germless phenotype of mes-1(bn7) was observed by the absence of the mex-5::GFP::PH signal through direct observation of epifluorescence."

Minor comments

1. The paper lacks a discussion of precedent in the literature for Wnt-independent Frizzled activity; this is a major finding that is being undersold in the current version of the manuscript.

Response:

Thank you very much for appreciating out finding. We have added the following paragraph to the Discussion section:

"Wnt-independent functions of Frizzled receptors

We have shown that lin-17/Fzd functions in a Wnt-independent manner to control SGP polarity, since the missing DTC phenotype of lin-17; cwn-2 and lin-17 mom-5 was completely rescued by ΔCRD-LIN-17. In addition, SGP polarity is normal in the quintuple Wnt mutant that has mutations in all the Wnt genes (Yamamoto et al., 2011). In seam cells, Wnt receptors including LIN-17/Fzd and MOM-5/Fzd appear to have Wnt-independent functions for cell polarization, as seam cells are still mostly polarized in the quintuple Wnt mutants, while they are strongly unpolarized in the triple receptor mutants (lin-17 mom-5 cam-1/Ror) (Yamamoto et al., 2011). In Drosophila, Fz/Fzd has been primarily considered to function Wnt-independently to coordinate planar cell polarity (PCP) between neighboring cells (Lawrence et al., 2007), though Fz function can still be regulated by Wnt, as PCP orientation can be directed by ectopically expressed Wnt proteins (Wu et al., 2013).

          In Drosophila, Fz regulates PCP by interacting with other PCP components including Van Gogh (Vang). In C. elegans, we found that vang-1/Vang does not appear to function with LIN-17/Fz, since most vang-1 single mutants and cwn-1 cwn-2 vang-1 triple mutants have two gonadal arms (215/216 and 58/58, respectively). As Fz interacts with Disheveled (DSH) in Drosophila PCP regulation, in C. elegans, the Disheveled homologs DSH-2 and MIG-5 regulate SGP polarity (Phillips et al., 2007). Therefore, LIN-17 might regulate the DSH homologs in a Wnt-independent manner. "

Added Reference：

1. Lawrence PA, Struhl G, Casal J. (2007). Planar cell polarity: one or two pathways? Nat Rev Genet. 8, 555-563.
2. Wu, J., Roman, A.C., Carvajal-Gonzalez, J.M., & Mlodzik, M. (2013). Wg and Wnt4 provide long-range directional input to planar cell polarity orientation in Drosophila. Nature Cell Biology, 15(9), 1045-1055.

Important: I think "Fig. 6 Germ cell independent migration of germ cells" title is a typo; should be "Germ cell independent migration of DTCs"

Response:

Thank you for pointing out the typo. We corrected it in the revised manuscript.

This is a very important experiment! I think a greater description of the mes-1 phenotype would be helpful, since loss of germline was not 100% penetrant in mes-1(bn7) hermaphrodites in Strome et al., 1995. The legend says "Germless mes-1 phenotype was confirmed by the absence of the mex-5::GFP::PH signal in the gonad." Consider adding a few sentences to the results (or methods, from which the mes-1 experiments are currently missing) describing that only mes-1 animals that lacked germline fluorescence were analyzed for DTC migration.

Response:

Thank you for providing the context. To address the concerns, we made the following changes to our manuscript:

1. In the Results section, we revised the sentence "We found that 84% of DTCs (n = 90) in germless mes-1 animals..." to "Among mes-1 animals that lack germ cells, we found 84% of DTCs (n = 90)...".
2. We also modified the sentence "We noticed that some germless mes-1 animals..." to "We noticed that some mes-1 animals that lack germ cells...".

Please correct "secreting the Notch ligand LAG-2" this is a membrane-bound, not secreted ligand

Response:

Thank you for your comment. In the revised manuscript, we modified the relevant sentence in the Introduction section as follows:

"Firstly, DTCs function as niche cells for germline stem cells, inhibiting their entry into meiosis by expressing the Notch ligand LAG-2 (Henderson et al., 1994)."

Fig 1. The qualitative loss of polarity would be better depicted with

a grayscale image instead of green-on-black.

Response:

Thank you for your suggestion. The GFP::POP-1 images are raw images of the green channel of the confocal microscopy. We believe that SGP polarity is clearly depicted by them.

Fig. 3 the presentation of these violin plots is confusing. The central text that reads "normal polarity, loss of polarity, reversed polarity" with arrows looks like a second Y axis label attached to the Z4 plot. I recommend rearranging. Consider shading the top, bottom, and central regions and explaining the meaning of the shading in the legend.

Response:

Thank you for your suggestions regarding the presentation of Figure 3. In response to your feedback, we have made the following modifications:

First, we moved the text and arrows from the center to the right side of the figure, creating a clearer layout. As you recommended, we applied shading to the top, bottom, and central regions of the violin plots. Additionally, to explain the meaning of the shading, we added a new explanation to the figure legend. Specifically, we included the following text:

"Values within the 95% CI (between the red lines; light green regions) indicate symmetric localization. Values below the lower red line (light blue regions) indicate reversed localization, while values above the upper red line (light red regions) indicate normal localization."

We applied the same modification to Supplemental Fig. 1.

Reviewer 2

Major comments

1- Are the effects of combining the different Wnts with the lin-17 allele specific to the n3091 allele? It would be important to test another allele, for example the sy277 allele has a similar phenotype and is available at CGC. A null would be even better if it is viable. Alternatively, lin-17(RNAi) could instead be used if efficient enough. This is important since the n3091 allele could differentially alter the binding to the various Wnts, resulting in their distinct phenotypes in that background. However, these distinct phenotypes may not be relevant in a wild-type context.

Response:

Thank you for your insightful comment. The lin-17(n3091) allele contains a nonsense mutation at the 35th codon, located between the second and third cysteine residues in the CRD domain (Wnt binding domain) (Sawa et al 1996). Therefore, it is highly unlikely that the N-terminal protein of 34 amino acids produced in lin-17(n3091) can bind to Wnts. In the revised manuscript, we added the missing-DTC phenotype of lin-17(n671) cwn-2 animals, which show a similar phenotype to lin-17(n3091) cwn-2. n671 is a reference allele in WormBase and has a nonsense mutation. Although sy277 has a deletion in the N-terminal region, its phenotype is weaker than that of n3091 and n671 (Sawa et al 1996).

In the revised manuscript, we described lin-17(n671) cwn-2, in the Table 1, Table S1 and added the following sentence.

"We observed a similar phenotype in lin-17(n671); cwn-2 double mutants, confirming that this genetic interaction is not allele-specific."

2- In the lin-17; mom-5 double mutant which lacks DTCs, are Z1 and Z4 there but they do not express DTC markers, or are they never born? A lineage analysis should be presented. Also, are Z2 and Z3 still there on their own? Please show images.

Response:

Thank you for your comments. We quantified sys-1p::GFP::POP-1 signals in Z1 and Z4 daughter cells of lin-17 mom-5 and have not observed any animals lacking Z1, Z4 or germ cells. In the revised manuscript, as Fig. S2, we added images of sys-1p::GFP::POP-1 localizations in SGP daughters, along with germ cells in lin-17 mom-5 as well as in lin-17 cwn-1 egl-20 cwn-2, both of which were not shown in the original manuscript. In response to Reviewer 1's comment, we also included examples of depth effects on fluorescence intensities in Fig. S2 with images of different focal planes.

Fig. S2 is quoted it at the end of the following sentence.

"Then, we quantified the ratios (on a logarithmic scale) of sys-1p::GFP::POP-1 signal intensities proximal to distal daughter cells in various genotypes (Fig. 3A and Fig. S2)."

The loss of polarity phenotype of lin-17 mom-5 has been described in Phillips et al. We missed to cite this in the original manuscript. We added the citation in the revised manuscript.

"These asymmetries were strongly disrupted and weakly affected in lin-17 mom-5 double and lin-17 single mutants, respectively, as described previously (Phillips et al., 2007; Siegfried et al., 2004)."

Minor comments

1- The term "mirror-symmetry" is redundant. Consider using "symmetry"

or "symmetrical polarity".

Response:

As noted in the cross-comment by Reviewer 1, we believe that "mirror-symmetry" is the appropriate term.

We think that "symmetry" implies the same lineage, whereas the relationship between the Z1 and Z4 lineages is not. "Mirror symmetry" was also used in Herman & Horvitz (1994) to describe the defect in the F lineage in lin-44/Wnt mutants as follows.

"we observed division patterns that were mirror symmetric to those of the wild type (Fig. 2). One plausible explanation is that the polarity of the first asymmetric cell division was reversed, causing the polarities of all subsequent asymmetric cell divisions also to be reversed."

2- "... they are permissively pushed distally by germ cells while proliferating" is confusing as it is unclear what proliferating cell you are referring to - germ cells or the DTC? proliferating? sense. Replace by: "they are pushed distally by proliferating germ cells"

Response:

Thank you for your helpful comment. We agree with your suggestion and modify the sentence as follows:

Original: "... they are permissively pushed distally by germ cells while proliferating" Revised: "... they are pushed distally by proliferating germ cells"

3- Fig. 2 is cited in the text before Fig. 1.

Response:

Thank you for pointing this out. Figure1 is mentioned in the Introduction before Figure 2 is referenced in the Result section in the original manuscript. We think the reviewer might be confused, as the POP-1 localization defect was shown in Figure 1. In response to the reviewer 1's comment, we moved the POP-1 localization images of the compound mutants to Figure 3. In addition, we noticed that in the original manuscript, Figure 1B was mentioned before Figure 1A in the Introduction. Therefore, we have modified the sentences in the Introduction.

The original sentence was:

"In the gonad, at the L1 stage, somatic gonadal precursor cells (SGPs), Z1 and Z4 have LH and HL polarity, respectively (Siegfried et al., 2004) (Fig. 1B). This mirror-symmetric polarity creates their mirror-symmetric lineages producing distal tip cells (DTCs) from the distal daughters (Z1.a and Z4.p) (Fig. 1B)."

The revised sentence now reads:

"In the gonad, at the L1 stage, somatic gonadal precursor cells (SGPs), Z1 and Z4 have LH and HL polarity, respectively, creating their mirror-symmetric lineages producing distal tip cells (DTCs) from the distal daughters (Z1.a and Z4.p) (Siegfried et al., 2004) (Fig. 1A and 1B)."

4- The results also suggest that MOM-5/Frizzled might be the receptor for Wnts regulating DTC production, as lin-17 mom-5 double mutants completely lack DTCs." Table 1 results rather suggest that lin-17 and mom-5 are the two frizzled receptor involved in DTC specification and that they are largely redundant.

Response:

As the reviewer noted, lin-17 and mom-5 function redundantly in DTC specification (SGP polarization). However, their functions are clearly different in terms of genetic interactions with Wnt genes (e.g. lin-17 cwn-2 but not mom-5 cwn-2 show the DTC-missing phenotype). We propose that MOM-5 but not LIN-17 functions as a receptor for Wnts.

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Referee #2

Evidence, reproducibility and clarity

This is an interesting paper where authors study how DTC specification and migration are genetically controlled. The results show a complex interaction between the various Wnt components with partially redundant roles for the ligands and receptors. This helps understanding how the two DTCs are specified and symmetrically polarized in vivo. In addition, authors convincingly show that DTCs can significantly migrate in the absence of any push by GSC proliferation.

Major

1. Are the effects of combining the different Wnts with the lin-17 allele specific to the n3091 allele? It would be important to test another allele, for example the sy277 allele has a similar phenotype and is available at CGC. A null would be even better if it is viable. Alternatively, lin-17(RNAi) could instead be used if efficient enough. This is important since the n3091 allele could differentially alter the binding to the various Wnts, resulting in their distinct phenotypes in that background. However, these distinct phenotypes may not be relevant in a wild-type context.
2. In the lin-17; mom-5 double mutant which lacks DTCs, are Z1 and Z4 there but they do not express DTC markers, or are they never born? A lineage analysis should be presented. Also, are Z2 and Z3 still there on their own? Please show images.

Minor

1. The term "mirror-symmetry" is redundant. Consider using "symmetry" or "symmetrical polarity".
2. "... they are permissively pushed distally by germ cells while proliferating" is confusing as it is unclear what proliferating cell you are referring to - germ cells or the DTC? proliferating? sense. Replace by: "they are pushed distally by proliferating germ cells"
3. Fig. 2 is cited in the text before Fig. 1.
4. "The results also suggest that MOM-5/Frizzled might be the receptor for Wnts regulating DTC production, as lin-17 mom-5 double mutants completely lack DTCs." Table 1 results rather suggest that lin-17 and mom-5 are the two frizzled receptor involved in DTC specification and that they are largely redundant.

Referees cross-commenting

Dear reviewer #1, with all due respect, I do do understand your point as the Fig. 1 in Bowerman shows lineages that are left-right "asymmetrical", not "non-mirror symmetrical"? In the paper we are reviewing, the Z1-Z4 lineages are anterior-posterior symmetrical. But this is a minor issue and we can wait to see what the authors are going to reply...

Significance

The paper is interesting but in its current form, the genetics results around DTC specification are somewhat complex and difficult to interpret. The results linked to DTC migration are easier to take home.

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Referee #1

Evidence, reproducibility and clarity

Summary

This manuscript illuminates the molecular regulation of mirror-image-symmetry in the C. elegans hermaphrodite somatic gonad precursor cells, reporting that a diversity of Wnt ligands and a Wnt receptor with Wnt-independent activity together polarize the two progenitors of the somatic gonad in opposite ways. The Wnt ligand genes act nonredundantly with respect to the polarity of the SGPs.

The conclusions arise from the careful interpretation of compound genetic mutants in the Wnt pathway, including the surprising finding of a Wnt-independent effect of lin-17/Frizzled on cell polarity that is bolstered by a new allele and construct of lin-17 that affect specifically its Wnt-interacting domain.

Major comments

While I find the genetic analyses compelling, I think the presentation of the data undermines the strength of the quantitative analysis of expression. Recommendations follow.

Genetic analyses: Major strength. Complex compound mutants are presented with ample sample sizes and careful interpretation of genetic interactions, presented clearly in tables that demonstrate large sample sizes. The new allele of lin-17 and delta-CRD LIN-17 construct reveal the surprising Wnt-independence of this Frizzled-type receptor. While of course I am interested in this Wnt-independent role, it is not necessary for the current manuscript to determine its mechanism.

Statistical analysis: When I first saw Fig. 1 B and C and read the associated results sections, I thought the qualitative polarity results reported in Fig. 1 would benefit from quantification, including at a minimum the sample size and penetrance of the phenotypes reported in Fig1B and 1C; it would be even better to quantify expression levels in the daughter cells. Then I discovered this appears to be what is reported in Figure 3. I think the presentation of these data are confusing. I recommend combining Figures 1 and 3 either before or after the results shown in Figure 2, so the reader's expectation for quantification is immediately satisfied.

Delving into the figure legend of Fig. 3 and the normalization procedure described in the Methods "Quantification of POP-1 asymmetry in the Z1 and Z4 division" raised concerns. The method therein described is overly complicated but also neglects background subtraction. My first question about this method: what range of distances between daughters is measured in Z? These distances are not discussed in absolute terms, and this is important for our understanding of how much correction for tissue depth might be necessary, as L1s are very thin.

To check my understanding, the authors use as a control a nuclear-localized GFP driven in the somatic gonad precursors in otherwise wild-type worms by the sys-1 promoter. They observe that the regression on a log scale of anterior:posterior (and vice versa) Z1 and Z4 daughter fluorescence over the distance between the daughters in the Z plane is fit by y = −0.034x + 0.0148, which is practically a slope of 0 and an intercept of 0. This means that they observed an ~1:1 ratio (as log(1)=0) of fluorescence in the anterior and posterior daughters of otherwise wild-type worms, at least across the range of very small X values of relevant distances between daughters (again, the relevant range of distances really matters and should be presented), making the normalization seem unnecessary.

Then based on this regression and 95% CI, the authors predict values that reflect true equivalence of fluorescence of POP-1::GFP in the two SGP daughters, compare the observed values to these predictions, and ultimately display in violin plots these differences of observed and expected. Correct?

Question: Is this complicated treatment the only way to detect differences in polarity of anterior and posterior daughters of Z1 and Z4? What happens if the authors measure GFP::POP-1 and calculate the following?

Z1p(MGV - background control from same focal plane)

Z1a(MGV - background control from same focal plane)

If this straightforward analysis shows asymmetric signal in the control that is made symmetrical or reversed in the mutants, the hypothesis would seem to be supported with a much more straightforward method. Samples could be analyzed separately in two bins by worm body position, which affects which cell is superficial in the sample. As it is, the Figure 3 Y axis label is hard to interpret without reading the methods at length, diminishing its impact.

Missing control: The sys-1 promoter-driven NLS-tagged fluorescent protein as a control to compare to the GFP::POP-1 is analyzed only in the wild-type, and apparently not in the mutants under consideration. Phillips et al. (2007) show that sys-1p transcriptional activity is equivalent between the SGP daughters in wild-type worms, but neither those results nor the method of normalizing to a sys-1p::GFP::NLS signal in this paper address the question of whether sys-1 promoter activity is equivalent in these cells in mutants upstream in the Wnt pathway. If the current method of normalization is to be used, it seems important to normalize to the sys-1p::GFP::NLS regression in each mutant background.

Missing methods: How was lin-17(mn589) generated? if this is the first report of this allele, full information on what the lesion is and how it was derived should to be reported.

The methods section lacks a description of how the mes-1 experiments were done, in terms of timing, duration, and temperature; mes-1(bn7) is a temperature sensitive allele.

Minor comments

The paper lacks a discussion of precedent in the literature for Wnt-independent Frizzled activity; this is a major finding that is being undersold in the current version of the manuscript.

Important: I think "Fig. 6 Germ cell independent migration of germ cells" title is a typo; should be "Germ cell independent migration of DTCs"

This is a very important experiment! I think a greater description of the mes-1 phenotype would be helpful, since loss of germline was not 100% penetrant in mes-1(bn7) hermaphrodites in Strome et al., 1995. The legend says "Germless mes-1 phenotype was confirmed by the absence of the mex-5::GFP::PH signal in the gonad." Consider adding a few sentences to the results (or methods, from which the mes-1 experiments are currently missing) describing that only mes-1 animals that lacked germline fluorescence were analyzed for DTC migration.

Please correct "secreting the Notch ligand LAG-2" this is a membrane-bound, not secreted ligand

Fig 1. The qualitative loss of polarity would be better depicted with a grayscale image instead of green-on-black.

Fig. 3 the presentation of these violin plots is confusing. The central text that reads "normal polarity, loss of polarity, reversed polarity" with arrows looks like a second Y axis label attached to the Z4 plot. I recommend rearranging. Consider shading the top, bottom, and central regions and explaining the meaning of the shading in the legend.

Referees cross-commenting

I have no comments to add to the points raised by Reviewer 2, other than to share my opinion that "mirror symmetry" is helpful terminology, as there are other possible developmental symmetries other than the "HL LH" mirror symmetry seen in the SGPs. For an example of a non-mirror developmental symmetry, see Fig. 1 of this dispatch by Bowerman 2006 https://www.sciencedirect.com/science/article/pii/S0960982206024481

Significance

Strengths: Rigorous genetics

Limitations: Data presentation, expression level analysis, communication of results.

Advance: Wnt signaling has highly conserved, ancient roles in the earliest polarity events in animal embryos, so learning how the Wnt signaling pathway dictates cell polarity, including a new Wnt-independent role for a major receptor, has broad implications for understanding cell polarity via this pathway in animal development. Another major advance is the finding that a conserved developmental signaling pathway with members that are redundant in certain contexts nonetheless elicits specific responses from certain cells during development, thus generating a diversity cell types.

Audience: These findings will be of interest to a broad audience interested in animal development, symmetry-breaking, complexity, and the Wnt-signaling pathway.

Additionally, a worm-specific audience will be interested in the finding that the DTCs are capable of germline-independent migration, as the new paradigm of Agarwal et al. ascribes DTC propulsion to germline pushing forces. How the DTC migrates is a longstanding question in our field.

Reviewer: I study developmental genetics and cell biology in the C. elegans hermaphrodite gonad. I have sufficient expertise to evaluate all of the experiments presented here. In my opinion, this is a highly valuable developmental genetics study with significant but easily addressable flaws in presentation.

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Reply to the reviewers

'The authors do not wish to provide a response at this time.'

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Referee #3

Evidence, reproducibility and clarity

Summary:

DNA double-strand breaks are harmful to cells. The authors used CRISPR-Cas9 to create DSBs in repetitive elements (Ty transposons) in the Saccharomyces cerevisiae genome. This method builds on previous ones that used the HO endonuclease or prokaryotic restriction enzymes. They used Cas9-based DSBs to assess the role of Tel1 (ATR) in DSB sensing, comparing it with the DSB-generating xenobiotic zeocin. The first part of the paper is fine, but it lacks information about the yield. The system is not as efficient as it is claimed to be, and it is slow. The second part of the paper is not well connected to the first half, and several controls are needed to make it sound.

Major comments:

1. A significant portion of the manuscript posits that the CRISPR-Cas9 system is capable of creating as many DSBs as the theoretical number of Ty targets. Nevertheless, it is clear that this is not the case. The authors can determine the number of breaks per construction. A plot of the frequency of average DSBs in chromosomes IV and III appears to be feasible based on the Southern blots presented, although this could give an underestimate, given that some broken chromosomes may become trapped in the well during DSB processing. However, the percentage of zero DSBs for a given chromosome is readily quantifiable, allowing the frequency of DSBs per cell to be determined through straightforward calculations. Once this has been achieved, the authors should revisit their assessments of Rfa1/Rad52/Tel1 foci formation and number.

2. OPTIONAL: It seems plausible that a saturation of DSBs per cell may occur when the actual number of DSBs is plotted against the theoretical maximum. In such a scenario, the introduction of additional Cas9 molecules could enhance the efficiency of DSB generation. This could be achieved by increasing the number of copies of the CAS9 gene.

3. The majority of the discussion chapter is dedicated to the second part of the paper (Tel1), yet no mention is made of the subject I have just commented on. Similarly, a comparison should be made between the Cas9-based system and previous methodologies for creating single and multiple DSBs (HO, I-SceI, restriction enzymes, radiation, radiomimetic drugs, etc.) in terms of efficiency, time to DSB, cell response, etc. It is surprising that the Cas9 system takes so long to generate DSBs and that the cell cycle profile indicates very little arrest in G2/M. This should also be discussed.

4. OPTIONAL: Since the lack of a strong G2/M arrest is intriguing, it would be good to do a Western blot of Rad53 to learn more about Cas9-based DSB sensing and the DNA damage checkpoint. Comparing it to both the well-established HO system (even a single HO cut) and radiation/radiomimetics would be ideal.

5. OPTIONAL: Given the lack of the G2/M arrest when Cas9 is expressed in asynchronous cultures, the authors may try to synchronize cells in G1 and G2/M before Cas9 is induced. This could help them find out if the G1 and G2/M peaks in Figure 2A are caused by DSBs leading to both a G1 and a G2/M arrest. Alternatively, they could film the cells after Cas9 is added and check microcolony formation.

6. The timeframes of Rad52 foci in Figure 5 indicate an anomalous pattern, which raises concerns about the validity of the experiment setup. The selected cells did not change their morphology throughout the 140-minute observation period. The unbudded cell remained unbudded, and the bud did not grow in six out of seven cells. For cells with small buds (cells 2 to 5), the expectation was that the bud would grow until the cell either became a dumbbell (indicative of a G2/M arrest) or divided its nucleus. Furthermore, no re-budding events were observed. Is this pattern real? Why is it so? Once this issue is addressed, could the duration of the Rad52 foci be quantified? Was a single z-plane taken? If so, some Rad52 foci that appear and/or disappear could reflect their migration to an on-focus or out-of-focus plane.

7. OPTIONAL: It would be beneficial to conduct a double labeling with known DSB factors that coexist with Tel1, or are downstream of it, in order to enhance the data shown in Figures 6 and 7. This would also address some of the queries raised about Tel1 clustering and location on the nuclear periphery.

8. In the experiment with the condensin mutant smc2-8, a temperature shift from 24 to 37 ºC is carried out, which represents a significant physiological change. Therefore, it is essential to include a parallel control with a WT strain to rule out the possibility that the unclustering of Tel1 foci is due to the temperature shift.

9. The sentence in the Discussion about condensin's role in maintaining Tel1 clustering is misleading. It could be interpreted as suggesting that condensin actively gathers Tel1 foci after DSBs (lines 650-651), when in fact condensin's function is simply to maintain Ty element clustering prior to DSBs, as the authors themselves cite in the text.

10. Taking into account the importance of Cas9/sgRNA plasmid constructions, PFGE and Southern blot in this work, the author should make the effort to describe them all in much more detail in M&M.

Minor comments:

1. In the Figure 7 legend, panel A is missing, and the text for the other panels is consequently misplaced.

2. The functional verification of the yEGFP-Tel1 construction (Fig. 6A & B) would be better presented as a supplementary figure. The same rationale applies to Figure 8.

3. Please include G2/M in the abbreviations.

4. Please clarify what is meant by "5h40". Is this 5 hours and 40 minutes? If so, please use alternative nomenclature.

5. Some sections of the text appear superfluous. For example, the definitions of mean, SEM and SD, and the rationale for choosing SEM over SD (lines 211 to 215), as well as the information about the purpose of PFGE in a figure legend (line 322).

Referees cross-commenting

After reading the comments of the other reviewers, I agree with them, and some of them raise the same concerns as I do.

Significance

General assessment: The study addresses the generation of multiple DSBs by Cas9 when an increasing number of targets are incorporated for cutting. The strategy to create an increasing dose of DSBs based on the different Ty elements is an innovative approach, although it is constrained by the nature of the target. The assessment of DSBs by PFGE plus Southern blot is well planned, although there is potential to exploit the obtained results further to assess the actual vs theoretical DSBs, saturation effect, etc. They then sought to use their dose-dependent system to examine the role of Tel1 in the DSB response, comparing Cas9 with zeocin. However, a comparison between the two strategies is challenging without prior knowledge of the number of DSBs present in each treatment. Overall, the study represents a promising effort to use Cas9 for the generation of multiple DSBs in yeast. Nevertheless, the system is constrained for further mechanistic studies on the DSB response due to its slow kinetics, low yields, and lack of expected DNA damage responses, particularly the G2/M checkpoint.

Advance: Despite such a dose-response assessment for the Cas9-based DSBs have not been performed in yeast, similar studies with sequence-specific DSBs have been done before in Lorraine Symington's lab (and others). Perhaps the Cas9-based system is simpler than the one that relies on multiple insertions of the HO cutting sequence, but it appears neither simpler than the one based on the expression of restriction enzymes nor more efficient. The Cas9 system has several limitations that make it less suitable for studying how cells react against multiple DSBs. It is slow, probably saturates after a few DSBs, and does not render a full DNA damage response. However, there is still value in understanding and making predictions about this important gene-editing method.

Audience: This paper is intended for a specialized audience, including those involved in basic research on DSB sensing and repair, particularly in yeast.

Field of expertise of the reviewer: Cell and molecular biology of S. cerevisiae, with a particular interest in the DNA damage response.

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Referee #2

Evidence, reproducibility and clarity

Summary.

The authors have developed a system to generate a variable number of double-strand breaks (DSBs) - specifically 1, 15, or 59 - in the genome of Saccharomyces cerevisiae in a galactose-inducible manner. This system relies on the galactose-inducible expression of Cas9 nuclease and the use of guide RNAs (gRNAs) targeting different classes of Ty elements. The efficiency of DSB induction was assessed through several methods, including monitoring cell viability in media containing galactose versus glucose, pulsed-field gel electrophoresis (PFGE), PFGE combined with Southern blotting using probes specific to different chromosomes, and the formation of foci by repair and checkpoint proteins. Specifically, the system was utilized to examine foci of the apical checkpoint kinase Tel1 and the repair factors RPA and Rad52.

From their experiments, the authors conclude that the system is capable of inducing DSBs in a controlled and dose-dependent manner, and that the DSBs are indeed induced at the chromosomal loci where the Ty elements reside.From microscopy experiments, the authors conclude that some DSBs are more persistent, while many are repaired via homologous recombination (HR). New DSBs can form as a result of the continuous expression of Cas9. Finally, Tel1 appears to form multiple foci after DSB induction and these foci localize to the nuclear periphery.

Major comments.

In my opinion, some experiments lack appropriate quantification, particularly those evaluating the dose-response effect and the efficiency of DSB induction. Additionally, some conclusions appear to be overestimated in relation to the experimental data. Further experiments and more detailed analyses would be beneficial to fully support the claims and conclusions presented. The data and methods are presented in a manner that should allow for reproducibility. The experiments are adequately replicated, but the statistical analyses are lacking in most of the figures. For example, in Figures 4B, 4C, and 6D, it would be helpful to indicate when the increase in cells containing repair foci is significant.

Additional experiments and modifications suggested:

• Figure 1B: Quantify the effect of DSB induction on cell viability with a dose-response curve. No differences are observed between the 0.5% gal and 1% gal conditions. The greatest effect on viability occurs in 2% gal and in the absence of sucrose, which might be due to the absence of sucrose. This should be addressed or discussed.
• Figure 1B and Figure 2: A quantification of galactose-induced Cas9 expression in the presence of different doses of galactose or over time, respectively, is a necessary control (mRNA or protein expression).
• Figure 2A: From the cell cycle analysis using FACS, the authors suggest that Cas9 induction causes checkpoint activation. This can be easily confirmed by using more direct methods for checkpoint activation, such as Rad53 phosphorylation. In addition, checkpoint activation should be monitored in synchronized cells released in galactose in order to evaluate whether Cas9-induced DSBs in Ty elements can trigger checkpoint in the first cell cycle or require more time. This reflects the timing of DSB induction.
• Figure 3: In addition to the restriction analysis, kinetics of DSB formation at specific Ty loci by classical Southern blot or qPCR is, in my opinion, necessary to demonstrate the effectiveness and efficiency of DSB induction over time, especially in relation to Cas9 expression.
• Figure 5: In this case as well, I believe that a quantitative analysis of the number of cells in the different conditions illustrated in the figure is necessary to understand the dynamics of repair and the formation of new DSBs. Some conclusions are somewhat strong, particularly the correlation between cell cycle phase and repair kinetics (long-lasting Rad52 foci versus short-lived Rad52 foci) and would require quantitative and statistical analysis. In addition, it is not clear to me why cells in which the Rad52 focus disappears do not proceed through the cell cycle (e.g., cells represented in rows 8 and 9 of the figure).
• Figure 7E and F: It would be interesting to see if the number of Rad52 foci per cell changes in the smc2-8 mutant to understand if RAD52 is required to form the repair center or if this depends on the clustering of Ty elements at the nuclear periphery.

Minor comments.

The authors have cited relevant literature to support their methodology, findings, and conclusions. The text and figures are clear. The descriptions in the text are precise and well-articulated, making the data easy to understand. The figures are well-designed and clearly labeled.

Specific suggestions:

• Figure 2C: Please change the order of the panel: place chromosome III at the top and chromosome IV at the bottom, as described in the text.
• Figure 4: The dose-response effect is not evident when monitoring repair foci. It appears that the proportion of cells showing RPA and Rad52 foci is generally low, especially after the formation of 59 DSBs. This is particularly concerning given that the strain in which 59 DSBs are induced already has 20% of cells with RPA foci at time = 0. The authors attribute the lower presence of foci to improved repair caused by the clustering of multiple lesions, but could it simply be due to lower Cas9 efficiency when there are many target sites?
• Related to Figure 4:The statement on line 400: "the proportion of nuclei displaying Rfa1 foci was consistently double than that of nuclei bearing Rad52 foci, probably reflecting the increased residence time of resected filaments in comparison with the process of homology search" is somewhat strong, considering that the proteins are labeled with different fluorophores, which might experience different rates of photobleaching.

I believe the proposed experiments can be completed in about 6 months. The request to monitor the kinetics of DSB formation at specific Ty elements in more detail might take more time if the PCR or Southern blot techniques need to be optimized. However, the authors may have other methods in mind that are more familiar to them for evaluating these kinetics, which could be equally valid.

Referees cross-commenting

I have carefully read the comments on the manuscript from the other reviewers. I noticed that many of our opinions coincide, and I am convinced that all the requests are appropriate.

Significance

My field of expertise centers on checkpoint regulation and homologous recombination in Saccharomyces cerevisiae. I have extensively utilized the galactose-inducible HO endonuclease system for inducing DSBs. I believe that developing additional systems to induce one or more localized DSBs in specific genomic regions is crucial for addressing unresolved questions regarding DSB response. An ideal system would also operate independently of galactose. Based on my experience, an effective system for DSB induction should induce breaks rapidly and simultaneously to produce innovative and reproducible results. From the data presented, I am uncertain whether the system developed in this work meets these criteria.

The development of a Cas9-based system capable of forming multiple DSBs could be an important tool for studying the DSB response, although I have doubts about how much it will truly enhance our understanding of damage repair. Other systems, cited by the authors, have been previously developed to produce multiple DSBs with different nucleases and using TY elements. Although the Ty-HO system to induce 1, 7, or 10 DSBs was developed in L. Symington's laboratory in 2004, the use of this system has been very limited, likely because it is not easy to monitor what happens to individual DSBs and because the efficiency of simultaneously inducing multiple DSBs may decrease with the increase in the number of target sites. I am concerned that the tool developed in this research article may have limited applicability, being relevant primarily to a small niche within the scientific community focused on DSB response, and thus might generate only a narrow interest. However, the latter part of the paper, which addresses the localization of Tel1, seems promising, despite being preliminary. Additionally, the development of a fluorescent variant of Tel1 is intriguing. This new variant appears to retain the protein's functions and forms well-visible foci within the cell, which seem to be brighter and more intense compared to those obtained with the variant previously developed by M. Lisby and R. Rothstein.

Strengths:

• The study presents an innovative system to induce a variable number of double-strand breaks (DSBs) in the genome of Saccharomyces cerevisiae using a galactose-inducible Cas9 and specific gRNAs for Ty elements.
• The authors use a variety of methods to evaluate DSB induction, including cell viability assays, PFGE, Southern blotting, and foci formation of repair and checkpoint proteins. This comprehensive approach provides sufficient evidence that DSBs are induced.
• The latter part of the article, focusing on the formation and localization of Tel1 foci, is particularly important. It sheds new light on the functions of Tel1 and its role in the DSB response. This finding is a significant preliminary indication that warrants further development, as the authors suggest in the discussion. I find the development of a tool to effectively visualize Tel1, which is a low-abundance protein in the cell, to be innovative and important for the community working in DSB repair and checkpoint.

Limitations:

• Some experiments lack appropriate quantification. For instance, a dose-response curve quantifying the effect of DSB induction on cell viability is missing. Additionally, quantification of galactose-induced Cas9 expression over time (mRNA or protein) is necessary.
• Statistical analyses are lacking in most figures. It is important to indicate when increases in cells containing repair foci are significant, particularly in Figures 4B, 4C, and 6D.
• The suggestion of checkpoint activation from cell cycle analysis using FACS should be validated with more direct methods, such as Rad53 phosphorylation, and monitored in synchronized cells to evaluate the timing of checkpoint activation.
• Further analysis is needed to demonstrate the effectiveness and efficiency of DSB induction over time, especially in relation to Cas9 expression. Monitoring the kinetics of DSB formation at specific Ty loci by classical Southern blot or qPCR would be beneficial.

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Referee #1

Evidence, reproducibility and clarity

The manuscript entitled "A CRISPR-Cas9-based system for the dose-dependent study of DNA double-strand breaks sensing and repair" by Coiffard and colleagues reports on a novel system to monitor the response to multiple DSBs in yeast by targetting Cas9 nuclease to Ty elements. The data is clearly presented, but there are several technical concerns and questions the conclusions of the manuscript. First of all, it is not clear how many DSB cells contain at any given time. The kinetics of DSB formation appears to be slow (hours) relative to the formation and progression of DNA repair foci (minutes). Tel1 foci are found primarily at the nuclear pheriphery, but given the slow kinetics and low number of foci, it is unclear whether the peripheral localization of Tel1 represents physiologically relevant repair and aberrant events where repair failed. Unfortunately, there is not assessment of the outcome of repair. Is the repair error-free or do the cells accummulate indels and/or structural changes at the Cas9 cleavage sites? Without this information, the assay will be of limited use for the scientific community.

Other major issues:

1. The introduction should reflect on the chemical structure of DSB ends and the fact that Cas9 remains bound to DNA after cleavage, which may delay repair.
2. Different Ty elements may be cut with different kinetics. The authors should compare their system to (Gnügge and Symington 2020).
3. The introduction should reflect on the physiological relevance of 15/59 DSBs.
4. What is the copy number of the Cas9 and gRNA plasmids? Could cell-to-cell variation in copy number explain the variation in the number of Tel1.
5. Do the authors observe mutations caused by leaky expression of gRNAs in the uninduced state?
6. Line 413: I don't think the data in figure 4 completely warrants the conclusion the "DSBs induced by Cas9 engage into HR in a dose-dependent manner". More foci with 15 DSBs than with 59 DSBs? Interference or other explanations? I think this discrepancy warrants additional discussion.
7. What is observed with a 1h pulse of Cas9 induction followed by glucose? Can the assay monitor the kinetics of repair?
8. The interpretation of foci in figure 5 is difficult to follow given that only 1 focus is observed while many DSBs are induced. Without further experimentation, these speculations should be moved to the Discussion.
9. Tel1 foci were always observed at the nuclear periphery (figure 6C). This information should be quantified and compared to Rad52, given that Rad52 foci have previously also been observed at the nuclear periphery for persistent DNA lesions (Whalen and Freudenreich, 2020; Nagai et al., 2008; Lisby et al., 2010). Do these foci perhaps reflect a small subset of Cas9-induced DSBs that are not repair?
10. Line 699: the notion that Tel1 senses DSBs is novel and does not fit well with the literature given that Tel1 is recruited to foci downstream of Mre11 (Lisby et al., 2004). Unless the authors can provide additional evidence, I suggest to rather write that Tel1 is an early transducer of the DNA damage response.

Minor suggestions:

1. The manuscript could benefit from correction of English grammar.
2. Figure 7F: please also include cells with 1 focus in the graph.
3. Figure 7: the labels A-F do not follow the legend.
4. Figure 3, legend: it should be stated in the legend, how long Cas9 was induced in this experiment.
5. Line 673: it could be noted that in mammalian cells, many more foci are observed, which is probably due to the larger size of the nucleus.
6. The conclusion that Tel1 behaves exceptionally in terms of the number of foci that are formed is perhaps an overstatement, since only three proteins were analyzed and the occurrence of 8 foci was rare (1:1000 cells).
7. Line 685: the word "form" indicates that the DSB are already at the nuclear periphery, when bound by Tel1. How do the authors exclude that Tel1 foci form all over the nucleus and then subsequently relocalize to the nuclear periphery? Time-lapse microscopy would be able to reveal where the Tel1 foci form.

Referees cross-commenting

I have read and agree with the comments of the other reviewers.

Significance

The manuscript entitled "A CRISPR-Cas9-based system for the dose-dependent study of DNA double-strand breaks sensing and repair" by Coiffard and colleagues reports on a novel system to monitor the response to multiple DSBs in yeast by targetting Cas9 nuclease to Ty elements. The data is clearly presented, but there are several technical concerns and questions the conclusions of the manuscript. First of all, it is not clear how many DSB cells contain at any given time. The kinetics of DSB formation appears to be slow (hours) relative to the formation and progression of DNA repair foci (minutes). Tel1 foci are found primarily at the nuclear pheriphery, but given the slow kinetics and low number of foci, it is unclear whether the peripheral localization of Tel1 represents physiologically relevant repair and aberrant events where repair failed. Unfortunately, there is not assessment of the outcome of repair. Is the repair error-free or do the cells accummulate indels and/or structural changes at the Cas9 cleavage sites? Without this information, the assay will be of limited use for the scientific community.

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Reply to the reviewers

The authors do not wish to provide a response at this time.

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Referee #3

Evidence, reproducibility and clarity

Protein kinases play an important role in regulating cell division and the dynamics of the actomyosin ring both in yeast and mammalian cells. While this raises the possibility that regulated protein dephosphorylation may also affects the division dynamics, few experimental studies have been reported. In the current study, Chrupcala and Moseley screen 9 non-essential protein phosphatases and show that the conserved protein phosphatase PP2A-B56 regulates division plane positioning in fission yeast. The authors show that Par1 localizes to the division site and that cells lacking Par1 have impaired levels of regulators of actomyosin ring positioning, including the anillin-like protein Mid1. Interestingly, restoring wildtype Mid1 levels partially suppresses the division defects observed in the par1∆ mutant.

The study is well-executed with attention to detail and careful phenotype quantification and analyses. The conclusions of the study are largely supported by experiments to yield the novel insights. This reviewer commends the authors on reporting the negative results regarding Mid1-3xSLiM mutant (Results section) and failure to detect physical interaction between Par1 and Mid1 (Discussion section). The authors provide an interesting discussion. Furthermore, they describe very relevant additional experiments but they restrict their execution to future studies that complement the current manuscript

Major concern

The authors show that par1∆ cells have impaired levels of Mid1, Cdc15 and Rga7, yet propose that Mid1 reduction is the principal cause of cytokinetic defects in par1∆ cells. Even though increase in Mid1 levels rescues par1∆ defects, it is possible that increased Mid1 levels suppress another primary defect (e.g. Rga7 increased levels). Thus, it would be interesting to perform the following two experiments: 1. The authors in the discussion say "we found that cytokinesis defects arise from decreased Mid1 levels" but this is not formally shown other than in par1∆ cells. Thus I would suggest monitor cytokinesis in cells with Mid1 levels directly reduced to levels comparable to those observed in par1∆ cells (as quantified in Fig 3B). Monitoring the effects of reduced Mid1 levels on Rga7 and Cdc15 would also be interesting. 2. Monitoring the levels of Rga7 and Cdc15 in par1∆ cells rescued by the second copy Mid1.

Minor concern

Even though definitive evidence for Par1 mechanism of Mid1 regulation might be difficult to obtain, the authors may choose to strengthen their work on the role of dephosphorylation at the actomyosin ring. For example, using the GFP-GFPnanobody pairing to force interaction between Mid1 and Par2 in par1∆ cells may provide support for dephosphorylation playing a more direct role in actomyosin ring positioning.

Referees cross-commenting

Consultation regarding Review 1:

Reviewer 3 shares the interest in points 1,3,5.

Relating to point 2: Not sure what the reviewer specifically wishes here.

Relating to point 4: Extensive analyses of many cytokinetic proteins in par1∆ cells, and importance of their levels for cytokinesis, would be interesting but perhaps beyond the scope of this study. I believe it would suffice to monitor Cdc15, Bgs1 and Rga7 in the 2xMid1 rescue that authors performed.

Consultation regarding Review 2: Reviewer 3 shares the interest in major points 1 (though authors do leave open the possibility that PP2A acts indirectly),3,4,5. Regarding point 2: This might be difficult to ascertain directly and instead it might suffice to show that Mid1 levels reduced to those observed in par1∆ phenocopy the par1 mutant's division defects.

Significance

The study is well-executed with novel findings on regulation of the cell division and the physiological roles of phosphatases. The study will benefit cell polarity and cell division research fields as well as researchers interested in roles of protein phosphatases.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this study, the authors investigates the functional relationship of Mid1, which plays a central role in the positioning of the contractile ring (CAR) in fission yeast, with protein phosphatases. Previous studies have shown that Mid1 is phosphorylated and modified by several kinases, but its relationship with phosphatases is not well understood except of Clp1. Therefore, the authors examined whether phosphorylase gene disruption strains were able to divide symmetrically. The authors found that cells lacking par1, which encodes the regulatory subunit of PP2A, frequently divided asymmetrically. Furthermore, they found that the par1 deletion mutant showed reduced protein levels of Mid1 in the cells. Furthermore, they showed that increasing the expression level of Mid1 can partially compensate for the mitotic abnormalities of the par1 strain. Since Par1 localizes to the division site, the authors also investigated the possibility of binding with Mid1, focusing on its SLiM motif. However, at this time, it is unclear whether direct binding of Par1 to Mid1 is necessary for Mid1 to exert its cellular functions, since no conspicuous cytokinesis abnormalities were observed in Mid1 with the mutation in the SLiM motif.

The focus of the study to be interesting and the clarity of the overall argument of the manuscript to be almost adequate. However, the authors should investigate or mention the following points. Also, a more appropriate and convincing way of presenting experimental results is required.

Major comments:

1. Does Par1 (PP2A) work in the dephosphorylation of Mid1? Looking at the band pattern of the western blot of Mid1 in Fig. 3E, there are several bands. This band shift probably indicates phosphorylation of Mid1, but is there any difference in the band shift between the wild-type strain and the par1 gene disrupted strain? Mid1 is phosphorylated in a cell cycle-dependent manner. So, it would be interesting to examine the cell cycle-dependent phosphorylation pattern using synchronous culture.
2. In Fig. 5A, par1- and ppa2-deficient strains show little nuclear localization of Mid1 in interphase cells. If little or no Mid1 enters the nucleus, could it be possible that the mitotic abnormality is not due to a reduction in Mid1 protein levels, but is directly due to an effect on the shuttling of Mid1 between the cell nucleus and cell surface?
3. The photographs showing cellular localization of proteins, such as Figs. 3, 4, 5, 6B, and 6C, are not convincing. The authors should show a typical picture of the cell population as supplemental figures, rather than trimming a single cell.
4. Is the Mid1-deficient strains impaired in the localization of Par1 to the division plane? As shown in Fig. 3A, the fluorescence of Cdc15 and Rga7 is predominantly enhanced in the par1 deletion strain (Fig. S2 also shows a protein with a significant difference in localization). The authors should consider these points and make more comprehensive discussion.
5. Fig. 3D and part of Fig. 5B also use the same photograph. This raises the suspicion that these data were taken only once. If so, the authors should do replicated experiments to assure the authenticity of the data. In addition, perhaps it is a careless mistake, but this way of presenting data should not be allowed unless specifically mentioned in the manuscript.

Minor point.

Typos. p. 11 Mid1-13my levels,.

Lack of uniformity in description p. 13 Methyl-2-benzimidazole and p. 15 methyl-2-benzimidazole In the figure of the paper Mid1-mNeonGreen and mid1-mNeonGreen

Significance

The importance of this study is that it deepens our knowledge of Mid1 protein of fission yeast, an important model organism for the study of cytokinesis. Although the possibility that Par1 is involved in the maintenance of Mid1 protein levels has been demonstrated, the molecular mechanism has not been clarified. It might be expected that anilline, which is similar to Mid1 in animal cells, might have a similar relationship to protein phosphatases, but there is no evidence for this. Therefore, at this moment, the effect of these findings may be limited to the fission yeast research community.

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Referee #1

Evidence, reproducibility and clarity

The study by Chrupcala and Moseley explores the role of the protein phosphatase PP2A-B56 in cytokinesis, particularly focusing on its regulatory subunit Par1 and its interaction with Mid1 in fission yeast (S. pombe). The authors report that in cells lacking Par1 (par1d), the contractile rings are mispositioned, and the levels of multiple cytokinetic proteins, including Mid1, are altered, leading to defects in cytokinesis. They suggest that the reduction in Mid1 levels is responsible for the observed cytokinetic defects and show that restoring Mid1 levels can partially rescue these defects. The study further demonstrates that Mid1 shuttling between the nucleus and cell membrane is impaired in par1d mutants. Thus, par1d seems to cause a range of defects, from altered protein levels to specifically impairing Mid1 functions. However, the study does not demonstrate a direct interaction between Par1 and Mid1, leaving the exact mechanism by which Par1 regulates Mid1 levels unclear.

The manuscript is well-written and investigates the lesser-studied role of protein phosphatases in cytokinesis, which is of general interest. However, a major criticism is the lack of evidence supporting a direct role of PP2A-B56 and its regulatory subunit Par1 in cytokinesis. It is possible that the phenotype observed in par1d cells results from a global impairment of protein regulation rather than a specific effect on Mid1. Indirect factors, such as changes in cell size/shape, could also produce similar phenotypes.

To strengthen the manuscript, the authors should consider the following points:

1. Changes in cell size/shape can influence Mid1 levels and nucleus positioning. Many par1d cells shown in the manuscript have bulgy morphologies. The manuscript should address whether cell size/morphology differences contribute to the observed phenotypes.
2. The quantification of nucleus positioning needs further consideration, as the positioning of the nucleus has been shown to be a function of cell size, etc.
3. The manuscript proposes that Par1 acts by reducing Mid1 levels and that restoring Mid1 levels can rescue the defects. However, given the known mechanisms of Mid1 localization, it is unclear why a reduction in Mid1 levels alone would lead to cytokinetic defects. To establish that the defects are due to Mid1 reduction, the authors should demonstrate that lowering Mid1 levels (e.g., using a low-expression promoter) phenocopies the cytokinesis defects observed in par1d cells.
4. As shown in par1d cells, other cytokinesis proteins have impaired levels. It should be investigated whether changes in the levels of other proteins (e.g., Cdc15, Rga7, Myp2, etc.) in par1d cells contribute to the cytokinetic defects. In general, it is not discussed how the par1d mutant can lead to differential levels of many cytokinesis proteins. Does the reduction of Mid1 explain this shift? It is conceivable that changes in the levels of other proteins could also produce similar cytokinesis defects.
5. The manuscript would benefit from additional functional rescue experiments. For example, expressing a phosphatase-dead version of Par1 in par1d cells could help determine if its phosphatase activity is necessary for cytokinesis.

Significance

The study by Chrupcala and Moseley explores the role of the protein phosphatase PP2A-B56 in cytokinesis, particularly focusing on its regulatory subunit Par1 and its interaction with Mid1 in fission yeast (S. pombe). The authors report that in cells lacking Par1 (par1d), the contractile rings are mispositioned, and the levels of multiple cytokinetic proteins, including Mid1, are altered, leading to defects in cytokinesis. They suggest that the reduction in Mid1 levels is responsible for the observed cytokinetic defects and show that restoring Mid1 levels can partially rescue these defects. The study further demonstrates that Mid1 shuttling between the nucleus and cell membrane is impaired in par1d mutants. Thus, par1d seems to cause a range of defects, from altered protein levels to specifically impairing Mid1 functions. However, the study does not demonstrate a direct interaction between Par1 and Mid1, leaving the exact mechanism by which Par1 regulates Mid1 levels unclear.

The manuscript is well-written and investigates the lesser-studied role of protein phosphatases in cytokinesis, which is of general interest. However, a major criticism is the lack of evidence supporting a direct role of PP2A-B56 and its regulatory subunit Par1 in cytokinesis. It is possible that the phenotype observed in par1d cells results from a global impairment of protein regulation rather than a specific effect on Mid1. Indirect factors, such as changes in cell size/shape, could also produce similar phenotypes.

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Reply to the reviewers

Reply to Reviewers

We would like to thank all the reviewers for their thorough reading and helpful comments. Below, please find our point-by-point response. The reviewer comments received through ReviewCommons have not been altered except for formatting.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

The authors extended the existing recombination-induced tag exchange (RITE) technology to show that they can image a subset of NPCs, improving signal-to-noise ratios for live cell imaging in yeast, and to track the stability or dynamics of specific nuclear pore proteins across multiple cell divisions. Further, the authors use this technology to show that the nuclear basket proteins Mlp1, Mlp2 and Pml39 are stably associated with "old NPCs" through multiple cell cycles. The authors show that the presence of Mlp1 in these "old NPCs" correlates with exclusion of Mlp1-positive NPCs from the nucleolar territory. A surprising result is that basket-less NPCs can be excluded from the non-nucleolar region, an observation that correlates with the presence of Nup2 on the NPC regardless of maturation state of the NPC. In support of the proposal that retention of NPCs via Mlp1 and Nup2 in non-nucleolar regions, simulation data is presented to suggest that basket-less NPCs diffuse faster in the plane of the nuclear envelope.

However, there are some points that do need addressing:

Major Points 1. Taking into account that the Nup2 result in Figure 4B forms the basis for one half of the proposed model in Figure 6 regarding the exclusion of NPCs from the nucleolar region of the NE, there is a relatively small amount of data in support of this finding and this proposed model. For example, the only data for Nup2 in the manuscript is a column chart in Figure 4B with no supporting fluorescence microscopy examples for any Nup2 deletion. Further, the Nup60 deletion mutant will have zero basket-containing NPCs, whereas the Nup2 deletion will be a mixture of basket-containing and basket-less NPCs. The only support for the localization of basket-containing NPCs in the Nup2 deletion mutant is through a reference "Since Mlp1-positive NPCs remain excluded from the nucleolar territory in nup2Δ cells (Galy et al., 2004), the homogenous distribution observed in this mutant must be caused predominantly by the redistribution of Mlp-negative NPCs into the nucleolar territory."

As suggested by the reviewer, we have added fluorescence microscopy examples for the Nup2 deletion to new Figure 4D. In addition, we have added data on Nup1 as suggested by reviewer 3. Since we observed a significant effect on nucleolar NPC density also upon depletion of Nup1 (new Figure 4A), we have overall revised the text and model to now reflect the shared role of Nup1 and Nup2.

We have also localized Mlp1-GFP in a nup2Δ background as well as in the Nup60ΔC background where Nup2 can no longer bind to the NPC. In both strains, Mlp1-containing NPCs remain excluded from the nucleolus as now shown in the new Figure 4E. Although we also observed partial Mlp1 mislocalization to a nuclear focus in the nup2Δ strain, such mislocalization was only minimal in the strain with the Nup2-binding domain in Nup60 deleted (nup60ΔC), supporting our conclusion that Nup2 contributes to nucleolar exclusion of NPCs independent of Mlp1. Similarly, Mlp1-positive NPCs remained excluded from the nucleolar territory in cells depleted of Nup1 (new Figure 4B).

1. The authors could consider utilizing this opportunity to discuss their technological innovations in the context of the prior work of Onischenko et al., 2020. This work is referenced for the statement "RITE can be used to distinguish between old and new NPCs" Page 2, Line 43. However, it is not referenced for the statement "We constructed a RITE-cassette that allows the switch from a GFP-labelled protein to a new protein that is not fluorescently labelled (RITE(GFP-to-dark))" despite Onischenko et al., 2020 having already constructed a RITE-cassette for the GFP-to-dark transition. The authors could consider taking this opportunity to instead focus on their innovative approach to apply this technology to decrease the number of fluorescently-tagged NPCs by dilution across multiple cell divisions and to interpret this finding as a measure of the stability of nuclear pore proteins within the broader NPC.

We apologize for this imprecise citation. We have modified the text to indicate that our RITE cassette was previously used in two publications. It now reads: "We used a RITE-cassette that allows the switch from a GFP-labelled protein to a new protein that is not fluorescently labelled (RITE(GFP-to-dark)) (Onischenko et al., 2020, Kralt et al., 2022)." Together with additional changes to the text throughout, we hope that our new manuscript version more clearly highlights the innovation of our approach relative to previous use cases.

1. The authors could also consider taking this opportunity to discuss their results in the context of the Saccharomyces cerevisiae nuclear pore complex structures published e.g. in Kim et al., 2018, Akey et al., 2022, Akey et al., 2023 in which the arrangement of proteins in the nuclear basket is presented, and also work from the Kohler lab (Mészáros et al., 2015) on how the basket proteins are anchored to the NPC. There is additional literature that also might help provide some perspective to the findings in the current manuscript, such as the observation that a lesser amount of Mlp2 to Mlp1 observed is consistent with prior work (e.g. Kim et al., 2018) and that intranuclear Mlp1 foci are also formed after Mlp1 overexpression (Strambio-de-Castillia et al., 1999).

Following the reviewer's suggestion, we extended our discussion of basket Nup stoichiometry and organization in the discussion section including most of the citations mentioned as well as the recent articles on the nuclear basket structure and organization (Stankunas & Köhler 2024 1038/s41556-024-01484-x, Singh et al. 2024 10.1016/j.cell.2024.07.020)

Minor Points 1. What is the "lag time" of the doRITE switching? Do the authors believe that it is comparable to the approximate 1-hour timeframe following beta-estradiol induction as shown previously in Chen et al. Nucleic Acids Research, Volume 28, Issue 24, 15 December 2000, Page e108, https://doi.org/10.1093/nar/28.24.e108

We thank the reviewer for suggesting we analyze the kinetics of RITE switching. We carried out quantitative real-time PCR on genomic DNA and found that the half-time of switching is below 20 min. The majority of the population is switched after 1 hour, similar to the results in Chen et al. This data is now included in Supplemental Figure 1A.

1. The authors could consider a brief explanation of radial position (um) for the benefit of the reader, in Figures 1E (right panel) and 2B (right panel), perhaps using a diagram to make it easier to understand the X-axis (um).

To address this, we have now included a diagram and refer to it in the figure legend and the text.

1. In Figure 1G, would the authors consider changing the vertical axis title and the figure legend wording from "mean number of NPCs per cell" to "mean labeled NPC # per cell" to reflect that what is being characterized are the remaining GFP-bearing NPCs over time?

Thank you for spotting this inaccuracy. We have changed the label to "mean # of labeled NPCs per cell".

1. In Figure 2C, the magenta-labeled protein in the micrographs is not described in the figure or the legend.

A description has been added in figure and legend.

1. In Figure S2A, there is an arrow indicating a Nup159 focus, but this is not described in the figure legend, as is done in Figure 2C.

A description has been added to the legend.

1. In Figure S3C, the figure legend does not match the figure. Was this supposed to be designed like Figure 3C and is missing part of the figure? Or is the legend a typographical error?

We apologize for this error and thank the reviewer for spotting it. The legend has been corrected (now Figure S4B).

1. In Figure S4B, the spontaneously recombined RITE (GFP-to-dark) Nup133-V5 appears in the western blot as equally abundant to pre-recombined Nup133-V5-GFP. In the figure legend, this is explained as cells grown in synthetic media without selection to eliminate cells that have lost their resistance marker from the population. In Cheng et al. Nucleic Acids Res. 2000 Dec 15; 28(24): e108, Cre-EBD was not active in the absence of B-estradiol, despite galactose-induced Cre-EBD overexpression. Would the authors be able to comment further on the Cre-Lox RITE system in the manuscript?

We note that also in the cited publication, cells are grown in the presence of selection to select (as stated in this publication) "against pre-excision events that occur because of low but measurable basal expression of the recombinase". Although the authors report that spontaneous recombination is reduced with the b-estradiol inducible system (compared to pGAL expression control of the recombinase only), they show negligible spontaneous recombination only within a two-hour time window. Indeed, we also observe low levels of uninduced recombination on a short timeframe, but occasional events can become significant in longer incubation times (e.g. overnight growth) in the absence of selection. It should be noted that in our system, Cre expression is continuously high (TDH3-promoter) and not controlled by an inducible GAL promoter. We have added the information about the promoter controlling Cre-expression in the methods section.

1. In Figure 6, the authors may want to consider inverting the flow of the cartoon model to start from the wild type condition and apply the deletion mutations at each step to "arrive" at the mutant conditions, rather than starting with mutant conditions and "adding back" proteins.

Following the suggestions of this reviewer as well as reviewer 3, we have modified our model to smore clearly represent the contributions of the different basket components.

Reviewer #1 (Significance (Required)):

Recent work has drawn attention to the fact that not all NPCs are structurally or functionally the same, even within a single cell. In this light, the work here from Zsok et al. is an important demonstration of the kind of methodologies that can shed light on the stability and functions of different subpopulations of NPCs. Altogether, these data are used to support an interesting and topical model for Nup2 and nuclear-basket driven retention of NPCs in non-nucleolar regions of the nuclear envelope.

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

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

In this study, Zsok et al. develop innovative methods to examine the dynamics of individual nuclear pore complexes (NPCs) at the nuclear envelope of budding yeast. The underlying premise is that with the emergence of biochemically distinct NPCs that co-exist in the same cell, there is a need to develop tools to functionally isolate and study them. For example, there is a pool of NPCs that lack the nuclear basket over the nucleolus. Although the nature of this exclusion has been investigated in the past, the authors take advantage of a modification of recombination induced tag exchange (RITE), the slow turnover of scaffold nups, the closed mitosis of budding yeast, and extensive high quality time lapse microscopy to ultimately monitor the dynamics of individual NPCs over the nucleolus. By leveraging genetic knockout approaches and auxin-induced degradation with sophisticated quantitative and rigorous analyses, the authors conclude that there may be two mechanisms dependent on nuclear basket proteins that impact nucleolar exclusion. They also incorporate some computational simulations to help support their conclusions. Overall, the data are of the highest quality and are rigorously quantified, the manuscript is well written, accessible, and scholarly - the conclusions are thus on solid footing.

We thank the reviewer for this assessment.

Reviewer #2 (Significance (Required)):

I have no concerns about the data or the conclusions in this manuscript. However, the significance is not overly clear as there is no major conceptual advance put forward, nor is there any new function suggested for the NPCs over nucleoli. As NPCs are immobile in metazoans, the significance may also be limited to a specialized audience.

We respectfully disagree with this assessment. First, our work demonstrates the use of a novel approach in the application of RITE that can be useful for other researchers in the field of NPC biology and beyond. For example, doRITE could be applied to study the properties of aged NPCs, an area of considerable interest due to links between the NPC and age-related neurodegenerative diseases.

Second, we characterize the interaction between conserved nuclear components, the NPC, the nucleolus and chromatin. While the specific architecture of the nucleus varies between species, many of these interactions are conserved. For example, Nup2's homologue Nup50 also interacts with chromatin in other systems, including mammalian cells, and thus may contribute to regulating the interplay between the nuclear basket and adjoining chromatin. This adds to our understanding of the multiple pathways and interactions that contribute to nuclear organization. Therefore, although the depletion of NPCs from the nucleolar territory in budding yeast may not be of direct importance, understanding the relationships between NPCs and their environment provide insight about nuclear organization throughout different eukaryotic lineages.

In the revised manuscript, we attempt to better highlight and discuss these aspects.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The manuscript of Zsok et al. describes the role of nuclear basket proteins in the distribution and mobility of nuclear pore complexes in budding yeast. In particular, the authors showed that the doRITE approach can be used for the analysis of stable and dynamically associated NUPs. Moreover, it can distinguish individual NUPs and follow the inheritance of individual NPCs from mother to daughter cells. The author's findings highlight that Mlp1, Mlp2, and Pml39 are stably associated with the nuclear pore; deletion of Mlp1-Mlp2 and Nup60 leads to the higher NPC density in the nucleolar territory; and NPCs exhibit increased mobility in the absence of the nuclear basket components.

The manuscript contains most figures supporting the data, and data supports the conclusions. However, authors need to include better explanations for figures in the text and figure legends. Lack of detailed explanation can pose challenges for non-experts. In addition, the authors jump over figures and shuffle them through the manuscript, which disrupts the flow and coherence of the manuscript.

We thank the reviewer for pointing this out. In response to the detailed comments given below, we have moved some figures and added more explicit explanations to the text to improve the flow and make it easier to follow. In addition, we have modified the figure legends throughout the manuscript to make them more accessible to the reader.

Major comments: - The nuclear basket contains Nup1, Nup2, Nup60, Mlp1, and Mlp2 in yeast. Nup60 works as a seed for Mlp1/Mlp2 and Nup2 recruitment and plays a key role in the assembly of nuclear pore basket scaffold (PMID: 35148185). Logically, the authors focused primarily on Nup60 in the current manuscript. However, NUP153 has another ortholog of yeast - Nup1, which has not been studied in this work. I recommend adjusting the title of the manuscript to: Nup60 and Mlp1/Mlp2 regulate the distribution and mobility of nuclear pore complexes in budding yeast. I also suggest discussing why work on Nup1 was not included/performed in the manuscript.

We thank the reviewer for suggesting we should test the role of Nup1. Although we had originally not considered it, since we were focusing on the interactors of Mlp1/2, we found that indeed Nup1 also contributes to nucleolar exclusion. We have therefore changed the title to "Nuclear basket proteins regulate the distribution and mobility of nuclear pore complexes in budding yeast".

• Figure 2B: I suggest choosing a more representative image for Pml39. It looks not like a stable component but rather dynamic as NUP60 or Gle1 based on figure showed in Figure 2B.

We thank the reviewer for pointing out this poor choice of panel. We selected a panel for the 14h timepoint that more clearly shows that individual foci can still be seen for Pml39 after this time. Due to its lower copy number, the foci are dimmer for Pml39 than the other stable Nups. Nevertheless, at both the 11 and 14 h timepoint, clear dots can be detected for Pml39, while e.g. Nup116 in the same figure exhibits a more distributed signal and the signal for Nup60 and Gle1 is no longer visible.

• Depletion of AID-tagged proteins needs to be supported by Western blot analysis with protein-specific antibodies, and PCR results should be included in supplementary data to demonstrate the homozygosity of the strains.

The correct genomic tagging of the depleted proteins by AID was confirmed by PCR. We include this PCR analysis for the reviewer below. Since we are working with haploid yeast cells, all strains only carry a single copy of the genes. Unfortunately, we do not have protein-specific antibodies against the depleted proteins. However, other phenotypes support the successful depletion of the protein: Mlp1-mislocalization upon Nup60 depletion, reduced transcript production in Pol II depletion (characterized previously: PMID: 31753862, PMID: 36220102), growth defect upon Nup1 depletion.

• Figure 5B: Snapshots of images from the movie are required. There are no images, only quantifications.

We have replaced the supplemental movie with a movie showing the detection by Trackmate as well as overlaid tracks. As requested, a snapshot of this movie was inserted in figure 5B. We have also moved the example tracks from the supplement to the main figure. Furthermore, we will deposit the tracking dataset in the ETH Research Collection to make it available to the community.

Description of figure legends is more technical than supporting/explaining the figure. For example, below my suggestions for Figure 1D. Please, consider more detailed explanation for other figures. (D) Left: Schematic of the RITE cassette. NUP of interest is tagged with V5 tag and eGFP fluorescent protein where LoxP sites flank eGFP. Before the beta-estradiol-induced recombination, the old NPCs are marked with eGFP signal, whereas new NPCs lack an eGFP signal after the recombination. ORF: open reading frame; V5: V5-tag; loxP: loxP recombination site; eGFP: enhanced green fluorescent protein. Right: doRITE assay schematic of stable or dynamic Nup behavior over cell divisions in yeast after the recombination.

We have modified the figure legends throughout the manuscript to make them more explanatory and helpful for the reader.

In addition, I recommend highlighting the result in the title of the figures. Please, re-consider titles for Figure S3.

We have split this figure to better group related results. The new figures S4 and S5 are entitled: " A RITE(dark-to-GFP) cassette to visualize newly assembled NPC. " and "Mlp1 truncations localize predominantly to non-nucleolar NPCs."

Minor: P.1 Line 31. Extra period symbol before the "(Figure 1A)".

Fixed

P.2 Line 10. Inconsistent writing of PML39 and MLP1. Both genes are capitalized. The same for P.4 Line 16. In some cases all letters are capitalized in other only the first one.

We are following the official yeast gene nomenclature by spelling gene names in italicized capitals and protein names with only the first letter capitalized. We are sorry that this can be confusing for readers more familiar with other model systems.

P.2 Line 18-22. The sentence is too long and hard to read. I recommend splitting it into two sentences.

We agree and have fixed this.

P.2-3 Line 46-47. The sentence is unclear. Suggestion: We expected that successive cell divisions would dilute the signal of labelled and stably associated with the NPC nucleoporins. By contrast, ...

We have modified the sentence to read: "When tagging a Nup that stably associates with the NPC, we expected that successive cell divisions would dilute labelled NPCs by inheritance to both mother and daughter cells leading to a low density of labelled NPCs. By contrast,..."

P.4 Line 17-21. Please, consider adding extra information and clarifying lines 19-21. For example, in Line 19 Figure 2B you can add that the reader needs to compare row 1 and row 4.

Thank you, we have fixed this as suggested.

P. 5 Line 15. When a number begins a sentence, that number should always be spelled out. You can pe-phrase the sentence to avoid it. Also, I recommend adding an explanation/hypothesis of why new NPCs are less frequently detected in nucleolar territory.

We have formatted the text. Interestingly, new NPCs are more frequently detected in the nucleolar territory than old NPCs. We have reformulated this section to make it clearer, also in response to the next comment.

P.5 Line 17-22. I recommend re-phrasing these two sentences. Logically, it is clear that Mlp1/Mlp2 loss mimics "old NPCs" to look more like "new NPCs", and for that reason, they are more frequently included in the nucleolar territory, but it is not clear when you read these two sentences from the first time.

We have reformulated this section to make it clearer.

P6. Line 16. No figure supporting data on graph (Figure 3B).

We have added fluorescent images of the nup2Δ strain to the figure (new Figure 4D).

P.7 Line 10-13. The sentence is unclear.

We have shortened the sentence and moved part of the content to the discussion in the next paragraph.

P.13,14 etc. If 0h timepoint has been used for normalization, why is it present on the graph?

The 0h timepoint is shown for comparison and to illustrate the standard deviation in the data.

P.15. Line 32-33. There is no image here. Potentially wrong description of the figure.

Thank you for spotting this. This was fixed (new Figure S4B).

Figures: - Inconsistent labeling of figures. For example, Fig.1, Fig.1S, Figure 2 etc.

Thank you, this has been corrected.

• Inconsistent labeling of figures. For example, Fig.1 G "mean number of NPCs per cell" - no capitalization of the first letter. Fig.1S "Fraction in population" is capitalized. In general, titles of axis should be capitalized.

Thank you for spotting this. This was fixed.

Suggestions for Figure 1D and Figure 6 are attached as a separate file.

We thank the reviewer for their suggestions to improve these figures. We have taken their recommendation and revised the figures accordingly (see also response to reviewer 1, minor point 8).

Reviewer #3 (Significance (Required)):

Zsok et al. used the recombination-induced tag exchange (RITE) approach, which is an interesting and powerful method to follow individual NUPs over time with respect to their localization and abundance. This approach has been used before in PMID: 36515990 to distinguish pre-existing and newly synthesized Nup2 populations and has been extended to other basket NUPs in this work. Using this method, the authors support the earlier data on basket nucleoporins and highlight new insights on how basket nucleoporins regulate NPCs distribution and mobility. Overall, the manuscript provides new details on the stability of nucleoporins in yeast and how these data align with the mass spectrometry and FRAP data performed earlier in other studies. The limitation of this study is the absence of data on Nup1. It was unclear why these data were not present. Additional data can be included on the dynamics of Pml39, for example, using the FRAP method. The dynamic of Pml39 at the pore was shown only using the doRITE method.

As suggested, we have tested the role of Nup1 (see above).

Unfortunately, we are not able to provide orthologous data for the dynamics of Pml39. As we discuss in the manuscript, FRAP is not suitable for the analysis of the dynamics of most nucleoporins in yeast due to the high lateral mobility of NPCs in the nuclear envelope and has previously generated misleading results for Mlp1. Furthermore, the low expression levels of Pml39 will make it difficult to obtain reliable FRAP curves for this protein. We therefore do not think that adding FRAP experiments with Pml39 will provide valuable insight.

However, in addition to the Pml39 doRITE result itself, our observation that the Pml39-dependent pool of Mlp1 exhibits stable association with the NPC supports the interpretation of Pml39 as a stable protein as well.

In general, this study represents a unique research study of basic research on nuclear pore proteins that will be of general interest to the nuclear transport field.

Field of expertise: nuclear-cytoplasmic transport, nuclear pore, inducible protein degradation. I do not have sufficient expertise in ExTrack.

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

The manuscript of Zsok et al. describes the role of nuclear basket proteins in the distribution and mobility of nuclear pore complexes in budding yeast. In particular, the authors showed that the doRITE approach can be used for the analysis of stable and dynamically associated NUPs. Moreover, it can distinguish individual NUPs and follow the inheritance of individual NPCs from mother to daughter cells. The author's findings highlight that Mlp1, Mlp2, and Pml39 are stably associated with the nuclear pore; deletion of Mlp1-Mlp2 and Nup60 leads to the higher NPC density in the nucleolar territory; and NPCs exhibit increased mobility in the absence of the nuclear basket components.

The manuscript contains most figures supporting the data, and data supports the conclusions. However, authors need to include better explanations for figures in the text and figure legends. Lack of detailed explanation can pose challenges for non-experts. In addition, the authors jump over figures and shuffle them through the manuscript, which disrupts the flow and coherence of the manuscript.

Major comments:

• The nuclear basket contains Nup1, Nup2, Nup60, Mlp1, and Mlp2 in yeast. Nup60 works as a seed for Mlp1/Mlp2 and Nup2 recruitment and plays a key role in the assembly of nuclear pore basket scaffold (PMID: 35148185). Logically, the authors focused primarily on Nup60 in the current manuscript. However, NUP153 has another ortholog of yeast - Nup1, which has not been studied in this work. I recommend adjusting the title of the manuscript to: Nup60 and Mlp1/Mlp2 regulate the distribution and mobility of nuclear pore complexes in budding yeast. I also suggest discussing why work on Nup1 was not included/performed in the manuscript.
• Figure 2B: I suggest choosing a more representative image for Pml39. It looks not like a stable component but rather dynamic as NUP60 or Gle1 based on figure showed in Figure 2B.
• Depletion of AID-tagged proteins needs to be supported by Western blot analysis with protein-specific antibodies, and PCR results should be included in supplementary data to demonstrate the homozygosity of the strains.
• Figure 5B: Snapshots of images from the movie are required. There are no images, only quantifications.
• Description of figure legends is more technical than supporting/explaining the figure. For example, below my suggestions for Figure 1D. Please, consider more detailed explanation for other figures. (D) Left: Schematic of the RITE cassette. NUP of interest is tagged with V5 tag and eGFP fluorescent protein where LoxP sites flank eGFP. Before the beta-estradiol-induced recombination, the old NPCs are marked with eGFP signal, whereas new NPCs lack an eGFP signal after the recombination. ORF: open reading frame; V5: V5-tag; loxP: loxP recombination site; eGFP: enhanced green fluorescent protein. Right: doRITE assay schematic of stable or dynamic Nup behavior over cell divisions in yeast after the recombination.

In addition, I recommend highlighting the result in the title of the figures. Please, re-consider titles for Figure S3.

Minor:

P.1 Line 31. Extra period symbol before the "(Figure 1A)".

P.2 Line 10. Inconsistent writing of PML39 and MLP1. Both genes are capitalized. The same for P.4 Line 16. In some cases all letters are capitalized in other only the first one.

P.2 Line 18-22. The sentence is too long and hard to read. I recommend splitting it into two sentences.

P.2-3 Line 46-47. The sentence is unclear. Suggestion: We expected that successive cell divisions would dilute the signal of labelled and stably associated with the NPC nucleoporins. By contrast, ...

P.4 Line 17-21. Please, consider adding extra information and clarifying lines 19-21. For example, in Line 19 Figure 2B you can add that the reader needs to compare row 1 and row 4.

P. 5 Line 15. When a number begins a sentence, that number should always be spelled out. You can pe-phrase the sentence to avoid it. Also, I recommend adding an explanation/hypothesis of why new NPCs are less frequently detected in nucleolar territory.

P.5 Line 17-22. I recommend re-phrasing these two sentences. Logically, it is clear that Mlp1/Mlp2 loss mimics "old NPCs" to look more like "new NPCs", and for that reason, they are more frequently included in the nucleolar territory, but it is not clear when you read these two sentences from the first time.

P6. Line 16. No figure supporting data on graph (Figure 3B).

P.7 Line 10-13. The sentence is unclear.

P.13,14 etc. If 0h timepoint has been used for normalization, why is it present on the graph?

P.15. Line 32-33. There is no image here. Potentially wrong description of the figure.

Figures:

• Inconsistent labeling of figures. For example, Fig.1, Fig.1S, Figure 2 etc.
• Inconsistent labeling of figures. For example, Fig.1 G "mean number of NPCs per cell" - no capitalization of the first letter. Fig.1S "Fraction in population" is capitalized. In general, titles of axis should be capitalized.

Suggestions for Figure 1D and Figure 6 are attached as a separate file.

Significance

Zsok et al. used the recombination-induced tag exchange (RITE) approach, which is an interesting and powerful method to follow individual NUPs over time with respect to their localization and abundance. This approach has been used before in PMID: 36515990 to distinguish pre-existing and newly synthesized Nup2 populations and has been extended to other basket NUPs in this work. Using this method, the authors support the earlier data on basket nucleoporins and highlight new insights on how basket nucleoporins regulate NPCs distribution and mobility. Overall, the manuscript provides new details on the stability of nucleoporins in yeast and how these data align with the mass spectrometry and FRAP data performed earlier in other studies. The limitation of this study is the absence of data on Nup1. It was unclear why these data were not present. Additional data can be included on the dynamics of Pml39, for example, using the FRAP method. The dynamic of Pml39 at the pore was shown only using the doRITE method.

In general, this study represents a unique research study of basic research on nuclear pore proteins that will be of general interest to the nuclear transport field.

Field of expertise: nuclear-cytoplasmic transport, nuclear pore, inducible protein degradation. I do not have sufficient expertise in ExTrack.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this study, Zsok et al. develop innovative methods to examine the dynamics of individual nuclear pore complexes (NPCs) at the nuclear envelope of budding yeast. The underlying premise is that with the emergence of biochemically distinct NPCs that co-exist in the same cell, there is a need to develop tools to functionally isolate and study them. For example, there is a pool of NPCs that lack the nuclear basket over the nucleolus. Although the nature of this exclusion has been investigated in the past, the authors take advantage of a modification of recombination induced tag exchange (RITE), the slow turnover of scaffold nups, the closed mitosis of budding yeast, and extensive high quality time lapse microscopy to ultimately monitor the dynamics of individual NPCs over the nucleolus. By leveraging genetic knockout approaches and auxin-induced degradation with sophisticated quantitative and rigorous analyses, the authors conclude that there may be two mechanisms dependent on nuclear basket proteins that impact nucleolar exclusion. They also incorporate some computational simulations to help support their conclusions. Overall, the data are of the highest quality and are rigorously quantified, the manuscript is well written, accessible, and scholarly - the conclusions are thus on solid footing.

Significance

I have no concerns about the data or the conclusions in this manuscript. However, the significance is not overly clear as there is no major conceptual advance put forward, nor is there any new function suggested for the NPCs over nucleoli. As NPCs are immobile in metazoans, the significance may also be limited to a specialized audience.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

The authors extended the existing recombination-induced tag exchange (RITE) technology to show that they can image a subset of NPCs, improving signal-to-noise ratios for live cell imaging in yeast, and to track the stability or dynamics of specific nuclear pore proteins across multiple cell divisions. Further, the authors use this technology to show that the nuclear basket proteins Mlp1, Mlp2 and Pml39 are stably associated with "old NPCs" through multiple cell cycles. The authors show that the presence of Mlp1 in these "old NPCs" correlates with exclusion of Mlp1-positive NPCs from the nucleolar territory. A surprising result is that basket-less NPCs can be excluded from the non-nucleolar region, an observation that correlates with the presence of Nup2 on the NPC regardless of maturation state of the NPC. In support of the proposal that retention of NPCs via Mlp1 and Nup2 in non-nucleolar regions, simulation data is presented to suggest that basket-less NPCs diffuse faster in the plane of the nuclear envelope.

However, there are some points that do need addressing:

Major Points

1. Taking into account that the Nup2 result in Figure 4B forms the basis for one half of the proposed model in Figure 6 regarding the exclusion of NPCs from the nucleolar region of the NE, there is a relatively small amount of data in support of this finding and this proposed model. For example, the only data for Nup2 in the manuscript is a column chart in Figure 4B with no supporting fluorescence microscopy examples for any Nup2 deletion. Further, the Nup60 deletion mutant will have zero basket-containing NPCs, whereas the Nup2 deletion will be a mixture of basket-containing and basket-less NPCs. The only support for the localization of basket-containing NPCs in the Nup2 deletion mutant is through a reference "Since Mlp1-positive NPCs remain excluded from the nucleolar territory in nup2Δ cells (Galy et al., 2004), the homogenous distribution observed in this mutant must be caused predominantly by the redistribution of Mlp-negative NPCs into the nucleolar territory."
2. The authors could consider utilizing this opportunity to discuss their technological innovations in the context of the prior work of Onischenko et al., 2020. This work is referenced for the statement "RITE can be used to distinguish between old and new NPCs" Page 2, Line 43. However, it is not referenced for the statement "We constructed a RITE-cassette that allows the switch from a GFP-labelled protein to a new protein that is not fluorescently labelled (RITE(GFP-to-dark))" despite Onischenko et al., 2020 having already constructed a RITE-cassette for the GFP-to-dark transition. The authors could consider taking this opportunity to instead focus on their innovative approach to apply this technology to decrease the number of fluorescently-tagged NPCs by dilution across multiple cell divisions and to interpret this finding as a measure of the stability of nuclear pore proteins within the broader NPC.
3. The authors could also consider taking this opportunity to discuss their results in the context of the Saccharomyces cerevisiae nuclear pore complex structures published e.g. in Kim et al., 2018, Akey et al., 2022, Akey et al., 2023 in which the arrangement of proteins in the nuclear basket is presented, and also work from the Kohler lab (Mészáros et al., 2015) on how the basket proteins are anchored to the NPC. There is additional literature that also might help provide some perspective to the findings in the current manuscript, such as the observation that a lesser amount of Mlp2 to Mlp1 observed is consistent with prior work (e.g. Kim et al., 2018) and that intranuclear Mlp1 foci are also formed after Mlp1 overexpression (Strambio-de-Castillia et al., 1999).

Minor Points

1. What is the "lag time" of the doRITE switching? Do the authors believe that it is comparable to the approximate 1-hour timeframe following beta-estradiol induction as shown previously in Chen et al. Nucleic Acids Research, Volume 28, Issue 24, 15 December 2000, Page e108, https://doi.org/10.1093/nar/28.24.e108
2. The authors could consider a brief explanation of radial position (um) for the benefit of the reader, in Figures 1E (right panel) and 2B (right panel), perhaps using a diagram to make it easier to understand the X-axis (um).
3. In Figure 1G, would the authors consider changing the vertical axis title and the figure legend wording from "mean number of NPCs per cell" to "mean labeled NPC # per cell" to reflect that what is being characterized are the remaining GFP-bearing NPCs over time?
4. In Figure 2C, the magenta-labeled protein in the micrographs is not described in the figure or the legend.
5. In Figure S2A, there is an arrow indicating a Nup159 focus, but this is not described in the figure legend, as is done in Figure 2C.
6. In Figure S3C, the figure legend does not match the figure. Was this supposed to be designed like Figure 3C and is missing part of the figure? Or is the legend a typographical error?
7. In Figure S4B, the spontaneously recombined RITE (GFP-to-dark) Nup133-V5 appears in the western blot as equally abundant to pre-recombined Nup133-V5-GFP. In the figure legend, this is explained as cells grown in synthetic media without selection to eliminate cells that have lost their resistance marker from the population. In Cheng et al. Nucleic Acids Res. 2000 Dec 15; 28(24): e108, Cre-EBD was not active in the absence of B-estradiol, despite galactose-induced Cre-EBD overexpression. Would the authors be able to comment further on the Cre-Lox RITE system in the manuscript?
8. In Figure 6, the authors may want to consider inverting the flow of the cartoon model to start from the wild type condition and apply the deletion mutations at each step to "arrive" at the mutant conditions, rather than starting with mutant conditions and "adding back" proteins.

Significance

Recent work has drawn attention to the fact that not all NPCs are structurally or functionally the same, even within a single cell. In this light, the work here from Zsok et al. is an important demonstration of the kind of methodologies that can shed light on the stability and functions of different subpopulations of NPCs. Altogether, these data are used to support an interesting and topical model for Nup2 and nuclear-basket driven retention of NPCs in non-nucleolar regions of the nuclear envelope.

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

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility, and clarity

Singh et al. analyze the expression and putative contribution of TEs in CD4+ T cells in HIV elite controllers. Through re-analysis of existing datasets, the authors describe broad differences in expression of TEs in ECs through analysis of RNA-seq and ATAC-seq data and come up with convincing examples where differentially-expressed innate immune genes correlate with increased accessibility of proximal TEs. Overall, the authors' conclusions are appropriately measured, though the manuscript text should be re-organized for clarity and a few further analyses are needed to support the main message of the paper.

Major comments

The manuscript would benefit from a re-organization of the figures to focus on TEs - in particular, Fig 1B, Fig 2, and Fig 3 reproduce known transcriptional differences between ECs and HCs and serve as quality controls for the authors' computational analysis. Conversely, Supplementary Fig 6 contains very interesting data on KZNF expression and should be included in the main figures.

Authors: Thank you for the suggestion. We agree that Figure S6 should be featured more prominently in the manuscript. Accordingly, we have now incorporated it into the main text as Figure 6. The TE-KZNF correlation plots, previously Figure 5C, have been relocated to this new figure to provide a cohesive presentation of all KZNF-related data within the same figure.

We’ve chosen to keep Figures 1B, 2, and 3 in their original places. We contend that they provide a foundational view of transcriptional variances in gene expression between patient groups, encompassing both previously identified and novel DEGs, which we believe warrants their placement in the main text. Furthermore, they serve as robust quality control measures for subsequent TE-centric transcriptional analyses. Given that there is no limitation in the number of figures in Genome Biology articles, we think it’s adequate to retain them as main figures.

It remains unclear whether differences in TE expression described are specific to ECs or to EC-like CD4+ T cell states. As there are plenty of datasets available that compare the transcriptome of naïve, activated, exhausted, and regulatory CD4+ T cells, the authors should compare the TE expression patterns observed in ECs to activated CD4+ T cells, particularly those with a Th1 and cytotoxic phenotype analogous to those observed in ECs, from healthy donors.

Authors: We thank the reviewer for this constructive suggestion to further study the foundations of HIV-1 elite control. In our initial study, we demonstrate that PBMCs from elite controllers (ECs) exhibit a heightened proportion of activated CD4+ T cells compared to PBMCs of healthy controls (HCs) and a heightened proportion of macrophages, naïve CD4+ T cells, and NK cells compared to PBMCs of treatment-naïve viremic progressors (VPs) (Figure 2D). Additionally, through clustering analysis of deconvoluted CD4+ T cell samples from elite controllers, we ascertain that the clustering pattern is not predicated on the CD4+ T cell subtype (Figure 3B). To further explore the reviewer’s inquiry, we compared the TE expression profile of ECs with that of unstimulated and stimulated CD4+ T cell subsets from HCs (data source: PMID 31570894), integrated into the revised manuscript as Figure S3B.

“Unsupervised clustering of these samples shows that the TE expression pattern of ECs is most similar to that of Th2 progenitor cells, which are associated with HIV-1-specific adaptive immune responses (61). Still, we observed that, for the majority of families, TE expression was higher on average in all EC CD4+ T cell subsets than in CD4+ T cell subsets from HCs, regardless of stimulation (Figure S3B). While a subset of TE families exhibited an expression pattern in ECs similar to that of activated CD4+ T cells of HCs (e.g., high expression of L1s and THE1B), multiple TE families appear to be upregulated in an EC-specific way (e.g., LTR12C and LTR7). Together, these findings underscore the unique immune cell composition, transcriptome, and retrotranscriptome of ECs.” [pg.13-14, L226-235]

While these observations are interesting, pursuing this question further falls beyond the scope of our study, as we note in the Discussion of the revised manuscript. We believe the reviewer’s inquiry pertains to a distinct research question, namely whether the potential for elite control of HIV-1 infection manifests as a detectable phenotype pre-infection within healthy CD4 T cell subsets (i.e., EC-like CD4+ T cell states) or is a unique phenotype that emerges solely after HIV-1 infection.

“Another outstanding question is whether the gene and TE signatures revealed by our analysis of ECs exist in the general population independent of HIV-1 infection or if they are driven by the initial infection. While this inquiry is beyond the scope of this study, we have presented here evidence of common TE signatures between EC CD4+ T cells and Th2 progenitors from HCs (Figure S3B) and established that ECs possess a unique CD4+ T cell retrotranscriptome with potential implications for natural HIV-1 control. Future studies designed to assess elite control prediction should explore whether these TE profiles can serve as predictive variables for whether an individual displays enhanced viral control.” [pg. 38, L663-671]

Therefore, while we appreciate the reviewer's suggestion and offer the addition of these preliminary findings, we believe that further investigation would be better suited for future studies specifically designed to address that question. Our manuscript aims to provide insight into the retrotranscriptome dynamics in ECs and their potential implications for natural HIV-1 control.

In Fig 1, the authors demonstrate differential expression of both innate immune genes and TEs, but the link between the two is unclear. Is there any enrichment in differential expression for TEs located proximal to innate immune genes? This type of analysis should be possible using the authors' own software to map TE expression to specific genomic loci.

__Authors: __Thank you for this excellent question. To answer this inquiry, we used the paired ATAC-seq and RNA-seq datasets for from ECs and HCs (used in Figures 1 and 4) to produce a new list of TE-gene pairs on which we could perform gene set enrichment analysis, the results of which have been integrated into the revised manuscript as Figure 4A.

“We used paired ATAC-seq – which measures chromatin accessibility – and RNA-seq datasets for ECs (n=4) and HCs (n=4) to create a list of TE-gene pairs where the TE locus and gene show increased accessibility and expression, respectively, in ECs compared to HCs (Table S7, see Methods for details). These loci and genes were paired based on proximity, with a maximum distance of 10kb between the TE locus and the gene’s transcription start site, to increase the likelihood of a direct cis-regulatory influence of the TE over the nearby gene. Subsequent gene set enrichment analysis revealed that these genes were predominantly involved in cellular activation, cytokine production, and immune response regulation (Figure 4A). The enrichment for differential accessibility of TE loci near genes involved in these pathways suggests that the distinct TE landscape observed in ECs may contribute significantly to a unique immune regulome in these individuals.” [pg. 21, L357-368]

Thus, we conclude that yes, there is an enrichment for immune-related genes with higher expression in ECs, proximal to differentially accessible TEs. We highlight six of these TE-gene pairs in Figure 4B-C. While we have high confidence in our analyses, future experimental validation is needed to confirm these regulatory relationships.

Optional: In Fig 3, the authors cluster CD4+ T cells based on transcriptomic profiles. It would be interesting to re-cluster these samples based on TE expression alone, given the differences in TE expression described in Fig 5.

__Authors: __Thank you for the suggestion. We agree that it would be valuable to assess how the EC clustering is altered when considering TE expression alone, as opposed to combining gene and TE family expression. To address this, we used the same graph-based k-nearest neighbors method to re-cluster the EC CD4+ T cell RNA-seq samples based only on locus-level TE expression, integrated into the revised manuscript as Figure S7.

“To further explore locus-level expression patterns, we re-clustered the same EC samples (n=128) using only locus-level TE expression. This again resolved four EC clusters (Figure S7A), which interestingly appeared even more distinct than those identified by gene and TE family expression (Figure 3A). The TE locus-based clusters (TL-Cs) aligned well with the gene and TE family clusters (GT-Cs), with an average 70% overlap in samples between each GT-C and its corresponding TL-C (Figure S7B), indicating high consistency (Table S8). The remaining 30% of samples that shifted between clusters did so consistently within individuals, not cohorts, maintaining heterogeneous TL-C compositions similar to the GT-Cs (Figures S7C & S5A). An exception to this heterogeneity was TL-C4, comprising 22 samples from GT-C1 that were almost entirely from the CD4+ T cell subsets of only four participants in the Jiang cohort (Figure S7C, Table S8). No other samples from the Jiang cohort shifted to this cluster from other GT-Cs, suggesting that these patterns reflect individual variation rather than cohort bias. Like the GT-Cs, each TL-C included samples from all five CD4+ T cell subsets and was largely heterogeneous (Figure S7C). Notably, TL-C2 mirrored corresponding GT-C3 in its overrepresentation of EM and TM cells, while TL-C1 uniquely showed an overrepresentation of naïve CD4+ T cells. Beyond sample composition, each TL-C was characterized by a unique pattern of expressed TE loci (Figure S7D). These signatures were heterogeneous across families, with subsets of variable loci from one TE family marking separate clusters (Figure S7E), some of which did not reach the threshold of significance in earlier analyses when analyzed at the family-level, like SVA-D. Many families maintained their cluster-specific signatures, like THE1B (a marker of GT-C2), for which the majority of variable loci were found in corresponding TL-C1. However, some TE families, like the L1s that marked GT-C1, showed more heterogeneous signatures with variable loci marking multiple TL-Cs. These findings underscore the need for future locus-level investigations with high-depth sequencing to fully capture the complexity of TE expression.” [pg. 27-28, L462-488]

We believe these findings not only validate the distinct clustering patterns observed but also highlight the potential of locus-level TE analysis to reveal additional layers of retrotranscriptomic diversity in EC CD4+ T cells.

Significance

The manuscript by Singh et al. describes for the first time the role of TEs in HIV elite controllers, suggesting that TEs may be co-opted for cis-regulatory function. This study builds off prior work demonstrating that HIV-infected CD4+ T cells activate LTR elements that may regulate the expression of interferon-inducible genes, demonstrating that ECs show further upregulation of innate immune genes. While these findings will need to be experimentally validated, this study constitutes a useful resource and adds to the growing body of evidence implicating TEs in cis-regulatory control of immune genes. This study will be of interest to basic scientists interested in genetic mechanisms of HIV control, and if further developed may comprise a useful source of biomarkers to predict viral kinetics in HIV-infected individuals. My expertise is in immunology, TE biology, and viral infection.

Authors: We greatly appreciate this positive evaluation of our manuscript and recognition of its significance in uncovering novel evidence of TE co-option for immune regulatory function in HIV-1 elite control, as well as the suggestion of promising avenues for future research in this field.

Reviewer #2

Evidence, reproducibility and clarity

The authors have re-analyzed published RNA-Seq data from CD4 T cells isolated from HIV elite controllers and reference cohorts, including HIV negative persons, viremic progressors and ART-treated persons. Their main finding is that in some of their comparisons, EC have higher levels of interferon-stimulated genes (ISG), paired with distinct expression patterns of transposable elements. The authors suggest that expression of transposable elements may induce altered expression of ISG, presumably due to immune recognition of TE. They also suggest that reduced expression of KZNF genes, which encode for transcription factors that can suppress TE, may be responsible for enhanced expression of TE. I have the following comments:

1. All data included in this manuscript derive from previously published data. A new dataset, specifically designed to focus on a high-resolution analysis of TE expression, would be better suited to address the proposed questions.

Authors: We agree that a new dataset tailored specifically for high-resolution analysis of TE expression would be optimal for addressing the proposed inquiries, and we emphasize this point in the Discussion of the revised manuscript.

“We found that distinct sets of innate immunity genes and restriction factors are upregulated in different EC clusters even in the absence of active viremia, suggesting that elevated basal expression of these factors plays a previously underappreciated role in the EC phenotype. Further studies will be necessary to cement this idea and would especially benefit from the integration of single-cell omics to dissect TE regulation and clustering in deconvoluted CD4+ T cells of ECs. We also acknowledge that our study is limited by the small number of EC individuals with available omics data, which likely limited our ability to identify significant relationships between transcriptome clustering and available participant metadata (Figure S5). While the rarity of ECs in the seropositive population makes it challenging to study this phenotype, the transcriptomic heterogeneity revealed by our analyses underscores the need for surveying larger and more diverse EC cohorts.” [pg. 37-38, L651-662]

Regrettably, we do not have access to elite controller samples (which are exceedingly rare), and as such the addition of a novel dataset was not feasible within the scope of this revision. Nevertheless, we assert that the publicly available sequencing data analyzed here is robust and suitable for locus- and family-level TE analysis. All sequencing runs were paired-end and of high depth, ensuring proper alignment to and high coverage of TEs at a locus-specific resolution. Additionally, we use in-house pipelines curated for TE analysis, to optimize the accuracy and quantity of TE-assigned reads (see Methods and our GitHub Repository for more details).

Authors: We agree that a new dataset tailored specifically for high-resolution analysis of TE expression would be optimal for addressing the proposed inquiries, and we emphasize this point in the Discussion of the revised manuscript.

“We found that distinct sets of innate immunity genes and restriction factors are upregulated in different EC clusters even in the absence of active viremia, suggesting that elevated basal expression of these factors plays a previously underappreciated role in the EC phenotype. Further studies will be necessary to cement this idea and would especially benefit from the integration of single-cell omics to dissect TE regulation and clustering in deconvoluted CD4+ T cells of ECs. We also acknowledge that our study is limited by the small number of EC individuals with available omics data, which likely limited our ability to identify significant relationships between transcriptome clustering and available participant metadata (Figure S5). While the rarity of ECs in the seropositive population makes it challenging to study this phenotype, the transcriptomic heterogeneity revealed by our analyses underscores the need for surveying larger and more diverse EC cohorts.” [pg. 37-38, L651-662]

Regrettably, we do not have access to elite controller samples (which are exceedingly rare), and as such the addition of a novel dataset was not feasible within the scope of this revision. Nevertheless, we assert that the publicly available sequencing data analyzed here is robust and suitable for locus- and family-level TE analysis. All sequencing runs were paired-end and of high depth, ensuring proper alignment to and high coverage of TEs at a locus-specific resolution. Additionally, we use in-house pipelines curated for TE analysis, to optimize the accuracy and quantity of TE-assigned reads (see Methods and our GitHub Repository for more details).

1. As the authors acknowledge, the described investigations are exploratory, and do not allow to draw firm conclusions. Mechanistic experiments are recommended to address the authors' hypotheses.

Authors: We agree and have duly acknowledged throughout the Discussion the exploratory nature of our investigations and the need for future mechanistic experiments to validate our model. Below are passages from the revised manuscript which we’ve added to emphasize these points.

“These findings underscore the need for future locus-level investigations with high-depth sequencing to fully capture the complexity of TE expression.” [pg. 28, L486-488]

“Each step in the model will require experimental work to be validated. First and foremost, it will be important to confirm that the TEs exhibiting increased transcript levels and accessibility in ECs are indeed boosting the innate immune response and control of HIV-1 in these individuals.” [pg. 34, L583-586]

“CRISPR-Cas9 editing was used in cell lines to demonstrate that a subset of MER41 elements function as enhancers driving the interferon-inducibility of several innate immune genes. However, the specific MER41 loci we identified here as differentially active in ECs have not been tested experimentally for enhancer activity. Thus, further work is warranted to confirm the regulatory function of these loci under the control of STAT1 or other immune TFs, as well as other TE families identified as targets of immune-related TFs (Figure S8).” [pg. 35, L594-600]

“Overall, our results reinforce the concept that TEs are important players in the human antiviral response (25,93) and uncover specific candidate elements for boosting cellular defenses against HIV-1 in ECs. We acknowledge that these associations are drawn from correlative patterns and manipulative experiments are needed to infer causality between chromatin changes at these TEs and increased expression of nearby immunity genes.” [pg. 36, L618-623]

“Further work is needed to validate TE-KZNF regulatory interactions in T cells, probe their connection to epigenetic variation at individual TE loci, and explore their repercussions on gene expression variation in CD4+ T cells, with and without HIV-1 infection.” [pg. 40, L715-718]

Thus, while we appreciate and agree with the suggestion of experimental validation, we contend that these experiments fall beyond the scope of the present study, which is a computational investigation providing insight into the EC retrotranscriptome and its potential implications for natural HIV-1 control.

1. An important limitation is that virological data of EC are not considered. For example, I believe it is a lot more likely that the upregulation of ISG in EC relates to ongoing low-level viral replication. The authors could analyze cell-associated HIV RNA and DNA levels and determine how they associate with ISG expression.

Authors: Thank you for bringing up this important consideration. It's worth noting that the public datasets used in our study reported undetectable viremia in the EC volunteers (PMIDs 30964004, 29269040, 32848246, 27453467). Nonetheless, we sought to address this limitation and explore the potential association between ISG expression and viremia as recommended by the reviewer. These analyses were integrated into the revised manuscript as Figure S6.

“To exclude the possibility that these gene expression signatures in ECs are associated with viremia, we quantified HIV-1 transcript levels in deconvoluted CD4+ T cell RNA-seq samples from ECs and ART-treated PLWH for comparison. In the original studies, all samples were reported to have undetected viremia by blood tests (9,37-39). Consistent with this, we found that the vast majority of the EC and ART samples taken from PBMCs exhibited very low HIV-1 transcript levels, with TPM values generally below 1. However, in samples originating from the lymph nodes of EC individuals (n = 22) (37), we detected HIV-1 expression in some subsets (Figure S6A&B). In agreement with the corresponding study (37), we found elevated HIV-1 transcript levels in germinal center and non-germinal center T follicular helper cells (GC Tfh & nGC Tfh, not included in our clustering analyses) -- and to a lesser extent in T effector memory (EM) cells (Figure S6A, average TPM This added analysis confirms that the increased expression of ISGs in ECs is not correlated with virological transcription and is therefore likely not to be driven by viremia.

1. KZNF genes seem downregulated in EC. Can the authors propose a reason/mechanism for that?

Authors: There is the possibility that KZNF regulatory loops are the cause of their transcriptional downregulation, which has been documented in embryogenesis (PMID 31006620) and cancer (PMID 33087347). We’ve incorporated this hypothesis into the Discussion as an additional consideration for the reader.

“These observations suggest that interindividual variation in KZNF expression in CD4+ T cells could explain why certain TEs are variably expressed and accessible across ECs. But what are the mechanisms underlying variation in ZNF expression? It is possible that TE-KZNF regulatory loops are involved, in which a copy of the TE family targeted by a KZNF is inserted near and regulates the KZNF gene, thereby introducing a negative feedback loop. This phenomenon has been documented in prior studies of KZNF activity in embryogenesis (51) and cancer (115).” [pg. 39-40, L705-711]

While we believe this is a viable hypothesis, it requires further experimentation to confirm the existence of this phenomenon and its impacts in the context of immune cells.

Significance

Overall, I think this is an interesting manuscript that proposes distinct and potentially important mechanisms that may contribute to immune control of HIV. My suggestions to improve the manuscript are complex and cannot be easily addressed through experimental work. I believe a possible option would be to publish the present manuscript without my proposed modifications but highlight the weaknesses of the current paper more clearly; mechanistic studies could then be deferred to a future study.

Authors: We appreciate the reviewer's positive assessment of our manuscript and their recognition of its significance in elucidating novel TE-derived mechanisms that may contribute to natural HIV-1 control. We agree that mechanistic studies are required to test our predictions. As the reviewer suggests, these would be complex experiments that we feel fall beyond the scope of this study. With the additions detailed above in response to the reviewer’s point #2, we believe that we have clearly highlighted the limitations of our work and emphasized the need for future experimentation to validate our findings.

Reviewer #3

Evidence, reproducibility, and clarity

Summary: This manuscript presents an analysis of published gene expression (RNA-seq and ATAC-seq) data from a couple of cohorts of HIV-infected elite controllers (EC), as compared to uninfected controls, (HC), virological progressors (VP). The authors report that HIV elite controllers may exhibit 4 distinct patterns of TE (and gene) expression and suggest that TE expression may drive some form of antiviral gene expression. Further, they show that heterogeneous TE expression may be determined by differential KZNF gene activity among the different clusters of elite controllers. These results are very interesting, even though the conclusions are very preliminary. It presents intriguing correlations between expression of certain TE groups of LINES and HERVs, and the clustering into 4 gene expression groups in EC and is a novel finding. That said, correlation is not causation, and the authors need to be more cautious in presenting their highly preliminary model in Figure 6.

Authors: We are grateful for the reviewer's insightful assessment of our manuscript, acknowledging the novelty and interest of our findings regarding TE expression patterns in HIV-1 elite controllers. We also appreciate their constructive feedback regarding the cautious interpretation of preliminary conclusions. In the revised manuscript, we have underscored the exploratory nature of our investigations and the need for future mechanistic experiments to validate our model.

“These findings underscore the need for future locus-level investigations with high-depth sequencing to fully capture the complexity of TE expression.” [pg. 28, L486-488]

“Each step in the model will require experimental work to be validated. First and foremost, it will be important to confirm that the TEs exhibiting increased transcript levels and accessibility in ECs are indeed boosting the innate immune response and control of HIV-1 in these individuals.” [pg. 34, L583-586]

“CRISPR-Cas9 editing was used in cell lines to demonstrate that a subset of MER41 elements function as enhancers driving the interferon-inducibility of several innate immune genes. However, the specific MER41 loci we identified here as differentially active in ECs have not been tested experimentally for enhancer activity. Thus, further work is warranted to confirm the regulatory function of these loci under the control of STAT1 or other immune TFs, as well as other TE families identified as targets of immune-related TFs (Figure S8).” [pg. 35, L594-600]

“Overall, our results reinforce the concept that TEs are important players in the human antiviral response (25,93) and uncover specific candidate elements for boosting cellular defenses against HIV-1 in ECs. We acknowledge that these associations are drawn from correlative patterns and manipulative experiments are needed to infer causality between chromatin changes at these TEs and increased expression of nearby immunity genes.” [pg. 36, L618-623]

“Further work is needed to validate TE-KZNF regulatory interactions in T cells, probe their connection to epigenetic variation at individual TE loci, and explore their repercussions on gene expression variation in CD4+ T cells, with and without HIV-1 infection.” [pg. 40, L715-718]

We hope these passages provide sufficient caution and clarity in the presentation of our scientific inquiry.

Major comments:

Overall, although preliminary, as the authors note, the results are interesting and worthy of follow-up. At this point, however, a number of issues arise that need further clarification and analysis before I would consider this study complete.

First, the analyses shown in Figures 3-5 based on data from studies on EC of CD4 cells are apparently motivated by the differential TE expression in total PBMCs shown in Fig 1 and 2. Yet, the TE groups (please don't use taxonomic terms like "subfamily") identified in Fig 2 and Fig 4 are completely different, with no overlap. This discrepancy underscores the possibility that the differential expression observed is, at least in part, due to the differences among the groups or clusters in cell type composition, as seen in Fig 2D and 3B which, themselves, could be a consequence of HIV infection and elite control (which has been shown to involve ongoing, albeit low-level, virus replication). This issue must be addressed.

Authors: Thank you for the suggestion. First, we’d like to clarify that the data used in Figures 1 and 2 were not both derived from PBMCs. Figures 1 and S1 examine the differential expression of TEs in EC CD4+ T cells compared to HCs and ART-treated PLWH, respectively. Figure 2 examines differential expression of TEs in EC PBMCs compared to treatment-naïve VPs. Second, regarding Figure 4B-C, the TE loci that we chose to highlight were not based on our results from the PBMC analysis in Figure 2, which is why there is no overlap in the TE families presented. Instead, we selected those TE-gene pairs based on 1) known function of the genes in immunity and/or HIV-1 restriction, 2) known contribution of the TE families to immunity, and 3) differential accessibility and expression of the TEs and genes respectively in ECs compared to HCs. Thus, Figure 4B-C represents select examples that we deemed particularly relevant to the EC phenotype. We have revised the manuscript to better explain the process of TE-gene pair identification and the rationale behind our selection for Figure 4B-C.

“We used paired ATAC-seq – which measures chromatin accessibility – and RNA-seq datasets from the CD4+ T cells of ECs (n=4) and HCs (n=4) (39) to create a list of TE-gene pairs where the TE locus and gene show increased accessibility and expression, respectively, in ECs compared to HCs (Table S7, see Methods for details). These loci and genes were paired based on proximity, with a maximum distance of 10kb between the TE locus and the gene’s transcription start site, to increase the likelihood of a direct cis-regulatory influence of the TE over the nearby gene.” [pg. 21, L357-363)

“In Figure 4B & 4C, we have highlighted six of the TE-gene pairs from Table S7 based on the gene’s function in HIV-1 restriction and the TE family’s known contribution to immune gene regulation.” [pg. 21, L369-371]

Regarding cell type composition, we acknowledge that the differences observed in the proportion of immune cell subtypes may contribute to the differential expression between ECs, VPs, and HCs (Figures 2D and S3A). However, we provide evidence that cell type composition cannot be the sole driver for the clustering of deconvoluted CD4+ T cell RNA-seq samples (Figure 3B and S5D). Cell subtype alone could not explain the observed clustering of EC samples by gene and TE family expression. Clusters 1 and 2, for example, had nearly identical subtype compositions, but were clearly separated on the UMAP (Figures 3A, 3B, and S5D). We remark on this in the Results of the revised manuscript.

“[W]e visualized the samples by cellular subtype, as identified in the original studies, to assess whether the clustering could be explained by CD4+ T cell subtype composition (Figure S5D). Clusters 1 and 2 were essentially indistinguishable in cell type composition, whereas Clusters 3 and 4 showed an overrepresentation of TM/EM and naïve/CM cell types, respectively (Figure 3B). Thus, cell subtype composition could only partially explain the clustering.” [pg. 16, L271-276]

The EC CD4+ T cell clusters also had unique gene ontology, gene & TE expression, and TE accessibility profiles (Figures 3C, 3D, 5). Moreover, while we do not have parallel RNA- and ATAC-seq data from similarly deconvoluted CD4+ T cells of ECs like those used in the clustering analysis (PMIDs 32848246 & 27453467), the original article from which we sourced the parallel RNA- and ATAC-seq data used in Figures 1 and 4 reported that these samples are predominantly effector memory CD4+ T cells (PMID 30964004). If new deconvoluted, multi-omic datasets from ECs become available, we would be interested in further exploring the contribution of cell type composition. However, the current data indicate that it is not a major contributor to the differential TE expression identified in our analyses.

Regarding the impact of ongoing HIV-1 replication upon the unique expression patterns in the EC participants, it's worth noting that the public datasets used in our study reported undetectable viremia in the EC volunteers (PMIDs 30964004, 29269040, 32848246, 27453467). Nonetheless, we sought to address this by quantifying HIV-1 transcription and exploring its potential association with interferon-stimulated gene (ISG) expression, a group of genes that we know would be reactive to active viremia. These analyses were integrated into the revised manuscript as Figure S6.

“To exclude the possibility that these gene expression signatures in ECs are associated with viremia, we quantified HIV-1 transcript levels in deconvoluted CD4+ T cell RNA-seq samples from ECs and ART-treated PLWH for comparison. In the original studies, all samples were reported to have undetected viremia by blood tests (9,37-39). Consistent with this, we found that the vast majority of the EC and ART samples taken from PBMCs exhibited very low HIV-1 transcript levels, with TPM values generally below 1. However, in samples originating from the lymph nodes of EC individuals (n = 22) (37), we detected HIV-1 expression in some subsets (Figure S6A&B). In agreement with the corresponding study (37), we found elevated HIV-1 transcript levels in germinal center and non-germinal center T follicular helper cells (GC Tfh & nGC Tfh, not included in our clustering analyses) -- and to a lesser extent in T effector memory (EM) cells (Figure S6A, average TPM Based on these results, we have concluded that the differential expression of genes and TEs in the EC clusters are not a consequence of low-level viral transcription in ECs.

Finally, a remark on TE nomenclature: The reviewer suggests that we use the term “TE groups” as opposed to taxonomic terms such as TE subfamily or TE family. We respectfully disagree. This nomenclature of TEs has been well defined (PMIDs 26612867, 26612867, 17984973) and is widely used in TE literature. Throughout the manuscript, we have conformed to the nomenclature used to annotate the human genome. One can debate the way TE families and subfamilies have been classified in Dfam (the database through which repetitive elements in the human genome have been annotated), but it is outside the scope of this study to revisit that nomenclature.

Similarly, of the 12 DE TE groups in EC in Fig 5A, only 3 overlap with the 16 in EC Fig S1.

Authors: This is correct, but we don’t believe it’s concerning. In Figure 5A, we are comparing the expression of TE families between separate EC clusters. In Figure S1, we are comparing the expression of TE families in ECs compared to ART-treated PLWH. These are fundamentally different comparisons and thus the differences in the identified DE-TEs between the two figures reflect the distinct biological contexts being investigated in each analysis.

Second, the introduction points out the strongly supported association between elite control and immunogenetic determinants, most notably specific HLA-B types, but also innate immunity factors. This cries out for inclusion of these factors in the analyses of this manuscript, in the format of Figure S4, for example, but none is to be found. The relevant genotypes are likely available in the metadata in the references cited, but, if not, could be inferred from the RNA-seq data.

Authors: Thank you for the recommendation. While our project’s primary focus is on the transcriptomic and epigenomic signatures, we agree that studying the HLA-B genotypes of all EC participants could provide valuable context for understanding the clustering of elite controllers. To explore this, we inferred the HLA-B alleles in each EC participant whose RNA-seq data was included in the clustering analysis, utilizing the arcasHLA tool (PMID: 31173059) on the total CD4+ T cell samples. We then validated these inferred HLA-B alleles against the available metadata from one of the source studies (PMID 27453467) and found that they matched for all participants. This strengthened our confidence in the accuracy of the HLA-B genotype inferences for the other samples where comprehensive HLA-B data was not provided.

In order to assess how these protective HLA-B alleles segregated between the four EC clusters derived from gene and TE family expression, we chose to visualize three of the most common alleles associated with HIV-1 elite control: HLA-B*27:03, *57:01, and *57:03 (PMIDs 30964004, 25119688, 21051598) (Figure R1, available in the Response to Reviewers PDF).

Our analysis revealed that these major protective alleles were not significantly overrepresented in any particular cluster. Consequently, we believe that HLA-B genotype does not have a major impact on the clustering observed in Figure 3.

It would also be very useful to present the KZNF data in Figure 5 the same way, since, looking at Fig 5C, the correlation of high and low KZNF expression, while clearly correlated with a that of few groups of elements, with clustering into specific groups does not appear to be well supported.

Authors: Thank you for the insightful suggestion. While the KZNF genes are included in the gene set used for the clustering analysis in Figure 3, we agree that clustering based solely on KZNF expression and displaying it as we have in Figures 3A and S5 could provide valuable insights. However, when we attempted to cluster the EC RNA-seq samples using only KZNF expression data, we were limited by the relatively low number of KZNF genes that showed sufficient variability across samples (n = 120). For robust statistical power, we require at least 200 features to reliably cluster the 128 EC CD4+ T cell samples. We believe this limitation does not diminish the relevance of KZNFs in the observed clustering patterns but rather highlights the nuanced role each KZNF plays in the regulation of the transcriptome. Each individual KZNF is responsible for the regulation of hundreds to thousands of TE loci (PMID 37730438). Thus, while a clustering approach based solely on KZNF expression may not be feasible, the integral role of KZNFs in modulating the transcriptome through TE regulation remains evident and supports their inclusion in Figure 6 of the revised manuscript.

In general, other than the cell type composition differences, there is no presentation of evidence for any biologically important feature associated with the clusters found.

Authors: We agree that the root cause of the transcriptomic differences between the EC clusters is hard to pin down but we do identify several distinctive features of the clusters that we believe are biologically significant. First, having extracted the lists of genes whose differential expression defined the four EC clusters, gene set enrichment analysis revealed that the clusters were functionally distinct, each characterized by a unique list of top GO terms (Figure 3C). Second, we provide evidence that KZNFs expressed in CD4+ T cells significantly bind to the candidate TE families whose expression defines each of these clusters (Figure 6D) and have significantly decreased expression in ECs compared to VPs (Figure 6C). This is corroborated by pairwise correlation analysis that revealed cluster-specific anticorrelation patterns between these KZNFs and their target TEs (Figure 6A). We present this data in support of our hypothesized KZNF-based mechanism for TE co-option in viral immunity. We do not yet have data indicative of the mechanism by which KZNF expression is in turn regulated. However, we speculate that negative feedback loops may be contributing to changes in KZNF expression.

“These observations suggest that interindividual variation in KZNF expression in CD4+ T cells could explain why certain TEs are variably expressed and accessible across ECs. But what are the mechanisms underlying variation in ZNF expression? It is possible that TE-KZNF regulatory loops are involved, in which a copy of the TE family targeted by a KZNF is inserted near and regulates the KZNF gene, thereby introducing a negative feedback loop. This phenomenon has been documented in prior studies of KZNF activity in embryogenesis (51) and cancer (115).” [pg. 39-40, L705-711]

Overall, our study presents preliminary evidence that the four EC clusters derived from gene & TE family expression may be distinguished by complex interplay of activators (Figure S8) and repressors (Figure 6) altering the activity of infection-responsive TE families to co-opt specific elements for immune regulatory function. While not yet validated in an experimental setting, we believe these results are of biological significance.

Third, the figures present values that have been very heavily analyzed, and it is difficult to impossible to infer what the underlying data look like. For example, with the exception of a few selected examples in Figs 4 and 5, individual provirus data are lacking. Nor can we tell how consistent the distribution of expression values within a TE group is, whether the TEs included solo LTRs (which constitute the majority of all ERVs), the possible contribution of other TFs to expression (with the exception of a brief mention of STAT1).

Authors: We respectfully disagree that the values presented in our figures are heavily analyzed. As this manuscript represents the first investigation of TEs’ role in HIV-1 elite control, we believe the most reasonable initial approach was to compile and visualize the data at the family level, rather than at the level of individual loci, which is harder to interpret due to mapping issues, commonly low transcription, and often idiosyncratic behavior of individual loci. Nonetheless, we did not limit our analysis to full-length HERVs (proviruses) and thus retain all solo LTR data in our analyses. This was added to the Methods of the revised manuscript.

“To facilitate comprehensive expression quantification, we curated a reference transcriptome by combining gene, TE, and HIV-1 genomic sequences. This was achieved by integrating the locus-level TE classification from RepeatMasker, the hg19 GenCode gene annotation,

and the HXB2 reference HIV-1 annotation. For the TEs, we removed simple repeats, SINE elements, and DNA transposons, retaining LINE and HERV loci, including all solo LTRs. We also removed any loci within gene exons/UTRs. The remaining sequences were appended in fasta format, and all sequences were annotated with their respective gene, TE locus, or HIV subunit and modeled in GTF format.” [pg. 55, L869-878]

For the sake of transparency, all relevant details on sequencing data analysis and the corresponding scripts are available in the Methods and our GitHub Repository.

Additionally, while most of our figures make comparisons at the family level, we do visualize multiple TE loci (Figure 4C) and provide a list of putative locus-level TE-gene pairs from which those shown in Figure 4C were selected (Table S7). In our revisions, we also re-clustered the 128 EC CD4+ T cell RNA-seq samples based only on locus-level TE expression, using the same graph-based k-nearest neighbors method as in Figure 3. The results of this new analysis have been integrated into the revised manuscript as Figure S7.

“To further explore locus-level expression patterns, we re-clustered the same EC samples (n=128) using only locus-level TE expression. This again resolved four EC clusters (Figure S7A), which interestingly appeared even more distinct than those identified by gene and TE family expression (Figure 3A). The TE locus-based clusters (TL-Cs) aligned well with the gene and TE family clusters (GT-Cs), with an average 70% overlap in samples between each GT-C and its corresponding TL-C (Figure S7B), indicating high consistency (Table S8). The remaining 30% of samples that shifted between clusters did so consistently within individuals, not cohorts, maintaining heterogeneous TL-C compositions similar to the GT-Cs (Figures S7C & S5A). An exception to this heterogeneity was TL-C4, comprising 22 samples from GT-C1 that were almost entirely from the CD4+ T cell subsets of only four participants in the Jiang cohort (Figure S7C, Table S8). No other samples from the Jiang cohort shifted to this cluster from other GT-Cs, suggesting that these patterns reflect individual variation rather than cohort bias. Like the GT-Cs, each TL-C included samples from all five CD4+ T cell subsets and was largely heterogeneous (Figure S7C). Notably, TL-C2 mirrored corresponding GT-C3 in its overrepresentation of EM and TM cells, while TL-C1 uniquely showed an overrepresentation of naïve CD4+ T cells. Beyond sample composition, each TL-C was characterized by a unique pattern of expressed TE loci (Figure S7D). These signatures were heterogeneous across families, with subsets of variable loci from one TE family marking separate clusters (Figure S7E), some of which did not reach the threshold of significance in earlier analyses when analyzed at the family-level, like SVA-D. Many families maintained their cluster-specific signatures, like THE1B (a marker of GT-C2), for which the majority of variable loci were found in corresponding TL-C1. However, some TE families, like the L1s that marked GT-C1, showed more heterogeneous signatures with variable loci marking multiple TL-Cs. These findings underscore the need for future locus-level investigations with high-depth sequencing to fully capture the complexity of TE expression.” [pg. 27-28, L462-488]

With this addition, we include significantly more data analyzed at the locus level, which we believe not only validate the distinct clustering observed in Figure 3, but also underscore the potential for locus resolution analysis to reveal additional layers of retrotranscriptomic diversity in EC CD4+ T cells.

Finally, we agree with the reviewer that TFs other than STAT1 may contribute to the observed changes in TE expression. To investigate this, we analyzed several TFs expressed in CD4+ T cells and, for TFs enriched over TEs of interest, subsequently examined the correlation between TF and target TE expression in the deconvoluted EC CD4+ T cell samples used for the clustering. The results of this analysis have been integrated into the revised manuscript at Figure S8.

“In addition to KZNF repressors, transcriptional activators may also drive the differential expression of specific TE families across ECs (83). To investigate this, we focused on transcription factors (TFs) expressed in CD4+ T cells and mined ChIP-seq data from the ENCODE Consortium (84) to identify TFs with binding enrichment to TE families of interest, selected for their elevated, cluster-specific expression in ECs (highlighted in Figures 4, 5, and S4). We then examined the correlation between TF and target TE expression in the deconvoluted CD4+ T cell samples from ECs used for our clustering analysis (Figure 3) (9,37). We observed several significant positive correlations between TF and TE expression across ECs (Figure S8). Thus, differential expression of immune-related TFs may also contribute to the variation in TE expression and cis-regulatory activity across ECs, in tandem with the repressive activities of KZNFs.” [pg. 30, L517-527]

This evidence supports the reviewer’s suggestion that other TFs may be contributing to the unique EC retrotranscriptome we profile in this study. These added analyses, mimicking those conducted for KZNFs in Figure 6B & 6D, demonstrate that transcriptional activators may indeed play a crucial role in shaping the TE landscape in ECs.

Other issues

Figure 1:

A) Log2 fold change of what? TPM values? Needs to be specified.

Authors: Thank you for pointing out this ambiguity. The log2-transformed fold change values plotted in Figure 1A refer to DESeq2-normalized expression. They were extracted from the results of the DESeq2 pipeline, which we applied to the raw count expression matrix (see our Methods for more details). Following your suggestion, we have clarified this point in the figure legend in the revised manuscript.

“Total detected genes and TE loci are plotted by log2-transformed fold change of DESeq2-normalized counts (EC vs. HC).” [pg. 10, L163-164]

We have similarly made these changes to any figure legend which was ambiguous in its description of the expression data.

Why Bonferroni correction? Usually BH q values or other less stringent adjustments are used nowadays.

Authors: In our analysis, we opted for the Bonferroni correction due to its well-established reliability and stringent control of the family-wise error rate when conducting multiple tests. Given the exploratory nature of our investigation and the desire to minimize the risk of false positive findings, we chose to employ this traditional correction method within our analytical pipelines.

B,C): Z-score of what? Scaled, normalized counts? Scaled TPM values?

Authors: Thank you again for highlighting this point of uncertainty. We now clarify this in the figure legend in the revised manuscript.

“Heatmap displaying the expression of the top differentially expressed genes in CD4+ T cells of ECs (n=4; red bar) vs. HCs (n=5; blue bar). Relative expression levels are representative of row-wise scaled, log2-transformed expression in transcripts per million (TPM). Heatmap coloration is based on the z-score distribution from low (gold) to high (purple) expression.” [pg. 11, L167-171]

Figure 2:

B) The blue font color is very difficult to see.

Authors: We have changed the blue font color to make it more easily distinguishable from the black.

C) This heatmap should demarcate or separate genes versus TE clades. If that's not possible, then the two should be shown separately.

Authors: We appreciate your suggestion regarding the heatmap presentation. While we understand the rationale for demarcating genes versus TE clades, we have chosen to retain the original figure layout. In this analysis, TEs were analyzed simultaneously with genes. The order in which they are shown was obtained by default clustering of the expression matrix using the hclust function. We chose to present them together and in this order to provide a comprehensive visualization of the differential expression patterns between the two groups and highlight the homogenous nature of gene and TE expression across VPs.

L191: How many groups (NOT families) and how many total elements were examined?

Authors: We begin with the RepeatMasker annotation of the hg19 assembly and filter out the SINE elements, DNA transposons, simple repeats, and all loci within gene exons/UTRs. These details are provided in the Methods of the revised manuscript, as was quoted above. In total, our analyses examine 1,104,828 loci from 603 TE groups (which we refer to as families). We apologize if this figure is not accurate to a separate classification of TEs into groups, rather than families. Any such method of grouping TEs is unfamiliar to us and outside of the Dfam annotation.

L198: 2B, not C

Authors: Thank you for catching this. The figures labelled were swapped in error and have been changed to reflect in Figure 2 to match the in-text references.

L205: Did the expressed proviruses have STAT1 sites?

Authors: Thank you for your question. The identification of LTR13’s increased expression in ECs compared to VPs was the result of a family level analysis which considered expression additively across the LTR13 loci in our annotation. To answer your question, we analyzed STAT1 ChIP-seq data from the ENCODE Consortium to characterize which LTR13 loci were bound by STAT1 (corroborated by motif prediction calls). We then integrated the EC RNA-seq data and found that the expressed LTR13 proviruses significantly overrepresented those with bound STAT1 sites (Figure R2, available in the Response to Reviewers PDF).

These data suggest that STAT1 binding may play a critical role in the transcriptional regulation of LTR13 in ECs, contributing to their differential expression profile. Further exploration into the contribution of activating, immune-related TFs is explored in Figure S8 in the revised manuscript.

L333: 10 kb is very close. Why was it chosen?

Authors: We chose 10 kb as our cutoff for selection because it allowed for very high confidence in the TE loci’s cis-regulatory capacity over the nearby genes. For transparency, we have made this clearer in the Results text of the revised manuscript.

“These loci and genes were paired based on proximity, with a maximum distance of 10kb between the TE locus and the gene’s transcription start site, to increase the likelihood of a direct cis-regulatory influence of the TE over the nearby gene.” [pg. 21, L360-363]

However, if desired, a less stringent cutoff could also be used with relative confidence (e.g., 50 kb).

L351-352: Again, correlation is not causation. How do the authors know it's not the other way around?

Authors: The candidates that we chose to display in Figure 4 (the figure to which these lines refers) are from MER41, ERV3-16, and LTR12C. Our lab and others have shown that these specific loci or other loci in these TE families are capable of regulating neighboring genes’ expression, with specific evidence in the context of immunity (PMID Smitha, Ed, APOBEC, etc.). Based on this knowledge, we believe that it’s most likely that TE-derived regulatory sequences are the cause of the increased restriction factor expression, rather than TE accessibility being a consequence of the transcriptional activation of the neighboring genes. However, we recognize that these results are correlative, as the reviewer notes, and we emphasize this in the revised manuscript. Most notably:

“We acknowledge that these associations are drawn from correlative patterns and manipulative experiments are needed to infer causality between chromatin changes at these TEs and increased expression of nearby immunity genes.” [pg. 36, L620-623]

Figure 4

B) Need to show a scale of the genome region, the orientation of both the gene and the TE, whether it is a solo LTR

Authors: Thank you for the suggestion. Genomic scale and orientation have been added to Figure 4C. All loci visualized were solo LTRs, save for HCP5, which is a lncRNA derived from a full-length ERV3 element.

Figure 5

A) Would benefit from also showing HCs

Authors: Thank you for the recommendation. The RNA-seq datasets used in this analysis do not include HC samples. Additionally, this analysis is meant to highlight differences in TE expression between the four EC clusters. Thus, we have chosen to keep Figure 5A as it appears in the original manuscript.

C) Would be helped by showing adjusted p-values, and also should show examples of non-correlating relationships between these KZNF genes and other TEs.

Authors: Thank you for the suggestion. All correlation analyses had adjusted p-values below 0.01, derived from corr.test in R. We’ve added this to the figure legends of Figure 6B [pg. 32, L539] and S8B [pg. 53, L835]. However, we have chosen not to integrate non-correlating examples into the revised manuscript for the sake of space.

Figure 6

Title: should start with "proposed model for.." or some such.

Authors: Thank you for the suggestion. The title has been changed to “Proposed model for the interplay of KZNFs and TEs regulating proximal antiviral gene expression in elite controllers of HIV-1” in the revised manuscript [pg. 34, L580-581].

L 537: Again, how do the alleles segregate in the clusters?

Authors: This question has been addressed in response to an earlier comment from Reviewer #3.

Generally, in the correlation analyses, I'd like to see adjusted p-values and examples of non-correlated results.

Authors: Thank you for the suggestion. As mentioned above, all correlation analyses have been annotated with the adjusted p-value threshold. Additionally, below we’ve included examples of non-correlated results from two analyses. First, we show a TE-gene pair whose increased TE accessibility in HCs compared to ECs does not correlate with increased expression of the proximal gene (Figure R3, available in the Response to Reviewers PDF). Notably, this gene does not play a role in HIV-1 infection response. Here, we show that genes with proximal (Second, we show the pairwise correlation and linear regression results of L1PA6 and ZNF2 (Figure R4, available in the Response to Reviewers PDF). ZNF2 is one of the KZNFs highlighted in Figure 6 for its low expression in ECs, anticorrelated to its repressive target LTR12C. On the other hand, L1PA6 is active in ECs, with variably high expression across samples. ZNF2 ChIP-exo revealed that ZNF2 has no capacity to bind to L1PA6 loci (adj. p-value = 1; PMID 37730438). Thus, even though both genes are variable across samples, we observe no significant (anti)correlation between the two variables (rho = 0.051 & p-value = 0.866).

While we have not integrated these results into the revised manuscript for the sake of space, we hope that the provided examples satisfactorily demonstrate the presence of non-correlated results in our analyses, further reinforcing the specificity and robustness of our significant findings.

Significance:

This study presents an in-depth analysis of the reverse transcriptome in Elite controllers. It will be of interest to both HIV researchers and those interested in the regulation of the human retrotranscriptome and its consequences.

Provides an avenue for future explanation into elite controllers and TE involvement in the phenotype.

Does a good job of placing the work in the context of existing lit, synthesizing other papers regarding TEs and immune control.

Potential immune regulatory involvement of specific HERV clades.

Authors: We’d like to thank the reviewer for their encouraging feedback. We’re pleased that they found our analysis of the EC retrotranscriptome to be of broad interest and appreciate their recognition of our efforts to synthesize existing literature, contextualizing our findings within the broader field. We agree that our study opens new avenues for exploring the role of TEs, particularly specific HERV clades, in not only the EC phenotype but immune regulation as a whole.