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Reply to the reviewers

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

*Randhawa and co-authors have studied various aspects of the regulation of lignocellulose degradation by the filamentous ascomycete fungus Penicillium funiculosum. Over-expression of the well-known transcription factor clr2 (which regulates cellulase gene expression in Neurospora and other ascomycetes) in a delta-mig1 strain did not result in an increase in cellulase activity. However, when combined with an increased Ca2+ concentration the cellulase activity in the medium did increase. Using RNA-Seq, the authors have identified a candidate regulator: Snf1. Indeed, a knockout confirms that this gene is involved in the posttranscriptional regulation of cellulase production, specifically by regulating the secretion of the cellulases. *

Major comments:

In general, the topic and results are interesting. There are a few issues that need to be addressed, however. The manuscript would benefit from some careful proofreading. For example, articles ('the', 'a') are frequently missing. Very informal language is sometimes used ('zilch effect'). Put a space between '1000bp', etc. It is 'kDa', not 'kD', etc.

Response – Thank you very much for the encouraging remarks. We have thoroughly checked the manuscript and have added the articles at appropriate places. We have also improved the manuscript’s language and removed any informal language used.

I am a bit puzzled by the choice of calcium source: CaCO3, up to 10 g/L. Calcium carbonate does not efficiently dissolve in water unless the pH is low. Fungi generally acidify their culture medium during growth. As such, calcium carbonate likely has a pH buffering effect. Therefore, the described effects may also be attributed to a more neutral pH of the medium, and not necessarily to an increase in calcium ions.

Response – We completely agree with the reviewer and had the same thought that the pH buffering effect of CaCO3 could be the reason for increased cellulase production. We ruled out this by using 50 mg/l CaCl2 solely in rest of the experiments performed in Fig. 3 and afterwards. We have also mentioned the same in the manuscript (lines 175-178).

The authors have performed RNA-Seq, but as far as I can tell the data has not been made publicly available. At least, the raw reads should be deposited in the Short Read Archive of NCBI (or a similar repository), and preferably also the expression values in GEO of NCBI (or a similar repository).

Response – We will comply and deposit the raw reads in the short read archive of NCBI. We will also be providing the differential analysis of transcription factors expressed under glucose and Avicel in NCIM1228 and ∆Mig1 in the supplementary information.

P21. Very little information is provided in the M&M regarding the gene expression analysis. Provide references to all the tools, as well as the version numbers. Were any non-default parameters used?

Response – We have added the complete information on tools and procedures used for RNA-seq data analysis. For differential expression profiling, all FPKM values were normalized to the library size using the R package, Edge R. The expression value for the transcript was calculated using the reads aligned & normalised it on library size (Total sequencing reads generated) & transcript length giving us FPKM value (Fragments Per Kilobase of transcript per Million mapped reads), and TPM value (Transcript per million reads), which is regarded as normalized expression value for a particular transcript. We have taken the number of reads which got aligned to the conserved transcripts (Present in both the comparison group i.e Wild Type Glu & Cellulose samples (S1, S2, S7 & S8) Vs MIG1 glu & Cellulose sample (S3, S4, S5 & S6) and performed the differential gene expression between the two groups. The excel sheet having differential expression profiling of transcription factors is available as supplementary data.

The authors claim that SSP1 CaMKK phosphorylates SNF1 AMPK (last title of the Results section). I don't see any evidence for a direct interaction between these two proteins. I will believe that they are in the same pathway, but if the authors want to claim a direct interaction then additional experiments will be required. E.g. Y2H.

Response – Ssp1 is known to phosphorylate SNF1 during nutritional stress in S. pombe and they were found to interact directly by Co-IP studies. Based on the literature, we planned to over-express Ssp1 in P. funiculosum.

Minor comments:

• Please add line numbers to the manuscript, this facilitates the review process.*

Response - Line numbers have been added.

*P14 "in all yeasts and filamentous fungi". I doubt that all fungi have been tested. *

Response - The phrase has been modified.

P18. "in diverse yeasts and fungi". Yeasts are also fungi.

Response - The phrase has been modified.

P16. "solves dual purpose". I think this is meant: "serves a dual purpose"?

Response - The phrase has been modified.

*P17, first paragraph: this seems very speculative to me, so it should probably be labeled as such. *

Response - The phrase has been modified.

P21. What reference genome is used? Please cite the paper.

Response - We have our own reference genome in lab which is yet to be published.

Fig 1B. These are reported as volcano plots, but to me it looks like an empty graph (no data points), only a number of genes.

Response - The pictures have been changed.

Fig 1D. What do the colors on the right represent? The colors on the right represents k-means clustering of the genes of transcription factors.

Response - The same has been added to the figure legend also.

On various places in the manuscript the term "three times in triplicate" is used. What is meant here, three technical replicates of each of the three biological replicates?

Response - Yes we mean the same and the phrase has been modified.

P46. "We aimed to sought"

Response - The phrase has been modified.

Abstract: The sentence "Further, Ca2+-signaling" should be rewritten, because currently is seems to suggest that SSP1 downregulates the phospho-HOG1 levels.

Response – As suggested by the Western blot in the Fig.4b, Snf1 gets phosphorylated only when dual signal of calcium and cellulose are present. Since we observed upregulated Ssp1 expression in Avicel (Fig. 4a), and increased Ssp1 expression could increase the phosphorylated Snf1 in the cell (Fig. 7i), our data suggests that Ssp1 phosphorylates Snf1 in a Ca2+-dependent manner. Further Hog1 was found in hyperphosphorylated state in ∆Snf1 (Fig 6e), thus we believe Snf1 AMPK downregulates phospho-Hog1 levels.

Reviewer #1 (Significance (Required)):

*In general, the topic and results are interesting. There are a few issues that need to be addressed, however. The manuscript would benefit from some careful proofreading. *

Response – We highly appreciate the encouraging words of the reviewer. We have addressed all the issues raised by the reviewer. The major ones included the language and readability of the text, which has been improvised. We have replaced the volcano plot figures, and will be uploading the RNA-seq data to the SRA database of NCBI and excel sheet of differential expression analysis of transcription factor will be added as a supplementary file to the manuscript.

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

• Randhawa et al. study the effect of loss of function of Snf1 kinase and calcium on the production of enzymes related to cellulose degradation in the fungus Penicillium funiculosum. *
• The manuscript is well structured and the researchers have done an enormous amount of work in constructing a number of mutant strains in this fungus. *
• Transcriptomics and proteomics support the conclusions reached with the strains generated.*

Response - Thank you very much for showing confidence in our research work, we are highly obliged by positive remarks on the manuscript.

The manuscript is long and suffers from an excess of results presented in figures. My main criticism focuses on the presentation of data on the cellular distribution of the ER and Golgi apparatus. The micrographs are inconclusive and it is not really clear what the authors are trying to show in these experiments. These results are not really necessary for the article and I suggest that they be removed from the article.

Response – We agree with the reviewers comments on data on the cellular distribution of the ER and Golgi apparatus. We have removed the micrograph data on the cellular distribution of the ER and Golgi apparatus (earlier Figure 3j and Figure 4r).

Reviewer #2 (Significance (Required)):

The authors have done an excellent job in producing a large number of strains carrying null alleles. In addition, they have used two broad analysis techniques that allow them to establish coherent hypotheses and corroborate them with the results.

Response – Thank you very much for the positive comments

The manuscript is difficult to understand in some sections because of the excessive amount of data and panels in the figures. The names designating each strain and given in full length in the graphs do not help either.

Response – Thank you very much for the valuable suggestion. We have reduced the number of graphs by including all enzymes assays in one concise graph in Figure 4. We have also shortened the names of strains and enzymes, in all the figures.

This work is of interest to all researchers interested in the integrity of signaling and regulatory pathways on extracellular enzymes of biotechnological interest.

*My interests focus on the cell biology of filamentous fungi, in particular on the molecular mechanisms and subcellular localization of elements involved in intracellular transport, signaling against environmental stresses and changes in transcriptional regulatory patterns. *

Response – Thank you once again for the encouraging remarks.

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

The manuscript submitted by Randhawa et al focus on the mechanism of cellulases secretion, the very important and basal question in the filamentous fungi, particularly for cellulases biotechnology. As the author said the molecular basis of cellulases production previously study mainly focuses on regulation mechanism at transcription level, the study of molecular mechanism of cellulases translation and secretion are much rare. Therefore the submitted work is very impressive me on the progress of this area. What they presented shown the Ca2+ is critical for the regulation of cellulases secretion by SNF-1, SSP1 and HOG1. The regulation might be caused by affecting the protein trafficking in ER and Golgi, the manuscript found the development of ER and Golgi shown changes by staining by ER-tracker and Bodipy under different conditions and mutants. The manuscript constructed a model about regulatory mechanism of Ca2+ on cellulases translation and secretion level. The present study is close to make significant progress in the cellulases regulation area.

Response – We appreciate the positive comments of the reviewer.

Major comments: I am really impressive for the great work in the manuscript, however, I think the more work do need for give the conclusion of paper.

1.In terms of dynamics development of ER and Golgi of strains, the very critical data for the conclusion of the paper, the current data is only by chemical staining. It is not robust, it will be needed by other methods, for example, GFP-labeling the marker of ER or Golgi.

Response – The manuscript focuses on the signaling events governing cellulase production, and secretion. Since ER and Golgi are the sites of protein production and secretion, we hypothesized, if the Ca2+ signaling affects post-transcriptional events, it must have had some impact on the dynamics of these organelles; and microscopy experiments suggested us the same. In the next set of experiments, we proved our hypothesis with the proteomics and functional analysis of Snf1, Ssp1, and Hog1 MAPK. Hog1 MAPK pathway is known to regulate protein trafficking and secretion in yeast. We here showed that Ca2+- dependent regulation of Hog1 MAPK and its downregulation by Snf1 AMPK is crucial to cellulase secretion.

2.Also the author try to suggest the cellulases were detained in the ER, not went into Golgi, therefore the secretome protein decreased. It is very much possibly but the evidence is not robust either, to trafficking the GFP-labelled CBH1 might be a good experiment to make it clear.

Response – Thank you very much for raising the query. The manuscript majorly focuses on the role of calcium signaling on cellulase translation and secretion. Further, we have studied two signaling proteins, Snf1 AMPK and Hog1 MAPK which are downstream to calcium signaling, and we found their crosstalk vital to cellulase secretion. We have not talked about cellulases being detained in the ER or Golgi, rather we focused on the signaling events regulating cellulase production and transport.

Since we had already ruled out the role of calcium in cellulase transcriptional activation, and ER and Golgi being major site of protein production in the cell; we performed microscopy experiments to see if the calcium signaling modifies ER and Golgi morphology during carbon stress. We found under-developed Golgi in the absence of calcium in wild type. This experiment helped us to build a hypothesis that calcium signaling might have role in downstream events like protein translation, and secretion. The hypothesis was proved by functional analysis of signaling proteins, Western blot and proteomics experiments. Further, microscopy experiments further strengthened our observation that Snf1 AMPK is downstream target of calcium signaling and has no role in the cellulase translation, but cellulase secretion.

Considering that we are not focusing on the protein trafficking of cellulase, the confocal microscopy experiments are not decisive, rather build supporting evidence for our hypothesis, as suggested by the second reviewer. We have proved our hypothesis of Ca2+-dependent post-transcriptional regulation of cellulase by proteomics, and other biochemical experiments. Nevertheless, we plan to perform the confocal experiments again to achieve pictures with higher resolution.

1.On page 9, please indicate the fold changes of the kinases genes talked about, snf1 and so on.

Response – We have added the Fold change in the expression of Snf1 and Ssp1 (line number 221).

2.The quality of microscopic figure is not good, should have one with higher resolution, even consider to present the electron microscope picture to give the er and Golgi dynamics changes the manuscript talked about(optional).

Response: We agree with the reviewer’s suggestion to add high resolution confocal images of mycelia in Fig 3j and Fig. 4o. We are in the process of repeating the confocal microscopy experiment. We will update the manuscript with improved microscopic pictures.

*3. The quality of Western plot need to be improved, particularly figure 4f，figure 7i， it is hard to give the conclusion based on the picture presented *

Response – We have replaced the pictures of western blots (Fig 4f, and Fig 7i) with high resolution images.

Reviewer #3 (Significance (Required)):

The manuscript submitted by Randhawa et al focus on the mechanism of cellulases secretion, the very important and basal question in the filamentous fungi, particularly for cellulases biotechnology. As the author said the molecular basis of cellulases production previously study mainly focuses on regulation mechanism at transcription level, the study of molecular mechanism of cellulases translation and secretion are much rare. Therefore the submitted work is very impressive me on the progress of this area. What they presented shown the Ca2+ is critical for the regulation of cellulases secretion by SNF-1, SSP1 and HOG1. The regulation might caused by affecting the protein trafficking in ER and Golgi, the manuscript found the development of ER and Golgi shown changes by staining by ER-tracker and Bodipy under different conditions and mutants. The manuscript constructed a model about regulatory mechanism of Ca2+ on cellulases translation and secretion level. The present study is close to make significant progress in the cellulases regulation area.

Response - Thank you for the positive comments on the manuscript.

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

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Referee #3

Evidence, reproducibility and clarity

The manuscript submitted by Randhawa et al focus on the mechanism of cellulases secretion, the very important and basal question in the filamentous fungi, particularly for cellulases biotechnology. As the author said the molecular basis of cellulases production previously study mainly focuses on regulation mechanism at transcription level, the study of molecular mechanism of cellulases translation and secretion are much rare. Therefore the submitted work is very impressive me on the progress of this area. What they presented shown the Ca2+ is critical for the regulation of cellulases secretion by SNF-1, SSP1 and HOG1. The regulation might caused by affecting the protein trafficking in ER and Golgi, the manuscript found the development of ER and Golgi shown changes by staining by ER-tracker and Bodipy under different conditions and mutants. The manuscript constructed a model about regulatory mechanism of Ca2+ on cellulases translation and secretion level. The present study is close to make significant progress in the cellulases regulation area.

Major comments

I am really impressive for the great work in the manuscript, however, I think the more work do need for give the conclusion of paper.

1.In terms of dynamics development of ER and Golgi of strains, the very critical data for the conclusion of the paper, the current data is only by chemical staining. It is not robust, it will be needed by other methods, for example, GFP-labeling the marker of ER or Golgi 2.Also the author try to suggest the cellulases were detained in the ER, not went into Golgi，therefore the secretome protein decreased. It is very much possibly but the evidence is not robust either, to trafficking the GFP-labelled CBH1 might be a good experiment to make it clear

Minors

1.On page 9, please indicate the fold changes of the kinases genes talked about, snf1 and so on. 2.The quality of microscopic figure is not good, should have one with higher resolution, even consider to present the electronmicroscope picture to give the er and Golgi dynamics changes the manuscript talked about(optional) . 3.The quality of western plot need to be improved, particularly figure 4f，figure 7i， it is hard to give the conclusion based on the picture presented

Significance

The manuscript submitted by Randhawa et al focus on the mechanism of cellulases secretion, the very important and basal question in the filamentous fungi, particularly for cellulases biotechnology. As the author said the molecular basis of cellulases production previously study mainly focuses on regulation mechanism at transcription level, the study of molecular mechanism of cellulases translation and secretion are much rare. Therefore the submitted work is very impressive me on the progress of this area. What they presented shown the Ca2+ is critical for the regulation of cellulases secretion by SNF-1, SSP1 and HOG1. The regulation might caused by affecting the protein trafficking in ER and Golgi, the manuscript found the development of ER and Golgi shown changes by staining by ER-tracker and Bodipy under different conditions and mutants. The manuscript constructed a model about regulatory mechanism of Ca2+ on cellulases translation and secretion level. The present study is close to make significant progress in the cellulases regulation area.

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

Randhawa et al. study the effect of loss of function of snf1 kinase and calcium on the production of enzymes related to cellulose degradation in the fungus Penicillium funiculosum. The manuscript is well structured and the researchers have done an enormous amount of work in constructing a number of mutant strains in this fungus. Transcriptomics and proteomics support the conclusions reached with the strains generated. The manuscript is long and suffers from an excess of results presented in figures. My main criticism focuses on the presentation of data on the cellular distribution of the ER and Golgi apparatus. The micrographs are inconclusive and it is not really clear what the authors are trying to show in these experiments. These results are not really necessary for the article and I suggest that they be removed from the article.

Significance

The authors have done an excellent job in producing a large number of strains carrying null alleles. In addition, they have used two broad analysis techniques that allow them to establish coherent hypotheses and corroborate them with the results. The manuscript is difficult to understand in some sections because of the excessive amount of data and panels in the figures. The names designating each strain and given in full length in the graphs do not help either. This work is of interest to all researchers interested in the integrity of signalling and regulatory pathways on extracellular enzymes of biotechnological interest.

My interests focus on the cell biology of filamentous fungi, in particular on the molecular mechanisms and subcellular localisation of elements involved in intracellular transport, signalling against environmental stresses and changes in transcriptional regulatory patterns.

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

Randhawa and co-authors have studied various aspects of the regulation of lignocellulose degradation by the filamentous ascomycete fungus Penicillium funiculosum. Over-expression of the well-known transcription factor clr2 (which regulates cellulase gene expression in Neurospora and other ascomycetes) in a delta-mig1 strain did not result in an increase in cellulase activity. However, when combined with an increased Ca2+ concentration the cellulase activity in the medium did increase. Using RNA-Seq, the authors have identified a candidate regulator: Snf1. Indeed, a knockout confirms that this gene is involved in the posttranscriptional regulation of cellulase production, specifically by regulating the secretion of the cellulases.

Major comments:

In general, the topic and results are interesting. There are a few issues that need to be addressed, however. The manuscript would benefit from some careful proofreading. For example, articles ('the', 'a') are frequently missing. Very informal language is sometimes used ('zilch effect'). Put a space between '1000bp', etc. It is 'kDa', not 'kD', etc.

I am a bit puzzled by the choice of calcium source: CaCO3, up to 10 g/L. Calcium carbonate does not efficiently dissolve in water unless the pH is low. Fungi generally acidify their culture medium during growth. As such, calcium carbonate likely has a pH buffering effect. Therefore, the described effects may also be attributed to a more neutral pH of the medium, and not necessarily to an increase in calcium ions. The authors have performed RNA-Seq, but as far as I can tell the data has not been made publicly available. At least, the raw reads should be deposited in the Short Read Archive of NCBI (or a similar repository), and preferably also the expression values in GEO of NCBI (or a similar repository). P21. Very little information is provided in the M&M regarding the gene expression analysis. Provide references to all the tools, as well as the version numbers. Were any non-default parameters used? The authors claim that SSP1 CaMKK phosphorylates SNF1 AMPK (last title of the Results section). I don't see any evidence for a direct interaction between these two proteins. I will believe that they are in the same pathway, but if the authors want to claim a direct interaction then additional experiments will be required. Eg Y2H.

Minor comments:

Please add line numbers to the manuscript, this facilitates the review process.

P14 "in all yeasts and filamentous fungi". I doubt that all fungi have been tested.

P18. "in diverse yeasts and fungi". Yeasts are also fungi.

P16. "solves dual purpose". I think this is meant: "serves a dual purpose"?

P17, first paragraph: this seems very speculative to me, so it should probably be labeled as such.

P21. What reference genome is used? Please cite the paper.

Fig 1B. These are reported as volcano plots, but to me it looks like an empty graph (no data points), only a number of genes.

Fig 1D. What do the colors on the right represent?

On various places in the manuscript the term "three times in triplicate" is used. What is meant here, three technical replicates of each of the three biological replicates?

P46. "We aimed to sought"

Abstract: The sentence "Further, Ca2+-signaling" should be rewritten, because currently is seems to suggest that SSP1 downregulates the phosphor-HOG1 levels.

Significance

In general, the topic and results are interesting. There are a few issues that need to be addressed, however. The manuscript would benefit from some careful proofreading.

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

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Reply to the reviewers

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

* Negreira et al. have studied aneuploidy in Leishmania selected using a "flash selection" with SbIII or miltefosine (MF). They provided evidence for the SbIII arm that a few parasites in the population with a specific genotype were enriched during drug selection, and these selected parasites with continuous drug pressure further present modifications in their ploidy. For MF selection they show a different scenario where first a minor population with a mutation in the MT gene is selected and with further passages with drugs, parasites with changes in ploidy are further enriched.*

* Here are some comments that hopefully will be helpful for the authors.*

* The plasticity of the Leishmania genome is fascinating. It is remarkable that these parasites can tolerate so many and frequent changes in ploidy. Either these changes are stochastic and serendipitous or as convey by the authors are part of the parasite arsenal to respond to a changing environment. They cleverly used single cell sequencing and bar-coded parasites in this well designed and well conducted study to assess the role of ploidy in parasite biology.*

1. Drugs are not inducing any of the changes observed, instead the drugs are selecting for parasites with different genotypes (e.g. polyploidy of chromosome 23 for SbIII or parasites with mutations in MT). This is an important conceptual difference and the authors need to change their text throughout starting at line 28.

R: We agree with the reviewer and adapted the text. These changes were introduced as follow:

Line 27 (line 19 in the new version):

“____we revealed that ____antimony-induced aneuploidy changes ____under antimony pressure____ result from the polyclonal selection of pre-existing karyotypes”

Line 201 (line 187 in the new version):

“____This approach revealed that____ the flash selection with SbIII ____induced led to____ a fourfold reduction in lineage diversity that stabilized between passages 3 to 4, leaving between 101 to 131 of detectable lineages”

Line 354 (line 381 in the new version)

“____The flash selection performed with miltefosine revealed a contrasting scenario where aneuploidy remained unchanged ____even after a stronger bottleneck induced by associated with the drug at passage 1, 25 µM and illustrated by the strong decrease in barcode diversity (from 453 to 7 lineages).”

• Line 170. Its is probably expected that no cells have increased copy of chromosome 23, 27 and 31 after single cell genomics. None of the first passages of the four SePOP are polyploid for chromosome 27. One possibility is that a subpopulation of cells with increased copy of chr. 23 (because of MRPA?) and 31 (because of ?) are first selected and in subsequent passages cells triploid for 27 are selected. Of note the ploidy of chr. 27 appears to decrease from passage 4 to 5 in SePOP1 which is unusual if the drug pressure is maintained.*

R: We agree with the reviewer that the aneuploidy changes seen in the SePOP1-4 can be explained by the initial selection of subpopulations of cells with a beneficial pre-existing dosage increase in one or two chromosomes (e.g., chromosome 23 and 31) followed by the selection of additional cumulative modifications emerging in subsequent time points. This conclusion was previously stated throughout the text and is also depicted by the minimum spanning tree in figure 1C, but we made some alterations in the text in order to better state this conclusion:

Line 100 (line 87 in new version):

“____Using single-cell genome sequencing, we could uncover the evolutionary paths that might have led to the emergence of such aneuploidy changes,____ which involved ____indicating a process of ____selection of pre-existing karyotypes complemented by further ____de novo cumulative ____alterations in chromosome copy number along evolution”

Line 168 (line 156 in new version):

“However, none of the sequenced promastigotes showed amplification of chromosomes 23, 27 and 31 concomitantly, and no pre-existing karyotype was identified with a pentasomy in chromosome 23 as observed in the SePOP3, suggesting that some of the aneuploidy ____modifications were generated along adaptation to SbIII changes seen in SePOP1-4 happened after initial exposure to SbIII.____”

Line 191 (line 177 in new version):

“Altogether, our single-cell data suggest that (i) aneuploidy changes observed in the SbIII-exposed populations are explained by the selection of pre-existing aneuploid cells, complemented by additional somy changes generated de novo during the experiment and initial selection of subpopulations bearing ____pre-existing chromosomal amplifications followed by the further selection of cumulative karyotypic modifications emerging in subsequent time points____ and (ii) that the aneuploidy changes seen in SePOP1-4 would have a polyclonal origin.”

Regarding the decrease of chr.27 in SePOP1 from passage 4 to 5, we believe this decrease is not very significant as its somy value (2.71) indicates that the majority of cells still display a trisomy for this chromosome. Moreover, this decrease coincides with the moment where a dosage increase (from ~3 to ~10 copies per haploid genome) in the MRPA locus happens exclusively in that population and in that passage (see supplementary figure S2B), which likely has a stronger impact in SbIII tolerance compared to the trisomy of chr27.

• Lane 194. I agree with the concept of the selection of pre-existing aneuploid cells but the additional somy changes observed are, in my opinion, just selected because these changes occur continuously.** *

R: The changes mentioned above starting at line 191 were also done in response to this comment.

Their barcoded strategy was interesting but it would appear that different lineages are enriched in the 4 SePOP. It would be of interest to test whether those lineages have similar ploidy at the onset. I am unclear of why they have to amplify the barcode prior sequencing. Could they just not get this info from the SePOP data; it is my understanding that the drug selection was done with the barcoded population. This would have facilitated the correlation barcode-specific ploidy.

R: We agree that it would have been interesting to integrate the single-cell genomics and the barcode data in order to determine if the selected lineages had similar karyotypes at the onset of the experiment. However, although the genome coverage of individual cells in the single-cell genomics method used in our study is enough to determine differences in chromosome copy number, it is not enough to evaluate, at sequence level, individual genomic loci such as the lineage barcodes. This is because the genome coverage per cell is too low (in our case 0,8x) meaning that most genomic loci are mapped by just a single sequence read or not mapped at all (10X Genomics, 2020). Thus, it was not possible to determine the lineage barcode of individual cells from the single-cell data.

Regarding the need for amplifying the barcodes: in contrast to WGS, a targeted amplification of the barcodes enabled us to obtain millions of reads covering the barcodes. This, in turn allowed quantifying accurately the frequency of each barcoded lineage.

This is now mentioned in the text starting in line 514 (548 in the new version):

“____Barcode amplification was done using the same DNA samples used for bulk whole genome sequencing. Targeted amplification of the barcodes is needed as the number of reads containing a lineage barcode (~50 pair end reads per sample on average in our case) in the whole genome sequencing data is insufficient for the determination of the frequency of each barcoded lineage in the parasite pool.____”

• The MF screen was harsh and the parasites selected (derived from few clones within the population when considering the time needed to expand) contained SNPs in MT. Difficult to compare the two screens. Passages with higher MF concentration led to major changes in ploidy but with few common features between the MePOP lines.*

R: The screen of the BPK282 strain under SbIII or miltefosine pressure provides two contrasting models and this is one of the interests of the present study. The BPK282 strain belongs to a population of L. donovani parasites from the lowlands of the Indian subcontinent, where parasites were exposed to strong SbIII pressure for decades, even more since these parasites are transmitted from human to human. This population is characterized by strong genomic variations affecting SbIII susceptibility, of which the intra-chromosomal amplification of MRPA is a well-known driver of SbIII pre-adaptation. BPK282 has this intrachromosomal amplification of MRPA and thus it is strongly pre-adapted to SbIII. In contrast, at the time of isolation of BPK282, miltefosine was not yet implemented in clinical practice in the Indian sub-continent (ISC). BPK282 is considered highly susceptible to miltefosine and pre-adaptation to this drug was not, until the present study, identified in this strain and in the ISC population it was isolated from. We performed the flash selection with both drugs to investigate if aneuploidy modulations would follow similar patterns in these two contrasting environments, one where the strain is pre-adapted, and another where it is highly susceptible.

We state this starting in line 242 (line 230 in the new version):

“The results described above demonstrated the importance of aneuploidy for parasite adaptation to high SbIII pressure together with the polyclonality of corresponding molecular adaptations. We aimed here to verify if the same features would be observed with another anti-leishmania drug, miltefosine. In contrast to SbIII, there was – at least before present study – no pre-adaptation known to miltefosine in the BPK282 strain, which is considered very susceptible to the drug (23).”

We also added two sentences in the discussion reiterating this contrast between SbIII and MF in BPK282:

Starting in line 353 (377 in the new version):

__“Finally, we assessed the role and dynamics of aneuploidy under strong pressure of another drug, miltefosine. _Noteworthy, BPK282 was isolated from the population endemic in the Gangetic plain, before miltefosine was implemented in the region (in sharp contrast to SbIII). Hence different results were expected for the scenario of genomic adaptation and clonal dynamics._” __

In addition, we believe that the results of the miltefosine flash selection further corroborate the notion that aneuploidy modulations seen in these drug selection experiments can happen de novo along adaptation to the drug. This was not well stressed in the manuscript and thus we included the following statement during in the discussion:

Starting at line 360 (line 387 in the new version):

“__This demonstrated that the strong bottleneck associated with initial exposure to miltefosine in the first passage did not impair the potential for aneuploidy modulations in later passages, and that these modifications depend on the strength of the stress caused by the drug. These observations are also in agreement with the notion of aneuploidy modulations happening de novo during adaptation to the drug as the aneuploidy profiles seen at passage 9 in the MePOPs exposed to 100 µM are also very different from the pre-existing karyotypes identified in the single-cell data of BPK282____.” __

• I am not asking for extra work but as a suggestion to help in linking ploidy with phenotype it would have been very interesting to look at 5 passages without drug (SbIII or miltefosine) to see whether a decrease in ploidy is correlated to a decrease in resistance.*

R: Unfortunately, we do not have access to the selected populations anymore, but we agree that characterizing these selected populations after keeping them for a few passages without drug would further strengthen the understanding of the relationship between aneuploidy modulations and SbIII tolerance.

Minor points

1. The environment studied (high drug pressure) is unlikely to occur in nature. The authors may wish to comment on how this may translate in the sand fly or in animals.

First of all, the population of L. donovani from which strain BPK282 originated has been naturally under high drug pressure since decades, given the anthroponotic nature of transmission in the Indian sub-continent and the absence of reported animal reservoir. An additional pressure came from the strong pollution with Arsenic, that is present in the lowlands where BPK282 was isolated (Perry et al., 2011). The same authors showed that chronic exposure to arsenic in drinking water can lead to resistance to antimonial drugs (cross resistance) in a mouse model of visceral leishmaniasis and concluded that arsenic contamination in the Gangetic plain may have played a significant role in the development of Leishmania antimonial resistance (Perry et al., 2013). This might explain why antimony resistance drivers like amplification of MRPA were already present in the populations even before antimony was implemented in the region (Imamura et al., 2016).

This is now mentioned in the text starting at line 311 (320 in the new version)

__ “_This pre-adaptation likely comes from the combination of high antimony pressure for decades, highly endemic pollution with arsenic – which can cause cross-resistance to antimonials (33, 34) – and anthroponotic transmission without animal reservoir_.”__

Secondly, in current study, we pushed further the parasite and experimentally exposed it to even higher drug pressure. Our flash selection approach was done as a general model to investigate the mechanisms that Leishmania exploits in order to adapt to sudden and strong environmental stresses, with a focus on aneuploidy changes. This is stated in the manuscript.

Starting at line 93 (line 79 in the new version):

“____In the present study we aimed to address these questions using a reproducible in vitro evolutionary model to study aneuploidy modulations and karyotype evolution in the context of adaptation to sudden environmental stresses, invoked here by the direct exposure to high concentrations of 2 drugs, trivalent antimonial (SbIII) or miltefosine (further called ‘flash selection’).”

In addition, for miltefosine, the concentrations used in our flash selection are lower than the concentrations found in the blood of treated patients or inside macrophages. Thus, for miltefosine, parasites are likely to be exposed to similar or even higher concentrations than those used in our study. We now highlight this in the discussion of the manuscript:

Starting at line 291 (line 290 in new version):

“This abrupt change in environments is also a characteristic of drug treatment. In the case of antimonials, measures made in patients treated for visceral leishmaniasis estimate a peak of 10 mg/L or ~82 µM of Sb in the blood after only 2 hours post drug administration (26). For miltefosine, blood concentrations can be as high as 70 µg/ml, or 172 µM after 72h (27). Moreover, bone marrow-derived macrophages exposed to 10 µM of miltefosine in vitro display intracellular concentrations of the drug as high as 323 µM after 72h (28). This illustrates that Leishmania parasites are directly exposed to sharp increases in drug concentrations – in the case of miltefosine, even higher than the concentrations used in this study – in patients upon drug administration.”

With respect to the importance of sand flies or animals in the environmental pressure, (i) animals play a negligible role given that transmission of L. donovani in the ISC is anthroponotic, without animal reservoir and (ii) the sand fly hosts the parasite for a short period of time (max 10 days), during which the parasite is not exposed to drugs.

• In Fig. S2 MRPA in SePOP1 is a signature of extrachromosomal amplification. *Was that studied?

R: We previously showed that amplification of MRPA in L. donovani encountered in the Indian sub-continent was intrachromosomal (Imamura et al., 2016); further amplification of that specific gene could occur by intrachromosomal expansion/contraction or indeed by episomal amplification. However, one of the core messages of present paper is that increased somy of chr23 automatically leads to increased dosage of the intra-chromosomal MRPA amplicon. We adapted the text in order to acknowledge the possibility of episomal amplification:

Starting at line 140 (line 127 in the new version):

“The BPK282 strain already contains a natural intra-chromosomal amplification of the MRPA gene that may bring a pre-adaptation to SbIII (____14____), and the locus might be subject to further ____intrachromosomal ____expansion ____or____,____ contraction____, or episomal amplification____.”

• For Chromosome 31 in the Sb screen, it would appear that the proximal (left) part is of lower copy number than the distal (right) portion of the chromosome. How could this have happened? Deletion of a portion of chromosome 31 for one allele? This has been described before (Mukherjee et al., 2013) in SbIII resistant lines as one telomeric end of Chr. 31 encodes AQP1, the route of entry of SbIII.*

R: The figures 1A and 2F and 3A do not indicate the copy number of intra-chromosomal segments as they reflect a single numeric value representing the somy of each chromosome at different time points (the x axis of the graphs). Thus, there is no information on differences between distal or proximal copy numbers inside a chromosome in those figures. The only figure showing read depth along the chromosome is fig S2B and corresponds to chr23 and not 31. It is indeed possible that there are telomeric deletions affecting AQP1 but this was not the scope of our study, since we were interested in understanding the reasons and possible drivers of increased gene dosage of chr31.

Reviewer #1 (Significance (Required)):

* The plasticity of the Leishmania genome is fascinating. It is remarkable that these parasites can tolerate so many and frequent changes in ploidy. Either these changes are stochastic and serendipitous or as convey by the authors are part of the parasite arsenal to respond to a changing environment. They cleverly used single cell sequencing and bar-coded parasites in this well designed and well conducted study to assess the role of ploidy in parasite biology.*

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

* Negreira et al. present a study that aims to understand the early evolution of aneuploidy. They use Leishmania, a protozoon parasite known for its genome plasticity, as model, and two drugs as stress inducers. In this work, they use single-cell genomics and lineage tracing to detect changes in chromosome copy numbers. They conclude that, although parasites tend to have genomes with unusual plasticity, aneuploidy dynamics depend on the stressor more than the organism.

* Further experiments:

1. Lines 121-124: I believe the authors should corroborate the statement that expansion of lineages that were fitter prior to drug exposure is stochastically by doing a statistical test comparing their obtained data and randomly generated simulated values. Given that there is still a considerable proportion of lineages with higher fitness and found in more than one passage, I believe this experiment/test would add strength to the conclusion.

R: We believe that the stochasticity per se is not the relevant aspect of our results, but the fact that the expansion of different lineages in different populations is followed by the emergence of the same somy changes in a set of chromosomes (23, 27, and 31), thus showing a process of convergent evolution. Therefore, we decided to reduce the emphasis on the stochasticity itself and adapted the text to highlight this process of convergence. This was done in the following parts of the manuscript:

Starting at line 98 (line 84 in new version):

“we revealed that changes in aneuploidy under ____SbIII____ pressure have a polyclonal origin, arising from the reproducible survival of a specific subset of lineages, which further expand stochastically differentially between independent replicates but converge to similar aneuploidy modifications”.

Starting at line 220 (line 205 in new version):

“____Moreover____, ____most of the positively affected lineages were enriched in only one of the SePOPs ____(Fig. 2C and fig. S3B)____ (figure 2C and supplementary figure S3B).____, suggesting that____. Altogether, these data indicate that____ (i) a subset of lineages was fitter to SbIII prior the drug exposure and (ii)____ their ____the further____ expansion ____of these surviving lineages was ____stochastically driven. divergent between independent replicates.____”

Starting at line 344 (line 366 in the new version):

“From 453 different traceable lineages, 303 consistently disappeared during SbIII exposure and 60 showed an increased frequency in at least one replicate. Most of these positively affected lineages were enriched in only one of the SePOP replicates, suggesting (i) higher tolerance to SbIII in a subset of lineages that reproducibly survived the flash selection and (ii) further expansion of these surviving lineages being stochastically driven. , including lineages which were dominating the population at the onset of the experiment (figure 2F), thus indicating that these lineages had a fitness disadvantage to SbIII compared to the other lineages. Among the surviving lineages, 60 could further expand in at least one of the SePOPs, leading to different clonal compositions in each population. Interestingly, changes in clonal composition in each SePOP coincide with the moments where changes in aneuploidy are observed in these populations, suggesting that these aneuploidy changes are due to the emergence of subsets of fitter lineages. Moreover, the observation that the same set of 3 chromosomes displayed dosage increases in all SePOP despite the fact that different lineages dominated each SePOP points to a process of convergent evolution, which further supports the notion of these chromosomes being under positive selection.”

Minor issues:

Fig. 1B: Add label to top horizontal axis, showing frequency of each karyotype.

R: A label stating ‘Number of Cells’ was added at figure 1B.

Lines 92-96: Could the authors postulate how and why pre-existing aneuploid cells seem to be selected upon SbIII exposure?

R: We believe that some aneuploidy changes, like the dosage increase of chromosome 23 (from 3 to 4 copies) offer an adaptive advantage to the cells bearing it by over-expressing genes related to SbIII tolerance. This was discussed in the manuscript.

starting at line 304 (314 in the new text):

“____Chromosome 23 bears the MRPA genes which encode an ABC-thiol transporter involved in the sequestration of Sb-thiol conjugates into intracellular vesicles (28). Amplification of MRPA genes through extra-or intra-chromosomal amplification is a well-known driver of experimental SbIII resistance. The line here used (BPK282) is remarkably pre-adapted to SbIII (18) – like other strains of the Gangetic plain – thanks to a pre-existing intra-chromosomal amplification of MRPA genes encountered in 200 sequenced L. donovani isolates of that region (13). The recurrent dosage increase of chromosome 23 observed here under SbIII pressure is a rapid way to further amplify the MRPA gene and this mechanism was likely selected instead of further amplifying MRPA genes intra-chromosomally.”

Fig. 3: Are panels B and C swapped in the figure or the reference swapped in the text? Fig. 3C seems to refer to the mutation (lines 173-179), whereas Fig. 3B seems to relate to the surviving lineages (lines 183-186).

R: Indeed, figures 3B and 3C were erroneously positioned in the panel. This is now fixed in the new version.

Lines 94-97: Could the authors comment on the advantages and disadvantages of such an aggressive selection method? I am not surprised with such a drastic decrease in lineage diversity in this context.

R: We now added a section at the beginning of the discussion commenting this:

starting at line 291 (line 281 in the new version):

“____Historically, adaptation in Leishmania was mainly addressed using a ‘gentle’ stepwise approach where parasite populations are exposed to progressively increasing drug concentrations in vitro over the course of months, allowing these populations to adapt to each concentration before proceeding to the next increment (19, 23-25). This approach is useful to reveal mechanisms promoting full resistance against that drug which emerge at the later time points where drug concentration is high, but it precludes the evaluation of mechanisms allowing parasites to cope with sudden and strong environmental changes as initial concentrations are often too permissive. Importantly, in nature, changes in environmental pressures are often abrupt rather than gradual, and therefore, demand for mechanisms which allow parasite populations to quickly adapt to the new environment.____”

And then on line 300 in the new version:

“____In____ the present study, we investigated the mechanisms governing the early adaptation of Leishmania promastigote populations to a direct exposure to high concentrations of two drugs – SbIII and miltefosine – as models of sudden environmental stresses.____”

Could the authors elaborate on what is different in chromosome 31 that makes it so prone to change?

R: We improved our discussion about the potential drivers of dosage increases for the other 2 chromosomes (chr 27 and chr 31) which, apart from chr23, are also consistently amplified under SbIII exposure.

Starting at line 320 (line 331 in the new version):

“_Regarding the other 2 chromosomes, chromosome 31 also bears a gene involved in antimony resistance, the sodium stibogluconate resistance protein gene (LdBPK310951.1). Interestingly, the ortholog of this gene displayed an increased copy number in L. braziliensis promastigotes experimentally selected for antimony resistance in vitro compared to non-selected lines (31). Moreover, this same study found a 50 kb intrachromosomal amplification affecting 23 genes (out of a total of 31 amplified genes) in chromosome 27 in the SbIII resistant line, with many of these genes displaying a copy number more than 10 times higher compared to the SbIII sensitive line (31). Among these genes, a WW domain/Zinc finger C-x8-C-x5-C-x3-H type - protein gene (LdBPK_270130.1 ortholog in L. donovani) was also the gene with the most upregulated expression compared to the SbIII-sensitive line. Importantly, CCCH type zinc finger proteins are known targets of antimony (32), and therefore, a higher expression of this gene might mitigate its inactivation by the drug.____” __

And for chromosome 31, we also discussed further its potential role in general response against drug-induced stresses.

Starting at line 360 (line 394 in new version):

“____At 100 µM, aneuploidy changes were specific to each of the 4 MePOP replicates, with the exception of chromosome 31 that consistently showed a higher somy than the control. The fact that an increase in copy number of chromosome 31 was observed under strong SbIII and miltefosine pressure, as well as under pressure of other drugs (24) might indicate that the dosage increase in this chromosome has also a general role against multiple types of stresses. ____Noteworthy, there are several ABC transporters in that chromosome (ABCC4-7 and ABCD3) which could play a role in drug efflux (36). Moreover, ontology analysis of chromosome 31 in L. braziliensis have demonstrated an enrichment of genes involved in iron metabolism which could play a role in general adaptation to oxidative stresses (37), but empirical evidence is still lacking.____”

Reviewer #2 (Significance (Required)):

* Aneuploidy can be well-tolerated, beneficial, or deleterious. Particularly, they can confer resistance against environment stresses, including drug pressures. This study aims to understand how aneuploidy arises. The authors approach this question using a model organism, Leishmania donovani, and two distinct drugs as environmental stressors. Using single-cell DNA sequencing and lineage tracing, the authors find that the appearance of aneuploidy is dependent on the drug used, which makes it dependent on the environmental stressor, rather than pre-determined. Importantly, they present a new barcoding method that may be useful to the field of experimental genome evolution.*

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

* This interesting, well written paper uses cutting edge technologies to address the evolutionary dynamics of changes in Leishmania donovani genomes in response to high drug pressure. Using single-cell genome sequencing and lineage tracing with a newly adapted cell barcoding system, the authors were able to follow aneuploid changes and lineage selection following exposures to high concentrations of either antimony or miltefosine. The main conclusions drawn from the careful bioinformatic analyses and methodic representation of 864 single cell genomes and 453 different traceable lineages were that for each drug exposure there was polyclonal selection of pre-adapted parasites complemented by de novo adaptions. Consistent changes in aneuploidy were associated with the populations selected by antimony, while miltefosine selected for populations that had a point mutation in a miltefosine transporter gene. These conclusions are well supported by the data.*

* Reviewer #3 (Significance (Required)):*

*3 comments, 3 responses *

Comment 1

*One general comment is that the contribution of pre-adapted lineages to the emergence of drug resistant populations under conditions of natural exposure is apt to be overstated from the current analysis. As the authors discuss, the L. donovani line used is already pre-adapted to resist antimony due, at least in part, to the amplification of the MRPA gene on chromosome 23. So it is expected that lineages adapted to strong antimony pressure will pre-exist in this line. It seems possible that the de novo adaptions that were observed, involving further copy number amplification of chromosome 23 and other chromosomes (e.g., chr 31), might be facilitated by their pre-existing aneuploides. Thus, the evolutionary dynamics observed might be very particular to these sorts of pre-conditioned cells. *

R: Although BPK282 is indeed pre-adapted to antimony due to an amplification of the MRPA locus, this strain is a clone, so this intra-chromosomal amplification is shared among all cells in the population. Thus, it is probable that this intra-chromosomal amplification alone is not the only reason why some lineages are better adapted to antimony than others, but its combination with variations in aneuploidy affecting chromosome 23. We agree that de novo adaptations were likely facilitated by the presence of pre-existing aneuploidies. This was already commented in answers to comments 2 and 3 of reviewer 1.

Comment 2

It should also be discussed that the culture condtions themselves may pre-condition the parasites for antimony resistance (and possibly other drugs). Continuous passage of L. donovani in axenic culture produced consistent patterns of aneuploid changes, including amplification of Chr 23 (Barja et al., Nat Ecol evol, 2017). Thus a potential caveat of the use of cultured promastigotes is that their culture adaptions might involve genes on the same chromosomes that confer drug resistance.

R: Indeed, and we are aware of the work of Barja et al 2017. However, the flash selection models characterize a competition assay between (sub)clonal lineages which are exactly in the same environment (lineages within each SePOP were in the same culture flasks). Thus, although culture adaptation might indeed lead to pre-conditioning against SbIII due to amplification of chr23, this pre-conditioning should affect the entire population and does not explain the differences in susceptibility to SbIII between the lineages within each SePOP. Moreover, the controls (maintenance in the same culture medium but without drug pressure) did not show any change in their aneuploidy, while SePOP showed an increase in somy of several chromosomes, including chromosome 23 (see fig.1A).

Comment 3

For the miltefosine selection, of the 7 lineages surviving in at least one of the MePOP replicates, only lineage 302 is represented more than once. What is the evidence that the adaptive mutations in the other 6 lineages were pre-existing and did not arise de novo?

R: We agree that evidence for pre-existing mutations is only present for lineage 302 and changed that in the text.

At line 29 (line 22 in the new version):

“In the case of miltefosine, early parasite adaptation was associated with independent pre-existing point mutations in a miltefosine transporter gene.”

Figs 3b and 3c are incorrectly referenced in the text.

R: Fixed in new version.

Discussion p. 8 - "Interestingly, the Gly160Asp mutation also correlated with the frequency of a specific lineage (lineage 27) and appeared in 3 of the 4 MePOPs, indicating that this was a pre-existing mutation found in that lineage." Lineage 302 would appear to be the correct lineage, not 27. Please clarify.

R: Indeed, the correct is lineage 302. This has now been fixed in the new version.

Additional modifications in the manuscript:

1) The mutation in the LdMT gene affecting the codon of amino acid 1016 was described as a Glutamate to stop codon mutation (Glu1016Stop), while in fact the original amino acid is a Serine (Ser1016Stop). This was corrected in the new version.

References

10X Genomics, 2020. How much of a single cell’s genome is amplified? [WWW Document]. 10X Genomics. URL https://kb.10xgenomics.com/hc/en-us/articles/360005108931-How-much-of-a-single-cell-s-genome-is-amplified- (accessed 4.3.23).

Imamura, H., Downing, T., Van den Broeck, F., Sanders, M.J., Rijal, S., Sundar, S., Mannaert, A., Vanaerschot, M., Berg, M., De Muylder, G., Dumetz, F., Cuypers, B., Maes, I., Domagalska, M., Decuypere, S., Rai, K., Uranw, S., Bhattarai, N.R., Khanal, B., Prajapati, V.K., Sharma, S., Stark, O., Schönian, G., De Koning, H.P., Settimo, L., Vanhollebeke, B., Roy, S., Ostyn, B., Boelaert, M., Maes, L., Berriman, M., Dujardin, J.-C., Cotton, J.A., 2016. Evolutionary genomics of epidemic visceral leishmaniasis in the Indian subcontinent. eLife 5, e12613. https://doi.org/10.7554/eLife.12613

Perry, M.R., Wyllie, S., Prajapati, V.K., Feldmann, J., Sundar, S., Boelaert, M., Fairlamb, A.H., 2011. Visceral Leishmaniasis and Arsenic: An Ancient Poison Contributing to Antimonial Treatment Failure in the Indian Subcontinent? PLoS Negl. Trop. Dis. 5, e1227. https://doi.org/10.1371/journal.pntd.0001227

Perry, M.R., Wyllie, S., Raab, A., Feldmann, J., Fairlamb, A.H., 2013. Chronic exposure to arsenic in drinking water can lead to resistance to antimonial drugs in a mouse model of visceral leishmaniasis. Proc. Natl. Acad. Sci. 110, 19932–19937. https://doi.org/10.1073/pnas.1311535110

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Referee #3

Evidence, reproducibility and clarity

This interesting, well written paper uses cutting edge technologies to address the evolutionary dynamics of changes in Leishmania donovani genomes in response to high drug pressure. Using single-cell genome sequencing and lineage tracing with a newly adapted cell barcoding system, the authors were able to follow aneuploid changes and lineage selection following exposures to high concentrations of either antimony or miltefosine. The main conclusions drawn from the careful bioinformatic analyses and methodic representation of 864 single cell genomes and 453 different traceable lineages were that for each drug exposure there was polyclonal selection of pre-adapted parasites complemented by de novo adaptions. Consistent changes in aneuploidy were associated with the populations selected by antimony, while miltefosine selected for populations that had a point mutation in a miltefosine transporter gene. These conclusions are well supported by the data.

Significance

One general comment is that the contribution of pre-adapted lineages to the emergence of drug resistant populations under conditions of natural exposure is apt to be overstated from the current analysis. As the authors discuss, the L. donovani line used is already pre-adapted to resist antimony due, at least in part, to the amplification of the MRPA gene on chromosome 23. So it is expected that lineages adapted to strong antimony pressure will pre-exist in this line. It seems possible that the de novo adaptions that were observed, involving further copy number amplification of chromosome 23 and other chromosomes (eg chr 31), might be facilitated by their pre-existing aneuploides. Thus the evolutionary dynamics observed might be very particular to these sorts of pre-conditioned cells. It should also be discussed that the culture condtions themselves may pre-condition the parasites for antimony resistance (and possibly other drugs). Continuous passage of L. donovani in axenic culture produced consistent patterns of aneuploid changes, including amplification of Chr 23 (Barja et al., Nat Ecol evol, 2017). Thus a potential caveat of the use of cultured promastigotes is that their culture adaptions might involve genes on the same chromosomes that confer drug resistance.<br /> For the miltefosine selection, of the 7 lineages surviving in at least one of the MePOP replicates, only lineage 302 is represented more than once. What is the evidence that the adaptive mutations in the other 6 lineages were pre-existing and did not arise de novo?

Figs 3b and 3c are incorrectly referenced in the text.

Discussion p. 8 - "Interestingly, the Gly160Asp mutation also correlated with the frequency of a specific lineage (lineage 27) and appeared in 3 of the 4 MePOPs, indicating that this was a pre-existing mutation found in that lineage." Lineage 302 would appear to be the correct lineage, not 27. Please clarify.

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Referee #2

Evidence, reproducibility and clarity

Negreira et al. present a study that aims to understand the early evolution of aneuploidy. They use Leishmania, a protozoon parasite known for its genome plasticity, as model, and two drugs as stress inducers. In this work, they use single-cell genomics and lineage tracing to detect changes in chromosome copy numbers. They conclude that, although parasites tend to have genomes with unusual plasticity, aneuploidy dynamics depend on the stressor more than the organism.

Further experiments:

Lines 121-124: I believe the authors should corroborate the statement that expansion of lineages that were fitter prior to drug exposure is stochastically by doing a statistical test comparing their obtained data and randomly generated simulated values. Given that there is still a considerable proportion of lineages with higher fitness and found in more than one passage, I believe this experiment/test would add strength to the conclusion.

Minor issues:

Fig. 1B: Add label to top horizontal axis, showing frequency of each karyotype. Lines 92-96: Could the authors postulate how and why pre-existing aneuploid cells seem to be selected upon SbIII exposure? Fig. 3: Are panels B and C swapped in the figure or the reference swapped in the text? Fig. 3C seems to refer to the mutation (lines 173-179), whereas Fig. 3B seems to relate to the surviving lineages (lines 183-186). Lines 94-97: Could the authors comment on the advantages and disadvantages of such an aggressive selection method? I am not surprised with such a drastic decrease in lineage diversity in this context.

Could the authors elaborate on what is different in chromosome 31 that makes it so prone to change?

Significance

Aneuploidy can be well-tolerated, beneficial, or deleterious. Particularly, they can confer resistance against environment stresses, including drug pressures. This study aims to understand how aneuploidy arises. The authors approach this question using a model organism, Leishmania donovani, and two distinct drugs as environmental stressors. Using single-cell DNA sequencing and lineage tracing, the authors find that the appearance of aneuploidy is dependent on the drug used, which makes it dependent on the environmental stressor, rather than pre-determined. Importantly, they present a new barcoding method that may be useful to the field of experimental genome evolution.

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Referee #1

Evidence, reproducibility and clarity

Negreira et al. have studied aneuploidy in Leishmania selected using a "flash selection" with SbIII or miltefosine (MF). They provided evidence for the SbIII arm that a few parasites in the population with a specific genotype were enriched during drug selection, and these selected parasites with continuous drug pressure further present modifications in their ploidy. For MF selection they show a different scenario where first a minor population with a mutation in the MT gene is selected and with further passages with drugs, parasites with changes in ploidy are further enriched.

Here are some comments that hopefully will be helpful for the authors.

The plasticity of the Leishmania genome is fascinating. It is remarkable that these parasites can tolerate so many and frequent changes in ploidy. Either these changes are stochastic and serendipitous or as convey by the authors are part of the parasite arsenal to respond to a changing environment. They cleverly used single cell sequencing and bar-coded parasites in this well designed and well conducted study to assess the role of ploidy in parasite biology.

1. Drugs are not inducing any of the changes observed, instead the drugs are selecting for parasites with different genotypes (e.g. polyploidy of chromosome 23 for SbIII or parasites with mutations in MT). This is an important conceptual difference and the authors need to change their text throughout starting at line 28.
2. Line 170. Its is probably expected that no cells have increased copy of chromosome 23, 27 and 31 after single cell genomics. None of the first passages of the four SePOP are polyploid for chromosome 27. One possibility is that a subpopulation of cells with increased copy of chr. 23 (because of MRPA?) and 31 (because of ?) are first selected and in subsequent passages cells triploid for 27 are selected. Of note the ploidy of chr. 27 appears to decrease from passage 4 to 5 in SePOP1 which is unusual if the drug pressure is maintained.
3. Lane 194. I agree with the concept of the selection of pre-existing aneuploid cells but the additional somy changes observed are, in my opinion, just selected because these changes occur continuously.
4. Their barcoded strategy was interesting but it would appear that different lineages are enriched in the 4 SePOP. It would be of interest to test whether those lineages have similar ploidy at the onset. I am unclear of why they have to amplify the barcode prior sequencing. Could they just not get this info from the SePOP data; it is my understanding that the drug selection was done with the barcoded population. This would have facilitated the correlation barcode-specific ploidy.
5. The MF screen was harsh and the parasites selected (derived from few clones within the population when considering the time needed to expand) contained SNPs in MT. Difficult to compare the two screens. Passages with higher MF concentration led to major changes in ploidy but with few common features between the MePOP lines.
6. I am not asking for extra work but as a suggestion to help in linking ploidy with phenotype it would have been very interesting to look at 5 passages without drug (SbIII or miltefosine) to see whether a decrease in ploidy is correlated to a decrease in resistance.

Minor points

1. The environment studied (high drug pressure) is unlikely to occur in nature. The authors may wish to comment on how this may translate in the sand fly or in animals.
2. In Fig. S2 MRPA in SePOP1 is a signature of extrachromosomal amplification. Was that studied?
3. For Chromosome 31 in the Sb screen, it would appear that the proximal (left) part is of lower copy number than the distal (right) portion of the chromosome. How could this have happened? Deletion of a portion of chromosome 31 for one allele? This has been described before (Mukherjee et al., 2013) in SbIII resistant lines as one telomeric end of Chr. 31 encodes AQP1, the route of entry of SbIII.

Significance

The plasticity of the Leishmania genome is fascinating. It is remarkable that these parasites can tolerate so many and frequent changes in ploidy. Either these changes are stochastic and serendipitous or as convey by the authors are part of the parasite arsenal to respond to a changing environment. They cleverly used single cell sequencing and bar-coded parasites in this well designed and well conducted study to assess the role of ploidy in parasite biology.

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Reply to the reviewers

We thank all reviewers for their comments and suggestions. The revised manuscript included new experiments they suggested and extensive text edits. Our point-by-point response is shown in bold.

Point-by-point description of the revisions

—----------------------------------------------------------------------------------------------------------------

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

Summary

In this manuscript, Blank et al. propose a link between cell-cycle dependent changes in metabolic flux and corresponding changes in TORC1 activity in yeast cells. Based on their findings, the authors propose that Bat1-dependent leucine synthesis from glucose increases as cells progress through G1 and that this activates TORC1 to drive cell cycle progression. Although the existence of cell-cycle dependent synthesis of leucine is a novel and exciting finding, several aspects of the proposed model are not sufficiently supported by experimental evidence, in particular the fact that the increase in Leu synthesis is causing the increase in TORC1 activity in late G1.

Major comments:

1. To show that the increase in Leu biosynthesis in S-phase is activating TOR, one would ideally want to blunt this increase in biosynthesis and assay TORC1 activity. Admittedly, this is difficult. So, instead, the authors study bat1- cells which have strongly impaired synthesis of BCAA including Leucine. The relevance of these bat1- cells to the proposed cell-cycle dependent model, however, is questionable for two reasons: 1) Although the authors state that "exogenous supplementation of BCAAs in all combinations suppressed the growth defect of bat1- cells, especially when valine was present", the spot assays in Figure 3 show visible rescues only when valine is present either alone or in combination, while supplementation of leucine or isoleucine does not seem to have any effect. Hence it appears that the bat1- phenotype is mainly due to limiting valine levels, not leucine levels. 2) The relevance of these results for understanding TORC1 regulation are questionable, since valine does not typically activate TORC1. Does addition of Leu to bat1- cells increase TORC1 activity ? RESPONSE: The reviewer’s comments were very valuable. We performed the suggested experiments (adding not only Leu but also Ile and Val) to bat1 cells and measuring phosphorylation of Rps6 (see new Figure 4D) and the DNA content of those cells (see new Figure 3C). We found that Leu weakly promotes cell cycle progression, compared to the addition of Val, which also leads to pronounced activation of TORC1 (>10-fold activation; see Figure 4D). We discuss these findings in the revised text.

We also note, as published by others and now discussed in the text, that in WT cells, exogenous addition of Leu (or any other BCAA) does not lead to sustained activation of TORC1 (see new Figure 4D). This is not surprising. As reported by the Hall lab (see PMID: 25063813, which we now cite), the Gtr-dependent activation of TORC1 by BCAAs mentioned by the reviewer is very transient. Hence, our new data, showing sustained TORC1 activation and cell cycle effects upon Val addition in bat1 cells, is exciting. They argue that bat1 cells serve as a highly sensitized background of low TORC1 activity, enabling the display of effects that are difficult to measure in WT cells.

TORC1 activity is known to depend on steady-state leucine concentrations in the cell rather than on leucine flux. Although the authors observe that the synthesis rate of leucine increases during G1 progression, this does not necessarily translate into increased leucine concentrations in the cell. To support the claim that the increase in TORC1 activity during G1 progression depends on leucine, the authors would need to show that, not only leucine synthesis, but also overall leucine levels in the cell increase during G1 progression.

RESPONSE: We did this experiment and now report the data (see new Figure EV2), using the Edman degradation-based assay. We found that changes in the steady-state levels of BCAAs had a similar pattern, and those changes were most significant for valine (rising 30-40% from late G1 to G2/M). Nonetheless, we note also that the kinetics of amino acid synthesis measured by our isotope tracing experiment need not match the steady-state levels of amino acids. Steady-state levels are affected by a multitude of parameters, only one of which is the rate of synthesis, as we now discuss in detail in the manuscript.

To test whether the increase in Leu biosynthesis in S-phase activates TORC1, a few different approaches could be tested: 1) Since leucine activates TORC1 through the Gtr proteins, the authors could test whether rendering TORC1 resistant to low leucine through expression of constitutively active Gtrs abolishes the cell-cycle dependence in TORC1 activity. 2) Leu could be added to the medium of wildtype cells in G1 to the amount necessary to cause an increase in intracellular Leu levels similar to those seen in S-phase to test whether this increases TORC1 activity.

RESPONSE: We did the suggested experiments, which are now shown in the new Figure 5. Leucine and valine accelerated the rise in TORC1 activity in G1. However, there were no noticeable downstream consequences in the kinetics of cell cycle progression. As we discuss in the text:

“A small acceleration of the rise in the levels of phosphorylated Rps6 was evident in both the leucine- and valine supplemented cells (Figure 5A,B). Nonetheless, there were no noticeable downstream consequences in the kinetics of cell cycle progression, in either the rate the cells increased in size or their critical size (Figure 5A; see values above the corresponding blots), consistent with the notion that TORC1 activity already is at a maximal level in these conditions…”

In Fig 2B one sees that Leu biosynthesis peaks at 150min and then drops again. The p-RpS6 blot in Fig. 5D, however, only goes up to 140 min and shows that TORC1 activity increases up to 140 min, but it doesn't show timepoints beyond 150 min when Leu biosynthesis drops again, and hence one would expect TORC1 activity to drop. If TORC1 activity were to drop from 150min onwards, this would strengthen the correlation between Leu biosynthesis and TORC1 activity.

RESPONSE: The reason for the drop in Figure 2 is trivial and does not affect the interpretation. As seen in Figure 1 (the experiment from which the data in Figure 2 are shown), by 180 min, the cells were entering a new cell cycle, evidenced by a reduction in cell size (Figure 1B) and in the fraction of budded cells (Figure 2B). At that point, there is a mix of mothers and daughters with very poor synchrony, making it impossible to conclude much about the drop in Leu synthesis (i.e., does it arise from the lack of new synthesis in mothers, daughters, or both?). In the experiment in Figure 5, the reviewer mentions (now those figures have moved to File S8 because we added more experiments in the figure) the experiment terminated when peak budding was reached, which was 140 min, within one cell cycle. Lastly, it is important to stress that every elutriation experiment is different. While the times are close, comparing various experiments on a time basis alone is inaccurate. Instead, the metric used in the field to compare different experiments is usually cell size, which we use in all other Figures except Figure 1 because, in that case, the experiment was a time-based, pulse-chase one.

Minor concerns:

1. In Figure EV4, the authors should highlight some of the metabolites that are significantly changed, in particular the BCAA. The figure is not very informative as currently presented. __RESPONSE: We have now labeled the BCAAs, and a few more metabolites as suggested (note the Figure is now EV5). __

Fig 2 - are "expressed ratios" the best term for metabolite levels? Unlike genes, where such heat maps are often used, the metabolites are not 'expressed'. How about 'relative metabolite level' instead?

RESPONSE: Good point. The axis now reads “relative abundance”.

Page 8: "We also measured the MID values from the media of the same cultures used to prepare the cell extracts." Where are these data? We don't see them in File S2?

RESPONSE: The data are in File S2 (there are many ‘sheets’ in the file). In sheets 3,4 are the MID values and the analysis from metabolites in the media.

Fig 4B - the x-axis labeling is missing for the bat1- cells

RESPONSE: Corrected. Note that new DNA content measurements are now shown in Figure 3C.

Although the authors state repeatedly that they show "for the first time in any system" that TORC1 activity is dynamic in the cell cycle, similar observations have already been made before, for instance showing high mTORC1 activity in the G1/S transition in the Drosophila wing disc or low mTORC1 activity during mitosis in mammalian cells (see PMIDs 28829944, 28829945, and 31733992). The text should be amended accordingly.

RESPONSE: Thank you. Corrected.

There are two entries for valine in File S1/Sheet8. Why?

RESPONSE: The reason is that they were detected in both analytical pipelines (primary metabolites and biogenic amines; primary metabolites were measured with GC-TOF MS, while biogenic amines with HILIC-QTOF MS/MS), which were combined in the Table. We did not describe it adequately in the previous version. We do now, in the Methods. We also note that the raw data from each method are shown in the corresponding supplemental files. We combined them in the Table used in the Figure for display purposes. We also note that the amino acids were also measured by another method (PTH-based HPLC). Hopefully, the new edits in the Methods clarify these points.

Reviewer #1 (Significance (Required)):

Significance

Despite the well-known effects of pharmacological or genetic manipulations of TORC1/mTORC1 on cell cycle progression, whether and how mTORC1 activity itself is physiologically coupled to cell cycle progression is still an insufficiently studied aspect. Hence this study provides an interesting link between cell-cycle dependent regulation of amino acid biosynthesis and TORC1 regulation. Importantly, the results of this study rely on centrifugal elutriation to obtain cell cycle synchronization, thus ruling out potential metabolic artifacts due to pharmacological methods. The observed changes in metabolic flux are therefore likely genuine and represent the major strength of the study. The major limitation is the lack of strong evidence supporting the notion that the increase in Leu biosynthesis at late G1 or S-phase is causing the increase in TORC1 activity.

The major advance is conceptual - that amino acid biosynthesis rates are cell-cycle dependent.

These results will be of interest to a broad audience of people studying the cell cycle, cell growth, TORC1 activity, cell metabolism and cancer.

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

This paper provides evidence that branched chain amino acid (BCAA) in the G1 phase of the cell cycle, fueled by pyruvate generated by glucose catabolism activates cell growth and allows cells to reach the critical size required for entry into S phase by activation of TORC1 signaling. Previous work had indicated that Leucine supplementation of a bat1 bat2 mutant, lacking both enzymes that catalyze BCAA from the alpha-keto acid precursors and starved on minimal medium, led to TORC1 activation. This work is significant in suggesting that BCAA synthesis from glucose is responsible for a cyclic activation of TORC1 necessary for a normal rate of cell growth in the G1 phase of the cell cycle.

The study employs metabolic flux analysis of metabolites derived from glucose following a pulse-chase with different isotopes of glucose in synchronized early G1 cells (obtained by elutriation) throughout one cell cycle. They claim that the only compelling changes in metabolites observed as the cell cycle proceeds was a decline in pyruvate containing only one heavy 13C carbon atom and a corresponding increase in Leu (M6) with 6 heavy carbon atoms, which is interpreted to indicate Leu synthesis from pyruvate that begins in early G1 and peaks at mitosis. They show that a bat1 mutant exhibits a slow-growth phenotype that can be mitigated only by valine (although they infer similar effects for Leu and Ile that I find unconvincing) and they observed reductions in all three BCAAs in different experiments that measure steady amino acid levels in different ways (although the results are compelling only for Val). They go on to show evidence that the bat1 mutation reduces birth and mean cell size and leads to an increased proportion of G1 cells in asynchronous cultures, and they claim that bat1 cells take much longer than WT to achieve the same size found when a synchronized WT culture reaches 50% budding (although they don't show the data for this last point.) Interestingly, they find that deleting BAT1 suppresses sensitivity to the TORC1 inhibitor rapamycin (Rap), consistent with the idea that the bat1 mutation impairs TORC1 activity in the same manner as Rap and that BCAA are required to activate TORC1 in WT cells to the level that can be impaired by Rap, as summarized in the model in Fig. 5F. Consistent with this, they present evidence that the bat1 mutation reduces TORC1 signaling as judged by diminished Rps6 phosphorylation (although it was not shown that this effect could be reversed by Val addition). They also show that TORC1 signaling/Rps6-P increases as the cell cycle progresses using elutriated early G1 cells, suggesting that TORC1 activity is periodic in the cell cycle (although they don't establish this periodicity through a second cell cycle).

General critique:

The conclusion that BCAA synthesis from glucose is responsible for a cyclic activation of TORC1 necessary for a normal rate of cell growth in the G1 phase of the cell cycle is potentially of considerable significance. There are however a number of puzzling aspects of the data that seem to weaken this conclusion. As described in greater detail below, it is difficult to explain why only Leu is synthesized from glucose during the cell cycle, and why only Val shows a marked reduction in the bat1 mutant that appears to be responsible for the slow-growth phenotype. In addition, there are important controls lacking of showing that a Val supplement can suppress the G1 delay and reduction in TORC1 signaling in the bat1 mutant. In addition, the evidence that TORC1 activity is periodic in the cell cycle is lacking and it needs to be shown that Rps6-P levels are periodic through at least a second cell cycle.

Major comments:

-Why don't they observe synthesis of Ile and particularly Val in the metabolic flux experiment of Fig. 1, especially considering that only Val appears to be critically required for normal cell growth in the bat1 mutant based on the results in Fig. 3B?

RESPONSE: We now show the actual plots and the errors of all the measurements in Figure 2 (instead of a heatmap we had shown before). Valine (M5) levels show a very similar trend to leucine (M6). The variance in the measurements was higher, though, and statistically, the valine changes were less significant. Hence, it was more appropriate to highlight the leucine changes. Lastly, the new DNA content data (Figure 3C) show an effect upon the addition of leucine, albeit less significant than that of valine addition.

-The data in Fig. 3B do not show a convincing increase in growth of the bat1 mutant with addition of Leu and Ile; and the stimulation by Val alone seems identical to that seen with Val in combination with Leu and Ile. Thus, it appears that the slow-growth of the bat1 mutant results only from reduced Val levels, not all 3 BCAAs, which is at odds with their interpretation of the data.

__RESPONSE. As mentioned above, the effect of valine is more pronounced than leucine's, but leucine does have consequences, best shown in the DNA content analysis (new Figure 3C). We also note that valine alone is insufficient to suppress the growth and cell cycle defects of bat1 cells. The latest data we have added (see Figures 3 and 5) are consistent with the interpretation that at least some de novo synthesis of BCAAs in the cell may be needed, explaining why exogenous BCAAs, including valine, are unable to correct the defects of bat1 cells fully. __

-they claim to see reductions in all three BCAAs in the bat1 mutant; however, no significant reduction was found for Leu in Fig. EV3, and only Val was altered by the 1.5-fold cut-off imposed on the MS metabolomics data in Fig. EV4 (which could be appreciated only by an in-depth examination of the supplementary data in File S1-the Val, Leu, and Ile dots should be labeled in Fig. EV4). In addition, the reductions in Ala and Gly showin in Fig. EV3 were not found in the MS analysis of Fig. EV4. It needs to be acknowledged that the metabolomics data show a marked reduction in the bat1 mutant only for Valine with little or no change in Leucine levels. This result is difficult to explain with the simple models shown in Fig. 3A and 5F, which requires additional comment. The authors should acknowledge the much greater effect of the bat1 mutation on Val levels versus Leu and Ile, revealed both by measuring the levels of BCAAs in the mutant and comparing the BCAAs for rescuing the slow-growth of the mutant, and explain how this can be reconciled with the results in Fig. 2 where only Leu and not Val or Ile synthesis was detected.

__RESPONSE. The perceived discrepancy in the steady-state measurements could easily arise from the different analytical methods used in each case. The differences are less substantial than the reviewer implies. For steady-state measurements in BAT1 vs. bat1 cells, we used the PTH-based method (which only detects amino acids) and two different MS-based pipelines (which detect various metabolites). From the MS-based analyses, the drop for all BCAAs was statistically significant. Although the magnitude of the drop was greater for valine (about 60% for valine vs. ~30% for isoleucine and leucine). Why is this a problem? __

As for the valine changes in the isotope tracing experiments, as we mentioned above, the trend for valine (M5) was similar to that of leucine (M6) (now, hopefully, that data is shown better in Figure 2). Furthermore, as we commented above (see response to Reviewer 1) and now stated in the text, our isotope tracing experiments measure only the rate of synthesis, which need not match the steady-state abundances. The latter are affected by a multitude of variables, including the turnover of proteins and amino acids, not to mention their partition into distinct intracellular pools.

__Lastly, please note that we have now added PTH-based measurements of amino acid levels in the cell cycle of wild type cells (new Figure EV2). As mentioned in our response to Reviewer 1, we found that changes in the steady-state levels of BCAAs had a similar pattern, and those changes were most significant for valine (rising 30-40% from late G1 to G2/M). __

-They need to add the data indicating that the bat1 mutant requires longer than WT cells to reach the ~35 fL volume at which 50% of WT cells are budded.

__RESPONSE: We added all that data (new Figure EV6) and discussed it better in the text. Note that our elutriation analyses allow accurate estimates of the G1 duration, which is at least 2x longer in bat1 vs. BAT1 cells. __

-It seems important to show that Val supplementation can suppress the overabundance of G1 cells in bat1 mutant cells shown in Fig. 4C; and can restore sensitivity to Rap and Rps6-P accumulation in bat1 mutant cells (in Fig.s 5A & B).

__RESPONSE: Excellent suggestions. We now present the requested experiments. The DNA content data are in Figure 3C, and the phospho-Rps6 data in the new Figure 4D are discussed in the text. Briefly, exogenous valine, and to a lesser extent leucine, suppressed the G1 accumulation, but not to wild type levels. Exogenous valine also substantially increased TORC1 activity (>10-fold). __

-It seems important to show that Rps6-P will decline in M phase and increase during a second cell cycle to establish that TORC1 activity actually fluctuates in the cell cycle instead of just by reduced by the manipulations involved in collecting young G1 cells by elutriation.

RESPONSE: The second cycle comment is not pertinent to our elutriation setup. The two-cycle approach should be used in arrest-and-release synchronizations to minimize arrest-related artifacts when cells continue to grow in size. This is why we used elutriation in the first place, as described in the text, to avoid such artifacts. In elutriations it is the first cycle, exclusively of daughter cells, that can be meaningfully scored. After that, the cells lose synchrony very fast because you have mothers (which grow in size very little) and daughters (which need to double in size until mitosis). Hence, the second cycle will be meaningless and impossible to interpret.

Reviewer #2 (Significance (Required)):

General Assessment:

Strengths: Evidence for BCAA biosynthesis from glucose in the G1 phase of the cell cycle, and evidence obtained from analyzing the bat1 mutant that BCAA synthesis underlies activation of TORC1 early in the cell cycle in a manner required to achieve the critical cell size necessary for G1 to S transition.

Weaknesses: Lack of evidence for Val biosynthesis in G1 despite evidence that Val limitation is more crucial than Leu limitation in the bat1 mutant; lack of confirmation that Val limitation underlies the delayed G1-S transition and reduced TORC1 signaling in the bat1 mutant; and lack of compelling evidence that TORC1 activity is periodic in WT cells.

Advance: This would be the first evidence that TORC1 activity varies through the cell cycle in a manner controlled by synthesis of BCAAs

Audience: This advance would be of great interest to a wide range of workers studying how the cell cycle is regulated and the role of TORC1 in controlling cell growth and division in normal cells and in human disease.

My expertise: Mechanisms of metabolic regulation of gene expression at the transcriptional and translational levels in budding yeast

**Referees cross-commenting**

Ref. #1's major comment 1 echoes my request for clarification about whether Leu, and not just Val, is limiting growth in the bat1 mutant, and also the need to determine which BCAA supplement to bat1 cells will restore TORC1 activity (which was also requested by Ref. #3).

I agree with this reviewer's request to provide evidence that Leu levels actually increase during G1 progression (comment #2). I also think the suggested experiments in Comment #3 are reasonable for their potential to provide stronger evidence that Leu production in the G1 phase of wild-type cells activates TORC1, as currently the argument is based on the finding of low TORC1 activation in bat1 cells (that seem to be limiting for Val vs. Leu). Comment #4 echoes similar requests made by both me and Ref. #3. Ref. #3's major comments 1 and 3 mirror two of my major comments. I wasn't convinced of the need to monitor Sch9 versus Rps6 phosphorylation as a read-out of TORC1 activity-does being a direct substrate truly matter? Regarding comment 5, I wasn't convinced of the need to include Rap-sensitive or -resistant control strains for the analysis in Fig. 5A. And regarding comment 4, while it would be interesting to examine if TORC1 regulates BCAA synthesis during cell cycle progression, this seems to be outside the scope of a demonstration that BCAA synthesis stimulates TORC1.

Thus, it seems we all agree on certain experiments that need to be carried out, and Ref. #1 has rightly proposed a few others with the potential to strengthen the evidence that Leu production during G1 phase mediates cyclic activation of TORC1

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

In this manuscript, Blank and colleagues measure the synthesis of various metabolites from glucose during cell cycle progression and observe an increased synthesis of branched-chain amino acids (BCAA) from the early G1 to late G1 phase. Interestingly, they also found a gradual increase in TORC1 activity from the early G1 to the S phase which is proposed to be dependent on BCAA synthesis.

Major comments:

1. The authors show that TORC1 activity increases from the early G1 to the S phase. TORC1 activity is sensitive to short-term starvations caused during changing media or centrifugations. Hence, the concern arises regarding the increased pattern of TORC1 activity during the cell cycle. Is it really a biological phenomenon or a cellular adaptation to experimental conditions? Can authors provide more support for this observation? Can authors monitor the cell cycle for the two cell cycles to confirm that TORC1 activity shows a wavy pattern? RESPONSE: The same point was also made by Reviewer #2. As we noted in our response above, “____The second cycle comment is not pertinent to our elutriation setup. The two-cycle approach should be used in arrest-and-release synchronizations to minimize arrest-related artifacts when cells continue to grow in size. This is why we used elutriation in the first place, as described in the text, to avoid such artifacts. In elutriations it is the first cycle, exclusively of daughter cells, that can be meaningfully scored. After that, the cells lose synchrony very fast because you have mothers (which grow in size very little) and daughters (which need to double in size until mitosis). Hence, the second cycle will be meaningless and impossible to interpret.____”

The authors use Rps6 phosphorylation as a read-out of TORC1 activity, which is not a direct substrate of TORC1. Analysis of the direct substrates of TORC1, such as phosphorylation of Sch9 will solidify the author's claim.

RESPONSE: The reviewers discussed this point (see their comments above). We agree with the opinion that Rps6 phosphorylation accurately reports on TORC1 activity (also used in the fly experiments we now cite, as requested by Reviewer 1). For all our experiments' objectives and conclusions, it doesn't matter if the phosphorylation of Rps6 lies more downstream than Sch9 phosphorylation.

Authors show that Bat1 lacking strain have reduced TORC1 activity. Can authors restimulate these cells with Leucin, Valine, and Isoleucine individually or in combination to identify the critical amino acid for the TORC1 activity?

RESPONSE: Yes, that is an excellent suggestion. We show the experiment in Figure 4D (see previous response). Valine showed pronounced activation (>10-fold).

The authors claim that increased BCAA synthesis is necessary for TORC1 activation. Since TORC1 is shown to be upstream of amino acid biosynthesis pathways, it will be interesting to check if TORC1 per se regulates BCAA synthesis during cell cycle progression. The authors could inhibit TORC1 by rapamycin treatment and monitor if the BCAA synthesis still shows cell cycle-dependent modulation.

RESPONSE: The reviewers also discussed this point (see their comments above). We agree with the view that it is a very substantial undertaking, well beyond the scope of this work.

In Figure 5A, the use of any rapamycin-sensitive and rapamycin-resistant strains as controls will strengthen their claim of TORC1 inhibition being epistatic to Bat1 deletion, since the rapamycin in minimal media might be less effective.

RESPONSE: Again, the reviewers also discussed this point (see their comments above). We agree that it will not add much to the conclusions in the context of all the data we show and the existing literature.

Minor comments:

1. The data of metabolic labeling, especially various species M1, M2, M3, etc., of an individual metabolite is difficult to understand for the general readers. Hence, a schematic explaining various species might be helpful. RESPONSE: We added a new Figure (EV1) delineating the carbons from glucose to valine and leucine.

Please describe the elutriation approach in more detail with media conditions and buffer conditions to understand the overall experimental setup.

RESPONSE: We now added this information (see the second section of the Materials and Methods).

Reviewer #3 (Significance (Required)):

Significance:

Overall, this study presents an interesting observation to the researchers working in TORC1 and cell cycle regulation.

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

In this manuscript, Blank and colleagues measure the synthesis of various metabolites from glucose during cell cycle progression and observe an increased synthesis of branched-chain amino acids (BCAA) from the early G1 to late G1 phase. Interestingly, they also found a gradual increase in TORC1 activity from the early G1 to the S phase which is proposed to be dependent on BCAA synthesis.

Major comments:

1. The authors show that TORC1 activity increases from the early G1 to the S phase. TORC1 activity is sensitive to short-term starvations caused during changing media or centrifugations. Hence, the concern arises regarding the increased pattern of TORC1 activity during the cell cycle. Is it really a biological phenomenon or a cellular adaptation to experimental conditions? Can authors provide more support for this observation? Can authors monitor the cell cycle for the two cell cycles to confirm that TORC1 activity shows a wavy pattern?
2. The authors use Rps6 phosphorylation as a read-out of TORC1 activity, which is not a direct substrate of TORC1. Analysis of the direct substrates of TORC1, such as phosphorylation of Sch9 will solidify the author's claim.
3. Authors show that Bat1 lacking strain have reduced TORC1 activity. Can authors restimulate these cells with Leucin, Valine, and Isoleucine individually or in combination to identify the critical amino acid for the TORC1 activity?
4. The authors claim that increased BCAA synthesis is necessary for TORC1 activation. Since TORC1 is shown to be upstream of amino acid biosynthesis pathways, it will be interesting to check if TORC1 per se regulates BCAA synthesis during cell cycle progression. The authors could inhibit TORC1 by rapamycin treatment and monitor if the BCAA synthesis still shows cell cycle-dependent modulation.
5. In Figure 5A, the use of any rapamycin-sensitive and rapamycin-resistant strains as controls will strengthen their claim of TORC1 inhibition being epistatic to Bat1 deletion, since the rapamycin in minimal media might be less effective.

Minor comments:

1. The data of metabolic labeling, especially various species M1, M2, M3, etc., of an individual metabolite is difficult to understand for the general readers. Hence, a schematic explaining various species might be helpful.
2. Please describe the elutriation approach in more detail with media conditions and buffer conditions to understand the overall experimental setup.

Significance

Overall, this study presents an interesting observation to the researchers working in TORC1 and cell cycle regulation.

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

This paper provides evidence that branched chain amino acid (BCAA) in the G1 phase of the cell cycle, fueled by pyruvate generated by glucose catabolism activates cell growth and allows cells to reach the critical size required for entry into S phase by activation of TORC1 signaling. Previous work had indicated that Leucine supplementation of a bat1 bat2 mutant, lacking both enzymes that catalyze BCAA from the alpha-keto acid precursors and starved on minimal medium, led to TORC1 activation. This work is significant in suggesting that BCAA synthesis from glucose is responsible for a cyclic activation of TORC1 necessary for a normal rate of cell growth in the G1 phase of the cell cycle.

The study employs metabolic flux analysis of metabolites derived from glucose following a pulse-chase with different isotopes of glucose in synchronized early G1 cells (obtained by elutriation) throughout one cell cycle. They claim that the only compelling changes in metabolites observed as the cell cycle proceeds was a decline in pyruvate containing only one heavy 13C carbon atom and a corresponding increase in Leu (M6) with 6 heavy carbon atoms, which is interpreted to indicate Leu synthesis from pyruvate that begins in early G1 and peaks at mitosis. They show that a bat1 mutant exhibits a slow-growth phenotype that can be mitigated only by valine (although they infer similar effects for Leu and Ile that I find unconvincing) and they observed reductions in all three BCAAs in different experiments that measure steady amino acid levels in different ways (although the results are compelling only for Val). They go on to show evidence that the bat1 mutation reduces birth and mean cell size and leads to an increased proportion of G1 cells in asynchronous cultures, and they claim that bat1 cells take much longer than WT to achieve the same size found when a synchronized WT culture reaches 50% budding (although they don't show the data for this last point.) Interestingly, they find that deleting BAT1 suppresses sensitivity to the TORC1 inhibitor rapamycin (Rap), consistent with the idea that the bat1 mutation impairs TORC1 activity in the same manner as Rap and that BCAA are required to activate TORC1 in WT cells to the level that can be impaired by Rap, as summarized in the model in Fig. 5F. Consistent with this, they present evidence that the bat1 mutation reduces TORC1 signaling as judged by diminished Rps6 phosphorylation (although it was not shown that this effect could be reversed by Val addition). They also show that TORC1 signaling/Rps6-P increases as the cell cycle progresses using elutriated early G1 cells, suggesting that TORC1 activity is periodic in the cell cycle (although they don't establish this periodicity through a second cell cycle).

General critique:

The conclusion that BCAA synthesis from glucose is responsible for a cyclic activation of TORC1 necessary for a normal rate of cell growth in the G1 phase of the cell cycle is potentially of considerable significance. There are however a number of puzzling aspects of the data that seem to weaken this conclusion. As described in greater detail below, it is difficult to explain why only Leu is synthesized from glucose during the cell cycle, and why only Val shows a marked reduction in the bat1 mutant that appears to be responsible for the slow-growth phenotype. In addition, there are important controls lacking of showing that a Val supplement can suppress the G1 delay and reduction in TORC1 signaling in the bat1 mutant. In addition, the evidence that TORC1 activity is periodic in the cell cycle is lacking and it needs to be shown that Rps6-P levels are periodic through at least a second cell cycle.

Major comments:

• Why don't they observe synthesis of Ile and particularly Val in the metabolic flux experiment of Fig. 1, especially considering that only Val appears to be critically required for normal cell growth in the bat1 mutant based on the results in Fig. 3B?
• The data in Fig. 3B do not show a convincing increase in growth of the bat1 mutant with addition of Leu and Ile; and the stimulation by Val alone seems identical to that seen with Val in combination with Leu and Ile. Thus, it appears that the slow-growth of the bat1 mutant results only from reduced Val levels, not all 3 BCAAs, which is at odds with their interpretation of the data.
• they claim to see reductions in all three BCAAs in the bat1 mutant; however, no significant reduction was found for Leu in Fig. EV2, and only Val was altered by the 1.5-fold cut-off imposed on the MS metabolomics data in Fig. EV3 (which could be appreciated only by an in-depth examination of the supplementary data in File S1-the Val, Leu, and Ile dots should be labeled in Fig. EV3). In addition, the reductions in Ala and Gly showin in Fig. EV2 were not found in the MS analysis of Fig. EV3. It needs to be acknowledged that the metabolomics data show a marked reduction in the bat1 mutant only for Valine with little or no change in Leucine levels. This result is difficult to explain with the simple models shown in Fig. 3A and 5F, which requires additional comment. The authors should acknowledge the much greater effect of the bat1 mutation on Val levels versus Leu and Ile, revealed both by measuring the levels of BCAAs in the mutant and comparing the BCAAs for rescuing the slow-growth of the mutant, and explain how this can be reconciled with the results in Fig. 2 where only Leu and not Val or Ile synthesis was detected.
• They need to add the data indicating that the bat1 mutant requires longer than WT cells to reach the ~35 fL volume at which 50% of WT cells are budded.
• It seems important to show that Val supplementation can suppress the overabundance of G1 cells in bat1 mutant cells shown in Fig. 4C; and can restore sensitivity to Rap and Rps6-P accumulation in bat1 mutant cells (in Fig.s 5A & B).
• It seems important to show that Rps6-P will decline in M phase and increase during a second cell cycle to establish that TORC1 activity actually fluctuates in the cell cycle instead of just by reduced by the manipulations involved in collecting young G1 cells by elutriation.

Referees cross-commenting

Ref. #1's major comment 1 echoes my request for clarification about whether Leu, and not just Val, is limiting growth in the bat1 mutant, and also the need to determine which BCAA supplement to bat1 cells will restore TORC1 activity (which was also requested by Ref. #3). I agree with this reviewer's request to provide evidence that Leu levels actually increase during G1 progression (comment #2). I also think the suggested experiments in Comment #3 are reasonable for their potential to provide stronger evidence that Leu production in the G1 phase of wild-type cells activates TORC1, as currently the argument is based on the finding of low TORC1 activation in bat1 cells (that seem to be limiting for Val vs. Leu). Comment #4 echoes similar requests made by both me and Ref. #3. Ref. #3's major comments 1 and 3 mirror two of my major comments. I wasn't convinced of the need to monitor Sch9 versus Rps6 phosphorylation as a read-out of TORC1 activity-does being a direct substrate truly matter? Regarding comment 5, I wasn't convinced of the need to include Rap-sensitive or -resistant control strains for the analysis in Fig. 5A. And regarding comment 4, while it would be interesting to examine if TORC1 regulates BCAA synthesis during cell cycle progression, this seems to be outside the scope of a demonstration that BCAA synthesis stimulates TORC1.

Thus, it seems we all agree on certain experiments that need to be carried out, and Ref. #1 has rightly proposed a few others with the potential to strengthen the evidence that Leu production during G1 phase mediates cyclic activation of TORC1

Significance

General Assessment:

Strengths: Evidence for BCAA biosynthesis from glucose in the G1 phase of the cell cycle, and evidence obtained from analyzing the bat1 mutant that BCAA synthesis underlies activation of TORC1 early in the cell cycle in a manner required to achieve the critical cell size necessary for G1 to S transition.

Weaknesses: Lack of evidence for Val biosynthesis in G1 despite evidence that Val limitation is more crucial than Leu limitation in the bat1 mutant; lack of confirmation that Val limitation underlies the delayed G1-S transition and reduced TORC1 signaling in the bat1 mutant; and lack of compelling evidence that TORC1 activity is periodic in WT cells.

Advance: This would be the first evidence that TORC1 activity varies through the cell cycle in a manner controlled by synthesis of BCAAs

Audience: This advance would be of great interest to a wide range of workers studying how the cell cycle is regulated and the role of TORC1 in controlling cell growth and division in normal cells and in human disease.

My expertise: Mechanisms of metabolic regulation of gene expression at the transcriptional and translational levels in budding yeast

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript, Blank et al. propose a link between cell-cycle dependent changes in metabolic flux and corresponding changes in TORC1 activity in yeast cells. Based on their findings, the authors propose that Bat1-dependent leucine synthesis from glucose increases as cells progress through G1 and that this activates TORC1 to drive cell cycle progression. Although the existence of cell-cycle dependent synthesis of leucine is a novel and exciting finding, several aspects of the proposed model are not sufficiently supported by experimental evidence, in particular the fact that the increase in Leu synthesis is causing the increase in TORC1 activity in late G1.

Major comments:

1. To show that the increase in Leu biosynthesis in S-phase is activating TOR, one would ideally want to blunt this increase in biosynthesis and assay TORC1 activity. Admittedly, this is difficult. So, instead, the authors study bat1- cells which have strongly impaired synthesis of BCAA including Leucine. The relevance of these bat1- cells to the proposed cell-cycle dependent model, however, is questionable for two reasons: 1) Although the authors state that "exogenous supplementation of BCAAs in all combinations suppressed the growth defect of bat1- cells, especially when valine was present", the spot assays in Figure 3 show visible rescues only when valine is present either alone or in combination, while supplementation of leucine or isoleucine does not seem to have any effect. Hence it appears that the bat1- phenotype is mainly due to limiting valine levels, not leucine levels. 2) The relevance of these results for understanding TORC1 regulation are questionable, since valine does not typically activate TORC1. Does additionn of Leu to bat1- cells increase TORC1 activity ?
2. TORC1 activity is known to depend on steady-state leucine concentrations in the cell rather than on leucine flux. Although the authors observe that the synthesis rate of leucine increases during G1 progression, this does not necessarily translate innto increased leucine concentrations in the cell. To support the claim that the increase in TORC1 activity during G1 progression depends on leucine, the authors would need to show that, not only leucine synthesis, but also overall leucine levels in the cell increase during G1 progression.
3. To test whether the increase in Leu biosynthesis in S-phase activates TORC1, a few different approaches could be tested: 1) Since leucine activates TORC1 through the Gtr proteins, the authors could test whether rendering TORC1 resistant to low leucine through expression of constitutively active Gtrs abolishes the cell-cycle dependence in TORC1 activity. 2) Leu could be added to the medium of wildtype cells in G1 to the amount necessary to cause an increase in intracellular Leu levels similar to those seen in S-phase to test whether this increases TORC1 activity.
4. In Fig 2B one sees that Leu biosynthesis peaks at 150min and then drops again. The p-RpS6 blot in Fig. 5D, however, only goes up to 140 min and shows that TORC1 activity increases up to 140 min, but it doesn't show timepoints beyond 150 min when Leu biosynthesis drops again, and hence one would expect TORC1 activity to drop. If TORC1 activity were to drop from 150min onwards, this would strengthen the correlation between Leu biosynthesis and TORC1 activity.

Minor concerns:

1. In Figure EV3, the authors should highlight some of the metabolites that are significantly changed, in particular the BCAA. The figure is not very informative as currently presented.
2. Fig 2 - are "expressed ratios" the best term for metabolite levels? Unlike genes, where such heat maps are often used, the metabolites are not 'expressed'. How about 'relative metabolite level' instead?
3. Page 8: "We also measured the MID values from the media of the same cultures used to prepare the cell extracts." Where are these data? We don't see them in File S2?
4. Fig 4B - the x-axis labeling is missing for the bat1- cells
5. Although the authors state repeatedly that they show "for the first time in any system" that TORC1 activity is dynamic in the cell cycle, similar observations have already been made before, for instance showing high mTORC1 activity in the G1/S transition in the Drosophila wing disc or low mTORC1 activity during mitosis in mammalian cells (see PMIDs 28829944, 28829945, and 31733992). The text should be amended accordingly.
6. There are two entries for valine in File S1/Sheet8. Why?

Significance

Despite the well-known effects of pharmacological or genetic manipulations of TORC1/mTORC1 on cell cycle progression, whether and how mTORC1 activity itself is physiologically coupled to cell cycle progression is still an insufficiently studied aspect. Hence this study provides an interesting link between cell-cycle dependent regulation of amino acid biosynthesis and TORC1 regulation. Importantly, the results of this study rely on centrifugal elutriation to obtain cell cycle synchronization, thus ruling out potential metabolic artifacts due to pharmacological methods. The observed changes in metabolic flux are therefore likely genuine and represent the major strength of the study. The major limitation is the lack of strong evidence supporting the notion that the increase in Leu biosynthesis at late G1 or S-phase is causing the increase in TORC1 activity.

The major advance is conceptual - that amino acid biosynthesis rates are cell-cycle dependent.

These results will be of interest to a broad audience of people studying the cell cycle, cell growth, TORC1 activity, cell metabolism and cancer.

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Reply to the reviewers

General Statement

We thank the reviewers for a thorough review that will help us to improve the manuscript in the revision process. In our opinion, all three reviewers found the manuscript interesting, novel, and relevant for a broader readership. The reviewers suggested performing additional analyses of cell quantification from existing brain tissue or from newly generated tissue. All reviewers identified several shared concerns that we are happy to address by additional experiments and analyses to improve our manuscript. The reviewers suggested including the Control Diet + LiPR treatment group to further characterize the effects of LiPR on adult neurogenesis outside the context of the High Fat Diet. Also, the reviewers suggested including built upon the analysis of tanycytes and their proliferation. Some of these analyses will require generating new experimental animals, however, most analyses can be performed from already available brain tissue or previously collected confocal microscope images. Because we had anticipated some of the possible concerns, we have placed mice in the experiment already in February 2023. These mice are in the 4-month treatment group of Control Diet + LiPR. We will collect the brain tissue at the end of May 2023 and will analyze it in June and July 2023. In April and May 2023, we will work on analyses from existing tissue or images as described in detail below. We estimate that the suggested analyses are all feasible and should be manageable in 3 months. In fact, we are pleasantly surprised by the favorable nature of the reviews, especially from the reviewer 1 and 3, which allowed us to address around 50% of comments already as demonstrated in this revision plan (see section 3). Therefore, we are confident that we will be able to address the remaining concerns to full satisfaction of all relevant reviewers’ comments.

Reviewer 1

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In this manuscript, Jorgensen and colleagues describe their findings on the action of a palmitoylated form of prolactin-release peptide (LiPR) on neural stem cells (NSC) in the adult mouse hypothalamus and adult mouse hippocampus. Their main conclusion is that LiPR can counteract the effects of high-fat diet (HFD) and rescue some of the adverse effects of HFD. Specifically, the authors provide evidence that: - Exposure to HFD reduces the number of presumptive adult neural stem cells (NSCs) in the adult hypothalamus, whereas exposure to LiPR reverses this trend. - The results suggest that LiPR reduces the proliferation of alpha-tanycytes and/or their progeny in the hypothalamus in the context of HFD, with Liraglutide acting similarly. In contrast, while LiPR also suppresses proliferation in the SGZ, Liraglutide works there in the opposite direction. - LiPR also helps the survival of adult-born hypothalamic neurons. - Reduction of proliferation by LiPR suggests a model where LiPR increases the number of NSCs presumably by reducing their rate of activation. - The results suggest that LiPR promotes expression of PrRP receptors in the hypothalamic neurons, suggesting that PrRP may act directly on such neurons (and tanycytes?) in vivo. - The authors also show that HFD and LiPR alter gene expression profiles of the MBH cells, with HFD, but not LiPR, inducing myelination-related genes. - Finally, they show that PrRP stimulates an increase in Ca2+ in in vitro-derived human hypothalamic neurons. - The authors conclude that LiPR may be reducing activation and proliferation of the hypothalamic stem cells and thereby preserve their pool from exhaustion, which was stimulated by HFD. The manuscript presents interesting data and is clearly written. There are several comments, mainly editorial.

RESPONSE: We thank the reviewer for the favorable and positive assessment of our manuscript and for finding our study to be interesting to a broad audience and well written, with most comments described by the reviewer as “editiorial”. Below, we address the reviewer’s concerns in a detailed revision plan.

1. • It is unclear why most of the experiments do not include the control+LiPR group. Even though the focus of the study was the action of LiPR in the context of HFD, questions remain regarding the action of LiPR per se. Is LiPR (or Liraglutide, for that matter) completely inactive on the normal diet background, with respect to neurogenesis in the hypothalamus and the hippocampus? Whether the Response is positive or negative, it would give a much better understanding of the action of LiPR - does it regulate neurogenesis in various physiological contexts, or does it only kick in with a particular type of diet? In fact, this was examined (see Supplementary figures), but only for the cells in culture and, when performed with animals, was limited to 7 and 21 days, rather than 4 months, which would have been much more informative.* RESPONSE: We thank the reviewer for this suggestion. We agree that including the Control Diet + LiPR group for the 4-month HFD group would complement the results from the 7 and 21 days. We will generate this treatment group for the 4-month HFD group and analyze the effect of LiPR on aNSC and adult-generated neurons. These mice in the 4-month treatment are in the experiment already from February 2023 and we plan to analyze their brain sections in June and July 2023.

2. The question above is also relevant when considering the conclusions on the potential depletion of the stem cell pool (again, whether in the hypothalamus or the hippocampus), particularly at the 4-month time point. The mice are ~6 months old by that time, and neurogenesis in both regions is expected to decrease by that time. Are LiPR or Liraglutide able to suppress or exacerbate this decrease? Can they be used to mitigate this decrease when mice are on a regular diet?*

RESPONSE: This concern will be addressed by analyzing the Control + LiPR mice for the 4-month HFD group (see our response to the point 1 above). We will analyze neural stem cells in the Hypothalamic Ventricular Zone and neural progenitors in the Median Eminence of these mice to address whether LiPR treatment changes the time-dependent decrease in both cell populations.

• A somewhat related issue is that, in most cases, only the percentage or the density of cells are shown on the graphs, rather than the absolute numbers (at least for some cases). This sometimes complicates the comparisons; for instance, does the surface of the hypothalamus change between 2 and 6 months of age? The tanycytes' number stays, apparently, the same (e.g., Fig. 2) but the production of new neurons is supposed to fall dramatically.*

RESPONSE: We thank the reviewer for this comment. We agree that the quantification of absolute number of cells is the preferable approach that we have used in our previous publications on subventricular (SVZ) or subgranular (SGZ) neurogenesis. However, hypothalamic adult neurogenesis is dispersed over much larger volume of tissue than neurogenesis in the SVZ or SGZ, which is confined to narrow tissue compartments. As we do not have access to a confocal microscope with stereological software, absolute quantification in entire MBH is not feasible. Nevertheless, we believe that our quantification of cell density provides an unbiased and informative approach that allowed us to compare the effects of LiPR and diet on the neurogenic process.

• The authors write "LiPR may prevent stem cells from exhaustion, induced by HFD" - but it is not clear that HFD indeed leads to exhaustion - there is no statistically significant difference in the number of the stem cells (alpha-tanycytes) between the control and HFD or between HFD at 1, 3, or 12 weeks.*

RESPONSE: We thank the reviewers for their insights. We adjusted the interpretation to better reflect our results. On line 442, we replaced the original statement “The lower cell activation may protect the stem cell pool from exhaustion elicited by the HFD“ with a new one, “The lower cell activation may protect the stem cell pool from exhaustion elicited by the HFD“.

• Numerous papers show that the rate of production of new adult hypothalamic neurons (mainly those derived from beta-tanycytes) drops drastically within the first several weeks of mouse life. Does HFD accelerate, and LiPR mitigate, this decrease? Perhaps one can calculate the numbers from the graphs, but it would help if this is explained in the text of the manuscript. Also, it is not always clear whether specific experiments were performed with the zones of the hypothalamic wall that only contain alpha-tanycytes.*

RESPONSE: Our results show that LiPR rescues the HFD-induced reduction in adult-generated hypothalamic neurons only in the context of 4-month HFD but not in the 7- and 21-day HFD. In the methods (line 877), we specify that “the Region of Interest (ROI) quantified included the MBH parenchyma with the Arcuate (Arc), DMN and Ventromedial (VMN) Nuclei and the Medial Eminence (ME)”. In the results of the revised manuscript (lines 301-303), we highlighted the areas of the ROI. Upon the request of Reviewer 3 (comment 14), we included new data on quantification of BrdU+ neurons in the Arcuate Nucleus (S.Fig.5O). This data show that 21d HFD increases the number of new neurons in ArcN, which is reversed by LiPR or Liraglutide (text added to results and discussion on lines 309-313 and 468-474, respectively). Finally, in the discussion (lines 464-488), it is stated that HFD and/or LiPR had no effect on number of new hypothalamic neurons or cells in the MBH parenchyma in the 7- and 21-day groups and this is discussed in the context of relevant literature.

• A sharp increase in PCNA+ cells in the hippocampus at the 21-day time point, both in the control and in the HFD and HFD/LiPR groups (Fig. S2f) is a little puzzling because neither the Dcx+ nor the Ki67+ cells show this increase.*

RESPONSE: We agree with the reviewer that this increase in the number of PCNA+ cells is puzzling. We quantified the number of PCNA+ cells twice by two different people, always getting the same result. Given that this is a minor result in a supplementary figure, we would prefer not analyzing this again, unless the reviewer would insist on it.

• The study deals with several agents and several processes; a simple scheme that summarizes authors' conclusions might help to better understand the relationships between those agents and processes.*

RESPONSE: We thank the reviewer for this useful suggestion. We included a summarizing schematic in the revised manuscript as the new Figure 6. We will update the schematic for the final revised manuscript, when we will incorporate the new analyses.

***Referee cross-commenting**

I agree, the lack of the LiPR group complicates the interpretation of the results. I also agree that the experiments with vimentin staining, calcium increase, and even with neurospheres do not add much to the main questions that this study attempts to Response, and I'd rather see a more thorough analysis of the activation and differentiation data. I also want to reiterate that the concept of LiPR/PrRP preventing the exhaustion of the hypothalamic stem cell pool is not clear, because it is not shown that this pool does actually get exhausted under normal or HFD conditions. This latter issue again requires the LiPR-alone group. Also, as a clarification - I wrote about 1 month required to compete the revision assuming that the authors actually have the data on the Control+LipR group or at least the specimens available, mainly because the supplementary material shows results with this group, at least with the neurospheres. If this group is fully missing, then the effort will obviously take a longer time.

Reviewer #1 (Significance (Required)):

The provided evidence suggests, for the first time, that PrRP prevents the loss of the neural stem cells population in the adult hypothalamus that was diminished by obesity and HFD. This finding might be interesting to a broad audience.

*

Reviewer 2

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*The authors examine the effect of an anorexigenic drug, LiPR in the context of treatment with high fat diet (HFD) and with a special focus on hypothalamic neural stem/progenitor cells and neurogenesis. The work is mostly based on mice and a barrage of different techniques (confocal imaging, cell cultures with time lapse, gene expression...) are used. The results are interesting because they address the yet-poorly understood implication of hypothalamic neurogenesis in food intake and energy balance. The results point at complex effects at different levels (neural stem cells, neurons, division, survival...). The experimental approach is sometimes thorough in the treatment of details on the one hand, it also lacks of consistency on the other, and as a result the conclusions lack strength. There is a number of experiments that sometimes seem unrelated and this hurts the comprehension of the manuscript, specially in lieu of the complexity of the results obtained.

*

RESPONSE: We thank the reviewer for finding our results interesting and relevant. We will strive to improve the consistency of our results in the revised manuscript to satisfy the reviewer’s concerns.

1. A major issue is the lack of a LiPR-only group, which would much facilitate the interpretation of the results. The effect of LiPR alone is however tested, but only in comparison with the Control in one of the in vitro experiments (S.Fig. 3) RESPONSE: We agree with the reviewer that expanding on the LiPR-only effect would facilitate the interpretation of the results (see concern 1 and 2 of reviewer 2). We want to emphasize, however, that we analyzed the HFD-independent LiPR effects not only in vitro but also in vivo by quantifying the number of BrdU+ cells and neurons in the MBH of mice exposed to 21-day HFD (S.Fig. 5 O-Q) and by including the Control Diet + LiPR in the RNAseq experiment (Fig.5C). Nevertheless, we will analyze the number of alpha tanycytes and proliferating cells for the 21-day Control Diet + LiPR treatment group. And we will generate mice treated with Control Diet + LiPR to complement the 4-month group. In this Control Diet + LiPR group, we will quantify the number of tanycytes and number of BrdU+ cells and neurons.

2. As plotted, in Fig 1B is difficult to interpret the effect of HFD and LiPR, might be using percentage and noting the statistical differences as in the other would help. It looks like HFD has no effect compared to control on weight and only at the end LiPR could have an effect. On the other hand, after 4 months, HFD mice are clearly above the controls and it is then, albeit when weight gain has reached a plateau, that LiPR has an effect. The election of these arbitrary paradigms and their drawbacks has to be better explained.*

RESPONSE: We thank the reviewer for the comment. We analyzed the effect of HFD and/or LiPR on the body weight for the 21-day group (Fig.1B) in the original manuscript (lines 111-115). The two-way, repeated measure ANOVA revealed no effect of the treatment on the body weight in the 7-day group, however, it revealed the effect of the duration of treatment on the body weight in the 21-day group. As suggested by the reviewer, we included the Control Diet + LiPR in the 21-day group (Fig.1B). We analyzed the data with ANOVA and found that the treatment has a statistically significant effect on the body weight, however, without any statistical difference between treatment groups (lines 112-116 in the revised manuscript). In addition, we will include the Control Diet + LiPR in the 4-month group.

Why was the proportion of GPR10+BrdU+MAP2+ cells only assessed in control mice and no in the experimental groups if its expression in overall neurons changes? This suggests that the receptor is expressed in neurons. Interestingly, exposure to 21d HFD reduced density of GPR10, which was rescued by LiPR administration (Fig.1L). Why was this time point chosen and not the longer-term one? What is the consequence of the alterations in the potential number of GPR10, specially in relation to the administration of LiPR? This clarification is important because a 14-day treatment was chosen for the in vitro experiments in which LiPR, but not HFD, seems to have an effect on cell proliferation. Might be it would have been more useful to use a paradigm in which HFD has an effect to better compare with in vivo work and for the rationale of the work. "Besides GPR10, we co-localized neuronal cytoskeleton structures with NPFFR2 in the MBH (Fig.1O-P)..." Why were not GPR10 and NPFF2 analyzed in a similar and consistent manner ? It is confusing.

RESPONSE: The proportion of GPR10+BrdU+Map2+ neurons was quantified to address whether new neurons express the PrRP receptor. We chose to analyze the proportion of GPR10+BrdU+Map2+ neurons at the 21d time-point because we had the most robust data for this or related time points in vitro and in vivo. We will emphasize this in the text. But we prefer not to analyze the effect of LiPR on the density or expression of GPR10 or NPFF2 for all time points. We consider this to be beyond the scope and focus of the manuscript.

The number of GFAP+ α-tanycytes is not significantly changed by HFD therefore LiPR does not rescue, but rather increases the number of GFAP+ α-tanycytes in the 7-day setting. There are no differences among groups later, the effect is lost by 21 days, therefore there is a transient excess of GFAP+ α-tanycytes which later "disappear" in the LiPR group. The authors state that LiPR rescues the decrease in "htNSCs", but after 21 days the number of the GFAP+ α-tanycytes is the same in all groups without the need of LiPR. There is no experimental follow up (addressing proliferation and survival of these cells) and the conclusions stated in the text (results and discussion) are not really supported by the data. The in vitro experiments could be a complement, but are no substitute for the missing in vivo exploration.

RESPONSE: We thank the reviewer for this comment. We agree that we did not correctly interpret the data. On line 158, we replaced the original statement “This suggests that short LiPR rescues HFD-induced reduction in the number of htNSCs” with a new one that reflects of date correctly, “This suggests that short LiPR increases the number of htNSCs. In our revision plan, we will quantify the number of proliferating tanycytes to complement our in vitro results.

• The fact that cell division is "rarely found" (Rax GFAP) experiments also push for further investigation. It is difficult to see that relevance of the inclusion of the vimentin staining experiment if there is no further exploration. The effect of LiPR is only transient, in the 7-day paradigm and as the parameter evaluated is the proportion of vimentin+ tanycytes among GFAP+ tanycytes it could only be reflecting increased expression of the filament. "Nevertheless, we did not observe a statistically different change in the area occupied by Rax+ tanycytes (Fig.2H)." Why did the authors use Rax only for this experiment if "GFAP+ α-tanycytes which are considered the putative htNSCs?" What is the justification for not seeing changes in relation to the results reported in Fig 2D-F? "Because Vimentin is associated with nutrient transport in cells and with metabolic response to HFD 52-54, we quantified the proportion of GFAP+ tanycytes expressing Vimentin (Fig.2F)." It is difficult to see that relevance of the inclusion of the vimentin staining experiment if there is no further exploration. The effect of LiPR is only transient, in the 7-day paradigm and as the parameter evaluated is the proportion of vimentin+ tanycytes among GFAP+ tanicytes it could only be reflecting increased expression of the filament.*

RESPONSE: Because Vimentin is a marker of neural stem cells and alpha tanycytes, we quantified the number of GFAP+Vimentin+ tanycytes to complement the quantification of GFAP+ alpha tanycytes. We are sorry that this was not clear, and we highlighted this connection in the revised manuscript (line 165). Because Rax is expressed in alpha tanycytes, we expected that LiPR will increase Rax in the Hypothalamic Ventricular Zone (HVZ). We agree with the reviewer that further investigation may be useful, and we will quantify the number of alpha tanycytes positive for Rax instead of determining only the volume of Rax+ tissue. We will quantify Rax+GFAP+ neural stem cells in the HVZ and Rax+GFAP+ neural progenitors (so-called beta tanycytes) in the Median Eminence to improve characterization of the cell dynamics in vivo.

• Why there is no Ki67 experiment in the 7-day paradigm if that is the timepoint in which changes in the number or proportion of GFAP+ tanycytes are observed? PCNA was then used but only in the 21-day paradigm. What is the interpretation and relevance of these data? What are the non-htNSCs proliferating cells, whose dynamics are different from the changes in the number or proportion of htNSCs that could be potentially related to changes in mitosis? Again, I think it would be much useful for the work to explore in detail the changes in the putative htNSCs than investing in experiments that only add confusion.*

__RESPONSE: __We apologize if the data presentation is confusing. We will include the quantification of the Ki67+ cells for the 7-day time point. In the MBH, many cell types undergo mitosis, including the oligodendrocyte precursor cells, microglia, astrocytes, and infiltrating macrophages. However, characterizing the identify of all these different cell types in response to the HFD and/or LiPR is beyond the scope of this study. To resolve whether HFD and/or LiPR influence proliferating aNSCs, we will quantify the proliferating cells in the HVZ, which will allow us to separate the proliferating aNSCs from all other proliferating cell types in the MBH.

• The inclusion of Liraglutide + HFD, (not Liraglutide alone) only in some of the experiments is pointless if there is no direct comparison with LiPR and a timepoint is missing. In S.Fig 3, Fig. 5 and S.Fig 7 LFD (low fat diet?) is used in several occasions as in: "on reducing number of PCNA+ cells in 21d protocol (one-way ANOVA (OWA), F(2,12) = 16.66, p = 0.0003) when compared to both LFD and HFD groups". Is this the control diet?*

RESPONSE: We apologize for the confusion caused by labelling the conditions of the Control Diet inconsistently. In some figures (e.g., Fig.2, S.Fig.3, Fig.4), we labelled the Control Diet as “Control”, whereas in some other figures (e.g., Fig.5, S.Fig.7) we labelled the Control Diet as “LFD” (Low Fat Diet). In all experiments and figures, the used Control Diet was identical. We unified the labelling of the Control Diet in all figures and in the text of the revised manuscript. Respectfully, we do not agree that including the Liraglutide data is pointless. We included the Liraglutide in the context of the HFD as a direct comparison with the HFD + LiPR group to demonstrate that the two anti-obesity compounds exert differential effects on adult neurogenesis. Such comparison has not been done before in analyzing adult neurogenesis and is valuable for better understanding of functions of these anti-obesity compounds.

• The final experiment shows that application of hPrRP31, a variation of LiPR, causes an immediate calcium increase in human induced pluripotent stem cell-derived hypothalamic nucleus. This finding is interesting in itself because it brings light about the function of the receptor/s. It would have been very useful to test what other receptors mentioned to bind LiPR is mediating the effect. In any case, the focus of the work are the neural stem/progenitor cells responsible for neurogenesis and the changes in their properties because of HFD and LiPR, therefore I would trade these experiments for a more thorough and detailed dissection of these effects.*

RESPONSE: We thank the reviewer for recognizing the relevance of the experiments with the hiPSC-derived neurons. As described in the comments above, we will conduct additional experiments to address the effect of LiPR on aNSCs and proliferation to more thoroughly as suggested by the reviewer.

Minor points: __ A.__ Introduce "GLP-1RA"

__RESPONSE: __We thank the reviewer for identifying this omission. We introduced the term in the revised manusript (line 50).

1. • "HFD-induced inflammation and astrogliosis in the hypothalamus 45,46, whereas the long (4mo) protocol leads to DIO" Are these notions exclusive?* __RESPONSE: __This statement emphasized that HFD-induced inflammation and astrogliosis precede obesity. We prefer to leave the statement as it is.
2. • LiPR displays no effects on astrocytes" "Displays" is not the correct term.* RESPONSE: We replaced the term “display” with the word “show” in the revised manuscript (line 342).

***Referee cross-commenting**

I think we all referees agree for the most part. The main concern stated by all of us is the lack of a LiPR-alone group. The rest of the concerns are also related or complementary. In my opinion the mostly common view by the referees is reasuring.

Reviewer #2 (Significance (Required)):

The strengths of the work are its novelty in the field and the variety of techniques employed. The work has the potential of unveiling mechanistic insight into the regulation of neural stem/progenitor cells and neurogenesis. The main audience of this work would be the community working on this field. The lack of experiments testing that the changes observed actually participate in food intake prevent the work from being of relevance for a broader audience (food intake, energy balance, obesity...). The limitations are the descriptive nature of the work and the lack of a consistent and systematic experimental design that would allow to extract solid conclusions upon to which build upon future research.

*

Reviewer 3

The work of Jörgensen et al describes the effect of a lipidized analogue of the prolactin releasing peptide (LiPR) on the mouse metabolism in response to high fat diet (HFD) and on hypothalamic and subgranular zone (SGZ) neurogenesis. They conclude that LiPR reduces body weight and improves metabolic parameters affected by HFD as well as it concomitantly stimulates neurogenesis in both niches the SGZ and the hypothalamus. The link between both effects is not demonstrated. The work is well conducted, the hypothesis is interesting and the experimental approach is adequate. The scope is wide and results are interesting, however a few aspects need to be further clarified. The manuscript is well written although the modification of some aspects would facilitate the reading such as the use of non described abbreviations for example.

RESPONSE: We thank the reviewer for the positive assessment of our manuscript and for recognizing its novelty and importance for the research in neurogenesis, endocrinology, and metabolism. We will strive to clarify and facilitate our conclusions to improve the manuscript.

1. • One concern in this study is the experimental groups. Authors analyze three groups control,HFD and HFD treated with LiPR. Authors conclude that the effects of LiPR are diet independent. However, given the results obtained by the authors on the effect of LiPR, the main question that arises in here is whether LiPR would have an effect on control mice. It seems tha a group is missing in the experimental design in which control ,mice are treated with LiPR during 7, 21 and the last two weeks of the 4 months. Author must include this information or at least argue the election of the experimental design.* RESPONSE: We thank the reviewer for this insight. We agree that including the Control Diet + LiPR in some of our analyses would improve the revised manuscript as also noted by Reviewer 2 (comment 1 and 2) and by Reviewer 2 (comment 1 and 2). In the original manuscript, we included the quantification of BrdU+ cells in the MBH for the Control Diet + LiPR in the 21-day group. To expand on these results, we will quantify the effects of LiPR on alpha tanycytes in the 21-day group. In addition, we will generate Control Diet + LiPR mice for the 4-month group to complement the HFD and HFD + LiPR data.

2. Body weight is found reduced by LiPR as well as other metabolic parameters in mice treated with LiPR during the last two weeks of the 4 Mo HFD. However, no effects on hypothalamic or SGZ neurogenesis are not observed in this experimental group. How do authors explain this results?*

__RESPONSE: __The 4-month group contains animals that are over 6-month-old, which display very low levels of cell proliferation and differentiation in comparison with the 7 and 21-day groups that contain mice that are 2 and 2.5 months old, respectively. It is possible that these low levels of neurogenesis did not allow us to detect any pro-neurogenic effects of LiPR. Alternatively, the low neurogenesis in older animals precludes us from detecting the adverse effects of the HFD, which are rescued by LiPR in younger animals.

• In figure 1 I-K images are not clear and better resolution images would help.*

RESPONSE: We provided images with higher resolution for Figure 1I-K of the revised manuscript.

• Authors conclude that LiPR is increasing the number of NSC by reducing their activation. However, authors show an induced increase in htNSC only in mice fed HFD for 7 days and not in the 21 day fed mice or the 4 mo fed mice (fig 2 d-f). In addition, authors test for the number of cells expressing Ki67 (fig 2 L), however, the number of Ki67+ alpha tanicytes is not shown.*

RESPONSE: We thank the reviewer for this insight. In the revised manuscript (line 158), we corrected the inaccurate statement that LiPR increased the number of aNSCs and did not rescue their number, which was also noted by Reviewer 1 (comment 5) and by Reviewer 2 (comment 4). In addition, we will quantify the number of Ki67+ cells in the Hypothalmic Ventricular Zone (HVZ), which will address whether LiPR affects proliferation of aNSCs. This concern parallels comment 6 of Reviewer 2.

• On figure 2B it seems that is alpha 2 tanicytes that are missing in response to HFD.*

RESPONSE: Indeed, the panel in Figure 2B shows that the HFD reduces the number of alpha tanycytes, including the alpha 2 tanycytes. This representative image supports our quantification results in Figure 2D-E.

• Are Fig 2 A-C images representative of mice fed HFD for 7 days?*

__RESPONSE: __Yes, the representative images in panels of Fig. 2A-C are from the 7-day group. However, the legend states that these images are from the 21-day group. This is an error that we corrected in the revised manuscript in the legend of Figure 2 (line 572). We apologize for this and thank the reviewer for double-checking.

• By looking at figure 2B it seems like the proportion of alpha tanicytes is higher in HFD since no or very few tanicytes are observed and almost all of them are alpha tanicytes.*

RESPONSE: Indeed, 7 days of HFD reduced the number of alpha 2 tanycytes, which occupy the ventral-lateral aspect of the 3rd ventricle. This reduction of alpha 2 tanycytes drives the lover proportion of GFAP+ alpha-tanycytes out of all GFAP+ tanycytes. We emphasized this in the text of the revised manuscript (line 435-437).

• In fig 2 d-f, an increase in the number of GFAP+ alpha tanicytes and its proportion as well as labelled with vimentin is observed in control mice fed with normal diet for 7 days compared with mice fed normal diet for 21 days. How do authors explain this difference?*

RESPONSE: There is no difference in the number of GFAP+ alpha tanycytes or proportion of GFAP+ alpha tanycytes between 7-day and 21-day Control Diet mice. We used the two-way, repeated measure ANOVA with the Bonferroni’s pots-hoc test and did not observe any statistical difference between these 2 quantifications for the Control Diet mice at 7 and 21 days. There is a statistical difference between 7-day and 21-day Control Diet mice in the proportion of GFAP+Vimentin+ tanycytes. This could be due to expansion of the Vimentin+ tanycytes in relatively young adult mice. Given that this is not a major point, we prefer not expanding its discussion in the manuscript.

• In fig 2 Why are the differences in RAX, KI67 and PCNA only present in mice fed HFD for 21 days?*

RESPONSE: We thank the reviewer for this question, which reflects a similar comment 6 of Reviewer 2. To improve consistency of the presented data, we will quantify the proliferating cells also for the 7-day time point. In addition, we will quantify the number of proliferating cells in the HVZ, which will allow us to address whether HFD and/or LiPR alter proliferation of tanycytes.

• Authors test for adult hippocampal neurogenesis in the three groups. DO images in fig S2 correspond to the 21 day treatment group?*

RESPONSE: Yes, the representative images in the Supplementary Figure 2 are from the 21-day group. This is stated in the figure legend.

• On fig S2 C, it seems that in HFD fed mice treated with LiPR newly generated neuroblasts are more differentiated have authors looked at DCX+ cell morphology?*

RESPONSE: We thank the reviewer for this observation. We have not analyzed the morphology of DCX+ cells or DCX+ neuroblasts in the SGZ. As the manuscript focuses on the hypothalamic and not hippocampal neurogenesis, we prefer not to analyze the morphology in the revised manuscript.

• In this same figure, it seems like the number of DCX+ neuroblasts and the number of newly generated neurons is reduced in mice of the 21 d group compared to the 7 day group. Is this statistically significant?*

RESPONSE: We used the two-way, repeated measure ANOVA with the Bonferroni’s pots-hoc test to analyze the DCX+ neuroblasts and neurons. We observed a statistically very significant effect of LiPR treatment on the number of DCX+ neuroblasts and neurons (page 10 of the original manuscript). However, the Bonferroni’s test did not reveal any difference between 7-day and 21-day treatment groups.

• There is a large reduction in the number of DCX+ cells from control 21 d treated mice to control 4 month treated mice. Is this statistically significat? How do authors explain this dramatic reduction?*

RESPONSE: Yes, there is statistically significant reduction in the number of DCX+ cells and DCX+ neurons in the SGZ between the 21-day and 4-month group S.Fig.2). This reduction is most likely a result of aging. The mice of the 21-day group were around 2.5 months of age when culled, whereas the 4-month group month mice were over 6.5-month-old. The decline in SGZ neurogenesis with age is well documented. Because this decrease in DCX+ cells in the SGZ is an obvious consequence of the animals’ age and because the hippocampal neurogenesis is not the primary focus of this manuscript, we prefer not to discuss this feature in the manuscript.

• Authors do not show the effect of HFD on BrdU+ neurons in the Arcuate. However, all data need to be shown.

*

RESPONSE: We stated (on page 12 of the original manuscript) that in the Arcuate Nucleus of the 21-day group, there was “a statistically significant increase of BrdU+ neurons by HFD compared to Control (data not shown)”. To satisfy reviewer’s comment, we incorporated this data in the S.Fig.5 as the new panel S.Fig.5O and added the following text (lines 309-313) to the revised manuscript: “However, in the ArcN, the primary nutrient and hormone sensing neuronal nucleus of MBH 4, there was a statistically significant difference in number of BrdU+ neurons due to treatment (OWA, F(3,15) = 3.97, p = 0.0029). Exposure to 21d HFD significantly increased the number of BrdU+ neurons in the ArcN, which was reversed by co-administration of LiPR or Liraglutide (S.Fig.5O).” In addition, we adjusted the relevant discussion (lines 468-472): “Our results show that the short and intermediate exposure to HFD does not change the number of newly generated, BrdU+ cells, neurons, or astrocytes in the MBH parenchyma, however, it increases the number of BrdU+ neurons in the primary sensing ArcN, which is reversed by the con-current administration of LiPR or Liraglutide” and (lines 474-476): “In addition, our results show that while LiPR does not change the number of new cells in the MBH parenchyma, it can rescue the increased production of new neurons in the ArcN in the context of the intermediate HFD exposure.”

*Reviewer #3 (Significance (Required)):

In general the manuscript includes a great amount of work to demonstrate the effect of LiPR on neurogenesis (hippocampal and hypothalamic). The scope is wide, and the hypothesis is really interesting. Authors may need to solve some issues in order to completely demonstrate their claims and conclusions, but once the work is done, it will be very valuable to understand the effect of pharmacological agents used in the field of endocrinology to treat metabolic disorders such as type 2 diabetes di type 2 diabetes. So far, no studies have been done in which the effect of this molecules have been described on SGZ and hypothalamic neurogenesis. Both the field of endocrinology and metabolism as well as the field of adult neurogenesis may benefit of a study of this type.*

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Referee #3

Evidence, reproducibility and clarity

The work of Jörgensen et al describes the effect of a lipidized analogue of the prolactin releasing peptide (LiPR) on the mouse metabolism in response to high fat diet (HFD) and on hypothalamic and subgranular zone (SGZ) neurogenesis. They conclude that LiPR reduces body weight and improves metabolic parameters affected by HFD as well as it concomitantly stimulates neurogenesis in both niches the SGZ and the hypothalamus. The link between both effects is not demonstrated. The work is well conducted, the hypothesis is interesting and the experimental approach is adequate. The scope is wide and results are interesting, however a few aspects need to be further clarified. The manuscript is well written although the modification of some aspects would facilitate the reading such as the use of non described abbreviations for example.

Major comments:

1. One concern in this study is the experimental groups. Authors analyze three groups control,HFD and HFD treated with LiPR. Authors conclude that the effects of LiPR are diet independent. However, given the results obtained by the authors on the effect of LiPR, the main question that arises in here is whether LiPR would have an effect on control mice. It seems tha a group is missing in the experimental design in which control ,mice are treated with LiPR during 7, 21 and the last two weeks of the 4 months. Author must include this information or at least argue the election of the experimental design.
2. Body weight is found reduced by LiPR as well as other metabolic parameters in mice treated with LiPR during the last two weeks of the 4 Mo HFD. However, no effects on hypothalamic or SGZ neurogenesis are not observed in this experimental group. How do authors explain this results?
3. In figure 1 I-K images are not clear and better resolution images would help.
4. Authors conclude that LiPR is increasing the number of NSC by reducing their activation. However, authors show an induced increase in htNSC only in mice fed HFD for 7 days and not in the 21 day fed mice or the 4 mo fed mice (fig 2 d-f). In addition, authors test for the number of cells expressing Ki67 (fig 2 L), however, the number of Ki67+ alpha tanicytes is not shown.
5. On figure 2B it seems that is alpha 2 tanicytes that are missing in response to HFD.
6. Are Fig 2 A-C images representative of mice fed HFD for 7 days?
7. By looking at figure 2B it seems like the proportion of alpha tanicytes is higher in HFD since no or very few tanicytes are observed and almost all of them are alpha tanicytes.
8. In fig 2 d-f, an increase in the number of GFAP+ alpha tanicytes and its proportion as well as labelled with vimentin is observed in control mice fed with normal diet for 7 days compared with mice fed normal diet for 21 days. How do authors explain this difference?
9. In fig 2 Why are the differences in RAX, KI67 and PCNA only present in mice fed HFD for 21 days?
10. Authors test for adult hippocampal neurogenesis in the three groups. DO images in fig S2 correspond to the 21 day treatment group?
11. On fig S2 C, it seems that in HFD fed mice treated with LiPR newly generated neuroblasts are more differentiated have authors looked at DCX+ cell morphology?
12. In this same figure, it seems like the number of DCX+ neuroblasts and the number of newly generated neurons is reduced in mice of the 21 d group compared to the 7 day group. Is this statistically significant?
13. There is a large reduction in the number of DCX+ cells from control 21 d treated mice to control 4 month treated mice. Is this statistically significat? How do authors explain this dramatic reduction?
14. Authors do not show the effect of HFD on BrdU+ neurons in the Arcuate. However, all data need to be shown.

Significance

In general the manuscript includes a great amount of work to demonstrate the effect of LiPR on neurogenesis (hippocampal and hypothalamic). The scope is wide, and the hypothesis is really interesting. Authors may need to solve some issues in order to completely demonstrate their claims and conclusions, but once the work is done, it will be very valuable to understand the effect of pharmacological agents used in the field of endocrinology to treat metabolic disorders such as type 2 diabetes di type 2 diabetes.

So far, no studies have been done in which the effect of this molecules have been described on SGZ and hypothalamic neurogenesis. Both the field of endocrinology and metabolism as well as the field of adult neurogenesis may benefit of a study of this type.

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Referee #2

Evidence, reproducibility and clarity

The authors examine the effect of an anorexigenic drug, LiPR in the context of treatment with high fat diet (HFD) and with a special focus on hypothalamic neural stem/progenitor cells and neurogenesis. The work is mostly based on mice and a barrage of different techniques (confocal imaging, cell cultures with time lapse, gene expression...) are used.

The results are interesting because they address the yet-poorly understood implication of hypothalamic neurogenesis in food intake and energy balance. The results point at complex effects at different levels (neural stem cells, neurons, division, survival...). The experimental approach is sometimes thorough in the treatment of details on the one hand, it also lacks of consistency on the other, and as a result the conclusions lack strength. There is a number of experiments that sometimes seem unrelated and this hurts the comprehension of the manuscript, specially in lieu of the complexity of the results obtained.

These are the detailed comments:

A major issue is the lack of a LiPR-only group, which would much facilitate the interpretation of the results. The effect of LiPR alone is however tested, but only in comparison with the Control in one of the in vitro experiments (S.Fig. 3)

As plotted, in Fig 1B is difficult to interpret the effect of HFD and LiPR, might be using percentage and noting the statistical differences as in the other would help. It looks like HFD has no effect compared to control on weight and only at the end LiPR could have an effect. On the other hand, after 4 months, HFD mice are clearly above the controls and it is then, albeit when weight gain has reached a plateau, that LiPR has an effect. The election of these arbitrary paradigms and their drawbacks has to be better explained.

Why was the proportion of GPR10+BrdU+MAP2+ cells only assessed in control mice and no in the experimental groups if its expression in overall neurons changes?

This suggests that the receptor is expressed in neurons. Interestingly, exposure to 21d HFD reduced density of GPR10, which was rescued by LiPR administration (Fig.1L). Why was this time point chosen and not the longer-term one? What is the consequence of the alterations in the potential number of GPR10, specially in relation to the administration of LiPR?

This clarification is important because a 14-day treatment was chosen for the in vitro experiments in which LiPR, but not HFD, seems to have an effect on cell proliferation. Might be it would have been more useful to use a paradigm in which HFD has an effect to better compare with in vivo work and for the rationale of the work.

"Besides GPR10, we co-localized neuronal cytoskeleton structures with NPFFR2 in the MBH (Fig.1O-P)..." Why were not GPR10 and NPFF2 analyzed in a similar and consistent manner ? It is confusing.

The number of GFAP+ α-tanycytes is not significantly changed by HFD therefore LiPR does not rescue, but rather increases the number of GFAP+ α-tanycytes in the 7-day setting. There are no differences among groups later, the effect is lost by 21 days, therefore there is a transient excess of GFAP+ α-tanycytes which later "disappear" in the LiPR group. The authors state that LiPR rescues the decrease in "htNSCs", but after 21 days the number of the GFAP+ α-tanycytes is the same in all groups without the need of LiPR. There is no experimental follow up (addressing proliferation and survival of these cells) and the conclusions stated in the text (results and discussion) are not really supported by the data. The in vitro experiments could be a complement, but are no substitute for the missing in vivo exploration.

The fact that cell division is "rarely found" (Rax GFAP) experiments also push for further investigation.

It is difficult to see that relevance of the inclusion of the vimentin staining experiment if there is no further exploration. The effect of LiPR is only transient, in the 7-day paradigm and as the parameter evaluated is the proportion of vimentin+ tanycytes among GFAP+ tanycytes it could only be reflecting increased expression of the filament. "Nevertheless, we did not observe a statistically different change in the area occupied by Rax+ tanycytes (Fig.2H)."

Why did the authors use Rax only for this experiment if "GFAP+ α-tanycytes which are considered the putative htNSCs?" What is the justification for not seeing changes in relation to the results reported in Fig 2D-F?

"Because Vimentin is associated with nutrient transport in cells and with metabolic response to HFD 52-54, we quantified the proportion of GFAP+ tanycytes expressing Vimentin (Fig.2F)." It is difficult to see that relevance of the inclusion of the vimentin staining experiment if there is no further exploration. The effect of LiPR is only transient, in the 7-day paradigm and as the parameter evaluated is the proportion of vimentin+ tanycytes among GFAP+ tanicytes it could only be reflecting increased expression of the filament.

Why there is no Ki67 experiment in the 7-day paradigm if that is the timepoint in which changes in the number or proportion of GFAP+ tanycytes are observed? PCNA was then used but only in the 21-day paradigm. What is the interpretation and relevance of these data? What are the non-htNSCs proliferating cells, whose dynamics are different from the changes in the number or proportion of htNSCs that could be potentially related to changes in mitosis? Again, I think it would be much useful for the work to explore in detail the changes in the putative htNSCs than investing in experiments that only add confusion.

The inclusion of Liraglutide + HFD, (not Liraglutide alone) only in some of the experiments is pointless if there is no direct comparison with LiPR and a timepoint is missing. In S.Fig 3, Fig. 5 and S.Fig 7 LFD (low fat diet?) is used in several occasions as in: "on reducing number of PCNA+ cells in 21d protocol (one-way ANOVA (OWA), F(2,12) = 16.66, p = 0.0003) when compared to both LFD and HFD groups". Is this the control diet?

The final experiment shows that application of hPrRP31, a variation of LiPR, causes an immediate calcium increase in human induced pluripotent stem cell-derived hypothalamic nucleus. This finding is interesting in itself because it brings light about the function of the receptor/s. It would have been very useful to test what other receptors mentioned to bind LiPR is mediating the effect. In any case, the focus of the work are the neural stem/progenitor cells responsible for neurogenesis and the changes in their properties because of HFD and LiPR, therefore I would trade these experiments for a more thorough and detailed dissection of these effects.

Minor points:

Introduce "GLP-1RA"

"HFD-induced inflammation and astrogliosis in the hypothalamus 45,46, whereas the long (4mo) protocol leads to DIO" Are these notions exclusive?

"LiPR displays no effects on astrocytes" "Displays" is not the correct term.

Referee cross-commenting

I think we all referees agree for the most part. The main concern stated by all of us is the lack of a LiPR-alone group. The rest of the concerns are also related or complementary. In my opinion the mostly common view by the referees is reasuring.

Significance

The strengths of the work are its novelty in the field and the variety of techniques employed. The work has the potential of unveiling mechanistic insight into the regulation of neural stem/progenitor cells and neurogenesis. The main audience of this work would be the community working on this field. The lack of experiments testing that the changes observed actually participate in food intake prevent the work from being of relevance for a broader audience (food intake, energy balance, obesity...).

The limitations are the descriptive nature of the work and the lack of a consistent and systematic experimental design that would allow to extract solid conclusions upon to which build upon future research.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Jorgensen and colleagues describe their findings on the action of a palmitoylated form of prolactin-release peptide (LiPR) on neural stem cells (NSC) in the adult mouse hypothalamus and adult mouse hippocampus. Their main conclusion is that LiPR can counteract the effects of high-fat diet (HFD) and rescue some of the adverse effects of HFD. Specifically, the authors provide evidence that:

• Exposure to HFD reduces the number of presumptive adult neural stem cells (NSCs) in the adult hypothalamus, whereas exposure to LiPR reverses this trend.
• The results suggest that LiPR reduces the proliferation of alpha-tanycytes and/or their progeny in the hypothalamus in the context of HFD, with Liraglutide acting similarly. In contrast, while LiPR also suppresses proliferation in the SGZ, Liraglutide works there in the opposite direction.
• LiPR also helps the survival of adult-born hypothalamic neurons.
• Reduction of proliferation by LiPR suggests a model where LiPR increases the number of NSCs presumably by reducing their rate of activation.
• The results suggest that LiPR promotes expression of PrRP receptors in the hypothalamic neurons, suggesting that PrRP may act directly on such neurons (and tanycytes?) in vivo.
• The authors also show that HFD and LiPR alter gene expression profiles of the MBH cells, with HFD, but not LiPR, inducing myelination-related genes.
• Finally, they show that PrRP stimulates an increase in Ca2+ in in vitro-derived human hypothalamic neurons.

• The authors conclude that LiPR may be reducing activation and proliferation of the hypothalamic stem cells and thereby preserve their pool from exhaustion, which was stimulated by HFD. The manuscript presents interesting data and is clearly written. There are several comments, mainly editorial.

• It is unclear why most of the experiments do not include the control+LiPR group. Even though the focus of the study was the action of LiPR in the context of HFD, questions remain regarding the action of LiPR per se. Is LiPR (or Liraglutide, for that matter) completely inactive on the normal diet background, with respect to neurogenesis in the hypothalamus and the hippocampus? Whether the answer is positive or negative, it would give a much better understanding of the action of LiPR - does it regulate neurogenesis in various physiological contexts, or does it only kick in with a particular type of diet? In fact, this was examined (see Supplementary figures), but only for the cells in culture and, when performed with animals, was limited to 7 and 21 days, rather than 4 months, which would have been much more informative.

• The question above is also relevant when considering the conclusions on the potential depletion of the stem cell pool (again, whether in the hypothalamus or the hippocampus), particularly at the 4-months time point. The mice are ~6 months old by that time, and neurogenesis in both regions is expected to decrease by that time. Are LiPR or Liraglutide able to suppress or exacerbate this decrease? Can they be used to mitigate this decrease when mice are on a regular diet?
• A somewhat related issue is that, in most cases, only the percentage or the density of cells are shown on the graphs, rather than the absolute numbers (at least for some cases). This sometimes complicates the comparisons; for instance, does the surface of the hypothalamus change between 2 and 6 months of age? The tanycytes' number stays, apparently, the same (e.g., Fig. 2) but the production of new neurons is supposed to fall dramatically.
• The authors write "LiPR may prevent stem cells from exhaustion, induced by HFD" - but it is not clear that HFD indeed leads to exhaustion - there is no statistically significant difference in the number of the stem cells (alpha-tanycytes) between the control and HFD or between HFD at 1, 3, or 12 weeks.
• Numerous papers show that the rate of production of new adult hypothalamic neurons (mainly those derived from beta-tanycytes) drops drastically within the first several weeks of mouse life. Does HFD accelerate, and LiPR mitigate, this decrease? Perhaps one can calculate the numbers from the graphs, but it would help if this is explained in the text of the manuscript. Also, it is not always clear whether specific experiments were performed with the zones of the hypothalamic wall that only contain alpha-tanycytes.
• A sharp increase in PCNA+ cells in the hippocampus at the 21-day time point, both in the control and in the HFD and HFD/LiPR groups (Fig. S2f) is a little puzzling because neither the Dcx+ nor the Ki67+ cells show this increase.
• The study deals with several agents and several processes; a simple scheme that summarizes authors' conclusions might help to better understand the relationships between those agents and processes.

Referee cross-commenting

I agree, the lack of the LiPR group complicates the interpretation of the results. I also agree that the experiments with vimentin staining, calcium increase, and even with neurospheres do not add much to the main questions that this study attempts to answer, and I'd rather see a more thorough analysis of the activation and differentiation data. I also want to reiterate that the concept of LiPR/PrRP preventing the exhaustion of the hypothalamic stem cell pool is not clear, because it is not shown that this pool does actually get exhausted under normal or HFD conditions. This latter issue again requires the LiPR-alone group. Also, as a clarification - I wrote about 1 month required to compete the revision assuming that the authors actually have the data on the Control+LipR group or at least the specimens available, mainly because the supplementary material shows results with this group, at least with the neurospheres. If this group is fully missing, then the effort will obviously take a longer time.

Significance

The provided evidence suggests, for the first time, that PrRP prevents the loss of the neural stem cells population in the adult hypothalamus that was diminished by obesity and HFD. This finding might be interesting to a broad audience.

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Reply to the reviewers

We thank both the reviewers for the positive comments, insightful suggestions, and constructive criticisms. We have added new calculations, analyzed new data, added new figures in the main text and the supplementary material, revised the main text and the supplementary text to address the reviewer’s comments. We believe the modifications have improved the quality of the manuscript. Below we append our point-by-point response to the reviewers’ comments.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): * Summary: The paper by Rajakaruna, Desai, and Das develops a multiscale computational model (PASCAR) to study design of features of CAR-T cells. Their ODE-based model captures cell-cell interactions using equilibrium receptor-ligand binding relations and a simplified representation of intracellular signaling. The cellular-scale model feeds into a population model including populations of CAR-T cells, healthy cells, and target cells. The model accounts for CAR-T cells with varying numbers of CARs; it accounts for target/healthy cells with varying numbers of ligands. The authors use published cytometry and cytotoxicity data to parameterize the model, which they show captures experimental trends well when they use a kinetic proofreading model to phenomenologically represent intracellular signaling. They then use an optimization approach to characterize features of the CAR-T cell design space that maximize killing of target cells while minimizing the destruction of healthy cells. Their conclusions are consistent with physical intuition and provide quantitative and generalizable predictions about biophysical parameters (including equilibrium binding constants, kinetic rates, etc.).

Major comments:

1. In general, the paper is logically laid out and easy to follow.*

Thank you for the positive comment.

However, some of the underlying mechanisms were not clear to me: * * (A) - In Figure 3A, it is not clear why the rate of lysis is greater for populations with an intermediate range of CAR abundances. I understand the authors' statement that "this is because the smaller number of CAR-T cells present in [higher or lower CAR expression groups]..." However, it is not clear why intermediate-level CAR-T cell populations are largest, noting that populations with higher receptor levels have larger proliferation rates. Is this simply reflecting the initial size of the populations, or is another mechanism at play? I think the paper would benefit from discussion/quantification of this.

We thank the reviewer for the insightful comment.

We have edited the main text (pages 8-9) and added a supplementary figure (Fig. S4) to address the above comment. The proliferation rate (ρRHUH) for a subpopulation of size TR increases almost linearly with R since ρRH ∝ R when CAR and HER2 interact with high affinity, i.e., KD≪ R and KD≪H. The distribution of CAR abundances at t=0 estimated by our method follows a lognormal distribution, i.e., the CAR T cell subpopulation size at an intermediate value of R is larger than that at larger values of R. However, given the estimated values for ρRHUH (RHUH decreases with time as the number of target cells are lysed by the CAR T cells. Therefore, the peak of the population remains at intermediate values of R values at later times, and both the initial distribution and the relatively smaller values of the estimated proliferation rate are responsible for the behavior.

*(B) - In Figure 2, C_N plateaus at HER2 density between 10^3 and 10^4. However, % lysis does not plateau until a density closer to 10^5. What is the underlying mechanism? I think the paper would benefit from its exploration.

*

Thank you for the comment.

We have modified the Figure 2 to show the percentage lysis at intermediate values of the HER2 density. The percentage lysis plateaus at similar HER2 concentrations as CN which is expected as the lysis and proliferation rates are proportional to CN. We have modified the text to explain the behavior.

• It is likely that CAR-T cells would encounter various ratios of target and healthy cells depending on patient, microenvironment, in vitro experimental design, etc. My understanding is that the optimization was done with equal numbers of healthy and target cells. It would be useful to explore how sensitive the optimization is to the ratio of target and healthy cells. This could provide useful design guidance to experimentalists.

*

Thank you for the comment.

We have carried out our Pareto front calculations at different healthy and tumor cell ratios namely, 1:4 and 4:1. The Pareto fronts show similar behavior as that shown for the 1:1 ratio in Figure 4 with small vertical and horizontal shifts in the curves compared to the 1:1. We have shown these additional results in the main text (page 11) and the Figure S8 in the supplementary material.

• Two assumptions of the population model initially surprised me. However, given the strong performance of the model, they seem to be well justified. Could the authors address the following with brief discussion?

(A) - The model essentially assumes a well-mixed population of cells, treating lysis and proliferation as second-order "reactions." However, individual cells likely encounter relatively few cells on the time scale of simulations. *

This is an excellent point.

We have added a supplementary text (Supplementary Text 3) and the text below in the Discussion section (page 14) to address the above comment.

PASCAR also assumes that the target and the T cells are well mixed. In vitro cytotoxicity experiments are carried out in culture wells and for the experiments in Hernandez-Lopez et al.6 Our estimates show that 99.8% of the T cells were partnered with target cells (details in Supplementary Text 3). However, depending on the number of target and T cells, the number of target cells in the immediate vicinity of a T cell is likely to be varied (details in Supplementary Text 3), therefore, a weighted sum for the target and T cells in Eq. (3)-(4) would be more appropriate. We plan to include that in a future study.

(B) - The model doesn't include "mixing" of CAR-T cell populations upon proliferation (i.e., a cell with R receptors can't divide and result in cell with a different number of receptors). Is this justified by the biology?

We thank the reviewer for raising this excellent point.

We have added the text below in the Discussion section (page 14) to address the above comment.

PASCAR assumes that the CAR abundances in single CD8+ T cells do not mix as they divide, i.e., daughter cells have the same CAR abundances as the mother cell. However, proteins in human cells (e.g., H1299 non-small cell lung carcinoma cell line) have been observed to mix due to cell division (Sigal et al., 2006) in time scales longer than two cell generations. It is unclear if the CARs follow the similar pattern as the CD8+ T cells proliferate. The doubling time scale for the faster proliferating CD8+ T cells in our model is ~1.7 days and the mean doubling time of the CD8+ T cell population is ~3 days, therefore, there will a negligible amount of mixing in the system due to cell proliferation if a similar mixing time scale as in Sigal et al. (Sigal et al., 2006) occurs for the CAR CD8+ T cells.

*Minor comments

1. A couple of figures appear to be mis-referenced in the paper: Figure 2D (end of paragraph, page 7) should presumably be Figure 2E, and Figure S3 should be S2 (end of page 7).*

Done.

• I don't know what "conv." stands for in the figures (I couldn't find the abbreviation in the captions or main text). *

We have changed all the “conv” abbreviations to “const.” indicating constitutive CAR T cells.

• Last pargraph of page 9: "However, decreasing K_D further starts decreasing lysis..." Given the previous sentences, this should presumably say "...increasing K_D further..."*

Done.

• I was initially confused by the second sentence of the last paragraph of Results. Based on the previous paragraph, I expected the sentence to read "when K_D increased" (not decreased) because of the statement "similar to constitutive CAR-T cells..." However, I think the underlying argument/conclusion is the same.*

Done.

• It is not completely clear to me what is being plotted in Figures 2D, 4B, and S1. Where do each of the points come from? Why do there appear to be sets of 4 points when there are more experimental data points in 2C?*

Thank you for the comment.

We have edited the figures and the captions to address the comment.

• What were the values of $\Delta_R$ and $\Delta_H$? (Or, alternatively, how many bins were chosen?) *

We have shown the ΔH and ΔR values in Figures S10-11 in the supplementary material.

7. It would be useful to define $\mu$ and $\sigma$ in the main text before they are introduced in Table 1.

Done.

*Reviewer #1 (Significance (Required)):

The paper addresses an important and timely problem, given the strong interest in designing CAR-T cells for cancer therapy. This paper adds to existing computational approaches (clearly summarized in the intro) by introducing a multiscale framework that includes cellular-level properties - like CAR binding affinities, etc. - with a population model that is important for capturing collective behavior of many cells. It is parameterized by experimental data and provides a framework for optimizing cells to maximize target cell killing while minimizing off-target killing of healthy cells. This will be of interest to computational groups in the field, can be extended to incorporate additional biology and/or data, and has the potential to provide useful guidance to experimental design of CAR-T cells. It could be highly impactful if combined with experiments in the future.*

We appreciate the positive comment.

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

In this study, Rajakaruna et al. propose a mathematical modeling framework called PASCAR to simulate the response of T cells engineered to express chimeric antigen receptors (CAR) against the oncogenic marker HER2 on target cells. The authors propose that the degree of T cell activation is given by a simple CAR occupancy model in the presence or absence of additional kinetic proofreading steps. A population-level ODE model is then used to describe proliferation and death of both target cells and T cells on longer time-scales as a function of the degree of T cell activation.

The model results are compared with recent experiments by Fernandez-Lopez et al. 2021 (ref. 6). showing that T cells respond to HER2 abundance on target cells in an ultrasensitive manner when using a circuit where a low-affinity synthetic Notch receptor (synNotch) for HER2 controls the expression of a high-affinity CAR for HER2. With this synNotch-CAR circuit, T cells are able to kill tumor cells with high HER2 abundance while sparing healthy cells with 100 fold less HER2.

The authors first fit model parameters in the situation where T cells constitutively express CARs of either low (Kd=210nM) or high (Kd=17.6nM) affinity and suggest that 7 kinetic proofreading steps are needed to account for the experimental data. For the situation where T cells are endowed with the synNotch-CAR circuit, the authors implicitly account for ultrasensitivity by assuming that CAR abundance is proportionnal to a Hill function of HER2 abundance on target cells. Using this asumption, they fit model parameters to experimental data. The authors use the model to predict the response (%lysis in target cells) for different initial numbers of T cells not used in the fitting procedure and conclude that the PASCAR model can generalize well to unseen situations.

The authors finally perform pareto optimization to investigate how optimization of lysis of tumor cell with high HER2 abundance and sparing of healthy cells with low HER2 can be performed simultaneously. They conclude by suggesting parameters values of the synNotch-CAR circuit optimizing the discrimination between healthy and tumor cells. * Major comments :

1(A) - In Figure 2A, the error bars are not properly drawn : the whiskers are not horizontal, not aligned and seem to have been placed manually. Some points and error bars are found outside plot limits. This is intriguing and suggests that this figure was edited manually. Error bars do not seem to agree with the original experimental data.

Thank you for pointing this out. We have modified Figure 2A. The extracted experimental data from Hernandez-Lopez et al.6 and the corresponding error bars are also provided in in an excel data file ”Error_bars_%lysis_&Predictions” available at the GitHub link.

*(B) The lines corresponding to the model results should be drawn using many more points(as compared to experimental results) to better illustrate the model behavior over the range of HER2 abundance values represented. Here, the representation of the model result is misleading (it suggests a linear increase of the %lysis between the 10^3 and 10^5 HER2 molecules/cell). *

Done. Reviewer 1 also pointed to this in Major comment #1B. We have modified Figures 2A-D,G and Figures 4A,D,F to address this. Please refer to our response to Reviewer #1 for details.

(C)The number of target and CAR T cells should be modified to correspond to those used in the original experiment (100000 target cells and 15000 CAR T cells for the model versus 20000 target cells and 10000 CAR T cells in the experiments).

The numbers (20000 of target cells and 10000 CAR T cells) we used in our model are taken from Hernandez-Lopez (see figure caption of Fig. 2 (page 2) in Hernandez-Lopez et al.). In addition, we reached out to Dr. Hernandez-Lopez and Dr. Wendell Lim to confirm the numbers of target cells and CAR T cells used in the experiments.

2 - An error is found in the expression of CN in the KP model. Indeed, Kd_tilde should be equal to Kd. Given that the authors do not provide the details of their calculation, it is not clear where the error comes from. A potential source of error could be the absence of the dissociation reaction of the CN complex in Fig.1 (see minor comments). The authors should correct the error and re-run the model with the correct expression for CN.

Thank you for raising this point.

The expressions shown in Eqs. 1 and 2 in our original manuscript is correct, however, it can be further simplified to get an expression where Kd_tilde is equal to K_d. We have included a detailed derivation of the equations in the supplementary material (Supplementary Text 1).

3(A) - The conversion scheme used to convert the association rate k_on (and also of the dissociation Kd) from units 1/(nM . s) to units 1/(molecules per cell . s) is not appropriate. Indeed, association rates depend on the diffusion of molecules which greatly differs between soluble (SPR measurements) and membrane-bound molecules. These different diffusion properties should be taken into account for the conversion.

We thank the reviewer for raising this point.

We have modified the main text (page 7) and added additional calculations (Supplementary Text 2) and a figure (Figure S1) in the supplementary material to address the comment. Briefly, we evaluated the role of diffusion in modifying the binding (k_on) and unbinding (k_off) rates following the approach developed by Eigen, Bell, Keizer, and others and found that for the rates used in Hernandez-Lopez et al, the changes in the rates are less than 20%, which does not lead to appreciable changes in in CN (Fig. S1). Therefore, including the effect of diffusion will affect the percentage lysis negligibly in this case.

*(B) Moreover, the conversion formula was found to give a Kd of 3.2 molecules/cell for the high affinity CARs and of 39 molecules/cell for the low affinity CARs. This information is important to interpret the results and should appear in the main text. As such, these Kd estimates are far below the typical number of CARs on T cells (10^3) and of HER2 ligands (from 10^3 to 10^6) on target cells. In this situation, the number of HER2:CAR complexes is entirely determined by the limiting component (HER2 or CAR) and does not depend on Kd (as shown in Fig.2B). Estimates of Kd in the 10^3 molecules/cell range would produce T cell responses that depend on Kd and potentially provide a good agreement between the NKP model and the experimental results shown in Fig.2A. The authors should therefore re-evaluate the performances of the NKP model. *

We agree with the reviewer regarding the above comment.

We have moved the conversion of K_D to the unit of molecules/cell. As we point out in our response to the previous comment, including the effect of diffusion in the reactions does not change the K_d appreciably and therefore, the NKP model would be unable to fit the results (% Lysis) shown in Fig 2A.

4(A) - KP model : The authors make a confusion about the performance of the KP model when stating : "The estimated value of the phosphorylation rate, kp (≈ 0.007 s -1 ) is larger than the ligand unbinding rate koff (≈ 10^-4 s^-1 ) indicating that the kinetic proofreading scheme is active in separating CAR-T cell responses across high and low affinity CARs". KP ligand discrimination is efficient when kp is much smaller than koff which is not the case here. To compensate for this inefficient discrimination, a large number N of proofreading steps is needed.

Thank you for the comment.

We have edited the sentence to address the comment (bottom of page 9 in the main text).

(B) The rate kp=7e-3 s^-1 sets a time-scale 1/kp = 2.5 min. The typical time scale for activation would then be N/kp = 17.5min which is much larger than the time-scale at which the KP mechanism operates during normal TCR signaling following pMHC-TCR engagement. Indeed, during TCR signaling, KP operates within a few tens of seconds as recently illustrated in a study by Mc Affee et al. (https://doi.org/10.1038/s41467-022-35093-9*) showing that LAT condensates appear typically 20 to 30 seconds following TCR engagement. *

Thank you for the comment and the reference.

We have edited the discussion to incorporate the above comment (last paragraph in page 12 in the main text).

5 - The authors should modify Fig.2D to produce a separate graph for each variable ("%lysis", "mean" and "var"). Indeed, differences are potentially hidden by plotting variables with different scales on the same graph. For the comparison of the % lysis between data and model, it would be helpful to have colors and shapes as in Fig.2A and Fig.2C. The authors should also represent the experimental and theoretical distributions of CAR molecules / T cell for the different experimental conditions (different abundance of HER2/target cell).

Done.

We have included Figures 2E and 4C in the main text to show comparisons between the means and variances.

6 - The "Cost" function in the methods section seems to be defined for only one experimental condition (corresponding to a given HER2 abundance). Please indicate that it is the sum over all experimental conditions that is minimized. Author show in Fig.2A the variable (%lysis) used in the cost function. Importantly, the other variables (mean and variance of CAR abundance at day 3) should also be plotted to help the reader appreciate the agreement between model and data.

Done.

We have edited the equation in page 15 in the main text to reflect the sum in the cost function.

We have included figures (Figs. 2F, 2H, 4B) in the main text and the supplementary material (Fig. S2A ) in the supplementary material quantifying comparisons between the data and our model.

7 - The author should quantify the goodness of the fit and analyze the sensitivity of the model results (the "Cost" function) with respect to parameter values. An investigation of the sensitivity of the model output as of function of all pairs of parameters would certainly highlight the fact that the estimates of parameters kp and N are correlated.

Done.

We have calculated correlations between the estimated parameters to address this. The results are shown in the main text (pages 10 and 17) and in Fig. S5 in the supplementary material.

8 - To model the ultrasensitive synNotch-CAR circuit, the authors assume that CAR expression is a Hill function of HER2 abundance on target cells. The parameters of this Hill function are estimated by fitting both the % lysis and CAR expression at day 3. The authors should evaluate the agreement between model results and experimental values by plotting CAR expression at day 3 (using CAR expression data from Supplementary Figure 1C in ref.6).

Done.

We have included the comparisons in Figure S12 in the supplementary material and mentioned those in the figure legend for Fig. 4 in the main text.

9 - In Figure 4C, for 12000 and 15000 T cells, experimental results show a non-ultrasensitive response (also Supp.Fig.S4E in ref 6) which does not agree well with the model results. Hence, the authors claim that the agreement is good does not seem to be supported. The model should also be tested using experimental data where synNotch and CAR receptors with different affinities are used (in particular Supp.Fig.S4D in ref.6 where a Hill coefficent of 0.6 is estimated for the med-low circuit). It will also be of interest to show that the model can reproduce results from Supp.Fig.S5A in ref.6 where cells with high and low CAR expression are used.

Thank you for the comment.

We have calculated the goodness of the fit R2 for our predictions of Fig. S4E in ref. 6 and our R2 = 0.97 suggest excellent agreement with the data. However, we agree that the data for E:T= 0.75 or E:T=0.6 in Fig. S4E in ref. 6 can be interpreted as a gradual increase, however, additional experiments at HER2 abundances 104 molecules/cell probably would be able to help distinguish a ultrasensitive response vs a gradual response at these E:T ratios.

We have tested model predictions for synNotch CAR T cells for higher affinity CAR (Fig. S4D, top panel, in ref. 6) which shows excellent agreement (Fig. 4F in our revised manuscript). We were unable to generate any prediction for the med-low synNotch circuit (Fig. S4D, bottom panel, in ref. 6) as the CAR expressions for the med-low synNotch circuit are not available in the manuscript. However, we have tested new model predictions (Figs. 2G-H) for constitutive CAR T cells (Fig. S5A in ref. 6) for high and low CAR expressions at different E:T ratios. The agreement between the data and the predictions is reasonable (R2 = 0.90) . Therefore, we believe we have confronted our model with responses generated by several types of CAR T cells and the excellent to reasonable agreements of the PASCAR model with the data provide confidence in the utility of our framework to investigate CAR T cell responses in vitro.

Minor comments :

*Figure 1 : left : The schematic of the CAR receptor is inaccurate. CD3z domains should be shown within the intra-cytoplasmic part of the receptor, and not as separate proteins. In addition to the CD3z domain, the 41BB domain should also be represented (see ref 6 by Fernadez-Lopez at al.). The arrow correspond to the dissociation of the complex C_N is missing. Without this reaction in the KP model, the steady state solution leads to C_i = 0 for i in [0, N-1]. All receptor-ligand complexes eventually reach and stay in the C_N state. *

We thank the reviewer for catching this.

We have made changes in the schematic figure to include the changes suggested above.

middle: In the schematic, add labels "lysis of target cell" and "proliferation" next to the corresponding parameters. The arrows should point from the label ("cancer cell" and "CAR-T cell") to the cell not to the other way around.

Done.

Right : this schematic is not useful and should either be removed or completed to provide useful information to the reader.

Done.

*Figure 2 : A - A reference to the article with the original data should be added in the legend. In line with the original article, "conv." should be replaced by "const." as an abbreviation for "constitutive". B and E - Please use a log scale for the y-axis. Have the authors represented an average of C0 and CN across the population at day 3? If so, that information should appear in the figure legend. *

Done.

Figure 3. To help the reader interpret these graphs, the authors should also show T_R(t) and U_H(t) on separate graphs. It would also be useful to show C_N as a function of R for H=10^6.2 both for high and low affinity CAR.

Done.

Figure 4. A - the number of target cells and CAR T cells do not correspond to those used in the original experiment (see major comment 1).

Done. Please check our response to comment #1(B) for Reviewer #2.

*B - Same comments as for Fig.2D. The legend appears to be incorrect ("% lysis" should be blue open circle and orange open squares). *

Done.

*C - Use more points to plot the model results. *

These figures have all the points plotted. However, some points are overlapping with some other.

Reviewer #2 (Significance (Required)):

The modeling framework is minimal but appropriate to describe CAR T cell activation and subsequent proliferation and lysis of target cells. The advantage of the simplicity of the model formulation is to allow a direct interpretation of the impact of the different parameters on the model output.

Thank you for the positive remark.

*However, the authors seldom discuss how the behavior of the model is controlled by the parameters and their values. Accordingly, the analysis of the theoretical results needs to be further developed. The authors should also quantify the goodness of fit and analyze the sensitivity of model results to parameter values. This will allow to evaluate how the experimental data constrain model parameters and to compare the performances of the different models. *

We have provided further explanation for some of the results obtain using our model (please refer to responses to comments # 1A, 3A, and 2B) for Reviewer 1), added a correlation analysis for the estimated parameters (Fig. S5 in the supplementary material), and included goodness of the fit (R2 values) for the fits and the model predictions. We believe these new results address the reviewers’ comments.

The authors should also assess the biological significance of their results by confronting their parameter estimates to related parameter estimates used in other models of T cell activation. The model should further be tested in other situations with different receptor affinities and cell numbers.

Thank you for the comment.

We have now tested new predictions for a highest affinity synN-CAR (Figure 4F) and for increasing and decreasing CAR abundances at different E:T ratios (Figure 2G-H). There are few models of CAR T cells (e.g., Rohrs et al. iScience, 2020) which use more detailed signaling models. Therefore, it will be difficult to compare the estimated values of parameters in our model and with those models. The time scales of signaling can also depend on the specifics of the CAR construct. Therefore, though it will be useful to compare different models and their parameter values developed so far, we believe this is outside the scope of this manuscript. We have included some potential directions for extending our current framework in the Discussion section (pages 12-14). If the editor and the reviewer strongly feel the need for including this in the current manuscript, we can do that.

This research should be of interest to readers specialized in the field of mathematical modeling of biological systems and in CAR-T cell immunotherapies.

Thank you for the positive comment.

*Fields of expertise of the reviewer : biophysics, mathematical modeling of biological systems, molecular mechanisms of T cell signaling and activation. *

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 #2

Evidence, reproducibility and clarity

Summary:

In this study, Rajakaruna et al. propose a mathematical modeling framework called PASCAR to simulate the response of T cells engineered to express chimeric antigen receptors (CAR) against the oncogenic marker HER2 on target cells. The authors propose that the degree of T cell activation is given by a simple CAR occupancy model in the presence or absence of additional kinetic proofreading steps. A population-level ODE model is then used to describe proliferation and death of both target cells and T cells on longer time-scales as a function of the degree of T cell activation.

The model results are compared with recent experiments by Fernandez-Lopez et al. 2021 (ref. 6). showing that T cells respond to HER2 abundance on target cells in an ultrasensitive manner when using a circuit where a low-affinity synthetic Notch receptor (synNotch) for HER2 controls the expression of a high-affinity CAR for HER2. With this synNotch-CAR circuit, T cells are able to kill tumor cells with high HER2 abundance while sparing healthy cells with 100 fold less HER2.

The authors first fit model parameters in the situation where T cells constitutively express CARs of either low (Kd=210nM) or high (Kd=17.6nM) affinity and suggest that 7 kinetic proofreading steps are needed to account for the experimental data. For the situation where T cells are endowed with the synNotch-CAR circuit, the authors implicitly account for ultrasensitivity by assuming that CAR abundance is proportionnal to a Hill function of HER2 abundance on target cells. Using this asumption, they fit model parameters to experimental data. The authors use the model to predict the response (%lysis in target cells) for different initial numbers of T cells not used in the fitting procedure and conclude that the PASCAR model can generalize well to unseen situations.

The authors finally perform pareto optimization to investigate how optimization of lysis of tumor cell with high HER2 abundance and sparing of healthy cells with low HER2 can be performed simultaneously. They conclude by suggesting parameters values of the synNotch-CAR circuit optimizing the discrimination between healthy and tumor cells.

Major comments:

1. In Figure 2A, the error bars are not properly drawn : the whiskers are not horizontal, not aligned and seem to have been placed manually. Some points and error bars are found outside plot limits. This is intriguing and suggests that this figure was edited manually. Error bars do not seem to agree with the original experimental data. The lines corresponding to the model results should be drawn using many more points(as compared to experimental results) to better illustrate the model behavior over the range of HER2 abundance values represented. Here, the representation of the model result is misleading (it suggests a linear increase of the %lysis between the 10^3 and 10^5 HER2 molecules/cell). The number of target and CAR T cells should be modified to correspond to those used in the original experiment (100000 target cells and 15000 CAR T cells for the model versus 20000 target cells and 10000 CAR T cells in the experiments).
2. An error is found in the expression of CN in the KP model. Indeed, Kd_tilde should be equal to Kd. Given that the authors do not provide the details of their calculation, it is not clear where the error comes from. A potential source of error could be the absence of the dissociation reaction of the CN complex in Fig.1 (see minor comments). The authors should correct the error and re-run the model with the correct expression for CN.
3. The conversion scheme used to convert the association rate k_on (and also of the dissociation Kd) from units 1/(nM . s) to units 1/(molecules per cell . s) is not appropriate. Indeed, association rates depend on the diffusion of molecules which greatly differs between soluble (SPR measurements) and membrane-bound molecules. These different diffusion properties should be taken into account for the conversion.

Moreover, the conversion formula was found to give a Kd of 3.2 molecules/cell for the high affinity CARs and of 39 molecules/cell for the low affinity CARs. This information is important to interpret the results and should appear in the main text. As such, these Kd estimates are far below the typical number of CARs on T cells (10^3) and of HER2 ligands (from 10^3 to 10^6) on target cells. In this situation, the number of HER2:CAR complexes is entirely determined by the limiting component (HER2 or CAR) and does not depend on Kd (as shown in Fig.2B). Estimates of Kd in the 10^3 molecules/cell range would produce T cell responses that depend on Kd and potentially provide a good agreement between the NKP model and the experimental results shown in Fig.2A. The authors should therefore re-evaluate the performances of the NKP model. 4. KP model : The authors make a confusion about the performance of the KP model when stating : "The estimated value of the phosphorylation rate, kp (≈ 0.007 s -1 ) is larger than the ligand unbinding rate koff (≈ 10^-4 s^-1 ) indicating that the kinetic proofreading scheme is active in separating CAR-T cell responses across high and low affinity CARs". KP ligand discrimination is efficient when kp is much smaller than koff which is not the case here. To compensate for this inefficient discrimination, a large number N of proofreading steps is needed.

The rate kp=7e-3 s^-1 sets a time-scale 1/kp = 2.5 min. The typical time scale for activation would then be N/kp = 17.5min which is much larger than the time-scale at which the KP mechanism operates during normal TCR signaling following pMHC-TCR engagement. Indeed, during TCR signaling, KP operates within a few tens of seconds as recently illustrated in a study by Mc Affee et al. (https://doi.org/10.1038/s41467-022-35093-9) showing that LAT condensates appear typically 20 to 30 seconds following TCR engagement. 5. The authors should modify Fig.2D to produce a separate graph for each variable ("%lysis", "mean" and "var"). Indeed, differences are potentially hidden by plotting variables with different scales on the same graph. For the comparison of the % lysis between data and model, it would be helpful to have colors and shapes as in Fig.2A and Fig.2C. The authors should also represent the experimental and theoretical distributions of CAR molecules / T cell for the different experimental conditions (different abundance of HER2/target cell). 6. The "Cost" function in the methods section seems to be defined for only one experimental condition (corresponding to a given HER2 abundance). Please indicate that it is the sum over all experimental conditions that is minimized. Author show in Fig.2A the variable (%lysis) used in the cost function. Importantly, the other variables (mean and variance of CAR abundance at day 3) should also be plotted to help the reader appreciate the agreement between model and data. 7. The author should quantify the goodness of the fit and analyze the sensitivity of the model results (the "Cost" function) with respect to parameter values. An investigation of the sensitivity of the model output as of function of all pairs of parameters would certainly highlight the fact that the estimates of parameters kp and N are correlated. 8. To model the ultrasensitive synNotch-CAR circuit, the authors assume that CAR expression is a Hill function of HER2 abundance on target cells. The parameters of this Hill function are estimated by fitting both the % lysis and CAR expression at day 3. The authors should evaluate the agreement between model results and experimental values by plotting CAR expression at day 3 (using CAR expression data from Supplementary Figure 1C in ref.6). 9. In Figure 4C, for 12000 and 15000 T cells, experimental results show a non-ultrasensitive response (also Supp.Fig.S4E in ref 6) which does not agree well with the model results. Hence, the authors claim that the agreement is good does not seem to be supported. The model should also be tested using experimental data where synNotch and CAR receptors with different affinities are used (in particular Supp.Fig.S4D in ref.6 where a Hill coefficent of 0.6 is estimated for the med-low circuit). It will also be of interest to show that the model can reproduce results from Supp.Fig.S5A in ref.6 where cells with high and low CAR expression are used.

Minor comments:

Figure 1 : left : The schematic of the CAR receptor is inaccurate. CD3z domains should be shown within the intra-cytoplasmic part of the receptor, and not as separate proteins. In addition to the CD3z domain, the 41BB domain should also be represented (see ref 6 by Fernadez-Lopez at al.). The arrow correspond to the dissociation of the complex C_N is missing. Without this reaction in the KP model, the steady state solution leads to C_i = 0 for i in [0, N-1]. All receptor-ligand complexes eventually reach and stay in the C_N state.

middle: In the schematic, add labels "lysis of target cell" and "proliferation" next to the corresponding parameters. The arrows should point from the label ("cancer cell" and "CAR-T cell") to the cell not to the other way around.

Right : this schematic is not useful and should either be removed or completed to provide useful information to the reader.

Figure 2 : A - A reference to the article with the original data should be added in the legend. In line with the original article, "conv." should be replaced by "const." as an abbreviation for "constitutive". B and E - Please use a log scale for the y-axis. Have the authors represented an average of C0 and CN across the population at day 3? If so, that information should appear in the figure legend.

Figure 3. To help the reader interpret these graphs, the authors should also show T_R(t) and U_H(t) on separate graphs. It would also be useful to show C_N as a function of R for H=10^6.2 both for high and low affinity CAR.

Figure 4. A - the number of target cells and CAR T cells do not correspond to those used in the original experiment (see major comment 1). B - Same comments as for Fig.2D. The legend appears to be incorrect ("% lysis" should be blue open circle and orange open squares). C - Use more points to plot the model results.

Significance

The modeling framework is minimal but appropriate to describe CAR T cell activation and subsequent proliferation and lysis of target cells. The advantage of the simplicity of the model formulation is to allow a direct interpretation of the impact of the different parameters on the model output. However, the authors seldom discuss how the behavior of the model is controlled by the parameters and their values. Accordingly, the analysis of the theoretical results needs to be further developed. The authors should also quantify the goodness of fit and analyze the sensitivity of model results to parameter values. This will allow to evaluate how the experimental data constrain model parameters and to compare the performances of the different models. The authors should also assess the biological significance of their results by confronting their parameter estimates to related parameter estimates used in other models of T cell activation. The model should further be tested in other situations with different receptor affinities and cell numbers.

This research should be of interest to readers specialized in the field of mathematical modeling of biological systems and in CAR-T cell immunotherapies.

Fields of expertise of the reviewer: biophysics, mathematical modeling of biological systems, molecular mechanisms of T cell signaling and activation.

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

Summary: The paper by Rajakaruna, Desai, and Das develops a multiscale computational model (PASCAR) to study design of features of CAR-T cells. Their ODE-based model captures cell-cell interactions using equilibrium receptor-ligand binding relations and a simplified representation of intracellular signaling. The cellular-scale model feeds into a population model including populations of CAR-T cells, healthy cells, and target cells. The model accounts for CAR-T cells with varying numbers of CARs; it accounts for target/healthy cells with varying numbers of ligands. The authors use published cytometry and cytotoxicity data to parameterize the model, which they show captures experimental trends well when they use a kinetic proofreading model to phenomenologically represent intracellular signaling. They then use an optimization approach to characterize features of the CAR-T cell design space that maximize killing of target cells while minimizing the destruction of healthy cells. Their conclusions are consistent with physical intuition and provide quantitative and generalizable predictions about biophysical parameters (including equilibrium binding constants, kinetic rates, etc.).

Major comments:

1. In general, the paper is logically laid out and easy to follow. However, some of the underlying mechanisms were not clear to me:

2. In Figure 3A, it is not clear why the rate of lysis is greater for populations with an intermediate range of CAR abundances. I understand the authors' statement that "this is because the smaller number of CAR-T cells present in [higher or lower CAR expression groups]..." However, it is not clear why intermediate-level CAR-T cell populations are largest, noting that populations with higher receptor levels have larger proliferation rates. Is this simply reflecting the initial size of the populations, or is another mechanism at play? I think the paper would benefit from discussion/quantification of this.

3. In Figure 2, C_N plateaus at HER2 density between 10^3 and 10^4. However, % lysis does not plateau until a density closer to 10^5. What is the underlying mechanism? I think the paper would benefit from its exploration.
4. It is likely that CAR-T cells would encounter various ratios of target and healthy cells depending on patient, microenvironment, in vitro experimental design, etc. My understanding is that the optimization was done with equal numbers of healthy and target cells. It would be useful to explore how sensitive the optimization is to the ratio of target and healthy cells. This could provide useful design guidance to experimentalists.

5. Two assumptions of the population model initially surprised me. However, given the strong performance of the model, they seem to be well justified. Could the authors address the following with brief discussion?

6. The model essentially assumes a well-mixed population of cells, treating lysis and proliferation as second-order "reactions." However, individual cells likely encounter relatively few cells on the time scale of simulations.

7. The model doesn't include "mixing" of CAR-T cell populations upon proliferation (i.e., a cell with R receptors can't divide and result in cell with a different number of receptors). Is this justified by the biology?

Minor comments

1. A couple of figures appear to be mis-referenced in the paper: Figure 2D (end of paragraph, page 7) should presumably be Figure 2E, and Figure S3 should be S2 (end of page 7).
2. I don't know what "conv." stands for in the figures (I couldn't find the abbreviation in the captions or main text).
3. Last pargraph of page 9: "However, decreasing K_D further starts decreasing lysis..." Given the previous sentences, this should presumably say "...increasing K_D further..."
4. I was initially confused by the second sentence of the last paragraph of Results. Based on the previous paragraph, I expected the sentence to read "when K_D increased" (not decreased) because of the statement "similar to constitutive CAR-T cells..." However, I think the underlying argument/conclusion is the same.
5. It is not completely clear to me what is being plotted in Figures 2D, 4B, and S1. Where do each of the points come from? Why do there appear to be sets of 4 points when there are more experimental data points in 2C?
6. What were the values of $\Delta_R$ and $\Delta_H$? (Or, alternatively, how many bins were chosen?)
7. It would be useful to define $\mu$ and $\sigma$ in the main text before they are introduced in Table 1.

Significance

The paper addresses an important and timely problem, given the strong interest in designing CAR-T cells for cancer therapy. This paper adds to existing computational approaches (clearly summarized in the intro) by introducing a multiscale framework that includes cellular-level properties - like CAR binding affinities, etc. - with a population model that is important for capturing collective behavior of many cells. It is parameterized by experimental data and provides a framework for optimizing cells to maximize target cell killing while minimizing off-target killing of healthy cells. This will be of interest to computational groups in the field, can be extended to incorporate additional biology and/or data, and has the potential to provide useful guidance to experimental design of CAR-T cells. It could be highly impactful if combined with experiments in the future.

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Reply to the reviewers

We would like to thank all reviewers for their time and effort invested into reviewing our manuscript.

Please find our responses to your comments, criticisms and suggestions below in blue.

__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __

Summary:

The manuscript by Vishwanatha et al. presents findings on the fission yeast transcription factor Cbf11, which is involved in regulating lipid synthesis. Changes in lipid metabolism often have detrimental effects on nuclear division (evidenced by the high percentage of cut phenotypes among strains with altered lipid content). Here the authors show that cbf11 deletion strains produce additional phenotypes such as changes to cohesion dynamics and altered chromatin modification within centromeric regions, in turn perhaps affecting microtubule attachment and proper chromosome distributions. This hypothesis is supported by the authors' finding of epistatic effects between cbf11 and cohesin loading and unloading.

Major comments:

While the evidence presented supports the hypothesis of altered cohesin loading as a major driver of observed mitotic defects, changes in the NE surface area are likely to also contribute to the phenotypes even in pre-anaphase stages.

• This is an interesting notion. We are only aware of NE overproduction and nuclear “flares” observed upon the Lipin phosphatase dysregulation (PMID 23873576).

• However, in our case we rather expect NE membrane shortage, not overproduction. Accordingly, we do see that the nuclear cross section area (thus likely also NE surface area) is smaller in cbf11KO compared to WT (see boxplots below). Is this what you are referring to? We are not sure how this would affect the pre-anaphase stages of mitosis.

Did the authors test any double deletions with regulators involved in decreasing lipid content (e.g. spo7, nem1, ned1) to counteract the role of Cbf11? This could be useful in assessing the relative contribution of cohesion dynamics and histone modifications.

• We previously published (PMID: 27687771) that cut6/ACC overexpression can indeed partially suppress the cut phenotype in the cbf11KO background. So lipid metabolism does play a role and does contribute to mitotic fidelity. In the current manuscript, we are showing that other factors contribute as well and that defects arise already prior to anaphase, which is not consistent with the simple notion of shortage of membrane building blocks during anaphase. We appreciate your suggestion on testing the relative contributions of these various factors to mitotic fidelity, but we have not tested any of the suggested double mutants.

A possible role of physical constraints dictated by the NE was already mentioned by the authors in the context of spindle bending and decreased elongation rates and some preliminary experimental data on this would be appreciated. Generation of strains, acquisition of some timelapses, and quantification of spindle elongation rate/buckling frequency should be feasible in a reasonable time frame.

• Assaying spindle parameters in Lipin-related mutants would indeed be interesting, but again, these are anaphase phenotypes. We are not sure how this is relevant for the pre-anaphase findings we report? Also, we unfortunately no longer have the personnel and capacity to carry out the suggested experiments.

The authors report mRNA levels of the centromere flanking genes per1 and sdh1 to be increased by 1.5x and decreased by 2x in comparison to WT. Could the authors elaborate on whether this is an expected trend? Kaufmann et al., 2010 reported low transcription of per1 when the surrounding regions are predominantly acetylated. Fig. 4A suggests a slight increase of H3K9ac at per1 and a decrease of transcription would be conceivable.

• We do not have any particular expectations regarding the expression levels of per1 and sdh1 in our system. We simply note that their expression changes in cbf11KO (in different directions) and this is accompanied by changes in H3K9 acetylation patterns.

• The increased histone acetylation at the per1 locus that you mention (Kaufmann et al., 2010) was only shown for H4K12ac, while we measured H3K9ac (these marks are deposited by different enzymes). The authors actually report that “The levels of histone H3 at per1 did not change significantly between the two growth conditions and strains”, so we do not think that paper is relevant for our study.

Fig. 3B indicates a catastrophic mitosis percentage of roughly 9.5% in cbf11∆ while in Fig. 1C 4% of all cells, or ˜31% of all mitotic events, is noted as abnormal. Could the authors clarify this discrepancy? Since Fig. 1 utilises time course data of 333 cells (please specify the number of analysed cells also in the legend), would the authors expect this data to be more trustworthy when compared to images of fixed cells? What were the criteria to assign divisions as catastrophic in fixed cells and which features were utilised to identify the 400 cells as mitotic?

• We typically do see higher proportions of cut cells in fixed samples than in live-cell imaging. We believe this has to do with the different fluorescence readouts for live vs fixed cells. We have added the following explanations to the methods:

“Please note that the observed frequencies of mitotic defects are not directly comparable between live and fixed cells. Following catastrophic mitosis, the dead cells rapidly lose histone-GFP fluorescence (imaging of live cells), but their DNA can still be visualized with DAPI for a much longer period (imaging of fixed cells), resulting in higher apparent defect frequencies in fixed cells.”

• Importantly, we always compared cbf11KO to WT grown and processed under the same conditions, and that is how we determined the significance of any defects.

• Mitotic defects were classified based on nuclear morphology both in live cells (histone signal) and in fixed cells (DAPI): Cells having the cut phenotype, or mis-segregated nucleus = 2 nuclei of different sizes, or septated cells with only one daughter cell having a nucleus, respectively.

• We have analyzed images of at least 400 cells *in total* from asynchronous populations (interphase + mitotic >= 400). We have modified the figure legend to make this fact more clear. In our experience, this is the standard way of reporting the frequency of mitotic defects in asynchronous yeast cell populations.

• We have specified the number of cells analyzed in Fig. 1C.

Minor comments:

Previous literature is, to the best of our knowledge, sufficiently referenced. The text is largely clear (some exceptions within the methods section will be elaborated on below). The figures, however, would benefit from graph titles and some minor formatting changes.

• Figures:

o Fig. 1: Specify the number of cells analysed in C within the legend as well. For B, please use colourblind-friendly schemes - especially since images are shown as merges only. The example of the "cut" phenotype appears small and crowded by surrounding cells. Especially the latter might affect mitotic fidelity. Under the assumption that this did not affect quantifications (WT seem fine) a less crowded cell would present a nicer example.

• We have changed Fig. 1 as requested.

o Fig. 3: Images shown in A add little benefit in their current form. What is the takeaway for the reader?

• We hope that the reader gets concrete information on cellular and nuclear morphology of the investigated strains, which would be otherwise difficult to reproduce by textual description.

Indicating that images represent DAPI staining and pointing out cells of interest with arrows/symbols would be helpful.

• Done.

The example shown for cbf11 appears to be dimmer in comparison and cell morphology is hard to interpret.

• The cbf11KO cells stain fainter with DAPI than cells of other strains. We do not know why. To increase the clarity of the image, we have now adjusted the brightness and contrast of the cbf11KO panel (and indicated this adjustment in the figure legend).

C feels misplaced in this figure and a title could improve readability.

• We have added a title and moved the panel to Fig. 4 (4D).

o Fig. 4: Graph titles needed, figure might work better in portrait

• We have added the required graph titles.

• We have recreated all ChIP-seq related figures to incorporate new data and to (hopefully) better highlight the differences between genotypes.

• Text:

o Mention median duration of mitosis in cbf11∆ (Fig. 2E) in text since WT is already noted;

• Done.

o Discussion, third paragraph: "TBZ [REF] and are prone to chromosome loss [...]". I assume this referred to minichromosome loss or have changes in ploidy/chromosome segregation been quantified?

• Changes in ploidy were indeed not quantified. We have changed the wording to “__mini__chromosome loss”. But please note that the Ch16 minichromosome is derived from regular Chromosome III and is a real chromosome, albeit a small one.

o Methods, Microscopy and image analysis:

How were fixed cells imaged (glass bottom dishes, plated on lectin, mounted on slides)?

Specify the CellR as widefield and provide details of the objective used (immersion and NA)

• We have added the following information to the relevant Methods section:

“Cells were applied on glass slides coated with soybean lectin, covered with a glass cover slip, and imaged using the 60X objective of the Olympus CellR widefield microscope with oil immersion (NA 1.4)”

Elaborate on "manual evaluation of microscopic images"

• We have extended the description of cell scoring:

“The frequency of catastrophic mitosis occurrence was determined by manual evaluation of microscopic images using the counter function of ImageJ software, version 1.52p (Schneider et al., 2012). At least 400 cells from the asynchronous populations were analyzed per sample and mitotic defects were scored based on nuclear morphology and septum presence/position. ”

For live cell microscopy, what was the estimated final density of cells within the 5 µl resuspension?

• Our estimate is 4-8 x 10^6 cells in 5 ul. We have added this information into the Methods.

What is meant by measuring the maximum section of plotted profiles? Is this the maximum distance of Hht1 signals within the entire time-lapse?

• We have changed the description:

“The nuclear distance was measured by using Hht2–GFP signals and converting the green channel images to binary, measuring the maximum distance between the Hht2-GFP signals using plot profile function in imageJ.”

Was spindle length quantified the same way?

• We have added the description:

“Spindle length was quantified by drawing a line along the length of the spindle (using mCherry-Atb2 signals) at each timepoint and measuring the length of the line using imageJ.”

Methods, ChIP-qPCR:

It is not clear which strains were used, this can only be guessed by the use of a GFP antibody suggesting GFP tagged chromatin to be precipitated. For people with expertise outside of ChIP assays, this should be specified

• We have listed the used strains in the ChIP-qPCR methods section.

Reviewer #1 (Significance (Required)):

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This manuscript presents a novel role for a transcription factor, one typically implicated in lipid metabolism, in chromatin modification and cohesin dynamics, with the possibility of this representing a more conserved process across ascomycetes. The mechanism of cbf11 regulation remains to be determined.

Place the work in the context of the existing literature (provide references, where appropriate).

This work helps link two bodies of work related to cell division that are usually considered in isolation, the regulation of lipid dynamics and the control of chromatin dynamics and cohesion. Some comparisons to phenotypes in closely related species would have helped provide a broader context (such as Yam et al., 2011, where the spindle morphologies in S. japonicus and response to cerulenin treatment might be of relevance to the work presented here).

• We now briefly discuss the semi-open mitosis of Sch. japonicus and the Yam et al. 2011 paper at the beginning of the Discussion.

State what audience might be interested in and influenced by the reported findings.

Molecular and cellular biologists with interests in nuclear remodelling, lipid metabolism, kinetochore assembly.

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.

Fission yeast biology, nuclear remodelling, microscopy. We are not qualified to make in-depth comments on the soundness of ChIP-Seq and ChIP-qPCR experiments.

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

This manuscript describes detailed mechanisms by which the cbf11 deletion showed the phenotype. They found that the cbf11 deletion altered pericentromeric chromatin states such as the level of cohesin and hypermethylation.

In general, their results are interesting and provide important insights into the relationship between lipid metabolism and chromosome segregation. The presented data are valuable for the community, but the authors should carefully re-assess their data.

Major comments:

1. Statistical analyses in some of the Fig.3B, 3C, 4B and S2 seem to be somewhat weird because p-values are too small for such a small number of experiments (three independent experiments) with large standard deviations. Please show all the data points in Fig. 2C-E, and provide raw values as a supplementary table for assessment of the data.

2. We now show individual data points for all barplots and boxplots and provide all source numerical data as supplementary tables. The details of the used statistical tests are given in the respective figure legends.

3. Pages 5-6: As for Fig. 4, the data is difficult to interpret because the trends of the ChIP-seq pattern of H3K9me2 between replicates look different: replicate 2 shows an increase of H3K9me2 signal, while replicate 1 shows almost no difference or weak if any. In such a case, the authors should repeat ChIP-seq one more time and confirm hypermethylation at these regions or confirm it by ChIP-qPCR.

4. We do not agree with this statement. It is true that the exact histone modification patterns are not identical between the two replicates, but this is likely due to the differences in chromatin extract preparation in replicate 1 vs replicate 2 (see Methods). Importantly, both replicates show pronounced differences in H3K9me2 patterns between WT and cbf11KO. We have changed the visualization style to better highlight the differences between WT and mutant (Fig. 4A, Fig. S2B, S3)).

5. Also, we have added one more biological replicate for the H3K9me2 ChIP-seq (Fig. S3) and performed the H3K9me2 ChIP-seq also in the Pcut6MUT strain with ~50% decreased expression of the cut6 gene (Cut6/ACC is the rate-limiting enzyme of fatty acid synthesis; cut6 is target of Cbf11) as 3 biological replicates (Fig. 4A and Fig. S3). Importantly, all replicates of both mutant strains show hypermethylated regions in the centromeres compared to WT.

Assuming that the pericentromeric regions are hypermethylated by cbf11 deletion, it is still unclear why the transcription from only dh, but not dg, regions increased although their ChIP-seq data indicated both dh/dg regions were hypermethylated. A similar question arises to the expression of per1 and sdh1. Both K9Ac and K9me2 modifications seem to unchange at both per1 and sdh1 loci, whereas the expression levels of these loci changed in the opposite direction. These results suggest that the transcription levels of the centromeric region are independent of their histone modification states.

• We do not know why dh expression differs from dg. But note that these are multi-copy repeats and it is very difficult to study individual copies separately. Our expression data, and partly also the ChIP-seq data represent “average” values across all the dh and dg copies present in the genome.

• Importantly, Figure 4A (and Fig. S2B, S3) show a large piece of the fission yeast chromosome (~57 kbp) and this scale does not allow making informed judgements about the state of histone modifications at a particular promoter locus.

• When we zoom in, we do see increased and decreased H3K9ac around the TSS of per1 and sdh1, respectively (2 replicates shown).

• A key question of this study is to understand the relationship between lipid metabolism and chromosome structures. However, the results presented are not enough to address this question. I request to distinguish whether the defects on pericentromeric regions are mediated by lipid metabolism or direct effect by cbf11 deletion. Cbf11 is a transcription factor and can directly bind to DNA, thereby there is a possibility that Cbf11 directly modulates the pericentromeric chromatin state without regulating lipid metabolism. This question can probably be addressed. As the authors have shown in their previous study (Prevorovsky et al., 2016), overexpression of cut6, which encodes acetyl coenzyme A carboxylase and is a target of cbf11, can bypass nuclear defects. If the overexpression of cut6 restores alteration on pericentromeric regions such as cohesin enrichment and hypermethylation, it suggests the defects are a secondary effect of the decrease of phospholipid biosynthesis.

• We agree that any rescue effects can be direct or indirect. And distinguishing between these two alternatives is unfortunately not straightforward.

• Our Cbf11 ChIP-seq data do not show Cbf11 binding to centromeres (PMID 19101542), suggesting that any impact of Cbf11 on centromeric chromatin is most likely indirect and mediated by some other, downstream, players.

• Instead of assaying cut6OE, we now show data that decreased cut6/ACC (a target of Cbf11) expression also leads to changes in histone methylation, similar to cbf11KO (Fig. 4A, Fig. S3). This suggests that lipid metabolism indeed can affect chromatin state (and the chromatin defects in cbf11KO are likely also lipid-related).

• We have recently shown (Princová et al., 2023, PMID: 36626368) that decreased fatty acid synthesis leads to changes in acetylation and expression of specific stress-response genes in S. pombe, and the whole process involves the histone acetyltransferases Gcn5 and Mst1. Therefore, instead of implicating membrane phospholipids, we rather suggest that lipid metabolism can affect chromatin acetylation/methylation and structure via HATs, potentially through acetyl-CoA, the common substrate of both FA synthesis and HATs. We now mention the Princová et al., 2023 paper in the Discussion section.

Minor comments:

1. Figure 3C: The legend says, "Values represent means + SD from 3 independent experiments". It meant "means {plus minus} SD"?

2. Corrected. Thank you for spotting this.

3. The relationship between phospholipid synthesis and mitotic fidelity is now discussed in the bioRxiv paper (https://doi.org/10.1101/2022.06.01.494365). It would be nice to discuss this paper.

4. Thank you for pointing out this reference. We now briefly mention this paper as a note that dysregulation of membrane phospholipid synthesis leads to mitotic phenotypes similar to cbf11KO.

Reviewer #2 (Significance (Required)):

Faithful chromosome segregation into daughter cells is crucial for cell proliferation. The authors previously reported that the deletion of cbf11, a transcription factor that regulates lipid metabolism genes, causes "cut (cell untimely torn)" phenotype (Prevorovsky et al., 2015; Prevorovsky et al., 2016). In this report, they examined detailed mechanisms by which the cbf11 deletion showed the phenotype, and found that the cbf11 deletion altered pericentromeric chromatin states such as the level of cohesin and hypermethylation. In general, their results are interesting and provide important insights into the relationship between lipid metabolism and chromosome segregation. The presented data are valuable for the community of basic science in the fields of chromosome biology and cell biology.

We are cell biologists working on chromosomes and the cell nucleus.

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

The Vishwanatha et al. manuscript examined the nature of the mitotic defect in cbf11 deletion cells. cbf11+ encodes a CSL transcription factor that regulates lipid metabolism genes in S. pombe. Loss of cbf11+ was previously shown to have a "cut" phenotype presumably due in part to aberrant regulation of its target gene cut6+ which encodes-acetyl CoA/biotin carboxylase involved in fatty acid biosynthesis (Zach et al. 2018). The authors hypothesized that the mitotic defect exhibited as chromosome missegregation in cbf11 deletion cells may be caused by alterations in cohesin occupancy and H3K9 methylation in centromeres. Cohesin occupancy was slightly higher in centromeric dh and dg repeats in the cbf11 mutant and loss of the cohesin-loader gene wpl1+ appeared to suppress the mitotic defect. The authors also showed by ChIP-Seq that H3K9 methylation was higher in the centromeric regions, as well as increased minichromosomal loss in the cbf11 mutant.

The discovery of increased cohesin occupancy and H3K9 hypermethylation in the centromeric regions of cbf11 deletion cells is novel and interesting. However, the main deficiency of the manuscript is that this discovery is underdeveloped. For example, the evidence linking the mitotic defect phenotype to these two processes was not well supported.

• We believe that the links have already been well established in the literature. The integrity of centromeric heterochromatin (H3K9me2) is known to be required for mitotic fidelity (eg. Clr4/HMT and Clr6/HDAC mutants with H3K9me2 deficiency have high minichromosome loss and/or show lagging chromosomes during mitosis - PMID: 19556509, PMID: 8937982, PMID: 9755190). Moreover, we stress the known interconnections and provide relevant citations in the Discussion:

“It is also important to note that heterochromatin, kinetochore function, cohesin occupancy, and gene expression are all interconnected and actually interdependent (Bernard et al., 2001; Folco et al., 2019, 5; Grewal and Jia, 2007; Gullerova and Proudfoot, 2008; Nonaka et al., 2002; Volpe et al., 2002)”

• We show in the manuscript altered cohesin occupancy in cbf11KO and show that mutations in cohesin loading factors do affect mitotic fidelity of cbf11KO. While we do agree that this connection can be developed further, we believe this is beyond the scope of our current project.

Moreover, there was no investigation in whether/how Cbf11 regulates cohesin occupancy or H3K9 methylation at the centromeres.

• This is true. But again, we believe this is beyond the scope of our current project.

Finally, the title and abstract provided an impression that lipid metabolism may influence cohesin occupancy and histone H3 hypermethylation at the centromeres, but this was not directly studied in the manuscript.

• We now provide H3K9me2 ChIP-seq data on the Pcut6MUT mutant deficient in fatty acid synthesis to show that lipid metabolism indeed can affect histone methylation at the centromeres (Fig. 4A, Fig. S3).

Centromeres are regions where sister chromatid cohesion is abolished last in mitosis. The observed higher levels of cohesin occupancy in the centromeric dh and dg repeats of cbf11 deletion cells could be the cause of chromosome missegregation, presumably because there is a delay or hinderance of cohesin removal from sister chromatids in mitosis. However, cohesin occupancy was carry out in asynchronous wild type and cbf11 deletion cultures, so it is unknown whether there is a delay of cohesion abolishment in mitosis. A cdc25-22 block and release experiment could better address this hypothesis.

• We acknowledge these limitations of our findings regarding cohesin occupancy in the paper:

“ Notably, centromeres are the regions where sister chromatin cohesion is abolished last during mitosis (Peters et al., 2008). Since cbf11Δ cells show altered cell-cycle and pre-anaphase mitotic duration compared to WT (Fig. 2), the observed difference in cohesin occupancy might merely reflect these changes in the timing of cell cycle progression. Alternatively, altered cohesin dynamics could play a role in the cbf11Δ mitotic defects.”

• We agree the issue could be addressed better using synchronous cell populations. However, the cdc25 or cdc10 block-release does not work well in cbf11KO (PMID: 27687771), and we currently do not have the capacity to perform less disruptive forms of cell cycle synchronization.

The observation that the spindle assembly checkpoint did not influence the mitotic catastrophe phenotype of cbf11 deletion cells suggests that the chromosome missegregation may not be mediated by defects in cohesin dynamics. How does Cbf11 influence cohesin dynamics in mitosis?

• There are clearly multiple contributors to the mitotic defects observed in the cbf11KO strain and we state this explicitly throughout the manuscript.

• We agree that it would be interesting in future to know more details about the link between Cbf11 and cohesin, but this is beyond the scope of our current project.

Does Cbf11 regulate transcription of cohesin genes or indirectly through defects in the centromere or condensins?

• Expression levels of cohesin and condensin genes are not affected by deletion of cbf11 (PMID: 26366556). We now mention these findings in the Results section.

There was no direct evidence that H3K9 hypermethylation at the centromeres contributes to the mitotic catastrophe phenotype of cbf11 deletion cells.

• This is true. However, the importance of H3K9me2 for mitotic fidelity has already been established in the literature (as we mention above).

It is also not clear whether Cbf11 directly or indirectly influences histone methylation at the centromeres of affect centromere function.

• When the Cbf11 protein is missing, centromeric histone methylation is different from normal (WT), and centromere function is not normal either - dh repeats are less expressed, minichromosome derived from ChrIII (so has a normal centromere) is 9x more frequently lost. So Cbf11 does affect these processes. The question remains, whether Cbf11 does this directly or indirectly. We favor the indirect route, as we have recently shown that H3K9 acetylation or methylation can be affected by shifting the balance between fatty acid synthesis (which is regulated by Cbf11) and histone acetyltransferase activity. We now mention these findings in the Discussion (Princová et al., 2023).

Based on a substantial number of protein-protein interactions of Cbf11 and gene products that affect chromatin function/silencing at the centromeres from the Pancaldi et al. 2012 study (e.g. HIR complex, Hrp1-Hrp3, Cnp1, Ino80 complex), I am surprised that these candidates were not mentioned in this study or investigated.

• Unfortunately, no DNase treatment was used during the affinity purification of Cbf11 in the study you mention. Therefore, the list of potential interactors is likely contaminated by irrelevant, DNA-mediated interactions with proteins sitting at nearby loci. This is why we have not pursued these candidates.

Also, it would be more comprehensive to examine defects in transcriptional silencing in the centromeric regions using an ade6+ or ura4+/FOA marker system rather than measuring expression of per1+ and sdh1+.

• We agree. We actually tried the ura4/FOA reporter system, but had problems constructing the reporter strains in the cbf11KO background. The resulting clones showed variable levels of FOA sensitivity (see figure of clones OC5-9 below), so we could not get a conclusive answer from this experiment and resorted to measuring the expression of pericentromeric genes.

Figure 1A shows that the "cut" and nuclear displacement phenotypes are independent. However, cut mutants can also generate a nuclear displacement phenotype [Samejima et al. (1993) J. Cell Sci. 105: 135-143]. Therefore, I am not sure whether the latter phenotype can be treated as entirely independent from "cut" mutants.

• We have made clarifications to Fig. 1A accordingly.

Reviewer #3 (Significance (Required)):

The discovery of increased cohesin occupancy and H3K9 hypermethylation in the

centromeric regions of cbf11 deletion cells is novel and interesting. However, the main deficiency of the manuscript is that this discovery is underdeveloped.

The results of this manuscript would be of considerable interest in the area of cell cycle research, transcription and chromatin structure and function.

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

Summary

In this paper Vishwanatha et al. analyze the mitotic phenotypes of cells lacking a regulator of lipid metabolism Cbf11. They propose that sister chromatid cohesion abnormalities and altered chromatin marks may contribute to the increased incidence of catastrophic mitosis. Additional experiments are required to improve the study and strengthen the authors' conclusions.

Major Comments

Both histone and alpha-tubulin tagging are known to aggravate mitotic errors in S. pombe. Before using these markers for live imaging, the authors should quantitate mitotic phenotypes in untagged cbf11∆ cells, as compared to the wild type. Using DAPI and Calcofluor staining (and ideally, also visualizing microtubules using anti- alpha-tubulin antibodies) the authors should measure the percentage of cells in mitosis and the percentage of cells that are going, or just went, through catastrophic mitosis, in asynchronous early-mid-exponential cell populations.

• We agree that tagging can affect protein function in numerous ways.

• The tagged versions of tubulin (mCherry-Atb2) and H3 (Hht2-GFP) used in our paper have been obtained from Phong Tran’s lab. These tagged alleles had been published (Nature Communications, PMID: 26031557) and used successfully to monitor mitotic defects including chromosome segregation errors and the cut phenotype.

• The analyses of mitotic and septation defects of asynchronous untagged cbf11KO cells that you suggest (except for the spindle visualization) were already done by us (PMID: 19101542, PMID: 26366556) and are in agreement with our present study. In brief, we showed that cbf11KO populations contain ~10-30% of cells with mitotic defects (eg. cut), depending on the cultivation conditions. They also show septation defects and altered cell morphology and shorter cell length.

In analyzing the dynamic of nuclear division, the authors claim that the interval between spindle formation and anaphase onset is "longer" and "more variable" in cbf11∆ cells compared to WT cells. The authors should provide proper statistical analysis of both differences to show that these differences are significant.

• We now show the required data and statistical testing as Fig. 2H.

The same goes for the authors' claim that mitotic duration is "more variable" in cbf11∆ cells compared to WT cells.

• The spread of values for both WT and cbf11KO is given in Fig. 2G.

As mentioned above, alternative estimates of possible perturbations of mitotic dynamics could be obtained by measuring the percentage of cells in different mitotic phases in asynchronous untagged cell populations, in order to avoid possible artifacts given by tagging histones and alpha-tubulin.

• As you mention above, to estimate their cell cycle stage, untagged cells would need to be fixed and stained to visualize the nucleus and septum. However, using fixed cbf11KO cells is not optimal for this purpose. cbf11KO have septation and cell separation defects (PMID: 19101542, PMID: 26366556). This results in increased numbers of cells having a (persistent) septum in the asynchronous population, which obscures any estimates of cell cycle stages, and this is why we observed live cells during a timecourse.

The fact that inactivation of SAC does not change the incidence of catastrophic mitoses shows that SAC is not involved and that there are likely no problems with kinetochore-microtubule attachments. Therefore, the authors' statement "These results suggest that SAC activity only plays a minor role (if any) in the mitotic defects observed in cbf11Δ cells" should be changed.

• We have changed the sentence to:

“These results suggest that SAC activity only plays a minor role (if any) in the mitotic defects observed in cbf11Δ cells, or that the defects are not caused by problems with kinetochore-microtubule attachment.”

Also, the authors' statement in the conclusion that "This indicates that proper microtubule attachment to kinetochores might be compromised and takes longer to achieve in cbf11Δ cells, possibly triggering the SAC" should be changed accordingly or further proof should be provided.

• This is probably a misunderstanding. We do not conclude that failed microtubule attachment to kinetochores is surely the cause of mitotic defects in cbf11KO. We merely describe our reasoning about structuring the project during its execution. We have rephrased the problematic sentence to improve clarity.

• We already state in the Discussion that the mitotic defects of cbf11KO may be caused by something completely different from microtubule attachment.

As pointed out by the authors, cohesion occupancy is affected by the cell cycle phases duration. Therefore, the authors should correct their data (Fig.3C) for the different duration of mitosis or measure cohesion occupancy in mitotically synchronized populations. If this is not possible, I suggest removing this piece of data altogether.

• We agree (and acknowledge in the paper) that the measurement of cohesin occupancy can be affected by duration of mitotic phases. However we do not see a straightforward way of normalizing for mitotic duration, as cohesin occupancy changes differentially at particular chromosomal loci.

• The suggested experiment of measuring cohesin occupancy in synchronized mitotic cells would likely help. However, as mentioned in our response to Reviewer 3 above, the cdc25 or cdc10 block-release does not work well in cbf11KO (PMID: 27687771), and the heat shock or drugs (eg. spindle poisons) would introduce confounding issues themselves. Unfortunately, we currently do not have the capacity to perform less disruptive forms of cell cycle synchronization.

• Since we show that mutations in cohesin loading factors can rescue mitotic fidelity of cbf11KO cells (Fig. 3B), we consider the data shown in Fig. 3C relevant. Therefore, we opt to keep Fig. 3C in the paper, and we do point out the potential limitations of these results in the Results section.

In Fig. 3A it is not clear what the authors mean by "morphological" differences between WT and cbf11∆ cells or between cbf11∆ cells and cbf11∆wpl1∆ cells. The authors should provide clearer images and indicate for each image which cells show morphological defects as an example.

• We now use arrows to highlight cells with nuclear defects in Fig. 3A.

• We now state examples of the cbf11KO-associated morphological defects in the text, together with a reference to the paper describing these defects in detail.

In Fig. 3A many cells in single or double cbf11∆ mutants show increased size typical of diploid cells. The authors should perform flow cytometry to test for possible diploidization in their mutants, as that would clearly affect any conclusions on mitotic defects rescue or enhancement.

• We previously published that cbf11KO cells show increased tendency for spontaneous diploidization (PMID: 19101542). When constructing cbf11KO strains, we always take care (including flow cytometry tests of DNA content) to exclude purely diploid clones, but the process of spurious diploidization is continuous and there are always diploid cells present in the cbf11KO culture.

• We mention diploidization as a possible mitotic outcome in cbf11KO cells in the first section of the Results.

As correctly pointed out by the authors, it is not clear if the increase in mitotic defects in cbf11∆ cells is entirely due to the perturbed lipid metabolism or to other factors being affected by Cbf11. A possible approach to prove this point, as suggested by the authors too, would be to test if the mitotic defects identified in cbf11∆ are common to other mutants of lipid metabolism that also show an increase in catastrophic mitotic events.

• We now show ChIP-seq data showing that centromeric H3K9 shows aberrant methylation patterns also in a hypomorphic cut6/ACC mutant (Pcut6MUT) (Fig. 4A, Fig. S3).

• We previously showed that the Pcut6MUT mutation predisposes fission yeast cells to catastrophic mitosis, and the defects manifest when Cut6 function is further weakened by limiting the supply of biotin (cofactor of Cut6) (PMID: 27687771).

Also, the authors' statement in the conclusion: "we have demonstrated several novel factors, not directly related to lipid metabolism, that affect mitotic fidelity in cells with perturbed lipid homeostasis" should be modified as it was not proven that these effects are not due to altered lipid metabolism.

• We agree that “it was not proven that these effects are not due to altered lipid metabolism”. However, the emphasis here is on the word “directly”. H3K9me2 and cohesin dynamics are not directly related to the metabolism of lipids. We have changed the phrasing to improve clarity.

Minor comments

The initial distinction (Fig. 1A) between "cut" and "nuclear displacement" phenotypes is somewhat confusing, especially since the authors are not investigating the different outcomes of a catastrophic mitosis. The two outcomes should be grouped together under the definition of "catastrophic mitosis" as it is done in the rest of the paper.

• We have changed Fig. 1A accordingly.

I do not think I understand the statement that "SAC abolition might actually suppress the mitotic defects of the cbf11∆ cells". The lack of SAC might aggravate defects in kinetochore-microtubule attachment or other aspects of spindle assembly. If the authors know of specific examples where the deletion of mad2 or the genes encoding other SAC components rescued the mitotic defects, they should cite those papers. Either way, this point needs clarification.

• We already provide an example in the Discussion:

“Intriguingly, SAC inactivation has been shown to suppress the temperature sensitivity of the cut9-665 APC/C mutant, which is also prone to catastrophic mitosis (Elmore et al., 2014)”

• We have now included this reference and explanation also at the point in the text that you are referring to.

Brightfield images in Fig. 1 would be clearer without the overlap of the fluorescence channels. The authors could also change the contrast of the images to highlight the septum.

• We have changed Fig. 1B as requested.

The length of spindle (shown in Fig. S1) is a more informative measurement for mitotic dynamics and should be used instead of the "nuclear distance" presented in Fig. 2.

• This might be true for a successful mitosis. But in case of defects (such as spindle detachment from the chromosomes, regressive merger of the daughter nuclei), these parameters become partially uncoupled and both are informative. We have therefore included the data from Fig. S1 in new Fig. 2C-D.

Generally, the authors could improve the data visualization by including in all the plots the single data points distribution along with the mean/median and error bars like it was done in Fig.2 C,D,E.

• Done.

Reviewer #4 (Significance (Required)):

The paper expands the knowledge on Cbf11, a still poorly characterized regulator of lipid metabolism. The idea that in addition to nuclear membrane limitation, perturbations of lipid metabolism might cause mitotic chromosome dynamics defects (for instance, through changing the protein acetylation levels), is interesting, but the authors should strengthen their conclusions by performing controls and further experiments.

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Referee #4

Evidence, reproducibility and clarity

Summary

In this paper Vishwanatha et al. analyze the mitotic phenotypes of cells lacking a regulator of lipid metabolism Cbf11. They propose that sister chromatid cohesion abnormalities and altered chromatin marks may contribute to the increased incidence of catastrophic mitosis. Additional experiments are required to improve the study and strengthen the authors' conclusions.

Major Comments

Both histone and alpha-tubulin tagging are known to aggravate mitotic errors in S. pombe. Before using these markers for live imaging, the authors should quantitate mitotic phenotypes in untagged cbf11∆ cells, as compared to the wild type. Using DAPI and Calcofluor staining (and ideally, also visualizing microtubules using anti- alpha-tubulin antibodies) the authors should measure the percentage of cells in mitosis and the percentage of cells that are going, or just went, through catastrophic mitosis, in asynchronous early-mid-exponential cell populations.

In analyzing the dynamic of nuclear division, the authors claim that the interval between spindle formation and anaphase onset is "longer" and "more variable" in cbf11∆ cells compared to WT cells. The authors should provide proper statistical analysis of both differences to show that these differences are significant. The same goes for the authors' claim that mitotic duration is "more variable" in cbf11∆ cells compared to WT cells. As mentioned above, alternative estimates of possible perturbations of mitotic dynamics could be obtained by measuring the percentage of cells in different mitotic phases in asynchronous untagged cell populations, in order to avoid possible artifacts given by tagging histones and alpha-tubulin.

The fact that inactivation of SAC does not change the incidence of catastrophic mitoses shows that SAC is not involved and that there are likely no problems with kinetochore-microtubule attachments. Therefore, the authors' statement "These results suggest that SAC activity only plays a minor role (if any) in the mitotic defects observed in cbf11Δ cells" should be changed. Also, the authors' statement in the conclusion that "This indicates that proper microtubule attachment to kinetochores might be compromised and takes longer to achieve in cbf11Δ cells, possibly triggering the SAC" should be changed accordingly or further proof should be provided.

As pointed out by the authors, cohesion occupancy is affected by the cell cycle phases duration. Therefore, the authors should correct their data (Fig.3C) for the different duration of mitosis or measure cohesion occupancy in mitotically synchronized populations. If this is not possible, I suggest removing this piece of data altogether.

In Fig. 3A it is not clear what the authors mean by "morphological" differences between WT and cbf11∆ cells or between cbf11∆ cells and cbf11∆wpl1∆ cells. The authors should provide clearer images and indicate for each image which cells show morphological defects as an example.

In Fig. 3A many cells in single or double cbf11∆ mutants show increased size typical of diploid cells. The authors should perform flow cytometry to test for possible diploidization in their mutants, as that would clearly affect any conclusions on mitotic defects rescue or enhancement.

As correctly pointed out by the authors, it is not clear if the increase in mitotic defects in cbf11∆ cells is entirely due to the perturbed lipid metabolism or to other factors being affected by Cbf11. A possible approach to prove this point, as suggested by the authors too, would be to test if the mitotic defects identified in cbf11∆ are common to other mutants of lipid metabolism that also show an increase in catastrophic mitotic events. Also, the authors' statement in the conclusion: "we have demonstrated several novel factors, not directly related to lipid metabolism, that affect mitotic fidelity in cells with perturbed lipid homeostasis" should be modified as it was not proven that these effects are not due to altered lipid metabolism.

Minor comments

The initial distinction (Fig. 1A) between "cut" and "nuclear displacement" phenotypes is somewhat confusing, especially since the authors are not investigating the different outcomes of a catastrophic mitosis. The two outcomes should be grouped together under the definition of "catastrophic mitosis" as it is done in the rest of the paper.

I do not think I understand the statement that "SAC abolition might actually suppress the mitotic defects of the cbf11∆ cells". The lack of SAC might aggravate defects in kinetochore-microtubule attachment or other aspects of spindle assembly. If the authors know of specific examples where the deletion of mad2 or the genes encoding other SAC components rescued the mitotic defects, they should cite those papers. Either way, this point needs clarification.

Brightfield images in Fig. 1 would be clearer without the overlap of the fluorescence channels. The authors could also change the contrast of the images to highlight the septum.

The length of spindle (shown in Fig. S1) is a more informative measurement for mitotic dynamics and should be used instead of the "nuclear distance" presented in Fig. 2.

Generally, the authors could improve the data visualization by including in all the plots the single data points distribution along with the mean/median and error bars like it was done in Fig.2 C,D,E.

Significance

The paper expands the knowledge on Cbf11, a still poorly characterized regulator of lipid metabolism. The idea that in addition to nuclear membrane limitation, perturbations of lipid metabolism might cause mitotic chromosome dynamics defects (for instance, through changing the protein acetylation levels), is interesting, but the authors should strengthen their conclusions by performing controls and further experiments.

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Referee #3

Evidence, reproducibility and clarity

The Vishwanatha et al. manuscript examined the nature of the mitotic defect in cbf11 deletion cells. cbf11+ encodes a CSL transcription factor that regulates lipid metabolism genes in S. pombe. Loss of cbf11+ was previously shown to have a "cut" phenotype presumably due in part to aberrant regulation of its target gene cut6+ which encodes-acetyl CoA/biotin carboxylase involved in fatty acid biosynthesis (Zach et al. 2018). The authors hypothesized that the mitotic defect exhibited as chromosome missegregation in cbf11 deletion cells may be caused by alterations in cohesin occupancy and H3K9 methylation in centromeres. Cohesin occupancy was slightly higher in centromeric dh and dg repeats in the cbf11 mutant and loss of the cohesin-loader gene wpl1+ appeared to suppress the mitotic defect. The authors also showed by ChIP-Seq that H3K9 methylation was higher in the centromeric regions, as well as increased minichromosomal loss in the cbf11 mutant.

The discovery of increased cohesin occupancy and H3K9 hypermethylation in the centromeric regions of cbf11 deletion cells is novel and interesting. However, the main deficiency of the manuscript is that this discovery is underdeveloped. For example, the evidence linking the mitotic defect phenotype to these two processes was not well supported. Moreover, there was no investigation in whether/how Cbf11 regulates cohesin occupancy or H3K9 methylation at the centromeres. Finally, the title and abstract provided an impression that lipid metabolism may influence cohesin occupancy and histone H3 hypermethylation at the centromeres, but this was not directly studied in the manuscript.

Centromeres are regions where sister chromatid cohesion is abolished last in mitosis. The observed higher levels of cohesin occupancy in the centromeric dh and dg repeats of cbf11 deletion cells could be the cause of chromosome missegregation, presumably because there is a delay or hinderance of cohesin removal from sister chromatids in mitosis. However, cohesin occupancy was carry out in asynchronous wild type and cbf11 deletion cultures, so it is unknown whether there is a delay of cohesion abolishment in mitosis. A cdc25-22 block and release experiment could better address this hypothesis. The observation that the spindle assembly checkpoint did not influence the mitotic catastrophe phenotype of cbf11 deletion cells suggests that the chromosome missegregation may not be mediated by defects in cohesin dynamics. How does Cbf11 influence cohesin dynamics in mitosis? Does Cbf11 regulate transcription of cohesin genes or indirectly through defects in the centromere or condensins?

There was no direct evidence that H3K9 hypermethylation at the centromeres contributes to the mitotic catastrophe phenotype of cbf11 deletion cells. It is also not clear whether Cbf11 directly or indirectly influences histone methylation at the centromeres of affect centromere function. Based on a substantial number of protein-protein interactions of Cbf11 and gene products that affect chromatin function/silencing at the centromeres from the Pancaldi et al. 2012 study (e.g. HIR complex, Hrp1-Hrp3, Cnp1, Ino80 complex), I am surprised that these candidates were not mentioned in this study or investigated. Also, it would be more comprehensive to examine defects in transcriptional silencing in the centromeric regions using an ade6+ or ura4+/FOA marker system rather than measuring expression of per1+ and sdh1+.

Figure 1A shows that the "cut" and nuclear displacement phenotypes are independent. However, cut mutants can also generate a nuclear displacement phenotype [Samejima et al. (1993) J. Cell Sci. 105: 135-143]. Therefore, I am not sure whether the latter phenotype can be treated as entirely independent from "cut" mutants.

Significance

The discovery of increased cohesin occupancy and H3K9 hypermethylation in the centromeric regions of cbf11 deletion cells is novel and interesting. However, the main deficiency of the manuscript is that this discovery is underdeveloped.

The results of this manuscript would be of considerable interest in the area of cell cycle research, transcription and chromatin structure and function.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes detailed mechanisms by which the cbf11 deletion showed the phenotype. They found that the cbf11 deletion altered pericentromeric chromatin states such as the level of cohesin and hypermethylation.

In general, their results are interesting and provide important insights into the relationship between lipid metabolism and chromosome segregation. The presented data are valuable for the community, but the authors should carefully re-assess their data.

Major comments:

1. Statistical analyses in some of the Fig.3B, 3C, 4B and S2 seem to be somewhat weird because p-values are too small for such a small number of experiments (three independent experiments) with large standard deviations. Please show all the data points in Fig. 2C-E, and provide raw values as a supplementary table for assessment of the data.
2. Pages 5-6: As for Fig. 4, the data is difficult to interpret because the trends of the ChIP-seq pattern of H3K9me2 between replicates look different: replicate 2 shows an increase of H3K9me2 signal, while replicate 1 shows almost no difference or weak if any. In such a case, the authors should repeat ChIP-seq one more time and confirm hypermethylation at these regions or confirm it by ChIP-qPCR. Assuming that the pericentromeric regions are hypermethylated by cbf11 deletion, it is still unclear why the transcription from only dh, but not dg, regions increased although their ChIP-seq data indicated both dh/dg regions were hypermethylated. A similar question arises to the expression of per1 and sdh1. Both K9Ac and K9me2 modifications seem to unchange at both per1 and sdh1 loci, whereas the expression levels of these loci changed in the opposite direction. These results suggest that the transcription levels of the centromeric region are independent of their histone modification states.
3. A key question of this study is to understand the relationship between lipid metabolism and chromosome structures. However, the results presented are not enough to address this question. I request to distinguish whether the defects on pericentromeric regions are mediated by lipid metabolism or direct effect by cbf11 deletion. Cbf11 is a transcription factor and can directly bind to DNA, thereby there is a possibility that Cbf11 directly modulates the pericentromeric chromatin state without regulating lipid metabolism. This question can probably be addressed. As the authors have shown in their previous study (Prevorovsky et al., 2016), overexpression of cut6, which encodes acetyl coenzyme A carboxylase and is a target of cbf11, can bypass nuclear defects. If the overexpression of cut6 restores alteration on pericentromeric regions such as cohesin enrichment and hypermethylation, it suggests the defects are a secondary effect of the decrease of phospholipid biosynthesis.

Minor comments:

1. Figure 3C: The legend says, "Values represent means + SD from 3 independent experiments". It meant "means {plus minus} SD"?
2. The relationship between phospholipid synthesis and mitotic fidelity is now discussed in the bioRxiv paper (https://doi.org/10.1101/2022.06.01.494365). It would be nice to discuss this paper.

Significance

Faithful chromosome segregation into daughter cells is crucial for cell proliferation. The authors previously reported that the deletion of cbf11, a transcription factor that regulates lipid metabolism genes, causes "cut (cell untimely torn)" phenotype (Prevorovsky et al., 2015; Prevorovsky et al., 2016). In this report, they examined detailed mechanisms by which the cbf11 deletion showed the phenotype, and found that the cbf11 deletion altered pericentromeric chromatin states such as the level of cohesin and hypermethylation. In general, their results are interesting and provide important insights into the relationship between lipid metabolism and chromosome segregation. The presented data are valuable for the community of basic science in the fields of chromosome biology and cell biology.

We are cell biologists working on chromosomes and the cell nucleus.

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Referee #1

Evidence, reproducibility and clarity

Summary:

The manuscript by Vishwanatha et al. presents findings on the fission yeast transcription factor Cbf11, which is involved in regulating lipid synthesis. Changes in lipid metabolism often have detrimental effects on nuclear division (evidenced by the high percentage of cut phenotypes among strains with altered lipid content). Here the authors show that cbf11 deletion strains produce additional phenotypes such as changes to cohesion dynamics and altered chromatin modification within centromeric regions, in turn perhaps affecting microtubule attachment and proper chromosome distributions. This hypothesis is supported by the authors' finding of epistatic effects between cbf11 and cohesin loading and unloading.

Major comments:

While the evidence presented supports the hypothesis of altered cohesin loading as a major driver of observed mitotic defects, changes in the NE surface area are likely to also contribute to the phenotypes even in pre-anaphase stages. Did the authors test any double deletions with regulators involved in decreasing lipid content (e.g. spo7, nem1, ned1) to counteract the role of Cbf11? This could be useful in assessing the relative contribution of cohesion dynamics and histone modifications.

A possible role of physical constraints dictated by the NE was already mentioned by the authors in the context of spindle bending and decreased elongation rates and some preliminary experimental data on this would be appreciated. Generation of strains, acquisition of some timelapses, and quantification of spindle elongation rate/buckling frequency should be feasible in a reasonable time frame.

The authors report mRNA levels of the centromere flanking genes per1 and sdh1 to be increased by 1.5x and decreased by 2x in comparison to WT. Could the authors elaborate on whether this is an expected trend? Kaufmann et al., 2010 reported low transcription of per1 when the surrounding regions are predominantly acetylated. Fig. 4A suggests a slight increase of H3K9ac at per1 and a decrease of transcription would be conceivable.

Fig. 3B indicates a catastrophic mitosis percentage of roughly 9.5% in cbf11∆ while in Fig. 1C 4% of all cells, or ˜31% of all mitotic events, is noted as abnormal. Could the authors clarify this discrepancy? Since Fig. 1 utilises time course data of 333 cells (please specify the number of analysed cells also in the legend), would the authors expect this data to be more trustworthy when compared to images of fixed cells? What were the criteria to assign divisions as catastrophic in fixed cells and which features were utilised to identify the 400 cells as mitotic?

Minor comments: Previous literature is, to the best of our knowledge, sufficiently referenced. The text is largely clear (some exceptions within the methods section will be elaborated on below). The figures, however, would benefit from graph titles and some minor formatting changes.

• Figures:
  • Fig. 1: Specify the number of cells analysed in C within the legend as well. For B, please use colourblind-friendly schemes - especially since images are shown as merges only. The example of the "cut" phenotype appears small and crowded by surrounding cells. Especially the latter might affect mitotic fidelity. Under the assumption that this did not affect quantifications (WT seem fine) a less crowded cell would present a nicer example.
  • Fig. 3: Images shown in A add little benefit in their current form. What is the takeaway for the reader? Indicating that images represent DAPI staining and pointing out cells of interest with arrows/symbols would be helpful. The example shown for cbf11 appears to be dimmer in comparison and cell morphology is hard to interpret. C feels misplaced in this figure and a title could improve readability.
  • Fig. 4: Graph titles needed, figure might work better in portrait
• Text:
  • Mention median duration of mitosis in cbf11∆ (Fig. 2E) in text since WT is already noted;
  • Discussion, third paragraph: "TBZ [REF] and are prone to chromosome loss [...]". I assume this referred to minichromosome loss or have changes in ploidy/chromosome segregation been quantified?
  • Methods, Microscopy and image analysis: How were fixed cells imaged (glass bottom dishes, plated on lectin, mounted on slides)? Specify the CellR as widefield and provide details of the objective used (immersion and NA) Elaborate on "manual evaluation of microscopic images" For live cell microscopy, what was the estimated final density of cells within the 5 µl resuspension? What is meant by measuring the maximum section of plotted profiles? Is this the maximum distance of Hht1 signals within the entire time-lapse? Was spindle length quantified the same way?

Methods, ChIP-qPCR:

It is not clear which strains were used, this can only be guessed by the use of a GFP antibody suggesting GFP tagged chromatin to be precipitated. For people with expertise outside of ChIP assays, this should be specified

Significance

Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This manuscript presents a novel role for a transcription factor, one typically implicated in lipid metabolism, in chromatin modification and cohesin dynamics, with the possibility of this representing a more conserved process across ascomycetes. The mechanism of cbf11 regulation remains to be determined.

Place the work in the context of the existing literature (provide references, where appropriate).

This work helps link two bodies of work related to cell division that are usually considered in isolation, the regulation of lipid dynamics and the control of chromatin dynamics and cohesion. Some comparisons to phenotypes in closely related species would have helped provide a broader context (such as Yam et al., 2011, where the spindle morphologies in S. japonicus and response to cerulenin treatment might be of relevance to the work presented here).

State what audience might be interested in and influenced by the reported findings. Molecular and cellular biologists with interests in nuclear remodelling, lipid metabolism, kinetochore assembly.

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.

Fission yeast biology, nuclear remodelling, microscopy. We are not qualified to make in-depth comments on the soundness of ChIP-Seq and ChIP-qPCR experiments.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In this paper, the authors present convincing experimental proof on why the BH3-only protein PUMA resists displacement by BH3-mimetics, while others such as tBID do not. Using a SMAC-mCherry based MOMP assay on isolated mitochondria, FRET in the presence of liposomes with a phospholipid composition similar to that of mitochondria as well as quantitative fast fluorescence lifetime imaging microscopy (F__�__rster resonance energy transfer - qF3) they show that the C-terminal region of PUMA (CTS), together with its BH3-domain, effectively "double-bolt" locks its interaction with BCL-XL and BCL-2 to resist displacement by the BCL-XL-specific BH3-mimetic A-1155463 or the BCL-2/BCL-XL inhibitor ABT-263 and AZD-4320. Although a similar mechanism has previously been published for BIM, the novel C-terminal binding sequence in PUMA is unrelated to that in the CTS of BIM and functions independent of PUMA binding to membranes. First, in contrast to BIM, PUMA contains multiple prolines and charged residues, and an unusually short span of hydrophobic amino acids, secondly, full length PUMA was more resistant to BH3-mimetic displacement than a PUMA mutant lacking the CTS (PUMA-d26) even in solution suggesting that the CTS of PUMA contributes to BH3-mimetic resistance even in the absence of membranes.<br /> The second, quite unexpected finding of this paper is that, in contrast to previous publications, the CTS of PUMA does not target the protein to mitochondria but to the ER. The authors show this by FLIM-FRET imaging and confocal microscopy, and they created mutants to identify the CTS residues (I175 and P180) that mediate binding to ER membranes.

The authors did an excellent job to show the mechanism of displacement resistance of PUMA from BCL-2 survival factors from different angles (in vitro, on isolated mitochondria, liposomes and inside living cells), generating respective BH3 and CTS mutants and also domain swaps with other BH3-only proteins such as tBID. Also, the unexpected finding that PUMA primarily localizes to the ER has been extensively scrutinized and the data presented are convincing.

Response:

We appreciate the favourable comments and that the reviewer found the data presented convincing.

Major comments:

I have only three questions which I like the authors to address before this MS can be published.

1) How can PUMA perform its pro-apoptotic action on MOMP from its site on the ER? Does PUMA eventually localize to MAMs (mitochondrial/ER contact sites)? Is it possible to co-IP PUMA with BCL-XL or BCL-2 from ER membranes or show such an interaction inside cells with PLA?

Response:

The reviewer raises an important point. One of the main conclusions from this paper is that the primary localization of exogenously expressed PUMA is at the ER. Our intent was to highlight the inherent specificity of the PUMA CTS sequence. However, we agree that identifying the localization of PUMA-BCL-XL complexes would add significantly to the manuscript. We carefully considered using co-IP or a proximity ligation assay (PLA) in order to investigate the localization of PUMA-BCL-XL complexes. In our experience the use of co-IP is very difficult to interpret due to the well characterized detergent-induced artifacts previously shown for BCL-2 family protein interactions (PMID: 9553144, PMID: 33794146). Moreover, PLAs are a proximity assay with a detection range of ~>20nm, and are difficult to quantify beyond enumerating frequency (ie counting spots). In contrast, the detection of FRET by fluorescence lifetime imaging microscopy (FLIM) is very sensitive to distance with a maximum that is <10nm, and the results can be interpreted quantitatively as apparent dissociation constants (manuscript Figures 2-3). Therefore we elected to use FLIM-FRET to address this question. We examined PUMA-specific interactions with BCL-XL at the ER and mitochondria by differentially segmenting the FLIM-FRET image data based on the signal from a mCherry-fused landmark expressed at the ER (mCherry-Cb5) or mitochondria (mCherry-ActA). This approach has similar spatial resolution to PLA yet retains more rigorous requirement for proximity and the quantitative interpretability of FLIM-FRET.

For these experiments we used a recently described the method of mitochondrial image segmentation using hyperspectral image data collected during FLIM-FRET imaging (Osterlund et al., 2023). In this approach, a watershed segmentation algorithm was used to identify mitochondria areas from mCherry-ActA images collected simultaneously with the FLIM data. The ER was identified in separate samples using the same approach with mCherry-Cb5 image data. Simultaneous collection of the images ensures that the data are not affected by movement within the cells. Example images showing the segmentation results for each organelle have been added to the manuscript as Figure 4 - Figure Supplement 2A.

The results of this FLIM-FRET experiment described in the text lines 581-598, revealed that VPUMA interacts with CBCL-XL within both ER and mitochondria-segmented ROIs (new Figure 4 - Figure Supplement 2B). These results can be explained by the fact that VPUMA is targeted to the ER, and BCL-XL is known to localize to the ER and mitochondria when bound to BH3 proteins in cells (Kale et al., 2018, PMID: 29149100). This result is similar to what we reported for BIK, another ER-localized BH3 protein that exerts its pro-apoptotic function from ER membranes (PMID: 11884414 and PMID: 15809295). Our recent data for ER localized BIK binding to mitochondria-targeted BCL-XL (Osterlund et al., 2023), suggests that, as the referee suggested, binding to occurs via a membrane-spanning interaction at MAMs (ER-mitochondia contact sites) and/or via relocalization of BIK and/or BCL-XL in response to their co-expression (Osterlund et al., 2023). Consistent with these interpretations, when expression of endogenous PUMA was upregulated in response to stress (Figure 4- figure supplement 3A-B), the amount of PUMA increased at both ER and mitochondria (Figure 4- figure supplement 3C). We have presented this data and interpretation on lines 599-621 and discussed the localization results and the similarity to BIK in the manuscript discussion, lines 1029-1035.

2) Since PUMA seems to be "double-bolt" locked to BCL-2 or BCL-XL via its BH3-domain and CTS, how can it act as a pro-apoptotic inducer? Is its main function to act as an inhibitor of BCL-2 and BCL-XL rather than a direct BAX/BAK activator? And if it acts as a BAX/BAK activator, how can it be released from BCL-2/ BCL-XL, for example by another BH3-only protein which is induced by apoptosis stimulation? Or would in this case PUMA remain bound to BCL-2/ BCL-XL in order to activate BAX/BAK (which would be a kind of new activation mechanism)?

Response:

We appreciate the reviewers queries and have clarified the text to indicate that our interpretation is that by binding to BCL-XL, PUMA releases active BAX that is sequestered by BCL-XL (as shown in Figure 1A for purified proteins). Double bolt locking increases both affinity and avidity of PUMA for BCL-XL enabling competition to favor PUMA binding and displacement of sequestered BAX. To further address the reviewers point we added two additional experiments now shown in figure supplements to Figure 1. The data shown in new Figure 1 – figure supplement 1A (described on lines 182-191 of the revised manuscript) demontrates that PUMA kills HCT116 and BMK cells but not HEK293 cells. New Figure 1 – figure supplement 1B shows that inhibition of BCL-XL and MCL-1 using BH3 mimetics is sufficient to kill HCT116 and BMK cells while HEK293 cells are not killed by even high concentrations of these BH3 mimetics. To kill HEK293 cells requires activation of BAX (described on lines 191-201). Together this data indicates that the primary pro-apoptotic function of PUMA is inhbiting BCL-XL and MCL-1 rather than by activating BAX. This data fits very well with PUMA double-bolt locking resulting in very tight binding of PUMA to BCL-XL and likely MCL-1 as the primary mode of PUMA mediated induction of cell death, at least in the three cell lines investigated here. The importance and role of PUMA mediated BAX activation is an interesting area of active investigation that is beyond the purview of the current paper.

3) Is PUMA still bound to the ER when it is transcriptionally induced by genotoxic stress. In this case, the extra amount of PUMA produced is supposed to directly activate BAX/BAK. Does it do this on the ER or on mitochondria?

Response:

The referee raises a very interesting point.

Interestingly, Zheng et al., 2022 highlighted a P53-dependent death response to genotoxic stress, which results in the extension of peripheral, tubular ER and promotes the formation of ER-mitochondria contact sites (PMID: 30030520). Furthermore, PUMA is transcriptionally activated by P53 (PMID: 17360476). Therefore, we hypothesized the induction of PUMA would increase the fraction of PUMA at ER membranes and MAMs. As the latter resemble mitochondria in micrographs of cells we anticipated an increase in apparent mitochondrial localization. To address this question experimentally, we treated MCF-7 cells with genotoxic stress and ER stressors and tracked the expression of endogenous PUMA by immunofluorescence. The results are described in the manuscript (line 603-613, page 28) and shown in Figure 4 figure supplement 3.

The immunofluorescence data confirmed that PUMA protein levels increase after genotoxic stress, as expected (Reference 39, 40 in the manuscript) and to a lessor but still significant extent after ER stress (Figure 4 figure supplements 3A and B). In response to stress the amount of PUMA increased at both ER and mitochondria, however, in unstressed cells the endogenous Puma co-localized more to the mitochondria than to the ER (Figure 4- figure supplement 3C). This suggests that similar to BIK localization of PUMA is dynamic. In particular, the abundance and localization of PUMA binding partners such as BCL-XL also affects PUMA localization (the new data are described on pages 27-28, Lines 591-621). As described above, the extra PUMA induced by genotoxic stress can indirectly activate BAX by binding BCL-XL and displacing sequestered activated BAX. Our FLIM-FRET data suggest PUMA can bind BCL-XL at both the mitochondria and the ER. Moreover, given the expansion of ER-mitochondrial contact sites that occurs during stress we cannot rule out the possibility that ER-localized PUMA can inhibit mitochondria-localized anti-apoptotic proteins (both BCL-XL and MCL-1) at the ER (for BCL-XL)and MAMs for both proteins.

Reviewer #1 (Significance):

Very significant contribution to the field. Quite novel

Reviewer #2 (Evidence, reproducibility and clarity):

This study by Pemberton and colleagues investigates interactions of pro-apoptotic PUMA with anti-apoptotic BCL-2 proteins, employing a variety of BH3-mimetics. The authors demonstrate that the PUMA/aa BCL-2 interactions are mediated not only via BH3-domain/groove interactions, but also dependent on a C-terminal sequence of PUMA. This mirrors (with distinct differences) what the authors have previously reported for BIM. They then, reveal that unexpectedly PUMA is often localising to the ER (as opposed to mitochondria), though this localisation is not important for the resistance of PUMA/BCL-2 complexes to BH3-mimetic treatment, authors speculate that ER localised PUMA may have a day job.

In my opinion, the study is important for several reasons, not least it strongly argues that BH3-mimetics are not optimal (in themselves) to promote apoptosis dependent on PUMA, and that approaches to disrupt the "double-lock" mechanisms should be sought - this has clear clinical importance, but equally important is it adds a new layer of complexity to how BCL-2 family members "work", how the double-lock mechanism is overcome in physiological apoptosis remains an open question, for instance. The data support the authors' conclusions, I have a few points that could be addressed.

Response:

The positive comments from the reviewer are greatly appreciated.

1 - The authors data in cells is consistent with a membrane recruitment effect of the PUMA CTS making a contribution to the resistance of PUMA/aa BCL-2 complexes to BH3-mimetics. What I found really intriguing, is that the CTS also influences affinity in the absence of membranes (Figure 1) - could the authors speculate why they think CTS may be affecting PUMA/aaBCL-2 binding in the absence of membranes ?

Response:

We agree with the reviewer that membrane binding contributes to BH3 mimetic resistant binding of PUMA to BCL-XL consistent with elegant data presented previously (Pécot et al., 2016; PMID: 28009301). However, we show in Figure 5D that mutants of VPUMA-d26 with restored membrane binding (VPUMA-d26-ER1 and VPUMA-d26-ER2) remain sensitive to BH3-mimetic displacement, indicating that membrane binding alone is not sufficient to confer resistance to BH3-mimetics. Furthermore, as the reviewer pointed out BH3 mimetic resistant binding is observed in the absence of membranes (Figure 1).

The data using purified proteins strongly suggests that the CTS of PUMA binds to BCL-XL and is directly involved in the protein-protein interaction. The fact that PUMA with the C-terminal fusion to the fluorescent protein Venus (PUMAV) still localizes to membranes in live cells (Figure 4 D,E) suggests that the C-terminus of PUMA does not span the membrane bilayer. Instead, we hypothesize that the C-terminus of PUMA binds peripherally to the membrane making it available to physically contribute to a protein interaction with anti-apoptotic proteins. This interpretation is consistent with the low hydrophobicity and high proline content (6 of 28 residues) of the amino acid sequence of the PUMA CTS as shown in Figure 6 and compared to the transmembrane tail anchor sequences of other proteins, including the BH3-protein BIK, in Figure 5 supplement 1. Binding of Bcl-XL by both the BH3 region and CTS of PUMA would increase both the affinity and avidity of the interaction. The presentation of this data has been revised to add clarity on pages Page 8, lines 215-223 and in the discussion (Lines 988-997 and 1044-1050).

2 - A minor point for clarification, are the mitochondria used in Fig 1A from BAX/BAK DKO cells ? - I had presumed so given exogenous BAX was added, but didn't note this in the text.

We indeed use mitochondria from BAX/BAK DKO cells and exogenous recombinant BAX in Figure 1A. This has now been added to the text on lines 166-180.

Reviewer #2 (Significance):

detailed in report above

Reviewer #3 (Evidence, reproducibility and clarity):

In this paper, Pemberton et al show that PUMA resists BH3-mimetic mediated displacement from BCL-XL via a novel binding site within its C-terminus of PUMA termed CTS (the last 26aa). Interestingly, the CTS of PUMA directs the protein to the ER membrane and residues I175 and P180 within the CTS are required for both ER localization and BH3-mimetic resistance.

Specific comments:<br /> 1 - BH3-mimetics kill cells by displacing sequestered pro-apoptotic proteins to initiate apoptosis. However, PUMA resists BH3-mimetic mediated displacement, and PUMA-d26 and PUMA I175A/P180A (CTS) do not. Thus, are these mutants sensitive to BH3-mimetics cell killing? In other words, do BH3-mimetics kill PUMA-/- cells that express either PUMA-d26 or PUMA I175A/P180A but not PUMA-/- cells that express wild type PUMA?

Response:

The reviewer raises a very interesting question that unfortunately we have been unable to address unambiguously. To answer this question requires separating the effects of PUMA on anti-apoptosis proteins and on activation of BAX and BAK as exogenous expression of express either PUMA-d26 or PUMA I175A/P180A is sufficient to kill PUMA-/- cells without the addition of a BH3 mimetic. To date we have been unable to identify mutants that inhibit anti-apoptotic proteins but that do not activate BAX and BAK as both PUMA-d26 and PUMA I175A/P180A have impaired BAX-activation function. This is additionally complicated by PUMA mediated inhibition of MCL-1, BCL-2 and BCL-W. Further, it isn’t possible to separate the function(s) using BAX/BAK knock-out cells because then PUMA induced cell death is completely abrogated. Understanding the direct activation of BAX by PUMA is an area of current investigation that is out of the scope of this paper as here we are focused on the interaction(s) of PUMA with anti-apoptotic proteins.

2 - The authors elegantly demonstrate using microscopic analysis that over expressed PUMA mostly localizes to the ER membrane. Since this is a major conclusion in the paper which is different than previously reported, the authors should confirm these findings using sub-cellular fractions followed by Western blot analysis. They should demonstrate that endogenous and over-expressed PUMA are mainly localized to the ER membrane and that the PUMA-d26 and PUMA I175A/P180A are mainly localized to the cytoplasm.

Response:

We appreciate that the reviewer found the microscopic analysis convincing. We also tested the idea of sub-cellular fractionation proposed by the reviewer.However, we have found it to be very difficult to separate mitochondria and MAMs. To address the question raised we instead performed new co-localization experiments,in addition to those reported for PUMA-d26 and the point mutants in Figure 6 (images in Figure 6 - figure supplement 3). The new experiments areforendogenous PUMA at steady state and with increased expressed in response to stress. These immunofluorescence experiments are reported in Figure 4 -figure supplements 3. We also added FLIM-FRET experiments in which ROIs were derived from areas of the cell enriched in either ER or mitochondria(Figure 4 - figure supplement 2). The results of these experiments indicate that PUMA localization is dynamic and are described in detail above in response to reviewer 1 question 3 and in the manuscript from line 579 to 621 and discussed on lines 1029-1036.

Reviewer #3 (Significance):

The advance in this paper is significant and the paper should be published once the specific comments are adequately addressed

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Referee #3

Evidence, reproducibility and clarity

In this paper, Pemberton et al show that PUMA resists BH3-mimetic mediated displacement from BCL-XL via a novel binding site within its C-terminus of PUMA termed CTS (the last 26aa). Interestingly, the CTS of PUMA directs the protein to the ER membrane and residues I175 and P180 within the CTS are required for both ER localization and BH3-mimetic resistance.

Specific comments:

1. BH3-mimetics kill cells by displacing sequestered pro-apoptotic proteins to initiate apoptosis. However, PUMA resists BH3-mimetic mediated displacement, and PUMA-d26 and PUMA I175A/P180A (CTS) do not. Thus, are these mutants sensitive to BH3-mimetics cell killing? In other words, do BH3-mimetics kill PUMA-/- cells that express either PUMA-d26 or PUMA I175A/P180A but not PUMA-/- cells that express wild type PUMA?
2. The authors elegantly demonstrate using microscopic analysis that over expressed PUMA mostly localizes to the ER membrane. Since this is a major conclusion in the paper which is different than previously reported, the authors should confirm these findings using sub-cellular fractions followed by Western blot analysis. They should demonstrate that endogenous and over-expressed PUMA are mainly localized to the ER membrane and that the PUMA-d26 and PUMA I175A/P180A are mainly localized to the cytoplasm.

Significance

The advance in this paper is significant and the paper should be published once the specific comments are adequately addressed

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Referee #2

Evidence, reproducibility and clarity

This study by Pemberton and colleagues investigates interactions of pro-apoptotic PUMA with anti-apoptotic BCL-2 proteins, employing a variety of BH3-mimetics. The authors demonstrate that the PUMA/aa BCL-2 interactions are mediated not only via BH3-domain/groove interactions, but also dependent on a C-terminal sequence of PUMA. This mirrors (with distinct differences) what the authors have previously reported for BIM. They then, reveal that unexpectedly PUMA is often localising to the ER (as opposed to mitochondria), though this localisation is not important for the resistance of PUMA/BCL-2 complexes to BH3-mimetic treatment, authors speculate that ER localised PUMA may have a day job.

In my opinion, the study is important for several reasons, not least it strongly argues that BH3-mimetics are not optimal (in themselves) to promote apoptosis dependent on PUMA, and that approaches to disrupt the "double-lock" mechanisms should be sought - this has clear clinical importance, but equally important is it adds a new layer of complexity to how BCL-2 family members "work", how the double-lock mechanism is overcome in physiological apoptosis remains an open question, for instance. The data support the authors' conclusions, I have a few points that could be addressed.

• The authors data in cells is consistent with a membrane recruitment effect of the PUMA CTS making a contribution to the resistance of PUMA/aa BCL-2 complexes to BH3-mimetics. What I found really intriguing, is that the CTS also influences affinity in the absence of membranes (Figure 1) - could the authors speculate why they think CTS may be affecting PUMA/aaBCL-2 binding in the absence of membranes ?
• A minor point for clarification, are the mitochondria used in Fig 1A from BAX/BAK DKO cells ? - I had presumed so given exogenous BAX was added, but didn't note this in the text.

Significance

detailed in report above

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Referee #1

Evidence, reproducibility and clarity

In this paper, the authors present convincing experimental proof on why the BH3-only protein PUMA resists displacement by BH3-mimetics, while others such as tBID do not. Using a SMAC-mCherry based MOMP assay on isolated mitochondria, FRET in the presence of liposomes with a phospholipid composition similar to that of mitochondria as well as quantitative fast fluorescence lifetime imaging microscopy (Förster resonance energy transfer - qF3) they show that the C-terminal region of PUMA (CTS), together with its BH3-domain, effectively "double-bolt" locks its interaction with BCL-XL and BCL-2 to resist displacement by the BCL-XL-specific BH3-mimetic A-1155463 or the BCL-2/BCL-XL inhibitor ABT-263 and AZD-4320. Although a similar mechanism has previously been published for BIM, the novel C-terminal binding sequence in PUMA is unrelated to that in the CTS of BIM and functions independent of PUMA binding to membranes. First, in contrast to BIM, PUMA contains multiple prolines and charged residues, and an unusually short span of hydrophobic amino acids, secondly, full length PUMA was more resistant to BH3-mimetic displacement than a PUMA mutant lacking the CTS (PUMA-d26) even in solution suggesting that the CTS of PUMA contributes to BH3-mimetic resistance even in the absence of membranes.

The second, quite unexpected finding of this paper is that, in contrast to previous publications, the CTS of PUMA does not target the protein to mitochondria but to the ER. The authors show this by FLIM-FRET imaging and confocal microscopy, and they created mutants to identify the CTS residues (I175 and P180) that mediate binding to ER membranes.

The authors did an excellent job to show the mechanism of displacement resistance of PUMA from BCL-2 survival factors from different angles (in vitro, on isolated mitochondria, liposomes and inside living cells), generating respective BH3 and CTS mutants and also domain swaps with other BH3-only proteins such as tBID. Also, the unexpected finding that PUMA primarily localizes to the ER has been extensively scrutinized and the data presented are convincing.

Major comments:

I have only three questions which I like the authors to address before this MS can be published.

1. How can PUMA perform its pro-apoptotic action on MOMP from its site on the ER? Does PUMA eventually localize to MAMs (mitochondrial/ER contact sites)? Is it possible to co-IP PUMA with BCL-XL or BCL-2 from ER membranes or show such an interaction inside cells with PLA?
2. Since PUMA seems to be "double-bolt" locked to BCL-2 or BCL-XL via its BH3-domain and CTS, how can it act as a pro-apoptotic inducer? Is its main function to act as an inhibitor of BCL-2 and BCL-XL rather than a direct BAX/BAK activator. And if it acts as a BAX/BAK activator, how can it be released from BCL-2/ BCL-XL, for example by another BH3-only protein which is induced by apoptosis stimulation? Or would in this case PUMA remain bound to BCL-2/ BCL-XL in order to activate BAX/BAK (which would be a kind of new activation mechanism)?
3. Is PUMA still bound to the ER when it is transcriptionally induced by genotoxic stress. In this case, the extra amount of PUMA produced is supposed to directly activate BAX/BAK. Does it do this on the ER or on mitochondria?

Significance

Very significant contribution to the field. Quite novel

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Reply to the reviewers

We are very pleased that our manuscript was well-received by each of the three reviewers. Reviewer #1 found our study to be “interesting”, “clear” and “convincing”. Reviewer #2 found our study to be “unprecedented” and of “considerable interest to cell biologists in the vision community and likely to the broader cell biology community”. Reviewer #3 believed that we reported “novel and significant findings into an important cell biological problem” and that our study “should be of broad interest to cell biologists and vision scientists”. This reviewer also stated that “it is strongly recommended for publication”.

Reviewer #1:

The paper of Lewis et al. presents an interesting study describing new information about the morphology of nascent discs and the role of peripherin in determining disc and incisure structure. I have only a few comments mostly about presentation.

We are happy that the reviewer liked our study.

1. Because this study employs both frog and mouse, the authors should be careful to give the species when describing their results. The naming of the species would be particularly important in the first paragraph of the Results section and the legend to the first data figure, Fig. 2.

We have clarified the text and figure legends to allow the reader to better follow which species each result came from.

1. It is unclear what Movie 1 adds to Fig. 3. This movie could perhaps be omitted.

We prefer to include the source data that the image in Figure 3 is derived from, particularly to stress that the pattern shown in a single z-section in this figure can be seen throughout the entire tomogram. We hope that the reviewer would agree.

1. Movies 3 and 5 either don't work or consist of single frames, which would be better illustrated as figures in the text rather than as supplementary movies.

We appreciate the reviewer catching that there were some technical issues with video playback. We have recompressed these videos and ensured that they will now play appropriately across a wide variety of computer specifications and video player applications.

1. The incisures in Fig. 4 will be difficult for many readers to visualize. My experience was that once I saw one of them, I began to see the others. The incisures in Fig. 5 are, on the other hand, very easy to see. If Fig. 5 had come before Fig. 4, I would have had no problem. The authors may wish to exchange these two figures or to supply a cartoon for one of the rods in Fig. 4, so that the reader can more easily understand what he or she should be trying to see.

We thank the reviewer for pointing out that some readers may have difficulties in fully appreciating the structure of incisures in this figure. We made two changes to improve the presentation of these images. First, we pseudo-colored several examples of enclosed discs in Figure 3, which highlights the structure of incisures. We also indicated one example of an incisure in these images with an arrowhead. Second, we pseudo-colored the example shown in Figure 4A to illustrate the same point, while still allowing the reader to view the three remaining examples in Figure 4 without any overlaid modifications.

1. It is unclear to me why the authors are so fond of their untested theory that incisures "likely serve to protect the flat lamellar disc membranes from undesirable deformations" but seem skeptical of the notion that incisures are present and especially numerous in rods of large diameter to aid longitudinal diffusion. The later notion is supported not only by theoretical calculations but also by common sense.

We appreciate this comment and, in fact, feel agnostic about both of these not mutually exclusive ideas. We removed the statement that the deposition of peripherin-2 in incisures likely serves to protect the flat lamellar disc membranes from undesirable deformations from the Introduction and rephrased the text in Discussion to stress that both functions are plausible and not mutually exclusive.

This manuscript presents a clear and convincing description of disc formation and the role of the protein peripherin in the formation of disc incisures.

Thank you for your kind comment.

Reviewer #2:

Summary: The manuscript by Lewis et al. focuses on the potential mechanisms underlying formation of incisures in rod photoreceptors. Incisures refer to the indentations that occur on the rim of the photoreceptor disc membranes. The presence of incisures has been noted for decades and have been identified across a number of species. The role of incisures is not entirely clear and the mechanisms governing their formation have largely been inferred from early transmission electron microscopy studies 40-60 years ago. More recent ultrastructural studies of rod outer segment discs from mice carrying mutant alleles of rhodopsin or periperhin-2 described changes in the length or presence of incisures, suggesting that these proteins likely play a fundamental role in incisure formation in mouse. The authors take advantage of advances in electron tomography to provide unprecedented analyses of incisure formation, size, and structural complexity in stacked discs within mouse photoreceptors. They also use genetic models to explore how rhodopsin and peripherin-2 contribute to incisure formation and length. The authors find that new discs are highly irregular in shape and do not contain incisures during disc formation. Incisures are only formed are discs are enclosed. They find that the incisures in adjacent discs always align adjacent to the ciliary axoneme. Intriguingly, they find evidence of physical connections on opposing sides of the incisure. Critically, they find that elevated levels of peripherin-2 increase incisure size and complexity while low levels of peripherin-2 prevent incisure formation. In contrast, reduced molar ratios of rhodopsin lead to smaller disc surface area but increased incisure complexity. These results lead the authors to conclude that incisure formation is mechanistically linked to the relative molar ratio of peripherin-2 to rhodopsin and that rods make a slight excess of peripherin-2 in order to drive proper disc closure. The excess peripherin-2 within the disc rim forces formation of an incisure.

We are happy that the reviewer liked our study.

Major comments<br /> 1. Line 145-146: the location of the incisure adjacent to the ciliary axoneme is an interesting observation indeed. As frogs have a number of incisures, is this a similar observation in species with multiple incisures or more exclusive to those species with a single incisure?

Indeed, we did observe that one of the many incisures in a frog disc is aligned with the ciliary axoneme. We have now included Supplementary Figure S2 to highlight this observation using an example of two adjacent cells.

1. While the presence of non-axonemal microtubules aligned with incisures in frog rods may provide an explanation for the number of incisures, the correlation with peripherin and rhodopsin content was lacking. In other words, do frog rods have considerably more peripherin-2 per disc than mouse rods?

This is a great question and one that we are interested in pursuing in the future. However, adapting the mass spectrometry-based protein quantification approach that we used to determine the absolute numbers of peripherin-2, ROM1 and rhodopsin molecules per disc was a significant undertaking that took several years (Skiba et al., PMID: 36711880). This approach is currently applicable to only a particular set of outer segment proteins in the mouse and cannot be automatically re-purposed to quantification of proteins in other species that have different amino acid sequences. Thus, designing, validating and employing this quantification protocol to all peripherin-2 and ROM1 isoforms along with rhodopsin in the frog would be a major undertaking that cannot be completed in the context of this revision. Nonetheless, we are very appreciative for the reviewer’s enthusiasm for this topic and plan to address this question in the future.

Minor comments<br /> 1. The location of the incisures are difficult to see in Figure 4. The arrowhead is pointing to a very low contrast area of the disc and the thin incisure can be seen, but it's difficult. If it is possible to pseudocolor the image in some way to highlight the disc vs the extramembrane space, it would be helpful.

We thank the reviewer for noticing this issue, which was also commented by another reviewer. As described above, we pseudo-colored several examples in Figures 3 and 4.

1. Line 138-142: As with any descriptive narrative of cell structures, it is important to ensure the reader can fully understand and appreciate the interpretation of the authors. The shape of the newly forming discs can be difficult to appreciate in Figure 4. The authors are strongly encouraged to perhaps take 1-2 examples and provide a drawing or schematic of the image that can be more clearly annotated to assist readers in finding the outline of the discs and incisures.

We appreciate this point and pseudo-colored the surfaces of new forming discs in the examples shown in Figure 4A. We feel that pseudo-coloring helps the reader better visualize not only the structure of incisures, but also the irregular shape of the newly forming discs as in this specific example.

Overall, the paper is well-written and organized logically. The figures are generally easy to interpret although some additional annotations would help readers identify incisures in some low-contrast images (see comments). The authors utilized state-of-the-art electron tomographic data and mouse genetics to address a fundamental question. This will be of considerable interest to cell biologists in the vision community and likely to the broader cell biology community on how peripheral/rim proteins can shape membrane.

It is needless to say that we are very pleased by these comments and that the reviewer found our study to be of considerable interest to the broader cell biology community.

The authors provide a well-reasoned model for how incisures form in mouse rod photoreceptors: a relative excess of peripherin-2 drives incisure formation. This agrees with their mass-spectrometry data and molar ratios of peripherin-2 and rhodopsin._ _The main concern and outstanding question is whether these results are specific to mouse photoreceptors? The experiments in Xenopus were limited and only found that a CRISPR knockout of one peripherin-2 ortholog prevented incisure formation. While this result agreed with the general model and how molar ratios of peripherin-2 contribute, the knockout phenotypes are different than that of mouse. Some hypotheses are mentioned to explain this, but none were tested. The authors provide a model that agrees with mouse data, but is this generalizable? The model should permit several predictions for incisure formation beyond that of mouse rods. It would be most helpful to look in a species with multiple incisures and calculate the molar ratios of rhodopsin and peripherin-2. Do Xenopus require significantly more peripherin-2 to form multiple incisures? Alternatively, is it possible for the authors to mine publically available proteomics studies to assess rhodopsin and peripherin-2 content from other species (e.g. human, non-human primates, rats, etc...) and correlate to incisure number and/or length? The study overall is interesting and thought-provoking, but the overall impact would be greatly enhanced if additional evidence was provided that their model is broadly generalizable given the variety in incisure number and photoreceptor disc morphology (e.g. surface area, diameter) across species.

Related to a comment above, we are interested in pursuing each of these directions in frogs and other species in future studies, although it is not feasible to accomplish the required body of work in the context of manuscript revision. There are three other photoreceptor tetraspanins homologous to peripherin-2 that remain to be quantified and knocked out in frogs, alone and in combinations (the xrds36 and xrds35 isoforms and the peripherin-2-like protein). Testing the function of each of these in incisure formation would be an endeavor spanning several years of work. Additionally, there are no publicly available proteomic datasets that would contain information allowing an accurate quantification of rhodopsin and these homologous tetraspanins in other species. This question would require us to adapt our protein quantification approach, which would indeed be valuable, but would take significant time to complete.

Reviewer #3:

Summary. This work explores the formation of incisures in rod outer-segment (OS) disks. The visual pigment rhodopsin is the major lamellar protein in rod OS disks, while peripherin is the major structural protein of the disk rim. The authors used wild-type, Rho+/- and Rds+/- mice to vary the ratio of rhodopsin to peripherin in vivo, and compared these ratios to incisure length and complexity in rod OS.

Comments. This study presents several new findings. The authors convincingly show by EM tomography that incisures only form after each OS disk has reached maturity (fully separated from the plasma membrane). This new finding corrects an earlier published observation. Next, they examined disk morphology in Rds+/- heterozygous null-mutant mice and showed that an ~50% reduction in peripherin levels resulted in rod OS disks with no incisures. They performed a similar study on Rho+/- heterozygotes mutants. This time they observed that an ~50% reduction in rhodopsin levels resulted in OS disks with excessively long incisures. MS analysis of rod OS proteins and quantitative analysis of the EM images showed that incisure length varies with the ratio of peripherin to rhodopsin. They further showed that wild-type rods contain a small excess of peripherin over the amount required to form mature disks with normal incisures. Finally, the authors examined the effects of peripherin levels in rods from Xenopus tropicalis, an animal containing large OS disks with multiple incisures and three homologs of peripherin. They used gene editing to generate Xenopus tropicalis with a null mutation in the xrds35 gene, which is most like mammalian peripherin. OS disks from xrds35-/- frogs contained no incisures by EM tomography, further supporting their hypothesis.

Thank you for this nice summary of our study.

Another protein in the rims of rod OS disks is ABCA4, an ATP-driven flippase that translocates PE conjugated to retinaldehyde from the lumenal to cytoplasmic leaflets of the disk membrane. Retinaldehyde is a toxic photoproduct of rhodopsin bleaching. It has been suggested that the large number of incisures in frog disks is due to the larger diameter of frog versus mouse rod OS, and hence the greater number of rhodopsins per disk. This relationship is thought to ensure sufficient ABCA4 flippase activity to process the larger flux of retinaldehyde released by rhodopsin in these wide disks during light exposure, and possibly to minimize the diffusion distance of retinaldehyde from the disk lamella to the rim. The authors' findings seem in conflict with this explanation. They may wish to comment on this facet of their results.

We agree that it is possible for incisures to promote the encounter rate between retinaldehyde and ABCA4 in the disc. We do not find this idea to conflict with any of our interpretations; rather, this may be a complementary function of incisures. However, we failed to find any place in the literature where this hypothesis has been explicitly proposed and feel uneasy to present it as a new idea of our own in discussion. Fortunately, these reviews are public and truly interested readers could appreciate this idea. It is also worth noting that the correlation between incisure number and disc diameter is not perfect. For example, owl monkey discs appear to have a large number of incisures despite having a similar diameter to the mouse (Kroll and Machemer, PMID: 4970987).

Significance. The manuscript presents novel and significant findings into an important cell biological problem, development of the rod OS. It is clearly written and the data are of high quality. The manuscript should be of broad interest to cell biologists and vision scientists. It is strongly recommended for publication.

We are glad that the reviewer found our study to be of broad interest.

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Referee #3

Evidence, reproducibility and clarity

Summary. This work explores the formation of incisures in rod outer-segment (OS) disks. The visual pigment rhodopsin is the major lamellar protein in rod OS disks, while peripherin is the major structural protein of the disk rim. The authors used wild-type, Rho+/- and Rds+/- mice to vary the ratio of rhodopsin to peripherin in vivo, and compared these ratios to incisure length and complexity in rod OS.

Comments. This study presents several new findings. The authors convincingly show by EM tomography that incisures only form after each OS disk has reached maturity (fully separated from the plasma membrane). This new finding corrects an earlier published observation. Next, they examined disk morphology in Rds+/- heterozygous null-mutant mice and showed that an ~50% reduction in peripherin levels resulted in rod OS disks with no incisures. They performed a similar study on Rho+/- heterozygotes mutants. This time they observed that an ~50% reduction in rhodopsin levels resulted in OS disks with excessively long incisures. MS analysis of rod OS proteins and quantitative analysis of the EM images showed that incisure length varies with the ratio of peripherin to rhodopsin. They further showed that wild-type rods contain a small excess of peripherin over the amount required to form mature disks with normal incisures. Finally, the authors examined the effects of peripherin levels in rods from Xenopus tropicalis, an animal containing large OS disks with multiple incisures and three homologs of peripherin. They used gene editing to generate Xenopus tropicalis with a null mutation in the xrds35 gene, which is most like mammalian peripherin. OS disks from xrds35-/- frogs contained no incisures by EM tomography, further supporting their hypothesis.

Another protein in the rims of rod OS disks is ABCA4, an ATP-driven flippase that translocates PE conjugated to retinaldehyde from the lumenal to cytoplasmic leaflets of the disk membrane. Retinaldehyde is a toxic photoproduct of rhodopsin bleaching. It has been suggested that the large number of incisures in frog disks is due to the larger diameter of frog versus mouse rod OS, and hence the greater number of rhodopsins per disk. This relationship is thought to ensure sufficient ABCA4 flippase activity to process the larger flux of retinaldehyde released by rhodopsin in these wide disks during light exposure, and possibly to minimize the diffusion distance of retinaldehyde from the disk lamella to the rim. The authors' findings seem in conflict with this explanation. They may wish to comment on this facet of their results.

Significance

The manuscript presents novel and significant findings into an important cell biological problem, development of the rod OS. It is clearly written and the data are of high quality. The manuscript should be of broad interest to cell biologists and vision scientists. It is strongly recommended for publication.

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Referee #2

Evidence, reproducibility and clarity

Summary: The manuscript by Lewis et al. focuses on the potential mechanisms underlying formation of incisures in rod photoreceptors. Incisures refer to the indentations that occur on the rim of the photoreceptor disc membranes. The presence of incisures has been noted for decades and have been identified across a number of species. The role of incisures is not entirely clear and the mechanisms governing their formation have largely been inferred from early transmission electron microscopy studies 40-60 years ago. More recent ultrastructural studies of rod outer segment discs from mice carrying mutant alleles of rhodopsin or periperhin-2 described changes in the length or presence of incisures, suggesting that these proteins likely play a fundamental role in incisure formation in mouse. The authors take advantage of advances in electron tomography to provide unprecedented analyses of incisure formation, size, and structural complexity in stacked discs within mouse photoreceptors. They also use genetic models to explore how rhodopsin and peripherin-2 contribute to incisure formation and length. The authors find that new discs are highly irregular in shape and do not contain incisures during disc formation. Incisures are only formed are discs are enclosed. They find that the incisures in adjacent discs always align adjacent to the ciliary axoneme. Intriguingly, they find evidence of physical connections on opposing sides of the incisure. Critically, they find that elevated levels of peripherin-2 increase incisure size and complexity while low levels of peripherin-2 prevent incisure formation. In contrast, reduced molar ratios of rhodopsin lead to smaller disc surface area but increased incisure complexity. These results lead the authors to conclude that incisure formation is mechanistically linked to the relative molar ratio of peripherin-2 to rhodopsin and that rods make a slight excess of peripherin-2 in order to drive proper disc closure. The excess peripherin-2 within the disc rim forces formation of an incisure.

Major comments

1. Line 145-146: the location of the incisure adjacent to the ciliary axoneme is an interesting observation indeed. As frogs have a number of incisures, is this a similar observation in species with multiple incisures or more exclusive to those species with a single incisure?
2. While the presence of non-axonemal microtubules aligned with incisures in frog rods may provide an explanation for the number of incisures, the correlation with peripherin and rhodopsin content was lacking. In other words, do frog rods have considerably more peripherin-2 per disc than mouse rods?

Minor comments

1. The location of the incisures are difficult to see in Figure 4. The arrowhead is pointing to a very low contrast area of the disc and the thin incisure can be seen, but it's difficult. If it is possible to pseudocolor the image in some way to highlight the disc vs the extramembrane space, it would be helpful.
2. Line 138-142: As with any descriptive narrative of cell structures, it is important to ensure the reader can fully understand and appreciate the interpretation of the authors. The shape of the newly forming discs can be difficult to appreciate in Figure 4. The authors are strongly encouraged to perhaps take 1-2 examples and provide a drawing or schematic of the image that can be more clearly annotated to assist readers in finding the outline of the discs and incisures.

Significance

Overall, the paper is well-written and organized logically. The figures are generally easy to interpret although some additional annotations would help readers identify incisures in some low-contrast images (see comments). The authors utilized state-of-the-art electron tomographic data and mouse genetics to address a fundamental question. This will be of considerable interest to cell biologists in the vision community and likely to the broader cell biology community on how peripheral/rim proteins can shape membrane.

The authors provide a well-reasoned model for how incisures form in mouse rod photoreceptors: a relative excess of peripherin-2 drives incisure formation. This agrees with their mass-spectrometry data and molar ratios of peripherin-2 and rhodopsin. The main concern and outstanding question is whether these results are specific to mouse photoreceptors? The experiments in Xenopus were limited and only found that a CRISPR knockout of one peripherin-2 ortholog prevented incisure formation. While this result agreed with the general model and how molar ratios of peripherin-2 contribute, the knockout phenotypes are different than that of mouse. Some hypotheses are mentioned to explain this, but none were tested. The authors provide a model that agrees with mouse data, but is this generalizable? The model should permit several predictions for incisure formation beyond that of mouse rods. It would be most helpful to look in a species with multiple incisures and calculate the molar ratios of rhodopsin and peripherin-2. Do Xenopus require significantly more peripherin-2 to form multiple incisures? Alternatively, is it possible for the authors to mine publically available proteomics studies to assess rhodopsin and peripherin-2 content from other species (e.g. human, non-human primates, rats, etc...) and correlate to incisure number and/or length? The study overall is interesting and thought-provoking, but the overall impact would be greatly enhanced if additional evidence was provided that their model is broadly generalizable given the variety in incisure number and photoreceptor disc morphology (e.g. surface area, diameter) across species.

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Referee #1

Evidence, reproducibility and clarity

Review of Lewis et al. 2023

The paper of Lewis et al. presents an interesting study describing new information about the morphology of nascent discs and the role of peripherin in determining disc and incisure structure. I have only a few comments mostly about presentation.

1. Because this study employs both frog and mouse, the authors should be careful to give the species when describing their results. The naming of the species would be particularly important in the first paragraph of the Results section and the legend to the first data figure, Fig. 2.
2. It is unclear what Movie 1 adds to Fig. 3. This movie could perhaps be omitted.
3. Movies 3 and 5 either don't work or consist of single frames, which would be better illustrated as figures in the text rather than as supplementary movies.
4. The incisures in Fig. 4 will be difficult for many readers to visualize. My experience was that once I saw one of them, I began to see the others. The incisures in Fig. 5 are, on the other hand, very easy to see. If Fig. 5 had come before Fig. 4, I would have had no problem. The authors may wish to exchange these two figures or to supply a cartoon for one of the rods in Fig. 4, so that the reader can more easily understand what he or she should bel trying to see.
5. It is unclear to me why the authors are so fond of their untested theory that incisures "likely serve to protect the flat lamellar disc membranes from undesirable deformations" but seem skeptical of the notion that incisures are present and especially numerous in rods of large diameter to aid longitudinal diffusion. The later notion is supported not only by theoretical calculations but also by common sense.

Significance

This manuscript presents a clear and convincing description of disc formation and the role of the protein peripherin in the formation of disc incisures.

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Reply to the reviewers

RC-2022-01805

We thank all reviewers for their careful analysis of our manuscript, constructive suggestions and support of our work.

Reviewer 1

The authors show that proximity of early mouse embryo blastomere chromosomes to the cell cortex activates the Polar Body Extrusion pathway to generate cell fragments. The authors use live cell imaging in control and Myo1C and dynein knockdown embryos to document accumulation of actin and myosin near chromosomes that come in close proximity to the cell cortex, which correlates with the increased fragmentation of the mutant blastomeres. The live imaging data are nicely presented and the results are well quantified. I have two major comments, and some minor comments on clarity, for the authors consideration in revising the manuscript.

Major comment:

1. The authors imply that Myo1 and dynein knockdowns result in an increase in the number of cells where chromosomes come in close proximity to the cell cortex. Apparently the spindle anchoring defects are meant to indicate that such defects are responsible for the increased frequency of abnormal chromosome proximity to the cortex. But the authors never actually document whether chromosomes in fact do come into proximity to the cortex more often in the mutant than in control embryos. The authors should clarify if they think the spindle anchoring defect does result in abnormal chromosome distributions. Can the authors somehow quantify a defect in overall chromosome positioning in mutant vs control blastomeres? Presumably the movies the authors already have could be used to provide such quantification?

We thank the reviewer for this opportunity to correct our previous assumptions. Following the reviewer’s suggestion, we tracked the distance between the cell surface and the center of the chromosomes cluster throughout mitosis. We found little difference in this distance between control and Myo1cKO embryos (Fig S3a), unlike what we had initially implied. This distance seemed more variable in Myo1cKo embryos than in control ones, suggesting that chromosome movements may be more erratic but analysis of this variation for individual cells did not show consistent differences between control and Myo1cKO embryos either (Fig S3b). Therefore, we cannot explain the increased signaling with differences in proximity of the chromosomes to the cortex during mitosis.

Instead, as already hinted in our initial manuscript as an additional factor, we find that signaling from chromosomes to the cortex can occur for an extended time in embryos with impaired spindle anchoring.

We had already measured that mitotic spindles persisted for a longer time in Myo1cKO embryos than in control ones (Fig 2b), as well as in ciliobrevin treated embryos as compared to DMSO treated ones (Fig S2b). To strengthen this data, we performed additional experiments in which we injected mRNA encoding fluorescent lamin-associated protein 2b (Lap2b-GFP) to track the breakdown and reassembly of the nuclear envelope. Consistent with the mitotic spindle persisting for a longer time in Myo1cKO embryos than in control ones, it generally takes more time for Myo1cKO embryos to reassemble their nuclear envelope than for control embryos (50 min vs 70 min, n = 8 control and 15 Myo1cKO embryos, p = 0.0161, Fig S3c-d, Movie 5). Taken together, the nuclear envelope and spindle data indicate that, although chromosomes are not closer to the cortex in Myo1cKO embryos than in control ones, they spend more time outside of the nucleus. This should give chromosomes extended opportunities to signal to the cortex and explains how difficulties with chromosome separation can lead to the hyper-activation of the polar body extrusion pathway.

We have revised our manuscript accordingly.

Near the end of the paper, the authors discuss how cell with bent/un-anchored spindles are more prone to fragmentation, referring to Figure 2. But Figure 2 does not document a correlation between blastomeres with bent spindles and increased fragmentation. Rather it shows an increase in bent spindles and in fragmentation in mutant vs control, but does show that they occur together. The authors should more accurately describe their results or provide such a correlation with additional data.

We thank the referee for pointing out this missing information.

To support our conclusions, we now provide additional analyses of mitosis duration in non-fragmenting and fragmenting cells from Myo1cKO embryos. When cells fragment, their mitosis is consistently longer, as measured from the persistence of the mitotic spindle, than when not fragmenting (Fig 2c). This provides a direct correlation between spindle defects and fragmentation.

We now present these analyses in the revised manuscript.

Finally, in describing the data in Figure 3, the authors refer to persistence of the spindle and bending of the spindle as indicating problems with anchoring. It is not clear to me how either spindle persistence or bending relate to anchoring. The authors should explain how they are related if they are, and it would be better if the authors could document spindle displacement relative to the cell center or cortex to make their point more directly that anchoring is defective.

We apologize for not making this clearer in our initial manuscript. As others noted before (Kotak et al, 2012; Mangon et al, 2021), poorly anchored spindles show larger displacements or rotations during mitosis. Spindle persistence and bending may not be directly related to spindle anchoring defects but could reflect broader issues with spindle assembly and function caused by spindle anchoring defects. Since a previous in vitro study had identified that Myo1cKO is important for spindle anchoring (Mangon et al 2021) and that ciliobrevin, known for compromising spindle anchoring, phenocopied these aspects, we had initially focused on anchoring defects in our conclusions. We still stand by our conclusion that our data suggest spindle anchoring defects. Nevertheless, we agree that our observations report more general spindle defects and that anchoring may be only one of the defective aspects. Instead of “spindle anchoring defects”, we now simply mention “spindle defects” unless specifically discussing spindle straightness and rotation.

Minor comment.

The authors document in Figure 3 that Myo1C KO blastomeres have an enhanced response, with more myosin accumulating at the cortex in response to chromosomes. Why does knocking out one non-muscle myosin lead to enhanced accumulation of another? The authors note this effect but provide no discussion as to how it occurs. Some clarification might be helpful.

In our manuscript, we report that chromosome proximity to the cortex is associated with Cdc42 activation, which leads to cortical actin recruitment (Fig 4a-d). We also observe that non-muscle myosin II (Myh9) is recruited to the cortex when chromosomes come near (Fig 3d-f). Importantly, these phenomena occur in control embryos as well and not only in Myo1cKO embryos.

We propose that this recruitment is further increased in Myo1cKO embryos (Fig 3f) because chromosomes spend more time outside of the nuclear envelope (Fig 2). This leads to fragmentation and is not specific to Myo1cKO since the same occurs after ciliobrevin treatment (Fig S2).

The authors provide a significant advance in our understanding of why early mammalian embryos, especially early human embryos, are so prone to fragmentation. Their data strongly support their conclusion that increased proximity of chromosomes to the cortex does lead to activation of the PBE response, which is an interesting and well documented finding. However, unless the authors can address my major comments and provide more direct evidence for increased displacement of chromosomes being responsible for increased fragmentation, they should revise their manuscript to acknowledge that they have not directly quantified chromosome positioning and thus do not conclusively document that it is responsible for increased fragmentation in the mutant oocytes.

We thank the reviewer for their thorough analysis of our data and for giving us the opportunity to correct some of the aspects of our study.

Reviewer 2

The manuscript "Ectopic activation of the polar body extrusion pathway triggers cell fragmentation in preimplantation embryos" by Pelzer and colleagues is focused on mechanism of cell fragmentation in early preimplantation embryos. This is an important issue, since fragmentation, with subsequent cell loss, has significant impact on early development of human embryos in vitro.

To study the cell fragmentation within the embryo, authors used mouse model system. However, since during the mouse preimplantation development blastomere fragmentation is less frequent than in human embryos, they used knockout of unconventional myosin-Ic to induce fragmentation of embryonic blastomeres with higher frequency and a similar morphology, known from human embryos.

Using their Myo1c KO, authors confirmed previous observation that reduction of myosin-Ic impairs spindle anchoring and they further show that the defects in spindle anchoring are linked to cell fragmentation. And that similar defects could be induced by chemical inhibition of dynein. Importantly, the defects in anchoring, causing aberrant spindle movements, bring spindle and chromosomal DNA to the proximity of the cell cortex. This induces local changes in concentration and organization of actin and myosin IIA and leads into fragmentation. Authors show that this pathway shares similarity with mechanism of polar body extrusion (PBE) during meiosis, namely that it requires active Cdc42-mediated actin polymerization or Ect2 signaling. And also, that important role in cell fragmentation is played by cell surface tension. Based on their results, authors propose that cell fragmentation within the embryo is triggered either by hyperactivation of PBE pathway in cells with normal surface tension, or by PBE pathway activation in cells with higher contractility.

This manuscript brings important information about mechanism, which might contribute to the high incidence of blastomere fragmentation in human embryos. I have not identified any important issues with experimental work or conclusions and therefore I recommend this paper for publication. The results from the mouse model system however need to be verified by further studies in human or similar embryos, which naturally exhibit higher fragmentation.

We thank the reviewer for their careful examination of our manuscript and data.

We agree that it would be important to verify the validity of our findings in other species. We have considered performing experiments with human embryos.

Ideally, we would need embryos in their early cleavage stages (zygote to 4-cell stages) to be able study fragmentation without perturbing morphogenetic movements, which begin at the 8-cell stage. Such early embryos are particularly rare, which further requires careful experimental design.

Ideally, such carefully designed experiment would not cause additional fragmentation (as we have mostly done in the present study) but rather reduce this deleterious process. In light of our experiments shown in Fig 4c-d, inhibiting Cdc42 would be a good way to reduce polar body extrusion signaling. Injection of DNCdc42 mRNA would be embryo-consuming to setup. We tried a Cdc42 chemical inhibitor on mouse embryos with unreliable results. Therefore, we do not yet feel confident in using precious human embryos with our currently available options.

Another complication is administrative since this project was funded by the ERC, which does not allow experimentation with human embryos.

As for studying the phenomenon in species other than mouse or human, we currently have limited access to other mammalian species. Generally, other mammalian embryos are less well characterized and, in particular, the species-specific fragmentation behavior would need to be characterized before initiating any attempt to reduce it.

We hope that the reviewers will agree that the current manuscript, describing and dissecting a previously unknown mechanism, makes sufficient advances to be published without the need to assess its evolutionary conservation.

This study revealed important mechanism, which might be responsible for inducing fragmentation of blastomeres in early preimplantation embryos. Authors use mouse knockout model system and therefore the results should be verified in other species, in which the embryos show higher fragmentation naturally. The manuscript provides evidence that pathway, leading into PBE in oocytes, remains operational also in embryos and might contribute to blastomere fragmentation in case when spindle loses anchoring to the membrane. The results of this manuscript should be of interest not only to the researchers in reproduction, but also to the general audience.

Reviewer 3

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

The manuscript discussed interesting and relevant topics in which the Authors addressed the effects of mouse Myo1C knock out on cell fragmentation and spindle anchoring defects. The authors found that fragmentation occurs in mitosis after ectopic activation of actomyosin contractility by signals emanating from DNA.

Reviewer #3 (Significance (Required)):

This is an excellent report dealing with significant technical methodologies. I find no fault in the methods, data analysis, or conclusions. I only have two comments. First, the authors should expand on the previous findings about the of the role of Myo1c during early preimplantation development. Second, the discussion should be expanded to compare the results of this study with those of previous/related studies (e.g., other factors involve in fragmentation and spindle anchoring). Finally, I was not able to open movie#2 and movie#8 so they may need to be re-uploaded.

We thank the reviewer for their careful assessment of our study.

We apologize for not discussing enough the previous research on Myo1c. To our knowledge, there is only one previous study reporting the effect of a point mutation on Myo1c on mouse ear physiology (Stauffer et al 2005). This is the first study on the role of Myo1c during mouse development. At this point, we would like to stress that our study, while partially based on the KO of Myo1c, is about cell fragmentation, which we induce experimentally in three independent ways: Myo1c KO, ciliobrevin treatment or Ect2 overexpression.

Regarding fragmentation, to our knowledge there is simply no convincing mechanism to explain this phenomenon. One study proposed that membrane threads connecting the cell surface to the zona pellucida could pull on cells and promote fragmentation (Derick et al 2017). However, fragmentation also occurs without zona pellucida, and hence without threads pulling on cells’ surfaces (Yumoto et al 2020). Other than that, fragmentation was associated with mitosis and general cytoskeleton defects, without no clear mechanism (Alikani 1999, Fujimoto et al 2011, Daughtry et al 2019).

We have now expanded these discussions.

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Referee #3

Evidence, reproducibility and clarity

The manuscript discussed interesting and relevant topics in which the Authors addressed the effects of mouse Myo1C knock out on cell fragmentation and spindle anchoring defects. The authors found that fragmentation occurs in mitosis after ectopic activation of actomyosin contractility by signals emanating from DNA.

Significance

This is an excellent report dealing with significant technical methodologies. I find no fault in the methods, data analysis, or conclusions. I only have two comments. First, the authors should expand on the previous findings about the of the role of Myo1c during early preimplantation development. Second, the discussion should be expanded to compare the results of this study with those of previous/related studies (e.g., other factors involve in fragmentation and spindle anchoring). Finally, I was not able to open movie#2 and movie#8 so they may need to be re-uploaded.

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Referee #2

Evidence, reproducibility and clarity

The manuscript "Ectopic activation of the polar body extrusion pathway triggers cell fragmentation in preimplantation embryos" by Pelzer and colleagues is focused on mechanism of cell fragmentation in early preimplantation embryos. This is an important issue, since fragmentation, with subsequent cell loss, has significant impact on early development of human embryos in vitro.

To study the cell fragmentation within the embryo, authors used mouse model system. However, since during the mouse preimplantation development blastomere fragmentation is less frequent than in human embryos, they used knockout of unconventional myosin-Ic to induce fragmentation of embryonic blastomeres with higher frequency and a similar morphology, known from human embryos.

Using their Myo1c KO, authors confirmed previous observation that reduction of myosin-Ic impairs spindle anchoring and they further show that the defects in spindle anchoring are linked to cell fragmentation. And that similar defects could be induced by chemical inhibition of dynein. Importantly, the defects in anchoring, causing aberrant spindle movements, bring spindle and chromosomal DNA to the proximity of the cell cortex. This induces local changes in concentration and organization of actin and myosin IIA and leads into fragmentation. Authors show that this pathway shares similarity with mechanism of polar body extrusion (PBE) during meiosis, namely that it requires active Cdc42-mediated actin polymerization or Ect2 signaling. And also, that important role in cell fragmentation is played by cell surface tension. Based on their results, authors propose that cell fragmentation within the embryo is triggered either by hyperactivation of PBE pathway in cells with normal surface tension, or by PBE pathway activation in cells with higher contractility.

This manuscript brings important information about mechanism, which might contribute to the high incidence of blastomere fragmentation in human embryos. I have not identified any important issues with experimental work or conclusions and therefore I recommend this paper for publication. The results from the mouse model system however need to be verified by further studies in human or similar embryos, which naturally exhibit higher fragmentation.

Significance

This study revealed important mechanism, which might be responsible for inducing fragmentation of blastomeres in early preimplantation embryos. Authors use mouse knockout model system and therefore the results should be verified in other species, in which the embryos show higher fragmentation naturally. The manuscript provides evidence that pathway, leading into PBE in oocytes, remains operational also in embryos and might contribute to blastomere fragmentation in case when spindle loses anchoring to the membrane. The results of this manuscript should be of interest not only to the researchers in reproduction, but also to the general audience.

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

The authors show that proximity of early mouse embryo blastomere chromosomes to the cell cortex activates the Polar Body Extrusion pathway to generate cell fragments. The authors use live cell imaging in control and Myo1C and dynein knockdown embryos to document accumulation of actin and myosin near chromosomes that come in close proximity to the cell cortex, which correlates with the increased fragmentation of the mutant blastomeres. The live imaging data are nicely presented and the results are well quantified. I have two major comments, and some minor comments on clarity, for the authors consideration in revising the manuscript.

Major comment:

1. The authors imply that Myo1 and dynein knockdowns result in an increase in the number of cells where chromosomes come in close proximity to the cell cortex. Apparently the spindle anchoring defects are meant to indicate that such defects are responsible for the increased frequency of abnormal chromosome proximity to the cortex. But the authors never actually document whether chromosomes in fact do come into proximity to the cortex more often in the mutant than in control embryos. The authors should clarify if they think the spindle anchoring defect does result in abnormal chromosome distributions. Can the authors somehow quantify a defect in overall chromosome positioning in mutant vs control blastomeres? Presumably the movies the authors already have could be used to provide such quantification?
2. Near the end of the paper, the authors discuss how cell with bent/un-anchored spindles are more prone to fragmentation, referring to Figure 2. But Figure 2 does not document a correlation between blastomeres with bent spindles and increased fragmentation. Rather it shows an increase in bent spindles and in fragmentation in mutant vs control, but does show that they occur together. The authors should more accurately describe their results or provide such a correlation with additional data.
3. Finally, in describing the data in Figure 3, the authors refer to persistence of the spindle and bending of the spindle as indicating problems with anchoring. It is not clear to me how either spindle persistence or bending relate to anchoring. The authors should explain how they are related if they are, and it would be better if the authors could document spindle displacement relative to the cell center or cortex to make their point more directly that anchoring is defective.

Minor comment.

The authors document in Figure 3 that Myo1C KO blastomeres have an enhanced response, with more myosin accumulating at the cortex in response to chromosomes. Why does knocking out one non-muscle myosin lead to enhanced accumulation of another? The authors note this effect but provide no discussion as to how it occurs. Some clarification might be helpful.

Significance

The authors provide a significant advance in our understanding of why early mammalian embryos, especially early human embryos, are so prone to fragmentation. Their data strongly support their conclusion that increased proximity of chromosomes to the cortex does lead to activation of the PBE response, which is an interesting and well documented finding. However, unless the authors can address my major comments and provide more direct evidence for increased displacement of chromosomes being responsible for increased fragmentation, they should revise their manuscript to acknowledge that they have not directly quantified chromosome positioning and thus do not conclusively document that it is responsible for increased fragmentation in the mutant oocytes.

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Reply to the reviewers

We thank the reviewers for their constructive feedback on our manuscript. They did a very comprehensive and helpful job of laying out some key areas that could be improved. We were heartened by the fact that there was a fair amount of overlap between the two reviewers, and that comments were largely addressable without further experimentation.

Below, we provide a summary of how we have attempted to address the comments and concerns from both reviewers. We also provide the rationale and action items for our responses. Overlapping comments from both reviewers have been consolidated and responded to together.

Comment 1 (Reviewer #1, Minor Comment 1 & Reviewer #2, Significance)

Both reviewers raised concerns about our choice to focus on essential genes in our CRISPRi screen, which could potentially underestimate the role of non-essential factors contributing to Tae1 sensitivity or resistance.

Rationale: We agree with the reviewers that including non-essential genes could provide additional insights into the roles of non-essential factors in Tae1 sensitivity and resistance. We believe our focus on essential genes contributes a unique perspective to the field, as there already exists a body of work that interrogates non-essential genes in this space. Here are some citations that represent this body. We will highlight these better in the manuscript.

Lin, H.-H.; Yu, M.; Sriramoju, M. K.; Hsu, S.-T. D.; Liu, C.-T.; Lai, E.-M. A High-Throughput Interbacterial Competition Screen Identifies ClpAP in Enhancing Recipient Susceptibility to Type VI Secretion System-Mediated Attack by Agrobacterium Tumefaciens. Front Microbiol 2020, 10, 3077. https://doi.org/10.3389/fmicb.2019.03077.

Hersch, S. J.; Sejuty, R. T.; Manera, K.; Dong, T. G. High Throughput Identification of Genes Conferring Resistance or Sensitivity to Toxic Effectors Delivered by the Type VI Secretion System; preprint; Microbiology, 2021. https://doi.org/10.1101/2021.10.06.463450.

Additionally, our screen was experimentally optimized for essential genes using our approach. The knockdown strategy is useful specifically for essential genes because E.coli is phenotypically very sensitive to essential gene perturbations (see more here: https://doi.org/10.1128/mBio.02561-21). While it would have been ideal to include non-essential genes too, doing so would require a different additional optimization that we believe would have diluted our bandwidth for this study. We do thank the reviewers for recognizing how much effort went into this!

We do acknowledge this is a limitation and want to make sure the readership is aware of that. Ideally, one could do more rigorous side-by-side comparisons between studies if the approaches, set-up, and assays are the same. Unfortunately, due to differences in experimental set-up, we could not directly compare with the non-essential screens. We hope others will pick up where we left off. Here are some action items we can take to increase the odds of that:

In the Introduction, we will mention other studies and highlight the need to investigate essential genes side-by-side with non-essential. (Lines 64-7) In the Discussion, we will add a sentence that acknowledges the importance of exploring non-essential genes for a more comprehensive understanding of Tae1 sensitivity and resistance. (Lines 484-5)

Comment 2 (Reviewer #1, Minor Comment 5 & Reviewer #2, Major Comment)

Both reviewers mentioned that the dormancy state in msbA-KD cells is not well characterized and its relationship with Tae1 resistance is not convincingly shown.

Rationale: We agree that our manuscript does not clearly pin down whether Tae1 resistance is linked to a true dormancy state. There are some intriguing similarities between what we observe and what is classically known as “dormancy” or “persistence”, which have specific definitions. Although we don’t yet have a concrete reason to think it’s NOT those states, we also don’t have sufficient data to point to it clearly being the same at a mechanistic or cellular level. This is merely a hypothesis that our work suggests. We would love to see others follow up on this, as we suspect there are overlaps and potentially additional cellular states that have yet to be clearly defined in this field of bacterial physiology.

Here is how we propose to address this concern:

We simplified our language to be more descriptive and less loaded in terms of nomenclature around dormancy or persistence. Namely, we are referring to the cells in a more descriptive way with “slowed growth.” This allows us to clearly describe what we observe without attempting to ascribe mechanism or anything beyond that. It doesn’t fundamentally change the overarching interpretation of our study. (Lines 444, 490,497-9) In the Discussion, we will add text emphasizing the need for follow-up studies to fully address whether there is indeed a connection between Tae1 resistance and slowed growth. (Lines 491-3)

Comment 3 (Reviewer #2, Major Comment)

The reviewer asks if the degradation of the sugar backbone is also required for lysis or if it is just the crosslinking step that is important.

Rationale: This is an astute point. We acknowledge that the degradation of the sugar backbone may play a role in lysis, and it’s predicted that this may be why the Pae H1-T6SS delivers a second PG-degrading toxin (Tge1), a muramidase that targets the sugar backbone. The most parsimonious conclusion from past studies by us and others is that Tae1 is critical for lysis, but not sufficient in the absence of any backbone-targeting enzyme. Indeed, many T6SS-encoding bacterial species also encode >1 type of PG-degrading enzyme, which may speak precisely to the reviewer’s point. However, it should also be noted that there may be endogenous enzymes with activities that can be leveraged alongside these toxins for the same effect.

Action items:

In the Discussion, we will add a sentence addressing the potential role of sugar backbone degradation in the lysis process and the need for future research on this topic. (Lines 524-6)

Comment 4 (Reviewer #1, Minor Comment 2)

The reviewer asks why lptC-KD leads to sensitivity to Tae1, while msbA-KD leads to resistance, considering both genes are implicated in LPS export.

Rationale: We appreciate the reviewer's careful attention to the underlying biology. They are absolutely correct in pointing this difference out. Our interpretation is that the different phenotypes may indicate that although the LPS biosynthesis superpathway intersects with PG synthesis, lptC and msbA may intersect with PG synthesis in distinct ways. We can address this concern through the following:

We will add a sentence in the Discussion section providing our interpretation of the different phenotypes observed for lptC-KD and msbA-KD. (Lines 508-13)

Comment 5 (Reviewer #1, Minor Comment 4)

The reviewer notes that the contribution of msbA to Tae1 resistance appears minor based on the results in Figure 3d.

Rationale: There are actually two aspects to this concern, which we note below. We found it difficult to fully capture it in the manuscript, but our thoughts are as follows.

(1) Technical viewpoint:

Bacterial competition experiments are inherently noisy. The quantitative read-out is easily impacted by a number of parameters, including cellular density, input ratio between competitor cell types, growth stage, and possibly other environmental factors that are difficult to predict. In general, our view is that we should avoid over-indexing on the degree of the phenotype, focusing more on the direction of the phenotype (loss of statistically-significant Tae1 sensitivity) and the fact that it is reproducible in our hands. Furthermore, our argument is bolstered by clear validation of the loss of Tae1 sensitivity through orthogonal lysis assays (Fig. 4a-c).

(2) Biological viewpoint

It is challenging to isolate the specific interaction between Tae1 and individual genetic determinants, as we think it’s a complex system with multiple factors simultaneously at play. It is crucial to acknowledge that the unique contribution of Tae1 is only a part of the T6SS. There may be other compensatory actions that influence the outcomes observed, such as upregulation of non-Tae1 toxins, regulation of system activation/firing, timing and location of T6S injections, etc. We think these are exciting possibilities and that more groups should delve into the context-dependent dynamics of the system. Although outside the scope of our manuscript, we would be open to suggestions for how we can further emphasize this point.

Comment 6 (Reviewer #2, Minor Comment)

The reviewer recommends that we discuss whether our findings are specific to Tae1 or if they can be extrapolated to other toxins.

Rationale: We understand the reviewer's interest in understanding the broader implications of our findings. Although our study focuses specifically on Tae1, we believe that our findings may provide insights into the mechanisms of sensitivity and resistance to other toxins that target the cell wall. However, experimentally investigating this would fall outside the scope of our current manuscript.

Additional Minor Revisions

Table 1: I would label MsbA and LptC as "LPS transport" and not "LPS synthesis" (Reviewer 1) Rationale: We agree that using “LPS transport” to describe the gene functions for lptC and msbA is more specific to their functions.

Table 1 was updated to change the “pathway/process” categorizations for lptC and msbA from “LPS synthesis” to “LPS transport”. In line with this comment, we also changed the pathway/process categorization for murJ (Lipid II flippase) to “PG transport”. Figure 3 legend: "...deformed membranes .........are demarcated in (g) and (h)" (Reviewer 1) We thank the reviewer for pointing out the missing text in this figure legend.

We corrected the error by adding the missing text back in Figure 3. Line 339-341: Supp. Fig. 9 should be Supp. Fig. 8 (Reviewer 1) Referenced Supp. Fig. was corrected. * Second, (L422-425) the authors conclude that their data demonstrate a "reactive crosstalk between LPS and PG synthesis". I disagree. There is no information in the paper that this is the case. The authors can only suggest that cross talk may occur. (Reviewer 2) We agree. Line 421-2: replaced “demonstrate” with “suggest” to soften the argument. *

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Referee #2

Evidence, reproducibility and clarity

Summary:

This study reports the finding that lipopolysaccharide integrity modulates bacterial sensitivity to a Type-6-secreted bacterial toxin. The authors used the Tae1 amidase produced by the P. aeruginosa T6SS and Escherichia coli bacteria as prey cells as a model system to test the effect of knockdowns in essential gene expression of the prey. This was accomplished by constructing a library of knockdown (KD) genes based on Crispr/Cas9 and selecting for those targets where E. coli prey is not killed. The screen revealed, as expected, that KD genes encoding cell wall synthesis assembly (and bamA, involved in OM protein assembly) enhanced the sensitivity to Tae1. In contrast, KD targets in genes involved in lipid metabolism and lipopolysaccharide synthesis conferred resistant to the amidase toxin. The authors hypothesized that non-PG components of the cell envelope may shape Tae1 toxicity and undertook a more detailed analysis of the effects of knocking down one of these genes, msbA, using a various biochemical and imaging approaches. The MsbA protein is an ATPase permease that plays an essential role in flipping newly synthesized lipid A across the bacterial inner membrane. The authors show that resistance to Tae1 in msbA-KD is independent of cell wall hydrolysis (meaning that the Tae1 remains active), PG synthesis is suppressed (despite PG is still Tae1 sensitive), and that protein synthesis and growth is suppressed. This latter observation suggests that the E. coli prey enters a persistent (dormant) state that protects it from Tae1 toxicity. The authors conclude that Tae1 susceptibility in vivo is determined by cross talk between essential cell envelope pathways and the general growth state of the cell.

Major comments:

This is a nice study unravelling cellular off target factors that affect the killing in vivo by a T6SS toxin. In that sense the study is novel since the interplay of T6SS effectors in the context of the physiological state of the prey cell has not been directly investigated. so this study adds new information to the literature in the field.

I have several comments concerning the interpretation of the results.

First, it is interesting that Tae1, being an amidase, can be the sole responsible for PG degradation. The enzyme cleaved the peptide bridges but has no effect on the PG backbone. The study was not designed to pick up autolysins (since only essential genes were targeted) but one would assume that degradation of the sugar backbone must also be required for lysis.

Second, (L422-425) the authors conclude that their data demonstrate a "reactive crosstalk between LPS and PG synthesis". I disagree. There is no information in the paper that this is the case. The authors can only suggest that cross talk may occur.

Third, Tae1 maximal effect is present when new PG is made, which also begs the question about the location of this protein in the PG mesh. Like B-lactam and other PG-active antibiotics, the effect of Tae1 requires active cell growth. This is also consistent with the authors' finding that the msbA-KD bacterial cells enter a state of dormancy or persistence, which will make them capable of overcoming Tae1 toxicity.

Fourth, an important outcome of protein synthesis inhibition and PG synthesis is increased oxidation and lipid peroxidation. This could also influence the results obtained in this study. It would be consistent with the other targets observed, which compromise lipid metabolism and membrane trafficking and secretion.

Referees Cross-commenting

Based on my own review and that of Reviewer 1, I think we both agree that there are 2 major limitations in this work: (i) the KD library only targets essential genes and this would potentially miss non-essential genes that when targeted for mutated could lead to synthetic lethal phenotypes that could be more revaling than a general defect protein synthesis, etc. and (ii) the dormancy state is not well characterized.

Despite these points the study is very nicely done with a huge amount of work.

Significance

This is an important study addressing experimentally the complexities of bacteria-bacteria interactions in the context of predator-prey interplay. The T6SS effectors affecting PG appear to have the same characteristics as known antibiotics and bacteria use similar strategies to protect themselves from PG attack. This is not only to increase growth as an escape approach but also to reduce it to a point in which the target cell cannot be effectively killed despite the presence of the toxin.

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Referee #1

Evidence, reproducibility and clarity

Summary

The manuscript by Trotta and co-workers investigates functions that shape E. coli envelope when cells are targeted by the cell-wall degrading toxin Tae1. The experimental setting employed by the authors is well thought and is based on the competition engaged by P. aeruginosa (Pae) expressing the type 6 secretion system (T6SS) against E. coli cells. In this context, the authors used an arrayed library of chromosomally encoded CRISPRi strains targeting essential genes of E. coli (knockdowns, KDs) to identify functions that increase or decrease E coli fitness following interbacterial competition with Pae cells expressing Tae1. The majority of genes whose depletion makes E. coli cells more sensitive to the toxin are implicated in PG synthesis while depletion of genes implicated in other cell envelope processes can result in toxin sensitivity or resistance. Among genes whose depletion makes E. coli cells more resistant to the toxin the authors selected those implicated in LPS biogenesis (msbA-KD and lpxK-KD), to investigate the hypothesis that non-PG components of the cell envelope may also shape Tae1 toxicity. While resistance to lpxK-KD to Tae1 could not be validated in the reconstructed strain likely due to a polar effect, the reconstituted msbA-KD gained Tae1 specific resistance. Further characterization of the msbA-KD revealed that Tae1 resistance is independent of cell wall hydrolysis and PG dynamics. By showing that both slow growth and decreased protein synthesis is specifically linked to Tae1 resistance in msbA-KD cells the authors suggest that a persistent state, induced by block of LPS biogenesis, helps depleted msbA cells to resist the toxic activity of Tae1. Overall, the experimental approach is solid, the developed in vivo screen to identify genetic interaction between secreted Tae1 and E coli is smart and well thought. I also acknowledge the huge work performed by the authors to characterize selected KD strains.

My few comments to the manuscript are reported below.

Major Comments

There are no major comments

Minor Comments

1. Why the authors limit the search of functions that help P. aeruginosa to antagonize E. coli cells only to essential genes? I understand that the available CRISPRi strains collection (developed by Carol Goss and co-workers) is only targeting essential genes, but the rationale for this choice should be discussed. This approach is inevitably underestimating the role of perhaps important non-essential factors contributing to Tae1 sensitivity/resistance.
2. It is intriguing that in Table 1 lptC is listed among the genes whose depletion leads to sensitivity to Tae1 whereas msbA transcriptional down regulation leads to resistance. Both LptC and MsbA are implicated in LPS export to the cell surface, and one would expect that their down-regulation leads to similar output in competition experiments with P. aeruginosa. Can the authors comment on that?
3. Based on results reported in Figure 3 d (fold change msbA-KD CFU) the contribution of MsbA to Tae1 resistance seems minor. Can the authors comment?
4. Based on the observation that msbA-KD cells arrest growth, do not divide, and decrease protein synthesis, the authors suggest that these cells enter a persistent state which could protect against Tae1 activity by passive tolerance. In support of this hypothesis the authors refer to published work (Roghanian et al. PloSOne 2019) showing that CRISPRi KD in lpxA (the first gene of the LPS biosynthetic pathway) triggers a dormancy state to respond to imbalances in outer membrane biogenesis. In this manuscript Roghanian and co-workers show that CRISPRi KD in lptA (encoding the periplasmic component of the LPS export machinery) share the same phenotypes as lpxA. These observations bring me again to the comment n. 2 above. I think that the authors should comment on this. In my opinion this is the weakest part of the manuscript as it is not convincingly showed i) that msbA-Kd cells enter a dormancy state, ii) how this dormancy state is related to Tae1 resistance.

Table 1: I would label MsbA and LptC as "LPS transport" and not "LPS synthesis"

Minor points

Figure 3 legend: "...deformed membranes .........are demarcated in (g) and (h)"

Line 339 341: Supp. Fig. 9 should be Supp. Fig. 8

Significance

This study aims at understanding how Tae1, a PG-degrading toxin secreted by T6SS specifically aids P. aeruginosa in antagonizing E. coli cells in vivo. By exploiting a smart in vivo genetic screen, the authors want to understand at the molecular level the interplay between Tae1 and essential functions in E. coli. The study of interspecies competition offers the possibility to investigate and dissect complex physiological processes and the interactions between them. The work is solid and the experimental plan well-conceived. However, the in vivo genetic screen is limited to the search of essential functions implicated to sensitivity or resistance to the secreted toxin. Such an approach is inevitably underestimating the role of perhaps important non-essential factors contributing to Tae1 sensitivity/resistance. Also, as indicated above, I think that the authors did not convincingly show i) that msbA-Kd cells enter a dormancy state, ii) how this dormancy state is related to Tae1 resistance.

Audience Broad audience / basic research

Expertise in outer membrane biogenesis

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Reply to the reviewers

The authors would like to thank the reviewers for their valuable comments and suggestions. We have carefully considered all of the points raised and revised our manuscript accordingly. In the rebuttal letter below, we have extensively discussed all the different concerns and adjustments we made to our work. In what follows the reviewers’ comments are in blue and the authors’ responses are in black. The additions and changes to the main and supplementary text of the manuscript are highlighted in yellow.

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

*In their paper entitled "CD38 promotes hematopoietic stem cell dormancy via c-Fos", Ibneeva et al., present a set of data predominantly from mouse HSCs where they explore the cell cycle kinetics and self-renewal capacity of LT-HSCs expressing (or not) CD38. They perform a series of sophisticated in vitro and in vivo experiments, including transplantations and single cell cultures and arrive at the conclusion that CD38 can fractionate LT-HSCs that are more deeply quiescent. Overall, it is an interesting question and would be of interest to experimental hematologists. That said, I had a number of issues that concerned me throughout the manuscript with regard to the robustness of the conclusions around CD38 and I have tried to detail these below.

Major concerns: *

*1) Novelty - It was unclear what the relationship of this CD38+ fraction had with other "segregators" of LT-HSCs - e.g., how does it compare with the Sca1 fractionation of Wilson et al, Cell Stem Cell 2015 or Gprc5c of Cabezas-Wallschied Cell 2017? Even if CD38 fractionated LT-HSCs, it was unclear what it would give beyond these two molecules (especially re: Sca-1 which is also a cell surface marker). *

Response:

We agree with the reviewer that further elaboration of this point with additional data would be helpful. We compared the expression of Sca-1 in the population of LT-HSCs (Lin- Kit+ Sca-1+ CD48- CD150+ CD34- CD201+) based on the gating strategy from the paper Wilson et al, Cell Stem Cell 2015. We found that all LT-HSCs (independent of CD38 expression) express Sca-1 at a high level and can be quantified as Sca-1hi (we have added these data in Fig. S2A). Thus, CD38 subfractionates LT-HSCs, and considering that we have shown that CD38+ are more quiescent (Fig. 3) and have higher repopulation capacity compared with CD38- LT-HSCs (Fig. 2E-G), we conclude that CD38 should be used in addition to Sca-1 to define dormant LT-HSCs.

We found that CD38+ dormant HSCs expressed Gprc5c mRNA at higher levels than CD38- LT-HSCs (Fig. 5D). Therefore, we cannot exclude that CD38+ and Gprc5c+ identify the same population of dormant HSCs. However, Cabezas-Wallscheid Cell 2017 used the reporter Gprc5c-EGFP mouse strain, which is not widely available. In contrast, we propose to use readily available antibodies against CD38 for efficient isolation of dormant HSCs. Moreover, to define CD38+ dormant HSCs, researchers do not need to use the CD38KO mice as a negative control, it would be sufficient to use total bone marrow cells to identify the CD38+ population for gating dHSCs (we have added this information to Fig. S2C and in the text: line 119-121: “We demonstrated that total bone marrow cells can be used to define the CD38+ fraction in the absence of CD38 knock-out mice (CD38KO) (Fig. S2C), providing the possibility of an internal positive control for easy identification of CD38+ cells”.

*Claims of CD38+ superiority in transplantation - I was surprised with the claim of CD38 negative cells being a less functional HSC when they are clearly still very strong in secondary transplantation assays. Both 38+ and 38- cells strongly repopulate secondary animals and only 5 mice were shown in the Figure. The legend suggests another experiment was undertaken, but these data are not presented. Did they substantially differ in their chimerism in primary and secondary animals? Was the magnitude of difference between the two fractions similar in both experiments? Is there a reason that the data could not be plotted on the same graph?

*

We have added the data from the second experiment to the graphs and changed the figure legend accordingly (Fig. 2D-H), now for primary transplantation n=8, for secondary transplantation n=6 vs 7. These data show the same trend of higher repopulation capacity of CD38+ LT-HSCs compared to CD38- LT-HSCs, although with the larger magnitude of difference in primary transplantation. We agree with the reviewer that CD38- LT-HSCs strongly repopulate secondary animals. However, the higher percentage of chimerism in peripheral blood and bone marrow for CD38+ LT-HSC progeny indicates their superior repopulation and self-renewal capacity compared to CD38- counterparts.

Also, the typical experiment to establish a quantitative difference in HSC production would be a limiting dilution analysis with a much larger number of recipient animals - without such data it is difficult to ascertain how different the two fractions really are.

While we appreciate the reviewer's suggestion to include additional data on the amount of repopulating HSCs, we respectfully disagree as we believe that this information is beyond the scope of the current study, which only aims to assess the functional superiority of CD38+ LT-HSCs over CD38- LT-HSCs in side-by-side comparisons. Assessment of donor-derived cells’ frequency in peripheral blood and bone marrow relative to the frequency of competitors after transplantation of the same amount of HSCs (so-called chimerism level) is a widely accepted assay in the field to demonstrate the difference in the functionality between two HSC fractions (Sanjuan-Pla et al., Nature 2013; Gekas C and Graf T, Blood 2015; Bernitz J.M et al. Cell 2016; and others, including papers cited by the reviewer: Wilson et al., Cell Stem Cell 2015 and Cabezas-Wallscheid et al., Cell 2017). A limiting dilution experiment will provide more detailed characteristics of two HSC fractions, namely the quantitative difference (how many cells from the sorted population can repopulate). However, this experiment will not significantly change our conclusion that the CD38+ LT-HSC fraction is superior in repopulation and self-renewal capacity compared to the CD38- LT-HSC fraction, as sufficiently demonstrated in Fig. 2E-G.

Furthermore the claim that CD38- HSCs do not ever produce CD38+ cells is a bit premature with so few mice and confusingly presented data (e.g., Fig 2I is 5 pooled mice in a single histogram plot - were these concatenated flow files? If so, how were they normalised? Did the other experiment look the same? And were all CD38+ HSCs capable of giving rise to both CD38+ and CD38- cells or was it a subfraction of mice/samples?).

The plot provided in Fig. 2I is a FACS analysis of pooled cells from mice transplanted with CD38+ or CD38- LT-HSCs (we added a detailed explanation in figure legend 2, lines 701-703). We provided data from the second experiment in Fig. S2G. All CD38+ LT-HSCs could give rise to both CD38+ and CD38- HSC; we added data in Fig. S2H.

Cell Cycle status differences and grades of quiescence - Ki67 and DAPI are really quite tricky for discerning G0 versus G1 and no flow cytometry plots are provided for the reader to assess how this has been done. Could another technique (e.g., Hoechst/Pyronin) be used to confirm the results? Perhaps more concerning is the variability of the assay in the authors own hands. If I am interpreting things correctly, the plots in 3G, 3H and 3I in the platelet depletion, pIpC and 5FU experiments are >10% higher in the CD38- control arm than the data in 3A which make me worried about the robustness of the cell cycle assay to distinguish G0 from G1.

Ki67 and DAPI staining is a widely accepted technique for distinguishing G0 from G1. We provide flow cytometry plots in Fig. S2F (original figures, S3B - updated figures), which the referee may have overlooked. We added a reference to the Fig. S3B to figure legend 3 to make it more transparent for the readers. We would like to clarify the reviewer’s concern regarding the slightly different frequency of CD38- cells in the G0 phase of the cell cycle at steady state in Fig. 3A (original figures). Fig. 3A compares the cell cycle stages between CD38- and CD38+ HSCs, while Fig. 3B compares the same parameters for CD38- vs CD38+ LT-HSCs, which are enriched for quiescent HSCs by using additional surface markers. Therefore, it is correct to compare the data for LT-HSCs under stress (Fig. 3G-I, original figures) with the data for LT-HSCs at steady state in figure 3B (original figures). To make it less confusing for the reader, since the entire Figure 3 is devoted to LT-HSCs, we have moved Figure 3A to the supplementary Figures (Fig. S3A).

All experiments for Fig. S3A&3A, 3F, 3G, and 3H (updated figures), were performed separately, and we did not compare mice from different experiments to avoid differences due to technical details. However, the groups of mice for each specific treatment (ctrl vs. treatment at different time points) were analyzed on the same day, using the same amount of cells, the same master mix of antibodies, and the same FACS machine and settings to compare ctrl vs. treated mice (we added this information in the Materials and Methods section, lines 388-391). In addition, we performed a BrdU incorporation assay and label retention assay using H2B-GFP mice, which support our finding that CD38+ LT-HSCs are more quiescent than CD38- cells in the steady state.

Minor points: Figure 3I was really confusing - it says it is the gating strategy for GFP retaining LT-HSCs, but only shows GFP versus cKit

We reformulated the figure legend for 3D: “Representative plot defining GFP+ cells in LT-HSCs.”

Figure 4B suggests that only 40% of CD38+ cells divide in the first 3 days - are there survival differences or are the cells sat there as single cells? It would be important to carry these further to see if cells eventually divide.

This is a relevant and crucial point addressed by the reviewer. We did not find any significant difference in the survival of cells. We have added this data to the supplementary data - Fig. S4Q-R.

Reviewer #1 (Significance (Required)):

I believe the study will be of interest to specialist readers in the HSC field, especially those working on quiescence and G0 exit. At present, I think the conclusion of a true subfractionation is a bit premature, but there are pieces of data that do look exciting and warrant further investigation. It was a little unclear how this would advance beyond Sca-1 or Gprc5c fractionation for finding more primitive HSCs, but having cleaner markers is always a useful advance for the field.

We thank the reviewer for his/her positive evaluation of our study. In our work, we compared several functional aspects of CD38+ and CD38- LT-HSCs:

1. We used four techniques (Ki67 and DAPI staining, BrdU incorporation assay, label retention assay, single-cell division tracing assay) and showed that CD38+ LT-HSCs are more quiescent than CD38- cells.
2. We performed a serial transplantation assay and found that although CD38- LT-HSCs have the long-term repopulation capacity, they repopulate significantly less effectively than CD38+ LT-HSCs.
3. We used a combination of surface markers (Lin- Kit+ Sca-1+ CD48- CD150+ CD34- CD201+) to define LT-HSCs; all of which belong to the Sca-1hi population according to Wilson et al, 2015. We further separated Sca-1hi LT-HSCs into CD38+ and CD38- cells and found that they differ in the repopulation capacity and quiescence in steady state and upon hematological stress. We conclude that CD38 surface staining should be used on top of Sca-1 to sort dormant LT-HSCs.
4. We found that CD38+ dormant LT-HSCs differ from CD38- cells in gene expression and response to CD38 and c-Fos inhibitors. CD38+ LT-HSCs are characterized by higher cytoplasmic Ca2+ and cell cycle inhibitor p57 levels than CD38- LT-HSCs. Thus, we demonstrated that CD38 is not only a marker but also has a functional role in mediating HSC dormancy. We discovered that CD38/cADPR/Ca2+/c-Fos/p57 axis regulates CD38+ HSC dormancy. Taken together, our findings demonstrate that CD38+ LT-HSCs have superior properties compared to CD38- LT-HSCs and can be classified as dHSCs, providing a simple approach for their isolation and further study. Moreover, we uncovered the CD38-mediated molecular mechanism regulating HSCs dormancy.

Regarding my own expertise - I have spent ~20 years in the field undertaking single cell assays of normal and malignant mouse and human HSCs, including many of the core functional assays described in this paper and consider myself very familiar with the topic area.

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

Although the experiments were well done and supported their testing hypothesis, but the overall novelty of the whole work is not that strong and this is because:

-the use of CD38 to identify/select and to test mouse LT-HSCs' function in vivo (although not commonly used nowadays) was demonstrated a few times more than 20 years ago (Randall, et al., 1996: PMID: 8639761 and Tajima et al., 2001; PMID: 11313250); in fact, the authors didn't even reference/acknowledge these papers which they should have done so; hence, most of the results in Fig.2 were already known (despite this current work gave a more detailed/better analysis);

We agree with the reviewer that the previous findings using CD38 to separate HSPCs should be appreciated; however, we would like to point out that while the studies by Randall, et al., 1996: PMID: 8639761 and Tajima et al., 2001; PMID: 11313250 employ only 3 markers to discriminate HSPC (Lin- Sca-1+ Kit+), in our study, we performed for the first time a very detailed characterization of CD38+ cells using surface markers that were not available 20 years ago. We analyzed not only the HSC compartment but also different populations of multipotent progenitors. Modern surface marker combinations for the LT-HSC isolation allow us to show that both populations: CD38- and CD38+, can be classified as LT-HSCs in contrast to the data of Randall et al, where the authors did not find any long-term repopulating activity in the CD38- KLS compartment. Moreover, we showed the hierarchical relationships between these two populations. We appreciate the previous findings and recommendations of the reviewer, and have added citations (Randall, et al., 1996: PMID: 8639761 and Tajima et al., 2001; PMID: 11313250) and comment in the discussion section, lines 267-271:

In contrast to previous studies reporting that only CD38+ HSPC compartment from adult mice contains LT-HSCs (42, 43), in our study we demonstrated using modern surface marker combinations for the isolation of LT-HSCs that while both populations: CD38- and CD38+, can be classified as LT-HSCs, only CD38+ LT-HSCs display characteristics of dormant HSCs (4).’’

-it is known the generic roles of CD38 in producing cADPR, ADPR, etc and these can induce Ca2+ oscillation in cells; despite that, it was nicely demonstrated here that in mouse HSCs cADPR was the main signalling mediator;

We thank the reviewer for pointing this out; indeed, it has not been shown before how Ca2+ is regulated in HSCs.

the roles of cADPR in human CD34+ were demonstrated (Podesta et al., 2023; PMID: 12475890: when CD34+ HSPCs were primed in vitro with cADPR it resulted in enhanced short-term while maintaining long-term (secondary transplant) engraftment in NOD/SCID mice, probably (mechanisms were not determined at that time) inducing cycling/expansion of human CD34+CD38+ progenitors while inhibiting cycling (hence, better long-term maintenance) of CD34+CD38- HSPCs); on this note; the data presented in Fig.4 K and S5 should be eliminated as it adds little to their story and it can be quite confusing when comparing to mouse data unless the authors wish to explore in a more detailed way the human part.

We appreciate the reviewer’s valuable suggestion. However, we respectfully disagree with their interpretation because we do not believe that the technical aspects of the cited paper (Podesta et al., 2003; PMID: 12475890) are robust enough to support their conclusions. Podesta et al. concluded that in vivo and in vitro treatment with a high dose of cADPR (25-fold higher than the physiological dose, according to the authors' estimation) stimulates the expansion of HSC and progenitor cells. At the same time, they did not use any surface markers to define populations and studied total mononuclear cord blood cells, so no conclusions can be drawn regarding CD34+ CD38+ and CD34+ CD38- dynamics. Unfortunately, we cannot confirm the reliability of the HSC engraftment data presented by Podesta et al. This is because they did not analyze the chimerism of human cells in peripheral blood and bone marrow for sixteen weeks post-transplantation, which is considered a standard time period for assessing long-term engraftment of human HSCs in the field (Brehm M.A. et al., Blood 2012, Cosgun K.N. et al., Cell Stem Cell 2014, Takagi S. et al. Blood 2012). Instead, they counted only some CD34+ cells at three and eleven weeks after transplantation. Therefore, the role of cADPR in the regulation of human HSC quiescence remained unknown.

In our original study, we showed that blocking the CD38 ecto-enzymatic activity stimulated both human HSC and mouse HSCs to exit from the G0 phase of the cell cycle. The role of CD38 enzymatic activity can be conservative for mice and humans and needs to be further investigated in future studies on human HSCs. For this reason, we decided to keep Fig. 4K and S6 in the paper.

-Ca2+ induction in cells can induce c-fos expression (as in an early response gene); in many cell types hence, it was not a surprising finding;

We agree with the reviewer that it has been shown previously that Ca2+ induction in cells could induce c-fos expression (as an early response gene to stress). However, we have shown for the first time that Ca2+ regulates c-Fos expression in LT-HSCs under steady-state conditions.

-c-fos was demonstrated to suppress cell cycle entry of dormant hematopoietic stem cells (Okada et al., 1999: PMID: 9920830).

In the cited publication (Okada et al., 1999: PMID: 9920830) the authors have only analyzed the in vitro proliferation and colony formation of Lin- Sca-1+ cells in the IFNα/β inducible c-Fos overexpression model. This population mainly contains progenitor cells and only 0.004% of dormant LT-HSCs (please find below an estimation of LT-HSC frequency). Therefore, the role of c-Fos in the regulation of dormant HSC cell cycle entry remained unexplored.

It would be useful to do ChIP-seq to determine to confirm that c-fos regulates p57 expression.

We have shown that inhibition of c-Fos transcriptional activity inhibits p57 expression (Fig. 6G). ChIP–seq with antibody against c-Fos will answer whether c-Fos directly activates the expression of p57. However, we can only isolate 200-300 CD38+ LT-HSCs from all bones of one mouse. Unfortunately, the ChIP-seq with such an amount of cells is technically very difficult, which explains the absence of publications using ChIP-seq for studying transcription factors in LT-HSCs. We added in the Discussion section that we couldn’t exclude indirect regulation of p57 expression by c-Fos, lines 307-308:” In contrast, although we couldn’t exclude indirect regulation of p57kip2 expression by c-Fos, our data clearly reveal that inhibiting the interaction between c-Fos and DNA in dHSCs reduced protein levels of the cell cycle inhibitor p57kip2 and stimulated cell cycle entry.”

So overall, many of the findings were already out there and the authors gathered many of the pieces of the puzzle and put them together (and demonstrated) in a nice and well-thought manner. This work does add useful information to the scientific community but unfortunately is not ground-breaking. It may contribute to other fields beyond hematopoiesis where CD38 function may play a role.

Thank you very much for the positive review of our work. As mentioned by the reviewer, CD38 is expressed by other normal (lymphocytes, Kupffer cells (Tarrago M.G. et al., Cell Metabolism 2018)) and cancer cells, e.g. hematological malignancies, lung cancer, prostate cancer (Hogan K.A. et al. Frontiers in Immunology, 2019),) but has not been studied in the context of quiescence regulation. Currently, anti-CD38 monoclonal antibodies are used to treat malignancies (Daratumumab) by mediating cytotoxicity (Lokhorst H.M et al., N. Engl. J. Med, 2015). However, the inhibition of CD38 enzymatic activity has not been used broadly. Therefore, our study can be groundbreaking and open new directions in anti-cancer therapy.

Reviewer #2 (Significance (Required)):

In this manuscript, the authors investigated the potential roles of CD38 (mainly) in mouse HSCs quiescent; the authors dissected the potential molecular mechanism by which this occurred, and it was via CD38/cADPR/Ca2+/cFos/p57Kip2. The authors used a combination of transplantation assays to test the importance of CD38 in vivo, followed by a series of simple in vitro experiments (mainly using pharmacological means) to dissect the molecular mechanisms. The manuscript is well-written/explained and the data presented is solid. There are no major issues in terms of reproducibility and clarity in this work.

We would like to thank the reviewer again for the detailed positive feedback.

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Referee #2

Evidence, reproducibility and clarity

Although the experiments were well done and supported their testing hypothesis, but the overall novelty of the whole work is not that strong and this is because:

• the use of CD38 to identify/select and to test mouse LT-HSCs' function in vivo (although not commonly used nowadays) was demonstrated a few times more than 20 years ago (Randall, et al., 1996: PMID: 8639761 and Tajima et al., 2001; PMID: 11313250); in fact, the authors didn't even reference/acknowledge these papers which they should have done so; hence, most of the results in Fig.2 were already known (despite this current work gave a more detailed/better analysis);
• it is known the generic roles of CD38 in producing cADPR, ADPR, etc and these can induce Ca2+ oscillation in cells; despite that, it was nicely demonstrated here that in mouse HSCs cADPR was the main signalling mediator;
• the roles of cADPR in human CD34+ were demonstrated (Podesta et al., 2023; PMID: 12475890: when CD34+ HSPCs were primed in vitro with cADPR it resulted in enhanced short-term while maintaining long-term (secondary transplant) engraftment in NOD/SCID mice, probably (mechanisms were not determined at that time) inducing cycling/expansion of human CD34+CD38+ progenitors while inhibiting cycling (hence, better long-term maintenance) of CD34+CD38- HSPCs); on this note; the data presented in Fig.4 K and S5 should be eliminated as it adds little to their story and it can be quite confusing when comparing to mouse data unless the authors wish to explore in a more detailed way the human part.
• Ca2+ induction in cells can induce c-fos expression (as in an early response gene); in many cell types hence, it was not a surprising finding;
• c-fos was demonstrated to suppress cell cycle entry of dormant hematopoietic stem cells (Okada et al., 1999: PMID: 9920830).

It would be useful to do ChIP-seq to determine to confirm that c-fos regulates p57 expression.

So overall, many of the findings were already out there and the authors gathered many of the pieces of the puzzle and put them together (and demonstrated) in a nice and well-thought manner. This work does add useful information to the scientific community but unfortunately is not ground-breaking. It may contribute to other fields beyond hematopoiesis where CD38 function may play a role.

Significance

In this manuscript, the authors investigated the potential roles of CD38 (mainly) in mouse HSCs quiescent; the authors dissected the potential molecular mechanism by which this occurred, and it was via CD38/cADPR/Ca2+/cFos/p57Kip2. The authors used a combination of transplantation assays to test the importance of CD38 in vivo, followed by a series of simple in vitro experiments (mainly using pharmacological means) to dissect the molecular mechanisms. The manuscript is well-written/explained and the data presented is solid. There are no major issues in terms of reproducibility and clarity in this work.

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Referee #1

Evidence, reproducibility and clarity

In their paper entitled "CD38 promotes hematopoietic stem cell dormancy via c-Fos", Ibneeva et al., present a set of data predominantly from mouse HSCs where they explore the cell cycle kinetics and self-renewal capacity of LT-HSCs expressing (or not) CD38. They perform a series of sophisticated in vitro and in vivo experiments, including transplantations and single cell cultures and arrive at the conclusion that CD38 can fractionate LT-HSCs that are more deeply quiescent. Overall, it is an interesting question and would be of interest to experimental hematologists. That said, I had a number of issues that concerned me throughout the manuscript with regard to the robustness of the conclusions around CD38 and I have tried to detail these below.

Major concerns:

1. Novelty - It was unclear what the relationship of this CD38+ fraction had with other "segregators" of LT-HSCs - e.g., how does it compare with the Sca1 fractionation of Wilson et al, Cell Stem Cell 2015 or Gprc5c of Cabezas-Wallschied Cell 2017? Even if CD38 fractionated LT-HSCs, it was unclear what it would give beyond these two molecules (especially re: Sca-1 which is also a cell surface marker)
2. Claims of CD38+ superiority in transplantation - I was surprised with the claim of CD38 negative cells being a less functional HSC when they are clearly still very strong in secondary transplantation assays. Both 38+ and 38- cells strongly repopulate secondary animals and only 5 mice were shown in the Figure. The legend suggests another experiment was undertaken, but these data are not presented. Did they substantially differ in their chimerism in primary and secondary animals? Was the magnitude of difference between the two fractions similar in both experiments? Is there a reason that the data could not be plotted on the same graph? Also, the typical experiment to establish a quantitative difference in HSC production would be a limiting dilution analysis with a much larger number of recipient animals - without such data it is difficult to ascertain how different the two fractions really are.

Furthermore the claim that CD38- HSCs do not ever produce CD38+ cells is a bit premature with so few mice and confusingly presented data (e.g., Fig 2I is 5 pooled mice in a single histogram plot - were these concatenated flow files? If so, how were they normalised? Did the other experiment look the same? And were all CD38+ HSCs capable of giving rise to both CD38+ and CD38- cells or was it a subfraction of mice/samples?). 3. Cell Cycle status differences and grades of quiescence - Ki67 and DAPI are really quite tricky for discerning G0 versus G1 and no flow cytometry plots are provided for the reader to assess how this has been done. Could another technique (e.g., Hoechst/Pyronin) be used to confirm the results? Perhaps more concerning is the variability of the assay in the authors own hands. If I am interpreting things correctly, the plots in 3G, 3H and 3I in the platelet depletion, pIpC and 5FU experiments are >10% higher in the CD38- control arm than the data in 3A which make me worried about the robustness of the cell cycle assay to distinguish G0 from G1.

Minor points:

Figure 3I was really confusing - it says it is the gating strategy for GFP retaining LT-HSCs, but only shows GFP versus cKit

Figure 4B suggests that only 40% of CD38+ cells divide in the first 3 days - are there survival differences or are the cells sat there as single cells? It would be important to carry these further to see if cells eventually divide.

Significance

I believe the study will be of interest to specialist readers in the HSC field, especially those working on quiescence and G0 exit. At present, I think the conclusion of a true subfractionation is a bit premature, but there are pieces of data that do look exciting and warrant further investigation. It was a little unclear how this would advance beyond Sca-1 or Gprc5c fractionation for finding more primitive HSCs, but having cleaner markers is always a useful advance for the field.

Regarding my own expertise - I have spent ~20 years in the field undertaking single cell assays of normal and malignant mouse and human HSCs, including many of the core functional assays described in this paper and consider myself very familiar with the topic area.

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Reply to the reviewers

Manuscript number: RC-2023-01862

Corresponding author(s): Lasse, Sinkkonen

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

In our manuscript we have aimed to take an unbiased and data-driven high-throughput approach for identification of transcription factors important for dopaminergic neuron differentiation via repeated, combined transcriptomics and epigenomics measurements. We also provide the research community with an extensive dataset enabling further studies on dopaminergic neurons beyond the scope of a single manuscript. We validate identified transcription factors not previously recognized being involved in mDAN differentiation. While we believe our approach is powerful in unbiased identification of central regulators, it does not focus only on factors that are unique for dopaminergic neurons. Importantly, the ranking of transcription factors is based on the epigenomic data of the target genes, rather than expression of transcription factors themselves. We have aimed for the genome-wide identification of pathways controlled by the identified transcription factors, for example through transcriptome analysis.

For practical reasons, to gain the sufficient depth of data to accomplish our aim, only one iPSC line was used for the initial data generation. However, we fully agree on the need for validation of the key findings and overall gene expression profiles in additional independent cell lines. Please find below our detailed point-by-point plan on addressing the reviewers’ comments.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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

In this study, Ramos and colleagues defined gene regulatory networks and transcriptional landscape during differentiation of a human iPSC reporter line into dopaminergic neurons. Several omic techniques (RNA-seq, ATAC-seq, chromatin-IP) and modelling (EPIC-DREAM) allowed them to identify putative effectors of dopaminergic differentiation LBX1, NHLH1 and NR2F1/2. Using overexpression and shRNA-mediated knock down experiments, the authors attempted to validate the hits.

This manuscript is very difficult to read and is confusing. The data are interesting, but they need to be presented in a more concise and readable way, in addition to be validated using additional iPSC lines. Below are few comments.

We thank Reviewer1 for taking the time to evaluate our manuscript and for providing valuable feedback towards improving it further. We were happy to read that the Reviewer1 found the study interesting with only a few caveats. Here we will outline a detailed plan to address those limitations.

In the revised manuscript we will do our best to improve the readability of the manuscript. However, since Reviewer2 has found that the manuscript is “well written, the research laid out in a clear way, and the experiments well thought”, it is somewhat difficult for us to identify the exact changes to introduce. Perhaps these are related to field-specific vocabulary or methodology, which we will aim to make more readable for broader audience.

We agree on the concern of Reviewer1 that different human iPSC lines can show significant variability due to their individual genetic backgrounds. We have observed differences in the rate of neuronal differentiation, depending on the iPSC line, and transcriptomic analysis reveals hundreds of differentially expressed genes between independent iPSC lines. Still, in a case of a single healthy donor, we don’t expect an intra-individual variability to alter conclusions regarding key regulators of fundamental processes such as differentiation. To carry out our multi-omic analysis in the sufficient depth that we have applied and using only purified dopaminergic neurons with a TH-mCherry-reporter inserted using genome editing, it was (also budget-wise) not considered to include multiple independent iPSC lines for the entire panel of experiments (as our ambition was not to characterize a specific mutation). However, to address this point, we have generated a second iPSC line from a healthy donor with TH-mCherry-reporter inserted through genome editing. To address the concerns regarding variability between different human iPSC lines we plan to:

1) Perform transcriptomic profiling of the second TH-mCherry-reporter line at selected time points of dopaminergic neuron differentiation to confirm the similarity of changes in cell identity at transcriptome level.

2) Perform TH staining upon LBX1 or NHLH1 knock-down in additional iPSC lines following dopaminergic neuron differentiation, to confirm their effect on differentiation across iPSC lines. To do this we will apply the Yokogawa high content image analysis that has been recently established in our laboratories. This will also be related to the next point regarding microscopy images of the dopaminergic neuron differentiation and the effect of transcription factors on this.

Only relative numbers and mRNA level normalized to control are presented in main figures. This is very confusing because there is no real quantification. Images of cultures to show increased/decreased number of dopaminergic neurons in non-FACS purified cultures following overexpression/knock down should be presented in main figures. It is recommended to add absolute quantification (percent of DAPI) and statistical analysis based on N=3 independent experiments.

Thank you for raising this point. We are happy to clarify the quantification of dopaminergic neuron numbers and mRNA levels. All quantifications of dopaminergic neuron numbers were based on the mCherry reporter inserted in the TH locus through genome editing and expressed together with endogenous TH. While mCherry can be detected using microscopy (as shown in Figure 1), the signal is significantly weaker than what can be achieved through antibody staining and quantitative analysis is therefore much more accurate when systematically performed using FACS analysis and controlled by using a cell line without the mCherry reporter. Moreover, the approach is direct and not dependent on antibody specificity. Therefore, all quantifications of dopaminergic neuron numbers in the manuscript were performed using FACS.

Most in vitro cell differentiation protocols show variability in their efficiency between independent experiments, which is typically reflected as variable expression levels of the different marker genes (Grancharova et al. 2021). This is also true for dopaminergic neuron differentiation and in our experiments the number of obtained dopaminergic neurons can vary between 5-20% while differentiations performed in parallel as part of the same experiment are typically very similar. Summarizing absolute numbers between independent experiments can lead to large variation while the relative effect of perturbation is reproducible. Therefore, our results are presented as relative changes in dopaminergic neuron numbers and mRNA levels.

Nevertheless, to increase confidence in the impact of NHLH1 and LBX1 on dopaminergic neuron differentiation, we propose, as already described above, to perform TH staining upon LBX1 or NHLH1 knock-down in additional iPSC lines following dopaminergic neuron differentiation. To visualize the observed impact on differentiation.

Based on images shown in figure S4, the effect of rapamycin is very low (no quantification is presented).

We apologize for the unclear Figure Legend for Figure S4 that did not specify what is visualized in the image. The images represent the transduction efficiency of the neurons based on the GFP reporter co-expressed with the short hairpin constructs. The mCherry levels, that are quantified in Figure 6G, are not visualized in these images. We will correct the Figure Legend accordingly. As mentioned in the last sentence on page 20 of the manuscript (referring to Supplementary Figure S4), rapamycin did not induce similar level of reduction in cell numbers as LBX1 knock-down alone did.

Are the three hits altered in dopaminergic neurons in Parkinson's disease and other synucleinopathies that could explain dysfunction of dopamine neurons in disease? Nurr1, EN1 and many other genes required for differentiation of dopaminergic neurons from pluripotent stem cells have their expression decreased in Parkinson's. It is expected that the expression of LBX1, NHLH1 and NR2F1/2 would change under disease condition.

We have investigated the expression of LBX1, NHLH1 and NR2F1/2 using recent meta-analysis of post-mortem brain tissue transcriptomes of Parkinson’s disease patients (Tranchevent, Halder, & Glaab, 2023). None of these TFs was found to be dysregulated in Parkinson’s disease patients. This is consistent with the fact that the expression of these factors is not restricted only to A9 midbrain dopaminergic neurons that are primarily degenerating in Parkinson’s disease but can be detected also in several other types of neurons (please see also our response in section 4).

However, NHLH1 expression is reduced in dopaminergic neurons derived from iPSCs of Parkinson’s disease patients carrying a LRRK2-G2019S mutation based on our published single cell RNA-seq data (Walter et al., 2021).

Beyond this, our results implicate NHLH1 in the regulation of miR-124, which in turn has been found to be downregulated in Parkinson’s disease patients and neuroprotective in different animal models of Parkinson’s disease (Angelopoulou, Paudel, & Piperi, 2019; Saraiva, Paiva, Santos, Ferreira, & Bernardino, 2016; Yang, Li, Yang, Guo, & Li, 2021; Zhang et al., 2022). Similarly, a recent analysis of single nuclei RNA-seq of midbrains from Parkinson’s disease patients, showed that targets of NR2F2 were enriched in the vulnerable dopaminergic neuron population, promoting neurodegeneration (Kamath et al., 2022). Indicating the involvement of the pathway in disease progression without a change in transcription factor expression.

Finally, a polymorphism in NHLH1 locus (rs2147472) is associated with schizophrenia while a polymorphism in LBX1 locus (rs12242050) is associated with Parkinson's disease, suggesting further involvement of these genes in disease risk.

We propose to include these findings in the revised manuscript and discuss them in the context of the current literature.

__Reviewer #1 (Significance (Required)): __

Interesting study that needs to be replicated using additional cell lines.

We would like to thank the reviewer for this positive conclusion and plan to address the key concerns using additional iPSC lines for transcriptome profiling and knock-down experiments.

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

In this manuscript Ramos et al. present a novel and comprehensive transcriptomic and epigenomic profile that identifies a series of key regulators of mDANs differentiation, providing functional validation and characterization of two newly associated TFs: LBX1 or NHLH1. In order to discover key regulators of mDAN differentiation the authors use their previous EPIC-DREM pipeline together with ATAC-seq data for the first time. Then, they focus their attention on those TFs with a more probable regulatory role by performing low input ChIP-seq for H3K27ac leading to the identification of 6 TFs as novel candidate regulators of mDAN differentiation under the control of super-enhancers at day 30 and day 50 of differentiation. In vitro knock down and overexpression of candidate TFs revealed LBX1, NHLH1 as important regulators of DAn differentiation. The authors then interrogate the role of these two TFs through RNA-seq and an Ingenuity Pathway Analysis (IPA)/g:Profiler and proposed regulation of the mature form miR-124 and cholesterol biosynthesis-related genes as the main processes controlled by NHLH1 and LBX1, respectively.

Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation. I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points listed below:

We would like to thank the reviewer for the detailed and overall positive evaluation of our work. We are grateful for the suggestions for improvements and below we detail our plan for addressing them.

Minor comments:

1. In page 15 the authors state "the list of 17 TFs was further explored to select the most promising candidates for functional analysis". However, they only named TCF4 and MEIS1 as examples of discarded TFs through literature search. It is not clear which of the remaining 15 TFs were discarded because of a literature search and which were by SE signal cutoff. Clarification is needed.

We will add clarification statements here, providing more evidence for the selection of our candidates. For that, we will add a supplementary figure showing the locus and expression of the 11 TFs that were not selected.

In page 15 the authors state "TFs, HOXB2, LBX1, NHLH1, NR2F1 (also known as COUP-TFI), NR2F2 (also known as COUP-TFII) and SOX4 were found to present the strongest SE signals and most dynamic gene expression profiles" however I could not find the data that corroborate this statement within tables or figures. Authors should provide hard data to support this statement.

With the clarification from point 1, point 2 will also be answered for a clear description and criteria of our selection.

In supplementary 3, in the IPA analysis some data appear with the warning "#¡NUM!" at the z-score. Some explanation should be given and if pertinent, added to the table legend.

Sorry for not clarifying that in the dataset. That term is produced when IPA cannot predict the Z-score and it is represented in our bar graphs in grey (see Figure 6B). We will add that information to the table header.

In methodology, some reagents and techniques appear with a code reference to catalog number and others don´t. Please keep it uniform throughout the text.

In this study, we performed most of the techniques using kits which contained all necessary reagents for it. We will better clarify which reagents were provided by the manufacturer and which ones were additional to the kits.

Supplementary table 1 has some TFs highlighted in yellow but there is no legend that explain what the yellow highlight symbolizes. Clarification is needed

This is an error and there should not be any TF highlighted in yellow. We apologize for the inconvenience. The highlights will be removed from the revised tables.

Format suggestions:

1. For an easier to follow flow between figure 3A and the main text, it would be helpful if NR2F1 and NR2F2 graphs in Figure 3A appeared next to each other or one above the other.

We will follow the recommendation from the reviewer, and we will change the order of the TFs in the figure to have both NR2F TFs next to each other.

Supplementary table 2.

Data is presented in a confused way. For example, the Top20_TFs_EPIC-DREM is presented as a list of names without divisions of type node or scoring annotations. It would be more informative and easy to follow if proper labeling and scoring is given within this spreadsheet without the necessity of navigating sup.table1 in parallel.

it would be preferable to have an extra sheet showing the comparison between both data sets (SE and EPICDREAM) before providing a final list of relevant TFs.

To the existing table containing the TFs controlled by SE, we are going to add the information regarding EPIC-DREM, namely, the rank those TFs got in each node together with their median ranking, and their best rank across nodes. That will give a good overview on how they look in both analyses. With this approach, some of the TFs controlled by SE will have no information regarding EPIC-DREM because their motif is not known according to the Jaspar database.

Reviewer #2 (Significance (Required)):

-General assessment:

Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation and description of new roles in DAn differentiation for two TF: LBX1 or NHLH1.

We thank the Reviewer2 for this assessment.

-Limitations: One limitation, than the authors themselves mentioned, is the possibility that promising candidate TFs involved in mDAn differentiation are discarded or not taken in account by the EPIC-DREAM algorithm.

We agree with this limitation and will make all the data available for the larger research community to use for follow-up work. Importantly, the data can be easily used for re-analysis using the same pipeline when improved databases become available.

-Audience: This manuscript focuses on factors involved in mDAN differentiation which targets a highly specific audience however their multiomic and functional methodology might attract broader audiences looking to apply similar pipelines and/or experimentation in different areas of research.

-My field of expertise:

I am a geneticist and neuroscientist with expertise in molecular biology and epigenomics focused on age related neurodegenerative disorders.

-Recommendation:

I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points.

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

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

No changes were introduced so far.

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.

Below we will provide a point-by-point response to concerns raised by the reviewers that we believe are outside of the scope of our study.

There is not data showing that the targets are specifically required for dopaminergic differentiation. One may argue that same targets may be identified and required for differentiation of other neuronal cell types. Hence, hits need to be validated for other neuronal cell types using knock in and shRNA mediated KO.

The novelty of this study resides in the use of epigenomic signatures to predict TF activity across differentiation and couple those predictions with the transcriptional changes occurring during this process to identify the TFs responsible for most of the transcriptional changes observed. Therefore, although our focus were TFs important for establishing cell identity, we did not select TFs with a selective/exclusive expression in these cells, namely, cell identity TFs. This gives another perspective regarding TF activity and their relevance for cellular processes like differentiation.

We believe the TFs identified in our study are likely to be involved in regulation in several other neuronal subtypes. There is a wide range of neuronal subtypes and selection and establishment of some of the those for testing of our factors seems biased but also outside of the scope of this study.

Are the neurons generated following overexpression/shRNA-mediated knock down of the three hits functional? Electrophysiological recordings could help.

What other functions are affected in dopaminergic neurons when targets are knocked-down? Is lysosomal activity changed? Is the level of synaptic proteins altered compared to control?

In order not to bias our approach towards particular phenotypes by selected analysis such as electrophysiological measurements or lysosomal activity assays, we performed a transcriptional profiling upon TF depletion for our selected candidates. Our transcriptional profiling highlighted the main pathways affected by the TFs and they are presented and discussed in our study. We exploit these data to find the processes controlled by our TFs that help to define dopaminergic neuron cell identity. We discussed them and tested the role of mTOR signaling and miR-124 as targets of our TFs. The results from the RNA-seq analysis did not indicate direct regulation of synaptic or lysosomal activity, and therefore we find such analysis to be outside of the scope of our study.

Moreover, since the knock-down of our candidate TFs is in general inhibiting dopaminergic differentiation, studying the dopaminergic neurons remaining after a knock-down risks focusing on cells that have either partially or completely escaped the knock-down. Thereby influencing the value of detailed analysis of their functionality.

References:

Grancharova, T., Gerbin, K.A., Rosenberg, A.B. et al. A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes. Sci Rep 11, 15845 (2021). https://doi.org/10.1038/s41598-021-94732-1

Angelopoulou, E., Paudel, Y. N., & Piperi, C. (2019). miR-124 and Parkinson’s disease: A biomarker with therapeutic potential. Pharmacological Research, 150. https://doi.org/10.1016/J.PHRS.2019.104515

Kamath, T., Abdulraouf, A., Burris, S. J., Langlieb, J., Gazestani, V., Nadaf, N. M., … Macosko, E. Z. (2022). Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease. Nature Neuroscience, 25(5), 588–595. https://doi.org/10.1038/S41593-022-01061-1

Saraiva, C., Paiva, J., Santos, T., Ferreira, L., & Bernardino, L. (2016). MicroRNA-124 loaded nanoparticles enhance brain repair in Parkinson’s disease. Journal of Controlled Release : Official Journal of the Controlled Release Society, 235, 291–305. https://doi.org/10.1016/J.JCONREL.2016.06.005

Tranchevent, L. C., Halder, R., & Glaab, E. (2023). Systems level analysis of sex-dependent gene expression changes in Parkinson’s disease. Npj Parkinson’s Disease 2023 9:1, 9(1), 1–16. https://doi.org/10.1038/s41531-023-00446-8

Walter, J., Bolognin, S., Poovathingal, S. K., Magni, S., Gérard, D., Antony, P. M. A., … Schwamborn, J. C. (2021). The Parkinson’s-disease-associated mutation LRRK2-G2019S alters dopaminergic differentiation dynamics via NR2F1. Cell Reports, 37(3). https://doi.org/10.1016/J.CELREP.2021.109864

Yang, Y., Li, Y., Yang, H., Guo, J., & Li, N. (2021). Circulating MicroRNAs and Long Non-coding RNAs as Potential Diagnostic Biomarkers for Parkinson’s Disease. Frontiers in Molecular Neuroscience, 14, 28. https://doi.org/10.3389/FNMOL.2021.631553/BIBTEX

Zhang, F., Yao, Y., Miao, N., Wang, N., Xu, X., & Yang, C. (2022). Neuroprotective effects of microRNA 124 in Parkinson’s disease mice. Archives of Gerontology and Geriatrics, 99. https://doi.org/10.1016/J.ARCHGER.2021.104588

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Referee #2

Evidence, reproducibility and clarity

In this manuscript Ramos et al. present a novel and comprehensive transcriptomic and epigenomic profile that identifies a series of key regulators of mDANs differentiation, providing functional validation and characterization of two newly associated TFs: LBX1 or NHLH1. In order to discover key regulators of mDAN differentiation the authors use their previous EPIC-DREM pipeline together with ATAC-seq data for the first time. Then, they focus their attention on those TFs with a more probable regulatory role by performing low input ChIP-seq for H3K27ac leading to the identification of 6 TFs as novel candidate regulators of mDAN differentiation under the control of super-enhancers at day 30 and day 50 of differentiation. In vitro knock down and overexpression of candidate TFs revealed LBX1, NHLH1 as important regulators of DAn differentiation. The authors then interrogate the role of these two TFs through RNA-seq and an Ingenuity Pathway Analysis (IPA)/g:Profiler and proposed regulation of the mature form miR-124 and cholesterol biosynthesis-related genes as the main processes controlled by NHLH1 and LBX1, respectively. Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation. I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points listed below:

Minor comments:

1. In page 15 the authors state "the list of 17 TFs was further explored to select the most promising candidates for functional analysis". However, they only named TCF4 and MEIS1 as examples of discarded TFs through literature search. It is not clear which of the remaining 15 TFs were discarded because of a literature search and which were by SE signal cutoff. Clarification is needed.
2. In page 15 the authors state "TFs, HOXB2, LBX1, NHLH1, NR2F1 (also known as COUP-TFI), NR2F2 (also known as COUP-TFII) and SOX4 were found to present the strongest SE signals and most dynamic gene expression profiles" however I could not find the data that corroborate this statement within tables or figures. Authors should provide hard data to support this statement.
3. In supplementary 3, in the IPA analysis some data appear with the warning "#¡NUM!" at the z-score. Some explanation should be given and if pertinent, added to the table legend.
4. In methodology, some reagents and techniques appear with a code reference to catalog number and others don´t. Please keep it uniform throughout the text.
5. Supplementary table 1 has some TFs highlighted in yellow but there is no legend that explain what the yellow highlight symbolizes. Clarification is needed

Format suggestions:

1. For an easier to follow flow between figure 3A and the main text, it would be helpful if NR2F1 and NR2F2 graphs in Figure 3A appeared next to each other or one above the other.
2. Supplementary table 2.
   • a. Data is presented in a confused way. For example, the Top20_TFs_EPIC-DREM is presented as a list of names without divisions of type node or scoring annotations. It would be more informative and easy to follow if proper labeling and scoring is given within this spreadsheet without the necessity of navigating sup.table1 in parallel.
   • b. it would be preferable to have an extra sheet showing the comparison between both data sets (SE and EPICDREAM) before providing a final list of relevant TFs.

Significance

General assessment:

Overall, the manuscript is well written, the research laid out in a clear way, and the experiments well thought. The novelty of this study lays in the combination of epigenomic and transcriptomic data at different time points in specific cells during DAn differentiation and description of new roles in DAn differentiation for two TF: LBX1 or NHLH1.

Limitations:

One limitation, than the authors themselves mentioned, is the possibility that promising candidate TFs involved in mDAn differentiation are discarded or not taken in account by the EPIC-DREAM algorithm.

Audience:

This manuscript focuses on factors involved in mDAN differentiation which targets a highly specific audience however their multiomic and functional methodology might attract broader audiences looking to apply similar pipelines and/or experimentation in different areas of research.

My field of expertise:

I am a geneticist and neuroscientist with expertise in molecular biology and epigenomics focused on age related neurodegenerative disorders.

Recommendation:

I believe the conclusions are supported by the results presented and therefore recommend this paper for publication after addressing some minor points.

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Referee #1

Evidence, reproducibility and clarity

In this study, Ramos and colleagues defined gene regulatory networks and transcriptional landscape during differentiation of a human iPSC reporter line into dopaminergic neurons. Several omic techniques (RNA-seq, ATAC-seq, chromatin-IP) and modelling (EPIC-DREAM) allowed them to identify putative effectors of dopaminergic differentiation LBX1, NHLH1 and NR2F1/2. Using overexpression and shRNA-mediated knock down experiments, the authors attempted to validate the hits.

This manuscript is very difficult to read and is confusing. The data are interesting, but they need to be presented in a more concise and readable way, in addition to be validated using additional iPSC lines. Below are few comments.

Only relative numbers and mRNA level normalized to control are presented in main figures. This is very confusing because there is no real quantification. Images of cultures to show increased/decreased number of dopaminergic neurons in non-FACS purified cultures following overexpression/knock down should be presented in main figures. It is recommended to add absolute quantification (percent of DAPI) and statistical analysis based on N=3 independent experiments.

There is not data showing that the targets are specifically required for dopaminergic differentiation. One may argue that same targets may be identified and required for differentiation of other neuronal cell types. Hence, hits need to be validated for other neuronal cell types using knock in and shRNA mediated KO.

Based on images shown in figure S4, the effect of rapamycin is very low (no quantification is presented).

Are the neurons generated following overexpression/shRNA-mediated knock down of the three hits functional? Electrophysiological recordings could help.

What other functions are affected in dopaminergic neurons when targets are knocked-down? Is lysosomal activity changed? Is the level of synaptic proteins altered compared to control?

Are the three hits altered in dopaminergic neurons in Parkinson's disease and other synucleinopathies that could explain dysfunction of dopamine neurons in disease? Nurr1, EN1 and many other genes required for differentiation of dopaminergic neurons from pluripotent stem cells have their expression decreased in Parkinson's. It is expected that the expression of LBX1, NHLH1 and NR2F1/2 would change under disease condition.

Significance

Interesting study that needs to be replicated using additional cell lines.

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Reply to the reviewers

Response to reviewer comments

Reviewer #1: Major Points

• I was hoping to see the gel run for various days of desiccation to support the conclusion that proteome remodeling occurs **during** the desiccation. Right now, the data in Fig. 2 come from a single day – 21 days post desiccation – so it still shows that proteomic remodeling happened during those 21 days but not exactly on which days. Response: Thank you for this suggestion. We have ourselves become quite interested in the exact nature and extent of proteome changes over time in this paradigm. Indeed, our findings in this study now open up so many future exciting directions including various possible molecular mechanisms that control this phenomenon. We are planning to carry out an extensive future study to compare the proteome of fresh and desiccated eggs quantitatively, and over time in order to explore these directions. At this stage though, a complete proteomic study is infeasible. However, our existing data still shows that Aedes eggs have acquired unique proteome – level changes which reiterates a distinct metabolic remodeling happening during the process of desiccation. We have added your point as an important consideration in the manuscript (L207-209).

• In Fig 2B: Unclear what you’re using as a reference to say that “45 proteins increased and 125 proteins decreased in amounts” (L147-148). Relative to fresh eggs that were laid 48 hours ago? Why is this a good reference instead of say, fresh eggs that are 21 days old (same age as the desiccated eggs)? Response: Thank you for this comment, and this helps us to now clarify this point. Throughout the study, “fresh eggs” refer to eggs that were 48 hours old, maintained in moist conditions and not subjected to desiccation. Placing the eggs under moist conditions for 48 hours after egg laying was critical to allow embryonic development (Clements, 1992, ISBN: 9780412401800; Mundim-Pombo et al., 2021, PMID: 34645492). By “desiccated eggs”, we mean fresh eggs (48 hours old) which are subsequently dried for a total period of 21 days by placing the eggs on Whatman filter paper. Therefore, the comparisons made in Figures 2 and 3 are between fresh eggs and desiccated eggs (21 days). Fresh eggs cannot be left for 21 days in moisture as they would hatch into larvae approximately between 48-72 hours after being laid (Clements, 1992; Mundim-Pombo et al., 2021, Rezende et al., 2008, PMID: 18789161). Therefore, the only possible comparison is of fresh eggs at a stage where it would have acquired desiccation tolerance, with the fully desiccated 21-day old egg. We have added new content in the methods section (L426-431) on how eggs were collected for various experiments including the ones described in Figure 2 and also included a figure (Figure S1D, L966-970) to demonstrate the same.

• L90-L91: "...dried for up to 21 days" But the methods section states that the eggs were dried for 10 days on Whatman filter paper. The 21 days refers to the fact that the authors looked at eggs that were stored for 21 days after the 10 days of desiccation, no? Isn't that why the x-axis goes up to 21 days in Fig. 1C? Please clarify. Response: As mentioned in the previous comment response, “21 days” refers to 48 hours of embryonic development (which was achieved by leaving the eggs in moist conditions for 48 hours), followed by 21 days of desiccation on Whatman paper. These dried eggs were then rehydrated to check the percentage hatching (Figure 1C). “21 days” does not mean 10 days of desiccation and 21 days of storage. We have accordingly modified the results section (L98-102), the figure (Figure 1A), its caption (L802-809) and the methods section (L401-415) to clarify and emphasize this point.

• • 1C: related to above. What does "0-day post desiccation" mean in the x-axis? Is these 10 days of desiccation on Whatman paper + 0 days of storage? Similarly, what is 12 days or 21 days post desiccation on the x-axis? These are 10 + 12 days and 10 +21 days respectively? Response: “0 days post desiccation” refers to fresh eggs that are 48 hours old post egg laying and not subject to desiccation. This has been the control throughout our study. As mentioned above, it does not mean 10 days of desiccation and 0 days of storage. We have rephrased the the results section (L98-102), the methods section (L401-415) and modified the illustration (Figure 1A) and its caption (L802-809)* appropriately to describe how the desiccation assay was performed.

• Methods section on desiccation is very unclear (related to above). I cannot determine what the days in Fig. 1C means based on this methods section and the main text (and caption for fig. 1C). Response: We acknowledge the original methods had very brief statements, and so we have substantially revised the text (L98-102), the methods section (L401-415) and improved the schematic (Figure 1A) and its caption (L802-809) to provide clarity on the experimental methods and on how the desiccation assays were designed. We have also included a separate section (L426-431) and an illustration (Figure S1D, L966-970) to demonstrate how samples were collected for various experiments.

• • 2A: What are "D1" and "D2"? These are two trials of desiccation? For each lane (e.g., D1), did you combine 150 eggs and lysed them together for the single lane in the gel? Specify these points in the caption. Response: Yes, D1 and D2 refer to two independently conducted trials from desiccated eggs. 150 eggs were used in each trial, where these eggs were combined and lysed together for protein extraction in order to ensure sufficient material for both the qualitative visualization on SDS-PAGE gel and proteomic analysis. We have incorporated your suggestions in the revised methods section (L443-447) figure legends (L841-845, 857-860)*.

• Related to above: Does the "21 day" correspond to 21 days **post** desiccation (i.e., "21" in the x-axis of Fig. 1C)? Or something else? Please specify in the figure caption. Response: Yes, 21 days in figure 1C corresponds to 21 days post-desiccation. We have clarified the definitions of fresh and desiccated eggs in response to comments 2, 3 and 4 above. To facilitate the understanding of how the desiccation assays were performed as well as the interpretations of Figure 1C, we have added text to the methods section (L401-415) and added explanations in the respective legends (Figure 1A, L802-809).

• L145-146: What is the emPAI score? Give a one-sentence explanation. * Response: The emPAI (exponentially modified protein abundance index) is an absolute quantitation method now widely used in proteomics, which allows comparisons of protein data acquired by LC-MS/MS (Ishihama et al., 2005, PMID: 15958392). The PAI is the protein abundance index, and this is proportional to the logarithm of absolute protein concentration, and since detection relies on mass spectrometry, PAI will indicate the ratio of observed to observable (due to inherent peptide/ionization and detection properties) peptides. The emPAI values of proteins from one sample can now be compared with those in another sample, especially those obtained in contiguous MS runs using the exact same method, to determine increasing or decreasing proteins (as we have done in this study). Thank you for pointing this out and we have added the necessary information to the text (L159-163)*.

Reviewer #2

• However, the method used to define the tolerance as anhydrobiosis, which forms the basis of this claim, is flawed. Physiological strategies for tolerating desiccation can be broadly divided into "desiccation avoidance," which involves maintaining the physiological state by preventing water loss, and "desiccation tolerance," which involves responding to water loss by changing the metabolic system. Desiccation tolerance can be further categorized into two types: hypometabolism, which reduces the metabolic rate, and ametabolism, which completely stops metabolism. The former is general desiccation tolerance, while the latter is defined as anhydrobiosis (Keilin, 1959. doi: 10.1098/rspb.1959.0013). To be classified as anhydrobiosis, the authors must demonstrate that metabolism, including respiration, has ceased completely, not merely that water content has been significantly reduced. The authors claim that the weight of Ae. aegypti eggs dropped by about 65% and that they were still able to hatch even after 21 days of desiccation, as evidence of anhydrobiosis. However, this only confirms the tolerance to desiccation exhibited by many insects living in arid regions. Response: We primarily see this study as a short discovery report that will open multiple directions of future inquiry in this space and we greatly appreciate these points. The responses below, therefore, are elaborate and considered, and we hope will clarify these points extensively.

We agree with the definition of anhydrobiosis as a phenomenon of the ability of some cells and animals to enter into a reversible state of suspended metabolism (ametabolism), and indeed we are admirers of the wonderful explanation of anhydrobiosis as explained by Keilin et al. in 1959. However, we would like to point out that even during such a state, the basal level of metabolism needed for cellular maintenance and repair has to exist (Bosch et al., 2021, PMID: 34347349; da Silva et al., 2019, PMID: 30266630; Garcia, 2011, PMID: 22116292; Pazos-Rojas et al., 2019, PMID: 31323038). The definitions of ‘hypometabolism’ and ‘ametabolism’ were made mostly in the 1950s, when it was primarily possible to only obtain bulk estimates of respiration or glycolysis. Indeed, in every known example of desiccation tolerance or dormancy, there is a decrease in the energetic arms of glycolysis and the TCA cycle (da Silva et al., 2019; Dinakar & Bartels, 2013, PMID: 24348488; Erkut et al., 2013, 2016, PMID: 24324795, PMID: 27090086; Hibshman et al., 2020, PMID: 33192606; Ryabova et al., 2020, PMID: 32723826; Thorat & Nath, 2018, PMID: 30622480; Zhang et al., 2019, PMID: 31019237). Entirely consistent with that, but with much more quantitative approaches that can make more complete inferences, our proteomics and metabolomics data (Figures 2 and 3), show a substantial decrease in glycolysis as well as the ATP and NADH producing arms of the TCA cycle (post α–ketoglutarate) clearly indicating a reduction in the key metabolic pathways involved in energy production (L245-255, Figure 3A).

However, what we are now able to observe, is that there is a rewiring of the central carbon metabolism to support the production of protective molecules such as polyamines (in this case). This would be from a rerouting of flux, away from energy metabolism, but when that happens, it is essential that this ‘carbon and nitrogen’ is put somewhere. Energy-producing pathways during desiccation now are only active at very low efficiencies. The desiccated Aedes aegypti eggs are therefore indeed hypometabolic, and this conclusion is not made merely based on the fact that the total water content and weight of desiccated eggs has been reduced. Note that it is practically almost impossible to measure active respiration in desiccated Aedes eggs, since these measurements require a water-environment (and the entire purpose is lost if we add back water to the desiccated eggs). We will also point out that more recent studies by Erkut et al., 2013 in nematodes, which primarily relied on some proteomic measurements, as well as limited metabolite measurements, already hint that such phenomena occur in bona fide anhydrobiotes such as the pre-conditioned dauer larvae of C. elegans. By using approaches similar to those in our studies, we anticipate that there is a tremendous amount of new learning to be obtained in this area, and we will be able to better revise the definitions of desiccation tolerance or anhydrobiosis.

A ~65% loss in mass, while also considering the mass of the egg shell (and therefore the actual loss of mass in the embryo will be an even higher percentage) is substantial. While we have revised the text thoroughly to avoid the description of these embryos as ‘true anhydrobiotes’, we have rephrased this as desiccation tolerant, and hope readers will appropriately consider these aspects.

Finally, regarding the point of “However, this only confirms the tolerance to desiccation exhibited by many insects living in arid regions.”, we agree, and point out that little or nothing is known about the pathways or means by which many of such insects (including insects that are major causes of diseases in human, livestock or agriculture) survive under extremely dry environments and till date remain phenomenological. Our entire molecular understanding of desiccation tolerance comes from a handful of model organisms such as yeasts, nematodes, and some tardigrades. This study demonstrates the systematic analysis of desiccation tolerance and survival under rapidly changing environmental conditions in a non-model insect - the mosquito, which is already known to have globally diversified due to its ability to adapt behaviorally and physiologically to environmental fluctuations (Diniz et al., 2017, PMID: 28651558; Halsch et al., 2021, PMID: 33431560 ; Miller & Loaiza, 2015, PMID: 25569303). Our study is a substantial advance in this regard, providing interesting insights into mechanisms of desiccation tolerance in mosquito eggs, a property which could in turn contribute to global expansion of this insect. We anticipate that this work will lay foundation to several studies to control the spread of Aedes mosquitoes.

Given the length of this report, we have chosen to avoid an extensive discussion section, and only briefly summarized this point (L65-78, L222-235, L245-255, L281-284).

• The lack of significant accumulation of trehalose and the absence of accumulation of IDPs suggest that the tolerance in the dried eggs is not anhydrobiosis, which means that the manuscript is actually a study of the desiccation tolerance of Aedes aegypti eggs (not anhydrobiosis, nor is it extreme desiccation tolerance). The manuscript should, therefore, be renamed as "Aedes aegypti eggs use rewired polyamine and lipid metabolism to survive desiccation". Response: We agree with your suggestion of changing the title of the manuscript and hence have renamed it to “Aedes aegypti eggs use rewired polyamine and lipid metabolism to survive desiccation”.

However, we believe that referring to organisms as anhydrobiotes just because of its ability to exclusively accumulate trehalose or IDPs during the desiccated state would be inappropriate, and hinders advances in this space. Please allow us a systematic explanation of the same, below, in three parts: on anhydrobiosis, on trehalose synthesis, and on IDPs.

Anhydrobiosis or desiccation tolerance involves drastic physiological changes during the induction of anhydrobiosis, survival during the desiccated state and exiting this state upon rehydration (Bosch et al., 2021; Crowe, 2014, PMID: 24548118; Dinakar & Bartels, 2013; Pazos-Rojas et al., 2019; Rajeev et al., 2013, PMID: 23739051; Ryabova et al., 2020). Multiple processes enable the cell to deal with physical challenges – to protect proteins and membranes, and to maintain cellular integrity (L49-59, L225-232) . Different molecular processes can be used to attain this. The role of trehalose has been best characterized, at a molecular level primarily in yeasts and in nematodes (Erkut et al., 2016). Trehalose functions to protect the integrity of the membrane by forming glass-like structures as well as functions as a protein chaperone (Crowe et al., 1998, PMID: 9558455; Erkut et al., 2011, PMID: 21782434; Tapia & Koshland, 2014, PMID: 25456447). In order for organisms to utilize trehalose, they must first accumulate it substantially, and this can only be done by shifting to very high rates of gluconeogenesis (Calahan et al., 2011, PMID: 21840858; Erkut et al., 2011, 2016; Tapia & Koshland, 2014). Two principles emerge from our own (Gupta et al., 2019, PMID: 31259691; Varahan et al., 2019, 2020, PMID: 31241462, PMID: 32876564; Varahan & Laxman, 2021, PMID: 34849891; Vengayil et al., 2019, PMID: 31604822), and other studies that have tried to a build systems-level understanding of various ways by which flux towards trehalose can be increased. First, cells need to have carbon reserves that can reroute towards trehalose biosynthesis, either if glycolytic flux is reduced and/or if gluconeogenic flux is increased. Second, the presumption of ‘ametabolic states’ is incorrect, since there is a primary reduction of glycolysis and respiration, with a concurrent rerouting of flux towards trehalose accumulation. While this flux re-routing is possible (through various means), as has been observed in several desiccation tolerant organisms like yeasts and nematodes, all of these organisms were present in media/growth conditions where various carbon sources are abundant. Note that a key point of this study is to highlight that mosquito eggs are different in their natural environment – eggs are essentially a ‘closed system’, with limited inputs of nutrients, and when in fresh water (where eggs are typically laid), these are very poor carbon sources (L79-85). Hence, rerouting of flux towards trehalose in such cases will be practically impossible.

Note that in this context, as a strategy to overcome desiccation stress, other organisms like tardigrades rarely accumulate trehalose but instead rely on intrinsically disordered proteins to survive desiccation (Boothby et al., 2017, PMID: 28306513; Hesgrove & Boothby, 2020, PMID: 33148259). Tardigrades, unlike nematodes or yeast are present typically in carbon-poor, water environments. Therefore, when viewed in the context we have explained above, unless they have suitable carbon stores, tardigrades will also be unable to ramp up trehalose production easily during the desiccation process. Therefore, it makes entirely more sense that tardigrades do not rely on trehalose, but instead utilize IDPs in desiccation tolerance. Before this study, there was no clear or established role for IDPs. Note that the function of the IDPs is also to protect proteins from denaturation, much like what was later found for trehalose (Crowe et al., 1998; Tapia & Koshland, 2014).

An alternate way to achieve similar physical ends would be to utilize polyamines. Studies suggest a critical role for polyamines in desiccation tolerance in nematodes, separate from trehalose (Erkut et al., 2013). The ability of polyamines at higher concentrations to protect DNA/RNA, or phase transition into glass-like forms (much like trehalose and IDPs) is extremely well established (Miller-Fleming et al., 2015, PMID: 26156863; Saminathan et al., 2002, PMID: 12202757). Therefore, our findings establishing a protective role for polyamines would be entirely consistent with interpretations made under these contexts (Figure 3A, 3B, L281-293).

Finally, we clarify the idea of desiccation tolerance in Aedes eggs. We establish the following – after desiccation, the eggs have substantially low glucose/glycolytic metabolism, and TCA cycle metabolites. This is seen both at the proteome and metabolite levels (Figure 2 and 3). In addition, we demonstrate substantially lower amounts of lipids, both in the desiccated state and after rehydration (Figure 2D and 2E). Our study points towards a novel aspect of how metabolic rewiring not only supports protection during the desiccated state, but also ensures reactivation of metabolism upon return of favourable conditions. In the Aedes eggs, desiccation tolerance which involves survival and sustenance of the pharate larvae inside the dried egg as well as the exit from this dried state upon exposure to water, can logically be achieved only by repurposing internal acetyl-CoA reserves, which come from increased fatty acid breakdown to synthesize polyamines (Figure 4E). The polyamines also confer protection from the consequences of desiccation together with other enzymatic antioxidants and molecular chaperones (Figure 2C). Fatty acids are utilized by the pharate larvae for its energetic needs during the dormant state as well as to fuel recovery upon rehydration.

Conclusively, we find that desiccation tolerance can be achieved not just by accumulating trehalose or IDPs, but also because of additional relevant mechanisms that are biochemically possible. Thereby, this study adds up to our current knowledge and understanding of possible ways by which cells can achieve the same end of desiccation tolerance, and survival upon rehydration.

• This manuscript provides interesting insights from the perspective of a metabolomic analysis for clarifying the mechanism of the "general" desiccation tolerance, not anhydrobiosis, in dried Ae. aegypti eggs. For instance, the accumulation of polyamines might contribute to desiccation tolerance. The authors suggest a relationship between the accumulation of polyamines and hatchability based on the fact that the inhibition of metabolic pathways resulted in a decrease in hatchability and polyamines. However, conclusive evidence of a causal relationship is not available. It is possible that the inhibitors disrupted metabolic pathways other than the polyamine synthesis, leading to a significant decrease in hatchability. Response: We understand the reviewer’s point made here; however, we would like to elaborate a clarification on this point. In order to test the role of polyamines in desiccation tolerance in Aedes eggs, we inhibited the polyamine biosynthetic pathway using difluoromethylornithine (DFMO). While there may be some non-target effects of a drug, as is true for every inhibitor, our choices of inhibitors were very deliberate and carefully considered. DFMO is extensively used as an anticancer drug to specifically target ornithine decarboxylase, the first (and rate-controlling) step of polyamine biosynthesis (LoGiudice et al., 2018, PMID: 29419804). Importantly, we made our inferences based on two sets of experiments with DFMO. First, we confirmed that DFMO reduces polyamine accumulation in desiccated eggs (Figure S4B). Next, we observed significant reductions in the hatching of desiccated eggs that were obtained from mosquitoes fed with the inhibitor (Figure 4A). This is consistent with a conclusion that the accumulation of polyamines is essential for desiccation tolerance. As critical controls, we included the hatching of fresh eggs obtained from mosquitoes fed with the inhibitor (in a concurrently conducted experiment, with the same sets of mosquitoes). These eggs showed a high percentage of hatching, which was very similar to the untreated controls (Figure 4A, L309-313). This minimizes the possibility that DFMO could inhibit other metabolic pathways that led to reduced hatching. While acknowledging the limitations of pharmacology, our data collectively (Figure 4A, S4B) are consistent with the likelihood that eggs where polyamine biosynthesis is inhibited, are sensitive to desiccation. Since it is not easily feasible to knock-out ODC in mosquitoes, which are still non-model organisms, it is practically implausible to do experiments that are more conclusive. In fact, such experiments as shown in this study have never been performed in mosquitoes (or other similar insects) before, and we therefore believe both the findings, and the approach (of feeding adult female mosquitoes with inhibitors before egg laying) to be substantial advances. We entirely anticipate that these approaches will stimulate future studies in the many new directions this study opens up.

• The quantitative values of polyamines were shown only as relative values based on the values in fresh eggs of the control group. Absolute quantification of polyamines, particularly ornithine, putrescine, and spermidine, should be essential for this manuscript. * Response: We thank the reviewer for raising an important point here. However, it is almost impossible to do such an experiment, since the concentrations of metabolites are usually calculated on the basis of total cellular volume of the extracted cells, normalized to the number of cells (Bennett et al., 2008, PMID: 18714298). Calculating the volume of a single egg, particularly that of a desiccated egg because of its distorted shape is almost impossible. Hence, the steady state levels of all measured metabolites including polyamines, are represented as relative values calculated using the peak areas which corresponds to the amount of the particular metabolite present in the sample, where we normalize to egg numbers (L878-879)*. We have provided the peak areas of all the measured metabolites in Table S4. Note that relative metabolite comparisons are a near-universally accepted approach to show fold-level increases or decreases and as a reference, we include this extensive methods paper where we and others discuss the different, appropriate ways of metabolite comparisons (Walvekar et al., 2018, PMID: 30345389).

• The statistical analysis throughout the study raises concerns, as the sole significant difference test employed is the student’s t-test. While this test is suitable for comparing two groups, it cannot be used for making comparisons between three or more groups. For instance, in the experiment depicted in Figure 4, a comparison of fresh and dried eggs in the control and inhibitor treatment combination would entail comparing four groups. To address this, a two-way analysis of variance ought to be conducted, followed by a post-test such as Bonferroni's or Tukey's multiple comparison test. Response: Thank you for allowing us to clarify this. In the case of our studies, it would be inappropriate to use a two-way ANOVA, and correct to use the unpaired t-tests, because we do not make any comparisons between three or more groups, although visually it might have appeared that way in Figure 4. The data in Figures 4A, 4B S4B and S4C consists of 4 samples – control fresh eggs, control desiccated eggs, treated fresh eggs and treated desiccated eggs. However, comparisons are only made between two groups at a time. These would be control fresh eggs versus control desiccated eggs; OR treated fresh eggs versus treated desiccated eggs OR control desiccated eggs versus treated desiccated eggs i.e., the comparisons are only made for the appropriate two sets. For illustrating these comparisons in an easily readable way, the graphs are presented together. A two-way ANOVA can only be used when comparisons are made between more than two groups or to determine the effect of 2 variables on an outcome which is not applicable in our case. Therefore, only an unpaired test (eg. a student t-test) is appropriate, and we make absolutely no point about multiple comparisons. We’ve included a table below, purely as a reference point, where the same comparisons were made using Wilcoxon’s ranked tests, which is a non-parametric test that only infers information in the magnitudes and signs of the differences between paired observations. Note that there is no change in the conclusions, nor is there any issue with significance for the specific samples compared (in the main manuscript figures). In addition to this, we have added additional text in the figure legends (L904-907, L918-921, L1015-1017, L1022-1025) and also modified the graphs in all the figures for a clearer illustration of the comparison sets.

Figure No.

Comparisons

__ Measurement__

__p value (Wilcoxon's rank-sum test) __

Significance

4A

Control fresh eggs vs Control desiccated eggs

% hatching

0.08143

ns

Treated fresh eggs vs Treated desiccated eggs

0.02857

*

Control desiccated eggs vs Treated desiccated eggs

0.02857

*

4B

Control fresh eggs vs Control desiccated eggs

% hatching

0.05714

ns

Treated fresh eggs vs Treated desiccated eggs

0.02857

*

Control desiccated eggs vs Treated desiccated eggs

0.02857

*

S4B (i)

Control fresh eggs vs Control desiccated eggs

Relative ornithine levels

0.02107

*

Treated fresh eggs vs Treated desiccated eggs

0.05907

ns

Control desiccated eggs vs Treated desiccated eggs

0.0294

*

S4B (ii)

Control fresh eggs vs Control desiccated eggs

Relative putriscene levels

0.02107

*

Treated fresh eggs vs Treated desiccated eggs

0.8857

ns

Control desiccated eggs vs Treated desiccated eggs

0.02857

*

S4B (iii)

Control fresh eggs vs Control desiccated eggs

Relative spermidine levels

0.02107

*

Treated fresh eggs vs Treated desiccated eggs

0.05907

ns

Control desiccated eggs vs Treated desiccated eggs

0.0294

*

S4C

Control fresh eggs vs Control desiccated eggs

Relative lipid levels

0.02107

*

Treated fresh eggs vs Treated desiccated eggs

1

ns

Control desiccated eggs vs Treated desiccated eggs

0.02857

*

*p<0.05, **p<0.01, ***p<0.001, ns - no significant difference.

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Referee #2

Evidence, reproducibility and clarity

This manuscript explores the mechanisms underlying extreme desiccation tolerance, known as anhydrobiosis, in Aedes aegypti eggs. The authors suggested that specific metabolites are involved in achieving this tolerance and provide a comparative metabolomic analysis between dried and fresh eggs to support their claim. In contrast to other anhydrobiotic animals, such as Polypedilum vanderplanki larvae and Artemia cysts, the dried eggs of Aedes aegypti do not accumulate large amounts of compatible solutes like trehalose or intrinsically disordered proteins (IDPs) such as LEA protein. Therefore, the authors want to contend that anhydrobiosis in Ae. aegypti eggs is achieved through a different mechanism.

Significance

However, the method used to define the tolerance as anhydrobiosis, which forms the basis of this claim, is flawed. Physiological strategies for tolerating desiccation can be broadly divided into "desiccation avoidance," which involves maintaining the physiological state by preventing water loss, and "desiccation tolerance," which involves responding to water loss by changing the metabolic system. Desiccation tolerance can be further categorized into two types: hypometabolism, which reduces the metabolic rate, and ametabolism, which completely stops metabolism. The former is general desiccation tolerance, while the latter is defined as anhydrobiosis (Keilin, 1959. doi: 10.1098/rspb.1959.0013). To be classified as anhydrobiosis, the authors must demonstrate that metabolism, including respiration, has ceased completely, not merely that water content has been significantly reduced. The authors claim that the weight of Ae. aegypti eggs dropped by about 65% and that they were still able to hatch even after 21 days of desiccation, as evidence of anhydrobiosis. However, this only confirms the tolerance to desiccation exhibited by many insects living in arid regions. The lack of significant accumulation of trehalose and the absence of accumulation of IDPs suggest that the tolerance in the dried eggs is not anhydrobiosis, which means that the manuscript is actually a study of the desiccation tolerance of Aedes aegypti eggs (not anhydrobiosis, nor is it extreme desiccation tolerance). The manuscript should, therefore, be renamed as "Aedes aegypti eggs use rewired polyamine and lipid metabolism to survive desiccation". This manuscript provides interesting insights from the perspective of a metabolomic analysis for clarifying the mechanism of the "general" desiccation tolerance, not anhydrobiosis, in dried Ae. aegypti eggs. For instance, the accumulation of polyamines might contribute to desiccation tolerance. The authors suggest a relationship between the accumulation of polyamines and hatchability based on the fact that the inhibition of metabolic pathways resulted in a decrease in hatchability and polyamines. However, conclusive evidence of a causal relationship is not available. It is possible that the inhibitors disrupted metabolic pathways other than the polyamine synthesis, leading to a significant decrease in hatchability. The quantitative values of polyamines were shown only as relative values based on the values in fresh eggs of the control group. Absolute quantification of polyamines, particularly ornithine, putrescine, and spermidine, should be essential for this manuscript. The statistical analysis throughout the study raises concerns, as the sole significant difference test employed is the Student's t-test. While this test is suitable for comparing two groups, it cannot be used for making comparisons between three or more groups. For instance, in the experiment depicted in Figure 4, a comparison of fresh and dried eggs in the control and inhibitor treatment combination would entail comparing four groups. To address this, a two-way analysis of variance ought to be conducted, followed by a post-test such as Bonferroni's or Tukey's multiple comparison test.

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Referee #1

Evidence, reproducibility and clarity

This manuscript valuably contributes to understanding how mosquito eggs survive desiccation: the authors establish that, during desiccation, the Ae. aegypti egg's TCA cycle and other metabolic pathways change in order to accumulate polyamines - these provide physical protection during desiccation - and breakdown of lipids which is required for both accumulating polyamines and fuel the recovery process once rehydration occurs (thereby helping the egg hatch after rehydration). The authors also establish that desiccation kills the eggs of another mosquito specie, An. stephensi, in which the above processes don't occur to provide protection during desiccation.

Much of the study uses mass spectrometry of desiccated eggs of Ae. aegypti to determine proteomic changes that occur during desiccation. Interestingly, these included increased superoxide dismutase, glutathione transferase, and theioredoxin peroxidase - all of these regulate the homeostasis of redox processes in cells. These are particularly interesting because, as the authors noted, other studies in different organisms had shown that Reactive Oxygen Species (ROS) are created during desiccation. These results thus suggest that the results of this study would be of interest to those studying desiccation of dauer C. elegans and yeast. Interestingly, recent studies have shown that ROS and glutathione (and other ROS-reducing enzymes) are the key determinants of whether yeast survives or not at extremely high and low temperatures. Some differences were observed though. For example, unlike in desiccated yeast and C. elegans, Intrinsically Disordered Proteins (IDPs) weren't upregulated during desiccation of the mosquito eggs.

For the most part, the experiments and analyses are rigorous and technically sound. The presentation and writing are clear, for the most part. But there are some aspects of the analyses and presentation that might benefit from clarifications. I specify these below.

I support the publication of this work with very minor revisions. The only additional experiment that I can recommend is in point #1 below (doing gel and mass spec on at least one intermediate day during desiccation instead of just at the final day (day 21) which is what has been done). But since mass spectrometry is expensive and time-consuming, this experiment is only suggested but not absolutely necessary. The authors' major conclusions are still valid without this additional experiment. It's just that we don't know how fast the proteomic changes are occurring during desiccation without some timcourse as the one that I suggest here. Perhaps this point can be mentioned as a deficiency of the current work in the discussion, in lieu of doing the additional experiment.

Major points:

1. I was hoping to see the gel run for various days of desiccation to support the conclusion that the proteome remodeling occurs during the desiccation. Right now, the data in FIg. 2 come from a single day - 21 days post desiccation - so it still shows that proteomic remodeling happened during those 21 days but not exactly on which days.
2. In Fig. 2B: unclear what you're using as a reference to say that "45 proteins increased and 125 porteins decreased in amounts" (L147-148). Relative to fresh eggs that were laid 48 hours ago? Why is this a good reference instead of, say, fresh eggs that are 21 days old (same age as the desiccated eggs)?
3. L90-L91: "...dried for up to 21 days" But the methods section states that the eggs were dried for 10 days on Whatman filter paper. The 21 days refers to the fact that the authors looked at eggs that were stored for 21 days after the 10 days of desiccation, no? Isn't that why the x-axis goes up to 21 days in Fig. 1C? Please clarify.
4. Fig. 1C: related to above. What does "0 day post desiccation" mean in the x-axis? Is this 10 days of desiccation on Whatman paper + 0 day of storage? Similarly, what is 12 days or 21 days post desiccation on the x-axis? These are 10 + 12 days and 10 +21 days respectively?
5. Methods section on desiccation is very unclear (related to above). I cannot determine what the days in Fig. 1C mean based on this methods section and the main text (and caption for fig. 1c).
6. Fig. 2A: what are "D1" and "D2"? These are two trials of desiccation? For each lane (e.g. D1), did you combine 150 eggs and lysed them together for the single lane in the gel? Specify these points in the caption.
7. Related to above: Does the "21 day" correspond to 21 days post desiccation (i.e., "21" in the x-axis of Fig. 1C)? Or something else? Please specify in the figure caption.
8. L145-146: What is emPAI score? Give a one-sentence explanation.

Significance

I support the publication of this work with very minor revisions. The only additional experiment that I can recommend is in point #1(doing gel and mass spec on at least one intermediate day during desiccation instead of just at the final day (day 21) which is what has been done). But since mass spectrometry is expensive and time-consuming, this experiment is only suggested but not absolutely necessary. The authors' major conclusions are still valid without this additional experiment. It's just that we don't know how fast the proteomic changes are occurring during desiccation without some timcourse as the one that I suggest here. Perhaps this point can be mentioned as a deficiency of the current work in the discussion, in lieu of doing the additional experiment.

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Reply to the reviewers

Manuscript number: RC-2022-01771

Corresponding author(s): Franck Pichaud and Rhian Walther

1. General Statements [optional]

We are grateful for the reviewers’ comments and suggestions. Both reviewers agree that our work addresses a poorly understood questions in biology and medicine, and that it will be of interest to the community of cell and developmental biologists.

We note that most of the comments/suggestions, especially from Rev#2, are concerned with the text. These include suggested references to be added, a need to expand on the Method description and suggested points of discussion. We have addressed all these issues in the revised manuscript.

Our work aims to understand which pathways control the basal geometry of epithelial cells, and how cells coordinate remodeling of their basal geometry to organize a tissue in 3D, from apical (top) to basal (bottom). This is a relatively understudied area, especially when compared to the breadth of work related to the pathways that control the apical geometry of epithelial cells.

The apical geometry of an epithelial cell is a direct function of the number of adherens junctions the cell shares with their neighbors. Suppression or extension of adherens junctions underpins apical geometry remodeling. Basally, this same cell will be attached to the basement membrane though integrin receptors. We use the fly retina, where cells adopt stereotyped basal geometry, to investigate whether and how integrin adhesion might induce cell basal geometry remodeling in morphogenesis.

The novel finding we report that a temporal sequence of event seems to underpin cell basal geometry remodeling in the retina, whereby i) laminin accumulates at specific location within the basement membrane, which is ii) accompanied by a concomitant accumulation of Dystroglycan (DG), and subsequently iii) integrin receptors are recruited to these sites of high Laminin-DG. This, along with our genetic experiments, suggests that a Laminin-DG-Integrin axis controls the basal geometry of retinal cells. In this axis, we envisage patterning of the basement membrane through Laminin-DG directs integrin recruitment, which in turn induces cell basal geometry remodeling. To our knowledge, this pathway in epithelial morphogenesis, spanning from ECM regulation to integrin polarization, has not been reported before. As the function of these components in basal adhesion is conserved across phyla, we anticipate our findings will be broadly relevant for our understanding of epithelial morphogenesis.

2. Description of the planned revisions

The main suggestion, common to both our reviewers, is that we should provide further re-assurance that the RNAi strains we use to target basement membrane components and the DG and integrin pathways are specific, and that these strains do not come with off-target effects.

We will follow this recommendation by i) including referencing when a line that we have used has been validated elsewhere, ii) by using at least two independent RNAi strains to target a gene of interest, iii) by making use of the deGrad-FP system (Caussinus et al., 2013) to target proteins instead of genes, iv) by making use of available mutant strains. This is all relatively straightforward, and I will detail the proposed experiments as part of the following point-by-point rebuttal and revision plan.

REVIEWER #1

Commenting on the need to provide further controls related to some of our RNAi experiments

1)* All the genetics experiments are based on RNAi induced knock-down approach. Although such an approach is easy to justify for genes associated with lethality when mutated, it becomes less relevant for non-lethal ones as Dystroglycan complex components (Dg, Dys, Sgc) for which null and viable mutants are published and available. The phenotype of such mutants should be provided. *

AND

*There is no data explaining how these RNAi lines were validated. The fact that it gives the phenotype expected by the authors is obviously not sufficient. This point is essential to exclude off-target effects and to be able to compare the different genotypes (see #2). For instance, the strong effect of sarcoglycan could be questioned. Is it really specific? If yes, is the difference with other Dystroglycan complex members only due to RNAi efficiency or does it have a specific function? *

AND

Line 255, "These perturbations led to a failure of bPS/Mys to accumulate at the grommet". Dg mutants are viable (PMID: 18093579); do they show consistent phenotypes?

__RE: __Our main methodology has been to use available RNAi strains to perturb composition of the basement membrane and to inhibit the expression of components of the DG and Integrin pathways. As pointed out by the reviewer, this approach allows us to assess the function of genes that might be embryonic lethal and allows us to specifically target the basal geometry remodeling step without perturbing earlier steps of retinal morphogenesis. This is important for the basement membrane and integrins, which are required although retinal tissue development. See for example: (Fernandes et al., 2014, Thuveson, 2019 #3787).

We are aware that mutant alleles are available for dg, dys and sgc allow for recovering adult homozygous (or trans-heterozygous) animals. However, based on our previous experience using mutants for which only very few flies make it to adulthood, we feel it is best not to examine those animals. Compensatory pathways might be at play that could mask a phenotype (Please see our recent work on the viable roughest null allele in cell intercalation (Blackie et al., 2021).

Therefore, we propose to induce mutant clones for dg, dys and sgc using the Flp/FRT system, using the strongest alleles that are available to us. Of note, in our experience stable proteins might not show a phenotype in small clones, but will develop a phenotype in larger ones, as the protein becomes further diluted upon multiple rounds of cell division. Bearing this in mind, we will generate animals where the whole retina is mutant for these genes. This will be done using the GMR-hid system (Stowers and Schwarz, 1999).

Specifically, we will target Dg, Dys and Sgc using:

Dystroglycan:

• The dg nonsense mutations, leading to expression of truncated proteins: DgO86 (stop codon at the R87 residue) and dgO43 (stop codon at the W462 residue) (Christoforou et al., 2008). While previous studies have suggested that these alleles are homozygous viable (Christoforou et al., 2008; Zhan et al., 2010), we have obtained this strain from the Bloomington Stock Centre, and note that no homozygous flies make it to adult. In preliminary work, we also note that clones mutant for the dgO86 allele generated with the flp-FRT system are very small, comprised of only one or two cells. This suggests that DG is required for cell proliferation or viability. These dg alleles are available on the G13 FRT which is not compatible with any FRT system designed to eliminate the wild type cells. To use the GMR-hid system, we will have to first recombine these dg alleles onto the appropriate FRT chromosome. Dystrophin:

• The dys3397 allele, which is semi-lethal P-element insertion in the dys Very few adult flies homozygous for this allele flies are recovered (Christoforou et al., 2008). We will have to recombine this allele onto an FRT chromosome to generate whole mutant retinas.

• The deficiency Df(3R)Exel6184, which removes the dys coding frame (Christoforou et al., 2008).

• We will also use dysE17, because it has been used before (Catalani et al., 2021; Cerqueira Campos et al., 2020; Mirouse et al., 2009). This lesion is a Q2807 Stop codon in the C-terminal region common to all 6 dys The Df(3R)Exel6184 and dysE17 alleles have been recombined onto FRT82B, which will allow us to make use of the GMR-hid system to generate whole mutant retinas. Sarcoglycan:

• Sgc (three subunits in Drosophila) using the deletion allele dscg169 (Allikian et al., 2007). We will have to recombine this mutation onto an FRT chromosome to generate whole mutant retinas. In addition, we will reproduce our RNAi phenotypes using additional available RNAi lines from stock centers and from previous studies, targeting different regions of dg, dys and scg. For dys we will use a validated RNAi line. For dg we will use a second RNAi line previously used in (Cerqueira Campos et al., 2020; Villedieu et al., 2023) For dys, we will use a second line previously used in (Cerqueira Campos et al., 2020). For Sarcoglycans, we will complement our work targeting scgd by also targeting scga.

Moreover, since a functional endogenously GFP-tagged Dg strain is now available (Villedieu et al., 2023) along with the Dys::GFP strain we have already used, we will target these proteins using the DeGrad-FP system (Caussinus et al., 2013). The main advantage with this system is that, as with RNAi, we can target a specific time window without affecting earlier steps in retinal morphogenesis. In addition, these experiments will address the possibility that DG and Dys might be stable in cells – inhibiting genes expression in flp-FRT induced clones does not always correlate with inhibiting protein function. We think that the well-established deGrad-GFP will be useful here to address the reviewer’s comment.

We trust these complementary approaches will more than address the reviewers’ comment by further ascertaining that the RNAi phenotypes we report here for Laminin, and the DG and integrin pathway, are specific.

Please note that we show in Fig.3 that the basal geometry phenotype we report for the talin RNAi, using an RNAi line reported in several previous studies (Lemke et al., 2019; Perkins et al., 2010; Xie and Auld, 2011; Xie et al., 2014), is comparable the phenotype we observed using the Flp-FRT system to induce mys1 mutant clones. So, we are confident this RNAi line is specific of talin. Nevertheless, we will also show results using second RNAi line targeting *talin. *

*- Authors claimed that laminin RNAi (or MMPs overexpression) affects cell geometry but why it is not analyzed by PCA? It is not consistent with the other figures. *

__RE: __To address this comment, we will provide the PCA analysis for the Laminin and MMP phenotypes.

__REVIEWER #2 __

• Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin __RE: __We have now included loss-of-function mutant clones for LanB1, using the LanB1KG003456 allele, showing defects in integrin localization resembling the LanB2 RNAi (please refer to section 3: revision already done, Section). We trust that this is good validation of the LanB2 RNAi strain. These new results have been added to Figure 6 (6E-6F).

RE: “This is the same for all the RNAi experiments”. Please refer to our response to Reviewer 1, above.

2) *As the authors write "Laminin-rich domains", I suppose that they assume that LanA/B1 accumulates in a restricted region of the BM. However, it has been reported that the majority of Laminin in the fly embryo is soluble and floating in the haemolymph (fly's 'blood' or body fluid) (PMID: 29129537). Therefore, the LanA/B1 observed in the figures might be just floating in the intercellular space and doing nothing on the BM. The authors should exclude this possibility to support their idea that Laminin localised in a specific region of the BM recruits Integrin. For example, does secreted GFP (PMID: 12062063) not behave in the same way as LanA/B1? Can the authors show that the LanA/B1 is indeed incorporated in the BM by FRAP or any methods? *

RE: While formally possible, our data suggest that it is unlikely that “LanA/B1 is just floating in the intercellular space and doing nothing on the BM”. For instance, our results show that the DG pathway component Scgd is required for accumulation of LanA::GFP (Fig.7E-F). The most likely explanation for this requirement is DG binding to Laminin fibers.

Nevertheless, we will follow up on the reviewer’s comment and perform FRAP on LanA::GFP, as this is relatively straightforward. We will also try the GFP secretion experiment using the suggested GFPsecr transgene generated by the Vincent lab in 2000.

3) Line 240. "RNAi against dSarcoglycan led to a decrease in LanA::GFP expression at the presumptive grommet at 20h APF (Figure 7F)". As to this result, the authors seem to interpret that Laminin is not recruited to the "specific BM domain" in grommet in the absence of Dg signalling. However, other possibilities exist, e.g., that the global expression level of Laminin was reduced, or that the intercellular space into which soluble Laminin (see the issue 4 above) flows was narrowed down. The authors should show the data that exclude (or at least reduce) these possibilities.

__RE: __Addressing Rev2 point (1) will rule out that Laminin is in soluble form. To address the comment that the global expression level of Laminin might be decreased, we will quantify the amount of LanA::GFP that is not at the grommet and compare wild type animals with the scgd ones.

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

__REVIEWER #1 __

• Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin __RE: __We have included new results – LanB2 loss of function – showing the role of Laminin in being required for Integrin localization in the secondary and tertiary pigment cells (revised Figure 6 – panels E-F)

Line 237: For this, we used both RNAi against LanB2 and a loss-of-function allele of LanB1. Consistent with our model, we found that in both cases bPS/Mys Integrin localization was affected. bPS/Mys failed to accumulate at the grommet, and instead was distributed at the basal plasma membrane into punctate domains (Figure 6A-F). In addition, these perturbation experiments affected cell basal geometry remodeling (Figure 6A, 6C, 6E).

2)* Methods section describing genetic conditions is really sketchy. The genotype corresponding to each figure is not provided and I guess that GMR-Gal4 has been used in all experiments using the Gal4 system but it is never clearly stated. *

__RE: __We have revisited the Methods section and Figure Legends to ensure all appropriate information is readily accessible to the reader. The reviewer is correct that the retinal GMR-Gal4 driver was used to express the RNAi used in this study.

3) PCA analysis. - In the WT situation it would be really informative to know which variable(s) is/are really discriminant between the two cell populations and then maybe to focus a bit more on these parameters. For instance, a PCA correlation circle plotting both cells and variables would be very helpful.

__RE: __We have followed the reviewer’s advice and amended the Methods section accordingly. We now provide the PCA correlation circle plotting both cells and variables in Suppl. Fig. 3, for talin RNAi and MysDN, and Suppl. Fig. 10 for DG and Scgd RNAi

*Methods: *

Line 522 : Principle component analysis

Principal component analysis (PCA) was carried out using the Scikit-learn library in Python. The Standard scaler package was used to standardize the data across all metrics before calculating the principal components. The PCA package was then used to perform the PCA. Metrics included in the PCA were as follows: extent, major axis length, minor axis length, eccentricity, roundness, circularity, area, cell shape index, perimeter.

The cell types (secondary and tertiary pigment cells) were assigned by following the cells in 3D to the apical surface where the cell types could be identified. Cells that could not be clearly assigned as either secondary or tertiary pigment cells were excluded from the PCA.

Extent is the area of an object divided by the area or the smallest rectangle (bounding box) that can fit around the object.

Major axis length is the longest line that can be drawn through an object.

Minor axis length is the line that can be drawn through an object which is perpendicular to the major axis.

__Eccentricity __is the ratio of the length of the short (minor) axis to the length of the long

(major) axis.

Roundness is a comparison of an object to the best fit circle of an object. The closer the object is to a perfect circle, the more round it will be.

Circularity is a measure of the smoothness of an object.

Cell shape index is a dimensionless parameter to describe cell shape. When cells have smaller contacts with their neighbours the cell shape index is small.

Correlation circle plots were generated using the mlxtend plotting package in python using the plot PCA correlation graph function.

• Please also see the graphs we now provide in Suppl. Fig.4*. *

We are also commenting on these results.

Line 174: To understand which parameters explained most of the variance in the PCA analysis we generated correlation circle plots (Supplementary Figure 4). For wildtype cells, perimeter and circularity contribute most to the variance between secondary and tertiary pigment cells along the PC1 axis. Eccentricity and minor axis length contribute most to variance along the PC2 axis (Supplementary Figure 4A). For talin RNAi and MysDN cells, the correlation circle plots are remarkably similar (Supplementary Figure 4B-C), indicating that these genetic perturbations have similar effects on cell basal geometry. To confirm this result, we performed PCA comparing secondary and tertiary pigment cells for these two genotypes. In both genotypes, cells fail to form discrete clusters (Supplementary Figure 4D-E). For the secondary pigment cells, expressing talin RNAi or MysDN leads to an increase in cell roundness. For the tertiary pigment cell, these genotypes lead to an increase in circularity (Supplementary Figure 4D-E). Examining the original segmentation data confirmed that, relative to wildtype cells, either genetic perturbation has a similar effect on key cell shape parameters (Supplementary Figure 4F-G).

*- In loss of function conditions, when the tissue is strongly affected, how do the authors recognize the two cell populations if PCA cannot? *

__RE: __In these genotypes, each cell type is identified based on their apical position and geometry. When a cell cannot be identified it is not included in the analysis. This allowed us to track the cells from apical to basal. We now make this clear in the Methods section.

Line 529: The cell types (secondary and tertiary pigment cells) were assigned by following the cells in 3D to the apical surface where the cell types could be identified. Cells that could not be clearly assigned as either secondary or tertiary pigment cells were excluded from the PCA.

- On the opposite, based on the provided image, Dys RNAi seems to have a mild effect and it seems that my eyes can easily recognize those two cell populations based on their shape. So why PCA cannot?

__RE: __We respectfully disagree with this comment. In the Dys RNAi, one cannot tell which is a secondary and which is a tertiary by visual inspection of the basal surface only. This is consistent with the PCA analysis, now described more thoroughly in Supplemental Figure 4. The Dys RNAi cells tend to remain elongated and they do not round up as much as the Scgd RNAi cells, which gives the false impression that the phenotype is closer to that of the wild type.

- Based on the proposed images, some phenotypes look clearly different depending on the genotype, e.g. Talin and Mys (figure 3) or Dys and Sgc (Figure 8). In other words, the fact that PCA cannot separate the cell pollutions in these different genotypes does not necessarily mean that their effect is identical. Could authors perform PCA analysis between mutants? If they are different, again it might be very interesting to identify the discriminating parameters.

RE: We did not claim the defect was identical__. __

The basal geometries look somewhat different depending on the genotype, and we envisage this is due to differences in RNAi strength and perhaps differences in protein stability. This is the case for Dys and Scgd, as outlined in the preceding point. With respect to talin and mys, none of the authors can distinguish by eye the talin RNAi from mys1 phenotypes. We have informally asked our institutional colleagues, and they were also unable to distinguish these genotypes.

Nevertheless, we have expanded our PCA analysis between phenotypes, considering one cell type at a time. This analysis shows that these phenotypes show partial overlap, outside of the wildtype range. While there are similarities, it does not reveal, however, any specific relationship between genes of interest (see previous).

Line 178: For talin RNAi and MysDN cells, the correlation circle plots are remarkably similar (Supplementary Figure 4B-C), indicating that these genetic perturbations have similar effects on cell basal geometry. To confirm this result, we performed PCA comparing secondary and tertiary pigment cells for these two genotypes. In both genotypes, cells fail to form discrete clusters (Supplementary Figure 4D-E). For the secondary pigment cells, expressing talin RNAi or MysDN leads to an increase in cell roundness. For the tertiary pigment cell, these genotypes lead to an increase in circularity (Supplementary Figure 4D-E). Examining the original segmentation data confirmed that, relative to wildtype cells, either genetic perturbation has a similar effect on key cell shape parameters (Supplementary Figure 4F-G).

*- From what I can understand, each PCA analysis has been done on a single retina. If true, more replicates should be included. If not true, the number of independent retinas should be mentioned. *

__RE: __All PCA analyses have been done using multiple retinas from different animals. We have clarified this in the figure legends.

4) Minor comments: - Globally, the article suffers from a lack of details, especially in the methods section and/or in figure legends.

RE: please see what we have done to address this comment, in section (2) above.

*- Also, several points could be advantageously discussed. For instance, why MMPs have different effects according to their specificity? Also, what could be the meaning of the nice differential pattern between integrin alpha subunits? *

__RE: __We were concerned this would be seen as too speculative by our reviewers. Following the reviewer’s advice, we are happy to share our current working model and speculations on this.

Results:

Line 242: Moreover, and consistent with basement membrane regulation being important for cell basal geometry remodeling, we found that degrading the basement membrane by expressing Matrix Metalloproteases MMP1 or MMP2 in retinal cells leads to a failure in bPS/Mys localization at the grommet and prevented cell basal geometry remodeling (Figure 6G-J). While recombinant Drosophila MMP1 and 2 can degrade Col-IV, only MMP2 can degrade Laminin (Wen et al., 2020). The MMP2 phenotype we observed in basal surface organization is stronger than that of the MMP1 overexpression. Our results, therefore, suggest that both Col-IV and Laminin play a role in controlling the basal geometry of retinal cells. This suggestion is consistent with our finding that both these basement membrane proteins are enriched at the grommet once cells have acquired their basal geometry.

Discussion:

Line 386: Integrins can bind to Col-IV and to Laminin (Hynes, 2002). Our experiments show that MMP2 overexpression leads to a stronger phenotype than MMP1. In addition to catalyzing Collagen-IV proteolysis, MMP2 can degrade Laminin, which is something MMP1 does not seem to be able to do (Wen et al., 2020). Therefore, our results suggest that both Col-IV and Laminin are required for cell basal geometry remodeling.

Line 408*: *

The cone cells express two Integrin receptors, ____a____PS1/Mew-____b____PS/Mys and ____a____PS2/if-____b____PS/Mys

We found that while the interommatidial cells express aPS1/Mew-bPS/Mys, the cone cells express both aPS1/Mew-bPS/Mys and aPS2/if-bPS/Mys. Thus, different cell types express different aPS subunits. It is not clear why the cone cells express two a-subunits. In the developing follicular epithelium of the fly oocyte, cells switch from expressing aPS1/Mew-bPS/Mys, to expressing aPS2/if-bPS/Mys (Delon and Brown, 2009). In this tissue, the developmental switch between aPS1 and aPS2 expression was shown to correlate with a change in stress fiber orientation. In addition, aPS1-bPS/Mys was also shown to be required to control F-actin levels basally. aPS1 mutant cells presented elevated levels of F-actin, a phenotype not seen in aPS2 mutant cells. Remarkably, in this tissue, aPS2-bPS/Mys, but not aPS1/Mew-bPS/Mys was able to recruit the integrin adapter Tensin. The authors envisaged that the aPS2 Tensin interaction might confer robustness in basal surface remodeling. With analogy to the follicular epithelium, we speculate that in the cone cells, aPS1-bPS/Mys and aPS2/Mew-bPS/Mys synergize in mediating robust attachment to the basement membrane, to ensure these cells do not detach as the retina lengthens along the apical-basal axis (Longley and Ready, 1995). We also note that in retinal development, the cone cells form new adherens and septate junctions at their basal feet (Banerjee et al., 2008). These cells, therefore, present two sets of adherens and Septate junctions. It is also possible that the atypical situation seen with the cone cells expressing two a subunits, is linked to the formation of these new junctions at the basal pole of these cells. It will be interesting to examine these possibilities, and to establish the role these two a-subunits play in cone cell morphogenesis. Further, the presence of two distinct integrin subunits within the cone cells may have implications when considering Integrin signaling during cone cell morphogenesis.

*- In Methods, a list of metrics is given for the PCA analysis but some look very similar and it would be helpful to define them briefly. *

RE: Please refer to what we have done to address this comment in section (2) above.

*- Figures are not always color-blind adjusted (e.g. dots on PCA graphs). *

__RE: __We have rectified this oversight.

__REVIEWER #2 __

1)* Line 169, "From these experiments, we conclude that Integrin adhesion is required for cell basal geometry remodeling during retinal morphogenesis". It has been long known that integrin is necessary for the gross morphogenesis of the eye (e.g., Zusman et al. 1993, PMID: 8076515). The authors need to cite these preceding researches and should clarify what new findings this new work adds to the previous knowledge. *

__RE: __Following the reviewer’s suggestion, we have added this reference which precedes (Longley and Ready, 1995)mentioned in the paper. Both references show that integrins are required for eye integrity and attribute this function to the contraction phase of retinal development. Notably, contraction occurs after cells have remodelled their basal geometry, which we have focused on in this study.

Line 128: The Integrin bPS subunit (Myspheroid, Mys) is required to maintain surface integrity late in retinal development, as the tissue surface undergoes basal contraction (Longley and Ready, 1995; Zusman et al., 1993).

4) Line 180, "Using available functional GFP protein traps [49, 50]", the authors investigate the behaviour of Laminin subunits LanA and LanB1. First, ref [50] is not relevant here and should be removed. Moreover, the Laminin-GFPs the authors used are not protein traps, but transgenic strains harbouring genes and most of their regulatory information, with the ORFs tagged with GFP [49]. Furthermore, while the ref [49] reported the functionality of LanB1-GFP, this reference did not fully address the functionality of LanA-GFP. The authors need another reference on it (PMID: 29129537), which demonstrated that LanA-GFP rescues LanA mutants.

5) Related to the issue above, in addition to LanA and LanB1, the authors examine the localisation of the following BM proteins using GFP-fusion: Perlecan/Trol, Collagen IV/Viking, Nidogen, and SPARC. The authors do not explicitly describe the nature of these GFP fusions, but I am afraid that the authors think all of them are "functional protein traps". However, in fact while Perlecan and Collagen IV are protein traps, Nidogen and SPARC are transgenics including regulatory sequences made in the ref [49]. This must be clarified. Moreover, to rely on the data obtained using these GFP fusions, their functionality must be confirmed by appropriate references or/and the authors' own data. For information, ref [62] showed the functionality of Perlecan-GFP and Collagen IV-GFP protein traps (they are both homozygous viable), and the Nidogen-GFP transgene rescues the BM deficiency of Ndg mutants (PMID: 30260959). These reports must be explained in the text, and I would like the authors collect and show more information.

__RE: __We have deleted ref 50. We thank the reviewer for flagging the issue with our referencing. We have now amended this section.

Line 204: To this end, we examined the localization and requirement of the Laminin A and B1 subunits (Laminina, LanA and Lamininb, LanB1), Perlecan/Trol, Collagen-IV/Viking (Col-IV), the glycoprotein Nidogen (Entactin/Ndg), and the secreted glycoprotein protein-acidic-cysteine-rich (Sparc), which are all components of the basement membrane (Walma and Yamada, 2020). For Laminin, Ndg and SPARC, we used strains generated from a fosmid library, and expressing a functional GFP-tagged transgene under the control of their own respective promoter (Dai et al., 2018; Matsubayashi et al., 2017; Sarov et al., 2016). For Col-IV and Perlecan, we used functional GFP exon-trap strains (Morin et al., 2001).

6) Line 200, "These specific patterns of expression for LamininA/B1, Collagen IV, Perlecan, Nidogen and Sparc". I have several comments here: - 5A. These patterns are discussed only using single optical sections. To highlight the difference in their localisation patterns more objectively, multiple sections and/or 3D images should be shown.

RE: (a) These are all projections of 3 to 5 confocal sections, and we have amended the manuscript to make this point clearer. (b) Following the reviewer’s advice, we now provide sagittal sections so the reader can better appreciate what is detected above and below the grommet. Please see new Fig. 5.

5B. Can the authors discuss, hypothesise, or speculate the biological meaning of the difference? * AND*

*5C. It has been reported that in the mammalian skin BM, different components show distinct localisation patterns (PMID: 33972551). It would be interesting to cite this paper and discuss the generality of the non-uniform distribution of BM components. *

__RE: __The revised manuscript offers a short discussion in this topic.

Line: 367 The idea that different cell types in a tissue can express different ECM components, and thus induce localized specialization of a basement membrane is well-supported by recent work in the mouse hair follicle. In this sensory organ, the architecture and composition of the basement membrane is highly specialized depending on the cell-cell and cell-tissue interface considered (Cheng et al., 2018; Fujiwara et al., 2011; Joost et al., 2016). Moreover, different cell populations – epithelial stem cells and fibroblasts, express different ECM components in the hair follicle (Tsutsui et al., 2021), supporting the notion that specific basement membrane organization contributes to cell-cell communication and overall 3D tissue architecture.

7) Line 215, "However, inhibiting the expression of Collagen IV, Ndg, Perlecan and Sparc individually, by expressing RNAi against these genes in all retinal cells, did not lead to defects in bPS/Mys localization". To conclude so, the authors must demonstrate that the used RNAis efficiently removed its target proteins.

__RE: __We have removed this section referring to Collagen IV, Ndg, Perlecan and Sparc.

Instead, we now focus solely on Laminin. Because Laminin accumulation at the presumptive grommet precedes that of the other ECM factors examined in our study, we favor a model in which Laminin plays a key role in promoting integrin localization.

8)* Line 222, "DG is required to organize the ECM in several experimental settings [42, 43, 45, 51]". Here, the authors must mention to a preceding paper that reported the eye deficiency of Dg mutant flies (PMID: 20463973), and discuss what new findings authors can add to the previous report. *

__RE: __We have followed this recommendation.

Line 441: We also note that a previous study showed that early in retinal development, DG localizes at the apical membrane of the photoreceptors. This study proposed that DG promotes elongation of these sensory neurons, independently to any potential role this surface receptor might play in basement membrane organization (Zhan et al., 2010). This conclusion was based on Df(2R)Dg248 mutant clones and trans-heterozygous retinas, where DG function was impaired not only in photoreceptors, but in all interommatidial cell types. Moreover, the basement membrane was not examined in this study. Our work, and the fact the bulk of retinal cell elongation occurs late in retinal development(Longley and Ready, 1995), is consistent with DG playing a role in retinal cell elongation and overall tissue thickening.

Under “Advance”:

*The 3D imaging of ommatidia development is beautiful and of good descriptive value. ** However, as mentioned in the major comments 1, 2, 3, and 8 above, I am afraid that the search of preceding literature seems insufficient, and it is often unclear what this manuscript add to existing knowledge. *

__RE: __The logic of how the reviewer links points 2, and 3 they raise as part of their review, to their assessment of how our work advances the field, is unclear to me. Their Points 2 and 3 have to do with making sure we better explain how the functional ECM transgenes were generated and by whom. The importance the reviewer places on points 2, 3 when considering the Advance our work provides to the field does not appear justified to me.

Point 1 refers to a previous study by Zusman et al., published in 1993. Using partial loss of function alleles and heat-shock inducible rescue constructs they show that bPS/Mys plays a role in eye development. They note that in adult eyes, retinal cells are not attached to their basement membrane. They show this is accompanied by a failure for the retina to elongate along the apical-basal axis. These phenotypes are consistent with a role for integrins in mediating attachment of epithelial cells to the basement membrane, and we are now referring to this work in the revised manuscript. A much more relevant reference to our work however, is (Longley and Ready, 1995), which we have used repeatedly in our manuscript to stress what was novel about our work.

Point 8 refers to a previous report implicating DG in photoreceptor elongation, which is a developmental phase that mostly occurs after the process we are studying here (please see Fig.3 of (Longley and Ready, 1995) for quantification using sections). The photoreceptors do no contribute basal profiles at the basal surface of the retina. The DysGFP signal we detect at this tissue surface, in the presumptive and established grommet, is clearly coming from the pigment cells, not from the photoreceptor axons which are found at this basal location. We now discuss this previous report, to make what is clearer what is novel about our own work.

.

Minor comments: - Line 85, "This is the case in the follicular epithelium for example". Here, the text would be more reader-friendly if the authors could clarify this is the follicular epithelium of the fly ovary.

__RE: __We have modified the text to address this comment.

- Line 203-, regarding all the experiments involving the Gal4-UAS system. Not all the readers are familiar with the system. A brief explanation on it should be added in the main text. Moreover, in the Results section, not in the Methods, the authors should show what Gal4 they used, and where is the Gal4 expressed.

__RE: __We have amended the manuscript accordingly.

*- Line 239, "We found that inhibiting the expression of the DG cofactor, dSarcoglycan [53] was most effective in inhibiting this pathway in retinal cells". Here, the authors should show the data. *

__RE: __This statement is based on the results shown in Fig.8 and Suppl. Fig.9, which make use of a PCA representation to quantify the Dg, Dys and dScg RNAi phenotypes in cell basal geometry. We have re-phrased this statement to make it clear that we are referring to the RNAi-based perturbation of these genes’ expression.

4. Description of analyses that authors prefer not to carry out

We will address all the reviewer comments as they will consolidate our findings.

Our further validation of the few RNAi lines used in our study that have not been used before in publications will also be valuable to the community.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Cell shape remodelling is essential for tissue morphogenesis. To model this event, the fruit fly Drosophila melanogaster has been widely used. In the pupal retina, ommatidial cells change their structure to form the photo-sensing machinery in the compound eye. Previous studies investigating this event mainly focused on the cell shape change at the apical plane. However, the cell shape at the basal side and the three-dimensional (3D) structure of the cells have been little studied.

In this manuscript, the authors address this issue by combining state-of-art 3D imaging and fly genetics. They report that at the initial stage of eye development, a basement membrane (BM) component Laminin accumulates at the basal side of the ommatidial cells in a manner dependent on the BM-receptor molecule dystroglycan (Dg). The authors propose that this Dg-dependent Laminin accumulation induces the polarisation of integrin at the basal surface, which is essential for proper ommatidia morphogenesis.

Major comments:

The beautiful images presented here provide interesting descriptions of the events occurring during eye development. Also, the authors propose an attractive and simple hypothesis that the Dg-dependent recruitment of Laminin leads to integrin polarisation and tissue morphogenesis. However, I'm afraid that this hypothesis is not supported enough by the presented data. In addition, the novelty of some conclusions and the reliability of a number of reagents used are unclear. Specific concerns are described below:

1. Line 169, "From these experiments, we conclude that Integrin adhesion is required for cell basal geometry remodeling during retinal morphogenesis". It has been long known that integrin is necessary for the gross morphogenesis of the eye (e.g., Zusman et al. 1993, PMID: 8076515). The authors need to cite these preceding researches and should clarify what new findings this new work adds to the previous knowledge.
2. Line 180, "Using available functional GFP protein traps [49, 50]", the authors investigate the behaviour of Laminin subunits LanA and LanB1. First, ref [50] is not relevant here and should be removed. Moreover, the Laminin-GFPs the authors used are not protein traps, but transgenic strains harbouring genes and most of their regulatory information, with the ORFs tagged with GFP [49]. Furthermore, while the ref [49] reported the functionality of LanB1-GFP, this reference did not fully address the functionality of LanA-GFP. The authors need another reference on it (PMID: 29129537), which demonstrated that LanA-GFP rescues LanA mutants.
3. Related to the issue above, in addition to LanA and LanB1, the authors examine the localisation of the following BM proteins using GFP-fusion: Perlecan/Trol, Collagen IV/Viking, Nidogen, and SPARC. The authors do not explicitly describe the nature of these GFP fusions, but I am afraid that the authors think all of them are "functional protein traps". However, in fact while Perlecan and Collagen IV are protein traps, Nidogen and SPARC are transgenics including regulatory sequences made in the ref [49]. This must be clarified. Moreover, to rely on the data obtained using these GFP fusions, their functionality must be confirmed by appropriate references or/and the authors' own data. For information, ref [62] showed the functionality of Perlecan-GFP and Collagen IV-GFP protein traps (they are both homozygous viable), and the Nidogen-GFP transgene rescues the BM deficiency of Ndg mutants (PMID: 30260959). These reports must be explained in the text, and I would like the authors collect and show more information.
4. Line 182-, LanA and LanB1 "accumulate at the center of the ommatidium, in a pattern resembling the grommet structure (Figure 4A and Supplementary Figure 4)"... "LamininA/B1 accumulation at the presumptive grommet precedes Integrin accumulation at this location. It suggests that localized Laminin might control Integrin localization in the interommatidial cells". Based on these results, the authors discuss that "generating specific polygonal geometries at the basal surface of cells starts with organizing the ECM to establish a pattern of Laminin-rich domains, distributed across the tissue basal surface" (Line 267).

As the authors write "Laminin-rich domains", I suppose that they assume that LanA/B1 accumulates in a restricted region of the BM. However, it has been reported that the majority of Laminin in the fly embryo is soluble and floating in the haemolymph (fly's 'blood' or body fluid) (PMID: 29129537). Therefore, the LanA/B1 observed in the figures might be just floating in the intercellular space and doing nothing on the BM. The authors should exclude this possibility to support their idea that Laminin localised in a specific region of the BM recruits Integrin. For example, does secreted GFP (PMID: 12062063) not behave in the same way as LanA/B1? Can the authors show that the LanA/B1 is indeed incorporated in the BM by FRAP or any methods? 5. Line 200, "These specific patterns of expression for LamininA/B1, Collagen IV, Perlecan, Nidogen and Sparc". I have several comments here: 5A. These patterns are discussed only using single optical sections. To highlight the difference in their localisation patterns more objectively, multiple sections and/or 3D images should be shown. 5B. Can the authors discuss, hypothesise, or speculate the biological meaning of the difference? 5C. It has been reported that in the mammalian skin BM, different components show distinct localisation patterns (PMID: 33972551). It would be interesting to cite this paper and discuss the generality of the non-uniform distribution of BM components. 6. Line 208, "we found that LanB2 RNAi leads to defects in bPS/Mys Integrin localization". Here, because the authors use only single RNAi, there remains the possibility that the observed phenotype was caused by an off-target effect. The authors should exclude this possibility by using another RNAi or mutants. This is the same for all the RNAi experiments. In case of LanB2, however, showing that one RNAi against LanB1 shows the same phenotype would be enough, because LanB1 is another single subunit of fly Laminin. 7. Line 215, "However, inhibiting the expression of Collagen IV, Ndg, Perlecan and Sparc individually, by expressing RNAi against these genes in all retinal cells, did not lead to defects in bPS/Mys localization". To conclude so, the authors must demonstrate that the used RNAis efficiently removed its target proteins. 8. Line 222, "DG is required to organize the ECM in several experimental settings [42, 43, 45, 51]". Here, the authors must mention to a preceding paper that reported the eye deficiency of Dg mutant flies (PMID: 20463973), and discuss what new findings authors can add to the previous report. 9. Line 240. "RNAi against dSarcoglycan led to a decrease in LanA::GFP expression at the presumptive grommet at 20h APF (Figure 7F)". As to this result, the authors seem to interpret that Laminin is not recruited to the "specific BM domain" in grommet in the absence of Dg signalling. However, other possibilities exist, e.g., that the global expression level of Laminin was reduced, or that the intercellular space into which soluble Laminin (see the issue 4 above) flows was narrowed down. The authors should show the data that exclude (or at least reduce) these possibilities. 10. Line 255, "These perturbations led to a failure of bPS/Mys to accumulate at the grommet". Dg mutants are viable (PMID: 18093579); do they show consistent phenotypes?

Minor comments:

1. Line 85, "This is the case in the follicular epithelium for example". Here, the text would be more reader-friendly if the authors could clarify this is the follicular epithelium of the fly ovary.
2. Line 203-, regarding all the experiments involving the Gal4-UAS system. Not all the readers are familiar with the system. A brief explanation on it should be added in the main text. Moreover, in the Results section, not in the Methods, the authors should show what Gal4 they used, and where is the Gal4 expressed.
3. Line 239, "We found that inhibiting the expression of the DG cofactor, dSarcoglycan [53] was most effective in inhibiting this pathway in retinal cells". Here, the authors should show the data.

Referee cross-commenting

This session includes comments from both reviewers

Reviewer 2: I almost totally agree with Reviewer 1, who is also mainly concerned about the functional analyses part of the paper while being impressed by the authors' beautiful imaging. One issue that Reviewer 1 and I apparently disagree with is the Estimated time to Complete Revisions: while they say 1-3 months, I say 3-6. However, actually I don't think this is a serious discrepancy. Thinking of the time to obtain flies and carry out their crosses necessary for the requested experiments, I'm afraid that the revision cannot be done in 1 month. However, if the authors are fortunate, they may finish the revision in 2-3 months. As I still think that the authors may struggle, I would say the time 2-6 months. I'd be glad if the comments of Reviewer 1 and me could complement with each other to help the revision of the manuscript.

Reviewer 1:As Reviewer #2 mentioned, there is a strong convergence of our opinions on this article, which should make the work of the authors easier. In fact, I hesitated between 1-3 or 3-6 months for the estimated revision time.

Reviewer2: Thank you Reviewer #1 for your response. I guess we (Reviewers #1 and #2) have reached an agreement now, haven't we?

Significance

General assessment:

The beautiful images presented here provide interesting descriptions of the events occurring during eye development. Also, the authors' hypothesis on the Dg-dependent recruitment of Laminin leading to integrin polarisation and tissue morphogenesis is simple and attractive. However, I'm afraid that this hypothesis is not supported enough by the presented data. In addition, the novelty of some conclusions and the reliability of a number of reagents used are unclear. Therefore, I cannot say that the conclusions of this manuscript are solid.

Advance:

The 3D imaging of ommatidia development is beautiful and of good descriptive value. However, as mentioned in the major comments 1, 2, 3, and 8 above, I am afraid that the search of preceding literature seems insufficient, and it is often unclear what this manuscript add to existing knowledge.

Audience:

If the issues mentioned above have been solved, this manuscript would be of general interest to researchers in various fields in cell and developmental biology. Would not be restricted to those using Drosophila.

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Referee #1

Evidence, reproducibility and clarity

Walther and colleagues address the role of the cell/ECM interface for cell shape, focusing on the basal domain of epithelial cells. More specifically, they present a thorough descriptive approach on the intricate morphology of the neuroepithelial cells composing Drosophila ommatidia in the retina and relate it to the organization of the basal lamina and the complexes interacting with it, Dystroglycan and integrins. Based on genetics and quantitative imaging approaches, they propose a linear mechanism where 1) Dystroglycan organizes the basal membrane 2) this organization guides the localization of integrins 3) integrin localization defines the shape of the basal domain.

Major comments:

First, the 3D description of the ommatidia organization is really nice and interesting, refreshing quite old data using better imaging tools. In particular, it illustrates the extreme difference in morphology between the apical pole and the basal pole of the cells composing the ommatidia, making it a paradigm for understanding how the basal shape is defined independently of the apical one. It also provides a nice and detailed spatiotemporal cartography of basement membrane components, integrin and dystroglycan. However, functional data are less convincing, mainly for technical issues, but could be really improved in a reasonable timeline. My criticisms mainly converge on two aspects of the experimental work :

1) Genetics :

• All the genetics experiments are based on RNAi induced knock-down approach. Although such an approach is easy to justify for genes associated with lethality when mutated, it becomes less relevant for non-lethal ones as Dystroglycan complex components (Dg, Dys, Sgc) for which null and viable mutants are published and available. The phenotype of such mutants should be provided.
• There is no data explaining how these RNAi lines were validated. The fact that it gives the phenotype expected by the authors is obviously not sufficient. This point is essential to exclude off-target effects and to be able to compare the different genotypes (see #2). For instance, the strong effect of sarcoglycan could be questioned. Is it really specific? If yes, is the difference with other Dystroglycan complex members only due to RNAi efficiency or does it have a specific function?
• Methods section describing genetic conditions is really sketchy. The genotype corresponding to each figure is not provided and I guess that GMR-Gal4 has been used in all experiments using the Gal4 system but it is never clearly stated.

2) Image analysis by PCA

After segmentation, authors analyzed their images by PCA using various parameters, which allows them to discriminate between two cell populations that correspond to SC and TC. Then, whatever the genotype they studied, PCA failed to separate those two cell populations leading the authors to propose that they all lead to similar morphological defects, arguing for a linear pathway Dystroglycan / BM / integrins. However, this approach raises many questions: - In the WT situation it would be really informative to know which variable(s) is/are really discriminant between the two cell populations and then maybe to focus a bit more on these parameters. For instance, a PCA correlation circle plotting both cells and variables would be very helpful. - In loss of function conditions, when the tissue is strongly affected, how do the authors recognize the two cell populations if PCA cannot? On the opposite, based on the provided image, Dys RNAi seems to have a mild effect and it seems that my eyes can easily recognize those two cell populations based on their shape. So why PCA cannot? - Based on the proposed images, some phenotypes look clearly different depending on the genotype, e.g. Talin and Mys (figure 3) or Dys and Sgc (Figure 8). In other words, the fact that PCA cannot separate the cell pollutions in these different genotypes does not necessarily mean that their effect is identical. Could authors perform PCA analysis between mutants? If they are different, again it might be very interesting to identify the discriminating parameters. - Authors claimed that laminin RNAi (or MMPs overexpression) affects cell geometry but why it is not analyzed by PCA? It is not consistent with the other figures. - From what I can understand, each PCA analysis has been done on a single retina. If true, more replicates should be included. If not true, the number of independent retinas should be mentioned.

Minor comments: - Globally, the article suffers from a lack of details, especially in the methods section and/or in figure legends.

Also, several points could be advantageously discussed. For instance, why MMPs have different effects according to their specificity? Also, what could be the meaning of the nice differential pattern between integrin alpha subunits?

In Methods, a list of metrics is given for the PCA analysis but some look very similar and it would be helpful to define them briefly.

Figures are not always color-blind adjusted (e.g. dots on PCA graphs).

Significance

This article addresses a relevant and still poorly answered question, which is how the shape of epithelial cells is defined on the side of their basal domain. Indeed, the vast majority of studies tackle this issue for the apical domain with the role of adherens junction and their regulators. Instead, here, the authors explore the role of BM/cell interface. They especially propose a specific sequence of events leading in fine to the proper subcellular targeting of integrins. Whereas many studies on other systems have reached similar conclusions on each of the different steps of this sequence, the main interest of the paper is to bring them together, allowing the proposal of a general framework. Of notice, they made it possible by first doing a nice description of their system. However, functional analysis is somehow superficial and does not really provide mechanistic clues for each step (i.e. how Dystroglycan allows BM assembly and/or secretion, how Integrins controls cell shape.... ).

Nonetheless, such an article might interest anyone working on tissue morphogenesis in vivo or ex vivo and wondering what the role of cell/BM interplay could be in its own system. Moreover, these protein complexes are highly conserved and involved in many diseases in humans. Thus, getting a more global understanding of their relationships is also relevant for readers working on the aetiology and pathophysiology of those diseases.

I am a developmental biologist interested in morphogenesis.

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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

The authors presented a comprehensive analysis of the effects of RBM12 on cAMP signaling and cAMP-induced transcription. All data point to hyperactivity in the absence of RBM12, suggesting that RBM12 negatively regulates cAMP signaling, particularly transcriptional response to CAMP in the nucleus. The authors found that increased expression of two adenylyl cyclase isoforms and reduced expression of PKA regulatory subunit and some isoforms of cAMP-destroying PDE is the molecular basis of excessive cAMP signaling in the absence of RBM12. The authors showed that two disease-associated RBM12 mutants are loss-of-function, as, in contrast to WT RBM12, they fail to normalize cAMP signaling and transcriptional response. The authors should be commended for confirming their findings in iPSC-derived neurons. The study is well performed and clearly described.

Significance

Identification of earlier unappreciated mechanism regulating cAMP signaling has broad implications.

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors have previously carried out a CRISPR screen of b2-AR regulators of transcriptional responses. In this manuscript, the authors proceed to characterize one of the top candidates from this screen, RNA-binding motif 12 (RBM12). They perform numerous studies in HEK293 cells and iPSC-derived neurons to show that loss of RBM12 leads to a hyperactive response of ligand-induced b2AR transcription, cAMP responses and PKA activity. This increase in transcriptional responses is independent in changes of b2AR receptor expression or internalization and can be observed upon activation of other Gs-coupled GPCRs, namely ligands for adenosine A1/2R and dopamine D1R. The hyperactive transcriptional responses can also be mimicked in wild-type cells with forskolin, isoproterenol plus phosphodiesterase inhibitor, or use of cAMP analog 8-CPT-cAMP. The authors also show that variants of RPM12, that lead to familial psychosis, show hyperactive responses to isoproterenol and cannot rescue loss of wild-type RPM12. Finally, transcriptomics of loss of RPM12 in iPSC-derived neurons show altered transcriptional profiles upon stimulation with b2AR agonists.

Major comments

Overall this is a comprehensive study of the effects of RBM12 on cAMP-dependent transcriptional responses. The study is highly rigorous and the authors generate several novel findings, supplying a mechanism for disease-altering variants for RBM12. There are a few issues that distract somewhat as detailed below.

1. The authors are highly focused on the GPCR responses; thus, they fail to discuss the fact that supplemental figures 1D-F show that the effects of RBM12 lie downstream of the receptor and are independent of b2AR. Stimulation with forskolin shows prominent enhancement of cAMP accumulation, pointing to enhancement of adenylyl cyclase and/or decreases in PDE activity. This effect should be quantitated. The is consistent with the fact that stimulation with multiple Gs-coupled receptors show similar enhancements. Given that, much of Fig 2, particularly 2E should be moved to supplemental. The order of figures may need to be re-examined.
   • 1b. The cAMP responses in Supplemental Fig 3 should also be quantitated with statistics.
2. The use of 8-CPT-cAMP is not appropriate as a pure cAMP analog. It not only activates PKG, but it can also increase cGMP due to inhibition of phosphodiesterases that breakdown cGMP (PDE5).
3. It is not clear why the authors are overexpressing the b2AR in the iPSC-derived neurons. The application of isoproterenol under conditions of overexpressed receptor is likely similar to stimulating the cell with forskolin or any agonist of an overexpressed Gs-coupled receptor. Thus it appears to be a stretch to call these "b2AR Targets". Moreover, although it is true that loss of PDE activity and/or RII subunits may contribute to loss of compartmentalization of signaling, overexpression of the GPCR could also lead to loss of compartmentalization. This must be discussed.
   • 3b. The actual list of genes in Table 3 should be shown (not just GO terms).
   • 3c. The fact that PDE1C was decreased could point to more than just cAMP-induced transcriptional changes. This is a dual PDE and its decrease may also increase cGMP.

Minor comments:

Missing "ISO" labels for Fig S2 C, D Need better labeling of bars for Fig 7B

Significance

This is a very rigorous and detailed study to characterize a novel regulator of cAMP signaling systems. Although the data do not support, RBM12 as a specific regulator of beta2AR signaling, it is nevertheless an important regulator of general cAMP signaling and could potentially have effects on cGMP signaling. The study has clinical value, as RPM12 variants drive familial psychosis but this study will also appeal to basic scientists.

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Referee #1

Evidence, reproducibility and clarity

RBM12 is an RNA-binding protein predominantly localized to the nucleus. Mutations in RBM12 have been linked to heritable psychosis and neurodevelopmental defects. The gene appeared in a previously published CRISPRi screen for potential regulators of cAMP signaling. The authors confirm that loss of RPM12 leads to increased basal and induced cAMP, activation of cAMP-dependent protein kinase, and induction of cAMP-CREB induced gene transcription. Similar effects were seen following direct activation of adenylyl cyclase and overexpression of the kinase catalytic subunit. The figures are clearly presented, well controlled, and show significant differences that largely support the central conclusion that RBM12 regulates cAMP. The authors have taken on an extremely challenging problem. The paper is very well written.

Significance

The significance of the findings are limited for a number of reasons, which the authors acknowledge as summarized under major comments. We know from prior CRISPRi work that RBM12 regulates cAMP signaling (ref. 8). The findings are publishable, but the advance is modest and the potential target audience is specialized.

Major comments.

First, the authors are not able to mechanistically link RBM12 to any particular component of the cAMP pathway. Without a mechanistic link, direct or otherwise, to proteins to make or degrade cAMP, the findings are descriptive.

OPTIONAL: Does RBM12 bind to and regulate a subset of mRNAs or proteins that are part of the pathway?

Second, while the link to psychosis and neurodevelopmental defects figures very prominently in the title and text, the link to psychosis is not supported by the approach and most of the text should be removed. The experiments performed here don't come close to recapitulating the physiological setting. Is there ever a situation where individuals don't express any RBM12 (patients with the variants will be heterozygous)?

OPTIONAL: What happens in a mouse model?

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Reply to the reviewers

We would truly like to thank all 3 reviewers for insightful, helpful and thus constructive comments.

Reviewer #1

Summary

In this manuscript, Lockyer et al. provide novel insights into the mechanism by which Toxoplasma gondii avoids parasite restriction in IFNγ-activated human cells. To identify potentially secreted proteins supporting parasite survival in IFNγ-activated human foreskin fibroblasts (HFF), the authors designed a CRISPR screen of Toxoplasma secretome candidates based on hyperLOPIT protein localization data. By this approach, they identified novel secreted proteins supporting parasite growth in IFNγ-activated cells. Among the gene identified, they found MYR3 a known component of the putative translocon in charge of protein export through the parasitophorous vacuole membrane. Therefore, the authors focused their investigations on GRA57, a dense granule protein of unknown function, which affects parasite survival to a lesser extent than the MYR component. The resistance phenotype conferred by GRA57 was confirmed by fluorescence microscopy. Importantly, the authors provide evidence that the protective function of GRA57 is not as well conserved in murine cells of the same type (MEF) as in HFF. To further explore the mechanism by which GRA57 protect the parasites in IFNγ-activated cells, the authors searched for protein partners by biochemistry. By immunoprecipitation and tandem mass spectrometry, they identified two other putative dense granule proteins, GRA70 and GRA71, which co-purified with GRA57-HA tagged protein. Noteworthy, both proteins were also found in the CRISPR screens with significant score conferring resistance. High-content imaging analysis confirmed the protective effect conferred by GRA57, GRA70, and GRA71 individually at similar levels. After ruling out an effect of tryptophan deprivation in parasite clearance, or a role of GRA57 in protein export normally mediated by the MYR translocon, and a role on host cell gene expression by RNA-Seq, the authors investigated the ubiquitination of the parasitophorous vacuole membrane, a marker previously thought to initiate parasite clearance. A reduction in ubiquitin labeling around the vacuole of mutant parasites is observed, which is quite surprising given the correlated increase in parasite clearance. The authors concluded that ubiquitin recruitment may not be directly linked to the parasite clearance mechanism.

Major comments

• Figure 2C. In this figure, the restriction effect of IFNγ is about 60% (or 40% survival) for RHdeltaUPRT parasites grown in HFFs, which is quite different from the 85% mentioned earlier in the results section. How was actually done the first assay? Settings with 60% restriction sounds reasonable and indicates that a substantial fraction of the parasite population evades the restrictive effect of IFNγ, which provides a clear rationale for the main objective of this study, namely the identification of effectors supporting parasite development in human cells in the presence of IFNγ.

This discrepancy in restriction likely arises from the differences in the parasites used in these assays and the measurements of restriction. The 85%/90% restriction initially mentioned is from the pooled CRISPR screens using the effector knockout pool. This restriction level was assessed by counting of parasites retrieved following infection of IFNg-stimulated HFFs. The 60% restriction of wildtype parasites seen in Figure 2 is a separate assay. This percentage was calculated by measuring total mCherry fluorescence area within infected HFFs. We expect the restriction of the pooled CRISPR population to be higher than in restriction assays performed with either wild type parasites or single genetic knockouts. We included the 85%/90% numbers to highlight that the HFFs were highly restrictive in the screen, but we have now removed references to these numbers in the results section to avoid confusion with later results that use more accurate measures of survival. We refer to this restriction level instead in the discussion section.

Optional comment: GRA70 and GRA71 were both copurified with GRA57, but what about GRA71 expression and localization? Is there a reason why this protein partner has not been studied further just like GRA70?

Tagging of GRA71 was attempted but was not successful in a first attempt. We have not re-attempted this tagging as Krishnamurthy et al 2023 (PMID: 36916910) recently tagged and localised GRA71, demonstrating it is also an intravacuolar dense granule protein with similar localisation to GRA57 and GRA70- we feel there is minimal value in us repeating this.

*Is there any change in GRA57, GRA70, and GRA71 localization and/or amount when cells were pretreated with IFNγ? *

Thank you for this suggestion, we have now conducted further investigation to address this. We checked the localisation of GRA57-HA and GRA70-V5 in IFNg-stimulated HFFs and found no change to their localisation. This data has been added in Supplementary Figure S4 in our revised manuscript. Alignment of our RNA-Seq data to the Toxoplasma genome, now included as Supplementary Data 4, also shows there is no significant up or downregulation in expression of any of the three proteins when HFFs are pretreated with IFNg.

Do they still form a complex in the absence of IFNγ?

We did not investigate this in this manuscript, however in Krishnamurthy et al 2023 (PMID: 36916910) CoIPs using GRA57 and GRA70 in the absence of IFNγ also identified these three proteins as interaction partners, so formation of the complex is likely IFNg-independent.

• In the absence of GRA70 or GRA71 is GRA57 expression and/or localization affected?*

We did not investigate this possibility in this manuscript, however doing so would require the generation of epitope tagged lines in knockout backgrounds. We believe this represents a significant body of work and would therefore be suitable for a future study focused on the further characterisation of this complex. The RNA-Seq data shows that GRA70 and GRA71 expression levels are not significantly different in the RH∆GRA57 strain (Supplementary Data 4) which we have now included as a statement in the results section.

• *Page 13, result section. To determine whether GRA57 has any direct or indirect effect on host cell gene expression, the authors performed RNA-Seq analysis of HFF cells pretreated or not with IFNγ. First, as for proteomic data, were the data deposited on GEO or another repository database? *

Second, were any effect detected on parasite gene expression? Reads alignment could be done using the T. gondii reference genome to determine whether IFNg or gra57 KO has any effect on parasite genes. Possibly, other secreted proteins not necessarily expressed at the tachyzoite stage and therefore not captured in the hyperLOPIT protein analysis are specifically expressed in these conditions.

We will deposit the RNA-Seq data on GEO prior to final publication. We did perform read alignment using the Toxoplasma gondii reference genome, and we agree it would be useful to include this analysis. We have now provided this data in Supplementary Data 4. Comparison of parasite gene expression between RH∆Ku80 and RH∆GRA57 revealed very few major changes (L2FC 2) that were also rescued in the RH∆GRA57::GRA57 line, irrespective of IFNg stimulation. Of the few genes that were up or downregulated in the RH∆GRA57 parasites, these were all uncharacterised. Collectively this data did not provide any mechanistic insight into the function of GRA57, and we think it unlikely the GRA57 phenotype is related to major changes in host or parasite gene expression. We have amended the manuscript to highlight this.

Optional comment: RNA-Seq analysis points to a clear induction of GBPs upon IFNγ treatment in HFF. Given the clear function of GBP in parasite clearance, have the authors ever hypothesized that GRA57 could be involved in preventing GBP binding to the PVM?

We have not tested if GBP recruitment is influenced by GRA57, however GBPs have previously been shown to be dispensable for restriction of Toxoplasma growth in HFFs (Niedelman et al 2013, PMID: 24042117) despite being robustly induced by IFNg stimulation (Kim et al 2007, PMID: 17404298). We have modified the manuscript to highlight this.

Minor comments

• Page 4, introduction, 8th paragraph. Regarding the role of IST, it might be less prone to controversy to state: 'a condition that may only be met in the early stages of infection.'

We agree and have changed this.

• Page 4, end of introduction. Changing '... indicating that the three proteins function in a complex'. Changing to '... indicating that the three proteins function in the same pathway.' might be more appropriate for the conclusion.

We agree and have changed this.

• Page 4, result section, first paragraph. 'strain specific and independent effectors'. Are the authors talking about strain-specific and non-strain-specific factors?

Yes- we have changed the text to reflect this.

- Page 6, result section. 'GRA25, an essential virulence factor in mice'. It is not clear to the reviewer how a virulence factor is essential since both parasite and mouse survival is achieved in the GRA25 mutant. I suggest to replace 'essential' by 'major'.

We agree and have changed this.

- Page 7. 'showing that GRA57 resides in the intravacuolar network (IVN) (Figure 2A)'. From the image shown, GRA57 clearly localizes into the PV, but it is hard to tell whether GRA57 is associated with the intravacuolar network. Colocalization assay or electron microscopy would be necessary to draw such conclusions.

We agree and have changed all references to this localisation as ‘intravacuolar’ instead of specifically the IVN.

- 'uprt locus'. Lower case letters and italic are generally preferred to designate mutants, whereas upper case letters are generally used for wild type alleles. (Sibley et al., Parasitology Today, 1991. Proposal for a uniform genetic nomenclature in Toxoplasma gondii).

We agree and have changed this.

- The authors mentioned in the introduction that ROP1 contributes to T. gondii resistance to IFNγ in murine and human macrophages. However, they did not comment on whether ROP1 was found important in the screen performed here in human HFF cells. It may be useful to reference ROP1 in Figure 1 as GRA15, GRA25, etc.

ROP1 was not found to be important in the HFF screens (+IFNg L2FCs in RH: -0.1, PRU: -0.46). As ROP1 was characterised as an IFNg resistance effector in macrophages, this discrepancy may therefore represent a cell type-specific difference, so we feel it is not relevant to highlight for the purposes of the screens presented here.

- Figure 2D. The authors compared the restriction effect of IFNγ on parasites grown in HFF and MEF host cells. However, as represented - % + IFNγ/- IFNγ - it cannot be estimated whether the parasites grew similarly in the two host cell types in the absence of IFN. Please indicate whether or not the growth was similar in both cell types.

As these restriction assays were not carried out concurrently and were designed to measure IFNg survival, we feel it would be inaccurate to compare parasite growth between the two cell types using this data. The focus of these experiments was to investigate the restrictive effect of IFNg across parasite strains, using the -IFNg condition to control for differences in growth rate or MOI. Therefore we feel it is appropriate for the focus of our manuscript to represent the data in this way.

- pUPRT plasmid. Any reference or vector map would be appreciated.

We have added the reference for this plasmid.

- Page 9, figure 3A, mass spectrometry analysis. I did not find the MS data in supplementals. Were the data deposited in on PRIDE database or another data repository?

The table was included as Supplementary Data 2, however this was not referred to in the main text. We have now amended the text to include this. The data will be deposited on PRIDE prior to final publication.

- Figures 3E and 3F. It might be worth mentioning, at least in the figure legend, that GRA3 localizes at PV membrane and is exposed to the host cell cytoplasm (to mediate interactions with host Golgi). The signal for GRA3 following saponin treatment is here an excellent control that should be highlighted, indicating that saponin effectively permeabilized the host cell membrane.

We agree and have updated the figure legend and the main text. We have also added a reference to Cygan et al 2021__ (__PMID: 34749525) in support of this data, which found GRA57, but not GRA70 or GRA71, enriched at the PVM.

• Page 11, section title. I think that the authors meant 'GRA57, GRA70 and GRA71 confer resistance to vacuole clearance in IFNγ-activated HFFs.'

We agree and have changed this.

• Page 11, in the result section comparing the effect of GRA57 mutant with MYR component KO, the authors are referring to host pathways that are counteracted by MYR-dependent effectors released into the host cell. It is not clear which pathways the authors are referring to.

It is not known exactly which host pathways mediate vacuole clearance or parasite growth restriction, or which MYR-dependent parasite effectors specifically resist these defences, therefore we have removed this statement from the text for clarity.

• Page 16, discussion, end of 4th paragraph. '... to promote parasite survival in IFNγ activated cells' sounds better.

We agree and have changed this.

• Page 22-23, Methods section, c-Myc nuclear translocation assays and elsewhere. Please indicate how many events were actually analyzed. For example, in this assay, to determine the median nuclear c-Myc signal, how many infected cells were analyzed for each biological replicate?

We have updated the methods section for the c-Myc nuclear translocation and ubiquitin-recruitment assays to include details on how many events were analysed.

**Referees cross-commenting**

Overall, I agree with most of the co-reviewers' remarks. I agree with reviewer #2 that this manuscript reports interesting data for the field of parasitology, but that the broad interest for immunologists is somewhat limited by the lack of a description of the mechanism by which these effectors oppose IFNgamma-inducible cell-autonomous defenses. I also agree with the other reviewers' comments regarding the GRA57, 70, and 71 heterotrimeric complex, which would require further description. In its present form, the manuscript undoubtedly represents an interesting starting point for further investigations and any additional data regarding the mode of interaction of the identified effectors and their function related or not to ubiquitylation would bring a significant added value.

Reviewer #1 (Significance (Required)):

Despite the fact that humans are accidental intermediate hosts for Toxoplasma gondii, the parasite may develop a persistent infection, demonstrating that it has effectively avoided host defenses. While Toxoplasma gondii has been extensively studied in mice, much less is known about the mechanisms by which the parasite establishes a chronic infection in humans. In this context, this article described very interesting data about the way this parasite counteracts human cell-autonomous innate immune system. This is a fascinating and important topic lying at the interface between parasitology and immunology. Indeed, the highly specialized secretory organelles characteristics of apicomplexan parasites are key to govern host-cell and parasite interactions ranging from host cell transcriptome modification to counteracting immune defense mechanisms. Overall, this article presents a significant contribution to the field of parasitology by identifying novel players involved in Toxoplasma gondii's evasion of human cell-autonomous immunity. Most conclusions are generally well supported by cutting-edge approaches and state of the art methods. Despite being a highly competitive field, this article stands out as the first screen designed specifically to identify virulence factors for human cells and extends our understanding of the secreted dense granule proteins resident of the parasitophorous vacuole. Importantly, the authors provide evidence that these players are active in different strain backgrounds and act in a way that is independent of the export machinery in charge of delivering effector proteins directly into the host cell. However, substantial further research is needed to fully understand the mechanism by which these novel players confer resistance to the parasite in IFNγ activated human cells and how their mode of action differs from that mediated by the translocation machinery (MYR complex). As a microbiologist and biochemist, I find this work of a particular interest to a broad audience, especially to parasitologists and immunologists, as it may unveil unexpected aspects of human innate immunity involved in parasite clearance with proteins unique to Apicomplexa phylum.

Reviewer #2

This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

Major:

- It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?

We did not use a specific statistical metric to define screen hits as strain-dependent vs strain-independent, but GRA57 was selected as a strain-independent hit based on having a L2FC of RH specific: TGME49_309600 (GRA71) & CST9

PRU specific: GRA35, GRA25, ROP17, GRA23 & GRA45

Strain-independent: MYR3, GRA57, TGME49_249990 (GRA70) & MYR1

This agrees with our selection of strain-independent hits. However, we feel that using either L2FC or Z-score cut-offs is equally arbitrary, and we would therefore prefer to leave the data displayed without these cut-offs. It is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore this further here.

*- The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work, *

We attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately failed to verify the MS data, but we believe this is primarily due to differences in the affinity matrix that we used for this pulldown (anti-V5 vs anti-HA) and would require further optimisation or generation of a GRA70-HA line. However, while these revisions were being performed, another group published data demonstrating through pulldown of GRA57 and GRA70 that these proteins interact with each other, GRA71, and GRA32__ (__Krishnamurthy et al 2023, PMID: 36916910). We also identified GRA32 as enriched in our MS data, but to a less significant degree than GRA70 and GRA71. Together we believe that this independent data set is a robust validation of our findings, and strongly justifies the conclusion that these proteins form a complex.

We agree with the reviewer that further biochemical characterisation of the complex will be an interesting avenue for future research, but we feel it would require a substantial amount of further work. As suggested, super-resolution microscopy of the 3 proteins would require the generation of either double or triple tagged Toxoplasma lines, or antibodies against one or more of the complex members. Again, we feel this would represent a substantial body of further work. Reconstitution of the complex in vitro would require recombinant expression and purification of multiple large proteins that are all multidomain and possibly membrane associated/integrated. Assuming a 1:1:1 stoichiometric assembly this complex would be 446kDa. Purification of such proteins and reconstitution of the complex in vitro is therefore likely to represent many challenges and we do not feel this would be trivial to accomplish.

- The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction

We agree with the reviewer that the possibility remains that GRA57 knockouts are cleared within the first hour of infection, and we have amended our text to reflect this. However, we think this is unlikely given that GRA57 knockouts are also less ubiquitinated in unstimulated cells, yet do not show any growth differences in unstimulated HFFs. Also considering the new data we have provided showing reduced recognition of GRA57 knockouts by the E3 ligase RNF213 (Figure 5D), we expect that the observed reduction in ubiquitination is highly likely to be unlinked to the increased susceptibility of GRA57 knockouts to IFNg. We have amended the discussion to state this conclusion more strongly.

The recently published manuscript that also identified GRA57/GRA70/GRA71 as effectors in HFFs showed that deletion of these effectors leads to premature egress from IFNg-activated HFFs__ (__Krishnamurthy et al 2023, PMID: 36916910). In light of this new data, we hypothesised that early egress could be causing the apparent reduction in ubiquitination. We have now provided data that disproves this hypothesis (Figure S10), as inhibition of egress did not rescue the ubiquitination phenotype. We also did not observe enhanced restriction of GRA57 knockout parasites at 3 hours post-infection (Figure S10B), suggesting clearance, or egress, happens after this time point.

We agree with the reviewer that determining the kinetics of IFNg restriction of these knockouts in HFFs would be interesting, however we feel this is more suited to future work. Imaging ubiquitin recruitment in live cells would also require the generation of new reporter host cell lines which would require a substantial amount of further work.

- Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

We agree that these experiments are within the scope of this study. We have now investigated the ubiquitin phenotype further by assessing the recruitment of M1, K48 and K63 ubiquitin linkages to the vacuoles of GRA57 knockouts. We observed depletion of both M1 and K63 linked ubiquitin. This data is now included in Figure 5 and Figure S8.

The E3 ligase RNF213 has recently been shown to facilitate recruitment of M1 and K63-linked ubiquitin to Toxoplasma vacuoles in HFFs (Hernandez et al 2022, PMID: 36154443 & Matta et al 2022, DOI: https://doi.org/10.1101/2022.10.21.513197 ). We therefore additionally assessed the recruitment of RNF213 to GRA57 knockouts, and found RNF213 recruitment was also reduced. Given that a reduction in RNF213 recruitment should correlate with a decrease in restriction, this data further supports our conclusion that the ubiquitin and restriction phenotypes are not causally linked. The observation that GRA57 knockouts are less susceptible to recognition by RNF213 also opens an exciting avenue for further research into the host recognition of Toxoplasma vacuoles by RNF213, for which currently the target is unknown.

Minor:

- For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries

We agree and have added this in.

- the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

As mentioned above, we agree that it is indeed interesting that there appear to be more strain-specific hits in the PRU screen, but we cannot speculate as to why this may be as we did not explore the reasons for this further in this manuscript. Without substantial further investigation it cannot be determined whether these represent true strain-specific differences or reflect technical variability between the independent screens. We therefore feel it is sufficient to highlight effectors with the strongest phenotypes in each screen, without drawing strong conclusions regarding strain-specificity.

**Referees cross-commenting**

My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

Reviewer #2 (Significance (Required)):

The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

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

Summary:

Toxoplasma gondii virulence and immune responsed upon infection in mice are well described. In contrast, little is known about human responses, particularly upon IFNγ-activation. However, host ubiquitination of the parasitophorous vacuole has been shown to be associated with parasite clearence in human cells.

Targeted CRISPR screens were used in the type I RH and type II Pru strain of Toxoplasma gondii to identify dense granule and rhoptry proteins. Human foreskin fibroblasts (HFFs) stimulated with IFNγ were used for infection of the knock-out parasites to identify guide RNAs and thus their corresponding genes to identify genes conferring growth benefits. Beside components of the MYR translocon, gra57 was identified. This gene was then knock-out or epitope-tagged in RH. The tagged line confirmed GRA57 localisation in the intravacuolar network confirming previously published work from another lab. Knock-out of gra57 lead to a moderate decrease in survival in HFFs, but not in mouse cells. Co-immunoprecipitation experiments with GRA57 identified 2 dense granule proteins that also display IFNγ-specific phenotypes with similar localisation as GRA57, and all are resistance factors in IFNγ-activated HFFs. Knock-out of GRA57 does not impact tryptophan metabolism, effector export of gene expression of the host cells. However, deletion of GRA57 or its interaction partners reduces ubiquitination of the parasitophorous vacuole.

Major comments:

This is a well executed study with informative, novel data. Here a few comments and questions:

- LFC cut-off of the CRISPR screen should be clearly stated.

We have amended this in the text.

- What is the rationale for using Prugniaud as the type II strain of choice and not ME49?

Both ME49 and PRU strains are widely used in the field, but as the PRU strain was used previously by our group for in vivo screens of Toxoplasma effectors (Young et al 2019 PMID: 31481656, Butterworth et al 2022 PMID: 36476844) ,using PRU here allows for direct comparison of our screening datasets.

- Figure 4A does not list all the significant genes that are then mentioned in the text below. This should be amended.

It is unclear what the reviewer is referring to here (Figure 4A displays restriction assay data).

*- RNA-Seq data is inadequately presented. Although, the actual genes regulated may be of secondary importance in this study, it would still be good to have a few key genes mentioned as a quality control statement. *

This was also raised by reviewer 1. We have now modified the manuscript to highlight that we observed robust induction of interferon-stimulated genes in our IFNg-treated conditions, but minimal differential gene expression between HFFs infected with the different parasite strains.

*- It is stated that "...GRA57 is not as important for survival in MEFs as in HFFS". With no significant change observed, it should be re-phrased to something like ""...indicatin that GRA57 is s important for survival in MEFs as in HFFS." *

We have re-phrased this statement.

*- Optional: GRA57 was described by the Bradley lab to be in the PV in tachyzoites and in the cyst wall in bradyzoites. Although it tissue cysts are not the focus of this paper and the knock-out is created also in a cyst-forming strain, it would have been useful to look for a phenotype of the knockout in cysts, in vitro at least, better both in in vitro and in vivo. In future, this could also be useful for the authors bringing in more citations. *

We agree with the reviewer that the impact of GRA57 on cyst formation would be an interesting topic for further exploration, however the focus of our study is on the role of secreted Toxoplasma effectors during the acute stages of infection.

Minor comments:

- Line numbers would be useful for an efficient review process.

We have added these to the revised manuscript.

- Strictly speaking, we have to talk about the sexual development taking place in felid and not feline hosts (Introduction; Felidae versus Felinae).

We have amended this in the text.

- Please insert spaces between numbers and units.

We have corrected this.

- Domain structures are presented, but maybe the AlphaFold 3D predictions could be added in a supplemental figure?

For GRA70 and GRA71 the AlphaFold 3D predictions are readily available on ToxoDB, whereas for GRA57 the prediction is not available due its size. We therefore independently analysed GRA57 using the full implementation of AlphaFold 2 (not ColabFold). We attempted submissions of putative discrete domains as well as the full-length protein, however both approaches yielded predictions with low confidence and low structural content, except for a ~100aa region of helical residues. We chose not to include the AlphaFold 3D predictions for all three proteins as the confidence for these predictions is low with pLDDT scores of commonly *- To improve the confidence of the co-immunoprecipitation, it would be necessary to use another tagged protein GRA70 or 71) and see if the same complex can be pulled down. Like this, one could also address what happens in a GRA57KO line? Do GRA70 and 71 stay together in the absence of GR57 forming a dimer? *

Reviewer 2 raised a similar point regarding the reciprocal pulldown, please see above for our detailed response to this. As suggested, we attempted a reciprocal pulldown using our GRA70-V5 line which unfortunately did not reconstitute the complex, but we believe this was due to technical differences in the epitope tag (V5 vs HA) and affinity matrix used. Overall, we believe that more detailed study of the assembly and biochemistry of this complex will require substantially more work and the generation of further cell lines, which would be beyond the scope of this study.

Reviewer #3 (Significance (Required)):

Significance:

This study endeavours to start closing an important knowledge gab of host defence in non-rodent hosts, especially humans. The data is solid using two different strains and yields novel insights into players of host cell resistance in humans against T. gondii. Using a targeted screening approach of rhoptry and dense granule proteins, they focused their interest on a subcategory of secreted proteins. The authors have not limited themselves to the screening and localisation study, but also investigated effect on host cells and host cell response. The identification of GRA57 being an important resistance factor and forming a heterodimer with GRA70 and GRA71 is novel. This study is of interest to cell biologists in the field of cyst-forming Coccidia, especially T. gondii and researchers interested in host resistance, parasite clearance by the host and parasite virulence.

I am a cell biologist working in Toxoplasma gondii and other Coccidians.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Toxoplasma gondii virulence and immune responsed upon infection in mice are well described. In contrast, little is known about human responses, particularly upon IFNγ-activation. However, host ubiquitination of the parasitophorous vacuole has been shown to be associated with parasite clearence in human cells.

Targeted CRISPR screens were used in the type I RH and type II Pru strain of Toxoplasma gondii to identify dense granule and rhoptry proteins. Human foreskin fibroblasts (HFFs) stimulated with IFNγ were used for infection of the knock-out parasites to identify guide RNAs and thus their corresponding genes to identify genes conferring growth benefits. Beside components of the MYR translocon, gra57 was identified. This gene was then knock-out or epitope-tagged in RH. The tagged line confirmed GRA57 localisation in the intravacuolar network confirming previously published work from another lab. Knock-out of gra57 lead to a moderate decrease in survival in HFFs, but not in mouse cells. Co-immunoprecipitation experiments with GRA57 identified 2 dense granule proteins that also display IFNγ-specific phenotypes with similar localisation as GRA57, and all are resistance factors in IFNγ-activated HFFs. Knock-out of GRA57 does not impact tryptophan metabolism, effector export of gene expression of the host cells. However, deletion of GRA57 or its interaction partners reduces ubiquitination of the parasitophorous vacuole.

Major comments:

This is a well executed study with informative, novel data. Here a few comments and questions:

• LFC cut-off of the CRISPR screen should be clearly stated.
• What is the rationale for using Prugniaud as the type II strain of choice and not ME49?
• Figure 4A does not list all the significant genes that are then mentioned in the text below. This should be amended.
• RNA-Seq data is inadequately presented. Although, the actual genes regulated may be of secondary importance in this study, it would still be good to have a few key genes mentioned as a quality control statement.
• It is stated that "...GRA57 is not as important for survival in MEFs as in HFFS". With no significant change observed, it should be re-phrased to something like ""...indicatin that GRA57 is s important for survival in MEFs as in HFFS."
• Optional: GRA57 was described by the Bradley lab to be in the PV in tachyzoites and in the cyst wall in bradyzoites. Although it tissue cysts are not the focus of this paper and the knock-out is created also in a cyst-forming strain, it would have been useful to look for a phenotype of the knockout in cysts, in vitro at least, better both in in vitro and in vivo. In future, this could also be useful for the authors bringing in more citations.

Minor comments:

• Line numbers would be useful for an efficient review process.
• Strictly speaking, we have to talk about the sexual development taking place in felid and not feline hosts (Introduction; Felidae versus Felinae).
• Please insert spaces between numbers and units.
• Domain structures are presented, but maybe the AlphaFold 3D predictions could be added in a supplemental figure?
• To improve the confidence of the co-immunoprecipitation, it would be necessary to use another tagged protein GRA70 or 71) and see if the same complex can be pulled down. Like this, one could also address what happens in a GRA57KO line? Do GRA70 and 71 stay together in the absence of GR57 forming a dimer?

Significance

This study endeavours to start closing an important knowledge gab of host defence in non-rodent hosts, especially humans. The data is solid using two different strains and yields novel insights into players of host cell resistance in humans against T. gondii. Using a targeted screening approach of rhoptry and dense granule proteins, they focused their interest on a subcategory of secreted proteins. The authors have not limited themselves to the screening and localisation study, but also investigated effect on host cells and host cell response. The identification of GRA57 being an important resistance factor and forming a heterodimer with GRA70 and GRA71 is novel. This study is of interest to cell biologists in the field of cyst-forming Coccidia, especially T. gondii and researchers interested in host resistance, parasite clearance by the host and parasite virulence.

I am a cell biologist working in Toxoplasma gondii and other Coccidians.

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Referee #2

Evidence, reproducibility and clarity

This paper reports high-quality genetic screening data identifying three novel Toxoplasma virulence factors (Gra57,70, and 71) that promote survival of two distinct Toxoplasma strains (type I RH and type II Pru) inside IFN-gamma primed human fibroblasts. Follow-up studies, exclusively focused on type I RH Toxoplasma, confirm the screening data. Gra57 IP Mass-Spec data suggest that Gra57, 70, and 71 may form a protein complex, a model supported by comparable IF staining patterns

Specific criticisms

Major:

• It is unclear what statistical metric was used to define screen hits as strain-dependent vs strain-independent. A standard approach would be to use a specific z-score value (often a z-score of 2) above or below best fit linear relationship between L2FCU for RH vs Pru as depicted in Fig.1D. Gra25 and Gra35 appear to be specific for Pru but it would be helpful to approach this type of categorization statistically. Also, such an analysis may reveal that only Pru-specific but not RH-specific hits were identified. Could the authors speculate why that would be?
• The paper proposes that Gra57, 70, and 71 form a heterotrimeric complex. This is based on the Mass-spec data from the original Gra57 pulldown, similar IF staining patterns, and comparable phenotypic presentation of the individual KO strains. However, only the MS data provide somewhat direct evidence for the formation a trimeric complex, and these data are by no means definitive. As this is a key finding of the MS, it should be further supported by additional biochemical data. Ideally, the authors should reconstitute the trimeric complex in vitro using recombinant proteins. Admittedly, this could be quite an undertaking with various potential caveats. Alternatively, reciprocal pulldowns of the 3 components could be performed. Super-resolution microscopy of the 3 Gra proteins might present another avenue to obtain more compelling evidence in support of the central claim of this work
• The ubiquitin observations made in this paper are a bit preliminary and the authors' interpretation of their data is vague. The authors may want to re-consider that ubiquitylated delta Gra57 PVs are being destroyed with much faster kinetics than ubiquitylated WT PVs. The reduced number of ubiquitylated delta Gra57 PVs compared to ubiquitylated WT PVs across three timepoints (as shown by the authors in Fi. S8) does not disprove the 'fast kinetics model.' To test the fast kinetics ubiquitin-dependent null hypothesis, video microscopy could be used to measure the time from PV ubiquitylation onset to PV destruction
• Related to the point above. We know that different ubiquitin species are found at the PVM in IFNgamma-primed cells but to what degree each Ub species exerts an anti-parasitic effect is not well established. The paper only monitors total Ub at the PVM. Could it be that delta Gra57 PVs are enriched for a specific Ub species but depleted for another? The authors touch on this in the Discussion but these are easy experiments to perform and well within the scope of the study. At least the previously implicated ubiquitin species M1, K48, and K63 should be monitored and their colocalization with Toxo PVMs quantified

Minor:

• For readers not familiar with Toxo genetics, the authors should include a sentence or two in the results section explaining the selection of HXGPRT deletion strains for the generation of Toxo libraries
• the highest scoring hits from the Pru screen (Gra35 &25) weren't investigated further. These hits appear to be specific for Pru. Some discussion as to why there are Pru-specific factors (but maybe not RH-specific factors) seems warranted

Referees cross-commenting

My reading of the comments is that there's consensus that this is a high quality study revealing novel Toxo effectors that undermine human cell-autonomous immunity and an important study in the field of parasitology. I might be the outlier that doesn't see much of an advance for the field of immunology since we don't really know what these effectors are doing, and the preliminary studies addressing this point are not well developed, with some confusing results.

My major comment #2 and rev#1's major comment #2 are, I think, essentially asking for the same thing, namely some more robust data on substantiating the formation of a trimeric complex.

My co-reviewers made great comments all across and I don't see any real discrepancies between the reviewers' comments - just some variation in what we, the reviewers, focused on

Significance

The discovery of a novel set of secreted Gra proteins critical for enhanced Toxoplasma survival specifically in IFNgamma primed human fibroblasts (but not mouse fibroblasts) is an important discovery for the Toxoplasma field. However, the study is somewhat limited in its scope as it fails to determine which, if any, specific IFNgamma-inducible cell-autonomous immune pathway is antagonized by Gra57 &Co. Instead, the paper reports that parasitophorous vacuoles (PVs) formed by Gra57 deletion mutants acquire less host ubiquitin than PVs formed by the parental WT strain. Because host-driven PV ubiquitylation is generally considered anti-parasitic, this observation is counterintuitive, and no compelling model is presented to explain these unexpected findings. Overall, this is a well conducted Toxoplasma research study with a few technical shortcomings that need to be addressed. However, in its current form, the study provides only limited insights into possible mechanisms by which Toxoplasma undermines human immunity. This study certainly provides an exciting starting point for further explorations.

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Referee #1

Evidence, reproducibility and clarity

Summary

In this manuscript, Lockyer et al. provide novel insights into the mechanism by which Toxoplasma gondii avoids parasite restriction in IFNγ-activated human cells. To identify potentially secreted proteins supporting parasite survival in IFNγ-activated human foreskin fibroblasts (HFF), the authors designed a CRISPR screen of Toxoplasma secretome candidates based on hyperLOPIT protein localization data. By this approach, they identified novel secreted proteins supporting parasite growth in IFNγ-activated cells. Among the gene identified, they found MYR3 a known component of the putative translocon in charge of protein export through the parasitophorous vacuole membrane. Therefore, the authors focused their investigations on GRA57, a dense granule protein of unknown function, which affects parasite survival to a lesser extent than the MYR component. The resistance phenotype conferred by GRA57 was confirmed by fluorescence microscopy. Importantly, the authors provide evidence that the protective function of GRA57 is not as well conserved in murine cells of the same type (MEF) as in HFF. To further explore the mechanism by which GRA57 protect the parasites in IFNγ-activated cells, the authors searched for protein partners by biochemistry. By immunoprecipitation and tandem mass spectrometry, they identified two other putative dense granule proteins, GRA70 and GRA71, which co-purified with GRA57-HA tagged protein. Noteworthy, both proteins were also found in the CRISPR screens with significant score conferring resistance. High-content imaging analysis confirmed the protective effect conferred by GRA57, GRA70, and GRA71 individually at similar levels. After ruling out an effect of tryptophan deprivation in parasite clearance, or a role of GRA57 in protein export normally mediated by the MYR translocon, and a role on host cell gene expression by RNA-Seq, the authors investigated the ubiquitination of the parasitophorous vacuole membrane, a marker previously thought to initiate parasite clearance. A reduction in ubiquitin labeling around the vacuole of mutant parasites is observed, which is quite surprising given the correlated increase in parasite clearance. The authors concluded that ubiquitin recruitment may not be directly linked to the parasite clearance mechanism.

Major comments

• Figure 2C. In this figure, the restriction effect of IFNγ is about 60% (or 40% survival) for RHdeltaUPRT parasites grown in HFFs, which is quite different from the 85% mentioned earlier in the results section. How was actually done the first assay? Settings with 60% restriction sounds reasonable and indicates that a substantial fraction of the parasite population evades the restrictive effect of IFNγ, which provides a clear rationale for the main objective of this study, namely the identification of effectors supporting parasite development in human cells in the presence of IFNγ.
• Optional comment: GRA70 and GRA71 were both copurified with GRA57, but what about GRA71 expression and localization? Is there a reason why this protein partner has not been studied further just like GRA70? Is there any change in GRA57, GRA70, and GRA71 localization and/or amount when cells were pretreated with IFNγ? Do they still form a complex in the absence of IFNγ? In the absence of GRA70 or GRA71 is GRA57 expression and/or localization affected?
• Page 13, result section. To determine whether GRA57 has any direct or indirect effect on host cell gene expression, the authors performed RNA-Seq analysis of HFF cells pretreated or not with IFNγ. First, as for proteomic data, were the data deposited on GEO or another repository database? Second, were any effect detected on parasite gene expression? Reads alignment could be done using the T. gondii reference genome to determine whether IFNg or gra57 KO has any effect on parasite genes. Possibly, other secreted proteins not necessarily expressed at the tachyzoite stage and therefore not captured in the hyperLOPIT protein analysis are specifically expressed in these conditions.
• Optional comment: RNA-Seq analysis points to a clear induction of GBPs upon IFNγ treatment in HFF. Given the clear function of GBP in parasite clearance, have the authors ever hypothesized that GRA57 could be involved in preventing GBP binding to the PVM?

Minor comments

• Page 4, introduction, 8th paragraph. Regarding the role of IST, it might be less prone to controversy to state: 'a condition that may only be met in the early stages of infection.'
• Page 4, end of introduction. Changing '... indicating that the three proteins function in a complex'. Changing to '... indicating that the three proteins function in the same pathway.' might be more appropriate for the conclusion.
• Page 4, result section, first paragraph. 'strain specific and independent effectors'. Are the authors talking about strain-specific and non-strain-specific factors?
• Page 6, result section. 'GRA25, an essential virulence factor in mice'. It is not clear to the reviewer how a virulence factor is essential since both parasite and mouse survival is achieved in the GRA25 mutant. I suggest to replace 'essential' by 'major'.
• Page 7. 'showing that GRA57 resides in the intravacuolar network (IVN) (Figure 2A)'. From the image shown, GRA57 clearly localizes into the PV, but it is hard to tell whether GRA57 is associated with the intravacuolar network. Colocalization assay or electron microscopy would be necessary to draw such conclusions.
• 'uprt locus'. Lower case letters and italic are generally preferred to designate mutants, whereas upper case letters are generally used for wild type alleles. (Sibley et al., Parasitology Today, 1991. Proposal for a uniform genetic nomenclature in Toxoplasma gondii).
• The authors mentioned in the introduction that ROP1 contributes to T. gondii resistance to IFNγ in murine and human macrophages. However, they did not comment on whether ROP1 was found important in the screen performed here in human HFF cells. It may be useful to reference ROP1 in Figure 1 as GRA15, GRA25, etc.
• Figure 2D. The authors compared the restriction effect of IFNγ on parasites grown in HFF and MEF host cells. However, as represented - % + IFNγ/- IFNγ - it cannot be estimated whether the parasites grew similarly in the two host cell types in the absence of IFN. Please indicate whether or not the growth was similar in both cell types.
• pUPRT plasmid. Any reference or vector map would be appreciated.
• Page 9, figure 3A, mass spectrometry analysis. I did not find the MS data in supplementals. Were the data deposited in on PRIDE database or another data repository?
• Figures 3E and 3F. It might be worth mentioning, at least in the figure legend, that GRA3 localizes at PV membrane and is exposed to the host cell cytoplasm (to mediate interactions with host Golgi). The signal for GRA3 following saponin treatment is here an excellent control that should be highlighted, indicating that saponin effectively permeabilized the host cell membrane.
• Page 11, section title. I think that the authors meant 'GRA57, GRA70 and GRA71 confer resistance to vacuole clearance in IFNγ-activated HFFs.'
• Page 11, in the result section comparing the effect of GRA57 mutant with MYR component KO, the authors are referring to host pathways that are counteracted by MYR-dependent effectors released into the host cell. It is not clear which pathways the authors are referring to.
• Page 16, discussion, end of 4th paragraph. '... to promote parasite survival in IFNγ activated cells' sounds better.
• Page 22-23, Methods section, c-Myc nuclear translocation assays and elsewhere. Please indicate how many events were actually analyzed. For example, in this assay, to determine the median nuclear c-Myc signal, how many infected cells were analyzed for each biological replicate?

Referees cross-commenting

Overall, I agree with most of the co-reviewers' remarks. I agree with reviewer #2 that this manuscript reports interesting data for the field of parasitology, but that the broad interest for immunologists is somewhat limited by the lack of a description of the mechanism by which these effectors oppose IFNgamma-inducible cell-autonomous defenses. I also agree with the other reviewers' comments regarding the GRA57, 70, and 71 heterotrimeric complex, which would require further description. In its present form, the manuscript undoubtedly represents an interesting starting point for further investigations and any additional data regarding the mode of interaction of the identified effectors and their function related or not to ubiquitylation would bring a significant added value.

Significance

Despite the fact that humans are accidental intermediate hosts for Toxoplasma gondii, the parasite may develop a persistent infection, demonstrating that it has effectively avoided host defenses. While Toxoplasma gondii has been extensively studied in mice, much less is known about the mechanisms by which the parasite establishes a chronic infection in humans. In this context, this article described very interesting data about the way this parasite counteracts human cell-autonomous innate immune system. This is a fascinating and important topic lying at the interface between parasitology and immunology. Indeed, the highly specialized secretory organelles characteristics of apicomplexan parasites are key to govern host-cell and parasite interactions ranging from host cell transcriptome modification to counteracting immune defense mechanisms. Overall, this article presents a significant contribution to the field of parasitology by identifying novel players involved in Toxoplasma gondii's evasion of human cell-autonomous immunity. Most conclusions are generally well supported by cutting-edge approaches and state of the art methods. Despite being a highly competitive field, this article stands out as the first screen designed specifically to identify virulence factors for human cells and extends our understanding of the secreted dense granule proteins resident of the parasitophorous vacuole. Importantly, the authors provide evidence that these players are active in different strain backgrounds and act in a way that is independent of the export machinery in charge of delivering effector proteins directly into the host cell. However, substantial further research is needed to fully understand the mechanism by which these novel players confer resistance to the parasite in IFNγ activated human cells and how their mode of action differs from that mediated by the translocation machinery (MYR complex). As a microbiologist and biochemist, I find this work of a particular interest to a broad audience, especially to parasitologists and immunologists, as it may unveil unexpected aspects of human innate immunity involved in parasite clearance with proteins unique to Apicomplexa phylum.

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Reply to the reviewers

Response to reviewers

We thank all reviewers for their comments and suggestions. In line, below, are our responses, marked in Bold. Textural changes in the manuscript are also marked in Bold.

Reviewer #1__ (__Evidence, reproducibility and clarity (Required)):

**Summary:**

Anuculeate red blood cell (RBC) is one of the interesting biological models that indicate the presence of eukaryotic circadian system independent of transcription-translation feedback. In this manuscript, the authors set up a new method for quantifying the circadian rhythmicity in RBC. The method called "Bloody Blotting" was developed through the careful and insightful investigation of "non-specific band" observed in the western blotting of peroxiredoxin, which has been used for the circadian monitoring of RBC. The authors characterized that the "non-specific' circadian-fluctuating signals, which can be observed by ECL imaging without any antibodies(-HRP), were attributed to ferrous-haem, but not ferric-haem, cross-linked to Hb upon cell lysis. Through the Bloody Blotting, this study suggests that the circadian fluctuation of ferrous-/ferric-haem exist in human and mouse RBC, and the period of rhythmicity is not affected by the canonical clock genes.

**Major comments:**

1)Although the authors conducted a careful biochemical evaluation of the "Bloody Blotting" signal, it is still unclear whether the changes in the Hb* (or Hb2*) signal corresponds to the changes in the ferrous-haem level in vivo. A direct perturbation on the level of in vivo ferrous-/ferric-haem is required. For example, is the Hb* (or Hb2*) signal decreased by the administration of amyl nitrite (in mice)?

__Thank you for the suggestion. We have addressed this and the second reviewer’s comment in a new Figures 4 & S4 and section titled “Effect of rhythms in metHb on vascular flow and body temperature”. __

For clarity, we have relabelled the schematic in A to “rest phase” and “activity phase” to consolidate data from humans and mice which both feature in the manuscript. We performed two experiments to test the model in Fig 4A and perturb metHb in vivo. The first is a direct perturbation of metHb levels in vivo with sodium nitrite, an oxidising agent that causes methaemoglobinemia. Reflecting our results ex vivo, RBC from differentially entrained mice sampled at the same external time, but 12h apart in terms of the light:dark cycle, contained significantly different metHb levels, with more metHb in the rest phase (revised Figure 4B, C). Whereas, RBCs from mice also given nitrite in their active phase contained more metHb (and thus lower Hb2* activity) than control (revised Figure 4B and S4). The second experiment tests the effect of sodium nitrite on core body temperature. Our hypothesis predicts that nitrite should accentuate the daytime drop in core body temperature, via the increased metHb-mediated production of NO to stimulate increased vasodilation (Ignacio et al 1981 and Cosby et al 2003). Revised figures 4D and E show that the effect of nitrite on body temperature (which has a large active vs inactive difference) is indeed daytime-specific.

Methods for these experiments have been added to Experimental Procedures.

2)The authors speculated that the higher PRX-SO2/3 signal during the first 24 hrs in mice is due to the sapling time at the resting phase (line ~235). The effect of sampling time should be easily tested by maintaining the mice group in 12-hr shifted L/D cycles and sampling the blood in the same o'clock (i.e., now the active phase). This type of experiment is also critical for the evaluation of Bloody Blotting because the level of Hb*/Hb2* signals may be affected by not only the circadian timing of mice but also the daily environmental fluctuation of a biochemistry laboratory (this is particularly important for the Bloody Blotting because some of the critical steps including the cross-linking between haem and Hb are supposed to occur in a test tube). If the signal of Bloody Blotting reflects the in vivo circadian rhythmicity, the 12-hr shifted L/D mice RBCs should have 12-hr shifted Bloody Blotting fluctuation pattern.

__We acknowledge this possibility. To test this, we sampled RBCs from mice kept under DL and LD conditions, as detailed in the new sections in the Experimental Procedures, harvesting blood at the same clock time. This gave us blood from mice in the “active” phase and “rest” phase – labels as per Figure 4B. Figure 4B shows that Hb2* signal significantly differs between mice in active and rest phases, even though these samples were collected and processed at the same external time. __

Separately from Hb2* activity, upon further reading of the literature we suspect that the higher PRX-SO2/3 signal detected in mouse RBCs (Fig 2) compared with human may be due to blood acidification during animal sacrifice by CO2. Additional text has been added to Supplementary Figure S3 to remark upon this, as follows:

"Interestingly, compared with human RBC time courses (Henslee et al., 2017; O’Neill and Reddy, 2011), we observed that murine PRX-SO2/3 immunoreactivity was extremely high during the first 24 hours of each 72-hour time course (Figure S3A). We attribute this to the different conditions under which blood was collected: blood was collected from mice culled by CO2 asphyxiation during their habitual rest phase by cardiac puncture and exposed immediately to atmospheric oxygen levels, whereas human blood was collected from subjects during their habitual active phase through venous collection into a vacuum-sealed collection vial. Thus, the initial high PRX-SO2/3 signal in mice may be related to CO2-acidification of the blood during culling, which affects PRX-SO2/3 but does not affect Hb oxidation status____."

3)Do the casein kinase inhibitors (ref: Beale, JBR 2019) affect the period of Bloody Blotting signals?

We have not experimentally addressed this as we consider it beyond the scope of the current study, which has instead focused on the in vivo relevance of the rhythms in metHb. Nevertheless, given the identical periodicity of PRX rhythms and Hb* rhythms (this paper), and the periodicity of PRX rhythms and rhythms in membrane conductance (Henslee et al, Nat Commun, 2018), we see no reason why the period lengthening of rhythms in membrane conductance reported in Beale et al, JBR, 2019 would not also been seen in PRX or Hb* rhythms.

**Minor comments:**

4)The authors quantify the dimer of Hb (Hb2*). This is important information but only explained in the supplementary figure legend. It should be explained in the main text. In addition, it is difficult to evaluate the fluctuation of Hb* (not Hb2*) because, as the authors stated, most of the Hb* signals are saturated. The saturation problem should be easily solved by reducing the sample loading volume. Quantification of Hb* is important at least experiments shown in figure 1A-G because the dimerization of Hb can be also affected by factors other than the in vivo ferrous-/ferric-haem conversion.

Thank you for pointing this out. Indeed the data throughout the original manuscript is Hb2*. We have brought this explanation into Figure 1 legend and labelled all figures consistently with Hb2*. We include quantification of Hb* and Hb2* of the in vivo metHb perturbation experiment (Figure 4) in the uncropped membranes shown in Supplementary Figure 4. The quantification of Hb* (Supplementary Figure 4D) gives the same result as the quantification of Hb2* (Figure 4B).

5)In the quantification of Hb2* (Figure 1A, 2E, 3C), were the signals normalized to Total Hb?

In the quantification of Hb2* throughout, signals were normalised to total protein through coomassie stain, apart from Figure 4B which used SYPRO Ruby. Each figure presents the Hb band of coomassie or SYPRO Ruby for simplicity, but the full gels are included in Supplementary Figures 1, 3 and 4.

6)The explanation and interpretation of the experiment shown in figure 3D should be more careful. The pulse-oximetry was conducted in normal working day conditions (real world setting) and thus should be affected by environmental and social daily signals.

__We have changed the section to the following (edits in bold): __

"Remarkably, in contrast to total Hb (SpHb) that displayed no significant 24h variation, the proportion of metHb (SpMet) in the blood exhibited a striking daily variation that rose during the evening and peaked during the night (Figure 3D). These subjects were in a real-world setting, and thus affected by environmental and social cues from a normal working day. However, the evening rise and night-time peak is consistent with ____the reduction in Hb2* activity at the end of the waking period in laboratory conditions (Figure 3B)____."

7)Typos at figure indicators in supplementary figure legends. Sup figure 1A legend refers to main figure "2" (should be 1), and figure S3 legend refers to main figure 1 (should be 3).

Thank you for pointing this out. We have corrected these legends.

Reviewer #1 (Significance (Required)):

The detection of circadian oscillation in RBC has been not easy because the experiment requires careful sample preparation and specific antibodies (Milev Methods Enzymol 2016) or a specific instrument for dielectrophesis (Henslee). The Bloody Blotting technic developed in this study will overcome this technical problem because Bloody Blotting does not rely on specific antibody and only requires conventional tools for western blotting. Because circadian biology of RBC is particularly important in the field of circadian research to evaluate the presence of eukaryotic circadian oscillator without transcription-translation feedback loops, this study will be interested a wide community of circadian clock researchers. This reviewer has expertise in the field of circadian genomics, biochemistry, animal experiments in mice as well as human.

Thank you for taking the time to read and constructively comment on our work

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

The study aims to provide a new tool for detecting the hemoglobin oxidative status named "Bloody blotting". It is based on redox- sensitive covalent linkage between the haem and the haemoglobin. This linkage is a consequence of an artifactual reaction provoked by the protein extraction, due to the lysis buffer's properties. In addition, using an in vitro (red blood cells) or in vivo (patients' blood) model the authors provide insight in the oscillating nature in the oxygen-carrying and nitrite reductase capacity of the blood, which is unaffected by the mutation of CK1εtau/tau and Fbxl3aafh/fh

**Major comments:**

In my honest opinion, the work does not provide interesting addition to what it is known in literature. The conclusions are summarized into a model (Fig.4) t, which is too speculative related to the amount and quality of results showed in the paper.

__We are disappointed by the reviewer's response. The physiological basis for daily rhythms in body temperature cooling is not currently understood, this work provides a testable basis for understanding it. Whilst we understand that the reviewer might not find immediate value in the biochemical mechanisms that initially informed our investigation, the recent publication of our investigation of human brain temperature rhythms (Rzechorzek et al., Brain, 2022) demonstrates that daily biological temperature rhythms are of broad interest (Altmetric score >2000). Daily temperature rhythms have almost exclusively been assumed to result from daily rhythms in heat production, yet the evidence for a contribution via daily rhythms of cooling is equally strong yet has received scant attention. __

__The speculative model that the reviewer refers to was a hypothesis that drew together multiple lines of published evidence for future experimental testing, not a conclusion, and was labelled as such in the original manuscript. To accommodate the reviewer's critique, however, we tested the model with new experiments, that are included in the revised Figure 4 and section titled “Effect of rhythms in metHb on vascular flow and body temperature”. __

For clarity, we have relabelled the schematic in A to “rest phase” and “active phase” to consolidate data from humans and mice which both feature in the manuscript, and described it as a hypothesis to avoid confusion. We performed two experiments to test this model. The first is a direct perturbation of metHb levels in vivo with sodium nitrite, an oxidising agent that causes methaemoglobinemia. RBCs from mice given nitrite in their active phase contain more metHb (and thus lower Hb2* activity) than control (Figure 4B). Reflecting our results ex vivo, RBC from mice sampled 12h apart contain significantly different metHb levels, with more metHB in the rest phase (Figure 4B, C). The second experiment tests the effect of sodium nitrite on core body temperature. Our model predicts that nitrite should further reduce core body temperature in the daytime, via the increased production of metHb (Figure 4C) and vasodilation (Ignacio et al 1981 and Cosby et al 2003). Figure 4D and E show that body temperature (which has a large active vs inactive difference) is further lowered upon nitrite treatment, and that this effect is restricted to the daytime, consistent with our hypothesis.

__Methods for these experiments have been added to Experimental Procedures. __

The title is misleading. The authors did not use any mutant for clock factors, but they used a kinase (CK1εtau/tau) and a ubiquitin ligase (Fbxl3aafh/afh) mutant, which are important in the regulation of proteins belonging to the clock machinery.

We respectfully disagree that the title was misleading. Mice and cultured cells/tissues that are mutant for CK1 and FBXL3 demonstrably show altered clock gene activity (See Godhino et al, Science, 2007, also Meng et al, Neuron, 2008, also Fig 2). Moreover, CK1 and FBXL3 are generally regarded as key components of the circadian clock due to their critical function in the regulation of clock proteins (e.g., Hirano et al, Nat. Struct. Mol. Biol., 2016). Being anucleate, RBCs lack the capacity for changes in clock gene activity and the period of oscillation is not affected by mutations that affect the activity and period of clock gene-oscillations in nucleated cells and whole mice. Since the rhythms of Hb oxidation persist in isolated RBCs, they cannot be dependent on clock gene activity and so must be considered to function independent of clock genes.

In light of the new data on mouse body temperature presented in revised Fig 4D/E, however, we have changed the title to better communicate the revised scope of the manuscript, as follows:

"Mechanisms and physiological function of daily haemoglobin oxidation rhythms in red blood cells"

Speaking of the specific points described in the paper, there are aspects that are not convincing. First, the bloody blotting is a consequence of a specific reagent contained in the lysis buffer used for the protein extraction, which reacts with the haemoglobin beta and alpha (as shown by Mass Spec). The peroxidase reaction is an artifact coming from this reaction, which simply follows the rhythmicity of peroxiding accumulation in the red blood cells, whose rhythmicity is known to be circadian. I do not really understand the utility of this technique, which anyway is limited to the specific lysis buffer, but for scientific reasons, researchers need often a different kind of lysis buffer. This means that the approach shows strong limitation to the chemical environment of the lysis buffer. I do not see in it a useful tool that can replace antibodies.

Apologies, we have not been clear enough. The bloody blotting is indeed a consequence of lysis, since that lysis condition fixes the cellular state at the time of lysis. In this case, the variation in Hb oxidation status is fixed at the time of lysis. The peroxidase activity we report is indeed revealed on membranes by the covalent interaction of the haem and Hb, which occurs at the point of lysis, and reports the oxidation state of the haem at the point of lysis. As we detail, haem exhibits peroxidase activity, so the signal we observe at molecular weights corresponding to Hb and Hb2 is peroroxidase activity due to covalently bound haem, where the peroxidase activity varies with the oxidation state of the haem. We have reorganised text associated with Figure 1, including changes to the final paragraph of the section to make explicitly clear that that the rhythm is due to a fixing of the redox state of Hb at the time of lysis – that a true underlying rhythm is revealed.

This technique is indeed limited to the observation of haem-peroxidase activity in RBCs on membranes. But as we explain in the manuscript, this is a far quicker and simpler method of observing RBC circadian rhythms than other methods, including immunoblotting for peroxiredoxins. Furthermore, it is common to change lysis buffer according to the downstream purpose.

Second, the oscillation in the peroxidase activity of PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.).

Many apologies, we do not understand the point. It is indeed correct that PRX-SO2/3 abundance oscillations have been reported in RBCs and other cells and organisms. Here we report another rhythm, separate to PRX: the rhythm in Hb:metHb. The PRX-SO2/3 blots serve as a positive control for rhythmicity.

Finally the circadian rhythms of red blood cells is already described and the corresponding author already published different papers about. The info provided in this paper do not add any new piece to the puzzle.

Respectfully, we report a novel rhythm in RBCs and demonstrate its functional relevance in vivo in humans (Figure 3) and mice (Figure 4), i.e., it is the identity of the rhythmic species that is novel, not that there are rhythms. What we further add with this study is that rhythms are not influenced by the cellular/organismal environment during RBC development (Figure 2), occur in vivo, in freely moving people (Figure 3) and metHb has a functionally significant role in body temperature rhythms (Figure 4). Furthermore, we report a novel technique for uncovering this rhythm in RBCs.

At this stage I do not consider the paper suitable for a publication. Other observations. Authors should describe how cells were synchronized.

RBCs in vitro were not synchronised by external cues. As reported in the Methods section, they were maintained at constant temperature after isolation. Fibroblasts were synchronised by temperature cycles as detailed and employed previously.

In experiments performed in vitro should be used the SD instead of the SEM.

We respectfully disagree. The SEM quantifies how precisely you know the true mean of the population - in each case we use it, we also present replicates’ data from which the mean is calculated (e.g. Fig 1A, Fig 2E, Fig 3B and Fig 4B). This gives the real scatter of the data, as a SD would.

**Minor comments:**

There are many English mistakes in the article, also errors in naming figures in the figure legends.

We have carefully re-examined the manuscript to find and fix these errors.

Figure 1B needs an appropriate loading control.

We have added the coomassie loading control to revised Figure 1B, with uncropped membranes shown in revised Supplementary Figure 1B

In experiments performed in vitro should be used the SD instead of the SEM.

SD vs SEM, see reply above.

Reviewer #2 (Significance (Required)):

Nature and significance of the advance At this stage I do not see any significance or advance in the field.

Compared to existing published knowledge. The The bloody blotting seems to be an original approach although full of limitation and based on artifactual reactions. PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.), therefore the paper does not add any new insight. The clock mutation do not affect the circadian rhythm in RBC is also known (O' Neill and Reddy, 2011). Therefore the results showed in the figure 3 support already published observations but do not add any particular insight.

It is unclear to us how the reviewer has misunderstood the scope and focus of the manuscript to such an extent. All previous work in this area by our own and other labs has been appropriately acknowledged. To reiterate the novel elements of this work:

- A daily rhythm of Hb redox state in mouse and human red blood cells, in vitro and in vivo. This was speculated about in O'Neill & Reddy (Nature, 2011) but never directly tested until now.

- That clock gene mutations that post-translationally regulate circadian period in nucleated mammalian cells do not affect circadian period in anucleate mammalian cells. O'Neill & Reddy (Nature, 2011) did not show this, rather we looked at (nucleated) fibroblasts that were deficient for Cry1/2 (a transcriptional repressor).

- A novel assay for measuring mammalian RBC rhythms - nowhere is it proposed that the assay would be useful in any other context, as the reviewer seems to imply.

- A mechanistic basis for understanding how daily rhythms in cooling of body temperature might arise, a poorly studied aspect of mammalian physiology.

__The elements in this work that are not completely novel are included as controls, they are not the focus of the manuscript e.g. PRX-SO2/3 rhythms have not previously been shown under these conditions in mouse RBCs, only human, so these blots are included as a control for rhythmicity in Fig2. Similarly, the period of oscillation of a genetically-encoded Cry1:Luc reporter in mouse fibroblasts would be predicted to be longer and shorter in Fbxl3 and Ck1 mutants, respectively, but nowhere this been published so we have included it as a control. __

Audience Chronobiologists, and medical science.

Fild of expertises (reviewer) Chronobiology, molecular biology, medical science.

**Referees cross-commenting**

I read your comment and they were very detailed. From my point of view I am very skeptical, as I discussed about the utility of the Bloody Blotting. Also the results showed in the paper are not very innovative fro my point of view. I would like to know what do you think about.

The rhythmicity is given by the elements present in the protein extraction. The reaction is given by the specific lysis buffer used in that experiment. Using another lysis buffer would not allow anybody to see some signal without a proper antobody. The authors claim that bloody blotting is useful because a researcher does not need to buy an antibody, but what if you don't work with a total total extract of proteins? In that case, you need to change the lysis buffer, and, therefore, the bloody blotting is not useful anymore. However, If you believe in that way, and you are two people agreeing in that, I will not oppose myself although I do not agree.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): **Summary:** Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

The manuscript "Clock gene-independent daily regulation of haemoglobin oxidation in red blood cells" describes a new assay for quantification of haemoglobin oxidation status (bloody blotting) in anucleate red blood cells". This study furthers our understanding of the role of a post-translational oscillator (PTO) in generating circadian rhythms in biology. The authors first describe how earlier work demonstrated 24h rhythms in the intensity of chemiluminescent bands on membranes blotted with protein from red blood cells (RBCs) in the absence of antibodies after exposure to ECL. They go on to address what these bands represent (through various approaches including the use of chemical inhibitors and mass spectrometry) and conclude that they are observing haemoglobin oxidation status. It is proposed that this assay represents a novel manner (complementary to earlier work) in which to report circadian rhythms in RBCs. The manuscript goes on to demonstrate the persistence of 24h rhythms in haemoglobin oxidation status in murine RBCs, including cells isolated from two clock mutant mice. Finally, the study utilises RBCs collected from human volunteers maintained under controlled conditions and demonstrate robust rhythms via "blood blotting", this data is presented alongside pulse co-oximetry data to examine physiological relevance of these rhythms.

**Major comments:** -Are the key conclusions convincing?

The key conclusions are well supported by the data. The discussion does become quite speculative, and this needs to be addressed.

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

The discussion around the physiological relevance of daily regulation of haemoglobin redox status is extensive (lines 366-403) as is the discussion on RBCs and the TTFL-less clock mechanisms (lines 405-429). Whilst interesting and well thought out, and well supported by the literature, these sections are very speculative and in my opinion should be toned down.

Thank you to the reviewer for both the compliment and suggestion. Indeed, these discussion sections were too long. We have reorganised the physiological relevance section to reduce its length and better accommodate the new data presented in the new experiments in Figure 4.

We have cut the TTFL-less section text by more than half.

-Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Further experiments would be required to support the discussion about the role of daily rhythms in haemoglobin oxidation status in regulating oxygen carrying capacity of the blood, vascular tone, body temperature and sleep-wake cycle. As the authors state, these experiments are beyond the scope of this study, but are of course of major interest. It would be more appropriate to limit the discussion to what has been demonstrated directly by the data presented, with just a few sentences speculating on physiological relevance.

__As above, we acknowledge that we were speculative in that section and we have curtailed the discussion as suggested. __

-Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

If the focus of the discussion is shifted as suggested, there is no need to pursue any further experiments. -Are the data and the methods presented in such a way that they can be reproduced? Yes. The methods are complete, and data presented very well. -Are the experiments adequately replicated and statistical analysis adequate?

Yes

**Minor comments:**

-Specific experimental issues that are easily addressable.

1.In the murine fibroblasts/RBC experiments in Figure 2 - what genotype were the wildtype controls? The main text suggests PER2::luc (line 226) but methods suggest Cry1:luc - could the authors clarify this?

__Thank you for pointing out this mistake, corrected text to Cry1:luciferase __

2.In figure 2B and 2D the blots show two samples for each time point (except for 72h where there is just one) are these technical repeats? This should be clarified.

Apologies, the labelling of this figure was not clear – for space reasons we only labelled every 2nd timepoint – the time course was 3-hourly. We have corrected the figure to label each timepoint.

3.The controls for the bloody blots are referred to as coomassie in Figure 1. In Figure 2, the controls for PRX-SO2/3 are referred to as "loading" but are coomassie stained gels - could this be standardised? Also Figure 2D - no controls? In Figure 3B controls are referred to as 'Total Hb from coomassie staining - I wasn't clear what this was.

Thank you. Throughout we have now labelled loading controls by their method (coomassie or SYPRO Ruby). Figure 2D is taken from the same gel as Figure 2B and so the same coomassie gel stain is used as a loading control. We have altered the figure legend to reflect this. Each figure presents the Hb band of coomassie or SYPRO Ruby for simplicity, but the full gels are included in Supplemetary Figures 1, 3 and 4. We have changed each figure legend to reflect: “coomassie stained gels were used as loading controls; the Hb band from the coomassie stained gel is shown”.

4.Figure 3A "S1" and "S2" stated in legend but only "S" used in the schematic

Many thanks for pointing this out. We have corrected the schematic to S1 and S2.

-Are prior studies referenced appropriately? Yes absolutely. -Are the text and figures clear and accurate? Mostly, few comments above.

-Do you have suggestions that would help the authors improve the presentation of their data and conclusions? No

Reviewer #3 (Significance (Required)): -Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

The study describes a rapid and relatively simple assay for observing 24h rhythms in RBC function. On a technical basis - this will likely be of significant use to others in the field. Further work examining rhythms in haemoglobin oxidation in RBCs in clock mutant mice confirms independence from the transcriptional-translational feedback loop, which further supports earlier work in this field. Finally, studies in humans (bloody blotting in combination with pulse co-oximetry) provide a glimpse into the functional relevance of these daily oscillations

-Place the work in the context of the existing literature (provide references, where appropriate).

The authors have done an excellent job of reviewing the literature in the field and contextualising their data. This current data is a significant advance in the field.

-State what audience might be interested in and influenced by the reported findings.

This work will be of interest to circadian biologists and adds weight to the relatively new concept of a post-translational oscillator (PTO). Further work showing the relevance of this PTO on physiological function will be of great interest.

-Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Circadian, Clock genes, mouse models,

I do not have a background in biochemistry and do not feel overly qualified to comment constructively on approaches taken to address what is driving the observed rhythmic peroxidase activity in RBCs (e.g NiNTA affinity chromatography, use of reductants to reduce thioester bonds and use of NEM to alkylate Hb cysteine residues).

**Referees cross-commenting**

In terms of the utility, as my review indicated, I do feel that this manuscript advances the field, providing a rapid and relatively simple way to measure rhythms in RBCs. Reviewer 1 explained this nicely in their significance summary.

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

The manuscript "Clock gene-independent daily regulation of haemoglobin oxidation in red blood cells" describes a new assay for quantification of haemoglobin oxidation status (bloody blotting) in anucleate red blood cells". This study furthers our understanding of the role of a post-translational oscillator (PTO) in generating circadian rhythms in biology. The authors first describe how earlier work demonstrated 24h rhythms in the intensity of chemiluminescent bands on membranes blotted with protein from red blood cells (RBCs) in the absence of antibodies after exposure to ECL. They go on to address what these bands represent (through various approaches including the use of chemical inhibitors and mass spectrometry) and conclude that they are observing haemoglobin oxidation status. It is proposed that this assay represents a novel manner (complementary to earlier work) in which to report circadian rhythms in RBCs. The manuscript goes on to demonstrate the persistence of 24h rhythms in haemoglobin oxidation status in murine RBCs, including cells isolated from two clock mutant mice. Finally, the study utilises RBCs collected from human volunteers maintained under controlled conditions and demonstrate robust rhythms via "blood blotting", this data is presented alongside pulse co-oximetry data to examine physiological relevance of these rhythms.

Major comments:

• Are the key conclusions convincing?

The key conclusions are well supported by the data. The discussion does become quite speculative, and this needs to be addressed.

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

The discussion around the physiological relevance of daily regulation of haemoglobin redox status is extensive (lines 366-403) as is the discussion on RBCs and the TTFL-less clock mechanisms (lines 405-429). Whilst interesting and well thought out, and well supported by the literature, these sections are very speculative and in my opinion should be toned down.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Further experiments would be required to support the discussion about the role of daily rhythms in haemoglobin oxidation status in regulating oxygen carrying capacity of the blood, vascular tone, body temperature and sleep-wake cycle. As the authors state, these experiments are beyond the scope of this study, but are of course of major interest. It would be more appropriate to limit the discussion to what has been demonstrated directly by the data presented, with just a few sentences speculating on physiological relevance.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

If the focus of the discussion is shifted as suggested, there is no need to pursue any further experiments.

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

Yes. The methods are complete, and data presented very well.

• Are the experiments adequately replicated and statistical analysis adequate?

Yes

Minor comments:

• Specific experimental issues that are easily addressable.

1.In the murine fibroblasts/RBC experiments in Figure 2 - what genotype were the wildtype controls? The main text suggests PER2::luc (line 226) but methods suggest Cry1:luc - could the authors clarify this?

2.In figure 2B and 2D the blots show two samples for each time point (except for 72h where there is just one) are these technical repeats? This should be clarified.

3.The controls for the bloody blots are referred to as Coomassie in Figure 1. In Figure 2, the controls for PRX-SO2/3 are referred to as "loading" but are Coomassie stained gels - could this be standardised? Also Figure 2D - no controls? In Figure 3B controls are referred to as 'Total Hb from Coomassie staining - I wasn't clear what this was.

4.Figure 3A "S1" and "S2" stated in legend but only "S" used in the schematic

• Are prior studies referenced appropriately?

Yes absolutely.

• Are the text and figures clear and accurate?

Mostly, few comments above.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

No

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

The study describes a rapid and relatively simple assay for observing 24h rhythms in RBC function. On a technical basis - this will likely be of significant use to others in the field. Further work examining rhythms in haemoglobin oxidation in RBCs in clock mutant mice confirms independence from the transcriptional-translational feedback loop, which further supports earlier work in this field. Finally, studies in humans (bloody blotting in combination with pulse co-oximetry) provide a glimpse into the functional relevance of these daily oscillations

• Place the work in the context of the existing literature (provide references, where appropriate).

The authors have done an excellent job of reviewing the literature in the field and contextualising their data. This current data is a significant advance in the field.

• State what audience might be interested in and influenced by the reported findings.

This work will be of interest to circadian biologists and adds weight to the relatively new concept of a post-translational oscillator (PTO). Further work showing the relevance of this PTO on physiological function will be of great interest.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Circadian, Clock genes, mouse models,

I do not have a background in biochemistry and do not feel overly qualified to comment constructively on approaches taken to address what is driving the observed rhythmic peroxidase activity in RBCs (e.g NiNTA affinity chromatography, use of reductants to reduce thioester bonds and use of NEM to alkylate Hb cysteine residues).

Referees cross-commenting

In terms of the utility, as my review indicated, I do feel that this manuscript advances the field, providing a rapid and relatively simple way to measure rhythms in RBCs. Reviewer 1 explained this nicely in their significance summary.

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

Summary

The study aims to provide a new tool for detecting the hemoglobin oxidative status named "Bloody blotting". It is based on redox-sensitive covalent linkage between the haem and the haemoglobin. This linkage is a consequence of an artifactual reaction provoked by the protein extraction, due to the lysis buffer's properties. In addition, using an in vitro (red blood cells) or in vivo (patients' blood) model the authors provide insight in the oscillating nature in the oxygen-carrying and nitrite reductase capacity of the blood, which is unaffected by the mutation of CK1εtau/tau and Fbxl3aafh/fh

Major comments:

In my honest opinion, the work does not provide interesting addition to what it is known in literature. The conclusions are summarized into a model (Fig.4) t, which is too speculative related to the amount and quality of results showed in the paper. The title is misleading. The authors did not use any mutant for clock factors, but they used a kinase (CK1εtau/tau) and a ubiquitin ligase (Fbxl3aafh/afh) mutant, which are important in the regulation of proteins belonging to the clock machinery.

Speaking of the specific points described in the paper, there are aspects that are not convincing. First, the bloody blotting is a consequence of a specific reagent contained in the lysis buffer used for the protein extraction, which reacts with the haemoglobin beta and alpha (as shown by Mass Spec). The peroxidase reaction is an artifact coming from this reaction, which simply follows the rhythmicity of peroxiding accumulation in the red blood cells, whose rhythmicity is known to be circadian. I do not really understand the utility of this technique, which anyway is limited to the specific lysis buffer, but for scientific reasons, researchers need often a different kind of lysis buffer. This means that the approach shows strong limitation to the chemical environment of the lysis buffer. I do not see in it a useful tool that can replace antibodies.

Second, the oscillation in the peroxidase activity of PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.). Finally the circadian rhythms of red blood cells is already described and the corresponding author already published different papers about. The info provided in this paper do not add any new piece to the puzzle.

At this stage I do not consider the paper suitable for a publication. Other observations. Authors should describe how cells were synchronized. In experiments performed in vitro should be used the SD instead of the SEM.

Minor comments:

There are many English mistakes in the article, also errors in naming figures in the figure legends. Figure 1B needs an appropriate loading control. In experiments performed in vitro should be used the SD instead of the SEM.

Significance

Nature and significance of the advance At this stage I do not see any significance or advance in the field.

Compared to existing published knowledge. The The bloody blotting seems to be an original approach although full of limitation and based on artifactual reactions. PRX-SO2/3 is well known to be circadian (Edgar et al., 2012. doi:10.1038/nature11088.), therefore the paper does not add any new insight. The clock mutation do not affect the circadian rhythm in RBC is also known (O' Neill and Reddy, 2011). Therefore the results showed in the figure 3 support already published observations but do not add any particular insight.

Audience Chronobiologists, and medical science.

Fild of expertises (reviewer) Chronobiology, molecular biology, medical science.

Referees cross-commenting

I read your comment and they were very detailed. From my point of view I am very skeptical, as I discussed about the utility of the Bloody Blotting. Also the results showed in the paper are not very innovative fro my point of view. I would like to know what do you think about.

The rhythmicity is given by the elements present in the protein extraction. The reaction is given by the specific lysis buffer used in that experiment. Using another lysis buffer would not allow anybody to see some signal without a proper antobody. The authors claim that bloody blotting is useful because a researcher does not need to buy an antibody, but what if you don't work with a total extract of proteins? In that case, you need to change the lysis buffer, and, therefore, the bloody blotting is not useful anymore. However, If you believe in that way, and you are two people agreeing in that, I will not oppose myself although I do not agree.

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

Summary:

Anuculeate red blood cell (RBC) is one of the interesting biological models that indicate the presence of eukaryotic circadian system independent of transcription-translation feedback. In this manuscript, the authors set up a new method for quantifying the circadian rhythmicity in RBC. The method called "Bloody Blotting" was developed through the careful and insightful investigation of "non-specific band" observed in the western blotting of peroxiredoxin, which has been used for the circadian monitoring of RBC. The authors characterized that the "non-specific' circadian-fluctuating signals, which can be observed by ECL imaging without any antibodies(-HRP), were attributed to ferrous-haem, but not ferric-haem, cross-linked to Hb upon cell lysis. Through the Bloody Blotting, this study suggests that the circadian fluctuation of ferrous-/ferric-haem exist in human and mouse RBC, and the period of rhythmicity is not affected by the canonical clock genes.

Major comments:

1. Although the authors conducted a careful biochemical evaluation of the "Bloody Blotting" signal, it is still unclear whether the changes in the Hb (or Hb2) signal corresponds to the changes in the ferrous-haem level in vivo. A direct perturbation on the level of in vivo ferrous-/ferric-haem is required. For example, is the Hb (or Hb2) signal decreased by the administration of amyl nitrite (in mice)?
2. The authors speculated that the higher PRX-SO2/3 signal during the first 24 hrs in mice is due to the sapling time at the resting phase (line ~235). The effect of sampling time should be easily tested by maintaining the mice group in 12-hr shifted L/D cycles and sampling the blood in the same o'clock (i.e., now the active phase). This type of experiment is also critical for the evaluation of Bloody Blotting because the level of Hb/Hb2 signals may be affected by not only the circadian timing of mice but also the daily environmental fluctuation of a biochemistry laboratory (this is particularly important for the Bloody Blotting because some of the critical steps including the cross-linking between haem and Hb are supposed to occur in a test tube). If the signal of Bloody Blotting reflects the in vivo circadian rhythmicity, the 12-hr shifted L/D mice RBCs should have 12-hr shifted Bloody Blotting fluctuation pattern.
3. Do the casein kinase inhibitors (ref: Beale, JBR 2019) affect the period of Bloody Blotting signals?

Minor comments:

1. The authors quantify the dimer of Hb (Hb2). This is important information but only explained in the supplementary figure legend. It should be explained in the main text. In addition, it is difficult to evaluate the fluctuation of Hb (not Hb2) because, as the authors stated, most of the Hb signals are saturated. The saturation problem should be easily solved by reducing the sample loading volume. Quantification of Hb* is important at least experiments shown in figure 1A-G because the dimerization of Hb can be also affected by factors other than the in vivo ferrous-/ferric-haem conversion.
2. In the quantification of Hb2* (Figure 1A, 2E, 3C), were the signals normalized to Total Hb?
3. The explanation and interpretation of the experiment shown in figure 3D should be more careful. The pulse-oximetry was conducted in normal working day conditions (real world setting) and thus should be affected by environmental and social daily signals.
4. Typos at figure indicators in supplementary figure legends. Sup figure 1A legend refers to main figure "2" (should be 1), and figure S3 legend refers to main figure 1 (should be 3).

Significance

The detection of circadian oscillation in RBC has been not easy because the experiment requires careful sample preparation and specific antibodies (Milev Methods Enzymol 2016) or a specific instrument for dielectrophesis (Henslee). The Bloody Blotting technic developed in this study will overcome this technical problem because Bloody Blotting does not rely on specific antibody and only requires conventional tools for western blotting. Because circadian biology of RBC is particularly important in the field of circadian research to evaluate the presence of eukaryotic circadian oscillator without transcription-translation feedback loops, this study will be interested a wide community of circadian clock researchers.

This reviewer has expertise in the field of circadian genomics, biochemistry, animal experiments in mice as well as human.

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Reply to the reviewers

Manuscript number: RC- 2023-01819

Corresponding author(s): Gernot Längst and Harald Wodrich

Full revision of the manuscript

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

2. Point-by-point description of the revisions

Dear Reviewers, thank you very much for your appreciation of our study and your input. In this point-to-point response, we amended our text marked in blue colour.

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

The authors have addressed the nucleoprotein structure of human adenovirus during the very early stages of infection, and its relationship to onset of expression of viral genes, using a combination of RNA-seq, MNase-seq, ChIP-seq and single genome imaging. They show that in the virion and the newly-infecting DNA, protein VII is precisely position at specific sites on the viral DNA, with greater accessibility at early gene promoters compared to other regions. Nucleosomes containing H3.3 replace specific protein VII at distinct positions at the transcription start sites of genes, which are then acetylated. Association with histones and nucleosomes occurs prior to transcription. These studies confirm and greatly expand on results already in the literature, and also elucidate a novel role for protein VII in orchestrating positioning of nucleosomes prior to initiation of transcription.

The authors provide excellent data in support of their conclusions and, in many instances, use alternative experiments (i.e. two different approaches) to support their claims. The details of methods are adequate (with small exceptions outlined below) and statistical methods appropriate.

Minor comments:

Line 561 "Protein VII molecules were exchanged for positioned nucleosomes at the +1 site of actively transcribed genes". This statement seems to suggest that the +1 position almost acts as a nucleating site, where replacement of a single, specific protein VII molecule at +1 is an initiating event, which then spreads from that site and into the rest of the gene. Data shown in Figure 6G and 6H shows that H3.3 appears to be found equally along the full length of E1A as early as 1 hr post infection (with no real "enhancement" at the +1 position), and that the overall levels simply increase over the next 4 hrs.

As the reviewer pointed out, the histone ChIP-seq peaks are broader than the +1 nucleosome region, extending into the transcribed regions of the gene. This is expected, as the mean length of the immunoprecipitated DNA is about 400bp long. Still, ChIP-seq peaks are in proximity to the transcription start site and overlap with the position of the +1 nucleosome. As we do not have the required resolution, we toned done our statement. The text now reads as follows: “Protein VII molecules were exchanged for nucleosomes downstream of the transcription start site, overlapping the +1 nucleosome site, of actively transcribed genes“ (line 568 ff).

Curiously, the authors chose not to use a wildtype virus for their studies - the virus contains a deletion in the E3 region. For clarity, I suggest that the authors should preferentially use an alternative designation for their virus rather than HAd-C5. Perhaps HAd-C5delE3 to differentiate this work from studies that truly use wildtype virus.

As requested by the reviewer we have updated the nomenclature to HAd-C5dE3 throughout the text and the figures.

The obvious limitation of the studies using the fluorescent TAF1-beta to label Ad genomes is that as protein VII is replaced by nucleosomes, the genomes would have declining detection by this method. Genomes devoid of protein VII would be "invisible".

Our MNase data show that within the first 4h only a fraction of pVII is removed from the viral genome e.g. at early genes, while most of the genome remains bound by protein VII. This should provide enough binding sites for TAF1-beta to label Ad genomes without a significant drop in the signal. Furthermore, our recent work (PMID:29997215, Fig. 1D) compared the TAF1-beta labelling system with a second in vivo detection system (AnchOR3) that directly labels the viral DNA independently of protein VII in the same cells. This direct comparison of two technically non-related methods to detect individual incoming adenoviral genomes in living cells showed the equivalence of both methods, at least for the first hours of infection showing that partial removal of protein VII does not affect the fluorescent TAF1-beta staining.

Line 275 "Interestingly, a central region of the viral genome (Late3) and a region between the E3 and E4 genes exhibited almost no peaks" for protein VII. The virus utilized in this study lacked at least part of the E3 region. Did this deletion "cause" this region to be devoid of protein VII? Is the same absence of protein VII peaks observed in a fully wildtype virus? Also, can the authors provide any speculation as to why the Late3 region also lacks protein VII?

We confirm the reviewer's observation. The region marked as Late3 and the region between E3 and E4 is present in the genome and is, as the reviewer observed, not chromatinized in our analysis. At this point, we can only speculate. We have two not mutually exclusive hypotheses. First, both regions could be involved in the proper packaging of the viral genome into the capsid. Physical constraints during packaging may preclude this region from being packaged into pVII. Second, as we observed that pVII positioning correlates with distinct DNA sequence patterns (revised Fig.4 D and E, see response to reviewer 3 for details), it might be that the sequence composition at the pVII depleted regions disfavour pVII assembly to keep those regions available for cellular factors that drive processes post genome delivery, such as transcription. Our time-resolved MNase analysis shows that indeed post genome delivery, this site in the Late3 region becomes protected (Fig. 5C), suggesting the binding of one or more cellular factors. As shown in Figure S6 we find conserved binding sites for several transcription factors at this MNase protected site.

Whether the chromatinization devoid regions would shift in position, remain in place or be chromatinized in a wildtype virus has to be addressed in the future and cannot be answered at this point. To address the comment, we have expanded the discussion (line 620 ff)

Line 569 "Reasons could be that the few genomes undergoing nucleosome assembly and active transcription produce the replication enzymes, whereas the bulk of genomes enters replication without activation as an elegant way to avoid repeated chromatinization." This argument may make sense in the context of a high MOI infection, but would certainly limit virus function during normal, pathogenic infection where the MOI is likely extremely low. Essentially, the authors data predicts that 80% of normal, low MOI infections don't progress to gene expression (at least during the first 4 hrs analyzed in this study).

We follow the argument of the reviewer. The high MOI in our study was necessary to perform the combined ‘omics’ approach to arrive at meaningful data within reasonable sequencing depth. To have equivalence we also used high MOI for the imaging approach. A detailed analysis for the effect of low MOI as well as positioning effects (see reviewer comment below) on transcriptional activation is an important question and will be addressed in future studies that require different techniques in addition. To address this comment, we have updated the discussion to emphasize the importance of MOI and positioning effects (line 587 ff).

Line 576 "This observation is in agreement with recent pVII-ChIP experiments showing transcription and replication independent pVII removal in early infection (Giberson et al., 2018; Komatsu and Nagata, 2012; Komatsu et al., 2011)." The authors can also state that histone and nucleosome deposition is also independent of transcription and replication, as has been alluded to in the same cited studies but proven more directly in this study.

We have changed the text accordingly (line 576 and 598).

Line 672 - the authors should be more definitive in the MOI that are used in all of their experiments. Line 672 states that an MOI of 3000 physical particles are applied per cell. There can be great variation between cell lines in how much virus binds to (and enters) a cell based on the surface levels of Ad receptors on different cell types. However, in general, 3000 is very high. Work by Wang et al. (PMID:24139403) showed that at an MOI of 200 or below most Ad will traffic correctly to the nucleus, whereas at an MOI above 200 there is a significant defect in Ad trafficking within the cell. How is this expected to affect all of the results in this study?

We agree with this and the other reviewer that this is an important issue. The actual dose of virus that enters a given cell is dependent on the concentration of virus particles in the inoculum and the time and temperature this inoculum is in contact with the cells and the cells respective susceptibility to the virus. We applied an infection dose of 3000 physical particles per cell in a defined volume (1ml) at 37˚C for 30min followed by inoculum removal. We prefer this description because with these infection conditions, we find on average well below 100 virus particles that enter the cell (=> This is e.g., reflected in the number of accumulating genomes shown in figure 2A). In contrast, this permits to have enough viruses inside the cell to perform the different “omics” techniques applied in our study to obtain meaningful results at reasonable sequencing depths. This experimental setting was carefully chosen in full awareness of the work by Wang et al., cited by the reviewer, to avoid e.g., overloading the nuclear import rate. Thus, our experimental conditions do not exceed the “MOI of >200” that would affect nuclear import rates. The number (>200) in the Wang et al. study refers to the number of virus particles inside the cell, the infection condition used in the Wang study was an MOI of 30 bound to Hela cells in the cold for 30min and warmed for 150min which is significantly more virus than we have used in our study. We have expanded the information on the MOI used in the material and methods section to clarify this point (line 685 ff).

Figure 5 is of low resolution and was difficult to read.

We thank the reviewer for spotting it. It seems that the Figure quality was compromised during the PDF conversion. We updated the Figures and checked the resolution after PDF conversion.

Figure S3 is missing a box from the top set of images indicating the region that is expanded in the detail picture.

We updated Figure S3

While I realize it is supplemental data, the difference in quality between the agarose gels shown in Figure S4A and S5A is shocking.

The nature of the experiments is very different and therefore the expected MNase digestion profiles on agarose gels look different. In Figure S5 viral particles were digested with MNase, resulting in a smeary decrease in DNA size. This looks very different from the regular MNase pattern of whole cells that is dominated by the regularly spaced nucleosomes in the heterochromatic regions of the genome. As pVII protects only about 70bp of DNA and its spacing is not as homogenous as the nucleosomal spacing, the pictures shown in Figure S5A were expected as they are.

Figure S7 is of low resolution.

We updated the Figures and checked the resolution after PDF conversion.

Reviewer #1 (Significance (Required)):

At least in the field of adenovirus research, this is a very important study. There has been considerable debate in the field regarding the timing and degree of protein VII removal and histone deposition, and the necessity of active transcription for these two events. The data provided in this manuscript clearly shows that some protein VII is removed from early active genes and replaced by nucleosomes, and that these events occur prior to initiation of transcription. The authors speculate that the specific placement of protein VII, a protamine-like protein, on the Ad genome prescribes where nucleosomes are placed. This finding should be of interest to a broad general audience, as it provides novel information on chromatin assembly within mammalian cell. Key words for this reviewer: adenovirus research, HAdV nucleoprotein structure

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

The submitted manuscript presents a detailed and comprehensive analysis of the adenoviral nucleoprotein complexes as infection progresses, starting with the "adenosome" assembled with pVII which are then progressively replaced with H3.3.-containing nucleosomes as the infection progresses. The submission presents a combination of in situ and populational analyses of the viral DNA accessibility and complexes through infection. I brief, the infecting viral genomes are assembled in some 250 adenosomes with pVII, which become progressively replaced as infection progresses with nucleosomes containing H3.3 and acetylated H3K17, starting at the active promoters of the E genes. Chromatin remodeling precedes transcription, and the accessibility differs for genes of different kinetic classes at differ times after infection, although there is no correlation between accessibility and H3.3. or acetylation content. Only about 20% of the genomes become transcriptionally active, though, which somewhat complicates the analyses of the populational studies of accessibility and occupancy. Overall, the study is well conceived, performed and presented. A few issues that deserve further analyses and discussion, as described below.

Major issues.

As figure 2 nicely shows, only about 20% of the intranuclear genomes become transcriptionally active. However, MNase and ChIP analyses cannot differentiate these genomes from the 80% that are transcriptionally inactive. The interpretation of the positioning of pVII (figure 4) or the changes in compaction of the adenoviral chromatin at different loci (figure 5) does not appear to consider this heterogeneity other than for a brief comment about the stringent MNase digestion in page 11. The authors favor a model in which the changes in compaction shown in figure 5, at mild MNase digestions, directly correlate with transcription of the respective genes. This could well be correct, and in fact the correlation may be underestimated as 80% of the genomes may not undergo any changes, but it may also be incorrect. The analyses presented cannot differentiate whether the changes in chromatin compaction occur in only a subset of genomes or in all the genomes, regardless of whether they are transcribed or not, or even only in the non-transcribed genomes (which appears extremely unlikely). This intrinsic limitation to the methods used (and I know of no better alternative) should be acknowledged and discussed for the benefit of the reader. This limitation also impacts the analyses of the lack of correlation between H3.3 and acetylated H3K27 occupancy and compaction.

A discussion is amended and located starting from line 571 in the text. “The heterogeneity of 80% inactive genomes and 20% activated genomes complicates the analysis of the MNase-seq data. High MNase concentrations do not differentiate between both states, and we suggest that low MNase conditions capture the dynamic viral proportion, changing and preparing its genome for gene activation. The data nicely suggest such a scenario, but there is the caveat that we catch an effect of the mixed population that we cannot differentiate.”

The analysis of the histone ChIP is discussed below.

Perhaps out of necessity to reach the required sensitivity, a high multiplicity of infection was used (although the actual moi is not stated, there are about 25-30 pVII foci/ per nuclei). The presentation, analyses and discussion of the results should emphasize this context. For example, one would presume that at low moi, when only one genome enters each cell, the percentage of transcriptionally active genomes in a given cell will be either 0 or 100%, but the "system" becomes saturated as more and more genomes enter the nucleus at higher moi resulting in only a subset of them being transcriptionally active. Along this line of reasoning, it is intriguing that the percentage of genomes estimated to be in nucleosomes at 4 hpi (14%) approaches the percentage of transcribed genomes.

This issue was also raised by reviewer 1 (see detailed comment above). The reviewer is correct that we chose to use a higher MOI to reach the required sensitivity in our different “Omics” assays. The imaging approach was adapted to reach the morphological equivalence to fit this analysis. We agree that it would be interesting to also study the MOI effect on transcriptional activation (as well as positioning effects, see comment below) but this requires different approaches and will be addressed in a future study. To address this comment (and others in this review) we revised the text in the discussion to emphasize the importance of MOI and possible other effects such as positioning (line 587 ff).

The changes in chromatin compaction presented in figure 5 are in some respect puzzling. The compaction of most of the late genes increases as infection progresses, at least for the first four hours, as the authors discuss. However, the L genes appear to be at least as accessible as the E ones at the early times, when only the E are transcribed to high levels. This appears counterintuitive, and may not be consistent with the main conclusion that increase accessibility to a given gen directly correlates to its transcriptional activity level. The data presented in Figure 5C deserves a more nuanced analysis and discussion, parsing out the changes in accessibility to each given gene at different times from the different accessibility to the different genes at any given time. The later does not appear to support the main conclusion reached by the authors that accessibility to each individual gen correlates with its transcriptional level.

We thank the reviewer for raising this point. While the viral genomes enter the nucleus, the viral chromatin structure is tightly condensed. Therefore, it is unlikely that after nuclear entry the viral chromatin undergoes further compaction. With our analysis, we expect to detect only decompaction of genomic sites relative to 0 hpi, when the virus has not entered the nucleus yet. At some sites and particularly at the Late genes the signal is decreasing, most likely due to normalization to sequencing depth and the variation in the number of viral genomes but not due to changes in compaction. We realized that the negative accessibility scores we used in the study are misleading and give a false impression. Therefore, we changed the analysis in that way, that negative values were not permitted and converted to zeros.

Additionally, we raised the temporal resolution of the analysis and compared the accessibility at all available timepoints against 0 hpi as suggested by the reviewer. Now, we clearly observe, that most accessibility changes are accomplished rapidly after nuclear import, already at 1 hpi and do not change much after, until 4 hpi. Regions of decompaction coincide with early expressed genes and occur before transcription, underscoring the conclusions made in the study. Nevertheless, while most genomic regions covering late genes do not show decompaction, we observed some local sites showing a high accessibility score. As transcription at those sites appears later in the life cycle of the virus, we can only speculate about the function e.g. as enhancer elements.

The Text and Figures were changed accordingly (line 347 ff).

New legend:

__C) __Profile illustrates HAd-C5dE3 genome coverage by low MNase-seq fragments. The average of two replicates is shown, except at timepoint 0 hpi where only one replicate was available. The accessibility score was calculated as the log(fold-change) between the indicated timepoint and 0 hpi. The score was assessed for each pVII peak (orange bars) and negative scores were set to 0. A new accessibility peak arising during infection in the Late3 region is marked by an asterisk. __D) __Boxplot showing the accessibility score distribution in each domain at each tested timepoint after infection.

Minor comments

The authors may wish to highlight in the discussion that the analyses are so far limited to a single adenovirus.

We have taken up the suggestion of the reviewer and included it in the discussion part, starting at line 607:

“The structural analysis is still limited to a single adenovirus genotype and it will be interesting to test whether these dynamic changes are conserved among other adenoviruses. Furthermore, reproducing such organization in adenoviral vectors could result in efficient and sustained transgene expression.”

The y-axes in the transcriptome figures (figure 1 B, S2) could be presented in Log(2) scale, such that transcript levels at all times can be appreciated in the same graph (the earlier times are just not visible in a linear scale)

As requested by the reviewer we changed the data to log2 scale. As there is no qualitative difference to the log10 scale, presented in the original version, we would like to keep the figure as it is. To highlight changes at early time points we generated the average expression of early genes in Fig1C.

As an information for the reviewer, we provide here the data plotted as log2 scale.

The (lack of) phenotype of the 24xMS2 binding site recombinant adenovirus used should be shown.

We observed no difference in phenotype between the parental and the MS2 modified virus. We updated Figure S3 and included a gel analysis and specific infectivity data to show this absence of difference.

The kymograph analyses presented in figure 3B appear to show that there are some sites of transcript accumulation sites which do not harbor viral genomes (i.e., green only tracks). Moreover, the interpretation of the TAF1beta-mCherry signal is complicated by the (fully expected) significant "background" signal. Although these results are consistent with those obtained by RNAscope/pVII staining, there appears to be intrinsic limitations to the system, which preclude reaching strong conclusions from it. These confirmatory analyses should probably be moved to the supplementary information section and removed from the main text and figures. The longer evaluation data mentioned as not shown in page 8 is critical to the conclusions and should be shown.

Here we disagree with the reviewer and prefer to keep the data as main figure. All (immobile) transcript accumulation sites are identified by the kymograph analysis and coincide with a genome while free transcripts show a high mobility that is not picked up in the kymograph analysis. This is independently verified in the provided supplemental movies. Depending on the positioning of the genome inside the living cell, accumulating transcripts can appear adjacent to or on top of a genome. This explains the slight shift between RNA and DNA signal for some genomes in the merged image of the kymograph. This is expected as only fully transcribed transcripts and not nascent transcripts are marked by MS2 (the MS2 loops are positioned in the 3’UTR). Also, all genomes (transcribing and non-transcribing) can be identified in the kymograph above background level. To clarify the representation, we have added labels to the kymograph to show which signal is DNA and RNA and a merge respectively. We are convinced that this data set is in strong support of our study, as it is the only technique that permits the discrimination of transcribing and non-transcribing genomes in living cells at real time.

As requested, we have also added two additional examples for a longer observation period (10min) into the supplemental data Fig. S3C.

Although the plot of cleavage frequency presented in figure S5 is clear, it would be beneficial to the reader if the actual peaks were also presented to compare their distribution (if any) in gDNA and virus particle.

In Figure S5 we wanted to test whether the regions lacking pVII peaks are resulting from the absence of pVII, protecting the DNA, and therefore being fully hydrolyzed by MNase, or whether this region is tightly packed by pVII thereby protecting DNA from MNase digestion. To test both possibilities we used a very limited MNase digestion approach, where even free DNA is not fully hydrolyzed, allowing the capture of DNA fragments. Therefore, the sequenced fragments comprise a mixture of protected and un-protected fragments. In this assay, the pVII protected fragments are not fully digested to the monomeric state, but a mix of mono-, di- and other multimers are present. As reflected by the fragment size distribution with the peak between 100-200 bp (Fig S5B), pVII dimers are predominantly enriched when compared to the high MNase digestion used to map pVII positions (compare to Fig4 B). Therefore, the peaks in the S5 data set have a low resolution and do not provide exact pVII positions (see below). Therefore, we would like to keep S5 as it is. We clarified this point in the text (line 279 ff)

Legend:

Fragment coverage plot of MNase digestions of gDNA (black) or Ad chromatin in virus particles (purple).

The mRNA analyses of selected transcription factors provides little information, as there is no context, there is variability between experiments, and in most cases the changes appear modest. As these results are not critical to the conclusions or analyses, perhaps the authors may wish to remove them from the manuscript. Alternatively, more in-depth analyses would be required.

We agree with the reviewer, that more information for the reader is needed. Therefore, we performed a statistical analysis of expression changes between 0 hpi and 4 hpi of the shown transcription factors using DESeq2. We added the corresponding log2(fold-change) and p-values to the figure. And adapted the text (line 471) and figure accordingly.

Legend:

Gene expression changes of transcription factors over the infection time course. P-values and log2(fold-changes) from differential gene expression analysis between 4 hpi and 0 hpi using DESeq2 are indicated. ns = not significant

It is unclear why the even distribution of H3.1-flag signal across the genome is considered indicative of no specific recruitment. The results presented are equally consistent with equal incorporation across the genome. Perhaps the authors have some additional information, such as an irrelevant antibody, input DNA, or the like, to support the conclusion. If so, that evidence should be presented and discussed. If not, the interpretation should be revisited. As an added complexity, endogenous H3.1 is normally expressed during S-phase. It is possible that Adenovirus infection may induce higher levels of expression of (untagged)endogenous H3.1, which would outcompete the tagged ectopically expressed histone. These analyses deserve a more nuanced and in-depth analysis.

We have taken several measures in the study to address the concern of the reviewer. We consider timepoint 0 hpi as background control as the viral genome has not entered the nucleus yet. Consistently, we observe very few reads mapped to the Ad genome regardless of antibody and construct used (Fig 6B). Additionally, all samples at 0 hpi cluster together in PCA (Fig 6C) and correlation analysis (Fig S7D)

H3.1 Flag tagged samples show at later timepoints (1 - 4hpi) slightly higher percentages of mapped reads to Ad, but plateau already at 1 hpi (Fig 6B) and cluster together in PCA (Fig 6C) and correlation analysis (Fig S7D) with 0 hpi samples. The low background signal starting at 1 hpi for H3.1 might arise due to the change of Ad genome location to the nucleus.

Even though, the number of Ad mapped reads at later timepoints was low in H3.1 Flag tagged samples, it could still be that they accumulate at few sites on the Ad genome indicating a specific deposition. We tested this by plotting the signal across the whole Ad genome (Fig S7E) and zooming into the data (compare scale of H3.3 and H3.1 plot), but we could not detect any reproducible local enrichments. To enable the reader a better comparison between the levels of H3.1 incorporation with H3.3 we put now both on the same scale (Fig 6D and Fig S7D) clearly showing that we cannot detect H3.1 incorporation at Ad genomes in the first 4 hours of infection. The H3.1 signal corresponds to the background noise. We think for two reasons, that it is very unlikely that endogenous H3.1 outcompetes the tagged H3.1:

• The time scale for the cells to transition into S-Phase and upregulate endogenous H3.1 would be only 1-2 hours in our timeseries experiments and therefore too short. To also show these experimentally we amended an experiment for the reviewer that is not included in the manuscript. The Western blots below show that the protein amount of H3 does not increase in the first 4hours of infection. Cells were infected and whole cell extracts were prepared 4hpi.
• As most cells are not in S-phase in our experiments, the expression levels of H3.3 variant is higher than H3.1. With the Flag ChIPs we can clearly show that the tagged H3.3 are not outcompeted by endogenous H3.3. As there is a high sequence similarity between H3.3 and H3.1 it is very unlikely that they behave in that regard differently.

  It is highly unlikely that the somewhat higher H3K27ac signal observed in the H3.3 than in the H3.1 expressing cells may result from higher H3.3. occupancy in the viral genome as speculated in page 13. The total levels of H3.3. are unlikely to increase by the ectopically expressed one, and even if they did it is not likely that the occupancy of the viral genome would be limited by the levels of H3.3. This speculation should be removed.

We removed the speculation.

Materials and methods are too concise. A longer more detailed version, as supplementary information, would be highly desirable.

We extended the materials and methods part.

Reviewer #2 (Significance (Required)):

The major strengths of this manuscript lie on its comprehensiveness, using several in situ and populational approaches to address biologically critical questions regarding the regulation of viral replication by chromatin and epigenetics. Experiments appears very well designed and performed and are mostly clearly presented. The interpretation analyses and discussion of the results may benefit from a more nuanced analysis of the issues posed by the existence of different populations of viral genomes in the cells infected at high moi and the accessibility across different genes at any given time versus the levels of transcription of the different genes, which appears not to be fully consistent with one of the main conclusions reached.

This study makes a very significant contribution, describing the dynamic changes in the adenoviral nucleoprotein complexes at the early times of infection and providing a full description of both the adenosomes and the nucleosomes in more and less transcribed loci. The results are properly analyzed in context of what is known about the regulation of viral gene transcription by chromatin dynamics in other systems, including similarities and differences. This study is likely to be of high interest to a wide audience, ranging from virologist to epigeneticists, to those working in gene therapy and vectored vaccines.

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

The manuscript "Adenoviral chromatin organization primes for early gene activation" combines RNA-seq, MNase-seq, ChIP-seq, and single genome and transcript imaging (immunofluorescence, RNA-scope, and live cell techniques) during early Adenovirus infection in vitro to characterise the spatiotemporal dynamics of viral chromatin organisation and association with gene transcription. The manuscript is an interesting read and the authors have combined multiple complimentary techniques to make a substantial contribution to understanding the early events occurring after nuclear import of viral genomes. Adenoviruses are important causes of human and animal pathology, are a useful model of non-integrating extra-chromosomal DNA virus infection in mammalian cells, and are useful vectors for vaccination and the discoveries may influence gene therapy DNA vector design. The chromatin organisation in adenovirus infection is distinct from other DNA viruses, and is relatively poorly understood compared to, for example, SV40 or herpesviruses. The manuscript describes an early transition from purely viral chromatin with Adenovirus protein pVII packaging the virus in virions, to a viral-human hybrid chromatin pattern with apparently strategically positioned H3.3 nucleosomes and viral pVII "Adenosomes" in the early hours after nuclear import of the viral genome. The data shows that packaged Adenoviruses are in a transcriptionally accessible form and gene expression occurs rapidly after infection, the combination of the MNase-seq data with ChIP-seq data is particularly interesting demonstrating and average ~238 adenosomes positioned by specific DNA code protecting 60-70bp of DNA, and that the genome is accessible at loci that also decondense on infection, with adenosomes being replaced by cellular H3.3 containing nucleosomes at distinct sites. Particularly they show that +1 H3K27 acetylated nucleosomes are acquired at the TSS of key early genes. The authors argue that their spatiotemporal data imply that this chromatin transition "primes" for early gene transcription. The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented. A few comments follow. Major concerns: • Abstract and Title: o the abstract and title suggest that because the chromatin changes are observed coincidentally or before transcriptional changes, and that this means that these chromatin changes "prime" (title) or are "required" and play a "central role" (abstract) in early gene expression. The temporal relationship would be consistent with chromatin changes being required for transcriptional changes, but do not imply necessity. Experiments to demonstrate the necessity of these changes for early gene transcription are lacking, and I recommend amending the text or additional experiments to provide this evidence directly.

We observe a clear timing of events, with chromatin opening, nucleosome assembly at the 5’ end of the gene followed by transcriptional activation, suggesting that these structural changes are essential for gene activation. Still, we cannot prove the direct dependency. Therefore we toned down the title of our manuscript and formulate the findings more conservatively.

The title now reads: “Changes in adenoviral chromatin organization precede early gene activation”

Results:o The IF data in Fig S1 is convincing, showing viral particles are accessible quickly in the nucleus. Although no statistics are provided for S1B and C, pVII foci appear at 0.5hpi and appear to mostly accumulate between 0.5hpi and 1hpi with further import between 1hpi and 4hpi. Can the authors be sure that a single pVII IF focus represents a single genome? If genomes tend to aggregate as they accumulate the number of foci per nucleus may not increase linearly with the number of genomes imported. Have the authors considered analysing the intensity of the individual pVII foci over the time points? A related question is whether the authors assume that all packaged virions contain intact complete viral genomes? Many viruses comprise some mixture of complete and incomplete packaged genomes, and the subsequent analyses determine the proportion of transcriptionally active copies with RNA-Scope to a single transcript E1A which lies at one end of the viral genome. Please comment explicitly on whether this is assumed and whether this assumption is realistic in light of known Adenovirus biology.

We appreciate the reviewer's concern. Several studies in the adenovirus field have shown equivalence between protein VII nuclear foci and individual genomes, including our own (PMID: 26332038). Probably the most accurate study was performed by Daniel Engels lab PMID: 19406166, who used nuclear protein VII foci to titrate viral as well as vector genomes. In contrast, a different study from Patrick Hearings lab PMID: 21345950 showed that past 4hpi, the number of nuclear protein VII foci gradually declines. Based on our experience and because our study is limited to 4 hpi we are confident that protein VII foci accurately reflect individual viral genomes.

Concerning genome packaging, adenovirus particles contain a single viral genome that is protected at each end by a covalently attached protein preventing its degradation. The packaging of adenoviruses is extremely efficient and only complete genomes are packaged into fully assembled particles. All viruses used in this study have been purified by double CsCl gradient purification. This density gradient based purification protocol removes all particles that are either empty or damaged or would contain partial genomes.

o The RNA-Seq data in Fig 1 and Fig S2 and Table S1 demonstrates transcription of early genes is barely observable at 1hpi but is observable by 2hpi and is clearly much increased by 4hpi. Fig 2C, visualising pVII foci directly within single cells, suggests that approximately 80% of foci are observed by 1hpi and a further 20% between 1hpi and 2hpi and little thereafter. These data convincingly demonstrate that nuclear import is rapid, typically occurring in the first hour. The E1A RNA-Scope data in figure 2, visualising individual mRNA transcripts of E1A, is more sensitive than the bulk RNA-Seq, and shows transcripts at 1hpi with clearly discernible transcription by 2hpi (2A&D) which suggests that transcription occurs early, by 2hpi. Thus transcription lags nuclear genome import by approximately one hour by these methods. However, the conclusions of the subsequent analyses depend on the chromatin changes clearly preceding, rather than being approximately coincident with transcription, therefore transcription being evident by 2hpi is relevant as figure 6A and D suggest that the chromatin remodelling is subtle before 2hpi on the bulk sequencing analyses. The authors should comment on this given the importance to their argument.

As stated by the reviewer we observe a clear lag between nuclear import and transcriptional activation. And we do also observe the largest changes in nucleosome occupancy (ChIP-seq data) between 1 and 2 hpi (Fig6A and D). Compared to 0hpi, we observe the strongest increase of nucleosome occupancy between 1hpi and 2hpi (4-8fold effect), whereas depending on the area a 2-3fold increase in occupancy can be observed from 2hpi to 4hpi (Fig6D). An effect that one would expect with chromatin structure preceding gene activation. Furthermore, the timing of nucleosome assembly perfectly matches the increase of MNase accessibility at 1 hpi, supporting our conclusions.

o The validation of the E1A probe specificity in Fig 2B looks convincing, but there are no data presented for multiple cells to reassure that this image is representative. The equivalent figure for 2D for the Ad5-GFP control would address this.

We include a large field overview with multiple cells for virus and vector control as new supplemental figure S2B showing that the RNAscope detection of the E1A transcript is highly specific.

o Figure 2E is presented as a colocalization analysis but appears to be a ratio of mRNA foci to pVII foci per cell. If this is an incorrect interpretation then some clarification in the figure legend would be helpful. If this interpretation of these data is correct, then it is not truly a colocalization analysis, as a single genome may give rise to multiple transcripts and so a ratio We apologize that this figure was not clear. The data are based on real colocalizations and represent the number of pVII dots positive for E1A normalized with the total number of nuclear pVII. We have clarified the figure legend accordingly.

o The live cell imaging experiments are elegant and convincing, but the agreement in Fig 3D of the % colocalization in MS2-BP data with the RNA-scope data is potentially misleading for the reasons outlined in the prior comment. Is the data in Fig 2E the same as the data in the right hand panel of Fig 3D. If so please comment on the n discrepancy (n=30 in 2E vs n=22 in 3D). The observation that 20% of genomes are transcriptionally active, via bursting or otherwise, is interesting, and would be consistent with the Suomalainen et al reference. The authors discuss two hypotheses to explain these findings: transcriptional bursting or a subset ~20% of genomes being transcriptionally active. This is an interesting and begs the question as to why this may occur. Assuming all imported genomes are intact (previous comment), it appears from the presented images that the foci at the radial periphery of the nucleus may be more frequently transcriptionally active, despite the nuclear periphery being enriched for heterochromatin. The authors might consider analysing the radial position of their TAF1B-mCherry genomes (active and inactive) as this might support position effect variegation rather than bursting as an explanation and they appear to already have the data to perform such analyses.

o In the presented images (Fig 3A and Fig S3) it appears a higher proportion of genomes than 20% appear to be transcriptionally active, particularly in the low MOI experiment. The authors may wish to comment on this and quantify whether the proportion of transcribing genomes was affected by the input MOI.

This and the previous comment concerning the influence of MOI, transcriptional bursting and the positioning effect of the genome on the transcriptional activity have also been in part raised above. As stated in our response to reviewer 1 we have used a high MOI in our experiments to have equivalence between all experimental approaches. We agree with the reviewers that all aspects (dose, bursting and positioning) merit a detailed investigation, which we plan in future studies. To be consistent and comparable in our comprehensive approach we decided to not include such studies here as they would address a different question. Nevertheless, to address this (and the above) comments we now mention positioning effects in the results (line 214) and enlarged the discussion (line 587 ff) where we especially raised awareness that such pertinent questions can be addressed with the tools presented in our study.

We also decided to visually separate the comparison of MS2 and RNAscope data to avoid misleading the reader. Furthermore, the RNAscope data have been replaced. The RNAscope data are indeed from Fig. 2. The difference in n was due to our mistake showing two different normalized data sets. Data were either normalized using total amount of nuclear protein VII (Fig. 2E) or the total amount of nuclear E1A signals (Fig 3D), which due to the more heterogenous signal did not include all cells. In the updated version both figures display data normalized by total amount of nuclear protein VII

o Fig 4C suggests that there is a large GC preference (or bias) in the pVII occupied regions. The authors may wish to comment on this and present a track with Adenovirus GC composition in Fig 4D.

We thank the reviewer for raising this point. As suggested by the reviewer we analysed the GC content under pVII peaks and in the linker DNA. Indeed, pVII occupied regions have a significant higher GC content indicating that pVII preferentially positions at GC rich regions. We included this analysis as an additional Figure 4E (line 302 f).

Legend:

Boxplot showing GC content of pVII occupied (pVII) or free (linker) regions. Two biological replicates are shown side by side and the p-value of a students t-test of the corresponding pairs is indicated above.

o Figure 6 presents convincing data showing H3.3. nucleosome positioning and acetylation at E1A and the data is nicely presented showing these changes occur early being observable by 2 and 4 hpi. Again, these changes are not convincingly prior to early gene activation but are certainly occurring early, and may occur prior to early gene activation at the level of individual foci, however, this is not demonstrated definitively.

This question belongs to the same context addressed by the reviewer above. Please refer to the answer given above.

Minor comments:

Introduction: o Paragraph 1 - Introduction for DNA viruses in general, but the authors appear to be talking about Adenoviruses specifically, "little is known about the structural organization of the genome" and "nuclear viral genomes could undergo different parallel fates", arguably these statements are not accurate for other DNA viruses (e.g. Epstein Barr Virus) suggest amending the wording for clarity.

The manuscript text was updated as suggested.

o paragraph 2 - Why do the authors say that Adenoviruses are prototypic DNA viruses?

We removed the term prototypic.

o Paragraph 3 - A recent study is referenced but multiple references are given.

The references were updated

o "Protein VII stays associated with the viral genome imported to the nucleus, while pV dissociates from the viral DNA following ubiquitylation (Puntener et al., 2011). The fate of the μ-peptide is not known". - The reference suggests that pV dissociates on entry to the cytoplasm and during capsid disassembly at the nuclear pore. I find this sentence confusing as it doesn't make it clear that pV is lost before nuclear entry which is important for interpreting the data.

We clarified this in the manuscript text

Results:

o Figure 5 is almost unreadable due to low resolution.

We updated the Figures and checked the resolution after PDF conversion.

o Reference to Fig 4C in text comes after Fig4D.

The order of Figure panels was changed accordingly.

Reviewer #3 (Significance (Required)):

The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented

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Referee #3

Evidence, reproducibility and clarity

The manuscript "Adenoviral chromatin organization primes for early gene activation" combines RNA-seq, MNase-seq, ChIP-seq, and single genome and transcript imaging (immunofluorescence, RNA-scope, and live cell techniques) during early Adenovirus infection in vitro to characterise the spatiotemporal dynamics of viral chromatin organisation and association with gene transcription. The manuscript is an interesting read and the authors have combined multiple complimentary techniques to make a substantial contribution to understanding the early events occurring after nuclear import of viral genomes.

Adenoviruses are important causes of human and animal pathology, are a useful model of non-integrating extra-chromosomal DNA virus infection in mammalian cells, and are useful vectors for vaccination and the discoveries may influence gene therapy DNA vector design. The chromatin organisation in adenovirus infection is distinct from other DNA viruses, and is relatively poorly understood compared to, for example, SV40 or herpesviruses. The manuscript describes an early transition from purely viral chromatin with Adenovirus protein pVII packaging the virus in virions, to a viral-human hybrid chromatin pattern with apparently strategically positioned H3.3 nucleosomes and viral pVII "Adenosomes" in the early hours after nuclear import of the viral genome. The data shows that packaged Adenoviruses are in a transcriptionally accessible form and gene expression occurs rapidly after infection, the combination of the MNase-seq data with ChIP-seq data is particularly interesting demonstrating and average ~238 adenosomes positioned by specific DNA code protecting 60-70bp of DNA, and that the genome is accessible at loci that also decondense on infection, with adenosomes being replaced by cellular H3.3 containing nucleosomes at distinct sites. Particularly they show that +1 H3K27 acetylated nucleosomes are acquired at the TSS of key early genes.

The authors argue that their spatiotemporal data imply that this chromatin transition "primes" for early gene transcription. The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented. A few comments follow.

Major concerns:

• Abstract and Title:
  • the abstract and title suggest that because the chromatin changes are observed coincidentally or before transcriptional changes, and that this means that these chromatin changes "prime" (title) or are "required" and play a "central role" (abstract) in early gene expression. The temporal relationship would be consistent with chromatin changes being required for transcriptional changes, but do not imply necessity. Experiments to demonstrate the necessity of these changes for early gene transcription are lacking, and I recommend amending the text or additional experiments to provide this evidence directly.
• Results:
  • The IF data in Fig S1 is convincing, showing viral particles are accessible quickly in the nucleus. Although no statistics are provided for S1B and C, pVII foci appear at 0.5hpi and appear to mostly accumulate between 0.5hpi and 1hpi with further import between 1hpi and 4hpi. Can the authors be sure that a single pVII IF focus represents a single genome? If genomes tend to aggregate as they accumulate the number of foci per nucleus may not increase linearly with the number of genomes imported. Have the authors considered analysing the intensity of the individual pVII foci over the time points? A related question is whether the authors assume that all packaged virions contain intact complete viral genomes? Many viruses comprise some mixture of complete and incomplete packaged genomes, and the subsequent analyses determine the proportion of transcriptionally active copies with RNA-Scope to a single transcript E1A which lies at one end of the viral genome. Please comment explicitly on whether this is assumed and whether this assumption is realistic in light of known Adenovirus biology.
  • The RNA-Seq data in Fig 1 and Fig S2 and Table S1 demonstrates transcription of early genes is barely observable at 1hpi but is observable by 2hpi and is clearly much increased by 4hpi. Fig 2C, visualising pVII foci directly within single cells, suggests that approximately 80% of foci are observed by 1hpi and a further 20% between 1hpi and 2hpi and little thereafter. These data convincingly demonstrate that nuclear import is rapid, typically occurring in the first hour. The E1A RNA-Scope data in figure 2, visualising individual mRNA transcripts of E1A, is more sensitive than the bulk RNA-Seq, and shows transcripts at 1hpi with clearly discernible transcription by 2hpi (2A&D) which suggests that transcription occurs early, by 2hpi. Thus transcription lags nuclear genome import by approximately one hour by these methods. However, the conclusions of the subsequent analyses depend on the chromatin changes clearly preceding, rather than being approximately coincident with transcription, therefore transcription being evident by 2hpi is relevant as figure 6A and D suggest that the chromatin remodelling is subtle before 2hpi on the bulk sequencing analyses. The authors should comment on this given the importance to their argument.
  • The validation of the E1A probe specificity in Fig 2B looks convincing, but there are no data presented for multiple cells to reassure that this image is representative. The equivalent figure for 2D for the Ad5-GFP control would address this.
  • Figure 2E is presented as a colocalization analysis but appears to be a ratio of mRNA foci to pVII foci per cell. If this is an incorrect interpretation then some clarification in the figure legend would be helpful. If this interpretation of these data is correct, then it is not truly a colocalization analysis, as a single genome may give rise to multiple transcripts and so a ratio <1 could be expected even if all pVII foci were transcribing genomes. In addition, the text suggests that because there is no statistically significant difference between 2hpi and 4hpi a plateau has been reached. However, the difference between 1h and 2h is barely significant (p=0.04) and the mean is increased between 2h and 4h, albeit non-significantly, so the plateau is not convincingly demonstrated. If the authors wish to perform a colocalization analysis rather than a ratio, they might assign each transcript to the nearest pVII IF focus within the nucleus and count the proportion of pVII foci with any transcripts assigned over time. Alternatively, they can amend the description of this analysis in the figure and text.
  • The live cell imaging experiments are elegant and convincing, but the agreement in Fig 3D of the % colocalization in MS2-BP data with the RNA-scope data is potentially misleading for the reasons outlined in the prior comment. Is the data in Fig 2E the same as the data in the right hand panel of Fig 3D. If so please comment on the n discrepancy (n=30 in 2E vs n=22 in 3D). The observation that 20% of genomes are transcriptionally active, via bursting or otherwise, is interesting, and would be consistent with the Suomalainen et al reference. The authors discuss two hypotheses to explain these findings: transcriptional bursting or a subset ~20% of genomes being transcriptionally active. This is an interesting and begs the question as to why this may occur. Assuming all imported genomes are intact (previous comment), it appears from the presented images that the foci at the radial periphery of the nucleus may be more frequently transcriptionally active, despite the nuclear periphery being enriched for heterochromatin. The authors might consider analysing the radial position of their TAF1B-mCherry genomes (active and inactive) as this might support position effect variegation rather than bursting as an explanation and they appear to already have the data to perform such analyses.
  • In the presented images (Fig 3A and Fig S3) it appears a higher proportion of genomes than 20% appear to be transcriptionally active, particularly in the low MOI experiment. The authors may wish to comment on this and quantify whether the proportion of transcribing genomes was affected by the input MOI.
  • Fig 4C suggests that there is a large GC preference (or bias) in the pVII occupied regions. The authors may wish to comment on this and present a track with Adenovirus GC composition in Fig 4D.
  • Figure 6 presents convincing data showing H3.3. nucleosome positioning and acetylation at E1A and the data is nicely presented showing these changes occur early being observable by 2 and 4 hpi. Again, these changes are not convincingly prior to early gene activation but are certainly occurring early, and may occur prior to early gene activation at the level of individual foci, however, this is not demonstrated definitively.

Minor comments:

• Introduction:
  • Paragraph 1 - Introduction for DNA viruses in general, but the authors appear to be talking about Adenoviruses specifically, "little is known about the structural organization of the genome" and "nuclear viral genomes could undergo different parallel fates", arguably these statements are not accurate for other DNA viruses (e.g. Epstein Barr Virus) suggest amending the wording for clarity.
  • paragraph 2 - Why do the authors say that Adenoviruses are prototypic DNA viruses?
  • Paragraph 3 - A recent study is referenced but multiple references are given.
  • "Protein VII stays associated with the viral genome imported to the nucleus, while pV dissociates from the viral DNA following ubiquitylation (Puntener et al., 2011). The fate of the μ-peptide is not known". - The reference suggests that pV dissociates on entry to the cytoplasm and during capsid disassembly at the nuclear pore. I find this sentence confusing as it doesn't make it clear that pV is lost before nuclear entry which is important for interpreting the data.
• Results:
  • Figure 5 is almost unreadable due to low resolution.
  • Reference to Fig 4C in text comes after Fig4D.

Significance

The manuscript is well written, uncovers important viral chromatin biology by combining multiple experimental techniques, and the data is generally very clearly presented

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Referee #2

Evidence, reproducibility and clarity

The submitted manuscript presents a detailed and comprehensive analysis of the adenoviral nucleoprotein complexes as infection progresses, starting with the "adenosome" assembled with pVII which are then progressively replaced with H3.3.-containing nucleosomes as the infection progresses. The submission presents a combination of in situ and populational analyses of the viral DNA accessibility and complexes through infection. I brief, the infecting viral genomes are assembled in some 250 adenosomes with pVII, which become progressively replaced as infection progresses with nucleosomes containing H3.3 and acetylated H3K17, starting at the active promoters of the E genes. Chromatin remodeling precedes transcription, and the accessibility differs for genes of different kinetic classes at differ times after infection, although there is no correlation between accessibility and H3.3. or acetylation content. Only about 20% of the genomes become transcriptionally active, though, which somewhat complicates the analyses of the populational studies of accessibility and occupancy. Overall, the study is well conceived, performed and presented. A few issues that deserve further analyses and discussion, as described below.

Major issues.

As figure 2 nicely shows, only about 20% of the intranuclear genomes become transcriptionally active. However, MNase and ChIP analyses cannot differentiate these genomes from the 80% that are transcriptionally inactive. The interpretation of the positioning of pVII (figure 4) or the changes in compaction of the adenoviral chromatin at different loci (figure 5) does not appear to consider this heterogeneity other than for a brief comment about the stringent MNase digestion in page 11. The authors favor a model in which the changes in compaction shown in figure 5, at mild MNase digestions, directly correlate with transcription of the respective genes. This could well be correct, and in fact the correlation may be underestimated as 80% of the genomes may not undergo any changes, but it may also be incorrect. The analyses presented cannot differentiate whether the changes in chromatin compaction occur in only a subset of genomes or in all the genomes, regardless of whether they are transcribed or not, or even only in the non-transcribed genomes (which appears extremely unlikely). This intrinsic limitation to the methods used (and I know of no better alternative) should be acknowledged and discussed for the benefit of the reader. This limitation also impacts the analyses of the lack of correlation between H3.3 and acetylated H3K27 occupancy and compaction.

Perhaps out of necessity to reach the required sensitivity, a high multiplicity of infection was used (although the actual moi is not stated, there are about 25-30 pVII foci/ per nuclei). The presentation, analyses and discussion of the results should emphasize this context. For example, one would presume that at low moi, when only one genome enters each cell, the percentage of transcriptionally active genomes in a given cell will be either 0 or 100%, but the "system" becomes saturated as more and more genomes enter the nucleus at higher moi resulting in only a subset of them being transcriptionally active. Along this line of reasoning, it is intriguing that the percentage of genomes estimated to be in nucleosomes at 4 hpi (14%) approaches the percentage of transcribed genomes.

The changes in chromatin compaction presented in figure 5 are in some respect puzzling. The compaction of most of the late genes increases as infection progresses, at least for the first four hours, as the authors discuss. However, the L genes appear to be at least as accessible as the E ones at the early times, when only the E are transcribed to high levels. This appears counterintuitive, and may not be consistent with the main conclusion that increase accessibility to a given gen directly correlates to its transcriptional activity level. The data presented in Figure 5C deserves a more nuanced analysis and discussion, parsing out the changes in accessibility to each given gene at different times from the different accessibility to the different genes at any given time. The later does not appear to support the main conclusion reached by the authors that accessibility to each individual gen correlates with its transcriptional level.

Minor comments

The authors may wish to highlight in the discussion that the analyses are so far limited to a single adenovirus.

The y-axes in the transcriptome figures (figure 1 B, S2) could be presented in Log(2) scale, such that transcript levels at all times can be appreciated in the same graph (the earlier times are just not visible in a linear scale)

The (lack of) phenotype of the 24xMS2 binding site recombinant adenovirus used should be shown.

The kymograph analyses presented in figure 3B appear to show that there are some sites of transcript accumulation sites which do not harbor viral genomes (i.e., green only tracks). Moreover, the interpretation of the TAF1beta-mCherry signal is complicated by the (fully expected) significant "background" signal. Although these results are consistent with those obtained by RNAscope/pVII staining, there appears to be intrinsic limitations to the system, which preclude reaching strong conclusions from it. These confirmatory analyses should probably be moved to the supplementary information section and removed from the main text and figures. The longer evaluation data mentioned as not shown in page 8 is critical to the conclusions and should be shown.

Although the plot of cleavage frequency presented in figure S5 is clear, it would be beneficial to the reader if the actual peaks were also presented to compare their distribution (if any) in gDNA and virus particle.

The mRNA analyses of selected transcription factors provides little information, as there is no context, there is variability between experiments, and in most cases the changes appear modest. As these results are not critical to the conclusions or analyses, perhaps the authors may wish to remove them from the manuscript. Alternatively, more in-depth analyses would be required.

It is unclear why the even distribution of H3.1-flag signal across the genome is considered indicative of no specific recruitment. The results presented are equally consistent with equal incorporation across the genome. Perhaps the authors have some additional information, such as an irrelevant antibody, input DNA, or the like, to support the conclusion. If so, that evidence should be presented and discussed. If not, the interpretation should be revisited. As an added complexity, endogenous H3.1 is normally expressed during S-phase. It is possible that Adenovirus infection may induce higher levels of expression of (untagged)endogenous H3.1, which would outcompete the tagged ectopically expressed histone. These analyses deserve a more nuanced and in-depth analysis.

It is highly unlikely that the somewhat higher H3K27ac signal observed in the H3.3 than in the H3.1 expressing cells may result from higher H3.3. occupancy in the viral genome as speculated in page 13. The total levels of H3.3. are unlikely to increase by the ectopically expressed one, and even if they did it is not likely that the occupancy of the viral genome would be limited by the levels of H3.3. This speculation should be removed.

Materials and methods are too concise. A longer more detailed version, as supplementary information, would be highly desirable.

Significance

The major strengths of this manuscript lie on its comprehensiveness, using several in situ and populational approaches to address biologically critical questions regarding the regulation of viral replication by chromatin and epigenetics. Experiments appears very well designed and performed and are mostly clearly presented. The interpretation analyses and discussion of the results may benefit from a more nuanced analysis of the issues posed by the existence of different populations of viral genomes in the cells infected at high moi and the accessibility across different genes at any given time versus the levels of transcription of the different genes, which appears not to be fully consistent with one of the main conclusions reached.

This study makes a very significant contribution, describing the dynamic changes in the adenoviral nucleoprotein complexes at the early times of infection and providing a full description of both the adenosomes and the nucleosomes in more and less transcribed loci. The results are properly analyzed in context of what is known about the regulation of viral gene transcription by chromatin dynamics in other systems, including similarities and differences. This study is likely to be of high interest to a wide audience, ranging from virologist to epigeneticists, to those working in gene therapy and vectored vaccines.

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Referee #1

Evidence, reproducibility and clarity

The authors have addressed the nucleoprotein structure of human adenovirus during the very early stages of infection, and its relationship to onset of expression of viral genes, using a combination of RNA-seq, MNase-seq, ChIP-seq and single genome imaging. They show that in the virion and the newly-infecting DNA, protein VII is precisely position at specific sites on the viral DNA, with greater accessibility at early gene promoters compared to other regions. Nucleosomes containing H3.3 replace specific protein VII at distinct positions at the transcription start sites of genes, which are then acetylated. Association with histones and nucleosomes occurs prior to transcription. These studies confirm and greatly expand on results already in the literature, and also elucidate a novel role for protein VII in orchestrating positioning of nucleosomes prior to initiation of transcription.

The authors provide excellent data in support of their conclusions and, in many instances, use alternative experiments (i.e. two different approaches) to support their claims. The details of methods are adequate (with small exceptions outlined below) and statistical methods appropriate.

Minor comments:

1. Line 561 "Protein VII molecules were exchanged for positioned nucleosomes at the +1 site of actively transcribed genes". This statement seems to suggest that the +1 position almost acts as a nucleating site, where replacement of a single, specific protein VII molecule at +1 is an initiating event, which then spreads from that site and into the rest of the gene. Data shown in Figure 6G and 6H shows that H3.3 appears to be found equally along the full length of E1A as early as 1 hr post infection (with no real "enhancement" at the +1 position), and that the overall levels simply increase over the next 4 hrs.
2. Curiously, the authors chose not to use a wildtype virus for their studies - the virus contains a deletion in the E3 region. For clarity, I suggest that the authors should preferentially use an alternative designation for their virus rather than HAd-C5. Perhaps HAd-C5delE3 to differentiate this work from studies that truly use wildtype virus.
3. The obvious limitation of the studies using the fluorescent TAF1-beta to label Ad genomes is that as protein VII is replaced by nucleosomes, the genomes would have declining detection by this method. Genomes devoid of protein VII would be "invisible".
4. Line 275 "Interestingly, a central region of the viral genome (Late3) and a region between the E3 and E4 genes exhibited almost no peaks" for protein VII. The virus utilized in this study lacked at least part of the E3 region. Did this deletion "cause" this region to be devoid of protein VII? Is the same absence of protein VII peaks observed in a fully wildtype virus? Also, can the authors provide any speculation as to why the Late3 region also lacks protein VII?
5. Line 569 "Reasons could be that the few genomes undergoing nucleosome assembly and active transcription produce the replication enzymes, whereas the bulk of genomes enters replication without activation as an elegant way to avoid repeated chromatinization." This argument may make sense in the context of a high MOI infection, but would certainly limit virus function during normal, pathogenic infection where the MOI is likely extremely low. Essentially, the authors data predicts that 80% of normal, low MOI infections don't progress to gene expression (at least during the first 4 hrs analyzed in this study).
6. Line 576 "This observation is in agreement with recent pVII-ChIP experiments showing transcription and replication independent pVII removal in early infection (Giberson et al., 2018; Komatsu and Nagata, 2012; Komatsu et al., 2011)." The authors can also state that histone and nucleosome deposition is also independent of transcription and replication, as has been alluded to in the same cited studies but proven more directly in this study.
7. Line 672 - the authors should be more definitive in the MOI that are used in all of their experiments. Line 672 states that an MOI of 3000 physical particles are applied per cell. There can be great variation between cell lines in how much virus binds to (and enters) a cell based on the surface levels of Ad receptors on different cell types. However, in general, 3000 is very high. Work by Wang et al. (PMID:24139403) showed that at an MOI of 200 or below most Ad will traffic correctly to the nucleus, whereas at an MOI above 200 there is a significant defect in Ad trafficking within the cell. How is this expected to affect all of the results in this study?
8. Figure 5 is of low resolution and was difficult to read.
9. Figure S3 is missing a box from the top set of images indicating the region that is expanded in the detail picture.
10. While I realize it is supplemental data, the difference in quality between the agarose gels shown in Figure S4A and S5A is shocking.
11. Figure S7 is of low resolution.

Significance

At least in the field of adenovirus research, this is a very important study. There has been considerable debate in the field regarding the timing and degree of protein VII removal and histone deposition, and the necessity of active transcription for these two events. The data provided in this manuscript clearly shows that some protein VII is removed from early active genes and replaced by nucleosomes, and that these events occur prior to initiation of transcription.

The authors speculate that the specific placement of protein VII, a protamine-like protein, on the Ad genome prescribes where nucleosomes are placed. This finding should be of interest to a broad general audience, as it provides novel information on chromatin assembly within mammalian cell.

Key words for this reviewer: adenovirus research, HAdV nucleoprotein structure

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Reply to the reviewers

*Summary: This paper illustrates the role of GDF15 in ganglionic eminence featuring the influence on neurogenesis and progenitor proliferation. Using the conventional knockout GDF-15 mouse lines, the authors provide a series of counting data indicating that GDF15 controls neurogenesis in the late embryonic or adult neural stem cells in the ventral forebrain. *

We would like to thank the reviewer to reading our manuscript and providing comments. As the reviewer highlights, we have used a conventional GDF15 knock-in knock-out model since the growth factor is expressed not only at tissue levels but also at a systemic level. Therefore, targeted ablation of GDF15 would be complicated and the results difficult to interpret. Moreover, GDF15 and its receptor GFRAL in normal conditions are expressed at very low levels in adult mice and mutant mice do not display any obvious developmental phenotype. However, GDF15 is characteristically expressed during development and our previous data have shown that its expression is particularly increased at later developmental ages and particularly in neural precursors, which are both the focus of our analysis. Although these previous analyses have long highlighted that GDF15 is particularly expressed in the V-SVZ and in the choroid plexus, its physiological role in this system remains to the best of our knowledge unknown. It is important to investigate this issue because, as we mention below, GDF15 expression is increased, including in NSCs, upon brain injury and aging. As the reviewer rightly mentions, using this conventional approach and straightforward quantitative analyses, instruments available and normally used for the investigation of biological phenomena, we have discovered that GDF15 directly affects the number of ependymal cells and neural stem cells thereby providing a first function for the expression of the growth factor in this region.

Major comments:

The entire story in this manuscript seems similar to the previous findings where the authors demonstrated the role of GDF15 in the hippocampal neural stem cells in relation to EGF and CXCR4.

As the reviewer rightly mentions we have in a previous paper investigated the effect of GDF15 on the stem cells of the hippocampus. However, we would like respectfully to disagree with the reviewer that our current manuscript describes similar findings. Whereas we have previously shown that the effect of GDF15 in hippocampal stem cells and neurogenesis is a transient inhibition of proliferation due to reduced EGFR expression, we here found that absence of GDF15 leads instead to increased proliferation and a permanent increase of stem cell number, besides of ependymal cells. As the reviewer rightly mentions, on the basis of our previous observations, also in this study we have analyzed EGFR expression and signalling. However, although our data clearly show that EGFR expression and more importantly signalling are altered also in this niche, we could show that the effect of GDF15 is more complex than altering EGFR signalling. Since we show for the first time in this study, that besides GDF15, neural progenitors also express GFRAL, our data point at a selective effect of GDF15 in the development of neural stem cell in the GE and in the deriving adult niche, which include change in EGFR signalling.

*The data and the conclusion presented here sound reasonable to me. The current manuscript, however, gave me the impression that the story is less impactful and rather descriptive. To improve the quality of the current draft, the authors may wish to clearly highlight the novelty of the findings, not simply apply the previous strategy to the other anatomical brain regions. Alternatively, the draft could emphasize the similarity of utilising the same biological strategies to control the number of adult stem cells in the distinct stem cell niche. *

We would like to thank again the reviewer for the positive consideration of our quantitative analyses, although we cannot agree with the referee’s conclusion about the impact of our current study. In particular, we would like to focus here not only on the specific findings of the two studies that, as we mentioned above, reach different conclusions, but also on the general meaning of our new study in the context of understanding the role of GDF15. Besides during development and aging, GDF15 expression is promptly upregulated in several pathologies including, cancer, injury and neurodegeneration. Indeed, a growing body of evidence has highlighted the possible role of GDF15 as stress hormone and mitokine, which could be part of a conserved mechanism of the body to signal and respond to stress. Within this context, it would be very important to understand the effect of GDF15 on stem cells, as it may prompt not only understanding of the physiological role of GDF15 but also of the mechanism underlying the response and contribution of stem cell to stress and injury. Supporting this view, it was recently shown that GDF15 is upregulated in quiescent neural stem cells following brain injury (Llorens-Bobadilla et al., 2015). However, the main problem in investigating the physiological role of GDF15 is that the expression of its recently discovered receptor is very limited in the brain. Therefore, our data here are important not only because of the novel effect that they describe for GDF15 in NSC development, but also because they show that GDF15 directly affects stem cell behavior. We have now followed the suggestion of the reviewer and re-edited several parts of the manuscript including editorial changes to highlight the relevance of our findings and the figures to better illustrate them. In particular, we have added also additional analyses to support our conclusions. These include data illustrated in Fig.1B, C; Fig. 4B, G and Fig. 5G.

*I also have two concerns which may help to improve the current draft. Firstly, I would suggest considering the data presentation as many of the counting data are not accompanied by representative images or a detailed description of the methods, which could impede the credibility of the data. For instance, it is not clear to me how the authors judged the apical vs subapical progenitor counting as neither the pictures nor the methods clearly specified how these are distinguished. Same to the Figures 3 and 8. *

We thank the reviewer for these suggestions, which we have now implemented in our revised manuscript. These changes include addition of representative images to Figs. 1, 2, 3, 4, 5, 6, as well as an illustration of how apical vs subapical cells were counted in Fig. 2A, an illustration showing how ependymal and single-ciliated cells were determined in Fig. 8A, and more detailed descriptions in the materials and methods section.

*I would also suggest the authors may wish to carefully review the text as many abbreviations are not properly stated (for example, what are P+ progenitors?), or not properly explained why the particular gene expression is analysed (EGF, Sox2, CXCR4). This is also applied to the anatomical jargon (like apical, subapical etc), which can be specified in the figure by introducing the cartoons or point-on images. *

We thank the reviewer for pointing these shortcomings out. We have now done a careful proofreading of the text and implemented changes in the relative figures.

The changes include: Explanation of Prominin-1-expressing (P+) progenitors (p. 10, line 35) and apical and subapical progenitors (p.3, line 11-23), as well as more detailed reasoning for the analysis of gene expression for EGFR, Sox2 and CXCR4 (p. 9, line 5-7; p.12, line 44 and following)

*Last not least, I should point out that the author uses less commonly used terminology. To my knowledge, the SVZ progenitors in the GE are now called basal progenitors (Bandler et al 2017 for example) and the word intermediate progenitors is used for the Tbr2+ IP cells in the developing cortex. MASH-1 is an old gene name as it is revised as Ascl1 (please refer to any recent papers and web databases such as Mouse Genome Informatics, and NCBI, for instance). The use of prevailed wording will help the readers to understand the presented story. *

We have now revised the terminology according to the reviewer’s suggestion.

Minor comments:

Abstract Line 12-15 is confusing: What does "genotype" means?

We used the term to refer to the genetic differences between WT and GDF15 mutant mice with respect to GDF15 expression. We apologize for the lack of clarity. We have now modified the paragraph in an effort to improve its clarity.

Introduction Despite the focus of this paper being on the proliferation in GE, the introduction mixed up the references describing the dorsal telencephalon. It's better to cite the ventral GE as some progenitor behaviours are different from the ones in the dorsal. Maybe it's better to dedicate more to describing the lineage trajectory in the ventral GE and the molecular players (such as EGF), which makes it harder to understand the rationales of the several experiments.

We would like to thank the reviewer for making this helpful comment. In the introduction we have tried to make two points: firstly, to clarify how the different progenitor types in the VZ can be distinguished based on the localization of their site of mitosis and secondly the importance of studying GDF15 in the context of NSCs in the subependymal zone of the lateral ventricle. For the first point we have several studies referring to dividing dynamics of radial glia in the developing cortex. This reflect the fact that many papers have studied both apical and subapical radial glia within the context of the developing cortex, unlike subapical progenitors, which were first discovered in the developing ganglionic eminence. A similar problem applies to the analysis of EGFR expression in the context of VZ progenitors, although we agree with the reviewer that it should introduced for a better understanding of our analyses. Therefore, we have now introduced several changes in our introduction to eliminate the shortcomings and to offset the imbalance in terms of citations.

Results

Fig1: V/SVZ -> VZ? I think V means ventricles while VZ is for the ventricular zone. Single-channel images should be presented to demonstrate the positive or negative cells for each antigen. Only a subset of progenitors in the adult SVZ is GDF15 positive although this is not described in the text.

We have now replaced V/SVZ with V-SVZ, meaning ventricular-subventricular zone, throughout the manuscript and added single channel images to all figures where it is relevant.

*Why the GDP15 staining was performed only in the adult sections, but not in E18 while the GFRAL is shown in both stages? The text claims "GDF15 is particularly expressed in the germinal region of the GE" but I did not find the data shown in this draft. *

The fact that GDF15 is expressed in the choroid plexus and in the subependymal region of the lateral ventricle was first observed in the neonatal rat brain and prompted us to investigate the hypothesis that GDF15 may affect NSCs. Moreover, in our previous manuscript we have confirmed that GDF15 is expressed in neural progenitors of the embryonic murine GE (Carrillo-Garcia et al., 2014). In this new manuscript, we have complemented these data by adding the missing information concerning the expression of the protein in the adult V-SVZ. Notably, we also investigate for the first time the expression of the receptor in this area. This is key issue in the field, since the expression of GFRAL has been reported only in few regions of the brain, which is in apparent contrast with the growing list of effects in which GDF15 has been involved. For completeness of information and to further strengthen our conclusions we have now added new set of images in figure 1B, C showing co-expression of GFRAL and EGFR.

*Line 24-25: I did not understand this statement. *

The sentence refers to the results published in our previous paper, as mentioned in our reply above, which illustrate expression of GDF15 in the GE at different ages of development and in the adult V-SVZ. In an effort to improve its clarity, we have now modified the sentence into: “Consistent with these observations, we have previously reported that in the GE, Gdf15 transcripts increase at late developmental age and remain high in the adult V-SVZ.”

Fig. 2 Line 33: what are apical P+ progenitors?

We apologize for this shortcoming. P+ is the abbreviation for Prominin-1 immunopositive progenitors. This information has been now added to the text.

*Fig. 2A: The total analysed cells are not described in M&M. *

We have now added this information in the relevant section of the manuscript (p. 7, lines 36-43).

*Fig 2 C and D. While the counting of apical or subapical progenitors has been done respectively, the representative images of which regions are judged as apical or subapical are not shown. This comment also applies to Line 41: I did not get the logic of how this analysis will be able to distinguish apical or subapical cell division. *

Mitotic apical and subapical progenitors have been detected on the basis of the position of their nuclei. Namely, mitosis was considered apical if the nucleus of the dividing cell was within two nuclei distance (~ 10 µm) of the apical surface, and considered subapical if the nuclei of the dividing cells was at a greater distance from the apical surface. Besides adding this information to the manuscript, see “Image analysis” in the “Materials and Methods” section, we have now illustrated our approach in the new Fig. 2B.

*Fig. 2 E and F: I am not sure why the proliferation was assessed in vitro whole mount cultures. IP injection in vivo animals would be more convincing. *

We have used the same whole mount preparation to determine changes in proliferation upon acute fixation of the tissue. We have then determined the effect of growth factors and pharmacological modulators in whole-mount explants preparation as this would allow us to test their effect in standardized conditions. For the sake of consistency, we have then used the same whole mount explant setting to investigate proliferation by means of IdU incorporation. We selected this mean of analysis because changes in proliferation were already detected upon tissue fixation, and direct exposure of the tissue to the pharmacological modulators allowed us to investigate the direct effect of the drugs on proliferation behavior. Using this setting, we have obtained data that are compatible and consistent with our analysis on acutely fixed preparations. We agree with the reviewer that these experiments could be also repeated by injecting the IdU in vivo, however this would be against the current animal 3R guidelines that prompt to minimize the use of animal in vivo experiments and only when they cannot be replaced by alternative approaches in vitro or ex vivo.

*Fig. 3 I am not sure the mitotic spindle orientation analysis is very informative to stand out as one independent figure. In some contexts (Noctor et al 2008), it does not correlate to the asymmetric or symmetric division modes. *

We would like to like to respectfully disagree on this issue. The reason why we think this data set is important is twofold. Firstly, previous papers pointing at changes in the number of NSCs in the GE, have established that this was caused by a change in the spindle orientation leading to the generation of extra SNP (Falk et al., 2017), indicating a role for the orientation of the mitotic spindle in this context. Since we observed that GDF15 promotes not only progenitor proliferation but also apical divisions, it is important to show that this effect does not reflect a change in spindle orientation. Secondly, these data set highlights an age-dependent effect on the orientation of the mitotic spindle that is fully consistent with previously published data supporting the solidity of our findings. However, since we did not see any significant differences between the WT and Gdf15-/- animals, we have decided to move this data to a supplementary figure (new supplementary figure S3).

*Fig. 4 I am not convinced by this data since how the fluorescent intensity is measured is not described. If the internal controls to adjust the staining variation among samples are not used, the data is not convincing to me. The representative pictures are not convincing either to claim the substantial differences. Perhaps immunoblotting is better to be employed to quantify the protein expression difference. *

We have now added additional pictures with higher magnification to show the difference in EGFR intensity, including a calibration bar (Fig. 4). Quantitative analysis showed a trend decrease at E18 which is strongly significant in the adult V-SVZ. We now also show analysis of phEGFR and modified extensively the relative result section (see also our reply to the comment on Evidence, reproducibility and clarity of reviewer 2). Furthermore, we have added the following paragraph to the methods section:

“For fluorescence intensity measurements of EGFR, slices stained at the same time with the same antibody solutions, and imaged on the same day with constant confocal microscope settings (laser intensity, gain, pixel dwell time), were measured using Fiji/ImageJ. Raw immunofluorescence intensity was normalized by subtracting background fluorescence levels, i.e. fluorescence in cells considered negative for EGFR. To rule out any unspecific secondary antibody binding, fluorescence was compared to slices incubated with secondary, but not primary antibodies (2nd only control); no difference was found between 2nd only control and cells considered negative in EGFR-labelled samples, or between 2nd only controls of different genotypes.”

*Fig. 5 This is very busy figure composed of mainly counting graphs of different experiments. I think at least it is better to separate the data in vivo or culture. *

We have now rearranged the figure according to the reviewer’s suggestion. We have moved, also according to Reviewer 2’s suggestions, some less relevant data to supplementary figure S4 (that is, previous panels G-J) and added confocal images to illustrate the results of previous panels C-E. We hope that this improved the focus and clarity of this figure.

*Fig. 6. Pictures of Day 2 and Day 7 should be presented to highlight the difference between them. *

We have now added pictures showing the cell culture at DIV2 and DIV7, as well as the different treatments, in Fig. 6A.

*Fig. 7 Mash-1 should be rephrased as Ascl1. *

We have now changed the name of the gene throughout the manuscript.

Fig. 8 A: I am not sure why these pictures are B&W even though the two antigens are stained. The main text needs more description since no explanation of FOP, b-catenin etc. The picture of GFD15 KO looks having massive numbers of FOP+ cells, which is not correlated to the counting, I guess?

We apologize for the lack of clarity. We have now added additional panels (Fig. 8A) to illustrate the rationale behind the analysis and to demonstrate how ependymal and single-ciliated cells were counted. We have added the following sections to the manuscript text:

Materials and methods:

“Both the β-catenin and fibroblast growth factor receptor 1 oncogene partner (FOP) primary antibodies are mouse monoclonal antibodies of the same immunoglobulin class. Therefore, for this double immunostaining both antigens were revealed using the same secondary antibody and each was distinguished based on the localization and morphology of the labelling, which is lining the cell boundaries or at the basal body of the cilia for β-catenin and FOP, respectively.”

Results:

“We here used β-catenin to label cell-cell contacts, thereby visualizing cell boundaries, and fibroblast growth factor receptor 1 oncogene partner (FOP), a centrosomal protein, to visualize the basal body of the cilia. As both β-catenin and FOP-antibodies where derived from the same host species, the antigens were labelled in a single fluorescent channel and differentiated based on label localisation and intensity (Fig. 8A). Cells with a single centrosome or centrosome pair (one to two FOP+ dots) were counted as single-ciliated (SC), whereas cells with more than two centrosomes, i.e. multiciliated cells, were counted as ependymal (Epen; Fig. 8A).”

We have also added the following paragraph to the figure legend:

“(A) Schematic showing counting of ependymal (Epen) and single-ciliated (SC) cells using FOP and β-catenin as markers. (a’) Closeup of WT image in (B), showing β-catenin, indicating cell-cell-contacts, and FOP, indicating ciliary basal bodies/centrosomes, in a single channel. Scale bar = 10 µm. (a’’) β-catenin and FOP labels are distinguished by location, morphology and label intensity, with FOP being single dots that are more intense than β-catenin and located within the cell boundaries. (a’’’) Cells containing one or two centrosomes were considered SC cells (red), while cells with more than two centrosomes were considered multiciliated and therefore Epen (blue).”

We would also like to point out to the reviewer that since FOP was used as a label for the ciliary base, the “massive numbers of FOP+ cells” (i.e., multiciliated cells) were indeed quantified in Fig. 8B (now 8C) as ependymal cells (Epen).

** Referees cross-commenting**

I agree with Reviewer #2's comment that despite the amount of data presented, they are not presented in a coherent manner. I would suggest revising carefully before submitting to any journals. As detailed above the manuscript has been revised to improve clarity and coherence according the reviewer’s suggestions.

Reviewer #1 (Significance (Required)):

*The presented finding of the role of GDF15 in the ventral progenitors are evident and a new finding has not been reported. Since the same effects and signalling pathways involved in adult hippocampus neurogenesis are previously published by the same authors, the impact of the current manuscript is limited. I think heightening the role of GDF15 in the biological context of ventral progenitors, or alternatively, making a comparison to the previous finding would greatly improve the quality of the draft. In my opinion, this work would be appealing to the community of neural stem cells but maybe not to the broad audience. My expertise is neurodevelopmental biology focusing on neuronal lineages and neurogenesis. *

We have already clarified that the effect that we report here of GDF15 on NSCs is not only novel, but is also very different from what we have previously observed in the hippocampus (see also our reply above to the comment on evidence, reproducibility and clarity of reviewer 1). Although many environmental signals and growth factors have been implicated in the regulation of NSC proliferation and self-renewal, GDF15 is, to our knowledge, one of the few factors directly regulating the number of apical NSCs. Following the suggestion of the reviewer, we have now revised our manuscript in order to highlight the difference between the two studies. Besides being important within the field of NSCs, we believe that our data are also important for understanding the physiological role of GDF15, whose expression is increased during development and in the response to a growing list of stressors. For such an understanding, it is essential to identify target cell populations which can directly respond to the growth factor. Our finding that the GDF15 receptor is expressed in NSCs provide first evidence that GDF15 can directly modulate stem cell development, providing a first function for the increase in its expression. Moreover, our observation that GFRAL continues to be expressed in adult NSCs opens up to the possibility that the increase in the expression of the growth factor in the stress response is to recruit/modulate stem cell behavior. Consistent with this scenario, it was recently observed that brain injury promoted and increase of GDF15 expression in NSCs (Llorens-Bobadilla et al., 2015).

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

* Here the authors explore the role of GDF15 during development of the adult neural stem cell niche at the lateral wall of the lateral ventricle using GDF15 knock-out mice. They find increased progenitor proliferation at neonatal stages and at 8weeks, compensated by neuronal death. Further they report that EGFR+ cells are arranged differently in the GDF15 mutants (in clusters rather than columns) with also lower levels of EGFR. This is surprising to me, as the authors observe an increase in proliferation. They then report that addition of EGF leads to an increase in prominin+ progenitors in the GDF15KO, but not the WT, but there is lower levels of EGFR in the KOs. They then block CXCR4, which is allegedly required for GDF15 to modulate EGFR expression, and they find that this blocking reduces proliferation mostly in WT cells. As can be seen from this summary, to me, the model of how GDF15 loss is supposed to increase proliferation is not clear. Even less so, when in adult SVZ, EGFR+ progenitors were increased, while EGFR was reduced at postnatal stages. Beyond this, the authors show convincingly that ependymal cells are increased at adult stages, while I see no data supporting their claim of NSCs to be increased (at least not reaching levels of significance).*

• Taken together, this manuscript contains a lot of data, but to me no coherent picture emerges. If the picture is that the higher proliferation rate of apical progenitors at E18 generates more ependymal cells, then this should be shown (by including analysis e.g. at P5 when ependymal cells emerge). How GDF15 would affect proliferation in general is also not clear to me - maybe an unbiased analysis by RNA-seq could help to separate the main effects from diving into known candidates that seem not to explain the main aspects.*

The reviewer mentions multiple aspects of our study that we would like to clarify. Therefore, we apologize for our lengthy reply.

Firstly, the apparent contradiction between the increase in progenitor proliferation despite the concomitant decrease in EGFR levels. It is long known activation of EGFR regulates multiple aspects of progenitor behavior including proliferation, migration and differentiation. It is also known that responsiveness of NSCs to EGF increases during development, a process that is paralleled by an increase in expression of EGFR expression increase with developmental age, peaking around the perinatal age. However, since NSCs start to slow their cell cycle and enter quiescence exactly at the same time in which the increase in responsiveness to EGF and in EGFR expression in NSCs during this same period, it is clear that there is not a linear correlation between NSC proliferation and EGFR expression. A possible explanation for this apparently counterintuitive observation is that the increase in EGFR expression is paralleled by an increase in the expression of Lrig1, a developmental negative regulator of EGFR (Jeong et al., 2020). Moreover, in our study we report that lack of GDF15 leads to a decrease in the expression of EGFR protein and not in the levels of EGFR mRNA. In light of the well-known feedback mechanism by which in the presence of high EGFR activation the receptor transits to late endosome for degradation, a decrease in the protein levels could actually represent a higher level of activation EGFR signalling in the mutant progenitors than in the wild type counterpart. This is consistent with the characteristics of punctuate EGFR immunostaining we see in the mutant tissue and our analysis of EGFR activation. Our data show that despite difference in activation kinetics, wild type and mutant progenitors similarly respond to exogenous stimulation with EGF. Moreover, there is no difference between the two genotypes in the expression of EGFR in mitotic cells, and blockade of EGFR more dramatically affects the proliferation of mutant than WT progenitors. Finally, exposure to exogenous EGF promotes the proliferation of WT but not mutant progenitors. Taken together, these observations suggest that endogenous activation of EGFR driving proliferation is higher in mutant than WT progenitors. Consistent with this hypothesis, our new data illustrated in Fig. 5A-G of the revised manuscript, show that EGFR is similarly phosphorylated in mutant and in WT progenitors and that levels of Phospho-EGFR are observed in regions with low levels of EGFR expression, especially in the postnatal V-SVZ.

With respect to the effect of CXCR4, we have investigated its effect with respect to the ability of GDF15 to promote EGFR expression at the cell surface and secondly with respect to affect proliferation in vivo and in vitro. Both experiments reveal a permissive role of the receptor, whose activity is necessary for GDF15 to promote EGFR expression at the cell surface and for cell to undergo proliferation. While these observations confirm in part our previously published data, the molecular mechanisms underlying these effects remain unclear. However, since AMD on its own does not affect EGFR expression in either WT or mutant progenitors, the two effects are not related. Despite the absence of a clear mechanism by which CXCR4 affects proliferation, our data indicate that the permissive effect of CXCR4 is more important for the proliferation of TAPs rather than for NSCs. Therefore, the different effect of CXCR4 inhibition between WT and mutant progenitors likely reflect the fact that the latter are enriched in NSCs.

Finally, the evidence that NSCs are significantly increased in the mutant V-SVZ is reported in Fig. 8. In this figure, it is clearly reported that compared to the WT counterpart at early postnatal stages, only the multiciliated ependymal cells are significantly increased in the mutant niche, whereas uniciliated progenitors display only a trend increase (Panel C). However, the total number of apical cells is increased in the mutant V-SVZ, indicating that the number of both cell types are likely increased. Consistent with this hypothesis, in panel G of the same figure we show that in the adult V-SVZ, when the ependymal cells are fully differentiated, also apical GFAP+ NSCs are significantly increased. Notably, in this figure we show that GFAP+ NSCs also display a primary cilium, an elongated morphology and lack multiple cilia, and therefore are not atypical ependymal cells. Finally, in supplementary table S6, we show no difference in terms of % of clone forming cells between dissociated cell preparations of the WT and mutant V-SVZ. These observations and our finding of increased Ki67 apical expression in the adult V-SVZ, illustrated in supplementary figure S2B, clearly show that apical NSCs are increased.

We have now introduced multiple modification in text and figures to clarify the mechanisms underlying the effect of GDF15 ablation on EGFR expression and activation and the differential effect of CXCR4 on WT and mutant progenitors. The new data set illustrating phosphoEGFR are illustrated in figure 4B, G. We have also modified figure 8 in an effort to illustrate more clearly the effect of lack of GDF15 on NSC number.

*Major comments: *

*1) Inconsistencies start already in Figure 1: The authors show expression of the receptor at neonatal stages (much higher) and adult stages, but GDF15 is shown only in adult stages and the citations of their previous work suggests that indeed it may not be present at this early stage in the GE VZ (p.8, line 10). If it is, please show. If it isn't, could it be that it is in the CSF and signals only to apical cells? *

As the reviewer rightly mentions, GDF15 is present in the CSF and signals mainly to apical cells, as it is known to be secreted by the choroid plexus (Böttner et al., 1999; Schober et al., 2001). However, we would like to respectfully disagree with the reviewer. In our previous work, we clearly showed that GDF15 expression increases at E16 and is highest at E18 in the GE, which is the reason we specifically chose this timepoint for analysis (compare Carrillo-Garcia et al. (2014), Fig. 1A). In this manuscript we have also shown that EGFR expressing progenitors in the GE express GDF15. As the expression of GDF15 at embryonic and neonatal ages has already been investigated by us and others for two decades (see also Schober et al. (2001)), we refrained from showing expression of GDF15 at these ages again. However, we have now modified the relevant result section to clearly highlight the existence of this previous findings.

*2) An overview of the KO phenotype by lower power pictures would be helpful. For example an overview over the GE and PH3 immunostaining WT and KO at comparable section levels. *

Our analysis is based on whole-mount preparation of the whole GE. To standardize our analysis the same number of pictures were taken at similar locations to obtain a quantification representative of the apical surface of the whole GE. Therefore, the areas of interest were not selected on the basis of the number of mitotic cells and the differences observed do not reflect a positional effect. Lower power pictures illustrating the whole GE, are unlikely to be helpful, because they would not show the nuclear immunostaining. However, we have now modified the relevant Material and Methods section as follows to describe the standardization of our quantitative analysis: “Whole mounts were imaged using a Leica TCS SP8 confocal microscope with a 40x or 63x oil immersion objective and LASX software (Leica). For the quantification, an average of three different regions of interest were chosen at fixed rostral, dorsal and ventral position of the GE or V-SVZ and averaged for the collection of a single data set.”

* 3) Figure 2B- where is the apical surface, where are we in the GE? Where was quantification done? *

We have now added images detailing the localization of apical and subapical cells in new Fig. 2A, as well as further clarifications of the imaging and quantification in the materials and methods section.

* 4) Clarify the part with EGF signaling and/or take a more comprehensive view by a proteomic or transcriptomic approach, as EGFR and CXCR4 which were already investigated previously, may not explain the phenotype. *

We agree with the reviewer that our data should prompt a more comprehensive approach. However, this is surely a work worthy of a separate manuscript, since we agree with the reviewer that changes in EGFR and CXCR4 do not fully explain the effect of GDF15 on proliferation. We have now clarified our conclusions about EGF signalling, modifying the relevant part in the result section. We have also modified the abstract as follows:

“From a mechanistic point of view, we show that active EGFR is essential to maintain proliferation in the developing GE and that GDF15 affects EGFR trafficking and signal transduction. Consistent with a direct involvement of GDF15, exposure of the GE to the growth factor normalized proliferation and EGFR expression and it decreased the number of apical progenitors. A similar decrease in the number of apical progenitors was also observed upon exposure to exogenous EGF. However, this effect was not associated with reduced proliferation, illustrating the complexity of the effect of GDF15.”

*5) Do the authors actually think that the effects on EGFR are in the cells expressing the GDF15 receptor? Then please show co-localization. *

As both EGFR and GFRAL are widely expressed in the embryonic GE (see Figs 1B and 4B), making overlap inevitable, we did previously not assume the need to show co-localization. We have now added images showing co-localization of EGFR and GFRAL in E18 and adult brain sections in Fig. 1B.

*6) Figure 5D shows virtually no apical mitosis in WT, but indeed there are apical mitosis in WT E18 GE as one can also see in panel 5A. *

We apologize for the confusion. In the manuscript, we use Ki67 and analysis of nuclear morphology to determine the number of cells undergoing cell division, i.e. in meta-, ana- or telophase and immunostaining with antibody with phH3+, which stains additionally cells also at late G2 and early mitotic stages. Consistent with this, the number of mitotic cells scored with Ki67 and quantified in Fig. 5D is smaller than the number of phH3+ cells that is illustrated in Fig. 5A. Throughout the manuscript, cells labeled by phH3 immunoreactivity are named “phH3+ cells”, as quantified in Fig. 5B, whereas with “dividing cells” we refer to cells with Ki67 labeling that show nuclear morphology of meta-, ana- or telophase. We have, also according to the suggestions of reviewer 1, added images of the whole mounts analyzed for Fig. 5D, as well as the following text in the materials and methods section: “Cells were considered dividing if the nuclei were labelled with Ki67 and the nuclear morphology showed signs of division, i.e. meta-, ana- or telophase, in DAPI and Ki67-channels. For the sake of clarity, “dividing cells” only refers to this way of detection, while cells positive for phH3 are termed “phH3+ cells”, as phH3 also labels cells in interphase and prophase, as well as late G2 phase.”

*7) For the effect on ependymal cell generation it could be good to include an intermediate age, such as P5-7, when ependymal cells differentiate, staining e.g. for Lynkeas or Mcidas, known fate determinants regulating ependymal cell differentiation at that time. *

Most of our research was performed in either E18 or adult animals, where ependymal cells are either not yet present or already fully differentiated. Since ependymal cell differentiation starts at birth, we used P2 animals to look at ependymal cell differentiation. As shown in Fig. 8B, C this age is appropriate to study early ependymal differentiation, as a lot of multiciliated ependymal cells are already present at this age, and the difference between WT and Gdf15-/- animals is clearly visible and significant. While another age or additional markers might be interesting, we argue that it would not add to the conclusion or significance of this paper, as we can see this phenotype already at age P2 and it can still be detected it in adult animals.

*Minor comments: *

*-) p. 8, subapical progenitors are mentioned in line 42 without explaining how they are defined. *

We have now added more detailed definitions of apical and subapical progenitors to the introduction.

*-) p.8, line 44: the word increased in mentioned 2x *

We have removed the additional word.

*-) In the description of Figure 8 C and D seem to have been mixed up. *

We have changed the description of Fig. 8.

* ** Referees cross-commenting***

*I also fully agree with the point that this manuscript is very difficult to read. I think that anyhow the results have to be reorganized to focus on the most important data, so rewriting will have to be done for clarity either way. *

We apologize for the lack of clarity we have now extensively modified and re-edited the manuscript in an effort to improve its clarity.

* Reviewer #2 (Significance (Required)): *

* Exploring signmalling factors important for the stem cell niche is important, and the GDF15 indeed seems to have an effect there. The problem is, that much has been done with this factor already, but of course a mechanistic understanding of whats going on is important and could be the strength of this manuscript. However, it is really not clear, which mechanisms causes what. What is clear, is that the increased proliferation of neuronal progenitors is counterbalanced by death. Its also clear that ependymal cells are increased, which is an interesting effect. But how and why is not clear and may be the best to focus in this paper. *

As the reviewer mentions, several publications focus on GDF15. However, there is only one publication investigating the effect of GDF15 on neural stem cells and this focuses on the hippocampus. Therefore, we would like to respectively disagree with the conclusion of the reviewer “that much has been done with this factor”. Moreover, a serious problem with previous studies investigating GDF15 is the fact that its receptor is scarcely expressed and therefore it is not clear if these studies investigate direct or indirect effects of the growth factor. Since we here for the first time show that neural stem cells in the GE and V-SVZ express GDF15-receptor GFRAL, our study for the first time show a direct involvement of GDF15 on proliferation, number of ependymal cells and, as detailed in our reply above, apical NSCs. This knowledge is not only relevant to the field of normal and cancer stem cells, but also within the context of the role of GDF15 as mitokine and as stress hormone (see also our reply to major comments 2 of reviewer 1). Therefore, although we agree with the reviewer that the molecular mechanisms underlying the effect of GDG15 need further investigation, our data are novel and of relevance to the general scientific community.

References:

Böttner, M., Suter-Crazzolara, C., Schober, A., Unsicker, K., 1999. Expression of a novel member of the TGF-beta superfamily, growth/differentiation factor-15/macrophage-inhibiting cytokine-1 (GDF-15/MIC-1) in adult rat tissues. Cell Tissue Res 297, 103-110.

Carrillo-Garcia, C., Prochnow, S., Simeonova, I.K., Strelau, J., Hölzl-Wenig, G., Mandl, C., Unsicker, K., von Bohlen Und Halbach, O., Ciccolini, F., 2014. Growth/differentiation factor 15 promotes EGFR signalling, and regulates proliferation and migration in the hippocampus of neonatal and young adult mice. Development 141, 773-783.

Falk, S., Bugeon, S., Ninkovic, J., Pilz, G.A., Postiglione, M.P., Cremer, H., Knoblich, J.A., Gotz, M., 2017. Time-Specific Effects of Spindle Positioning on Embryonic Progenitor Pool Composition and Adult Neural Stem Cell Seeding. Neuron 93, 777-791 e773.

Jeong, D., Lozano Casasbuenas, D., Gengatharan, A., Edwards, K., Saghatelyan, A., Kaplan, D.R., Miller, F.D., Yuzwa, S.A., 2020. LRIG1-Mediated Inhibition of EGF Receptor Signaling Regulates Neural Precursor Cell Proliferation in the Neocortex. Cell Rep 33, 108257.

Llorens-Bobadilla, E., Zhao, S., Baser, A., Saiz-Castro, G., Zwadlo, K., Martin-Villalba, A., 2015. Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury. Cell Stem Cell 17, 329-340.

Schober, A., Böttner, M., Strelau, J., Kinscherf, R., Bonaterra, G.A., Barth, M., Schilling, L., Fairlie, W.D., Breit, S.N., Unsicker, K., 2001. Expression of growth differentiation factor-15/ macrophage inhibitory cytokine-1 (GDF-15/MIC-1) in the perinatal, adult, and injured rat brain. J Comp Neurol 439, 32-45.

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Referee #2

Evidence, reproducibility and clarity

Here the authors explore the role of GDF15 during development of the adult neural stem cell niche at the lateral wall of the lateral ventricle using GDF15 knock-out mice. They find increased progenitor proliferation at neonatal stages and at 8weeks, compensated by neuronal death. Further they report that EGFR+ cells are arranged differently in the GDF15 mutants (in clusters rather than columns) with also lower levels of EGFR. This is surprising to me, as the authors observe an increase in proliferation. They then report that addition of EGF leads to an increase in prominin+ progenitors in the GDF15KO, but not the WT, but there is lower levels of EGFR in the KOs. They then block CXCR4, which is allegedly required for GDF15 to modulate EGFR expression, and they find that this blocking reduces proliferation mostly in WT cells. As can be seen from this summary, to me, the model of how GDF15 loss is supposed to increase proliferation is not clear. Even less so, when in adult SVZ, EGFR+ progenitors were increased, while EGFR was reduced at postnatal stages. Beyond this, the authors show convincingly that ependymal cells are increased at adult stages, while I see no data supporting their claim of NSCs to be increased (at least not reaching levels of significance). Taken together, this manuscript contains a lot of data, but to me no coherent picture emerges. If the picture is that the higher proliferation rate of apical progenitors at E18 generates more ependymal cells, then this should be shown (by including analysis e.g. at P5 when ependymal cells emerge). How GDF15 would affect proliferation in general is also not clear to me - maybe an unbiased analysis by RNA-seq could help to separate the main effects from diving into known candidates that seem not to explain the main aspects.

Major comments:

1. Inconsistencies start already in Figure 1: The authors show expression of the receptor at neonatal stages (much higher) and adult stages, but GDF15 is shown only in adult stages and the citations of their previous work suggests that indeed it may not be present at this early stage in the GE VZ (p.8, line 10). If it is, please show. If it isn't, could it be that it is in the CSF and signals only to apical cells?
2. An overview of the KO phenotype by lower power pictures would be helpful. For example an overview over the GE and PH3 immunostaining WT and KO at comparable section levels.
3. Figure 2B- where is the apical surface, where are we in the GE? Where was quantification done?
4. Clarify the part with EGF signaling and/or take a more comprehensive view by a proteomic or transcriptomic approach, as EGFR and CXCR4 which were already investigated previously, may not explain the phenotype.
5. Do the authors actually think that the effects on EGFR are in the cells expressing the GDF15 receptor? Then please show co-localization.
6. Figure 5D shows virtually no apical mitosis in WT, but indeed there are apical mitosis in WT E18 GE as one can also see in panel 5A.
7. For the effect on ependymal cell generation it could be good to include an intermediate age, such as P5-7, when ependymal cells differentiate, staining e.g. for Lynkeas or Mcidas, known fate determinants regulating ependymal cell differentiation at that time.

Minor comments:

• p. 8, subapical progenitors are mentioned in line 42 without explaining how they are defined.
• p.8, line 44: the word increased in mentioned 2x
• In the description of Figure 8 C and D seem to have been mixed up.

** Referees cross-commenting**

I also fully agree with the point that this manuscript is very difficult to read. I think that anyhow the results have to be reorganized to focus on the most important data, so rewriting will have to be done for clarity either way.

Significance

Exploring signmalling factors important for the stem cell niche is important, and the GDF15 indeed seems to have an effect there. The problem is, that much has been done with this factor already, but of course a mechanistic understanding of whats going on is important and could be the strength of this manuscript. However, it is really not clear, which mechanisms causes what. What is clear, is that the increased proliferation of neuronal progenitors is counterbalanced by death. Its also clear that ependymal cells are increased, which is an interesting effect. But how and why is not clear and may be the best to focus in this paper.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This paper illustrates the role of GDF15 in ganglionic eminence featuring the influence on neurogenesis and progenitor proliferation. Using the conventional knockout GDF-15 mouse lines, the authors provide a series of counting data indicating that GDF15 controls neurogenesis in the late embryonic or adult neural stem cells in the ventral forebrain.

Major comments:

The entire story in this manuscript seems similar to the previous findings where the authors demonstrated the role of GDF15 in the hippocampal neural stem cells in relation to EGF and CXCR4.

The data and the conclusion presented here sound reasonable to me. The current manuscript, however, gave me the impression that the story is less impactful and rather descriptive. To improve the quality of the current draft, the authors may wish to clearly highlight the novelty of the findings, not simply apply the previous strategy to the other anatomical brain regions. Alternatively, the draft could emphasize the similarity of utilising the same biological strategies to control the number of adult stem cells in the distinct stem cell niche.

I also have two concerns which may help to improve the current draft.

Firstly, I would suggest considering the data presentation as many of the counting data are not accompanied by representative images or a detailed description of the methods, which could impede the credibility of the data. For instance, it is not clear to me how the authors judged the apical vs subapical progenitor counting as neither the pictures nor the methods clearly specified how these are distinguished. Same to the Figures 3 and 8.

I would also suggest the authors may wish to carefully review the text as many abbreviations are not properly stated (for example, what are P+ progenitors?), or not properly explained why the particular gene expression is analysed (EGF, Sox2, CXCR4). This is also applied to the anatomical jargon (like apical, subapical etc), which can be specified in the figure by introducing the cartoons or point-on images.

Last not least, I should point out that the author uses less commonly used terminology. To my knowledge, the SVZ progenitors in the GE are now called basal progenitors (Bandler et al 2017 for example) and the word intermediate progenitors is used for the Tbr2+ IP cells in the developing cortex. MASH-1 is an old gene name as it is revised as Ascl1 (please refer to any recent papers and web databases such as Mouse Genome Informatics, and NCBI, for instance). The use of prevailed wording will help the readers to understand the presented story.

Minor comments:

Abstract Line 12-15 is confusing: What does "genotype" means?

Introduction Despite the focus of this paper being on the proliferation in GE, the introduction mixed up the references describing the dorsal telencephalon. It's better to cite the ventral GE as some progenitor behaviours are different from the ones in the dorsal.

Maybe it's better to dedicate more to describing the lineage trajectory in the ventral GE and the molecular players (such as EGF), which makes it harder to understand the rationales of the several experiments.

Results

Fig1: V/SVZ -> VZ? I think V means ventricles while VZ is for the ventricular zone

Single-channel images should be presented to demonstrate the positive or negative cells for each antigen. Only a subset of progenitors in the adult SVZ is GDF15 positive although this is not described in the text. Why the GDP15 staining was performed only in the adult sections, but not in E18 while the GFRAL is shown in both stages? The text claims "GDF15 is particularly expressed in the germinal region of the GE" but I did not find the data shown in this draft.

Line 24-25: I did not understand this statement.

Fig. 2 Line 33: what are apical P+ progenitors ?

Fig. 2A: The total analysed cells are not described in M&M.

Fig 2 C and D. While the counting of apical or subapical progenitors has been done respectively, the representative images of which regions are judged as apical or subapical are not shown. This comment also applies to Line 41: I did not get the logic of how this analysis will be able to distinguish apical or subapical cell division.

Fig. 2 E and F: I am not sure why the proliferation was assessed in vitro whole mount cultures. IP injection in vivo animals would be more convincing.

Fig. 3 I am not sure the mitotic spindle orientation analysis is very informative to stand out as one independent figure. In some contexts (Noctor et al 2008), it does not correlate to the asymmetric or symmetric division modes.

Fig. 4 I am not convinced by this data since how the fluorescent intensity is measured is not described. If the internal controls to adjust the staining variation among samples are not used, the data is not convincing to me. The representative pictures are not convincing either to claim the substantial differences. Perhaps immunoblotting is better to be employed to quantify the protein expression difference.

Fig. 5 This is very busy figure composed of mainly counting graphs of different experiments. I think at least it is better to separate the data in vivo or culture.

Fig. 6. Pictures of Day 2 and Day 7 should be presented to highlight the difference between them.

Fig. 7 Mash-1 should be rephrased as Ascl1.

Fig. 8 A: I am not sure why these pictures are B&W even though the two antigens are stained. The main text needs more description since no explanation of FOP, b-catenin etc. The picture of GFD15 KO looks having massive numbers of FOP+ cells, which is not correlated to the counting, I guess? written

** Referees cross-commenting**

I agree with Reviewer #2's comment that despite the amount of data presented, they are not presented in a coherent manner. I would suggest revising carefully before submitting to any journals.

Significance

The presented finding of the role of GDF15 in the ventral progenitors are evident and a new finding has not been reported. Since the same effects and signalling pathways involved in adult hippocampus neurogenesis are previously published by the same authors, the impact of the current manuscript is limited. I think heightening the role of GDF15 in the biological context of ventral progenitors, or alternatively, making a comparison to the previous finding would greatly improve the quality of the draft. In my opinion, this work would be appealing to the community of neural stem cells but maybe not to the broad audience. My expertise is neurodevelopmental biology focusing on neuronal lineages and neurogenesis.

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Reply to the reviewers

General comments:

We thank the reviewers for recognizing the importance of our work and for their supportive and insightful comments.

Our planned revisions focus on addressing all the comments and especially in further elucidating the molecular mechanism underpinning our observations, their consequences for cell phenotypes and reproducing our observations in an additional cell line. Our revision plan is backed up in many cases by preliminary data.

Our submitted manuscript demonstrated that DNMT3B’s recruitment to H3K9me3-marked heterochromatin was mediated by the N-terminal region of DNMT3B. Data generated since submission suggest that DNMT3B binds indirectly to H3K9me3 nucleosomes through an interaction mediated by a putative HP1 motif in its N-terminal region.

Specifically, we have found that DNMT3B can pull down HP1a and H3K9me3 from cell extracts and that this interaction is abrogated when we remove the N-terminal region of DNMT3B (revision plan, figure 1a). Using purified proteins in vitro, we have shown binding of DNMT3B to HP1a that is dependent on the presence of DNMT3B’s N-terminus suggesting that the interaction with HP1a is direct and that this mediates DNMT3B’s recruitment to H3K9me3 (revision plan, figure 1b). Alphafold multimer modelling identified that DNMT3B's N-terminus binds the interface of a HP1 dimeric chromoshadow domain through a putative HP1 motif. Two point mutations in this motif ablate DNMT3B’s interaction with HP1a in vitro (revision plan, figure 1b - DNMT3B L166S I168N).

We propose to further characterize DNMT3B’s interaction with HP1a in vitro and determine the significance of these observations in cells by microscopy in a revised manuscript. Together with the other proposed experiments and analyses, we believe the extra detail regarding the molecular mechanisms through which DNMT3B is recruited to H3K9me3 heterochromatin will help address the reviewer’s comments.

Point by point response:

We have reproduced the reviewer’s comments in their entirety and highlighted them in blue italics.

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

This paper by Francesca Taglini, Duncan Sproul, and their coworkers, examines the mechanisms of DNA methylation in a human cancer cell line. They use the human colorectal cancer line HCT116, which has been very widely used to look at epigenetics in cancer, and to dissect the contribution of different proteins and chromatin marks to DNA methylation.

The authors focus on the role of the de novo methyltransferase DNMT3B. It has been shown in ES cells in 2015 that its PWWP domain directs it to H3K36me3, typically found in gene bodies. More recently, the authors showed similar conclusions in colorectal cancer (Masalmeh Nat Comm 2021). Here they examine, more specifically, the role of the PWWP. The conclusions are described below.

Major comments:

• *1-I feel that this paper has several messages that are somewhat muddled. The main message, as expressed in the title and in the model, is that the PWWP domain of DNMT3B actively drags the protein to H3K36me3-marked regions. Inactivation of this domain by a point mutation, or removal of the Nter altogether, causes DNMT3B to relocate to other genomic regions that are H3K9me3-rich, and that see their DNA methylation increase in the mutant conditions. This first message is clear.

We thank the reviewer for their positive comments on our observations. However, we note that our results suggest that removal of the N-terminal region has a different effect to point mutations in the PWWP domain. The data we present suggest that the N-terminus facilitates recruitment to H3K9me3 regions.

The second message has to do with ICF. A mutant form of DNMT3B bearing a mutation found in ICF, S270P, is actually unstable and, therefore, does not go to H3K9me3 regions. I feel that here the authors go on a tangent that distracts from message #1. This could be moved to the supp data. At any rate, HCT116 are not a good model for ICF. In addition, a previous paper has looked at the S270P mutant, and it did not seem unstable in their hands (Park J Mol Med 2008, PMID: 18762900). So I really feel the authors do not do themselves a favor with this ICF angle.

While we agree with the reviewer that HCT116 cells as a cancer cell line are not a good model for ICF1 syndrome, our observation that S270P destabilizes DNMT3B is important to consider in the context of this disease. In addition, the S270P mutant was reported to abrogate the interaction between DNMT3B and H3K36me3 (Baubec et al 2015 Nature PMID: 25607372) making it important to compare it to the other mutations we examine. In our revised version of the manuscript, we propose to move these data to the supplementary materials and add a statement to the discussion noting the caveat that HCT116 cells are likely not to model many aspects of ICF1.

With regard to the differences between our results and that of Park et al, we note that stability of the S270P mutant was not assessed in that study whereas we directly assess stability in vitro and in cells. We propose to add discussion of this previous study to the revised manuscript.

2-I feel that some major confounders exist that endanger the conclusions of the work. The most worrisome one, in my opinion, is the amount of WT or mutant DNMT3B in the cells. It is clear in figure 4C that the WT rescue construct is expressed much more than the W263A mutant (around 3 or 4 times more). Unless I am mistaken, we are never shown how the level of exogenous rescue protein compares to the level of DNMT3B in WT cells. This bothers me a lot. If the level is too low, we may have partial rescue. If it is too high, we might have artifactual effects of all that DNMT3B. I would also like to see the absolute DNA methylation values (determined by WGBS) compared to the value found in WT. From figure S1A, it looks like WT is aroun 80% methylation, and 3BKO is around 77% or so. I wonder if the rescue lines may actually have more methylation than WT?

The rescue cell lines do express DNMT3B to a greater level than observed endogenously. In our manuscript we controlled for this effect by generating the knock-in W263A cells and, as reported in the manuscript, we observe similar effects to the rescue cells (manuscript, figure 2d) suggesting that our observations are not driven by the overexpression.

We also expressed ectopic DNMT3B from a weaker promoter (EF1a) in DNMT3B KO cells but did not include these data in the submitted manuscript. We have previously shown that this promoter expresses DNMT3B at lower levels than the CAG promoter used in the submitted manuscript (Masalmeh et al 2021 Nature Communications PMID: 33514701). Bisulfite PCR of representative non-repetitive loci within heterochromatic H3K9me3 domains show that we observe similar gains of methylation with DNMT3BW263A (revision plan, figure 2).

Revision plan figure 2. Expression of DNMT3BW236A from a weaker promoter leads to increased DNA methylation at selected H3K9me3 loci. Barplot of mean methylation by BS-PCR at H3K9me3 loci alongside the H3K4me3-marked BRCA2 promoter in DNMT3B mutant cells were DNMT3B is expressed from the EF1 promoter. P-values are from two-sided Wilcoxon rank sum tests.

To reinforce that our conclusions are not solely a result of the level of DNMT3B expression, we propose to include these data in the revised manuscript.

The reviewer is also correct that by WGBS, the rescue cell lines have higher levels of overall DNA methylation than HCT116 cells. We will note this in revised manuscript and include HCT116 cells in a revised version of Figure S1e.

3-I guess the unarticulated assumption is that the gain of DNA methylation seen at H3K9me3 region upon expression of a mutant DNMT3B is due to DNMT3B itself. But we do not know this for sure, unless the authors test a double mutant (PWWP inactive, no catalytic activity). I am not necessarily asking that they do it, but minimally they should mention this caveat.

The hypothesis that the gains in DNA methylation at H3K9me3 loci result from the direct catalytic activity of DNMT3B is supported by our observation that a catalytic dead DNMT3B does not remethylate heterochromatin (manuscript, figures 1d and e). However, we acknowledge that we have not formally shown that the additional DNA methylation seen with DNMT3BW263A are a direct result of its catalytic activity. We will conduct an analysis of the effect of catalytically dead DNMT3BW263A on DNA methylation at Satellite II and selected H3K9me3 loci and include this in the revised manuscript.

4-I am confused as to why the authors look at different genomic regions in different figures. In figure 1 we are looking at a portion of the "left" arm of chr 16. But in figure 2B, we now look at a portion of the "right" arm of the same chromosome, which has a large 8-Mb block of H3K9me3, and is surprisingly lowly methylated in the 3BKO. This seems quite odd, and I wonder if there is a possible artifact, for instance mapping bias, deletion, or amplification in HCT116. Showing the coverage along with the methylation values would eliminate some of these concerns.

By choosing different regions of the genome for different figures, we intended to reassure the reader that our results were not specific to any one region of the genome. In the revised manuscript, we propose to display a consistent genomic region between these figures.

With regard to the low levels of DNA methylation in H3K9me3 domains in DNMT3B KO cells, H3K9me3 domains are partially methylated domains which have reduced methylation in HCT116 cells (see page 5 of the manuscript):

… we found that hidden Markov model defined H3K9me3 domains significantly overlapped with extended domains of overall reduced methylation termed partially methylated domains (PMDs) defined in our HCT116 WGBS (Jaccard=0.575,p=1.07x10-6, Fisher’s test).

These domains lose further DNA methylation in DNMT3B KO cells leading to the low methylation level noted by the reviewer. The methylation percentages calculated from WGBS are based on the ratio of methylated to total reads. Thus, a lack of coverage generates errors from division by zero rather than the low values observed in this domain in DNMT3B KO cells.

We include a modified version of figure 2b from the manuscript below. This includes coverage for the 3 cell lines (revision plan, figure 3). Although WGBS coverage is slightly reduced in H3K9me3 domains, reads are still present and overall coverage equal between different cell lines.

While we could potentially include the coverage tracks in revised versions of figures, we note that doing so for multiple cell lines would make these figures extensively cluttered and it would likely be difficult to observe the differences in DNA methylation in these figure panels due to shrinkage of the other tracks.

Minor comments:

1-The WGBS coverage is not very high, around 2.5X on average, occasionally 2X. I don't believe this affects the findings, as the authors look at large H3K9me3 regions. But the info in table S2 was hard to find and it is important. I would make it more accessible.

In the revised manuscript we will specify the mean coverage in the text to ensure this is clearer.

2-It would be nice to have a drawing showing exactly what part of the Nter was removed.

We will add this in the figure in the revised manuscript.

3-some figures could be clearer. I was not always sure when we were looking at a CRISPR mutant clone (W263A) versus a piggyBac rescue.

In the revised manuscript we will clarify in the figure labels to ensure it is clear which data were generated using CRISPR clones.

4-unless I am mistaken, all the ChIP-seq data (H3K9me3, H3K36me3 etc) come from WT cells. It is not 100% certain that they remain the same in the 3BKO, is it? This should be discussed.

We performed ChIP-seq on both HCT116 and 3BKO cell lines and used ChIP-seq data from the 3BKO cell line for the rescue experiments where DNMT3Bs were expressed in 3BKO cells. We will ensure this is clearer in the revised version.

Reviewer #1 (Significance (Required)):

Strengths:

The experiments are for the most part well done and well interpreted (save for the limitations mentioned above). The techniques are appropriate and well mastered by the team. The paper is well written, the figures are nice. The authors know the field well, which translates into a good intro and a good discussion. The bioinformatics are convincing.

Limitations:

All the work is done in a single cancer cell line. One might assume the conclusions will hold in other systems, but there is no certainty at this point.

We acknowledge this limitation. To demonstrate that our results are applicable beyond HCT116 cells, we will include analysis of experiments on an independent cell line in the revised manuscript.

HCT116 are not the best model system to study ICF, which mostly affects lymphocytes

At present, I feel that the biological relevance of the findings is fairly unclear. The authors report what happens when DNMT3B has no functional PWWP domain. I am convinced by their conclusions, but what do they tell us, biologically? Are there, for instance, mutant forms of DNMT3B expressed in disease that have a mutant PWWP? Are there isoforms expressed during development or in certain cell types that do not have a PWWP? In these cell types, does the distribution of DNA methylation agree with what the authors predict?

As stated in response to point 1, although we acknowledge the limitations of HCT116 cells as a model of ICF, we believe are finding that the S270P mutation results in unstable DNMT3B are still important to consider for ICF syndrome.

We are not aware of reports of mutations affecting the residues of DNMT3B’s PWWP domain we have studied. Our preliminary analysis suggests that although mutations in DNMT3B’s PWWP domain are frequent, residues in the aromatic cage such as W263 and D266 are absent from the gnomAD catalogue (Karczewski et al 2020 Nature, PMID: 32461654). This suggests that they are incompatible with healthy human development.

A number of different DNMT3B splice isoforms have been reported. These include DDNMT3B4 which lacks the PWWP domain and a portion of the N-terminal region (Wang et al 2006 International Journal of Oncology, PMID: 16773201). DDNMT3B4 is proposed to be expressed in non-small cell lung cancer (Wang et al 2006 Cancer Research, PMID: 16951144).

We will include analysis of gnomAD and discussion of these points in the revised manuscript.

In its present state, I feel the appeal of the findings is towards a semi-specialized audience, that is interested in aberrant DNA methylation in cancer and other diseases. This is not a small audience, by the way.

We thank the reviewer for their comments and the suggestion that our findings are of interest to a cross-section of researchers.

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

Note, we have added numbers to the comments made by reviewer 2 to aid cross-referencing.

In this manuscript, Taglini et al., describe an increased activity of DNMT3B at H3K9me3-marked regions in HCT cells. They first identify that DNA methylation at K9me3-marked regions is strongly reduced in absence of DNMT3B. Next, the authors re-express DNMT3B and DNMT3B mutant variants in the DNMT3B-KO HCT cells and assess DNA methylation by WGBS where they identify a strong preference for re-methylation of K9me3 sites. Based on genome-wide binding maps for DNMT3B, including the mutant variants, they address how the localization of DNMT3B relates to the observed changes in methylation.

Major points:

• The authors show increased reduction of mCG at H3K9me3 (and K27me3) sites in absence of DNMT3B. This is based on correlating delta %mCG with histone modifications in 2kb bins. I find this approach to not fully support the major claim. First, the correlation coefficients are very small -0.124 for K9me3 and -0.175 for K27me3, and just marginally better compared to, for example, K36me3 that does not seem to have any influence on mCG according to Sup Fig S1b. While I agree that mCG seems more reduced at K9me3 in absence of DNMT3B (e.g. in Fig 1a), is there a better way to visualize the global effect? The delta mCG Boxplots based on bins are not ideal (this applies to many figures using the same approach in the current manuscript).*

Our choice to examine the global effects using correlations in windows across the genome was motivated by similar previous analyses in other studies (for example: Baubec et al. 2015 Nature PMID 25607372, Weinberg at al 2021 Nature PMID:33986537, Neri et al 2017 Nature PMID: 28225755). These global analyses result in modest correlation coefficients because the vast majority of genomic windows are negative for a given mark. For this reason, we included specific analyses of H3K36me3, H3K9me3 and H3K27me3 domains in the manuscript (eg manuscript figure 1b, c and d) which reinforce the conclusions drawn from our global analyses.

However, we acknowledge that while our data support a specific activity at H3K9me3 marked heterochromatin, these are not the only changes in DNMT3B KO cells as DNMTs are promiscuous enzymes that are localized to multiple genomic regions. We will add discussion of this point to the revised manuscript.

2. Second, the calculation based on delta mCpG does not allow to see how much methylation was initially there. For example, S1b shows a median decrease of ~ 10% in K9me3 and ~7-8% in H3K4me3. What does this mean given that the starting methylation for both marks is completely different?

Following this point, the authors mention that mCG is already low at K9me3 domains in HCT cells (compared to other sites in the genome). I am curious if this may influence the accelerated loss of methylation in absence of DNMT3B? Any comments on this?

The observation that there is a greater loss at H3K9me3 domains than H3K27me3-only domains which also have low DNA methylation levels in HCT116 argue that the losses are not solely driven by the lower initial level of methylation in H3K9me3 domains. Our analyses later in the manuscript also support a specific activity at H3K9me3. In addition, we propose to reinforce this point through further data on exploring how DNMT3B interacts with HP1a (see general comments, revision plan figure 1).

However, we acknowledge the possibility that part of the loss seen at H3K9me3 domains in DNMT3B KO cells could be in part a result of their low initial level of methylation. In the revised manuscript we propose to include discussion of this possibility.

3. One issue is the lack of correlation in DNMT3B binding to H3K9me3 sites in WT cells (Fig 3). How does this explain the requirement for DNMT3B for maintenance of methylation at H3K9me3? While some of the tested mutants show some weak increase at K9me3 sites, these are not comparable to the strong binding preferences observed at K36me3 for the wt or delta N- term version.

Using ChIP-seq we cannot say that DNMT3BWT does not bind at H3K9me3, only that it binds here to a lower level than at K36me3-marked loci. The normalized DNMT3BWT signal at H3K9me3 domains is higher than the background signal from DNMT3B KO cells (manuscript figure 3d) supporting the hypothesis that DNMT3BWT localizes to H3K9me3. This hypothesis is also supported by the observation that the correlation between DNMT3BDN and H3K9me3 is reduced compared to that of DNMT3BWT (manuscript figure 6c compared to figure 3c).

There are several reasons why the apparent enrichment of DNMT3B at H3K9me3 may appear weaker than at H3K36me3 by ChIP-seq. Previous work has also suggested that formaldehyde crosslinking fails to capture transient interactions in cells (Schmiedeberg et al. 2009 PLoS One PMID: 19247482). H3K9me3-marked heterochromatin is also resistant to sonication (Becker et al. 2017 Molecular Cell PMID: 29272703) and this could further affect our ability to detect DNMT3B in these regions using ChIP-seq. Our new data also suggest that DNMT3B binds to H3K9me3 indirectly through HP1a (see general comments, revision plan figure 1) and this may also lead to weaker ChIP-seq enrichment at H3K9me3 compared to the direct interaction with H3K36me3 through DNMT3B’s PWWP domain.

We propose to add discussion of these issues to the revised manuscript.

4. Following the above comment, what about other methyltransferases in HCT cells? Could DNMT1 or DNMT3A function be altered in absence of DNMT3B, and the observed methylation changes could be indirectly influenced by DNMT3B? The authors could create a DNMT-TKO HCT cell line and re-introduce DNMT3B in this background and measure methylation to exclude that DNMT1 or DNT3A could have an influence. In this case, only H3K9me3 should gain DNA methylation.

As discussed in response to reviewer 1 (point 3), we propose to examine the changes in DNA methylation upon expression of catalytically dead DNMT3BW263A to further strengthen the evidence that DNMT3BW263A is directly responsible for the increased DNA methylation at H3K9me3-marked loci.

5. DNMT3B lacking N-terminal shows reduced K9me3 methylation & some localization by imaging. While the presented experiments show some support for this conclusion, I suggest to re-introduce a W263A mutant lacking the N-terminal part and measure changes in DNA methylation at H3K9. This should help to test the requirement for the N-terminal regions and further indicate which protein part (PWWP or N-term) is more important in regulating the balance between K9me3 and K36me3.

We have performed this experiment and the data are shown in manuscript figure S6c and d. The results of these experiments show that DNMT3BΔN+W263A cells showed less methylation at H3K9me3 loci than DNMT3BW263A cells, supporting a role for the N-terminus in recruiting DNMT3B to H3K9me3-marked heterochromatin. In the revised version, we will ensure that these data are more clearly indicated.

In the first paragraph of the discussion, the authors state: "Our results demonstrate that DNMT3B is recruited to and methylates heterochromatin in a PWWP- independent manner that is facilitated by its N-terminal region." Same statement is found in the abstract. This contradicts the ChIP-seq results that do not indicate a recruitment of DNMT3B to heterochromatin, and the N- terminal deletions are not fully supporting a role in this targeting since there is no localization to K9me3 to begin with. While changes in methylation are observed, it remains to be determined if this is indeed through direct DNMT3B delocalization or indirectly through influencing the remaining DNMTs.

As discussed above, there are several potential reasons why DNMT3B ChIP-seq signal at H3K9me3 is weak (reviewer 2, point 3). The additional experiments we propose to include in the revised manuscript could reinforce this statement by clarifying whether DNMT3B is directly responsible for methylating H3K9me3-marked regions (reviewer 1, point 3) and by delineating the role of the putative HP1a motif in DNMT3B’s N-terminal region (general comments, revision plan figure 1).

Reviewer #2 (Significance (Required)):

Advance: detailed analysis of DNMT3B mutants in relation to K9me3. Builds up on previous studies. Audience: specialised audience

We thank the reviewer for their insights.

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

• *In this work, Taglini et al. examine how the de novo DNA methyltransferase DNMT3B localizes to constitutive heterochromatin marked by the repressive histone modification H3K9me3. The authors utilize a previously generated DNMT3B KO colorectal carcinoma cell line, HCT116 to study recruitment and activity of DNMT3B at constitutive H3K9me3 heterochromatin. The authors noted preferential decrease of DNA methylation (DNAme) at regions of the genome marked with H3K9me3 in DNMT3B KOs. The authors then rescued the deficiency through overexpression of WT and catalytic dead DNMT3A/B and confirmed that DNA methylation increase at H3K9me3+ region in the WT DNMT3B, but not catalytically inactive mutant nor DNMT3A. To examine which protein domains may be mediating DNMT3B's recruitment to H3K9me3 regions, the authors designed a series of mutants, primarily focusing on the PWWP domain which normally recognizes H3K36me3. In the PWWP mutants, DNMT3B binding to the genome is altered, showing depletion at some H3K36me3-marked regions and gain at H3K9me3 heterochromatin, which coincides with DNAme increase at satellites. In contrast, the clinically relevant ICF1 mutation S270P, shows DNMT3B protein destabilization and no such loss of DNAme at heterochromatin. Finally, the authors truncate the N-terminal portion of DNMT3B, and saw that this region of the protein is necessary for heterochromatin localization and subsequent DNAme of H3K9me3+ regions.

The experiments are well done with extensive controls, and the results are interesting and convincing. The structure of the manuscript could be improved for clarity and flow - for example, the PWWP mutations and truncations should be mentioned and compared together. I also found the section on ICF1 mutant to be out-of-place.

As described above (reviewer 1, point 1), we propose to move these data to the supplementary materials in the revised manuscript.

• *

More emphasis should be placed on the N- terminal mutant as this region seems to be critical to heterochromatin recruitment, and this may address whether the interaction to H3K9me3 is direct or indirect.

As described above (general comments), the revised manuscript will include experiments clarifying the nature of DNMT3B’s interaction with H3K9me3. Our preliminary data support that it is an indirect interaction mediated through HP1a (revision plan figure 1).

Finally, while the epigenetic crosstalk is well-examined in this work, I would strongly urge the authors to add RNA-seq data to determine the transcriptional consequence of such chromatin disruptions (e.g. are repetitive sequences up-regulated in DNMT3B KOs?).

As suggested by the reviewer, we propose to generate and analyse RNA-seq data in the revised manuscript to understand the impact of DNMT3B on transcriptional programs.

Comments

1. A potential caveat to the study is the use of a single cell line - colorectal cancer cell HCT116 - to draw major conclusions on the function of DNMT3B. It is worth noting the Baubec et al. study examining DNMT3B recruitment to H3K36me3 was mainly performed in murine embryonic stem cells (mESCs). It would greatly strengthen the study if the authors could perform similar type of data analysis on an independent DNMT3B KO cell line. For example, does DNMT3B localize to H3K9me3 regions in WT mESCs?

As described above in response to reviewer 1, we will include analysis in an additional cell line in the revised manuscript to demonstrate that our results are generalizable beyond HCT116 cells.

2. Did the PWWP mutant W263A show the expected loss of DNAme at H3K36me3-marked regions? In other words, was there evidence of DNAme redistribution in loss at H3K36me3+ regions and inappropriate gain at H3K9me3+ regions? Please perform intersection analysis of DMRs with other epigenomic marks (e.g. H3K27me3, H3K36me3, CpG shores) in the PWWP mutants.

Our analysis of DNMT3B KO cells (manuscript figure s1d) show that losses of DNA methylation in these cells are not correlated with H3K36me3 in gene bodies suggesting that DNMT3A and DNMT1 are sufficient to compensate in maintaining their methylation in DNMT3B KO cells. To clarify this point for the DNMT3BW263A knock in clones, in the revised manuscript we will directly examine whether these cells show loss of methylation at H3K36me3 marked gene bodies in a similar analysis and add discussion of these results.

The study would also be strengthen greatly with the in addition of biochemical studies to confirm direct loss of binding, and possibly gain of H3K9me3 binding, in the DNMT3B PWWP mutants.

As detailed above (general comments, revision plan figure 1), our data suggest that DNMT3B interacts indirectly with H3K9me3 through an HP1 motif in its N-terminal region. We will undertake further biochemical studies on this interaction which will be included in the revised manuscript. Specifically we will focus on using EMSAs with synthetic nucleosomes to clarify the degree to which the HP1a interaction is responsible for binding of DNMT3B to H3K9me3 modified nucleosomes.

We also propose to undertake in vitro biochemical characterization of the effect of DNMT3B PWWP mutations on interaction with H3K36me3 using synthetic nucleosomes. However, we note that in the manuscript we have shown similar effects using two independent point mutations that are predicted to affect H3K36me3 binding (W263A and D266A) and deletion of the entire PWWP domain.

3. Examining the tracks in Figure 3A,B, the PWWP mutants showed almost indiscriminate increase across the genome, and not specifically to H3K9me3-marked regions. Would ask the authors to speculate as to why the ChIP-seq of DNMT3B mutants do not recapitulate the heterochromatin co-localization shown by immunofluorescence.

As discussed in response to reviewer 2 (point 3) we believe that the weak DNMT3B ChIP-seq signal at H3K9me3 loci is likely due to the nature of the interaction that DNMT3B has with chromatin in these regions. We will add discussion of these points to the revised manuscript.

4. It's a shame that the ICF1 mutation S270P was not characterized to the same extent as the PWWP mutants. Would consider adding WGBS for this clinically relevant mutation.

We have shown that this mutant does not produce stable protein in vitro or in our cells and we observe little difference in DNA methylation at selected loci. As WGBS is expensive, we believe that carrying out this experiment is not an efficient use of limited research resources.

5. Figure 7 - please draw in the ICF1 and the N-terminal mutations in the model figure. Also provide legends.

We will modify the manuscript to include these details in the revised manuscript.

Reviewer #3 (Significance (Required)):

This is an intersting study on a timely subject. It will be of interest to multiple fields from epigenetics to development and cancer. My expertise is in cancer, epigenetics, development.

We thank the reviewer for highlighting the broad interest of our study.

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Referee #3

Evidence, reproducibility and clarity

In this work, Taglini et al. examine how the de novo DNA methyltransferase DNMT3B localizes to constitutive heterochromatin marked by the repressive histone modification H3K9me3. The authors utilize a previously generated DNMT3B KO colorectal carcinoma cell line, HCT116 to study recruitment and activity of DNMT3B at constitutive H3K9me3 heterochromatin. The authors noted preferential decrease of DNA methylation (DNAme) at regions of the genome marked with H3K9me3 in DNMT3B KOs. The authors then rescued the deficiency through overexpression of WT and catalytic dead DNMT3A/B and confirmed that DNA methylation increase at H3K9me3+ region in the WT DNMT3B, but not catalytically inactive mutant nor DNMT3A. To examine which protein domains may be mediating DNMT3B's recruitment to H3K9me3 regions, the authors designed a series of mutants, primarily focusing on the PWWP domain which normally recognizes H3K36me3. In the PWWP mutants, DNMT3B binding to the genome is altered, showing depletion at some H3K36me3-marked regions and gain at H3K9me3 heterochromatin, which coincides with DNAme increase at satellites. In contrast, the clinically relevant ICF1 mutation S270P, shows DNMT3B protein destabilization and no such loss of DNAme at heterochromatin. Finally, the authors truncate the N-terminal portion of DNMT3B, and saw that this region of the protein is necessary for heterochromatin localization and subsequent DNAme of H3K9me3+ regions.

The experiments are well done with extensive controls, and the results are interesting and convincing. The structure of the manuscript could be improved for clarity and flow - for example, the PWWP mutations and truncations should be mentioned and compared together. I also found the section on ICF1 mutant to be out-of-place. More emphasis should be placed on the N-terminal mutant as this region seems to be critical to heterochromatin recruitment, and this may address whether the interaction to H3K9me3 is direct or indirect. Finally, while the epigenetic crosstalk is well-examined in this work, I would strongly urge the authors to add RNA-seq data to determine the transcriptional consequence of such chromatin disruptions (e.g. are repetitive sequences up-regulated in DNMT3B KOs?).

Comments

1. A potential caveat to the study is the use of a single cell line - colorectal cancer cell HCT116 - to draw major conclusions on the function of DNMT3B. It is worth noting the Baubec et al. study examining DNMT3B recruitment to H3K36me3 was mainly performed in murine embryonic stem cells (mESCs). It would greatly strengthen the study if the authors could perform similar type of data analysis on an independent DNMT3B KO cell line. For example, does DNMT3B localize to H3K9me3 regions in WT mESCs?
2. Did the PWWP mutant W263A show the expected loss of DNAme at H3K36me3-marked regions? In other words, was there evidence of DNAme redistribution in loss at H3K36me3+ regions and inappropriate gain at H3K9me3+ regions? Please perform intersection analysis of DMRs with other epigenomic marks (e.g. H3K27me3, H3K36me3, CpG shores) in the PWWP mutants. The study would also be strengthen greatly with the in addition of biochemical studies to confirm direct loss of binding, and possibly gain of H3K9me3 binding, in the DNMT3B PWWP mutants.
3. Examining the tracks in Figure 3A,B, the PWWP mutants showed almost indiscriminate increase across the genome, and not specifically to H3K9me3-marked regions. Would ask the authors to speculate as to why the ChIP-seq of DNMT3B mutants do not recapitulate the heterochromatin co-localization shown by immunofluorescence.
4. It's a shame that the ICF1 mutation S270P was not characterized to the same extent as the PWWP mutants. Would consider adding WGBS for this clinically relevant mutation.
5. Figure 7 - please draw in the ICF1 and the N-terminal mutations in the model figure. Also provide legends.

Significance

This is an intersting study on a timely subject. It will be of interest to multiple fields from epigenetics to development and cancer.

My expertise is in cancer, epigenetics, development.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Taglini et al., describe an increased activity of DNMT3B at H3K9me3-marked regions in HCT cells. They first identify that DNA methylation at K9me3-marked regions is strongly reduced in absence of DNMT3B. Next, the authors re-express DNMT3B and DNMT3B mutant variants in the DNMT3B-KO HCT cells and assess DNA methylation by WGBS where they identify a strong preference for re-methylation of K9me3 sites. Based on genome-wide binding maps for DNMT3B, including the mutant variants, they address how the localization of DNMT3B relates to the observed changes in methylation.

Major points:

The authors show increased reduction of mCG at H3K9me3 (and K27me3) sites in absence of DNMT3B. This is based on correlating delta %mCG with histone modifications in 2kb bins. I find this approach to not fully support the major claim.

First, the correlation coefficients are very small -0.124 for K9me3 and -0.175 for K27me3, and just marginally better compared to, for example, K36me3 that does not seem to have any influence on mCG according to Sup Fig S1b. While I agree that mCG seems more reduced at K9me3 in absence of DNMT3B (e.g. in Fig 1a), is there a better way to visualize the global effect? The delta mCG Boxplots based on bins are not ideal (this applies to many figures using the same approach in the current manuscript).

Second, the calculation based on delta mCpG does not allow to see how much methylation was initially there. For example, S1b shows a median decrease of ~ 10% in K9me3 and ~7-8% in H3K4me3. What does this mean given that the starting methylation for both marks is completely different?

Following this point, the authors mention that mCG is already low at K9me3 domains in HCT cells (compared to other sites in the genome). I am curious if this may influence the accelerated loss of methylation in absence of DNMT3B? Any comments on this?

One issue is the lack of correlation in DNMT3B binding to H3K9me3 sites in WT cells (Fig 3). How does this explain the requirement for DNMT3B for maintenance of methylation at H3K9me3? While some of the tested mutants show some weak increase at K9me3 sites, these are not comparable to the strong binding preferences observed at K36me3 for the wt or delta N-term version.

Following the above comment, what about other methyltransferases in HCT cells? Could DNMT1 or DNMT3A function be altered in absence of DNMT3B, and the observed methylation changes could be indirectly influenced by DNMT3B? The authors could create a DNMT-TKO HCT cell line and re-introduce DNMT3B in this background and measure methylation to exclude that DNMT1 or DNT3A could have an influence. In this case, only H3K9me3 should gain DNA methylation.

DNMT3B lacking N-terminal shows reduced K9me3 methylation & some localization by imaging. While the presented experiments show some support for this conclusion, I suggest to re-introduce a W263A mutant lacking the N-terminal part and measure changes in DNA methylation at H3K9. This should help to test the requirement for the N-terminal regions and further indicate which protein part (PWWP or N-term) is more important in regulating the balance between K9me3 and K36me3.

In the first paragraph of the discussion, the authors state: "Our results demonstrate that DNMT3B is recruited to and methylates heterochromatin in a PWWP- independent manner that is facilitated by its N-terminal region." Same statement is found in the abstract. This contradicts the ChIP-seq results that do not indicate a recruitment of DNMT3B to heterochromatin, and the N-terminal deletions are not fully supporting a role in this targeting since there is no localization to K9me3 to begin with. While changes in methylation are observed, it remains to be determined if this is indeed through direct DNMT3B delocalization or indirectly through influencing the remaining DNMTs.

Significance

Advance: detailed analysis of DNMT3B mutants in relation to K9me3. Builds up on previous studies.

Audience: specialised audience

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Referee #1

Evidence, reproducibility and clarity

Summary:

This paper by Francesca Taglini, Duncan Sproul, and their coworkers, examines the mechanisms of DNA methylation in a human cancer cell line. They use the human colorectal cancer line HCT116, which has been very widely used to look at epigenetics in cancer, and to dissect the contribution of different proteins and chromatin marks to DNA methylation.

The authors focus on the role of the de novo methyltransferase DNMT3B. It has been shown in ES cells in 2015 that its PWWP domain directs it to H3K36me3, typically found in gene bodies. More recently, the authors showed similar conclusions in colorectal cancer (Masalmeh Nat Comm 2021). Here they examine, more specifically, the role of the PWWP. The conclusions are described below.

Major comments:

1. I feel that this paper has several messages that are somewhat muddled. The main message, as expressed in the title and in the model, is that the PWWP domain of DNMT3B actively drags the protein to H3K36me3-marked regions. Inactivation of this domain by a point mutation, or removal of the Nter altogether, causes DNMT3B to relocate to other genomic regions that are H3K9me3-rich, and that see their DNA methylation increase in the mutant conditions. This first message is clear.

The second message has to do with ICF. A mutant form of DNMT3B bearing a mutation found in ICF, S270P, is actually unstable and, therefore, does not go to H3K9me3 regions. I feel that here the authors go on a tangent that distracts from message #1. This could be moved to the supp data. At any rate, HCT116 are not a good model for ICF. In addition, a previous paper has looked at the S270P mutant, and it did not seem unstable in their hands (Park J Mol Med 2008, PMID: 18762900). So I really feel the authors do not do themselves a favor with this ICF angle. 2. I feel that some major confounders exist that endanger the conclusions of the work. The most worrisome one, in my opinion, is the amount of WT or mutant DNMT3B in the cells. It is clear in figure 4C that the WT rescue construct is expressed much more than the W263A mutant (around 3 or 4 times more). Unless I am mistaken, we are never shown how the level of exogenous rescue protein compares to the level of DNMT3B in WT cells. This bothers me a lot. If the level is too low, we may have partial rescue. If it is too high, we might have artifactual effects of all that DNMT3B. I would also like to see the absolute DNA methylation values (determined by WGBS) compared to the value found in WT. From figure S1A, it looks like WT is aroun 80% methylation, and 3BKO is around 77% or so. I wonder if the rescue lines may actually have more methylation than WT? 3. I guess the unarticulated assumption is that the gain of DNA methylation seen at H3K9me3 region upon expression of a mutant DNMT3B is due to DNMT3B itself. But we do not know this for sure, unless the authors test a double mutant (PWWP inactive, no catalytic activity). I am not necessarily asking that they do it, but minimally they should mention this caveat.<br /> 4. I am confused as to why the authors look at different genomic regions in different figures. In figure 1 we are looking at a portion of the "left" arm of chr 16. But in figure 2B, we now look at a portion of the "right" arm of the same chromosome, which has a large 8-Mb block of H3K9me3, and is surprisingly lowly methylated in the 3BKO. This seems quite odd, and I wonder if there is a possible artifact, for instance mapping bias, deletion, or amplification in HCT116. Showing the coverage along with the methylation values would eliminate some of these concerns.

Minor comments:

1. The WGBS coverage is not very high, around 2.5X on average, occasionally 2X. I don't believe this affects the findings, as the authors look at large H3K9me3 regions. But the info in table S2 was hard to find and it is important. I would make it more accessible.
2. It would be nice to have a drawing showing exactly what part of the Nter was removed.
3. some figures could be clearer. I was not always sure when we were looking at a CRISPR mutant clone (W263A) versus a piggyBac rescue.
4. unless I am mistaken, all the ChIP-seq data (H3K9me3, H3K36me3 etc) come from WT cells. It is not 100% certain that they remain the same in the 3BKO, is it? This should be discussed.

Significance

Strengths:

The experiments are for the most part well done and well interpreted (save for the limitations mentioned above). The techniques are appropriate and well mastered by the team. The paper is well written, the figures are nice. The authors know the field well, which translates into a good intro and a good discussion. The bioinformatics are convincing.

Limitations:

All the work is done in a single cancer cell line. One might assume the conclusions will hold in other systems, but there is no certainty at this point.

HCT116 are not the best model system to study ICF, which mostly affects lymphocytes

At present, I feel that the biological relevance of the findings is fairly unclear. The authors report what happens when DNMT3B has no functional PWWP domain. I am convinced by their conclusions, but what do they tell us, biologically? Are there, for instance, mutant forms of DNMT3B expressed in disease that have a mutant PWWP? Are there isoforms expressed during development or in certain cell types that do not have a PWWP? In these cell types, does the distribution of DNA methylation agree with what the authors predict?

In its present state, I feel the appeal of the findings is towards a semi-specialized audience, that is interested in aberrant DNA methylation in cancer and other diseases. This is not a small audience, by the way.

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Reply to the reviewers

1. General Statements

We thank all the reviewers for their time and effort in the peer-review process. We appreciate the positive reflections on the study and the feedback comments which were well thought-out and articulated. Considering these comments has led us to deeper reflections on the conceptualization of the mechanisms at play, and we hope that our responses here and revisions of the manuscript have improved the presentation of the data and our interpretation of these complex matters. As a result, we have now incorporated a new supplementary figure 5 and present a new model figure with the corresponding comments in the text.

1. Point-by-point description of the revisions

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

In this manuscript Sanchez-Martinez et al investigated the function of ntc, a Drosophila homologue of FBXO7. The mechanisms by which mutations in this protein cause autosomal recessive PD are poorly understood. The protein has previously been implicated in PINK1/Parkin mitophagy however the mechanistic detail is lacking. The data presented here provide an important insight into the molecular functions of ntc as well as mitophagy in vivo in general. Ntc was found to promote ubiquitination of mitochondrial proteins which is counteracted by USP30. The basal ubiquitination regulated by these two enzymes is proposed to act as a permissive factor for the initiation of Pink1/Parkin mitophagy. The conclusions are based on strong data in vivo and there is a lot to like about this paper. The analyses are done rigorously, conclusions are balanced and well supported and there is a lot of conceptual novelty in the dataset. At the same time the paper raises some questions with regard to the role of mitophagy, at least in Drosophila. Not all of these could be answered during revisions but it would be useful to address the points outlined below.

1. The functional measurements such as climbing, flight and lifespan are used to complement the data on mitophagy and mitochondrial health. However, it is clear that these do not correlate. Ntc KOs and Pink1/Parkin flies have reduced climbing and flight ability, however ntc KO does not affect mitochondrial function. In case of Pink1/Parkin the assumption is that impaired fly functionality is due to damaged mitochondria. This is clearly not the case with Ntc. How relevant is climbing/flight/lifespan to the role of Ntc in mitophagy?

• The reviewer raises a very good point, and we agree that there isn’t a strict, linear connection between the cellular process of mitophagy and the presentation of organismal neuromuscular phenotypes such as motor behaviours and lifespan. Considering this point further, starts to highlight the complexity of the situation at hand: it is becoming clear that there are many different forms of mitophagy, and these perform different functions in cellular remodelling and homeostasis. And, of course, there are many ways to interfere with neuromuscular function (as well as lifespan). So, it follows that some forms of mitophagy may dramatically impact neuromuscular homeostasis when disrupted, while others may not. We and others have described that basal mitophagy is minimally by loss of Pink1/parkin in vivo, so the organismal phenotypes clearly do not relate to this biology. But it is currently unclear how the phenotypes may relate to physiologically relevant stress-induced mitophagy as the precise nature of this, as well as the methods to experimentally manipulate it, are lacking.

Here, we are initially documenting new phenotypes for ntc, with no bias for the mechanistic cause, all of which are worthy of description to gain a holistic view of the overall contribution of this gene function to organismal integrity. It is clear from the literature that ntc/FBXO7 has multiple functions, for instance, regulating proteasome function and caspase activation, so it follows that genetic loss is likely to impinge on multiple cellular functions causing pleiotropic effects.

We have always been careful not to consider (or claim) that the organismal phenotypes, such as motor function or lifespan, are specifically due to defective mitophagy but are an overall readout of the health and functioning of the neuromuscular system. Nevertheless, these phenotypes are useful in investigating manipulations that improve or worsen the effect of Pink1/parkin (or ntc) mutants, which, a priori, may or may not also modulate mitophagy. While we have documented these new organismal phenotypes of ntc mutants and analysed the impact on basal and stress- induced mitophagy, we have not drawn a cause-effect link between the two but a correlation at least. Nevertheless, this issue raises some important considerations for the field in terms of the different kinds of mitophagy, and when they may be needed, and the impact on cells, systems or whole-organisms when they are defective. This issue is explored more in answer to Q3 below.

1. Fig 3 is somewhat patchy, mitophagy is shown for USP30 and ntc KO epistasis but climbing index used in OE setting. These data do not match, and it feels like experiments that are shown are the ones that worked. The relevance of climbing index to mitophagy is unclear as mentioned above. Does KO/OE of ntc and USP30 affect levels of mitochondria, e.g. Marf used as a maker for mitochondria in Fig. 1? And if not why not, considering that ntc/USP30 but not PINK/Parkin control basal mitophagy?

• The main purpose of the data in Figure 3 was to document the impact of ntc manipulation on basal mitophagy, and by extension to link this to the known mitophagy regulator, USP30, whose loss of function has been documented to promote stress-induced mitophagy. Here, we successfully demonstrated USP30 RNAi and ntc OE cause an increase in mitophagy, and established their genetic relationship. However, it is very important to our modus operandi that we have orthogonal evidence for this relationship and understand the impact at an organismal level. As the reviewer indicates, the obvious choice that would align with the mitophagy data would be to assess whether loss of ntc prevents a USP30 RNAi phenotype. However, in our hands USP30 knockdown using the same RNAi line had no discernible impact on viability or behaviour in adult flies, precluding this experiment. Of note, we did observe a detrimental impact of USP30 knockdown on adult viability using a different RNAi line (KK) but this has 2 known off-targets so this result is unreliable. An alternative approach to genetically test the antagonistic relationship between USP30 and ntc, and equally valid in our view, is to assess whether ntc OE can counteract a USP30 OE phenotype. Here, we were fortunate that USP30 OE does indeed provoke an organismal phenotype, and this was suppressed by ntc OE consistent with the mitophagy data. It is unfortunate that the more obvious option was not workable on this occasion, but we hold that the genetic relationship was nevertheless substantiated as expected, albeit with alternative manipulations. Importantly, we established the validity of this approach by demonstrating the known genetic interaction between USP30 and parkin, whereby USP30 OE locomotor phenotype is suppressed by parkin OE (now, Fig. S3D). To substantiate this approach more clearly, we have now added to the text (lines 200-201) and figure (Fig. S3C) the lack of observable effect by USP30 knockdown as noted above.

As to the second point, assessing whether levels of mitochondria are changed by ntc/USP30 manipulations; according to the immunoblot presented in new Supplementary Figure 5A (and replicates), the levels of ATP5a are not notably changed by ntc O/E or USP30 RNAi. Marf levels are also unchanged though this is not shown. This is in line with our expectations since, as discussed above, ntc/USP30 are only one set of regulators of one type of mitophagy and several others exist. The reviewer will likely be aware that the levels of mitochondria are tightly regulated and fine-tuned for the specific need in different tissue types, and that substantial changes to mitochondrial content can be catastrophic for cell and tissue viability. While this is relatively straightforward to achieve in cultured cells, substantial reductions in mitochondrial content are non-viable in an in vivo context. Of course, in physiological conditions, rates of degradation are kept in fine-balance with biogenesis and proliferation so non-catastrophic alterations in mitochondrial content are usually counteracted by compensatory changes in proliferation or degradation.

1. What is the role of mitophagy in the maintenance of mitochondrial function in Drosophila in general? Pink1/Parkin KO assumed to result in dysfunctional mitochondria due to impairment of damage-induced mitophagy which is a minor contributor to mitophagy as has previously been published by the authors and confirmed in this dataset using mitophagy reporter. At the same time ntc is clearly required for mitophagy, but mitochondria remains structurally and functionally intact in ntc KO. The most straightforward interpretation of these data is that Pink1/Parkin contribute little to mitophagy in flies and their effect on mitochondria and fly function is independent of mitophagy. Instead ntc (and USP30) strongly regulate mitophagy but mitophagy is not important for the maintenance of mitochondrial function. The effect of ntc on fly function is also independent of its role in mitophagy/mitochondria. Unless there is an alternative explanation the entire dataset would need to be reinterpreted and discussed differently.

• We agree that this is an important point raised by the study findings and needs to be clearly articulated in the text, but we don’t think it is as simple as whether ‘mitophagy’ contributes to mitochondrial and organismal integrity. First, as mentioned above, it is becoming apparent that it is crucial for the field to clearly and consciously distinguish between basal and induced forms of mitophagy. Basal mitophagy is likely, though not yet proven, to be an important component of mitochondrial quality control in metazoans and largely act in a house-keeping manner providing continual surveillance of mitochondrial quality and quantity. As such, like many other critical biological processes, it is likely to be supported in a ‘belt-and-braces’ manner by several mechanisms working in parallel with a degree of functional redundancy. In contrast, induced mitophagy is presumed to be quiescent until stimulated into action at specific times for specific purposes. For instance, it is assumed, though not yet proven, that PINK1/Parkin stress-induced mitophagy is stimulated in response to some kind of physiological stress or damage to mitochondria that may be catastrophic if left unchecked.

We and others have shown before that PINK1/Parkin are minimally involved in basal mitophagy in vivo but they are well-established to promote stress-induced mitophagy. In contrast, we have found that ntc regulates basal mitophagy and, we posit, facilitates PINK1/Parkin mitophagy by providing the initiating ubiquitination. How does this map onto the mitochondrial/organismal phenotypes? There are clear disruptions to energy-intensive, mitochondria-rich tissues inPink1/parkin mutants which are not grossly affected in ntc mutants. On the other hand, ntc mutants show a dramatically short lifespan, much shorter than Pink1/parkin mutants, while other measures of mitochondrial integrity are fine.

The Pink1/parkin phenotypes are consistent with a catastrophic loss of tissue integrity caused by the lack of a crucial protective measure (induced mitophagy) for a specific circumstance (we think, mitochondrial ‘damage’ arising from a huge metabolic burst). In contrast, while loss of ntc causes a partial (but not complete) loss of basal mitophagy, these same tissues appear to be able to cope with this impact on house-keeping QC but importantly are also able to mount a stress-induced response via Pink1/parkin still being present. On the other hand, it should be remembered that ntc is known to perform other important cellular functions, such as regulating proteasome function and caspase activation, and it is perhaps loss of these functions that causes the dramatic loss of vitality.

Importantly, although Pink1/parkin do not contribute to basal (steady-state) mitophagy, we think it is not appropriate to think of Pink1/parkin mitophagy as a ‘minor’ contribution. Since, under the particular triggering conditions of damage or stress that stimulate Pink/parkin mitophagy, apparently only Pink1/parkin can perform this role in certain Drosophila tissues, and this stress- induced mitophagy is crucial to tissue integrity, as exemplify by the fact that increasing basal mitophagy via ntc O/E still is not sufficient to rescue Pink1 mutants. In this specific context, this is a major mitophagy pathway.

In summary, the connection between mitochondrial autophagic degradation and mitochondrial/organismal health is not a simple one and we would avoid conflating different aspects of mitochondrial QC with the expectation that the consequences of their dysfunction would be the same. Nevertheless, these well-considered feedback comments have crystallised the need to elaborate these ideas in the Discussion where we have added a new section (lines 359-387).

Reviewer #1 (Significance (Required)):

Very strong genetic data presented; novel functions for human Park15 homologue in Drosophila; mechanistic insight into the ubiquitination of mitochondria by two opposing enzymes. Overall very interesting paper but interpretation is less clear which needs to be addressed.

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

In this ms. Sanchez-Martinez and colleagues study the role of the ub ligase FBXO7, in regulating mitophagy - highlighting that mutations in FBXO7 associate with Parkinson's disease and defects in mitochondrial homeostasis. Using the fly as model, they carry out a series of expts. investigating ntc (Drosophila ortholog of FBX07) demonstrating that it can functionally rescue Parkin but not PINK1 deficiency. Expanding on this, they propose a model whereby ntc/FBX07 regulates basal mitophagy and also acts as a priming Ub-ligase for Parkin mediated mitophagy, finding that the dub USP30 counteracts these ntc function. Overall the data are robust and support the authors' conclusions and model, the manuscript is well written and I think can be accepted as is.

• We thank the reviewer for their appreciation of the work and the time taken to provide supportive feedback.

While outside the scope of this study to understand why, I find it very interesting that ntc cannot rescue the PINK1 deficient phenotype, argues that PINK1 may be having additional effects beyond regulating mitochondrial ubiquitylation.

• We entirely agree with the reviewer, this is a very intriguing finding. Indeed, there are several examples in the literature showing that PINK1 performs additional functions than just triggering mitophagy. But in the current context we interpret these data as further support for a clear mechanistic distinction between basal mitophagy and stress-induced mitophagy as discussed at length to the other reviewers’ comments.

Reviewer #2 (Significance (Required)):

Importance for understanding the role of FBX07 function - relevant for Parkinson's disease, also demonstrates a role for it in priming for PINK1/Parkin dependent mitophagy.

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

In this manuscript, Sanchez-Martinez et al characterise the role of nutracker (ntc), the presumed Drosophila orthologue of human FBXO7 (whose gene is mutated in autosomal recessive PD), in mitophagy and phenotypes associated with neurodegeneration in flies (climbing index, dopaminergic neuron loss, rough eye phenotype, and others). FBXO7 (human) has been previously shown to restore parkin (not Pink1) phenotypes and mitochondrial morphology in Drosophila and implicated in Pink1-parkin mitophagy, however the role of ntc in basal mitophagy and its genetic interaction with USP30 has not been previously reported. Key findings include: evidence for functional homology between ntc and FBXO7, and that Ntc/FBXO7 is required for basal mitophagy (and reverses USP30 function) in a Pink1-parkin independent manner.

FBXO7/ntc is clearly an important regulator of mitophagy and its overexpression can suppress Parkin phenotypes, however FBX07/ntc has not been studied as intensively as Parkin and Pink1, therefore this work represents important insight into mitophagy regulators (broad interest to many overlapping fields).

However, in addition to minor points and controls requested below, some further characterisation of the signals on the mitochondria induced by ntc/FBXO7 would improve the novelty of the study and the mechanistic insight provided. For example, the authors look at total ubiquitin and pS65- Ub, whereas if they looked at specific substrates that they mention in the discussion (e.g. OMM and translocon proteins) it would allow a less speculative discussion.

Figure 1: The authors show that overexpression of ntc can rescue Parkin null phenotypes but not Pink1 phenotypes. In very similar experiments, overexpression of FBXO7 (human) has been shown to rescue Parkin phenotypes but not pink1 phenotypes (Burchell), appropriately mentioned by the authors.

The western blots are not terribly clear and would benefit from quantification (particularly H).

• We had previously performed the quantification on replicate experiments but had considered that the result was clear enough without quantification, and that including a quantification may make the figure too crowded. However, we have now added the quantification to the figure to support these results.

Specific ubiquitylated substrates like translocon proteins would be very interesting (alternatively, this could be provided in figure 7).

• We agree that this would be a very interesting aspect to investigate, but we feel that since the emphasis of the current study is clearly on the regulation of mitophagy, and not on specific substrates as has been published elsewhere (Phu et al., Mol Cell, 2020 (Ref. 42); Ordureau et al., Mol Cell, 2020 (Ref. 39)), investigating the impact of ntc and USP30 on the ubiquitination of the translocon would be a distraction to the focus of the study.

If the rough eye phenotype is highly homogenous, state in text otherwise, a relative roughness quantification would be more informative.

• The rough eye phenotype described here is indeed highly stereotyped and homogeneous. We have added this comment to the text for clarity (lines 124-126).

Figure 2: Although mainly in agreement with Burchell et al findings (that there is no disruption of mito morphology or dopaminergic neuron loss caused by ntc loss), the loss flight ability in the ntc mutant is partially discrepant with Burchell et al (results not shown in Burchell et al). Can the authors explain the discrepancy? It is important finding that the ntc functionally orthogue of FBXO7 and differs from the Burchell et al conclusions.

• The reviewer raises a good point regarding discrepant interpretations with earlier preliminary work that we didn’t specifically elaborate in the current manuscript. For the Burchell et al study we performed a series of non-exhaustive analyses with reagents that were most readily available at the time. The flight data described in Burchell et al (as ‘data not shown’) were done with what we now know to be a hypomorphic allele, which did not give a strong flight defect that we were expecting to see as a phenocopy of parkin mutants. Moreover, experiments aimed at testing the functional homology sought to rescue the only reported ntc phenotype at that time – male sterility – which did not work. It is worth noting that GAL4/UAS-mediated expression is known to be very inefficient in the male germline, so we originally interpreted the lack of rescue with this caveat in mind. It is also worth adding that, subsequent to the Burchell et al study, we have seen that expression of FBXO7 can rescue the caspase-3 activation in ntc mutant spermatocytes, supporting their functional homology. Importantly, during the Burchell et al study we did not have reagents to test the effects of ntc overexpression, obtained subsequently, which have provided compelling data that support a functional homology between ntc and FBXO7. At the time of writing the current manuscript we did not specifically revisit the Burchell et al text to note this strongly stated conclusion. We realise that this requires unequivocal clarification and thank the reviewer for pointing this out. We have amended the text to clarify this important point (lines 285-293).

Figure 7: A,B. It is not clear that mitochondria have been enriched - can the authors show on mitochondria or show the fractionation quality?

• Mitochondrial enrichment is a standard procedure in our lab, with consistently acceptable results, so we apologize for omitting a demonstration of this. We have now added these data to a new supplementary figure S5A. The corresponding information has also been added to the text (line 253). We have also extended this analysis to now show that total ubiquitination is not changed in ntc OE or USP30 RNAi, highlighting the specificity for accumulated ubiquitination on the mitochondria. This has been added to supplementary figure S5Band text lines 253-254.

C/D. The text that accompanies these figures needs further explanation and clarification and I found this result hard to understand without referring to the discussion. I think the authors are concluding that pS65 is ubiquitylated by FBXO7? I think this should be re-written in the results section. If it is a major point that the authors want to make, a complementary approach would be advised - possibly human cells/mass spectrometry.

• We apologise that this was confusing and have simplified the text accordingly to improve the clarity (lines 260-262). While this specific analysis is not a major point of the study, it provides a useful additional measure of how ntc/USP30 contributes to mitochondrial ubiquitination which *is* a key focus of the study so we have revised the Discussion to better highlight this point (lines359- 387).

As for Figure 1, specific ubiquitylated substrates at the OMM such as the translocon subunits would be informative.

• As discussed above, the role of USP30, at least, on ubiquitination of protein import in the translocon has been documented elsewhere and further specific analysis on this here would be a distraction from the main focus of the study.

Minor points

Figure 8 model and discussion: Nice discussion. However, unless protein import/ubiquitylation of translocon factors/localisation of FBXO7 to the translocon is shown in the manuscript, I would recommend more clarity in the figure legend to emphasise what is speculation based on other papers and what are new findings from the paper.

• This is a fair point and we agree that it is good to be clear about which aspects of the working model are reflections of the data presented here and which are extrapolation/speculation from the literature. We have modified the figure and the figure legend accordingly.

Reviewer #3 (Significance (Required)):

FBXO7/ntc is clearly an important regulator of mitophagy however its mechanism of action has not been studied as intensively as Parkin and Pink1, therefore this work contains important insight into mitophagy regulators.

It will be of broad interest to many overlapping fields, and has translational impact in that mitophagy is disrupted in many diseases and FBXO7 itself is mutated in Parkinson's disease.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Sanchez-Martinez et al characterise the role of nutracker (ntc), the presumed Drosophila orthologue of human FBXO7 (whose gene is mutated in autosomal recessive PD), in mitophagy and phenotypes associated with neurodegeneration in flies (climbing index, dopaminergic neuron loss, rough eye phenotype, and others). FBXO7 (human) has been previously shown to restore parkin (not Pink1) phenotypes and mitochondrial morphology in Drosophila and implicated in Pink1-parkin mitophagy, however the role of ntc in basal mitophagy and its genetic interaction with USP30 has not been previously reported. Key findings include: evidence for functional homology between ntc and FBXO7, and that Ntc/FBXO7 is required for basal mitophagy (and reverses USP30 function) in a Pink1-parkin independent manner.

FBXO7/ntc is clearly an important regulator of mitophagy and its overexpression can suppress Parkin phenotypes, however FBX07/ntc has not been studied as intensively as Parkin and Pink1, therefore this work represents important insight into mitophagy regulators (broad interest to many overlapping fields).

However, in addition to minor points and controls requested below, some further characterisation of the signals on the mitochondria induced by ntc/FBXO7 would improve the novelty of the study and the mechanistic insight provided.

For example, the authors look at total ubiquitin and pS65-Ub, whereas if they looked at specific substrates that they mention in the discussion (e.g. OMM and translocon proteins) it would allow a less speculative discussion.

Figure 1: The authors show that overexpression of ntc can rescue Parkin null phenotypes but not Pink1 phenotypes. In very similar experiments, overexpression of FBXO7 (human) has been shown to rescue Parkin phenotypes but not pink1 phenotypes (Burchell), appropriately mentioned by the authors.

The western blots are not terribly clear and would benefit from quantification (particularly H). Specific ubiquitylated substrates like translocon proteins would be very interesting (alternatively, this could be provided in figure 7).

If the rough eye phenotype is highly homogenous, state in text otherwise, a relative roughness quantification would be more informative.

Figure 2: Although mainly in agreement with Burchell et al findings (that there is no disruption of mito morphology or dopaminergic neuron loss caused by ntc loss), the loss flight ability in the ntc mutant is partially discrepant with Burchell et al (results not shown in Burchell et al). Can the authors explain the discrepancy? It is important finding that the ntc functionally orthogue of FBXO7 and differs from the Burchell et al conclusions.

Figure 7: A,B. It is not clear that mitochondria have been enriched - can the authors show on mitochondria or show the fractionation quality? C/D. The text that accompanies these figures needs further explanation and clarification and I found this result hard to understand without referring to the discussion. I think the authors are concluding that pS65 is ubiquitylated by FBXO7? I think this should be re-written in the results section. If it is a major point that the authors want to make, a complementary approach would be advised - possibly human cells/mass spectrometry.

As for Figure 1, specific ubiquitylated substrates at the OMM such as the translocon subunits would be informative.

Minor points

Figure 8 model and discussion: Nice discussion. However, unless protein import/ubiquitylation of translocon factors/localisation of FBXO7 to the translocon is shown in the manuscript, I would recommend more clarity in the figure legend to emphasise what is speculation based on other papers and what are new findings from the paper.

Significance

FBXO7/ntc is clearly an important regulator of mitophagy however its mechanism of action has not been studied as intensively as Parkin and Pink1, therefore this work contains important insight into mitophagy regulators.

It will be of broad interest to many overlapping fields, and has translational impact in that mitophagy is disrupted in many diseases and FBXO7 itself is mutated in Parkinson's disease.

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

In this ms. Sanchez-Martinez and colleagues study the role of the ub ligase FBXO7, in regulating mitophagy - highlighting that mutations in FBXO7 associate with Parkinson's disease and defects in mitochondrial homeostasis. Using the fly as model, they carry out a series of expts. investigating ntc (Drosophila ortholog of FBX07) demonstrating that it can functionally rescue Parkin but not PINK1 deficiency. Expanding on this, they propose a model whereby ntc/FBX07 regulates basal mitophagy and also acts as a priming Ub-ligase for Parkin mediated mitophagy, finding that the dub USP30 counteracts these ntc function. Overall the data are robust and support the authors' conclusions and model, the manuscript is well written and I think can be accepted as is.

While outside the scope of this study to understand why, I find it very interesting that ntc cannot rescue the PINK1 deficient phenotype, argues that PINK1 may be having additional effects beyond regulating mitochondrial ubiquitylation.

Significance

Importance for understanding the role of FBX07 function - relevant for Parkinson's disease, also demonstrates a role for it in priming for PINK1/Parkin dependent mitophagy.

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 #1

Evidence, reproducibility and clarity

In this manuscript Sanchez-Martinez et al investigated the function of ntc, a Drosophila homologue of FBXO7. The mechanisms by which mutations in this protein cause autosomal recessive PD are poorly understood. The protein has previously been implicated in PINK1/Parkin mitophagy however the mechanistic detail is lacking. The data presented here provide an important insight into the molecular functions of ntc as well as mitophagy in vivo in general. Ntc was found to promote ubiquitination of mitochondrial proteins which is counteracted by USP30. The basal ubiquitination regulated by these two enzymes is proposed to act as a permissive factor for the initiation of Pink1/Parkin mitophagy. The conclusions are based on strong data in vivo and there is a lot to like about this paper. The analyses are done rigorously, conclusions are balanced and well supported and there is a lot of conceptual novelty in the dataset. At the same time the paper raises some questions with regard to the role of mitophagy, at least in Drosophila. Not all of these could be answered during revisions but it would be useful to address the points outlined below. 1. The functional measurements such as climbing, flight and lifespan are used to complement the data on mitophagy and mitochondrial health. However, it is clear that these do not correlate. Ntc KOs and Pink1/Parkin flies have reduced climbing and flight ability, however ntc KO does not affect mitochondrial function. In case of Pink1/Parkin the assumption is that impaired fly functionality is due to damaged mitochondria. This is clearly not the case with Ntc. How relevant is climbing/flight/lifespan to the role of Ntc in mitophagy? 2. Fig 3 is somewhat patchy, mitophagy is shown for USP30 and ntc KO epistasis but climbing index used in OE setting. These data do not match, and it feels like experiments that are shown are the ones that worked. The relevance of climbing index to mitophagy is unclear as mentioned above. Does KO/OE of ntc and USP30 affect levels of mitochondria, e.g. Marf used as a maker for mitochondria in Fig. 1? And if not why not, considering that ntc/USP30 but not PINK/Parkin control basal mitophagy? 3. What is the role of mitophagy in the maintenance of mitochondrial function in Drosophila in general? Pink1/Parkin KO assumed to result in dysfunctional mitochondria due to impairment of damage-induced mitophagy which is a minor contributor to mitophagy as has previously been published by the authors and confirmed in this dataset using mitophagy reporter. At the same time ntc is clearly required for mitophagy, but mitochondria remains structurally and functionally intact in ntc KO. The most straightforward interpretation of these data is that Pink1/Parkin contribute little to mitophagy in flies and their effect on mitochondria and fly function is independent of mitophagy. Instead ntc (and USP30) strongly regulate mitophagy but mitophagy is not important for the maintenance of mitochondrial function. The effect of ntc on fly function is also independent of its role in mitophagy/mitochondria. Unless there is an alternative explanation the entire dataset would need to be reinterpreted and discussed differently.

Significance

Very strong genetic data presented; novel functions for human Park15 homologue in Drosophila; mechanistic insight into the ubiquitination of mitochondria by two opposing enzymes. Overall very interesting paper but interpretation is less clear which needs to be addressed.

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Reply to the reviewers

To the Editor,

We thank the reviewers for their generous efforts on behalf of our manuscript. We were very pleased to see that reviewer #1 considered our manuscript a “very detailed, high quality paper” that “serves as an important resource for the field.” And that Reviewer #2 concurred, writing that our manuscript is “valuable because it generates large datasets on the NK/ILC family from human blood that can be deposited in repositories”, and that it is of “special relevance to the HIV field because it examines viral infection effects on these subsets.”

We believe the revisions made in response to the reviewers suggestions have improved the presentation of our data. Our responses to each reviewer comment are listed below in bold font. References mentioned here are listed in a bibliography at the end of this document.Changes to the text are also highlighted in the manuscript.

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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. *

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

Wang and colleagues present a very detailed, high quality paper describing phenotyping, transcriptional, epigenetic and functional differences between NK cells and ILCs in human peripheral blood. They overlay these studies which descriptions of differences in these populations in healthy and HIV infected (viremia, ART-treated, controllers), extending a their previous 2020 study.

Overall, this paper serves as an important resource for the field. There are areas in which the manuscript could be modified to improve clarity and detail regarding rigor.

My comments below are all addressable and therefore I class them all as 'major'.

1. The authors describe ILCs as being 'permanently deleted' in HIV infection. As written, this can be misinterpreted to suggest complete ablation of this cell subset. This is clearly not the case. Moreover, there looks to be some restoration of ILCs in ART-treated participants. I suggest revising text to quantify the level of cell loss or replace the word deleted with reduced. The reviewer is correct that ILCs are not completely ablated. In response to this important point we have clarified the text, as follows:

- As indicated on page 5, we have changed the text to, “ILCs are decreased in the blood and intestinal lamina propria in people living with HIV-1, even after viremia has been suppressed by antiviral therapy”.

- In the Results on page 13, we have changed the text to “ILCs were decreased in all subgroups of people living with HIV-1”.

__- The text on page 18 was changed to, “The percentage of ILCs in the Lin–CD56– population decreased from 37.5% in HIV-1– controls to 7.34% in people living with HIV-1 who are viremic; the reduction in ILCs was not fully restored by ART (24.38%) or in spontaneous controllers (18.16%)” __

- As indicated on page 26 in the Discussion, we have changed the text to “HIV-1 infection permanently reduces ILCs but not NK cells”.

1. The HIV EC are not clearly discussed in the paper and are not distinguished from viremic controllers. If ILCs are permanently reduced in this group, what does this suggest for the role of this cell subset in HIV control? Plasticity between ILC and NK cells is described. Is this plasticity relevant at all for HIV control, elite or otherwise? We thank the reviewer for asking us to clarify these important points in the manuscript:

2. Though elite controllers suppress HIV-1 viremia to undetectable levels, ILC numbers are still decreased in these individuals. Additionally, we do not detect an inverse correlation between ILC numbers and viremia. Further, our previous publication showed that ILC reductions in HIV-1 infection correlate inversely with markers of systemic inflammation, for example sCD14 _(Wang et al_, 2020b)_. Like other people living with HIV-1 infection, elite controllers have elevated microbial translocation, suggestive of disturbed gut homeostasis (Brenchley _et al, 2006)_. Some studies have even reported higher rates of cardiovascular disease in elite controllers, presumably as a result of higher levels of systemic inflammation_ (Crowell et al. 2015; Caetano et al. 2022).____ Taken together, these observations suggest that ILCs play no direct role in control of HIV-1 replication. These points are discussed on page 26-27. __

3. The fascinating question of plasticity between ILCs and NK cells is one we have explored extensively, both in our previous work and in the current manuscript. Plasticity has been reported between intraepithelial ILC1s and NK cells from tumor tissue, and between ILC3s and NK cells in tonsil _(Moreno-Nieves et al_, 2021; Raykova _et al, 2017; Cortez _et al, 2017)_. When ILC2s are cultured in vitro with IL-12, IFN-γ and TBX21 expression are upregulated to the levels of CD56hiNK cell (Lim _et al, 2016)_, indicating possible plasticity between ILC2 and CD56hiNK cells under certain inflammatory conditions. In HIV-1 infection, we do not detect correlations between the decrease in ILCs and increase in NK cells. In fact, the total NK cells did not change in HIV-1+ people who are viremic, on ART, or viremic controllers [(Figure 2A and 2B in (Wang _et al, 2020b)____]. Our transcriptome analysis indicates that, during HIV-1 infection, ILCs are likely depleted by inflammation-induced apoptosis (Figure 4I and Supplemental Table 6). However, whether blood ILCs (ILCPs or ILC2s) can upregulate TBX21 and EOMES to give rise to NK cells during HIV-1 infection is an interesting question worth further investigation. We now discuss the reviewer’s question on pages 27. __ III. The authors write that ILCs have a primary role in cytokine secretion and then distinguish the ILCs from NKs....but NKs also secrete cytokines. Can the authors please clarify their text?

__The reviewer is correct that, in this context, our statement is confusing. We have therefore deleted the sentence “Typical of cells with a primary role in secretion of cytokines…” on page 10. This change helps to focus the text on the specific genes that distinguish these subsets. __

1. Discussion (an also Results) - The authors use pseudotime analysis to show that CD56+NK cells sit between the ILCs and CD56-NKs. When initially described in the Discussion they don't really interpret this in terms of their (and others' observations) that CD56-NKs are increased in disease. Other groups have suggested CD56-NKs are dysfunctional. Here you show different granzyme profiles between CD56+ and CD56- NKs. Elsewhere you distinguish CD56dim and CD56- NKs in terms of functionality in HIV+ donors, though these cells cluster on the pseudotime analysis. The IL-2 and CD4+ T cell data add another layer of complexity.
2. Altogether, I am unclear on the authors' interpretation of their collective data.
3. Can you please clarify whether you are suggesting that CD56dimNKs are a distinct from CD56+ NKs but functional as opposed to CD56-NKs that are dysfunctional? __It would be best to respond to these two questions together. It is important to remember that our manuscript describes the characterization of these cells using several techniques. __

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- By flow cytometry, NK cells can be separated into CD56hi, CD56dim, and CD56neg populations (Figure 1F). Pseudotime analysis of our single cell RNA-Seq data showed that, at the transcriptional level, CD56dim and CD56neg NK cells are very similar to each other and cluster together (Figures 3A and 3F). Nonetheless, as compared with CD56dim NK cells, CD56neg NK cells express lower levels of GZMA and PRF1 (Supplemental Figure 4A), and produce less IFN-γ upon cytokine stimulation (Figures 8H and 8J). And when CD56neg NK cells are sorted and assessed by Bulk RNA-Seq, which gives deeper coverage than single cell RNA-Seq, metabolic gene expression is altered with respect to CD56dim NK cells (Figure 9).

__- Our pseudotime analysis based on single cell RNA-Seq showed that CD56hiNK cells form a distinct cluster, but that they share some transcriptional features with both the ILC cluster and the CD56dim/CD56–NK cell cluster (Figures 3A-3C and 3F). __

- In people living with HIV-1, as assessed by flow cytometry, CD56neg NK cells increase in number at the expense of CD56dim NK cells (Figure 7C). And yes, we show here that CD56neg NK cells expand in tissue culture if CD4+ T cells are depleted from the PBMCs, and addition of exogenous IL-2 prevents the switch from CD56dimNK cells to CD56–NK cells (Figures 8A-8C). These points are covered in the Results and Discussion on pages 11 and page 29-30.

1. Are CD56-NKs end-stage or can they be rescued? IL-2 treatment of CD56–NK cells from people living with HIV-1 can be converted back to CD56dimNK cells, though function is not fully restored to the level of CD56dimNK cells ____(Mavilio ____et al_, 2005)_. ____In our manuscript, we showed that IL-2 or IL-15 treatment can prevent CD56dimNK cells from becoming dysfunctional CD56–NK cells, and that these cytokines maintain mTOR activity (Figure 8A-D, 8G, 8I and Figure 10). Taking together our data with the published data, CD56–NK cells appear to be an end-stage dysfunctional cell. Whether conditions can be found for full restorage of function is an important question that is worth pursuing. This point is now stated on page 30.

2. CD8 T cell number remain high post ART and very much drive the ongoing inverted CD4:CD8 ratio. Have the authors consider how and if the need of these cells for IL-2 impact recovery of NK cell populations? __Two orthogonal experiments indicate that IL-2 secreted by CD4+ T cells, but not from CD8+ T cells, is responsible for the recovery of CD56dimNK cells after treatment with ART. In our ex vivo culture of PBMCs, depletion of CD4+ T cells , but not of CD8+ T cells, was associated with increased numbers of CD56–NK cells (Supplemental Figure 6A). Additionally, the IL-2 concentration in plasma from people living with HIV-1 correlated with CD4+ T cell numbers, but not with CD8+ T cell numbers (Figures 8N and 8O). __

3. Discussion: It would be very helpful if the authors can pull this large volume of data together in a summary paragraph and possibly also a graphic. __We thank the reviewer for the suggestion. We have now summarized our data in the discussion (page 31). Additionally, we have added bullet points with a 2 sentence summary to accompany our graphic abstract (page 3). __

4. "Thus, GZMK+CD8+T cells appear to be the adaptive counterpart of CD56hiNK cells, representing an intermediate state between cytokine producing CD4+T cells and cytotoxic CD8+T cells" For me, the 'cytokine-producing CD4+ T cells' comes from in from left field here. Can the authors please clarify or was the intent to write cytokine-producing CD8+ T cells? To clarify what we meant we need to discuss two points:

First, NK cells can be considered as the innate immune counterparts of CD8+T cells, in that both cell types are cytotoxic killer cells. In contrast, ILCs may be considered as the innate counterparts of non-cytotoxic CD4+ T helper cells. For example, like Th1 cells, ILC1 cells are TBX21+ producers of IFN-γ. And like Th2 cells, ILC2s are GATA3+ producers of IL-13. The relationship between ILCs and NK cells is similar to the relationship between CD4+T help cells and CD8+ cytotoxic T cells _(Vivier et al_, 2018)____. __

Second, previous studies showed that GZMK+CD8+T cells are distinguished from other CD8+T cells by higher expression of IL7R, TCF7, IFN-γ and TNF-α, and by decreased cytotoxic activity ____(Jonsson ____et al_, 2022)_. Thus, the relationship between GZMK+CD8+T cells and GZMK–CD8+T cells may be similar to the relationship between CD56hiNK cells (GZMK+, IL7R+, TCF7+, higher IFN-γ production and lower cytotoxicity) and cells from the CD56dim and CD56–NK cell cluster (GZMK–, IL7R–, TCF7–, lower IFN-γ production and higher cytotoxicity). In response to this comment we have modified the text on page 25-26 to make these points more clear.

VII. Methods: Were Pearson correlations applied because of the large 'n' or were data first tested for normality?

After checking the normality, nonparametric spearman correlation was performed for panels that failed the normality test. For panels with smaller n, the normality test may be not applicable, and Pearson correlation was performed.

VIII. Methods: Please clearly justify the use of t-tests throughout the manuscript rather than non-parametric based tests.

We tested the normality before performing parametric or nonparametric test. The t-test, Wilcoxon test or Mann-Whitney test used in our analysis was now specified for each panel in the legend. For panels that calculate cell percentage or numbers with smaller n, normality test may be not applicable, t-test was performed as done previously in these papers, Figure 7C and 7D in _(Wang et al_, 2020a)_, and in Figure 3 and 4 in (Xue _et al, 2022)____. We confirmed these analyses with two statisticians in our institute. __

1. Methods: How many cells were acquired in flow cytometry? How many ILCs were acquired in healthy/HIV+ donors for these studies? Did this create limitations on interpretation of phenotypic data? ILCs constitute roughly 1,000 cells per million PBMCs. To assess ILCs, we acquired data from 500,000 PBMCs, or roughly 500 ILCs per sample. NK cells constitute roughly 100,000 cells per million PBMCs, we acquired data from 200,000 PBMCs, or roughly 20,000 NK cells per sample. For each patient group, whether HIV-1-negative, HIV-1 viremic, etc, we analyzed PBMCs from at least 19 blood donors. This information is now stated in the “Flow cytometry” section in “Methods”, on page 35.

2. Methods: How was the cytokine concentrations used for in vitro assays determined? The cytokine concentrations were determined according to previous publication and our previous studies ____(Romee ____et al_, 2016, 2012; Wang _et al_, 2020b; Silverstein _et al_, 2022)_. We have added the related references to the “Stimulation conditions” section in “Method”, on page 36.

3. Figure: In Figure 4A, should it be '+ILC- ' or '+ILC' (no negative symbol)? __Yes, the reviewer is correct. We have deleted the typo “-”. __

XII. It's unclear from you single cell analysis how many cells were acquired for each of the 4 subsets. I assume less ILCs were analyzed. If so, can you please clarify for the non-bioinformatician how your bioinformatic analysis took these differences into account?

Indeed, we were concerned that we might have too few ILCs and therefore set up conditions so that similar numbers of each cell type were assessed. To accumulate enough ILCs for single cell analysis, ILCs were sorted from 3 donors. ILCs, CD56hi, CD56dim and CD56–NK cells were sorted in parallel from the same 3 donors. The sorting strategy is shown in Supplemental Figure 1D. Equal numbers of ILCs, CD56hi, CD56dim and CD56–NK cells were sorted and then mixed together before library preparation. In total, libraries were generated from 5,210 single cells using 10 x genomics. 1,478 ILC2s, 897 ILCPs, 1,116 CD56hiNK cells, and 1,486 CD56dim and CD56–NK cells were shown in UMAP. We have added this information in Figure 3 legend.

Reviewer #1 (Significance (Required)):

Overall, this paper serves as an important resource for the field.

There are areas in which the manuscript could be modified to improve clarity and detail regarding rigor (see above). The overall message of this work for understanding HIV pathogenesis is unclear but can be addressed.

__We appreciate the reviewer’s efforts on behalf of our manuscript. By carefully addressing the reviewer’s questions and comments, we believe that the rigor of our manuscript is improved and that the message regarding HIV-1-induced abnormalities of ILCs and NK cells are clarified. __

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

Summary: This manuscript makes use of RNA sequencing, ATAC-Seq and single cell sequencing to compare transcriptomes and identify relationships between various ILC/NK cell subsets in blood from uninfected controls. The authors describe differences in genes and pathways between subsets and determine that CD56high NK cells are an intermediate-connecting cell type between ILC progenitors and CD56dim NK cells. The authors show how HIV infection (viremic, ART+ or controllers) effect ILC/NK cell gene expression and pathways. One of the genes enhanced in NK cells is AREG which the authors demonstrate to be upregulated in IL/NK subsets in response to PMAI/I stimulation or stimulation with IL-2 or IL-15. The authors demonstrate that TGFB1 or knockout of RUNX3 modulates the frequency of AREG+ NK cells, implicating the Wnt signaling pathway in AREG regulation. Authors show that in PLWH, the frequency of AREG+ NK cells is altered. Authors also report that with HIV infection there is an increase in CD56neg NK cells with corresponding loss of CD56dim cells. Using healthy control PBMCs, the authors suggest differentiation of CD56dim into CD56neg is prevented by CD4 T cell production of IL-2 but promoted by TGFB1. Also in healthy controls, the authors run metabolomics to describe how CD56dim are more metabolically functional than CD56neg and link this to MTOR activity.

Major:

• Which population(s) of NK cells (high, dim or negative for CD56) are responding to stimulation with IL-2 and IL-15 by upregulating AREG in Figure 5? Does this NK population have higher expression of receptors for IL-2 and IL-15 (in single cell seq data or by flow) and if so how does receptor expression change in PLWH? We thank the reviewer for these excellent questions. In response, we have added the new figure below to Supplemental Figure 4E. In general, CD56hi NK cells have higher AREG RNA and protein than do the other NK cell subsets. After IL-2 or IL-15 stimulation, all three NK cell populations upregulate AREG, though CD56hiNK cells produce significantly higher levels of AREG than do either CD56dim or CD56negNK cells.

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__Consistent with the above results, the expression of IL2RB, IL2RG and IL15RA was higher in CD56hiNK cells than in CD56dimNK cells (new Supplemental Figure 4F). __

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We examined IL2RA, IL2RB, IL2RG, and IL15RA expression in the different NK cell subsets from the three groups of people living with HIV-1, ART, viremic, and controllers. As compared with cells from HIV-1-negative people, the only clearly significant difference for these four genes was a reduction in IL2RB expression in CD56– and CD56dim NK cells from viremic people. These points are now mentioned on page 15.

• Does signaling through IL-2 or IL-15 converge on the TCF7/Wnt signaling pathway? For example, is it possible to knockdown TCF7 then stimulate with IL-2 or IL-15 and measured AREG+ cells with the idea that loss of TCF7 would result in reduced cytokine-induced activation of AREG if mediated through the Wnt pathway? Indeed, this question was very important to us and we attempted to answer it with lentiviral vector-mediated knockdown with shRNA and with electroporation of Cas9 ribonucleoprotein complexes (RNPs). Though we were successful in decreasing TCF7 protein, to render NK cells competent for lentiviral transduction or for electroporation with the Cas9 RNPs, NK cells must first be cultured for at least 7 days in high concentration IL-2. This treatment with IL-2 activates AREG production before TCF7 protein is decreased. These points are now discussed in the manuscript on page 29.

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• In Figure 8 the authors demonstrate that CD4 T cells promote the maintenance of CD56dim NK cells likely through production of IL-2. Was AREG expression examined in these studies and if so could you conclude that CD4 T cells also promote AREG expression in this population? Yes, indeed, CD4+T cells, or IL-2, promote AREG production by CD56dimNK cells. Depletion of CD4+T cells from PBMCs decreased AREG production and addition of exogenous IL-2 was sufficient to restore AREG production by NK cells when CD4+ T cells were depleted (see figure below, left panel). Additionally, IL-2 neutralizing antibodies decreased AREG production by NK cells in total PBMC cultures (right panel). These results are now included as Supplemental Figure 6G and 6H in the revised manuscript.

• In Figure 8D, although significant, the increase in percentage from 2-4% of the CD56neg is a minimal change. Could a higher dose of IL-2 be tested to see more substantial changes? Similar comment for Figure 10D, although significant, the increase in percentage from 2-4% of the NK population to CD56neg is not a large change with rapamycin. Could a higher dose be tested (that does not kill the cells) or a different inhibitor be used to see more substantial changes? More robust changes would strengthen the conclusions. First to clarify, as concerns figure 8D, we believe the reviewer is referring to IL-2 neutralizing antibody, not IL-2. Regarding Figure 8D, which used IL-2 neutralizing antibody at 4 ug/ml, we tried a higher dose (10 ug/ml) but this caused cell death. We also tried longer culture time with 4 ug/ml IL-2 neutralizing antibody, and found the cell viability decreases after 7 days culture. Similarly, the limited in vitro culture time in the presence of anti-IL-2 antibody restricted experimental conditions in Figure 10D.

• In Figure 10, was the increase in pmTOR with IL-2/IL-15 stimulation specifically observed in CD56dim cells (rather than total NK cells or CD56neg cells)? This would strength the conclusion that CD56dim is more metabolically functional than CD56neg. Our data indicate that the mTOR response to IL2 in CD56–NK cells was similar to that in CD56+NK cells (including CD56dim and CD56hi) (see figure below). This result is consistent with a previous report showing that, when stimulated with exogenous IL-2, sorted CD56– NK cells become CD56dimNK cells ____(Mavilio ____et al_, 2005)_. Our data extend this observation by showing that upregulation of CD56 on CD56neg NK cells by IL-2 correlates with mTOR upregulation.

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Minor

• Approximately how many ILC from PLWH were submitted for single cell sequencing: Since this cell population was depleted in HIV+, was there a sufficient number of cells to interpret/publish DEG results in Figure 4? In Figure 4, ILCs were sorted from HIV-1 negative donors and different groups of PLWH, and then these cells were subjected to bulk-RNA seq using CEL-Seq2. In Figure 4, for most blood donors, whether HIV-1 negative or PLWH, well over 1,000 ILCs were sorted and used to construct high quality libraries (see figure below, was included as new Supplemental Figure 3C). PCA plots also showed that all ILC libraries clustered together as a distinct population (Figure 4A), indicating the quality of all ILC libraries was comparable.

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• On page 13 of text, it is a bit disconcerting to jump to Figure 7 then back to Figure 4. Would recommend reorganizing text or figure panels to flow better. __We have deleted the premature mention of Figure 7A. __

• Supplemental Figure 1 and 2: Dots for ILC population are purple in color but legend is mislabeled as pink color.

• Supplemental Figure 4: Dots for HIV+ controller are purple in color, but legend is mislabeled as pink in color. Thank you, these mistakes in the labeling of the colors have been corrected.

• Would recommend adding quantification of Figure 5A for all donors tested. In fact, the AREG production by CD56hi, CD56dim, CD56–NK cells and ILCs from HIV-1 negative donors were quantified in Figure 6A.

• Figure 5B for IFNg it appears that part of the positive population is not gated on and thus percentages are likely higher if the gate is adjusted. We thank the reviewer for the suggestion. The gating for IFN-γ has been adjusted in the revised manuscript, as suggested. The original Figure 5B and associated histogram Figure 5C were replaced accordingly. Please see the newly gated figure below:

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• For Figure 5F, need to add to text and panel that PMA/I was used to stimulate as described in figure legend. Current figure and text read that the Wnt agonist alone was responsible for levels depicted in NK subsets. We now state that PMA+ionomycin was used to stimulate the cells in Figure 5F and 5G, and in the text (page 16 and 28).

• On the x-axis of Figure 6F-J does "Lin-TBX21+" refer to Total NK cells? If so then would recommend sticking with nomenclature "Total NK" as in Figure 6A-B. The x-axis of Figure 6F-J was changed to “total NK (Lin–TBX21+)”.

• Would recommend labeling gates for populations of interest in Supplemental Figure 5B-C, Supplement Figure 6A, Figure 8A. The populations of interest (CD56dimNK, CD56–NK, and ILCs) were labeled as suggested.

• Other groups have shown that CD56neg are increased in HIV, functionally impaired and correlate with loss of CD4 T cells. Would recommend citing their studies. The original manuscript had mentioned only Mavilio et al. The revised manuscript now has a more complete list of references ____(Cao ____et al_, 2021; Mavilio _et al_, 2005; Cocker _et al_, 2022; Alter _et al_, 2005; Barker _et al_, 2007)_. These references are cited in the Introduction (page 5) and in the Results (page 20).

• Would recommend adding quantification of Figure 8A for all donors tested. Also, for the 3rd flow plot does "NK+CD4" mean purified NK cells + autologous CD4 T cells? If so, then would clarify in figure legend. It may strengthen conclusion for IL-2/IL-15 to show differentiation of NK cells is not contact dependent with T cell via transwell assay. __Quantification for all donors in Figure 8A has been added to the revised manuscript (Figure 8A, right panel). The figure legend has been modified accordingly. As far as the comment about contact-dependence, the fact that CD56dimNK cells were maintained by IL-2 in the absence of CD4+ T cells demonstrates that direct contact with CD4+ T cells is not required. __

Reviewer #2 (Significance (Required)):

Significance: This study is valuable because it generates large datasets on the NK/ILC family from human blood that can be deposited in repositories and of special relevance to the HIV field because it examine show viral infection (controlled or not) effects these subsets.

The strengths of the study are the cohort of PBMC samples utilized (HIV-, HIV+ viremic, HIV+ ART+ and HIV+ controlled), the multi-omics approach for transcriptome and epigenetics and the in vitro mechanistic studies identifying key regulators of NK cell differentiation/function.

The study advances the field "mechanistically" by providing key targets that may be subject to therapeutic modulation in PLWH such as AREG, IL-2 signaling, IL-15 signaling, Wnt signaling, and mTOR activity (although more work will need to be done to examine these pathways using PBMCs from HIV+). This study advances the field "conceptually" by providing large datasets for others to mine if deposited.

The audience for this study "basic research" such as immunologists in the HIV field or immunologist interested in ILC/NK biology.

My field of expertise is infectious disease (HIV, SARS-CoV-2), basic immunology (ILC, NK, B cell) and autoimmune disease. Would recommend additional reviewers for assessment of metabolic genes/pathways in Figure 9-10.

__We were very pleased to see that the reviewer thought our manuscript makes a valuable contribution to HIV-1 immunology and ILC/NK biology, that it advances mechanistic understanding of pathogenesis in people living with HIV-1, and that it provides a valuable data resource. __

- In Figure 9, the metabolism related genes were defined as canonical targets of, or regulated by, MTOR signaling, in previous publications ____(Saxton & Sabatini, 2017; Bayeva ____et al_, 2012)_.

- In Figure 10, we used pMTOR, pAKT, p4EBP1, pS6 and CD71 to monitor MTOR pathway activation as reported previously by others ____(Marçais ____et al_, 2014)_. We have cited this paper on pages 23 and 24.

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