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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.

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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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References for Response to Reviewers

Alter G, Teigen N, Davis BT, Addo MM, Suscovich TJ, Waring MT, Streeck H, Johnston MN, Staller KD, Zaman MT, et al (2005) Sequential deregulation of NK cell subset distribution and function starting in acute HIV-1 infection. Blood 106: 3366–3369

Barker E, Martinson J, Brooks C, Landay A & Deeks S (2007) Dysfunctional natural killer cells, in vivo, are governed by HIV viremia regardless of whether the infected individual is on antiretroviral therapy. AIDS 21: 2363–2365

Bayeva M, Khechaduri A, Puig S, Chang H-C, Patial S, Blackshear PJ & Ardehali H (2012) mTOR regulates cellular iron homeostasis through tristetraprolin. Cell Metab 16: 645–657

Brenchley JM, Price DA, Schacker TW, Asher TE, Silvestri G, Rao S, Kazzaz Z, Bornstein E, Lambotte O, Altmann D, et al (2006) Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat Med 12: 1365–1371

Caetano DG, Ribeiro-Alves M, Hottz ED, Vilela LM, Cardoso SW, Hoagland B, Grinsztejn B, Veloso VG, Morgado MG, Bozza PT, et al (2022) Increased biomarkers of cardiovascular risk in HIV-1 viremic controllers and low persistent inflammation in elite controllers and art-suppressed individuals. Sci Rep 12: 6569

Cao W-J, Zhang X-C, Wan L-Y, Li Q-Y, Mu X-Y, Guo A-L, Zhou M-J, Shen L-L, Zhang C, Fan X, et al (2021) Immune Dysfunctions of CD56neg NK Cells Are Associated With HIV-1 Disease Progression. Front Immunol 12: 811091

Cocker ATH, Liu F, Djaoud Z, Guethlein LA & Parham P (2022) CD56-negative NK cells: Frequency in peripheral blood, expansion during HIV-1 infection, functional capacity, and KIR expression. Front Immunol 13: 992723

Cortez VS, Ulland TK, Cervantes-Barragan L, Bando JK, Robinette ML, Wang Q, White AJ, Gilfillan S, Cella M & Colonna M (2017) SMAD4 impedes the conversion of NK cells into ILC1-like cells by curtailing non-canonical TGF-β signaling. Nat Immunol 18: 995–1003

Crowell TA, Gebo KA, Blankson JN, Korthuis PT, Yehia BR, Rutstein RM, Moore RD, Sharp V, Nijhawan AE, Mathews WC, et al (2015) Hospitalization Rates and Reasons Among HIV Elite Controllers and Persons With Medically Controlled HIV Infection. J Infect Dis 211: 1692–1702

Jonsson AH, Zhang F, Dunlap G, Gomez-Rivas E, Watts GFM, Faust HJ, Rupani KV, Mears JR, Meednu N, Wang R, et al (2022) Granzyme K+ CD8 T cells form a core population in inflamed human tissue. Sci Transl Med 14: eabo0686

Lim AI, Menegatti S, Bustamante J, Le Bourhis L, Allez M, Rogge L, Casanova J-L, Yssel H & Di Santo JP (2016) IL-12 drives functional plasticity of human group 2 innate lymphoid cells. J Exp Med 213: 569–583

Marçais A, Cherfils-Vicini J, Viant C, Degouve S, Viel S, Fenis A, Rabilloud J, Mayol K, Tavares A, Bienvenu J, et al (2014) The metabolic checkpoint kinase mTOR is essential for IL-15 signaling during the development and activation of NK cells. Nat Immunol 15: 749–757

Mavilio D, Lombardo G, Benjamin J, Kim D, Follman D, Marcenaro E, Angeline O’Shea M, Kinter A, Kovacs C, Moretta A, et al (2005) Characterization of CD56–/CD16+ natural killer (NK) cells: A highly dysfunctional NK subset expanded in HIV-infected viremic individuals. Proc Natl Acad Sci U S A 102: 2886–2891

Moreno-Nieves UY, Tay JK, Saumyaa S, Horowitz NB, Shin JH, Mohammad IA, Luca B, Mundy DC, Gulati GS, Bedi N, et al (2021) Landscape of innate lymphoid cells in human head and neck cancer reveals divergent NK cell states in the tumor microenvironment. Proc Natl Acad Sci U S A 118

Raykova A, Carrega P, Lehmann FM, Ivanek R, Landtwing V, Quast I, Lünemann JD, Finke D, Ferlazzo G, Chijioke O, et al (2017) Interleukins 12 and 15 induce cytotoxicity and early NK-cell differentiation in type 3 innate lymphoid cells. Blood Adv 1: 2679–2691

Romee R, Rosario M, Berrien-Elliott MM, Wagner JA, Jewell BA, Schappe T, Leong JW, Abdel-Latif S, Schneider SE, Willey S, et al (2016) Cytokine-induced memory-like natural killer cells exhibit enhanced responses against myeloid leukemia. Sci Transl Med 8: 357ra123

Romee R, Schneider SE, Leong JW, Chase JM, Keppel CR, Sullivan RP, Cooper MA & Fehniger TA (2012) Cytokine activation induces human memory-like NK cells. Blood 120: 4751–4760

Saxton RA & Sabatini DM (2017) mTOR Signaling in Growth, Metabolism, and Disease. Cell 169: 361–371

Silverstein NJ, Wang Y, Manickas-Hill Z, Carbone C, Dauphin A, Boribong BP, Loiselle M, Davis J, Leonard MM, Kuri-Cervantes L, et al (2022) Innate lymphoid cells and COVID-19 severity in SARS-CoV-2 infection. Elife 11

Vivier E, Artis D, Colonna M, Diefenbach A, Di Santo JP, Eberl G, Koyasu S, Locksley RM, McKenzie ANJ, Mebius RE, et al (2018) Innate Lymphoid Cells: 10 Years On. Cell 174: 1054–1066

Wang J, Xu Y, Chen Z, Liang J, Lin Z, Liang H, Xu Y, Wu Q, Guo X, Nie J, et al (2020a) Liver Immune Profiling Reveals Pathogenesis and Therapeutics for Biliary Atresia. Cell 183: 1867–1883.e26

Wang Y, Lifshitz L, Gellatly K, Vinton CL, Busman-Sahay K, McCauley S, Vangala P, Kim K, Derr A, Jaiswal S, et al (2020b) HIV-1-induced cytokines deplete homeostatic innate lymphoid cells and expand TCF7-dependent memory NK cells. Nat Immunol 21: 274–286

Xue R, Zhang Q, Cao Q, Kong R, Xiang X, Liu H, Feng M, Wang F, Cheng J, Li Z, et al (2022) Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612: 141–147

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Referee #2

Evidence, reproducibility and clarity

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?
• 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?
• 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?
• 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.
• 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.

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?
• 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.
• 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.
• Would recommend adding quantification of Figure 5A for all donors tested.
• 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.
• 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.
• 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.
• Would recommend labeling gates for populations of interest in Supplemental Figure 5B-C, Supplement Figure 6A, Figure 8A.
• 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.
• 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.

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.

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

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.
2. 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?
3. 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?
4. 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.
   • a. Altogether, I am unclear on the authors' interpretation of their collective data.
   • b. 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?
   • c. Are CD56-NKs end-stage or can they be rescued?
   • d. 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?
5. 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.
6. "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?
7. Methods: Were Pearson correlations applied because of the large 'n' or were data first tested for normality?
8. Methods: Please clearly justify the use of t-tests throughout the manuscript rather than non-parametric based tests.
9. 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?
10. Methods: How was the cytokine concentrations used for in vitro assays determined?
11. Figure: In Figure 4A, should it be '+ILC- ' or '+ILC' (no negative symbol)?
12. 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?

Significance

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.

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Reply to the reviewers

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

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Summary:

In this manuscript, Roberts et al. hypothesised that the 5:2 diet (a popular form of IF, a dietary strategy within the Intermittent fasting that is thought to increase adult hippocampal neurogenesis - AHN) would enhance AHN in a ghrelin-dependent manner. To do this, the Authors used immunohistochemistry to quantify new adult-born neurons and new neural stem cells in the hippocampal dentate gyrus of adolescent and adult wild-type mice and mice lacking the ghrelin receptor, following six weeks on a 5:2 diet. They report an age-related decline in neurogenic processes and identify a novel role for ghrelin-receptor in regulating the formation of new adult

born neural stem cells in an age-dependent manner. However, the 5:2 diet did not affect new neuron or neural stem cell formation in the dentate gyrus, nor did alter performance on a spatial learning and memory task. They conclude that the 5:2 diet used in their study does not increase AHN or improve associated spatial memory function.

Major comments:

One criticism might be the fact that many aspects are addressed at the same time. For instance it is not fully clear the role of ghrelin with respect to testing the DR effects on AHN. Although the link between ghrelin, CR and AHN is explained by citing several previous studies, it is difficult to identify the main focus of the study. Maybe this is due to the fact that the Authors analyse and comment throughout the paper the different experimental approaches used by different

Authors to study effect of DR to AHN. This is not bad in principle, since I think the Authors have a deep knowledge of this complex matter, but all this results in a difficulty to follow the flow of the rationale in the manuscript.

We appreciate the reviewer’s critique regarding the rationale of the studies presented in the manuscript.

The role of ghrelin in the regulation of AHN by dietary interventions such as CR and IF is a major interest of our lab and is the main focus of the study. We, and others, have shown that ghrelin mediates the beneficial effects of CR on AHN. It is often assumed that ghrelin will elicit similar effects in other DR paradigms. We selected the 5:2 diet since it is widely practiced by humans, but it has not been well tested experimentally.

We sought to empirically test how the neurogenic response to 5:2 differed between mice with functional and impaired ghrelin signaling.

Given that plasma ghrelin levels and AHN are reduced during ageing, we also wanted to determine if 5:2 diet could slow or even prevent neurogenic decline in ageing mice.

We will re-write the manuscript to ensure that our primary aim is clearly presented. We will also reanalyze the data, with genotype and 5:2 diet as key variables. To help maintain focus, the variable of age will be analyzed separately. This amendment will, we hope, help the reader follow the narrative of our manuscript.

Another major point: the Discussion is too long. The Authors analyse all the possible reasons why different studies obtained different results concerning the effectiveness of DR in stimulating adult neurogenesis. Thus, the Discussion seems more as a review article dealing with different methods/experimental approaches to evaluate DR effects. We know that sometimes different results are due to different experimental approaches, yet, when an effect is strong and clear, it occurs in different situations. Thus, I think that the Authors must be less shy in expressing their conclusions, also reducing the methodological considerations. It is also well known that sometimes different results can be due to a study not well performed, or to biases from the Authors.

In our discussion, we felt that it was particularly important to be as rigorous as possible in contextualizing our findings with other published data, whilst highlighting methodological differences. Our aim was to be as precise as possible when comparing findings across studies, however, this resulted in the narrative drifting from the key objectives of our study – namely, to determine the effect of 5:2 diet on neurogenesis and whether or not ghrelin-signalling regulated the process. We will amend the text of the discussion to ensure that the key points of our study are only compared and contrasted with relevant studies in the field. We thank the reviewer for their candid comment.

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Minor comments:

• This sentence: "There is an age-related decline in adult hippocampal neurogenesis" cannot be put in the HIGHLIGHTS, since is a well known aspect of adult hippocampal neurogenesis

The reviewer is correct to state this. Our study replicates this interesting age-related phenomenon. However, we will remove it from the ‘Highlights’ section.

• Images in Figure 5 are not good quality.

We apologise for this oversight. We will review each figure and panel to ensure that high-resolution images, that are appropriately annotated, are used throughout the manuscript.

• In general, there are not a lot of images referring to microscopic/confocal photographs across the entire manuscript.

We structured the manuscript with a limited number of figures and associated microscope captured panels, with the aim of presenting representative images to illustrate the nature and quality of the IHC protocols. However, we will amend the figures for the revised manuscript to provide representative microscopy images, with each group included and clearly annotated.

• The last sentence of the Discussion "These findings suggest that distinct DR regimens differentially regulate neurogenesis in the adult hippocampus and that further studies are required to identify optimal protocols to support cognition during ageing" is meaningless in the context of the study, and in contrast with the main results. Honestly, my impression is that the Authors do not want to disappoint the conclusions of the previous studies; an alternative is that other Reviewers asked for this previously.

We do not believe that this statement is contradictory to our findings, as distinct DR paradigms do appear to regulate AHN in different ways. However, we agree that we can be more explicit with regards to our own study findings and will prioritize the conclusions of our study over those of the entire field during revision.

Reviewer #1 (Significance (Required)):

value the significance of publishing studies that will advance the field.

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

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In this manuscript, Roberts et al. investigate the effect of the 5:2 diet on adult hippocampal neurogenesis (AHN) in mice via the ghrelin receptor. Many studies have reported benefits of dietary restriction (DR) on the brain that include increasing neurogenesis and enhancing cognitive function. However, neither the mechanisms underlying the effects of the 5:2 diet, nor potential benefits on the brain, are well understood. The authors hypothesize that the 5:2 diet enhances AHN and cognitive function via ghrelin-receptor signaling. To test this, they placedadolescent and adult ghrelin receptor knockout or wild type mice on either the 5:2 or ad libitum (AL) diet for 6 weeks, followed by spatial memory testing using an object in place (OIP) task. The authors also assessed changes in AHN via IHC using multiple markers for cell proliferation and neural stem cells. The authors observed a decrease in AHN due to age (from adolescent to adult), but not due to diet or ghrelin-receptor signaling. While loss of the ghrelin-receptor impaired spatial memory, the 5:2 diet did not affect cognitive function. The authors conclude that the 5:2 diet does not enhance AHN or spatial memory.

We thank the reviewer for this summary. We note that there was a significant reduction in new neurones (BrdU+/NeuN+) cells in GHS-R null animals, regardless of age or diet (3 way ANOVA of age, genotype and diet (sexes pooled): Genotype P = 0.0290). These data suggest that the loss of ghrelin receptor signalling does impair AHN. However, we will re-analyse our data in light of reviewer 1 comments to remove ‘age’ as a variable. The new analyses and associated discussion will be presented in our revised manuscript.

The authors use a 5:2 diet but fail to provide a basic characterization of this dietary intervention. For example, was the food intake assessed? In addition to the time restriction of the feeding, does this intervention also represent an overall caloric restriction or not? According to the provided results, the 5:2 diet does not appear to regulate adult hippocampal neurogenesis contrary to the authors' original hypothesis. Did the authors measure the effects of the 5:2 diet on any other organ system? Do they have any evidence that the intervention itself resulted in any well documented benefits in other cell types? Such data would provide a critical positive control for their intervention.

This is an important point raised by the reviewer. Currently, we carefully quantified weight change across the duration of the study. However, we do not know whether the 5:2 diet reduced overall food intake or whether it impacted the timing of feeding events. To overcome this limitation, we will now test what impact the 5:2 dietary regime has on food intake and the timing of feeding. This study will allow us to correlate any changes with 5:2 diet. In addition, we have collected tibiae to quantify skeletal growth and have collected both liver and plasma (end point) samples which will be used to assess changes in the GH-IGF-1 axis. These additional studies will allow us to characterise the effects of the 5:2 paradigm on key indicators of physiological growth. These new data will be incorporated into the revised manuscript.

Based on the effects of ghrelin in other dietary interventions, the authors speculate that the effect of the 5:2 diet is similarly mediated through ghrelin. However, the authors do not provide any basic characterization of ghrelin signaling to warrant this strong focus on the GSH-R mice. While the GSH-R mice display changes in NSC homeostasis and neurogenesis, none of these effects appear to be modified by the 5:2 diet. Thus, the inclusion of the GSH-R mice does not seem warranted and detracts from the main 5:2 diet focus of the manuscript.

The role of ghrelin signalling via its receptor, GHSR, is a central tenet of our hypothesis. The loxTB-GHS-R null mouse is a well validated model of impaired ghrelin signalling, in which insertion of a transcriptional blocking cassette prevents expression of the ghrelin receptor (ZIgman et al.2005 JCI). We have previously shown that this mouse model is insensitive to calorie restriction (CR) mediated stimulation of AHN, in contrast to WT mice (Hornsby et al. 2016), justifying its suitability as a model for assessing the role of ghrelin signalling in response to DR interventions, such as the 5:2 paradigm. Whilst our findings do not support a role for ghrelin signalling in the context of the 5:2 diet studied, we did follow the scientific method to empirically test the stated hypothesis. While critiques of experimental design are welcome, the removal of these data may perpetuate publication bias in favour of positive outcomes and is something we wish to avoid.

Neurogenesis is highly sensitive to stress. The 5:2 diet may be associated with stress which could counteract any benefits on neurogenesis in this experimental paradigm. Did the authors assess any measures of stress in their cohorts? Were the mice group housed or single housed?

We thank the reviewer for raising this point. We have open-field recordings that will now be analysed to assess general locomotor activity, anxiety and exploration behaviour. Additionally, we will assess levels of the stress hormone, ACTH, in end point plasma samples. These datasets will be incorporated into the revised manuscript.

The authors state that the 5:2 diet led to a greater reduction in body weight (31%) in adolescent males compared to other groups. However, it appears that the cohorts were not evenly balanced and the adolescent 5:2 male mice started out with a significantly higher starting weight (Supplementary Figure 1). The difference in starting weight at such a young age is significantly confounding the conclusion that the 5:2 diet is more effective at limiting weight gain specifically in this group.

We thank the reviewer for highlighting this limitation. In the revision we will re-focus our discussion around the Δ Body weight repeated measures data, which compares the daily body weight of each group to its baseline value - thereby normalising any intergroup differences in starting weight. Furthermore, we will restructure figures 1 and S1 so that figure 1 presents only the repeated measure Δ Body weight data, while data for body weight both at baseline and on the final day of the study will be presented in figure S1.

The authors count NSCs as Sox2+S100b- cells. However, the representative S100b staining does not look very convincing. Instead, it would be more appropriate to count Sox2+GFAP+ cells with a single vertical GFAP+ projection. Alternatively, the authors could also count Nestin-positive cells. Additionally, the authors label BrdU+ Sox2+ S100B- cells as "new NSCs". However, it appears that the BrdU labeling was performed approximately 6 weeks before the tissue was collected (Figure 1A). Thus, these BrdU-positive NSCs most likely represent label retaining/quiescent NSCs that divided during the labeling 6 weeks prior but have not proliferated since. As such, the term "new NSC" is misleading and would suggest an NSC that was actively dividing at the time of tissue collection.

We apologise for presenting low-resolution images – these will be replaced by high-resolution images in the revised manuscript. In this study we have quantified the actively dividing BrdU+/Sox2+/S100B- cells that represent type II NSCs (rather than GFAP+ or Nestin+ type I NSCs) that have incorporated BrdU within the time period of the 6-week intervention. We appreciate the reviewer’s comments concerning the “new NSCs” terminology. We agree that we should be more specific in clarifying that the NSCs identified are those labelled during the 1st week of the 6-week intervention. We will amend this throughout the revised manuscript by re-naming these cells as 6-week old NSCs.

Overall, this manuscript lacks a clear focus and narrative. Due to a lack of an affect by the 5:2 diet on hippocampal neurogenesis, the authors mostly highlight already well-known effects of aging and Grehlin/GSH-R on neurogenesis. Moreover, the authors repeatedly use age-related decline and morbidities as a rational for their study. However, they assess the effects of the 5:2 diet on neurogenesis only in adolescent and young mature but not aged mice.

To provide greater clarity, and in accordance with reviewer 1’s comments, we will amend the text throughout to provide a focus on the data obtained. The objective of the changes will be to re-enforce the original study narrative. In relation to the use of the term ‘age-related decline’ or ‘age-related changes’, we think that these are appropriate to our study. Physiological ageing doesn’t begin at a specific point of chronological time, but is a process that is continuously ongoing. Indeed, our data is in agreement with previous studies reporting an age-related reduction in AHN at 6 months of age (e.g Kuhn et al.1996).

Minor Points

The authors combine the data from both male and female mice for most bar graphs. While this does not appear to matter for neurogenesis or behavioral readouts, there are very significant sexually dimorphic differences with respect to body size and weight. As such, male and female mice in Figure 1D,F should not be plotted in the same bar graph.

We agree that sexual dimorphism exists with respect to body size and weight. We used distinct male and female symbols for each individual animal on these bar graphs, but do agree with the reviewer that sexual dimorphic differences should be emphasized. To achieve this, we will include additional supplementary graphs presenting the sex differences in starting weight, final weight, and weight change versus starting weight.

The Figure legends are very brief and should be expanded to include basic information of the experimental design, statistical analyses etc.

We thank the reviewer for this comment. We will provide specific experimental detaisl in the revised figure legends.

Many figures include a representative image. However, it is often unclear if that is a representative image of a WT or mutant mouse, or a 5:2 or control group (Figure 2A, 3A, 4A, 5A).

We structured the manuscript with a limited number of figures and associated microscope captured panels, with the aim of presenting representative images to illustrate the nature and quality of the IHC protocols. However, we will amend the figures for the revised manuscript to provide representative microscopy images, with each group included and clearly annotated.

It would be helpful to provide representative images of DCX-positive cells in Figure 3A-F. Additionally, the authors should include a more extensive description of how this quantification was performed in the method section.

We will revise the manuscript to provide representative high-resolution Dcx+ images displaying cells of each category. The method will also be revised to include a detailed description of how the quantification and classification was performed.

The authors state "the hippocampal rostro-caudal axis (also known as the dorsoventral[] axis". However, the rostral-caudal and dorsal-central axis are usually considered perpendicular to one another.

We agree that the dorso-ventral and rostral-caudal axes are anatomically distinct. The terms are often used interchangeably in the literature, which can lead misinterpretations (e.g the caudal portion of dorsal hippocampus is often mislabelled as ventral hippocampus). To avoid ambiguity, mislabelling or misidentification, we will include a supplementary figure detailing our anatomical definitions of the rostral and caudal poles of the hippocampus, alongside representative images and the bregma coordinates.

Reviewer #2 (Significance (Required)):

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Understanding the mechanisms of a popular form of intermittent fasting (5:2 diet) that is not well understood is an interesting topic. Moreover, examining the effect of this form of intermittent fasting on the brain is timely. Notwithstanding, while the authors use multiple markers to validate the effect of the 5:2 diet on adult hippocampal neurogenesis, concerns regarding experimental design, validation, and data analysis weaken the conclusions being drawn.

We thank reviewer 2 for this significance statement. We will revise the manuscript, as mentioned above, to clarify the experimental design, improve presentation of the data, and re-focus the narrative of the primary aims of the study.

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

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Summary

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In this study, Roberts and colleagues used a specific paradigm of intermitted fasting, the 5:2 diet, meaning 5 days ad libitum food and 2 non-consecutive days of fasting. They exposed adolescent and adult wild-type mice and ghrelin receptor knockout mice (GHS-R-/-) for 6 weeks to this paradigm, followed by 1 week ad libitum food. They further used the "object in place task" (OIP) to assess spatial memory performance. At the end of the dietary regime, the authors quantified newborn neurons and neural stem cells (NSCs) by immunohistochemistry. Roberts

et al. show that the 5:2 diet does not change the proliferation of cells in the hippocampus, but report an increased number of immature neurons (based on DCX) in all the mice exposed to the 5:2 diet. This change however did not result in an increased number of mature adult-born neurons, as assessed by a BrdU birthdating paradigm. The authors further show diet-independent effects of the ghrelin receptor knockout, leading to less adult born neurons, but more NSCs in the adolescent mice and a lower performance in the OIP task.

Major comments:

The main conclusion of this study is that a specific type of intermitted fasting (5:2 diet) has no effects on NSC proliferation and neurogenesis. As there are several studies showing beneficial effects of intermitted fasting on adult neurogenesis, while other studies found no effects, it is important to better understand the effects of such a dietary paradigm.

The experimental approaches used in this manuscript are mostly well explained, but it is overall rather difficult to follow the results part, as the authors always show the 4 experimental groups together (adolescent vs adult and wt vs GHS-R-/-). They highlight the main effects comparing all the groups, which most of the time is the factor "age". Age is a well-known and thus not surprising negative influencer of adult neurogenesis. Instead of focusing on the main tested factor, namely the difference in diet, the authors show example images of the two age classes

(adolescent vs adult), which does not underly the major point they are making. Most of the time, they do not provide a post hoc analysis, so it is difficult to judge if the results with a significant main effect would be significant in a direct 1 to 1 comparison of the corresponding groups. The authors point out themselves that previous rodent studies did not use such a 5:2 feeding pattern, so having diet, age and genotype as factors at the same time makes the assessment of the diet effect more difficult.

The manuscript would improve if the authors restructure their data to compare first the diet groups (adolescent wt AL vs 5:2 and in a separate comparison adult wt AL vs 5:2) and only in a later part of the results check if the Ghrelin receptor plays a role or not in this paradigm.

We thank the reviewer for these comments. In line with comments from the other reviewers we will re-formulate the presentation of our datasets. We will remove ‘age’ as a key variable as age related changes are to be expected. For the revision, we will separate the adolescent and adult mouse data sets, plotting individual graphs for both. This should provide a clearer focus on 5:2 responses in both assessed genotypes.

This re-configuration will impact the data being analysed and, therefore, the statistical analysis presented. In our original manuscript post hoc analyses were performed, however, only significant post hoc comparisons were highlighted (e.g figure 5). Non-significant post hoc comparisons have not been presented. In the method section of the revised manuscript, we will clarify that we’ll report post hoc differences when they are observed.

During our study design, we decided to assess diet and genotype in parallel - as part of the same analysis. This seemed to us to be the most appropriate statistical method, so that we assessed dietary responses in both WT and GHS-R null mice.

As this 5:2 is a very specific paradigm, it is furthermore difficult to compare these results to other studies and the conclusions are only valid for this specific pattern and timing of the intervention (6 weeks). It remains unclear why the authors have not first tried to establish a study with wildtype mice and a similar duration as in previous studies observing beneficial effects of intermitted fasting on neurogenesis. Like this, it would have been possible to make a statement if the 5:2 per se does not increase neurogenesis or if the 6 weeks exposure were just too short.

The reviewer raises this relevant point which we considered during the study design period. Given that we had previously reported significant modulation of AHN with a relatively short period of 30% CR (14 days followed by 14 days AL refeeding (Hornsby et al.2016)), we predicted that a 6 week course on the 5:2 paradigm (totalling 12 days of complete food restriction over the 6 week period) would provide a similar dietary challenge. The fact that we did not observe similar changes in AHN with this 5:2 paradigm is notable.

The graphical representation of the data could also be improved. Below are a few

examples listed:

1.) Figure 1 B and C, the same symbol and colours are used for the adolescent and adult animals, which makes the graphs hard to read. One colour and symbol per group throughout the manuscript would be better.

We thank the reviewer for this comment. We will amend the presentation of the graphs throughout the manuscript to ensure that they are easier to interpret.

2.) The authors found no differences in the total number of Ki67 positive cells in the DG. However, Ki67 staining does not allow to conclude the type of cell which is proliferating. It would thus strengthen the findings if this analysis was combined with different markers, such as Sox2, GFAP and DCX.

Double labelling of Ki67 positive cells would allow for further insight into the identity of distinct proliferating cell populations. However, quantifying Ki67 immunopositive cells within the sub-granular zone of the GCL, as a single marker, is commonly used in studies of AHN. Given that studies of intermittent fasting, calorie restriction and treatment with exogenous acyl-ghrelin report no effect on NPC cell division, we decided not to pursue this line of inquiry.

3.) In Figure 3, the authors say that the diet increases the number of DCX in adolescent and adult mice, which is not clear when looking at the graph in 3B. Are there any significant differences when directly comparing the corresponding groups, for instance the WT AL vs the WT 5:2? It is further not clear how the authors distinguished the different types of DCX morphology-wise. The quantification in C and D would need to be illustrated by example images. Furthermore, the colour-code used in these graphs is not explained and remains unclear

While the 3 way ANOVA does yield a significant overall effect for diet, we agree that it is indeed difficult to see a difference on the graph, although the mean values of the adolescent 5:2 animals are more prominent than the AL counterparts. Mean +/- SEM will be provided in the supplementary section of the revised manuscript. Furthermore, we will clarify the method used to identify distinct DCX+ morphologies, include representative high-resolution images of each DCX+ cell category, and amend the colour coding to avoid misinterpretation.

4) In Figure 5, the authors show that the number of new NSCs is significantly increased in the adolescent GHS-R-/- mice, independent of the diet, but this increase does not persist in the adult mice. They conclude that "the removal of GHS-R has a detrimental effect on the regulation of new NSC number..." this claim is not substantiated and needs to be reformulated. As the GHS-R-/- mice have a transcriptional blockage of Ghrs since start of its expression, would such an effect on NSC regulation not result in an overall difference in brain development, as ghrelin is also important during embryonic development?

This is an interesting point. However, we disagree that the statement "the removal of GHS-R has a detrimental effect on the regulation of new NSC number..." is unsubstantiated, since it does not exclude any developmental deficits in these mice that may account for the differences observed. Nonetheless, we will rephrase the sentence to clarify our intended point and remove any ambiguity.

5.) In Figure 6, the authors asses spatial memory performance with a single behavioral test, the OIP. As these kind of tests are influenced by the animal's motivation to explore, it's anxiety levels, physical parameters (movement) etc., the interpretation of such a test without any additional measured parameters can be problematic. The authors claim that the loss of GHS-R expression impairs spatial memory performance. As the discrimination ratio was calculated, it is not possible to see if there is an overall difference in exploration time between genotypes. This would be a good additional information to display.

We thank the reviewer for this insight. We have open-field recordings that will now be analysed to assess general locomotor activity, anxiety and exploration behaviour. These data, alongside exploratory time of the mice during the OIP task will be incorporated into the revised manuscript.

Besides these points listed above, the methods are presented in such a way that they can be reproduced. The experiments contained 10-15 mice per group, which is a large enough group to perform statistical analyses. As mentioned above, the statistical analysis over all 4 groups with p-values for the main effects should be followed by post hoc multiple comparison tests to allow the direct comparison of the corresponding groups.

Reviewer #3 (Significance (Required)):

In the last years, growing evidences suggested that IF might have positive effect on health in general and also for neurogenesis. However, a few recent studies report no effects on neurogenesis, using different IF paradigms. This study adds another proof that not all IF paradigms influence neurogenesis and shows that more work needs to be done to better understand when and how IF can have beneficial effects. This is an important finding for the neurogenesis field, but the results are only valid for this specific paradigm used here, which limits its significance. The reporting of such negative findings is however still important, as it shows that IF is not just a universal way to increase neurogenesis. In the end, such findings might have the potential to bring the field together to come up with a more standardized dietary intervention paradigm, which would be robust enough to give similar results across laboratories and mouse strains, and would allow to test the effect of genetic mutations on dietary influences of neurogenesis.

We thank the reviewer for their insightful and thorough feedback.

1. 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.

The manuscript has not been revised at this stage.

2. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

• *

We have included in our replies to the reviewers a description of the amendments that we will make to our manuscript. Two requested revisions stand out as being unnecessary or cannot be provided within the scope of a revision.

The first was the request to perform the 5:2 study in older mice. This an interesting suggestion, however, the expense and time needed to maintain mice into old age (e.g >18 months) cannot be provided within the scope of our revision. In addition, given that we report no effect of the 5:2 paradigm on AHN in adolescent (7 week old) and adult (7 month old) mice, there is less justification for such a study in older mice.

The second request, that we disagree with, was to remove data relating to the GHS-R null mice (see reviewer 2, point 2). The role of ghrelin signalling via its receptor, GHS-R, is a central tenet of our hypothesis. Whilst our findings do not support a role for ghrelin signalling in the context of the 5:2 diet studied, we followed the scientific method to empirically test the stated hypothesis. While critiques of experimental design are welcome, the removal of such data may perpetuate publication bias in favour of positive outcomes and is something we wish to avoid.

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 study, Roberts and colleagues used a specific paradigm of intermitted fasting, the 5:2 diet, meaning 5 days ad libitum food and 2 non-consecutive days of fasting. They exposed adolescent and adult wildtype mice and ghrelin receptor knockout mice (GHS-R-/-) for 6 weeks to this paradigm, followed by 1 week ad libitum food. They further used the "object in place task" (OIP) to assess spatial memory performance. At the end of the dietary regime, the authors quantified newborn neurons and neural stem cells (NSCs) by immunohistochemistry. Roberts et al. show that the 5:2 diet does not change the proliferation of cells in the hippocampus, but report an increased number of immature neurons (based on DCX) in all the mice exposed to the 5:2 diet. This change however did not result in an increased number of mature adult-born neurons, as assessed by a BrdU-birthdating paradigm. The authors further show diet-independent effects of the ghrelin receptor knockout, leading to less adult born neurons, but more NSCs in the adolescent mice and a lower performance in the OIP task.

Major comments:

The main conclusion of this study is that a specific type of intermitted fasting (5:2 diet) has no effects on NSC proliferation and neurogenesis. As there are several studies showing beneficial effects of intermitted fasting on adult neurogenesis, while other studies found no effects, it is important to better understand the effects of such a dietary paradigm.

The experimental approaches used in this manuscript are mostly well explained, but it is overall rather difficult to follow the results part, as the authors always show the 4 experimental groups together (adolescent vs adult and wt vs GHS-R-/-). They highlight the main effects comparing all the groups, which most of the time is the factor "age". Age is a well-known and thus not surprising negative influencer of adult neurogenesis. Instead of focusing on the main tested factor, namely the difference in diet, the authors show example images of the two age classes (adolescent vs adult), which does not underly the major point they are making. Most of the time, they do not provide a post hoc analysis, so it is difficult to judge if the results with a significant main effect would be significant in a direct 1 to 1 comparison of the corresponding groups. The authors point out themselves that previous rodent studies did not use such a 5:2 feeding pattern, so having diet, age and genotype as factors at the same time makes the assessment of the diet effect more difficult. The manuscript would improve if the authors restructure their data to compare first the diet groups (adolescent wt AL vs 5:2 and in a separate comparison adult wt AL vs 5:2) and only in a later part of the results check if the Ghrelin receptor plays a role or not in this paradigm.

As this 5:2 is a very specific paradigm, it is furthermore difficult to compare these results to other studies and the conclusions are only valid for this specific pattern and timing of the intervention (6 weeks). It remains unclear why the authors have not first tried to establish a study with wildtype mice and a similar duration as in previous studies observing beneficial effects of intermitted fasting on neurogenesis. Like this, it would have been possible to make a statement if the 5:2 per se does not increase neurogenesis or if the 6 weeks exposure were just too short.

The graphical representation of the data could also be improved. Below are a few examples listed:

1. Figure 1 B and C, the same symbol and colours are used for the adolescent and adult animals, which makes the graphs hard to read. One colour and symbol per group throughout the manuscript would be better.
2. The authors found no differences in the total number of Ki67 positive cells in the DG. However, Ki67 staining does not allow to conclude the type of cell which is proliferating. It would thus strengthen the findings if this analysis was combined with different markers, such as Sox2, GFAP and DCX.
3. In Figure 3, the authors say that the diet increases the number of DCX in adolescent and adult mice, which is not clear when looking at the graph in 3B. Are there any significant differences when directly comparing the corresponding groups, for instance the WT AL vs the WT 5:2? It is further not clear how the authors distinguished the different types of DCX morphology-wise. The quantification in C and D would need to be illustrated by example images. Furthermore, the colour-code used in these graphs is not explained and remains unclear.
4. In Figure 5, the authors show that the number of new NSCs is significantly increased in the adolescent GHS-R-/- mice, independent of the diet, but this increase does not persist in the adult mice. They conclude that "the removal of GHS-R has a detrimental effect on the regulation of new NSC number..." this claim is not substantiated and needs to be reformulated. As the GHS-R-/- mice have a transcriptional blockage of Ghrs since start of its expression, would such an effect on NSC regulation not result in an overall difference in brain development, as ghrelin is also important during embryonic development?
5. In Figure 6, the authors asses spatial memory performance with a single behavioral test, the OIP. As these kind of tests are influenced by the animal's motivation to explore, it's anxiety levels, physical parameters (movement) etc., the interpretation of such a test without any additional measured parameters can be problematic. The authors claim that the loss of GHS-R expression impairs spatial memory performance. As the discrimination ratio was calculated, it is not possible to see if there is an overall difference in exploration time between genotypes. This would be a good additional information to display.

Besides these points listed above, the methods are presented in such a way that they can be reproduced. The experiments contained 10-15 mice per group, which is a large enough group to perform statistical analyses. As mentioned above, the statistical analysis over all 4 groups with p-values for the main effects should be followed by post hoc multiple comparison tests to allow the direct comparison of the corresponding groups.

Minor comments:

The authors should provide more information in the figure legends and always show representative images of the parameters analyzed. Some of the images are also of low resolution and should be replaced with higher resolution images (for instance Fig. 5A). The significant P values of the multiple comparison between groups should be added into the figures.

Significance

In the last years, growing evidences suggested that IF might have positive effect on health in general and also for neurogenesis. However, a few recent studies report no effects on neurogenesis, using different IF paradigms. This study adds another proof that not all IF paradigms influence neurogenesis and shows that more work needs to be done to better understand when and how IF can have beneficial effects. This is an important finding for the neurogenesis field, but the results are only valid for this specific paradigm used here, which limits its significance. The reporting of such negative findings is however still important, as it shows that IF is not just a universal way to increase neurogenesis. In the end, such findings might have the potential to bring the field together to come up with a more standardized dietary intervention paradigm, which would be robust enough to give similar results across laboratories and mouse strains, and would allow to test the effect of genetic mutations on dietary influences of neurogenesis.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

In this manuscript, Roberts et al. investigate the effect of the 5:2 diet on adult hippocampal neurogenesis (AHN) in mice via the ghrelin receptor. Many studies have reported benefits of dietary restriction (DR) on the brain that include increasing neurogenesis and enhancing cognitive function. However, neither the mechanisms underlying the effects of the 5:2 diet, nor potential benefits on the brain, are well understood. The authors hypothesize that the 5:2 diet enhances AHN and cognitive function via ghrelin-receptor signaling. To test this, they placed adolescent and adult ghrelin receptor knockout or wild type mice on either the 5:2 or ad libitum (AL) diet for 6 weeks, followed by spatial memory testing using an object in place (OIP) task. The authors also assessed changes in AHN via IHC using multiple markers for cell proliferation and neural stem cells. The authors observed a decrease in AHN due to age (from adolescent to adult), but not due to diet or ghrelin-receptor signaling. While loss of the ghrelin-receptor impaired spatial memory, the 5:2 diet did not affect cognitive function. The authors conclude that the 5:2 diet does not enhance AHN or spatial memory.

Major Points

1. The authors use a 5:2 diet but fail to provide a basic characterization of this dietary intervention. For example, was the food intake assessed? In addition to the time restriction of the feeding, does this intervention also represent an overall caloric restriction or not? According to the provided results, the 5:2 diet does not appear to regulate adult hippocampal neurogenesis contrary to the authors' original hypothesis. Did the authors measure the effects of the 5:2 diet on any other organ system? Do they have any evidence that the intervention itself resulted in any well documented benefits in other cell types? Such data would provide a critical positive control for their intervention.
2. Based on the effects of grehlin in other dietary interventions, the authors speculate that the effect of the 5:2 diet is similarly mediated through grehlin. However, the authors do not provide any basic characterization of grehlin signaling to warrant this strong focus on the GSH-R mice. While the GSH-R mice display changes in NSC homeostasis and neurogenesis, none of these effects appear to be modified by the 5:2 diet. Thus, the inclusion of the GSH-R mice does not seem warranted and detracts from the main 5:2 diet focus of the manuscript.
3. Neurogenesis is highly sensitive to stress. The 5:2 diet may be associated with stress which could counteract any benefits on neurogenesis in this experimental paradigm. Did the authors assess any measures of stress in their cohorts? Were the mice group housed or single housed?
4. The authors state that the 5:2 diet led to a greater reduction in body weight (31%) in adolescent males compared to other groups. However, it appears that the cohorts were not evenly balanced and the adolescent 5:2 male mice started out with a significantly higher starting weight (Supplementary Figure 1). The difference in starting weight at such a young age is significantly confounding the conclusion that the 5:2 diet is more effective at limiting weight gain specifically in this group.
5. The authors count NSCs as Sox2+S100b- cells. However, the representative S100b staining does not look very convincing. Instead, it would be more appropriate to count Sox2+GFAP+ cells with a single vertical GFAP+ projection. Alternatively, the authors could also count Nestin-positive cells. Additionally, the authors label BrdU+ Sox2+ S100B- cells as "new NSCs". However, it appears that the BrdU labeling was performed approximately 6 weeks before the tissue was collected (Figure 1A). Thus, these BrdU-positive NSCs most likely represent label-retaining/quiescent NSCs that divided during the labeling 6 weeks prior but have not proliferated since. As such, the term "new NSC" is misleading and would suggest an NSC that was actively dividing at the time of tissue collection.
6. Overall, this manuscript lacks a clear focus and narrative. Due to a lack of an affect by the 5:2 diet on hippocampal neurogenesis, the authors mostly highlight already well-known effects of aging and Grehlin/GSH-R on neurogenesis. Moreover, the authors repeatedly use age-related decline and morbidities as a rational for their study. However, they assess the effects of the 5:2 diet on neurogenesis only in adolescent and young mature but not aged mice.

Minor Points

1. The authors combine the data from both male and female mice for most bar graphs. While this does not appear to matter for neurogenesis or behavioral read-outs, there are very significant sexually dimorphic differences with respect to body size and weight. As such, male and female mice in Figure 1D,F should not be plotted in the same bar graph.
2. The Figure legends are very brief and should be expanded to include basic information of the experimental design, statistical analyses etc.
3. Many figures include a representative image. However, it is often unclear if that is a representative image of a WT or mutant mouse, or a 5:2 or control group (Figure 2A, 3A, 4A, 5A).
4. It would be helpful to provide representative images of DCX-positive cells in Figure 3A-F. Additionally, the authors should include a more extensive description of how this quantification was performed in the method section.
5. The authors state "the hippocampal rostro-caudal axis (also known as the dorso-ventral [] axis". However, the rostral-caudal and dorsal-central axis are usually considered perpendicular to one another.

Significance

Understanding the mechanisms of a popular form of intermittent fasting (5:2 diet) that is not well understood is an interesting topic. Moreover, examining the effect of this form of intermittent fasting on the brain is timely. Notwithstanding, while the authors use multiple markers to validate the effect of the 5:2 diet on adult hippocampal neurogenesis, concerns regarding experimental design, validation, and data analysis weaken the conclusions being drawn.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Roberts et al. hypothesised that the 5:2 diet (a popular form of IF, a dietary strategy within the Intermittent fasting that is thought to increase adult hippocampal neurogenesis - AHN) would enhance AHN in a ghrelin-dependent manner. To do this, the Authors used immunohistochemistry to quantify new adult-born neurons and new neural stem cells in the hippocampal dentate gyrus of adolescent and adult wild-type mice and mice lacking the ghrelin receptor, following six weeks on a 5:2 diet. They report an age-related decline in neurogenic processes and identify a novel role for ghrelin-receptor in regulating the formation of new adult born neural stem cells in an age-dependent manner. However, the 5:2 diet did not affect new neuron or neural stem cell formation in the dentate gyrus, nor did alter performance on a spatial learning and memory task. They conclude that the 5:2 diet used in their study does not increase AHN or improve associated spatial memory function.

Major comments:

I think that the key conclusions are convincing and no further experiments are required. Maybe some parts of the text should be rewritten (see below). The methods are presented in such a way that they can be reproduced, and the experiments adequately replicated with proper statistical analysis.

• One criticism might be the fact that many aspects are addressed at the same time. For instance it is not fully clear the role of ghrelin with respect to testing the DR effects on AHN. Although the link between ghrelin, CR and AHN is explained by citing several previous studies, it is difficult to identify the main focus of the study. Maybe this is due to the fact that the Authors analyse and comment throughout the paper the different experimental approaches used by different Authors to study effect of DR to AHN. This is not bad in principle, since I think the Authors have a deep knowledge of this complex matter, but all this results in a difficulty to follow the flow of the rationale in the manuscript.
• Another major point: the Discussioni is too long. The Authors analyse all the possible reasons why different studies obtained different results concerning the effectiveness of DR in stimulating adult neurogenesis. Thus, the Discussion seems more as a review article dealing with different methods/experimental approaches to evaluate DR effects. We know that sometimes different results are due to different experimental approaches, yet, when an effect is strong and clear, it occurs in different situations. Thus, I think that the Authors must be less shy in expressing their conclusions, also reducing the methodological considerations. It is also well known that sometimes different results can be due to a study not well performed, or to biases from the Authors.

Minor comments:

• This sentence: "There is an age-related decline in adult hippocampal neurogenesis" cannot be put in the HIGHLIGHTS, since is a well known aspect of adult hippocampal neurogenesis
• Images in Figure 5 are not good quality.
• In general, there are not a lot of images refferring to microscopic/confocal photographs across the entire manuscript.
• The last sentence of the Discussion "These findings suggest that distinct DR regimens differentially regulate neurogenesis in the adult hippocampus and that further studies are required to identify optimal protocols to support cognition during ageing" is meaningless in the context of the study, and in contrast with the main results.

Honestly, my impression is that the Authors do not want to disappoint the conclusions of the previous studies; an alternative is that other Reviewers asked for this previously.

Significance

The significance of this study relies on the fact that adult neurogenesis field (AN) has been often damaged by the search of "positive" results, aiming at showing that AN does occur "always and everywhere" and that most internal/external stimuli do increase it. This attitude created a bias in the field, persuading many scientists that a result in AN is worthy of publication (or of high impact factor publication) only when a positive result is found.

The Author cite in the Discussion the work by Gabarro-Solanas et al. (preprint), which share the same conclusion although analysing different aspect of neurogenesis. Both seems sound studies, substantially balanced in their conclusions.

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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

In this manuscript, Veen and colleagues investigate a role for hairy-related 6 (her6) and prospero homeobox 1 (prox1) in the regeneration of photoreceptors in the zebrafish retina. The candidate genes, her6 and prox1 were chosen for investigation based on initial analyses of genes implicated in the dedifferentiation of cells in the Drosophila brain. Using a chemogenetic approach to ablate long-wavelength cone photoreceptors (red cones), the authors investigate the role of her6 and prox1 in mediating the regenerative response of Müller glia and progenitors derived from these cells in the production of photoreceptors. They use morpholinos to identify that her6 and prox1 likely cross regulate each other.

The authors state that the loss of Her6 leads to an increase in the number of photoreceptors after injury. However, the marker they use is a Zpr1 antibody, which marks both red and green cones but no other cone type nor rods. Thus what they show is an increase in red and green cones, not all photoreceptors. More markers would be needed to make that point. It also remains possible that the loss of Her6 leads to an increase in the number of overall cells generated after red cone ablation and thus Her6 is not exclusively linked with photoreceptor regeneration. Previous work has shown that while the regenerative response is biased toward the production of cell-types lost as a result of injury, other cell-types are also generated (Powell et al., 2016). It would be important to follow up whether other retinal cell-types are also generated. While her6 and prox1 in conjunction might regenerate (some?) photoreceptors, her6 alone might have a broader effect.

The segue between the first and second sections of the results felt abrupt to me. I wonder whether the authors could instead follow this sequence:

1. Identifying candidate genes that regulate de-differentiation in the Drosophila. 2.Investigating whether the candidate genes play a role in the dedifferentiation that Müller cells must go through in their regenerative response.
2. To test point 2, set up an ablation paradigm - MTZ mediated red cone ablation etc. etc.<br /> The authors noted several genes as being mediators of dedifferentiation in the Drosophila. What was their reasoning for choosing the 2 they ended up pursuing in the zebrafish retina?

I do not understand why an increase in the number of PH3+ cells in Her6 MO treated retinae indicates a faster cell-cycle? Could it also mean that cells are stuck in the G2-M phase (marked by PH3) longer? There are other approaches to measure whether cell-cycle is sped up, as the authors suggest.

Minor suggestions to improve clarity:

Introduction: Noel et al., 2021, in my view, is not necessarily the reference most appropriate for a broad statement of how vision works. It appears to be specifically about zebrafish photoreceptors.

Methods:<br /> Fish husbandry and set up<br /> The authors state that the light/dark cycle in their fish facility is 12/12h. To my knowledge, this is not the standard practice; it is usually 14/10h light/dark respectively. (See Aleström et al., Laboratory Animals 2020, Vol. 54(3) 213-224).

No references are given for the Tg(lws2:nfsb-mCherry) and the Tg (her4.1:dRFP) lines. Additionally, the reference for Tg(gfap:eGFP) is incorrect. This should be Bernardos and Raymond 2006 Gene Expr Patterns 2006 Oct;6(8):1007-13. doi: 10.1016/j.modgep.2006.04.006.

Immunostaining fish samples<br /> 'The slides were covered with Parafilm and tissue was dampened.....'<br /> Here a reader may be confused by whether the authors refer to retinal tissue - presumably the authors mean paper tissue to create a humidified chamber.

Microscopy and analysis<br /> A bit more detail here would help - for example, are the 40x and 60x objective air or immersion objectives?

Analysis of zebrafish retinal sections<br /> It is unusual to quantify the percentage of cells expressing specific markers by counting voxels. Can the authors clarify why they took this approach?

Are the micrographs depicted in the figures, single confocal planes, max. intensity projections of several confocal planes?

mCherry labelled photoreceptors are sometimes difficult to appreciate in the micrographs of the main figures - eg. Fig 2C. Here it appears that mCherry is not expressed by the entire cell. It would help to show just the mCherry on its own.

Fig 4 -GHI - it would help to see the double labelled cells prox1 and zpr1 at higher magnification

Supplementary Fig 3 - PCNA, not GS is depicted in pink. GS not GFAP is in green?

Significance

This is an interesting study that pursues candidate genes derived from Drosophila screens to highlight pathways that might operate in the regeneration of cells that were previously ablated in the vertebrate CNS.

Approaches such as the ones used in this study contribute to our overall knowledge about coaxing regeneration in the context of the mammalian CNS.<br /> The work here should be of interest to biologists interested in regeneration.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Veen and colleagues assesses two transcription factors, and makes the novel conclusion that they regulate each other in a manner that is required for photoreceptor regeneration in zebrafish. The work is potentially exciting, because similar findings from zebrafish have found traction in translation to mammals, where regeneration of photoreceptors has surprising promise to treat blindness.

The authors have been ambitious in there approach to the problem by disrupting these genes in the adult retina, which is the appropriate context required to assess photoreceptor regeneration. Because technologies for conditional gene ablation are not very available in zebrafish, these authors use electroporation of morpholinos to accomplish their goals. Where most researchers have abandoned this very challenging approach, it seems these authors have found some success.

Together the technical feat and intriguing conclusions combine, in my opinion, to make this paper worthy of serious consideration for publication. I would hate to see it not be made available for public consumption. Its' merits are strong, but some shortcomings in communication and interpretation nevertheless should be addressed. I suggest Major and minor concerns below.

I suspect that doing further experiments would be asking a lot of the authors at this point, but I point out some possible experiments that would improve the manuscript if my suspicion is wrong. Without further experiments, I suggest much of the writing needs to be carefully qualified and less deterministic.

Major concerns:

1. The authors need to quantify impacts of MO without MTZ (or with MTZ on wildtype fish without the nfsb Tg). Alternatively, the interpretation needs to be softened considerably. Observations made include increased proliferation and more PR, but these are not clearly connected by the data in a way that allows you to claim "more regeneration". A plausible alternative is that the MO was protective, and the MTZ did not kill as many PR cells when the genes were knocked down. Moreover, Figure 3 shows that only one of two proliferation markers is increased (how to explain?) and only at one timepoint, so this may be a fluke. I suggest softening the conclusions to state that gene knockdown increased proliferation and led to increased PR abundance, thus implying improved regeneration (and provide the alternative interpretation). E.g. the punchy titles of Figures 3, 5, 7 are not supported by the data; neither is text in Discussion bottom of page 15.
2. Fig 5 quantification of proliferation is needed if the interpretation is about regeneration (see comment 1). Instead, the conclusions could be reworked to match the data.
3. I'm unclear on why these experiments couldn't have been completed in mutant zebrafish. Are they not viable?
4. Sequence and chemistry of the MO knockdown reagents must be provided. If they are similar to previously published MO reagents (several for both gene targets have been published) then this might be used to improve confidence of MO efficacy. Were the MOs modified to facilitate electroporation? The gene targets also must be listed with less ambiguity, e.g. when "prox1" is mentioned, do you mean prox1a? Without these details, the experiments fail to provide enough info to permit replication.
5. A suggestion to improve the text [no need for new experiments]: The Discussion should address assumptions about MO knockdowns in regards to: a) efficacy, and b) specificity. E.g. (a) future experiments might challenge the efficacy by measuring the abundance of genes that are regulated by prox1 and her6. E.g. (b) future experiments should challenge the specificity of the MO reagents by testing to see if the same result is attained with disparate MO oligos, by phenocopy with CRISPR, performing the work in mutants (I assume rescuing the knockdown by replacing the target gene is not feasible by electroporation, but that would be ideal).
6. The claim that Prox1 is in PR (Figure 4 title) is not convincing. Does the scRNA-Seq confirm this, and why not invoke this data to clarify more concretely? Figure 4A shows a lot of green prox1 signal, but that is very inconsistent with what is shown in Figure 4G, where no prox1 signal is observed in the PR. On page 12, which relates to this Figure, the authors instead say that Prox1 is detected in PR after injury (a big difference compared to title of Fig4!). Fig4I' shows some signal in the area of the PR, but the overlap of the signals is not convincing and it looks to mostly be adjacent to the zpr1 signal; maybe it is Muller glia or some other cone type, or rod cells. If it is Muller glia or rods, then the interpretation needs to be adjusted. Regardless, it is unclear if this is in LWS cones, which is presumably what regenerates after LWS cone ablation(?)
7. Figures showing prox1 or Hes1 IHC (Fig 2, 4, 6, 7 & Supps) - how many replicates were evaluated (how many individual fish were assessed) to determine that these IHCs are representative.
8. Some of the data, i.e. some photomicrographs of IHC, are used repeatedly in separate Figures. I cannot find a comment in the manuscript acknowledging this. Panel F is identical in Figures 3 and 5, and panel 7E is identical to Supp panel S5E. My opinion here is mixed: I think re-using these Figures is marginally ok if it is explicitly and repeatedly described (e.g. in Methods, Results, and Fig Legends), but I also think that if the authors have replicated the experiments sufficiently, then they will surely have some other micrographs to use. My opinion is tipped into grumpy and worried about good data integrity, because in both cases the lines that indicate retinal layers are drawn in different places between the replicated panels; that could happen out of sloppy-ness or instead could be a ploy to help hide the Figure recycling. I prefer to assume the authors are of good intent and have made an error (indeed the panels are all meant to represent the same control treatments) but I would not want the manuscript published without explicitly rectifying this issue. Minimally the replicate micrographs should be explicitly acknowledged. My search for other duplicated panels was not exhaustive.

Minor points:

• a) Page numbers and line numbers would make it less work to prepare a constructive critique of this paper. Similarly, the Figures need Figure numbers.
• b) On the histograms, does each dot represent an individual fish? (i.e. an independent biological replicate).
• c) It would be lovely to learn that left vs. right eyes were used as internal controls in each case, and then the authors could plot the difference between control & treatment within each individual. Perhaps this would allow normalizations or more powerful statistical tests, and then the PCNA data would be more aligned with the conclusions, for example.
• d) Figure 5: expected to see quantification of PH3 here, akin to Figure 3.
• e) P. 6 secondary antibodies probably did not come from ZIRC
• f) More should be done to acknowledge past papers examining Her6, Hes1 and Prox1 in vertebrate retina.
• g) I do not see how the final section of the manuscript (beginning with "Insulinoma" to the end of the Discussion is relevant to the paper. A very odd ending to this manuscript. Some sentences (especially beginning the section with a topic sentence) would be need to be added if this writing is to remain.
• h) The final two sentences of the Abstract were interesting - these ideas are unfortunately not Discussed again later in the manuscript.
• i) What is the source of the transgenic zebrafish line Tg(lws2:nfsb-mCherry) ? Is it maybe from Wang...Yan 2020 PLOS BIOL (PMID: 32168317)? If yes, it would be ideal to provide an allele number. If no, construction of this line should be described.
• j) Bottom page 4 says "Two transgenic lines used were crossed" but only one line is mentioned.
• k) Then on page 7, the text says "Zebrafish line Tg(her4.1:dRFP/gfap:GFP/lws2:nfsb-mCherry) for red cone ablation, ..." which muddies the waters even further.
• l) When the antibody zpr1 is described, it is mentioned as a "zinc finger" (many instances throughout). This is incorrect, and the words "zinc finger" can be removed.
• m) It would be useful to state in Methods, and at first occurrence in figure legends, that the antibody ZPR1 labels double cones (the red & green cones), and these make up about half of the cone photoreceptor population. (i.e. not all cones are evaluated in this work).
• n) Figure 2 desperately needs a panel describing methodological timeline, similar to Fig 1D. It is really hard to figure what happened when (e.g. when did ablation occur? When was the MO delivered?). This also should be described more explicitly in the Methods, which seem quite vague on this point: Electroporated fish went straight into MTZ?
• o) Throughout the authors refer to injury, e.g. hpi = hours post injury. I don't think this represents the methods very well at all, because they have ablated the cells, not injured them. Injured cells don't regenerate (because they are not dead). This miswording contributes to confusion interpreting the Figures, which are not decipherable as stand-alone items.
• p) There is a really weird yellow dotted line that spans between and ACROSS adjacent panels in Figure 2. It covers the white line separating panels F' & G', and then again in F" and G".
• q) Fig 2, it is evident that Hes1 protein is not eliminated so you cannot claim it is "not expressed". It is perhaps reduced in abundance, but signal is still obviously present.
• r) Title of Figure 6 needs to rewritten: LLPS may be occurring, but until you manipulate both LLPS and Prox1 together, you cannot claim that they act through one another.
• s) Figure 7 title needs to be rewritten: PR are not quantified here.
• t) Figure 6: I am deeply incredulous that applying any chemical to zebrafish for only two minutes can alter cell differentiation, except perhaps via toxicity. Perhaps examples of similar impacts can be provided from the literature to make it seem more credible that the mechanism here is LLPS in retinal cells.

The following minor comments are all captured under the notion that the Figure Legends all need to be re-written by a senior colleague. Figures+legends should be interpretable as stand-alone items. All these Figures fail this minimal standard. Below are some issues, but really I'd suggest starting with a blank slate.<br /> - u) Figure 1 must mention Drosophila. So very very confusing to read this believing it is about zebrafish.<br /> - v) Figure 1 what is "deadpan"?<br /> - w) Fig 2 title, how do you know these progenitors are MG-derived?<br /> - x) Fig 2, define abbreviation MG<br /> - y) Fig 2 title, Hes1 is less abundant, but that might be from alternative mechanisms other than "reduced expression" (e.g. altered PTMs, increased clearance, LLPS, etc)<br /> - z) Fig 3 legend is a jumble of oddity. At least three distinct signals are supposedly labelled in pink(?). separately, What about the mCherry - is it also in pink?<br /> - aa) Most Figures: we know they are micrographs, so you don't need to lead the Description saying "micrographs of...". Instead, describe the logic of the experiment and the overall interpretations.<br /> - bb) All Figures: it is really odd to list the data (averages & variances, including implausible significant digits on each) for every treatment - that is what the histograms are meant to convey.<br /> - cc) Figure 4 should send the reader to the Supplemental so they know that the no-primary control experiment is available.<br /> - dd) Fig 6B legend - explain what the chemicals are meant to do (e.g. "block LLPS").<br /> - ee) Fig 6 define "2m" = 2 minutes?<br /> - ff) Several more abbreviations are not defined: hpi, ONL, etc...

Significance

The manuscript by Veen and colleagues assesses two transcription factors, and makes the novel conclusion that they regulate each other in a manner that is required for photoreceptor regeneration in zebrafish. The work is potentially exciting, because similar findings from zebrafish have found traction in translation to mammals, where regeneration of photoreceptors has surprising promise to treat blindness.

Together the technical feat and intriguing conclusions combine, in my opinion, to make this paper worthy of serious consideration for publication. I would hate to see it not be made available for public consumption. Its' merits are strong, but some shortcomings in communication and interpretation nevertheless should be addressed

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Referee #1

Evidence, reproducibility and clarity

The authors interrogate the roles of prox1a and her6 in red/green cone photoreceptor regeneration in zebrafish. Using scRNAseq, they find evidence that her6 is expressed in MG and suggest it is downregulated in Müller glia-derived progenitor cells during retinal regeneration. Further, they show that knockdown of her6 leads to an increase in the rate of cone PR regeneration and suggest that this is due to increased proliferation of retinal progenitors. The authors then demonstrate that prox1a is expressed in differentiated PRs and that knockdown inhibits PR regeneration. An exploration of the role of Liquid-Liquid Phase<br /> Separation (LLPS) in prox1a signaling is provided. Finally, the authors show that her6 knockdown leads to increased prox1a expression and that prox1a knockdown leads to loss of her6 expression, suggesting both factors impact expression of the other to regulate the kinetics of PR regeneration in the zebrafish retina.

Major comments:

1. As presented, the "downregulation" of her6 in MG-derived progenitors is not convincing due to a discrepancy between the single cell data and antibody staining. For "downregulation" to be the case, MG and/or progenitor cells would need to be expressing her6 and then lose it over time during regeneration. The single cell data are consistent with that showing expression of her6 in quiescent and proliferating MG, but the antibody staining is not. In addition, regarding the single cell data, the colors used to discriminate early PRs and PRs are nearly identical. The authors need to make clear which clusters correspond to MG-derived progenitor cells using clear labeling. Regarding antibody labeling, prior to cone PR ablation, the expression of Her6 (ie Hes1 staining) appears to be localized to interneurons and RGCs, not MG - yet the single cell data suggest this is not the case. Either the single cell data or the antibody labeling may be correct, but not both. The authors should provide a figure showing co-labeling of Her6/Hes1 and a MG marker such as GS or GFAP. In the absence of that result, the antibody data is most consistent with her6 expression either not being expressed in MG and MG-derived progenitor cells or expressed in only a subset of progenitors - both of which conflict with the single cell data.
2. The her6 MO results are not convincing with respect to data interpretation. Knockdown of her6 results in more cone PRs at 72 hpi and more progenitor proliferation at the same time point (as assessed by pH3 immunostaining). For an increase in progenitor proliferation to explain the increase in cone PRs the observation would need to precede the increase in cones, not be coincident with it. Alternative hypotheses need to be explored, for instance, whether her6 downregulation alters cell fate choices in progenitor cells. The precocious increase in prox1 expression at 48 hpi (Figure 6A-E) would be consistent with that idea, leading either to precocious cone differentiation or a stronger bias toward the red/green cone PR fate.
3. The connection to Liquid-liquid Phase Separation (LLPS) is the weakest component of the study given the following issues:
4. a. Prox1 staining as punctate and/or liquid-like assembly associated is not convincingly demonstrated, this would require much higher magnification, quantification, and co-localization with factors known to aggregate in liquid-like assemblies.
5. b. The reagents used to modulate LLPS are not specific to Prox1 and will alter all LLPS-associated signaling events.
6. c. What assay was used to set "effective and consistent" outcomes at a concentration of 5% 1,6-HD anda treatment time of 2 minutes? Was toxicity or any other assay performed to assess the specificity of 1,6-HD effects in vivo at this concentration?
7. d. Shouldn't the regenerated cone cells be expressing the lws2:nfsb-mCherry transgene? This is especially troubling given that the lws2:nfsb-mCherry transgene is evident at the periphery in both conditions whereas the zpr1antibody (i.e., Arr3a expression) appears to differentially label more central areas of the retina.
8. e. Controls are needed to demonstrate that the expression of other transcription factors is normal following 1,6-HD treatment.
9. f. Controls are needed to show that zpr1 staining / Arr3a expression is not selectively altered by 1,6-HD treatment, eg are other markers of red/cones similarly altered?
10. Statistical methods are not provided for the Dros portion of the study. Please indicate which statistical tests were used and what corrections performed for multiple comparisons.
11. A control is need to show that the antibody against mammalian Hes1 labels Her6 in zebrafish.

Minor Comments:

1. Please provide alleles for all transgenic resources used as this will help to ensure others are able to replicate these findings and/or use equivalent resources for their own research.
2. "Zebrafish injury model" section states that two transgenic lines were used to create a PR ablation line but only one line is listed? Please clarify. Also the larval ablation model should be included in the Methods.
3. Montgomery et al ablated rods not cones. As cells exhibit differential sensitivity to cell ablation methods - including rods and cones - it would be preferable if a reference for NTR/Mtz-mediated cone cell ablation was cited.
4. Figure S2: Since fish between 10-12 months were used, there is no need to mention the ablation results with the younger fish as it is unrelated to the study.
5. 24 hpi as first observation of MG-derived progenitors (Fig. S3D-D') is very weak, data is not convincing. Please update the figure or revise the manuscript. Figure 2 is much better, simply referencing Figure 2E' would suffice.
6. Edit needed: "They began proliferating at 48hpi, as indicated by clusters of PCNA+ cells across the inner nuclear cell layer (INL; Figure S3E-E'), also observed by at 72 and 96 hpi (Figure S3F-F' and G-G' EE', outlined).
7. her6 has been previously implicated in retinal regeneration previously and references to these studies should be included in the intro and/or discussion. See: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643038/<br /> https://bmcdevbiol.biomedcentral.com/articles/10.1186/1471-213X-6-36<br /> https://www.jneurosci.org/content/34/43/14403
8. Please confirm her6 MO-based downregulation with a more quantitative method.
9. "DamID" should be defined at the first instance (first paragraph of Results) as DNA adenine methyltransferase Identification.
10. zpr-1 is an antibody not a protein and should not be capitalized (per "Zpr1+ cells").
11. According to ZFIN, prox1 is now prox1a, the authors use both. It would be helpful to stay consistent and use the most recent nomenclature to refer to prox1a.

Referees cross-commenting

Reviewer #2 / Major concern #1: Unclear how one assesses the effects of gene knockdown on regeneration in the absence of cell ablation? The reviewer should clarify what "impacts" they would like quantified (e.g., developmental effects, etc) . For instance, assessing effects of gene knockdown on PR number developmentally (with egg injections of morpholino and/or sgRNAs for Crispants) would help to account for potential gene knockdown effects on progenitor differentiation (i.e., causing a fate bias toward red cones) or protection (e.g., absence of normal levels of cell death/TUNEL).

Reviewer #2 / Major concern #8: Repeated micrographs: In my view this should not be done at all. Please use different examples of control data between comparisons of her6 and prox1a knockdowns - even if these experiments were al performed in parallel there should be additional control samples from each assay and across a minimum of three biological repeats.

Reviewer #3, Paragraph 2 ("The authors state that the loss of Her6..."): Pulse-chase experiments demonstrating an increase in the number of newly generated red cones - by virtue of co-labeling with pulse marker and Lws2 transgene - would help to address this concern and clarify the degree of bias exhibited in the red cone ablation paradigm (an issue discussed by all reviewers and which the authors are familiar with, eg, DOI: 10.1186/s13064-017-0089-y)

Significance

This work is significant in interrogating the function of two classic factors associated with regulation of neurogenesis and neuronal differentiation in Drosophila in the context of cone photoreceptor regeneration in zebrafish. My expertise lies in the latter paradigm and I have restricted my evaluation to that portion of the study. The novelty of the study is diminished somewhat by recent reports of the role of Notch signaling in retinal regeneration in zebrafish - thus, it would follow that a major signaling component of the Notch pathway, Hes1/Her6, would likely be involved as well. Indeed, expression changes for her6 during regeneration have been previously reported (see refs above). However, the present study does add a functional characterization which is a significant advance. The advent of stimulation of retinal regenerative capacity in mice by Tom Reh and colleagues adds intrigue to the study of retinal regeneration in zebrafish as the insights generated are being applied in a way that suggests possible relevance to human disease. Thus defining conditions that lead to accelerated retinal regeneration is of widespread interest. However, several issues raised in the detailed review above diminish my enthusiasm for the study overall.

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Reply to the reviewers

General Statements:

We want to thank the reviewers for their time in assessing our study, their positive feedback and their constructive comments that we address here. We have carefully assessed each reviewers’ comments and address them point-by-point below. We appreciate that the reviewers consider our study to be relevant and advance the field in better understanding mitochondrial stress responses.

There is a consensus as to why we have inhibited rather than depleted PHGDH via genetic means. PHGDH knockout is developmentally lethal at embryonal day 13.5, and therefore whole-body knockout crossings were not an option (Yoshida et al. JBC 2004). Furthermore, we and colleagues have shown that mitochondrial disease induces a complex systemic response including e.g., metabokines FGF21 and GDF15, secreted by the affected muscle and heart and modifying metabolism in the whole organism, indicating that tissue-specific knockouts are not optimal to resolve mechanisms in pathophysiology. Further, potential serine supplementation from other tissues could mask the effects on the muscle. Next, the slowly progressive phenotype of the mitochondrial myopathy mouse, fully manifesting close to 2 years of age, make double-transgenic strategies resource-intensive. Therefore, a PHGDH inhibitor, the specificity of which we carefully explore, was the approach of choice.

Serine rich diet: We did consider a serine-rich diet. However, our evidence indicates that exogenous serine is not sufficient to rescue the phenotype in cultured cells. This suggests that the enzymatic intracellular localization of de novo serine synthesis is of crucial importance.

Off-target effects: We agree and are aware that using a small-molecule molecule for pharmacological inhibition bears risks of off-target effect, as Arlt et al. 2021 reported for NCT-503 neuroblastoma cells lines. In agreement with the reviewers about the relevance of our results, the observation that we do not observe any meaningful alterations in WT muscle, even at the advanced age where metabolic muscle fitness is already challenged, whilst the myopathy phenotype worsens in myopathic mice support an on-target effect of NCT-503.

Therefore, we had carefully considered the suggested experiments during the initial study and decided that PHGDH inhibitor was the best choice to answer our questions about importance of de novo serine biosynthesis.

Reviewer #1 (Evidence, reproducibility and clarity):

In this manuscript the authors describe interesting results regarding amino acid metabolism under conditions of mitochondrial stress. The authors used a selective PHGDH inhibitor (compound NCT503), and document that de novo serine synthesis is essential to sustain phospholipid biosynthesis, redox homeostasis, mitochondrial function, and mitochondrial protein synthesis in Deletor mice and in cell culture under mitochondrial stress. Interestingly, serine supplementation does not rescue those metabolic alterations, indicating a specific mitochondrial stress-dependent mechanism of serine utilization by the cells.<br /> In addition, the authors attempted to explore the role of serine in phospholipid biosynthesis under in vivo and ex vivo mitochondrial stress, and this is the weakest part of the manuscript. The specific reduction of mitochondrial PE using the PHGDH is interesting, and it can provide some clues into how mitochondrial membranes are synthetized under mitochondrial stress, but additional experimental approaches should be incorporated to generate a more robust body of evidence.

In summary, the manuscript should be improved by using additional experimental approaches. In addition, genetic intervention through PHGDH gain or loss of function can help to elucidate the molecular mechanisms.

We thank the reviewer for this proposition. As explained above in detail, knockout of PHGDH in mice is lethal in embryogenesis. Secondly, the cell culture studies show that in a mitochondrial disease background, cell-intrinsic de novo serine biosynthesis becomes essential. Therefore, the phenotype of a conditional knockout in skeletal muscle is likely to be drastic and does not reflect the situation of an adult-onset disease. Thirdly, if the gain- and loss of mutant double transgenics would live, the Deletor mice are an actual slowly progressing disease model, which manifests the phenotype at around two years. Therefore, this experiment takes three years, is not feasible and was considered to be of little informativeness. We carefully explored the specificity of PHGDH inhibition by NCT503 and found it to serve best the questions asked.

The authors could also incorporate metabolomic studies using C13 serine or C13 glucose supplemented culture medium to differentiate carbons that come from extracellular serine or from de novo synthesis.

We thank reviewer #1 for her/his comments. The exogenous serine supplementation shows minor rescue of the phenotype in cultured cells but decreases somewhat the mitochondrial integrated stress response markers in serine-containing medium, suggesting some amelioration of the PHGDH inhibition.

We have now incorporated new data of D3-serine flux in cells to the manuscript (p.14, FigS3G, p.33). These data indicate that some serine uptake occurs by the cells and that is not significantly affected by mitochondrial translation inhibition (actinonin), even if the combination of PHGDH plus actinonin is not viable.

Manuscript Figure S3G. D3-serine uptake in C2C12 cells with and without actinonin. Normal non-labelled serine was used as a negative control for D3-serine contamination. D3-serine amounts were measured in culture media and in cells; biological replicates (n=3). In media, fold change (FC) was calculated relative to the sample with normal serine media without actinonin. In cells, FC was determined against cells in D3-serine without actinonin. No traces of D3 label were detected in normal serine samples. ACT = actinonin.

Additional questions: Is PHDGH subcellular localization modulated under mitochondrial stress conditions?

This is an interesting question. The insufficient resolution in muscle sections did not give a conclusive answer. We have, however, performed new immunofluorescence experiments in cells to investigate this potential mechanism and used a newly generated PHGDHKO cell line as a negative control (Rev Fig.1). The conclusions have been added to the manuscript (page 14, FigS4E,F, p.34). In essence, no altered localization of PHGDH in cells is observed – the localization appears to be cytoplasmic. The increasing PHGDH amount in increasing actinonin concentration is apparent, however.

Revision Figure 1. Immunofluorescent staining of HEK293 and C2C12 cells. WT and PHGDHKO HEK293 cells were used as controls for the PHGDH antibody. (Hoechst staining of nucleus, blue; anti-PHGDH, green; mitochondrial outer membrane protein TOM20, red. EtOH = ethanol; ACT = actinonin; INH = PHGDH inhibitor.

Is the extracellular serine uptake impaired under mitochondrial stress?

As explained above, we have now incorporated data of D3-labeled serine uptake in cells to the manuscript, quantifying the uptake from the media and the levels in the cells. Mitochondrial translation inhibition by actinonin does not affect uptake, indicating that the uptake per se is not impaired. However, the de novo synthesis being essential in metabolic stress strongly points to the intracellular site to be important.

While the extracellular serine can only little compensate for de novo synthesis, our new data, qPCR analysis on muscle of all mouse groups to quantify serine tRNA levels (MTTS1) indicates the levels to be increased, suggesting insufficient charging of the tRNA (MTTL1 as negative control; Rev Fig. 2). In addition, the presumed mitochondrial serine transporter SFXN1 is upregulated on protein level (p.11, Fig S2H, p.32). These data suggest that cell-intrinsic serine synthesis supports mitochondrial translation in metabolic stress situations.

Revision Figure 2. RNA expression of mitochondrial tRNAs for leucine (MTTL1) and serine (MTTS1) in skeletal muscles of wild type (WT) and Deletor (DEL) mice; RT-qPCR. WT VEH n=6, DEL VEH n=5, WT INH n=7, DEL INH n=8. VEH = vehicle; INH = PHGDH inhibitor; FC = fold change.

ATF4 is a transcription factor that regulates amino acid transport. In this connection, is ATF4-dependent serine transporters modulated under mitochondrial stress or under PHGDH treatment?

As explained above, SFXN1 transporter was upregulated in protein level. In postmitotic skeletal muscle, the responses are under ATF5, as shown in Forsström, Jackson et al. Cell Metab 2019.

Regarding the alteration in redox homeostasis, mitochondrial function and mitochondrial protein synthesis. Is the alteration of phospholipid synthesis upstream of all of those mitochondrial alterations?

The authors are unclear of what the reviewer means by “all those mitochondrial alterations”. The disease in the Deletor mice is caused by dominant patient-homologous mutation of mitochondrial DNA helicase Twinkle, and in the cell culture model we block mitochondrial protein synthesis with actinonin. Therefore, the primary defect in both mice and cells is intramitochondrial, in mtDNA replication and protein synthesis, and the stress responses are a consequence of the primary mitochondrial dysfunction. The consequent secondary stagewise mitochondrial integrated stress response, ISRmt, affects both mitochondria and rest of the cell, with effects in metabolism and nuclear genome transcription. We have pioneered the discovery of ISRmt in mice and humans with mitochondrial defects/diseases in several studies (e.g., Cell Metab 2016, 2017, 2019, 2020). This response includes a remarkable remodeling of one-carbon metabolism, with major changes in methyl cycle, transsulfuration and nucleotide synthesis of the whole cell. PS synthesis is dependent of methyl groups deriving from the one-carbon cycle -driven methyl cycle. Therefore, the original mitochondrial replisome dysfunction causes a stagewise, progressive disease process, which is upstream of all the other responses. Phospholipid synthesis alteration, however, has high potential to modify mitochondrial membranes that can aggravate disease during its progression.

The temporal metabolomics of cultured cells did show PE accumulation at later time points than on mitochondrial translation or other crucial cellular metabolites, suggesting its alterations to be a consequence rather than upstream. Indeed, further studies are needed to dig deeper into the dynamics of phospholipid synthesis in mitochondrial dysfunction.

Additionally, in my opinion the results should be reorganized, because in the current format the manuscript is fragmented, and several panels lose the symmetry.

The authors are unclear what the reviewer means by the panels losing symmetry. Without suggestions it is hard to make changes, but we have carefully reviewed the clarity of the presentation.

Major concerns.

Figure 1

The authors should incorporate the Deletor mice amino acid levels in muscle. How does the PHGDH inhibitor treatment modulate the other non-essential amino acids?

We have quantified all 11 non-essential amino acids from Deletor muscle by targeted metabolomics in combination or individually and added the new graphs (p.10, Figure S2D,E, p.32).

Manuscript Figure S2D. Total non-essential amino acid quantification (NEAAs) from targeted metabolomics of skeletal muscles of WT and DEL mice. WT VEH n=6, DEL VEH n=5, WT INH n=7, DEL INH n=8. VEH = vehicle; INH = PHGDH inhibitor. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.

Manuscript Figure S2E. Individual NEAAs from targeted metabolomics of skeletal muscles of WT and DEL mice. WT VEH n=6, DEL VEH n=5, WT INH n=7, DEL INH n=8. VEH = vehicle; INH = PHGDH inhibitor. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.2. The authors should incorporate the data of WT + vehicle and wt + NCT503 mice in figures 1C, 1D, 1F and 1G, in order to compare properly the effects of PHGDH inhibitorThe authors should incorporate the data of WT + vehicle and wt + NCT503 mice in figures 1C, 1D, 1F and 1G, in order to compare properly the effects of PHGDH inhibitor.The authors should incorporate the data of WT + vehicle and wt + NCT503 mice in figures 1C, 1D, 1F and 1G, in order to compare properly the effects of PHGDH inhibitor.

The authors should incorporate the data of WT + vehicle and wt + NCT503 mice in figures 1C, 1D, 1F and 1G, in order to compare properly the effects of PHGDH inhibitor.

We agree with the reviewer`s notion to depict all groups. No COX- fibres exist in our vehicle or inhibitor-treated WT mice and we have added this data into the manuscript in p.31, commented on in p.6, Figure S1B, also below).

Manuscript Figure S1B. Histochemical analysis of combined cytochrome-c-oxidase (COX) and succinate dehydrogenase (SDH) activity in muscles of treated WT mice. (Brown fibres indicate high COX activity, translucent – low COX activity). Lower panel shows immunohistochemical detection of the mTORC1 downstream target - phosphorylated ribosomal S6 (p-S6). INH = PHDGH inhibitor.

Figure 1H. Are there any effect of PHGDH in the mtDNA of WT mice? The authors might incorporate this information, to show properly that mitochondrial stress led to dependence of serine to sustain muscle homeostasis.

In addition to the mtDNA deletion analysis, we have assessed mtDNA copy number via qPCR (p.27, Figure S1D). No mtDNA deletions exist in WTs and their mtDNA copy number raised slightly. These new data are included in p. 6 and Figure S1D, p.31.

Manuscript Figure S1D. Mitochondrial DNA copy number analysis in muscles of WT and DEL mice. Measured with qPCR (n=5-8/group). VEH = vehicle; INH = PHGDH inhibitor. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.

Figure 2.<br /> Interestingly the authors observe a decrease in total mitochondrial lipids content, and an increase in mitochondrial PE content in Deletor mice compared to WT mice. These results suggest an alteration in the phospholipids flux between mitochondria and endoplasmic reticulum in this model of mitochondrial disease. Moreover, PHGDH treatment appears to be able to rescue this alteration. Some question related this issue: What is the expression of genes involved in the balance PC, PE, PS? Are the PSS1, PSS2, PSD and PEMT expression altered?

Previous Deletor muscle studies cohorts showed non-altered PEMT in diseased mice of similar age nor was PEMT and PSD significantly altered RNAseq data from patients in contrast to PHGDH (Forsstrom et al., 2019; Rev Fig.3). 20-months old Deletor do not show any alteration in PEMT (qPCR quantification below) when analysed from total muscle. Whether these are changed in single fibers (mosaic manifestation of the disease) we cannot exclude.

Revision Figure 3. Left: RNA expression of PEMT in skeletal muscles of 20-months-old WT and DEL mice. Measured with qPCR; n=9. Right: Gene expression of PEMT, PSD, and PHGDH enzymes in muscles of mitochondrial myopathy patients. Measured with RNA-Seq; n=8 in control; n=4 in patients. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.

Regarding the phospholipid synthesis. Is mitochondria or endoplasmic reticulum ultrastructure altered in Deletor mice muscle? The authors should explain the possible mechanisms.

We have extensively characterized the pathology in Deletor muscle in publications previously (Tyynismaa et al. PNAS 2005; Tyynismaa Hum Mol Genet 2010 for morphology, especially, but also as a part of the physiology studies Nikkanen et al. Cell Metab 2016; Khan et al. Cell Metab 2017; Forsström, Jackson et al. Cell Metab 2019 and Mito et al. Cell Metab 2022). The morphological changes in mitochondria are quite extensive, and these are comparable to those found in patients with similar mutations: enlarged mitochondria with distorted and few cristae, various inclusions. We presume the reviewer means sarcoplasmic reticulum in skeletal muscle. The extent of the muscle pathology in vivo suggests that ER structure is changed as everything else is: the diseased muscle fibers are full of abnormal mitochondria in the Deletors, with only few traces of myofibers or fibrils. These fibers become more prevalent after PHGDH inhibition. Furthermore, the mice – WT and Deletors – show age-induced prevalence of non-pathology-related accumulation of sacroplasmic aggregates (so called tubular aggregates) as a known feature of C57Bl6 mice.

New Figure S1 C, p.31 and below (Rev Fig.4): Ultrastructure of Deletor muscle shows enlarged mitochondria both inside the fibers (Fig S1C left panel, arrows) and subsarcolemmally, further aggravated by PHGDH inhibition. Tubular aggregates, as shown in the inhibitor treated Deletor in the Figure S1C seen in the light-microscopic image on the right (arrows), are part of the mouse substrain characteristics, not disease associated. These changes are also, as white aggregates inside the fibers in all genotypes and treatment groups We now added the light and electron microscopic image analysis to Figure S1 C. We now added the electron microscopic images to the manuscript as S1C (commented on p.6), indicating the aggravated phenotype of PHGDH inhibitor.

Revision Figure 4. Left: Panel from the Manuscript Fig. S1C. Representative images of transmission electron microscopy (TEM) in skeletal muscles of WT and DEL mice. Black arrows – enlarged mitochondria. Right: Representative images from light microscopy (LM) of muscles of the same groups as in Left. Black arrows – tubular aggregates. VEH = vehicle; INH = PHGDH inhibitor.

We have also performed analysis of mitochondrial ultrastructure in cells (see below) showing specific lipid accumulation. As mentioned above the alterations in PE levels are detected metabolically at later time points and after substantial loss of mitochondrial translation.

We added this new data as Figure S3E, p.33, commented on p.6.

Manuscript Figure S3E. Representative images of TEM analysis of 12h-treated C2C12 cells. The cells were incubated in media with or without serine and treated with either actinonin, PHGDH inhibitor or a combination of both. White arrow – myelinosome-like membranous lipid aggregates; M = mitochondria. INH = PHGDH inhibitor; ACT = actinonin; Ser = serine.

Supplementary figure 3D<br /> Based on the metabolomic studies, the authors propose a time-dependent decrease in PSAT1 and phosphoserine in cells under mitochondrial stress (Figure S3D). To elucidate the direct role of PHGDH, the authors should analyze the phosphoserine and different phospholipid (described in figure 2E) in presence of PHGDH inhibitor. This will help to understand the link between the endogenous serine synthesis and mitochondrial PE accumulation.

The temporal metabolomics show a time-dependent decrease of the metabolite phosphoserine (not PSAT1). Here, we show the PE and PC dynamics at 6 and 24 hours. These data have now been added to the manuscript, figure S3I, p.33, commented on page 14.

Manuscript Figure S3I. Normalised PC and PE pools in C2C12 cells measured with untargeted metabolomics after treatments with actinonin, PHGDH inhibitor, or a combination of two for 6 and 24 hours. n=6-11/group; ACT = actinonin; INH = PHGDH inhibitor.

Figure 3H shows a decrease in phosphoserine in the presence of PHGDH inhibitor but this figure is asymmetric compared to figure 3I. Can the authors use another experimental approach to detect the specific mitochondria phospholipid levels (used in Figure 2 for instance)?

Figure 3H shows a decrease in phosphoserine in the presence of mitochondrial dysfunction induced through actinonin alone. Figure 3I shows a further decrease in phosphoserine when actinonin-treated cells are treated with the PHGDH inhibitor (phosphoserine is marked now also in our genetic model of mtDNA depletion, Figure 3I). The data are from untargeted metabolomics, parallel analysed samples, and therefore are comparable for the levels.

We have now grouped the time-wise dynamics of the annotated phospholipids from the untargeted metabolomics decreasing at a late stage of the temporal treatment, suggesting that the consequences on of mitochondrial dysfunction by translation inhibition clearly precede phospholipid alterations in cells. (Figure below, also added to the manuscript as Figure S3D, p.33, and commented in the text in p. 13)

Manuscript Figure S3D. Heatmap of selected top significantly altered metabolites in primary human myoblasts temporally treated with actinonin; untargeted metabolomics (n=5-6, run in technical duplicates). ACT = actinonin.

The authors should incorporate mitochondrial PE analysis in figure 3 to link the cellular studies described in this figure with the studies done in Deletor mice muscle.

Figure 3 I-K. The authors suggest an alteration in glutathione redox state and a further increase in mitochondrial superoxide production in cells treated with the PHGDH inhibition under mitochondrial stress. What are the total glutathione levels under these conditions? Could GSH regeneration improve the mitochondrial function and mitochondrial protein synthesis? Is extracellular serine able to rescue the reduced glutathione levels?

We have previously shown in vivo (Nikkanen et al. Cell Metab 2016), in Deletors, that in the skeletal muscle and the heart glucose carbons show flux via serine to glutathione, without changes in steady-state levels of glutathione, indicating high usage. In our targeted metabolomics assay from muscle, the steady-state glutathione amount was below the limit of quantitation. The levels of glutathione oxidized vs reduced are dynamic, however. Our untargeted set shows a trend towards an increase. The GSSG/GSH data (Rev Fig.5 Right) was included already in the original manuscript, as Figure S2C, and the time-wise dynamics is here shown for the reviewer (Rev Fig.5 Left).

Revision Figure 5. Left: Ratio between oxidized (GSSG) and reduced (GSH) glutathione temporally measured in primary human myoblasts with untargeted metabolomics; n=10-16/group. Right: Ratio between glutathione forms in muscles of WT and DEL mice. Measured by targeted metabolomics; WT VEH n=6, DEL VEH n=5, WT INH n=7, DEL INH n=8; VEH = vehicle; INH = inhibitor. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.

Minor concerns. Figure 3B,C does not show the statistical analysis so please incorporate this information.<br /> Include quantification of figure 3 E and supplementary figure S3E.

We have added this information to both figures.

Figure S3E. Improve flow cytometry histogram, the cell population data values cannot be observed.

We have improved the resolution.

Some methods and primers included in the material and methods section are not used in the manuscript.

We have carefully edited the materials and methods sections for any unnecessary data.

Reviewer #1 (Significance):

In this manuscript the authors describe interesting results regarding amino acid metabolism under conditions of mitochondrial stress. The authors used a selective PHGDH inhibitor (compound NCT503), and document that de novo serine synthesis is essential to sustain phospholipid biosynthesis, redox homeostasis, mitochondrial function, and mitochondrial protein synthesis in Deletor mice and in cell culture under mitochondrial stress. Interestingly, serine supplementation does not rescue those metabolic alterations, indicating a specific mitochondrial stress-dependent mechanism of serine utilization by the cells.<br /> This data will be relevant to better understand the connection between alterations in mitochondrial function and amino acid metabolism in cells, and in organisms.

We thank the reviewer #1 for the constructive comments and acknowledgement of interesting results and significance.

Reviewer #2 (Evidence, reproducibility and clarity):

Experiments reported in this manuscript indicate that NCT-530- an inhibitor of the de novo serine biosynthetic enzyme PHGDH- worsens mitochondrial pathology.<br /> Systemic administration of NCT-503 decreased serine levels only in Deletor mice- ubiquitously expressing a homologous dominant patient mutation in mitochondrial twinkle helicase.<br /> NCT-503 treatment induced a further increase of the mitochondrial integrated stress response in Deletor mice. Moreover, the metabolic profile and lipid balance was further modified by NCT-503 administration to Deletor mice. The relevance of NCT-503 treatment was finally evaluated in cellular systems exposed with different treatments inducing mitochondrial insults.

COMMENTS:

NCT-530 specificity should be confirmed by reducing PHGDH levels by either siRNA or even better by CRISPR-Cas9-mediated gene deletion.

Firstly, we find clear results in Deletor mice but no pathology in WT mice treated with NCT-530. We interpret the reviewer to mean our cellular model, because in vivo siRNA or CRISPR-Cas9 approach is not feasible in mice that manifest the disease at 2 years of age. The compound is inhibiting the activity of PHGDH. The specificity has been described in the paper of Pacoult et al. (2016) previously, and as we find decreased serine and response of increased PHGDH transcript, the results are well consistent to what has previously been described. However, to respond to this reviewer, we have now added new data on a PHGDHKO in HEK293 cells (Figure S4E,F,p34 and below, commented on page 14).

Manuscript Figure S4F. Quantification of population doublings of WT and PHGDHKO cells treated with actinonin, PHDGH inhibitor or a combination of both. Immunofluorescence images are presented in Rev Fig 1. The data is presented as average values of three independent experiments; VEH = vehicle; INH = inhibitor; ACT = actinonin; KO = knockout; EtOH = ethanol. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001; **** p ≤0.0001.

If the serine biosynthetic pathway is causally relevant to worsen the mitochondrial pathology, supplementing Deletor mice with a serine-rich diet or intraperitoneal injection of serine is expected to improve pathology.

This is an exciting suggestion which we did consider. However, based on our cell experiments, we show that in the context of mitochondrial disease, the de novo serine synthesis becomes essential for cellular viability, and this cannot be compensated by extracellular serine supplementation alone. This suggests that the intracellular localization of serine synthesis is essentially important, which is an interesting finding and warrants further investigation, not in the scope of this paper.

Reviewer #2 (Significance):

Limited significance in the field.

Reviewer #3 (Evidence, reproducibility and clarity):

This study concludes that de novo serine biosynthesis fueled by PHGDH activity is an adaptive mechanism counteracting defects in mitochondrial function observed in mitochondrial myopathies, while being dispensable for mitochondrial function of healthy muscle. By using a mouse model of mitochondrial myopathies, as well as cells treated with different mitochondrial poisons, authors show that de novo serine biosynthesis is specifically needed to preserve phospholipid synthesis, some degree of mitochondrial function, and mitigate mitochondrial ROS production. These conclusions are drawn mostly from the data obtained from experiments treating cells and mutant mice with NCT-503, an inhibitor of PHGDH. The main limitation is that the approach does not allow us to discern whether the phenotypes observed are explained by on-target and muscle autonomous actions of NCT-503. The list of major concerns are as follows:

We thank reviewer #3 for her/his comments.

1) The authors do not cite the study by Vandekeere et al. Cell Metabolism 2018 that define the KO of PHGDH in endothelial cells. This study demonstrates that serine derived from PHGDH is required to synthesize heme to preserve mitochondrial function in endothelial cells. In addition, they demonstrate that the absence of PHGDH increases mitochondrial ROS and decreases electron transport chain function. These previous studies can indicate that the worsening of the muscle phenotype in the mice treated with NCT-503 might be driven by its actions in endothelial cells, and not in muscle. In addition, it raises the question on why NCT-503 has no effect on muscle of 24-month-old mice, which have a decline in metabolic fitness.

This is an interesting comment, and we indeed have now included the missing reference. Firstly, we find it interesting as well that NCT-503 has no significant health-related effect in WT background in the tissues that we analysed but shows harmful effects upon mitochondrial dysfunction. Our aged WT mice, even at the age of 24 months, show no signs of respiratory chain deficiency in skeletal muscle and this is consistent with numerous other studies on old mice. Therefore, the metabolic fitness decline that the reviewer mentions does not make PHGDH induction essential in normal aging.

Secondly, we are aware of the Vandekeere et al. study on endothelial cells. Our mouse is a constitutive transgenic mouse, and therefore isolated changes in single tissues as in the endothelial KO mouse, because of metabolite signaling between cell types, is hard to compare with ours. In our mice, expressing a dominant patient mutation in the mitochondrial replicative helicase Twinkle, PHGDH induction in the skeletal muscle is a key component of the mitochondrial integrated stress response (ISRmt) as we described in e.g., in Tyynismaa et al. 2010 and Forsström, Jackson et al. Cell Metab 2019. Similar findings have been reported by Kuhl et al. ELife 2017 and other groups. We have also shown that PHGDH expression is dependent on prior induction of FGF21 in the skeletal muscle (Cell Metab 2019). We cannot fully exclude a contribution of endothelial cells in the Deletor phenotype, but the upregulation of PHGDH is robust exactly in the Deletor muscle, and endothelial cells show no phenotype in ultrastructural analysis of these mice. Therefore, our strong view is that in order to study the effect of PHGDH induction caused by primary mitochondrial disease in the skeletal muscle, the use of an inhibitor is the first choice.

Hence, we find that the Vandekeere et al. study supports our findings in that increasing PHGDH could represent an adaptive response to support electron transport chain function and decreasing mitochondrial ROS in a tissue where PHGDH is induced.

In this respect, we would also like to emphasize that a 1-month treatment of old mice, as in our study, differs from a life-long tissue-specific knockout, which is not a natural disease presentation nor an avenue to understand whether the opposite – increasing PHGDH activity – could represent a viable treatment option.

2) No experiments with PHGDH knockdown are performed in vitro (or ideally in vivo using muscle electroporation if possible) to confirm the specificity of NCT-503. This is validation is key in muscle, as the on-target actions of NCT-503 were mostly shown in different cancers with low and high PGHDH expression (Pacold et al). Therefore, whether there are no off-target effects of NCT-503 in muscle is still unknown and should be defined.

Firstly, we find effects in serine levels and also response to PHGDH expression, and effects on ISRmt. As argued, full-body genetic ablation is associated with lethality in early embryogenesis and is not an option. Muscle-specific PHGDH-KO was considered, but with a late-manifesting mouse is a several-year experiment and therefore was not considered to be in the scope of this article. In an addition to the arguments, we raised above, muscle electroporation or intramuscular AAV infection would represent the only option, but with limited spreading in adult aged mice (our own and colleagues’ experience), and in a mosaic disease would offer low information.

We have however, now added data on a PHGDHKO HEK293 cell line with and without pharmacologically inducing mitochondrial dysfunction, p.14, Fig S4E,F.

3) The data showing that exogenous serine supplementation cannot override the effects of NCT-503 treatment on mitochondria could also be compatible with an off-target effect of NCT-503 in models of mitochondrial dysfunction (see point 2). If the experiments suggested in point number 2 demonstrate on-target action of NCT-503, authors should then decrease the expression of the mitochondrial transporter of serine (SFXN1) to demonstrate that specific intracellular pools of serine are needed to mitigate mitochondrial dysfunction. If the authors' conclusion is conclusive, knock-down of SFXN1 should be highly toxic in in vitro and in vivo models of mitochondrial myopathies, while having barely any effect in controls (similarly to serine deprivation in vitro being almost harmless).

Thank you for the comment. To our knowledge and based on our own experience elsewhere, the role of SFXN1 as the only serine transporter is still somewhat unclear (Kory et al, Science 2018; see also below). Therefore, the relevant metabolomic serine-dependent changes, PHGDH response and effects on the relevant stress response to our opinion are strong evidence of on-target-effects.

4) The authors list SFXN1 in the antibodies used in the paper, but I could not find any data. Are the protein levels of SFXN1 changed in mice with mitochondrial myopathies? Do they strongly correlate with PHGDH expression?

We actually had probed SFXN1 ab in the muscle but because of the extent of the data in the paper, had decided to omit the data – but accidentally left it in the methods. We do see an increase in the abundance of SFXN1 in treated Deletors. However, as also explained above, although SFXN1 has been described as a serine transporter, it is not fully characterized, also transports other amino acids, and has some redundancy with other SFXN family members requiring multiple KDs to get the required effect (Kory et al., Science 2018).

These data are now included to Figure S2H (p.32 and below) and commented in page 11.

Manuscript Figure S2H. Evaluation of SFXN1 protein expression in muscles of WT and DEL mice with Western Blotting. Left: image of the SFXN1 bands and the total protein lanes. Right: quantification of the band intensities. n=5/group; VEH = vehicle; INH = inhibitor. Significance levels: n.s. = p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001.

4) Serine tracing experiments would be required to conclude that serine is needed for phospholipid synthesis. Otherwise, the defects observed can just be downstream of mitochondrial dysfunction and ISRmt activation.

Thank you for the point. Our study aims to characterize the role of PHGDH in skeletal muscle, in a mitochondrial disease. In vivo tracing experiments would be out of the scope for this study. We have modified our conclusions in respect to this in page 11.

5) Authors use isolated mitochondria from muscle to determine whether OPA1 processing is changed in mice with mitochondrial myopathy and treated with NCT-503. The blots show higher total OPA1 content per mitochondria in the group with the greatest mitochondrial dysfunction, depolarization, and ROS production (myopathy + NCT-503). These data strongly suggest that dysfunctional mitochondrial from dysfunctional fibers do not survive the isolation procedure, showing just OPA1 content of resilient mitochondria surviving isolation. Indeed, heme depletion (as expected from PHGDH inhibition Vadekeere et al. Cell Metabolism 2018) and OPA1 processing catalyzed by OMA1 activation by ROS and depolarization can both activate the mitochondria ISR, via HRI. Therefore, authors should analyze OPA1 processing in total lysates to include mitochondria from dysfunctional fibers. Maybe increased OMA1 activity as a result of increased mitochondrial ROS and depolarization could explain the exacerbation of the mitochondrial ISR induced by NCT-503 treatment.

The notion that stress-induced OPA1 processing present in mitochondrial myopathy is a valid one, which we did ask in a previous study in the Deletor mouse (Forsstrom et al., Cell Metabolism 2019). Surprisingly, we did not detect any significant levels of OPA1 processing in the Deletors, in the total cell lysates in that paper, nor in a previous paper of mitochondrial myopathy treated with Atkins diet (Ahola et al. EMBO Mol Med, 2016). The differential centrifugation protocol is unlikely to induce selection bias at the high centrifugation forces used (10000xg). We have previously described the increase in stress-related proteins in mitochondria-enriched fractions (Forsstrom et al., Cell Metabolism, 2019) and Rev Fig.6, below from the current study.

Revision Figure 6. Immunoblot analysis of mitochondria-enriched fractions from skeletal muscles of all treatment groups from this study. n=5/group; VEH = vehicle; INH = inhibitor.

Reviewer #3 (Significance):

This study is significant, as it aims to understand the pathways that mediate adaptation to mitochondrial myopathies and such knowledge is necessary to find novel therapeutic targets. It could be of high interest to basic researchers studying metabolism and mitochondrial regulation, as well as to clinicians treating mitochondrial diseases.

We appreciate the reviewer noting its importance and emphasizing the significance of this pathway for its clinical relevance.

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Referee #3

Evidence, reproducibility and clarity

This study concludes that de novo serine biosynthesis fueled by PHGDH activity is an adaptive mechanism counteracting defects in mitochondrial function observed in mitochondrial myopathies, while being dispensable for mitochondrial function of healthy muscle. By using a mouse model of mitochondrial myopathies, as well as cells treated with different mitochondrial poisons, authors show that de novo serine biosynthesis is specifically needed to preserve phospholipid synthesis, some degree of mitochondrial function, and mitigate mitochondrial ROS production. These conclusions are drawn mostly from the data obtained from experiments treating cells and mutant mice with NCT-503, an inhibitor of PHGDH. The main limitation is that the approach does not allow us to discern whether the phenotypes observed are explained by on-target and muscle autonomous actions of NCT-503. The list of major concerns are as follows:

1. The authors do not cite the study by Vadekeere et al. Cell Metabolism 2018 that define the KO of PHGDH in endothelial cells. This study demonstrates that serine derived from PHGDH is required to synthesize heme to preserve mitochondrial function in endothelial cells. In addition, they demonstrate that the absence of PHGDH increases mitochondrial ROS and decreases electron transport chain function. These previous studies can indicate that the worsening of the muscle phenotype in the mice treated with NCT-503 might be driven by its actions in endothelial cells, and not in muscle. In addition, it raises the question on why NCT-503 has no effect on muscle of 24-month-old mice, which have a decline in metabolic fitness.
2. No experiments with PHGDH knockdown are performed in vitro (or ideally in vivo using muscle electroporation if possible) to confirm the specificity of NCT-503. This is validation is key in muscle, as the on-target actions of NCT-503 were mostly shown in different cancers with low and high PGHDH expression (Pacold et al). Therefore, whether there are no off-target effects of NCT-503 in muscle is still unknown and should be defined.
3. The data showing that exogenous serine supplementation cannot override the effects of NCT-503 treatment on mitochondria could also be compatible with an off-target effect of NCT-503 in models of mitochondrial dysfunction (see point 2). If the experiments suggested in point number 2 demonstrate on-target action of NCT-502, authors should then decrease the expression of the mitochondrial transporter of serine (SFXN1) to demonstrate that specific intracellular pools of serine are needed to mitigate mitochondrial dysfunction. If the authors' conclusion is conclusion, knock-down of SFXN1 should be highly toxic in in vitro and in vivo models of mitochondrial myopathies, while having barely any effect in controls (similarly to serine deprivation in vitro being almost harmless).
4. The authors list SFXN1 in the antibodies used in the paper, but I could not find any data. Are the protein levels of SFXN1 changed in mice with mitochondrial myopathies? Do they strongly correlate with PHGDH expression?
5. Serine tracing experiments would be required to conclude that serine is needed for phospholipid synthesis. Otherwise, the defects observed can just be downstream of mitochondrial dysfunction and ISRmt activation.
6. Authors use isolated mitochondria from muscle to determine whether OPA1 processing is changed in mice with mitochondrial myopathy and treated with NCT-503. The blots show higher total OPA1 content per mitochondria in the group with the greatest mitochondrial dysfunction, depolarization, and ROS production (myopathy + NCT-503). These data strongly suggest that dysfunctional mitochondrial from dysfunctional fibers do not survive the isolation procedure, showing just OPA1 content of resilient mitochondria surviving isolation. Indeed, heme depletion (as expected from PHGDH inhibition Vadekeere et al. Cell Metabolism 2018) and OPA1 processing catalyzed by OMA1 activation by ROS and depolarization can both activate the mitochondria ISR, via HRI. Therefore, authors should analyze OPA1 processing in total lysates to include mitochondria from dysfunctional fibers. Maybe increased OMA1 activity as a result of increased mitochondrial ROS and depolarization could explain the exacerbation of the mitochondrial ISR induced by NCT-503 treatment.

Significance

This study is significant, as it aims to understand the pathways that mediate adaptation to mitochondrial myopathies and such knowledge is necessary to find novel therapeutic targets. It could be of high interest to basic researchers studying metabolism and mitochondrial regulation, as well as to clinicians treating mitochondrial diseases.

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Referee #2

Evidence, reproducibility and clarity

Experiments reported in this manuscript indicate that NCT-530- an inhibitor of the de novo serine biosynthetic enzyme PHGDH- worsens mitochondrial pathology.

Systemic administration of NCT-503 decreased serine levels only in Deletor mice- ubiquitously expressing a homologous dominant patient mutation in mitochondrial twinkle helicase.

NCT-503 treatment induced a further increase of the mitochondrial integrated stress response in Deletor mice. Moreover, the metabolic profile and lipid balance was further modified by NCT-503 administration to Deletor mice. The relevance of NCT-503 treatment was finally evaluated in cellular systems exposed with different treatments inducing mitochondrial insults.

Comments:

1. NCT-530 specificity should be confirmed by reducing PHGDH levels by either siRNA or even better by CRISPR-Cas9-mediated gene deletion.
2. If the serine biosynthetic pathway is causally relevant to worsen the mitochondrial pathology, supplementing Deletor mice with a serine-rich diet or intraperitoneal injection of serine is expected to improve pathology.

Significance

Limited significance in the field.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript the authors describe interesting results regarding amino acid metabolism under conditions of mitochondrial stress. The authors used a selective PHGDH inhibitor (compound NCT503), and document that de novo serine synthesis is essential to sustain phospholipid biosynthesis, redox homeostasis, mitochondrial function, and mitochondrial protein synthesis in Deletor mice and in cell culture under mitochondrial stress. Interestingly, serine supplementation does not rescue those metabolic alterations, indicating a specific mitochondrial stress-dependent mechanism of serine utilization by the cells.<br /> In addition, the authors attempted to explore the role of serine in phospholipid biosynthesis under in vivo and ex vivo mitochondrial stress, and this is the weakest part of the manuscript. The specific reduction of mitochondrial PE using the PHGDH is interesting, and it can provide some clues into how mitochondrial membranes are synthetized under mitochondrial stress, but additional experimental approaches should be incorporated to generate a more robust body of evidence.<br /> In summary, the manuscript should be improved by using additional experimental approaches. In addition, genetic intervention through PHGDH gain or loss of function can help to elucidate the molecular mechanisms. The authors could also incorporate metabolomic studies using C13 serine or C13 glucose supplemented culture medium in order to differentiate carbons that come from extracellular serine or from de novo synthesis.

Additional questions:

Is PHDG subcellular localization modulated under mitochondrial stress conditions?

Is the extracellular serine uptake impaired under mitochondrial stress?

ATF4 is a transcription factor that regulates amino acid transport. In this connection, is ATF4-dependent serine transporters modulated under mitochondrial stress or under PHGDH treatment?

Regarding the alteration in redox homeostasis, mitochondrial function and mitochondrial protein synthesis. Is the alteration of phospholipid synthesis upstream of all of those mitochondrial alterations?

Additionally, in my opinion the results should be reorganized, because in the current format the manuscript is fragmented, and several panels lose the symmetry.

Major concerns.

Figure 1<br /> 1. The authors should incorporate the Deletor mice amino acid levels in muscle. How does the PHGDH inhibitor treatment modulate the other non-essential amino acids?<br /> 2. The authors should incorporate the data of WT + vehicle and wt + NCT503 mice in figures 1C, 1D, 1F and 1G, in order to compare properly the effects of PHGDH inhibitor<br /> 3. Figure 1D is mislabeled<br /> 4. Figure 1H. Are there any effect of PHGDH in the mtDNA of WT mice?

The author might incorporate this information, to show properly that mitochondrial stress led to dependence of serine to sustain muscle homeostasis

Figure 2

Interestingly the authors observe a decrease in total mitochondrial lipids content, and an increase in mitochondrial PE content in Deletor mice compared to WT mice. These results suggest an alteration in the phospholipids flux between mitochondria and endoplasmic reticulum in this model of mitochondrial disease. Moreover, PHGDH treatment appears to be able to rescue this alteration. Some question related this issue:<br /> What is the expression of genes involved in the balance PC, PE, PS?, Are the PSS1, PSS2, PSD and PEMT expression altered?<br /> Regarding the phospholipid synthesis. Is mitochondria or endoplasmic reticulum ultrastructure altered in Deletor mice muscle?<br /> The authors should explain the possible mechanisms.

Figure 3

Based on the metabolomic studies, the authors propose a time-dependent decrease in PSTA1 and phosphoserine in cells under mitochondrial stress (Figure 3D). To elucidated the direct role of PHGDH, the authors should analyze the phosphoserine and different phospholipid (described in figure 2E) in presence of PHGDH inhibitor. This will help to understand the link between the endogenous serine synthesis and mitochondrial PE accumulation.<br /> Figure 3H shows a decrease in phosphoserine in the presence of PHGDH inhibitor but this figure is asymmetric compared to figure 3I. Can the authors use another experimental approach to detect the specific mitochondria phospholipid levels (used in Figure 2 for instance)?<br /> The authors should incorporate mitochondrial PE analysis in figure 3 to link the cellular studies described in this figure with the studies done in Deletor mice muscle.<br /> Figure 3 I-K. The authors suggest an alteration in glutathione redox state and a further increase in mitochondrial superoxide production in cells treated with the PHGDH inhibition under mitochondrial stress. What are the total glutathione levels under these conditions? Could GSH regeneration improves the mitochondrial function and mitochondrial protein synthesis? Is extracellular serine able to rescue the reduced glutathione levels?

Minor concerns.

Figure 3B,C does not show the statistical analysis so please incorporate this information.

Include quantification of figure 3 E and supplementary figure S3E.

figure S3E. Improve flow cytometry histogram, the cell population data values cannot be observed.

Some methods and primers included in the material and methods section are no used in the manuscript.

Significance

In this manuscript the authors describe interesting results regarding amino acid metabolism under conditions of mitochondrial stress. The authors used a selective PHGDH inhibitor (compound NCT503), and document that de novo serine synthesis is essential to sustain phospholipid biosynthesis, redox homeostasis, mitochondrial function, and mitochondrial protein synthesis in Deletor mice and in cell culture under mitochondrial stress. Interestingly, serine supplementation does not rescue those metabolic alterations, indicating a specific mitochondrial stress-dependent mechanism of serine utilization by the cells.

This data will be relevant to better understand the connection between alterations in mitochondrial function and amino acid metabolism in cells, and in organisms.

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Reply to the reviewers

Reviewer #1:

Major comments:

1. In figure 4 and throughout the text they refer to AVP neurons as being "directly" inhibited by glucose but they do not show this. They do show membrane depolarization in response to decreased glucose form 2.5 to 0.1 mM; however this could easily be an indirect effect. To demonstrate that they directly sense glucose, it is necessary to block presynaptic input (generally done with the sodium channel blocker tetrodotoxin). In addition, the example recordings are not very good. The neurons seem to just decrease action potential frequency throughout the recording, despite the membrane depolarization. In the methods section, they state that cell resistance was measured but they do not report it. Looking at the traces, it appears that resistance does not change in response to decreased glucose which further supports a presynaptic action. In order to characterize these neurons as directly glucose sensing, they must use a blocker of presynaptic activity such as tetrodotoxin and the resistance values should be reported. They should also choose better representative traces.

Answer: By “directly inhibited” we do not refer to a cell-autonomous effect but to a direct glucose responsiveness. Our cFOS experiments (Fig 3) and preexisting in vivo fiber photometry data (Kim et al., PMID: 34787082; Mandelblat-Cerf et al., PMID: 27989461) reported activation of AVP neurons in conditions of hypoglycemia. We further wanted to verify that the activation was triggered directly by the decreased glucose levels and not by accompanying systemic signals (for example insulin levels, stress response, etc). Furthermore, we aimed at identifying if inactivation of Tmem117 in AVP neurons affects their glucose responsiveness (at the circuit level). Therefore, we decided to take into account the synaptic inputs. Of course, this approach provides information at the circuit level (that we consider essential for CRR characterization) but is also accompanied with highly variable firing patterns. We focused our analysis on the membrane potential, as an indicator of cell excitability state that based on our previous experience was proven to be consistently affected in glucose sensing neurons regardless of cell-type, inputs or topology (Strembitska et al., PMID: 36180454; Kessler et al., PMID: 34622169; Quenneville et al., PMID: 32839348; Quenneville et al., PMID: 32839348; Labouèbe et al., PMID: 29218531; Steinbusch et al., PMID: 27422385; Labouèbe et al., PMID: 27322418; Lamy et al., PMID: 24606905). We used membrane resistance as an indicator of our patch-clamp quality. As specified in the Materials and Methods section (pages 33, lines 702-704), neurons with an access resistance >25 MΩ or changing by >20% during the recording were excluded from the analysis.

We understand that the term “directly affected” has created some confusion since it could be interpreted as cell-autonomous effect. Therefore, we have exchanged the term as follows:

• Results section (page 14, line 281): “To determine whether AVP neurons were directly sensitive to hypoglycemia and whether Tmem117 would modify this sensitivity” was converted to “To determine whether AVP neurons were sensitive to the decreased glucose availability and whether Tmem117 would modify this sensitivity”
• Discussion section (page 27, lines 548-553): “Thus, AVP neurons can directly respond to hypoglycemia. Interestingly, a recent study reported that hypoglycemia can also activate AVP neurons indirectly, through afferent connections arising from GI neurons of the basolateral medulla (Kim et al., 2021). Thus, AVP neurons are part of a brainstem-hypothalamus neuronal circuit where hypoglycemia can be sensed by neurons located at multiple sites to activate the secretion of AVP leading to increased secretion of GCG.” was converted to “Thus, AVP neurons can respond to decreased glucose levels. Interestingly, a recent study reported that hypoglycemia can also activate AVP neurons through afferent connections arising from GI neurons of the basolateral medulla (Kim et al., 2021). Thus, AVP neurons are part of a brainstem-hypothalamus neuronal circuit where hypoglycemia can be sensed by neurons located at multiple sites to activate the secretion of AVP leading to increased secretion of GCG.”

Regarding the membrane resistance measurements, 6 out of 7 AVPTM117KO GI cells and 6 out of 9 AVPTM117WT GI cells show a decrease in conditions of low glucose suggesting a cell autonomous effect, at least in a subpopulation of AVP GI neurons. We realized, based on the reviewer’s comment, that not including these data can lead to misinterpretation of the results, therefore we included the membrane resistance measurements in figure 4 and added the corresponding explanation in the Results section (page 15, lines 295-298).

Regarding the representative traces, those included in the figure clearly show the increase in membrane potential that was consistent in all our GI cells. Choosing a representative trace for firing patterns is unrealistic given the high variability observed without the use of synaptic blockers.

1. The relationship between the AVP/Tmem117 knockout and glucagon secretion is also not very convincing - especially in terms of the data showing that the effect seen at 1-week reverses by 3 weeks post knockdown in males when the neurons have died. First, the effect is very slight to begin with. Second, and more of a concern, the over secretion is still evident at the later time point, it just did not reach significance. This could have been due to the difference in sample size. Sample size in the early timepoint is 15-16 and in the late time point is reported as 8-9 (almost half the size), and in fact when I try to count data points, it really looks like the WT sample size is only 5. To make the conclusion that the effect disappears at the later time it is necessary to increase sample size so that the studies are equivalent. This is very important because these data are what is supporting the hypothesis that Tmem117 in AVP neurons regulates (inhibits) glucagon secretion. It is also concerning that the increased glucagon secretion in the knockout did not translate into differences in the depth of hypoglycemia raising concerns about physiological relevance.

Answer: The sample size required based on our power calculations for these traits (plasma glucagon and copeptin) in male mice is 8 animals per group. Figure 2 consists of data merged from two individual experiments, both of which showed a significant effect. Figure 6 is the 3rd replication of our phenotypic observations with the addition of a later timepoint measurement. The number of animals included is 9 AVPTM117WT and 8 AVPTM117KO. Only for copeptin levels (panel F), as stated in the figure legend, the n is 8 mice per group, since for one of the AVPTM117WT mice the baseline levels of copeptin were undetectable by ELISA. Counting the datapoints in a 2D graph can cause misinterpretation since similar values can be depicted as overlapping points. We had decided to present in Fig 6 only the later timepoint that corresponds to the IF results reporting AVP cell death. We now realize based on the reviewer’s comment that this can cause misinterpretation of the phenotypic outcome, therefore we have exchanged graphs E-G (containing 2 timepoints) for the corresponding graphs that contain all 3 timepoints. We believe that with this addition the concern will be addressed since with the same sample size there is a significant increase in glucagon and copeptin secretion in AVPTM117KO mice at the early timepoint that is no longer evident at the later timepoint.

Regarding the concern of effect size, we acknowledge the fact that we are reporting a small effect. But the CRR is controlled by a highly complex network of hypoglycemia activated neurons, which are located in different brain regions and act in an integrated manner to induce glucagon secretion through autonomic control, the HPA axis, and the secretion of AVP (reviewed in Steinbush et al., PMID: 26163755; Thorens, PMID: 34989155). Therefore, in this highly redundant and robust system, suppressing only one hypoglycemia sensing node, cannot be expected to have major consequence on the glucagon secretion. Nevertheless, we provide solid evidence that inactivation of Tmem117 in AVP neurons leads to a slight upregulation of AVP secretion that is accompanied by a subsequent increase in the glucose-stimulating hormone glucagon. To further highlight this observation, we added in figure 6 the correlation analysis between glucagon and copeptin levels of each tested sample that is reporting a significant positive correlation in both genotypes.

Regarding the concern for physiological relevance, we would like to clarify that in this model we have not followed the reestablishment of normoglycemia over time. The timepoint of blood collection and glycemic measurement was selected based on our previous experience. We have performed multiple times full insulin tolerance tests in various mouse models, and we selected here (and in other papers: Picard et al., PMID: 35339728; Strembitska et al., PMID: 36180454) the one-hour time point because this is always the nadir of the glycemic curves, where CRR has the strongest effect in stimulating the secretion of CRR hormones. Glycemia at this time point depends on the insulin sensitivity of the animal. Thus, finding similar glycemia at 60 minutes after insulin injection indicates similar insulin sensitivity in AVPTM117WT and AVPTM117KO mice, further supporting that the increased glucagon secretion is directly correlated to the increased AVP secretion.

1. It is also curious that the females in proestrus were similar to males but that females in other stages showed no effect. If, as mentioned in the discussion, estradiol was involved one would expect that males would be more similar to females in stages with low estradiol. The sex difference should be discussed more fully since it does seem to be one of the strong conclusions in the study

Answer: We acknowledge that the discussion of the sex-specific phenotype was minimal in the initial version of our manuscript. We consider this finding very important for the general understanding of sex-differences in CRR, therefore we have included it in our main figures. But given that our observations remain in the phenotypic level our intention was to keep the discussion minimal and not add a lot of speculations that would not be supported by experimental data. We have performed all our further mechanistic analysis only in male mice and therefore we are not able to comment about this sex specific effect with evidence on the underlying mechanism. Of course, we have our literature-based explanation/hypothesis for the observed phenotype and given that this point was raised by all 3 reviewers, we have expanded this section of the discussion (pages 26, lines 520-542).

1. The discussion needs to be adjusted in line with the findings.

Answer: Several adjustments both in the Results and Discussion sessions have been incorporated based on the reviewers’ comments. Details on the adjustments for each point raised can be found in the corresponding answer.

Minor comments:

The BXD mice should be defined and the reason for their use explained

Answer: We did not include further information in the first place because all the details and rational for the use of BXD mice were included in the original publication of this genetic screen (Picard et al., PMID: 35339728). We have now added further details on the origin of the BXD mouse lines accompanied with the corresponding reference (page 4, lines 83-85).

The abbreviation cQTL needs to be defined at first use in the text of the results

Answer: We added the abbreviation (page 5, line 102).

Reviewer #2:

Minor comments:

1. Page 5, line 111: Fig1 shows Tmem117 expression in the hypothalamus. Tmem117 expression in AVP neurons in the PVN and SON are highlighted, however, AVP is not only expressed in these areas in the hypothalamus and in extra-hypothalamic areas. Was Tmem117 also expressed in AVP neurons beyond these areas?

Answer: Based on our immunofluorescent detection Tmem117 is only observed in AVP neurons of the PVN and SON. This pattern (Fig 1A), the percentage of AVP-Tmem117 co-positive cells in these two areas (Fig 1H, I) and the presence of strong positive staining in the neurohypophysis (Fig 1B) points towards specific expression in magnocellular neurosecretory AVP cells. We do not observe staining in other AVP positive neurons. An example can be found in Fig 1A where Tmem117 staining is localized at the PVN and SON, but not at the Suprachiasmatic nucleus.

1. Page 10, line 219: Fig2 shows CRR responses after conditional Tmem117 ko in AVP neurons. A sex-dependent difference was observed in the increased CRR after Tmem117 inactivation. A similar pattern between males and females during pro-estrus in copeptin and glucagon was observed. Was AVP expression and AVP neuron response to hypoglycemia also analyzed in both sexes, and in the case of females, were the results analyzed during the pro-estrus phase?

Answer: Unfortunately, a detailed analysis of the mechanistic underpins of the estrus phase-dependent phenotype in female mice would exceed the spectrum of this initial characterization. We do believe that this finding is of great importance for the general understanding of sex-differences in CRR, therefore we have included it in our main figures. But our observations remain at the phenotypic level. We have performed all our further mechanistic analysis only in male mice. Of course, we have our literature-based explanation/hypothesis for the observed phenotype and given that this point was raised by all 3 reviewers, we have expanded this section of the discussion (pages 26, lines 520-542).

1. Many of the experiments performed after Fig 3 (glucose responsiveness of AVP neurons, ER stress and ROS production, Ca2+ imaging) are mainly analyzed in the SON, but not the PVN. What is the rationale for analyzing only the SON in some experiments? Analyzing the PVN is of relevance due to its known role in CRR triggering.

Answer: The reason for focusing our single-cell level analysis only in SON is its homogeneous population of AVP-Tmem117 double positive cells (Fig 1I). In the PVN about 10% of AVP neurons are negative for Tmem117 (Fig 1H). Therefore, we would be unable to distinguish between the cells with Tmem117 inactivation and those that did not express it at the first place. We agree with the reviewer that PVN is of great relevance for CRR induction, and this is why for all our in vivo phenotyping experiments we aimed at targeting both areas. Our AVPTM117KO mouse model is generated by injection of the AAV6-AVP-icre virus in the posterior pituitary and is targeting both regions (Fig S2E). This model was used for all our phenotypic analyses (Fig 2, 6) and for the fiber photometry recordings (Fig 5H-M). The Ca2+ signal we obtain is a collective output of AVP magnocellular terminals in the posterior pituitary deriving from both PVN and SON. But when it comes to the analysis of responses at the single-cell level, not having the ability to identify the subpopulation of Tmem117 negative cells could have a big impact on the results and their interpretation.

1. Page 16, line 346: Again here, only BIP expression in AVP neurons is shown in the SON. I would be interested in looking at expression in AVP neurons in the PVN. Same goes for activation of these neurons (measured by Ca2+) after insulin administration.

Answer: As mentioned above, we did not quantify these single-cell events in AVP neurons of the PVN due to the inability of effectively distinguishing between cells with Tmem117 inactivation and those that do not express Tmem117. But based on our supporting data (activation of PVN AVP cells in Fig 3 and decreased number of AVP PVN cells upon Tmem117 inactivation in Fig 6) we believe that the effect of Tmem117 inactivation is comparable between the Tmem117-positive AVP cells of both areas.

1. What would be the role of Tmem117 in CRR triggering after hypoglycemia? Would its expression change during hypoglycemic conditions versus normoglycemia? Would that be different in models of pathology, like diabetes? Speculation of its physiological role in the discussion might be of help to visualize its role beyond inactivation.

Answer: We focused our discussion strictly on the acquired results, but we agree with the reviewer than including speculations regarding the physiological role of Tmem117 would enhance the communication of our results. We have incorporated some extra points focusing on the possible role of Tmem117 expression in physiological conditions and pathological states in the Discussion section (pages 27-28, lines 562-571). Furthermore, we have included some extra data from microdissected PVN and SON tissue reporting downregulation of Tmem117 in the SON as a physiological response to insulin-induced hypoglycemia (Fig 3G-I).

Reviewer #3:

Major comments:

1) The in vivo calcium data is not convincing (e.g. Figure 5j). Why is the activity not similar to that normally seen from populations of neurons (and in particular AVP neurons) in vivo - bursting/spikes. This is just a background increase in calcium... not an increase in activity?

Answer: Our aim with the fiber photometry experiments was not to detect the activity of AVP neurons but to evaluate the Ca2+ fluctuations in AVP neuronal terminals, directly at the site of AVP exocytosis. The activity of AVP neurons in conditions of hypoglycemia has been previously detected with similar techniques at the level of cell bodies in the SON (Kim et al., PMID: 34787082; Mandelblat-Cerf et al., PMID: 27989461) and was further supported by our cFOS and ephys experiments (Fig 3, 4). These experiments showed that inactivation of Tmem117 did not affect the activation of AVP neurons in conditions of hypoglycemia. But our phenotypic characterization revealed higher secretion of AVP (Fig 2) and our in vitro (Fig S3) and in vivo (Fig 5A-G) data demonstrated increased ER stress and ROS in AVPTM117KO cells. Therefore, we wanted to assess if this increased stress is associated with increased Ca2+ at the level of neuronal terminals which would enhance AVP exocytosis. The acquired signal is a collective output of AVP magnocellular neurons from PVN and SON and although it is not resembling the pattern observed from cell body recordings, we believe it provides important information for the output of this circuit in conditions of hypoglycemia. Our results do not only report that Tmem117 inactivation is accompanied by an increase in intracellular Ca2+ at the level of nerve terminals/exocytosis, but further demonstrate (for the first time to our knowledge) that the activity reported in conditions of hypoglycemia is linked, as expected, to an increase in local Ca2+ levels at the posterior pituitary. All the essential controls for normalization and interpretation have been taken into account as mentioned in the methods section (page 31, lines 655-668). GCaMP7 signal recordings are corrected for artifacts and photobleaching. The isosbestic channel is subtracted to correct for non-GCaMP7 related signal artifacts and then at a second stage (during the DF/F0 normalization) the traces are corrected for photobleaching by the running average algorithm. Furthermore, 60min recordings of mice injected with saline are used as control for determining the increased signal after insulin injection. We have now added a clarification on the origin of the recorded signal in the Results section (page 18, line 368).

2) Why would this response be oestrus cycle dependent? This is given very little thought in the manuscript.

Answer: We acknowledge that the sex-specific phenotype observed was not thoroughly discussed in the initial version of our manuscript. We included the data in a main figure since we consider them as an important piece of information supporting the crucial role of sex hormones in the regulation of CRR. On the other hand, since our observations remain in the phenotypic level, we thought it would be better to keep the discussion on the topic short and not include various literature-based hypothesis that would not be supported by experimental data. We have performed all our further mechanistic analysis only in male mice and therefore we are not able to comment about this sex specific effect with evidence on the underlying mechanism. We have now realized based on the reviewers’ comments that further discussion on the topic is needed, therefore we have expanded this section of the Discussion (page 26, lines 520-542).

Minor comments:

1) How does the regulation make sense in the context of AVPs normal role in whole body physiology?

Answer: We focused our discussion strictly on the acquired results, but it has become evident based on the reviewers’ comments that a discussion of the broad picture regarding the role of AVP and Tmem117 in physiological and pathological conditions would strengthen the interpretation of our results. We have now incorporated some extra points focusing on the possible role of Tmem117 expression in physiological conditions and pathological states in the Discussion section (pages 27-28, lines 562-571).

2) What do the authors think about the role of AVP in islets, some authors suggest it actually has the opposite effect.

Answer: The literature on the role of AVP on pancreatic islets shows indeed some controversies. A couple of studies suggest an action on beta cells and insulin secretion without any effect on glucagon secretion. On the other hand, the evidence on the stimulation of glucagon secretion is supported by numerous studies over the years and has been convincingly demonstrated with various ex vivo and in vivo models. Our study, in line with these findings, verifies the increased AVP secretion in conditions of hypoglycemia and reports a positive correlation between the levels of circulating copeptin and glucagon (Fig 6H).

3) Why have the authors left the recent study by Kim, Rorsman and colleagues et al. 2022 (eLife) to the discussion? This manuscript is an important piece in this field and should be introduced in the introduction.

Answer: We have now added the AVP induced glucagon secretion, accompanied by the supporting references (including the reference mentioned by the reviewer) as integral part of the CRR in our Introduction section (pages 3-4, lines 68-71).

4) The intro should also mention that islets also can intrinsically regulate glucagon release.

Answer: We focused our introduction exclusively to the brain triggered glucagon secretion, but in accordance with the reviewer’s comment we have now incorporated the intra-islet regulation and supporting literature in the Introduction (page3, line 55).

5) Has tmem117 shown up in any human screens of hypogylcaemia risk? Do tmem117 variants show in any osmoregulation or insipidus risk?

Answer: Not to our knowledge.

6) Please clarify what N= is? What is a statistical unit in each case in the graphs. It is not clear when it is a mouse, cell or experiment. This should be justified in each case.

Answer: We have now justified the corresponding values in all figure legends.

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Referee #3

Evidence, reproducibility and clarity

Glucagon secretion counters hypogylcaemia, and this is in part regulated by the CNS.

The authors investigated the role of hypothalamic Tmem117, which was previously identified in a genetic screen as a regulator of the counter-regulatory response to hypoglycaemia. They show that Tmem117 is expressed in vasopressin neurons and that inactivation of Tmem117 in AVP neurons increases hypoglycemia-induced vasopressin secretion leading to higher glucagon secretion. They show this is oestrus cycle phase-dependent.

The manuscript is generally well written and the results of good quality. The findings are important.

I have some comments:

Major:

1. The in vivo calcium data is not convincing (e.g. Figure 5j). Why is the activity not similar to that normally seen from populations of neurons (and in particular AVP neurons) in vivo - bursting/spikes. This is just a background increase in calcium... not an increase in activity?
2. Why would this response be oestrus cycle dependent? This is given very little thought in the manuscript.

Minor:

1. How does the regulation make sense in the context of AVPs normal role in whole body physiology?
2. What do the authors think about the role of AVP in islets, some authors suggest it actually has the opposite effect.
3. Why have the authors left the recent study by Kim, Rorsman and colleagues et al. 2022 (eLife) to the discussion? This manuscript is an important piece in this field and should be introduced in the introduction.
4. The intro should also mention that islets also can intrinsically regulate glucagon release.
5. Has tmem117 shown up in any human screens of hypogylcaemia risk? Do tmem117 variants show in any osmoregulation or insipidus risk?
6. Please clarify what N= is? What is a statistical unit in each case in the graphs. It is not clear when it is a mouse, cell or experiment. This should be justified in each case.

Significance

This is important, but some of the in vivo calcium data is weak. The manuscript is also riddled with self-citations and doesn't really put the results into a broader context. In addition, the finding is in a sense piggybacking off a recent revelation in the field (AVP neuron importance) using a technique that is available to the authors (Tmem117 manipulation) rather than trying to address the hypothesis/question with THE BEST approach to answer the question. The question feels like: "how can we use our mouse model to probe this AVP neuron hypothesis ourselves". Strange. The finding that the results are only relevant during a particular phase of the oestrus cycle feels a bit random and lacks adequate explanation. I still like the manuscript very much, the experiments are very technically challenging, the results are very interest, the data (mostly) of excellent quality, and the relevance to the field highly important, but I feel there is still room for improvement (unlikely more experiments, definitely some thinking and rewritting).

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Referee #2

Evidence, reproducibility and clarity

The present study led by Gaspari, et al shows a new role for Tmem117 as a major regulator in the CRR. The authors used a variety of techniques to demonstrate that Tmem117 innactivation modulates hypoglycemia-induced responses by regulating AVP action in the hypothalamus. I found the rationale of the study interesting, the results seem to support the author's claims and conclusions seem to be proper. However there are some minor issues that needs to addressed:

1. Page 5, line 111: Fig1 shows Tmem117 expression in the hypothalamus. Tmem117 expression in AVP neurons in the PVN and SON are highlighted, however, AVP is not only expressed in these areas in the hypothalamus and in extra-hypothalamic areas. Was Tmem117 also expressed in AVP neurons beyond these areas?
2. Page 10, line 219: Fig2 shows CRR responses after conditional Tmem117 ko in AVP neurons. A sex-dependent difference was observed in the increased CRR after Tmem117 inactivation. A similar pattern between males and females during pro-estrus in co-peptin and glucagon was observed. Was AVP expression and AVP neuron response to hypoglycemia also analyzed in both sexes, and in the case of females, were the results analyzed during the pro-estrus phase?
3. Many of the experiments performed after Fig 3 (glucose responsiveness of AVP neurons, ER stress and ROS production, Ca2+ imaging) are mainly analyzed in the SON, but not the PVN. What is the rationale for analyzing only the SON in some experiments? Analyzing the PVN is of relevance due to its known role in CRR triggering.
4. Page 16, line 346: Again here, only BIP expression in AVP neurons is shown in the SON. I would be interested in looking at expression in AVP neurons in the PVN. Same goes for activation of these neurons (measured by Ca2+) after insulin administration.
5. What would be the role of Tmem117 in CRR triggering after hypoglycemia? Would its expression change during hypoglycemic conditions versus normoglycemia? Would that be different in models of pathology, like diabetes? Speculation of its physiological role in the discussion might be of help to visualize its role beyond inactivation.

Significance

The study of Gaspari, et al proposes a new player in the hypoglycemia-induced CRR, specially affecting AVP modulation of CRR. The results and conclusions are of relevance to the field, as the mechanisms mediating hypoglycemia-induced CRR and how these are affected during pathology are not completely understood. The study is relevant in the metabolic field and for a neuroendocrinology public.

Field of expertise: Neuroendocrinology, hypoglycemia, diabetes.

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Referee #1

Evidence, reproducibility and clarity

In this study the authors convincingly demonstrate that the hypothalamic protein Tmem117 protects against elevated ROS and cell death in AVP neurons, and that knocking out Tmem117 in these neurons leads to cell death. However, the major thrust of this paper was to demonstrate that Tmem117, in AVP neurons plays a role in hypoglycemia counter regulation (per the title). The data supporting this hypothesis are much less convincing.

Major comments

There are 2 major findings of this study that are problematic (#1 and 2 below). Further experiments are needed to support their conclusions.

1. In figure 4 and throughout the text they refer to AVP neurons as being "directly" inhibited by glucose but they do not show this. They do show membrane depolarization in response to decreased glucose form 2.5 to 0.1 mM; however this could easily be an indirect effect. To demonstrate that they directly sense glucose, it is necessary to block presynaptic input (generally done with the sodium channel blocker tetrodotoxin). In addition, the example recordings are not very good. The neurons seem to just decrease action potential frequency throughout the recording, despite the membrane depolarization. In the methods section, they state that cell resistance was measured but they do not report it. Looking at the traces, it appears that resistance does not change in response to decreased glucose which further supports a presynaptic action. In order to characterize these neurons as directly glucose sensing, they must use a blocker of presynaptic activity such as tetrodotoxin and the resistance values should be reported. They should also choose better representative traces.
2. The relationship between the AVP/Tmem117 knockout and glucagon secretion is also not very convincing - especially in terms of the data showing that the effect seen at 1-week reverses by 3 weeks post knockdown in males when the neurons have died. First, the effect is very slight to begin with. Second, and more of a concern, the over secretion is still evident at the later time point, it just did not reach significance. This could have been due to the difference in sample size. Sample size in the early timepoint is 15-16 and in the late time point is reported as 8-9 (almost half the size), and in fact when I try to count data points, it really looks like the WT sample size is only 5. To make the conclusion that the effect disappears at the later time it is necessary to increase sample size so that the studies are equivalent. This is very important because these data are what is supporting the hypothesis that Tmem117 in AVP neurons regulates (inhibits) glucagon secretion. It is also concerning that the increased glucagon secretion in the knockout did not translate into differences in the depth of hypoglycemia raising concerns about physiological relevance.
3. It is also curious that the females in proestrus were similar to males but that females in other stages showed no effect. If, as mentioned in the discussion, estradiol was involved one would expect that males would be more similar to females in stages with low estradiol. The sex difference should be discussed more fully since it does seem to be one of the strong conclusions in the study
4. The discussion needs to be adjusted in line with the findings.

Minor comments:

The BXD mice should be defined and the reason for their use explained<br /> The abbreviation cQTL needs to be defined at first use in the text of the results

Significance

The observation that Tmem117 is protective against ROS and cell death is interesting. However, the other conclusions are not supported by the data which decreases the overall significance of the study

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Reply to the reviewers

Thank you for the rapid and favorable reviews of our manuscript entitled “Long-Read Genome Assembly and Gene Model Annotations for the Rodent Malaria Parasite Plasmodium yoelii 17XNL.” We particularly appreciated that both reviewers had substantial, detailed expertise with the sequencing and assembly of Plasmodium genomes, and valued their questions and suggestions to ensure high rigor of our work. We have addressed all of the reviewers’ comments in the revised manuscript, and have provided a point-by-point response to each below.

Response to Reviewers

Note: Point-by-point responses are provided in italics below each reviewer comment below. Line numbers referenced in our responses refer to their final line position in the Track Changes version of the manuscript.

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

The manuscript entitled "Long-Read Genome Assembly and Gene Model annotation for the Rodent Malaria Parasite P. yoelii 17XNL" is a well-written manuscript providing updates and important observations about the genome assembly and annotation of this specific non-lethal isolate. The group overall did a great job showing how the application of newer technologies such as long-read DNA and direct RNA sequencing to generate top-quality genomes to be used as a reference for the community. Here are some comments about the work presented:

Response: Thank you for your positive feedback and suggestions on how to clarify these findings. We have improved the revised manuscript based on your feedback and suggestions below.

Major comments: - The authors added several result information across the methods section. Making the text repetitive, since the same is also presented in the results section. Please revise the method section to remove results from this section.

Response: We agree and have streamlined both the Results and Methods sections to remove redundancy in these descriptions.

• Some methods are also redundant in the Result section. For example, in line 141-142, the group describe which DNA extraction kit they used (again this is correctly mentioned in the methods section).

Response*: We agree and have removed minutiae such as these from the Results section. These details remain in the Methods section to ensure reproducibility. *

• Besides important, the group added several information about method comparison between base call accuracy and sequencing methods. I agree that having this information in the supplemental material is great, but I would be careful to not focus too much on those, since most of the observations are already well-known by the community and focus more in the biological relevance of what is being generated with the newly updated genome.

Response: The advances in base calling algorithms do make substantial improvements to the Nanopore reads. We have only included a short description of this in the main manuscript and feel this is an appropriate amount of context for the typical reader. Those that love these details and want to dig further can find this content in our supplemental information.

• The group did a great job generating two versions of the genome, and an updated gene annotation set using long-read sequencing. But the major question is, how about alternative splicing? They mention the use of it (line 350) but I don't see any result about how many alternative transcripts were observed, and if they were differentially detected in different life stages of the sets used for the RNA sequencing. This is a very important result to be added since one of the key pieces of information that long-read RNA sequencing brings for Genome annotation.

Response: We have now expanded this description in the manuscript to note that 866 genes are predicted to have multiple transcript isoforms (Lines 240-241). Moreover, we have now generated a Supplemental Table 4 that lists these isoforms in the revised manuscript. As we have not conducted further validation of this large number of transcript isoforms, we have left the description at this level.

• Same observation as above for potential long ncRNAs.

Response: We agree that lncRNAs are a fascinating aspect of the biology of the parasite, but a proper analysis of this class of RNA is far outside of the scope of this current study. Automatic identification approaches with Nanopore data will likely yield high numbers of false positives, which require manual curation for rigorous annotation. We hope others can use these data to accelerate such studies as well.

• From what I understand the Hifi run was able to generate a gapless genome assembly and the ONT run did not. What was the final coverage for each? From my experience with P. falciparum genomes, ONT even with the rapid kit was able to generate chromosomal level assemblies if the coverage was >100x (but again, this is not a rule). Add those valuable observations about the depth so the reader can check if other variables in the comparison should be made.

Response: This is a particularly interesting aspect of not only our datasets, but of other Plasmodium genomes as well. This issue occurs at least in part due to the presence of many repeated elements in the subtelomeric regions. It is important to note that these repeated elements do not resolve into a single haplotype in an assembly due to conflicting information, not due to lack of coverage. For instance, regions may differ by only a few nucleotides that each have significant read support. We are particularly interested in a recent preprint that concludes that P. falciparum harbors extrachromosomal plasmids with these var sequences present (doi.org/10.1101/2023.02.02.526885). *If this observation is supported via peer review, this interpretation could also begin to explain our results with P. yoelii 17XNL as well. *

• Also be sure that the structural comparisons between the genomes are not the ones used after running ragtag.py. If so, there is a high chance of structural bias in the scaffolded contigs.

Response: We apologize for the confusion. We did not use ragtag for the PacBio assembly, and all structural and variant comparisons were done using the PacBio assembly. However, we did use ragtag for the Nanopore assembly that is included in this study as an additional resource to our community. These data were not used for variant calling though.

• How Prokka differed from Braker2 for the Mitochondria/API annotation? This needs to be very well described since prokka is made for prokaryotic organisms and not for eukaryotic ones. And Braker2 uses a custom build dataset for training, which I believe contains known information about MIT/API for Plasmodium species.

Response: We first applied Braker2 to the organellar genomes and identified only 6 genes in the apicoplast genome and only 2 genes in the mitochondrial genome. Due to their prokaryotic origin, we then tested if Prokka could alleviate this issue. To do so, we applied Prokka to the 17X reference genome and found that it detected all of its annotated organellar genes. Therefore, we also applied Prokka to our Py17XNL genome to annotate the genes found on the apicoplast and mitochondrial genomes. As a final validation check, the gene annotations on these two organellar genomes are effectively identical between 17X and 17XNL. This is consistent with the sequencing results and assemblies that show that the apicoplast genome is identical and the mitochondrial genome differs in a single, notable deletion in 17XNL.

• Figure 5B, what is the peak observed in the mitochondria? What genes? Repeats?

Response: What appears to be an inward pointed trough actually reflects the deletion of bases in 17XNL compared to the 17X assembly. We have clarified this in the manuscript on Lines 296-297 and in the legend of Figure 5.

Minor comments: - For Oxford nanopore sequencing using the ligation kit, did the group check for potential chimeric reads generated by the protocol?

Response*: We did. We used the adapter trimming software, Porechop, to identify and bin chimeric reads that were eliminated from the dataset. This method is described in the Makefile associated with the manuscript. *

• Check if all species are italicized (for example, line 187 P. yoelii is not)

Response: We have italicized this instance of P. yoelii and have reviewed the document to search for any other words that should be italicized.

• In methods add the parameters for minimap2 for the direct RNA alignment

Response*: We would encourage readers to view our MakeFile that has all of the commands and parameters used for the bioinformatic work reported here. *

• For variant calling, I would use a minimum of 10x coverage to make a variant call instead of 5x. Besides looking well reproducible between all checks, I would be careful mainly with the single bp deletions with a such low threshold.

Response: Read counts for the called variants were generally greater than 20. Moreover, we took these validations a step further and manually curated these variants using the data from multiple sequencing platforms used in this study to ensure high rigor in making these variant calls. We have further clarified this in the revised manuscript.

• In some parts of the methods, the authors mentioned slight modifications in some protocols (for example, lines 443 and 454), besides well described in the text, could you highlight what were the modifications in the text? This will facilitate many other researchers to understand why those modifications were needed.

Response: We have clarified these modifications in the revised manuscript. In short, these modifications consisted of: 1) For the HMW gDNA prep kit, an agitation speed of 1500 rpm was used as opposed to the recommended 2000 rpm due to limitations of our instruments. 2) A slow end over end mixing by hand was preferred over using a vertical rotating mixer as yield was consistently greater with this change. 3) For the RNeasy kit, the lysate was passed through a 20-gauge needle for homogenization of the sample. Instead of an on-column DNaseI treatment, the RNA was treated with DNaseI off of the column to promote complete DNA digestion. 4) A second elution from the RNeasy column was performed in order to improve yield.

• As mentioned in the major, the data analysis method section needs rework to remove results from the text.

Response: We have revised the manuscript accordingly.

• The group mentioned that small contigs not mapping to Py17X were discarded. What are those? Repeats? Contamination?

Response: These contigs were of mouse origin, as P. yoelii was grown in Swiss webster mice in this work. We have clarified this in the revised manuscript on Lines 183-184.

Reviewer #1 (Significance (Required)):

This work generated a strong method and resource for a better genome assessment of P. yoelii for the community. As I mentioned in my comments, some more details about the findings such as alternative splicing and lncRNAs may strengthen them even more the publication. I know that comparative analysis between Py17X and XNL is not in the scope here, but more information about it, such as a synteny plot would be great for the community to understand that they can rely on this new reference genome. I've been working with eukaryotic and prokaryotic genomes for more than a decade and I have a lot of experience with all the methods presented. I believe that potentially the depth generated for the ONT data may be one of the factors for not reaching the chromosomal level of this isolate, since HiFI was. The group did a great job on the method description, and I believe that the community will be very happy to incorporate this genome as one of the references for this organism.

Response: We are thrilled that you value the data and the rigor of our approaches. We also believed that a direct comparison between 17X and 17XNL strains is critical. Because of this, we provided details of this comparison in Figures 5 and 6, as well as in supplemental files. Because our colleagues often use these strains interchangeably, it is important for our community to know what differences are present between the parental 17X and the cloned 17XNL line. While substantial identity exists between the 17X and 17XNL strains, there are many variants between them, including many that affect genes that are known to have essential functions for the parasite. For this reason and more, we believe the true 17XNL genome assembly will be a preferred reference once it is fully integrated into PlasmoDB.

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

The paper has three distinct parts, 1. Assembly of the P. yoelii yoelii 17XNL 2 Annotation of the genome and adding UTR regions 3. Comparing the sequence of 17XNL with 17X .

Assembly: The authors present a novel assembly for the P. yoelii yoelii 17XNL genome. They used two different approaches, comparing Oxford Nanopore (ONT) long reads + Illumina DNA with PacBio Hifi. None of the approaches generated a telomer to telomer assembly so sequences from the 17X reference was used to fill in the mssing sequence.

Response: Please also see the comment from Reviewer 1 and our response. The presence of many repeated elements in the subtelomeric regions leads to the challenges noted here about a telomere-to-telomere assembly, as well. The presence of these elements means that the sequences do not resolve into a single haplotype in an assembly due to conflicting information, not due to lack of coverage. Because of this, we have chosen to harmonize the selected haplotype at these subtelomeric regions with that of 17X, while still acknowledging and providing the complex data associated with the subtelomeric regions.

Annotation Next, they generated long reads (ONT)and Illumina RNA-Seq to improve the annotation. Although, their annotation is not better than the current P. yoelii 17X reference genome in PlasmoDB, they could predict the UTR regions and alternative splice sites due to the 3' capturing approach and long reads. Having the UTR annotated and potentially having alternative splice sides is useful for the field.

Response: We agree that the additional gene model annotations for both UTRs and alternative transcript isoforms is a valuable resource to our community. We are working with PlasmoDB currently to make these data readily accessible.

17XNL - 17X comparison The author compared the 17XNL with the 17X reference. Both genomes were done with Pacbio, and it should be noted that P. yoelii has a GC content of probably ~23% with several homopolymer tracks. Further, the 17XNL genotype was obtained from a 17X culture, so the genomes are expected to be very similar as the author noted in the introduction. The authors found ~2000 differences; some are in genes, but many are indels, which very well could be sequencing errors. Finally, the authors claim that this genome could become relevant for the community as new reference to perform analysis. As their genome is so similar to 17X and they have to show that their annotation is at least as good as the current 17X reference genome (manual curated) and the difference are not due to sequence error in 17X or 17XNL.

Response: As we describe below, we have taken multiple steps to inspect the quality of the 17X genome assembly (it is very robust), to call variants between strains, and to validate them using our data across multiple sequencing platforms and via manual curation. Because of this, we view these as true variants between the 17X and 17XNL genomes

Major comments Overall I struggle to see the need for a "NEW" P. yoelii reference. It would be good to state how similar these genomes are - they are basically identical. As the 17XNL is curated manually, it would have made more sense to me to start from that one and then generate the UTR annotation and include splice sides. This could be easily loaded into an alternative Web-apollo track and then merged to the current annotation to be useful to the community.

Response*: We chose to generate a new reference assembly for 17XNL because the current one is from 2002, remains in >5000 contigs, has gene identifiers that do not align with other current Plasmodium gene models (e.g., PY00204 vs. PY17X_0502200), and historically has had problematic gene models attributed to individual genes. This clean start ensures that users can know the provenance of the underlying data that created the genome assembly and gene models. *

I wonder if many of the differences the authors found between 17X and the 17XNL reference are true. The authors are correct that some differences between 17X and 17XNL are true. I could not find any evidence of genome polishing with tools like Pilon or ICORN to correct sequencing errors, I wonder if these differences are sequencing errors.

Response: The PacBio-based assembly received no error correction or polishing. It should be noted that all variants that were called automatically were also manually verified using data from multiple sequencing platforms generated in this study. Moreover, for coding sequences, we imposed a threshold that 80% of all reads at the variant’s location needed to support the variant in order to be considered true. Through these strict thresholds, we eliminated many potential variants that only had support from one sequencing platform. We highlight several variants that were confirmed through multiple datasets in Table 2.

Did the authors look into the reads of the NCBI - GCA_900002385.2 - assembly? Maybe they could use the underlying Illumina reads if theirs don't have enough coverage. Also, the differences between 17X and 17XNL could be that the reference is wrong. How many pseudo genes did they obtain? Are there more or less than in the current reference?

To confirm the calls, could you also map the 17XNL reads against the 17X reference and see if they are still true. As the same time, map the 17X illumina reads to see if the reference is correct at this state. When looking at the alignments, it can be seen that many different are in low complexity/repetitive regions.

Response: We analyzed both their raw and assembled data to compare them with our results, and we determined that the 17X data and assembly were robust and that these difference likely reflect true variance between the strains. The 17X reference has 57 pseudogenes that are annotated as pir, fam-a/c, or others. Overall, there were 1057 pir genes annotated in the 17X genome, whereas we annotated 1048 for our Py17XNL genome. There were 302 fam-a/b genes annotated in the 17X genome, whereas we annotated 301 for our Py17XNL genome. As noted above, we confirmed variant calls using data from multiple sequencing platforms in this study as well as through manual curation.

The authors sequence their genome with a HiFi Pacbio run and also ONG + DNASeq... but why did they not get 16 chomromes out? For example the current P. yoelii reference was assembled directly into far less pieces than theirs [P. chabaudi assembles into 16 pieces]. Could it be a different read depth or is it the fragment length? Could the authors please comment on that. Also, if there were contigs, why did they fill the sequence with 17X sequence, rather than keeping gaps? So in the end, their sequence is a hybrid, of 17X and 17XNL, right?

Response: Please see our responses above to both Reviewer 1 and 2 regarding the heterogeneity of the subtelomeric regions that indicate that a single haplotype is not readily called. This is not due to insufficient read depth, but rather we believe it reflects something fascinating about Plasmodium genomes in these regions. A recent preprint (doi.org/10.1101/2023.02.02.526885) provides one possible interpretation for this observation.

Why do you think you had less coverage of CCS read around the telomer ends? Do you think it is a systematic issue of the PacBio Hifi? Did you see any evidence of Illumina or ONT reads - or could it be that while culturing the telomer ends dropped off?

Response: See our response above about the challenging nature of the subtelomeric regions of Plasmodium genomes. As above, this is not an issue of coverage per se, but rather of heterogeneous related sequences that are not readily resolved into a single haplotype. In order to minimize the risk of sequencing a genome of a mixture of heterogeneous parasites, we sequenced “Pass 0” parasites received directly from BEI Resources to ensure this genome reflects the established P. yoelii 17XNL clone.

I realised that the authors used a lot of primary tools. I wonder why they chose that path, as there are several tools to do automatic finishing for long read assemblies: Assemblosis, ARAMIS, MpGAP or ILRA. Especially the last one focuses on Plasmodium genomes. Please comment.

Response: We initially started our bioinformatic analyses using established tools such as these. Specifically, we first tried Companion and ILRA, but the results were not superior to those we achieved with the workflow we describe in this manuscript, which also provided greater parameter control.

Also, for the annotation, could it not be better to transfer the manually curated genome annotation with LIFT off or RATT? All these tools are widely used in the generation of reference genomes in the parasitology field. I annotated their sequence with Companion, and although their gene models are good and some of the Companion calls might need improvement, overall, the Companion results look more exact to me.

Response: Companion was the original tool we used for the generation of gene models. While we found that for a pre-package software platform it performed excellently, we found it to be insufficiently customizable and the results were not sufficiently accurate from our assessment. Additionally, lifting over information always raises the risk of imposing a different perspective on what is truly present. We believe that a high quality, de novo assembly is always preferable, and therefore chose this workflow.

The code is very well organised, and it was easy to follow. Are you planning to put it on a GitHub repository?

Response: We appreciate this recognition. We believe clear reporting of the bioinformatics work is critical for rigor and reproducibility. Yes, all of this will also be provided in GitHub to benefit the wider community.

For the annotation in the attachment, there were two files. I had a look at them and they were quite different. As 17X and this genome are basically identical (Response: The two gff files represent either a Nanopore only or hybrid Nanopore+Illumina-based model. The latter produced a more comprehensive annotation of gene models, which is what we have proceeded with. However, we provided both in case end users find value in the Nanopore only annotation which has a 3’ bias due to the mechanism of how sequencing occurs via this approach.

We have found meaningful variations in genome sequence that potentially impact biological function (see Discussion). Therefore, we maintain that these genomes are not basically identical and are useful to the malaria research community for these reasons and more.

It is excellent that the genome is submitted to NCBI. Why are there 18k proteins? Are these the alternative spliced forms?

Response*: We are not certain how this interpretation might have arisen, as we only have reported 7047 potential transcript isoforms to NCBI based upon our data. *

Minor The current Py 17X genome in PlasmoDB is a Pacbio assembly (https://plasmodb.org/plasmo/app/record/dataset/TMPTX_pyoeyoelii17X), but not part of the 2014 paper. It was submitted later to NCBI than the paper the authors cite. Also, the current P. berghei Pacbio genome is from Fougère et al. PLoS Pathog 2016;12(11):e1005917.

Response: We have now made a detailed note about the Py17X PacBio dataset in our revised manuscript on Lines 186-187. Mentions of the current P. berghei genome assembly had already cited the Foug’ere et al. publication.

I tried to open the supplemental tables, but they were all in pdf rather than excel and split over several pages. Two had missing information, e.g. UTR per gene. From the name of the tables, I had an idea of what they should contain, but for a re-submission, it would be good to have them in the correct format.

Response: We agree that provision of the PDFs of the supplemental files is not the ideal way to review these analyses. The complete data was also provided in the Excel files provided to Review Commons. We will ensure that the affiliate journal receives the Excel files for completion’s sake.

To me, the beginning of the results reads a bit like an introduction (the part which sequencing technology to use)

Response: We agree, and as noted to Reviewer 1 above, we have streamlined this section of the revised manuscript.

Could you add to the tables: Sequence Coverage of the three technology, how many contigs you had before ordering the contigs and the number of pseudogenes in the annotation?

Response: This information is now provided in Supplemental Table 3 in the revised manuscript.

I struggle with the section header line 229-230 that the new sequence is more complete as it is a hybrid assembly with 17X. Alternatively, please explain how the consensus was built.

Response: We agree and have revised this section header for accuracy.

The authors correctly state that ONG is great, lines 333ff, but why does it not generate telomer-to-telomer chromosomes in this case? Please discusss.

Response: Please see our response to this above for remarks made by both Reviewer 1 and 2. We have also added clarifying text in our revised manuscript discussing why this may have occurred.

Reviewer #2 (Significance (Required)):

General assessment As mentioned above, I struggle to see this as a strong leap for the malaria community to use this genome, as it is so similar to the current 17X genome, which is manually curated in plasmodb. Response: We agree that it is important to know how similar the genomes of 17X and the cloned 17XNL strain are. It is perhaps even more important to know what the key differences are as well. In this study, we have asked and answered these questions, and identified 2000+ variants between the strains. We have manually curated several of the variants that impact the expression of essential/important genes, and found that biologically meaningful differences exist (see Discussion). Finally, we have also provided additional information on the gene models of 17XNL, including an experimental definition of UTRs and transcript isoforms. Together, we hold that these data will not only match those currently available for 17X, but will exceed them. We are currently working with PlasmoDB to make these data readily accessible to our community.

Advance The authors should make the comparison of ONT and PacBio HiFi clearer and discuss why the technologies still don't generate telomer-to-telomer sequences. From the biological side, none of the found differences were related to the different phenotype between 17X and 17XNL.

Response: We have provided these comparisons and all related data to the reader in this manuscript, as well as through public depositories. Please see above for our responses as to why a true telomere-to-telomere assembly is challenging with Plasmodium parasites, and for a recent preprint that might provide an explanation for this. Finally, the phenotypic differences between 17X and 17XNL are variable, which might reflect differences in individual parasite stocks as has been historically seen in the spontaneous development of lethality in multiple laboratories. While we do not find any particular genetic difference correlates with a specific phenotype, these data using the cloned 17XNL parasite available from BEI provides a robust reference with a defined parasite stock.

Audience: I do agree that adding the UTR sequence will be useful for those working with P. yoelii as a model, or who want to do comparative UTR analysis across species.

Response: We agree that this additional gene model information will be valuable. We are working with PlasmoDB to make this information readily available and are already integrating it into our ongoing studies.

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Referee #2

Evidence, reproducibility and clarity

The paper has three distinct parts,

1. Assembly of the P. yoelii yoelii 17XNL 2 Annotation of the genome and adding UTR regions
2. Comparing the sequence of 17XNL with 17X .

Assembly: The authors present a novel assembly for the P. yoelii yoelii 17XNL genome. They used two different approaches, comparing Oxford Nanopore (ONT) long reads + Illumina DNA with PacBio Hifi. None of the approaches generated a telomer to telomer assembly so sequences from the 17X reference was used to fill in the mssing sequence.

Annotation Next, they generated long reads (ONT)and Illumina RNA-Seq to improve the annotation. Although, their annotation is not better than the current P. yoelii 17X reference genome in PlasmoDB, they could predict the UTR regions and alternative splice sites due to the 3' capturing approach and long reads. Having the UTR annotated and potentially having alternative splice sides is useful for the field.

17XNL - 17X comparison The author compared the 17XNL with the 17X reference. Both genomes were done with Pacbio, and it should be noted that P. yoelii has a GC content of probably ~23% with several homopolymer tracks. Further, the 17XNL genotype was obtained from a 17X culture, so the genomes are expected to be very similar as the author noted in the introduction. The authors found ~2000 differences; some are in genes, but many are indels, which very well could be sequencing errors.

Finally, the authors claim that this genome could become relevant for the community as new reference to perform analysis. As their genome is so similar to 17X and they have to show that their annotation is at least as good as the current 17X reference genome (manual curated) and the difference are not due to sequence error in 17X or 17XNL.

Major comments

Overall I struggle to see the need for a "NEW" P. yoelii reference. It would be good to state how similar these genomes are - they are basically identical. As the 17XNL is curated manually, it would have made more sense to me to start from that one and then generate the UTR annotation and include splice sides. This could be easily loaded into an alternative Web-apollo track and then merged to the current annotation to be useful to the community.

I wonder if many of the differences the authors found between 17X and the 17XNL reference are true. The authors are correct that some differences between 17X and 17XNL are true. I could not find any evidence of genome polishing with tools like Pilon or ICORN to correct sequencing errors, I wonder if these differences are sequencing errors. Did the authors look into the reads of the NCBI - GCA_900002385.2 - assembly? Maybe they could use the underlying Illumina reads if theirs don't have enough coverage. Also, the differences between 17X and 17XNL could be that the reference is wrong. How many pseudo genes did they obtain? Are there more or less than in the current reference?

To confirm the calls, could you also map the 17XNL reads against the 17X reference and see if they are still true. As the same time, map the 17X illumina reads to see if the reference is correct at this state. When looking at the alignments, it can be seen that many different are in low complexity/repetitive regions. The authors sequence their genome with a HiFi Pacbio run and also ONG + DNASeq... but why did they not get 16 chomromes out? For example the current P. yoelii reference was assembled directly into far less pieces than theirs [P. chabaudi assembles into 16 pieces]. Could it be a different read depth or is it the fragment length? Could the authors please comment on that. Also, if there were contigs, why did they fill the sequence with 17X sequence, rather than keeping gaps? So in the end, their sequence is a hybrid, of 17X and 17XNL, right?

Why do you think you had less coverage of CCS read around the telomer ends? Do you think it is a systematic issue of the PacBio Hifi? Did you see any evidence of Illumina or ONT reads - or could it be that while culturing the telomer ends dropped off?

I realised that the authors used a lot of primary tools. I wonder why they chose that path, as there are several tools to do automatic finishing for long read assemblies: Assemblosis, ARAMIS, MpGAP or ILRA. Especially the last one focuses on Plasmodium genomes. Please comment.

Also, for the annotation, could it not be better to transfer the manually curated genome annotation with LIFT off or RATT? All these tools are widely used in the generation of reference genomes in the parasitology field. I annotated their sequence with Companion, and although their gene models are good and some of the Companion calls might need improvement, overall, the Companion results look more exact to me. The code is very well organised, and it was easy to follow. Are you planning to put it on a GitHub repository? For the annotation in the attachment, there were two files. I had a look at them and they were quite different.

As 17X and this genome are basically identical (<2k variants), would it not be better to transfer the genes from the 17X genome and then add the UTR (see comment before)? The 17X is manually curated. Table 1 and figure 4 show that it is far better. I doubt that the community would use this genome, if the annotation is not lifted over.

There are two gff files in the supplemental. Which one is better? It is excellent that the genome is submitted to NCBI. Why are there 18k proteins? Are these the alternative spliced forms?

Minor

The current Py 17X genome in PlasmoDB is a Pacbio assembly (https://plasmodb.org/plasmo/app/record/dataset/TMPTX_pyoeyoelii17X), but not part of the 2014 paper. It was submitted later to NCBI than the paper the authors cite. Also, the current P. berghei Pacbio genome is from Fougère et al. PLoS Pathog 2016;12(11):e1005917. I tried to open the supplemental tables, but they were all in pdf rather than excel and split over several pages. Two had missing information, e.g. UTR per gene. From the name of the tables, I had an idea of what they should contain, but for a re-submission, it would be good to have them in the correct format. To me, the beginning of the results reads a bit like an introduction (the part which sequencing technology to use) Could you add to the tables: Sequence Coverage of the three technology, how many contigs you had before ordering the contigs and the number of pseudogenes in the annotation? I struggle with the section header line 229-230 that the new sequence is more complete as it is a hybrid assembly with 17X. Alternatively, please explain how the consensus was built. The authors correctly state that ONG is great, lines 333ff, but why does it not generate telomer-to-telomer chromosomes in this case? Please discusss.

Significance

General assessment As mentioned above, I struggle to see this as a strong leap for the malaria community to use this genome, as it is so similar to the current 17X genome, which is manually curated in plasmodb.

Advance The authors should make the comparison of ONT and PacBio HiFi clearer and discuss why the technologies still don't generate telomer-to-telomer sequences. From the biological side, none of the found differences were related to the different phenotype between 17X and 17XNL.

Audience: I do agree that adding the UTR sequence will be useful for those working with P. yoelii as a model, or who want to do comparative UTR analysis across species.

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Referee #1

Evidence, reproducibility and clarity

The manuscript entitled "Long-Read Genome Assembly and Gene Model annotation for the Rodent Malaria Parasite P. yoelii 17XNL" is a well-written manuscript providing updates and important observations about the genome assembly and annotation of this specific non-lethal isolate. The group overall did a great job showing how the application of newer technologies such as long-read DNA and direct RNA sequencing to generate top-quality genomes to be used as a reference for the community. Here are some comments about the work presented:

Major comments:

• The authors added several result information across the methods section. Making the text repetitive, since the same is also presented in the results section. Please revise the method section to remove results from this section.
• Some methods are also redundant in the Result section. For example, in line 141-142, the group describe which DNA extraction kit they used (again this is correctly mentioned in the methods section).
• Besides important, the group added several information about method comparison between base call accuracy and sequencing methods. I agree that having this information in the supplemental material is great, but I would be careful to not focus too much on those, since most of the observations are already well-known by the community and focus more in the biological relevance of what is being generated with the newly updated genome.
• The group did a great job generating two versions of the genome, and an updated gene annotation set using long-read sequencing. But the major question is, how about alternative splicing? They mention the use of it (line 350) but I don't see any result about how many alternative transcripts were observed, and if they were differentially detected in different life stages of the sets used for the RNA sequencing. This is a very important result to be added since one of the key pieces of information that long-read RNA sequencing brings for Genome annotation.
• Same observation as above for potential long ncRNAs.
• From what I understand the Hifi run was able to generate a gapless genome assembly and the ONT run did not. What was the final coverage for each? From my experience with P. falciparum genomes, ONT even with the rapid kit was able to generate chromosomal level assemblies if the coverage was >100x (but again, this is not a rule). Add those valuable observations about the depth so the reader can check if other variables in the comparison should be made.
• Also be sure that the structural comparisons between the genomes are not the ones used after running ragtag.py. If so, there is a high chance of structural bias in the scaffolded contigs.
• How Prokka differed from Braker2 for the Mitochondria/API annotation? This needs to be very well described since prokka is made for prokaryotic organisms and not for eukaryotic ones. And Braker2 uses a custom build dataset for training, which I believe contains known information about MIT/API for Plasmodium species.
• Figure 5B, what is the peak observed in the mitochondria? What genes? Repeats?

Minor comments:

• For Oxford nanopore sequencing using the ligation kit, did the group check for potential chimeric reads generated by the protocol?
• Check if all species are italicized (for example, line 187 P. yoelii is not)
• In methods add the parameters for minimap2 for the direct RNA alignment
• For variant calling, I would use a minimum of 10x coverage to make a variant call instead of 5x. Besides looking well reproducible between all checks, I would be careful mainly with the single bp deletions with a such low threshold.
• In some parts of the methods, the authors mentioned slight modifications in some protocols (for example, lines 443 and 454), besides well described in the text, could you highlight what were the modifications in the text? This will facilitate many other researchers to understand why those modifications were needed.
• As mentioned in the major, the data analysis method section needs rework to remove results from the text.
• The group mentioned that small contigs not mapping to Py17X were discarded. What are those? Repeats? Contamination?

Significance

This work generated a strong method and resource for a better genome assessment of P. yoelii for the community. As I mentioned in my comments, some more details about the findings such as alternative splicing and lncRNAs may strengthen them even more the publication. I know that comparative analysis between Py17X and XNL is not in the scope here, but more information about it, such as a synteny plot would be great for the community to understand that they can rely on this new reference genome.

I've been working with eukaryotic and prokaryotic genomes for more than a decade and I have a lot of experience with all the methods presented. I believe that potentially the depth generated for the ONT data may be one of the factors for not reaching the chromosomal level of this isolate, since HiFI was. The group did a great job on the method description, and I believe that the community will be very happy to incorporate this genome as one of the references for this organism.

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Reply to the reviewers

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

Banerji and colleagues measure DUX4 and target gene expression over a time course in doxycycline-inducible myoblasts to estimate kinetic parameters and rates underlying the transition of non-expressing cells through DUX4-expressing cells to cell death. They then use these parameters to model the rate of appearance of DUX4+ cells, DUX4 target gene expression, etc. in cells from FSHD patients, and derive a model that predicts that over 100 days around one fourth of cells die while less than 1% of cells express DUX4 or its target genes at any given time. This is somewhat similar to what is seen in FSHD patients, where DUX4 expression is infrequent in cultured cells, while patients eventually have substantial muscle loss. The experiments are well-designed and explained clearly.

We thank the reviewer for their kind comments on our manuscript and for acknowledging the similarity between our model simulation and both cell biological and clinical features of FSHD.

Reviewer #1 (Significance (Required)):

The primary significance of this study is that the field has a sense that the damage seen in patient muscle is not congruent with the low expression of DUX4 in patients, and the model showing many cells dying with only a few cells expressing DUX4 at any given time suggests that overall damage can be greater than that observed in any particular snapshot.

However, it is important not to conflate low frequencies of DUX4+ nuclei in cultured myoblasts with "DUX4 being difficult to detect" as in p 12, Discussion. DUX4 is difficult to detect, indeed basically not detected, in muscle biopsy specimens, but in cells in vitro, DUX4 is fairly easy to detect, albeit in quite low numbers of cells. Since the study evaluates cells in vitro, it is important to make clear that the situation in vivo is qualitatively different from that seen in vitro, namely DUX4 not being detected, and the authors should clarify this importance difference.

We appreciate this important point that DUX4 detection is extremely challenging in FSHD patient muscle biopsies, compared to in vitro cell culture of FSHD myoblasts and myotubes, a topic we considered at length in our recent review (Banerji and Zammit 2021, https://doi.org/10.15252/emmm.202013695).

Detection of DUX4 mRNA in muscle biopsies is highly variable requiring nested RT-qPCR, and protein detection is even less robust, though this has been achieved via western blot in less affected FSHD muscle (Tassin et al., 2013, DOI: 10.1111/j.1582-4934.2012.01647.x) and using a proximity ligation assay (Beermann et al. 2022, doi: 10.1186/s13104-022-06054-8). DUX4 target gene expression is detected in inflamed FSHD muscle but less robustly in non-inflamed muscle, indicating the recent presence of DUX4 protein, and also implying that a systemic component may activate DUX4 expression in vivo.

Conversely in cell culture of FSHD muscle cells, DUX4 mRNA is typically found in 0.5-3.8% of myonuclei (van den Heuvel et al., 2019, https://doi.org/10.1093/hmg/ddy400) and protein in around 1/1000 myonuclei (Snider et al., 2010, https://doi.org/10.1371/journal.pgen.1001181). Arguably these levels are still very low and DUX4 is still ‘difficult to detect’ in vitro, for example DUX4 expression is never seen above the limit of detection in bulk RNA-seq of FSHD patient cultured myoblasts. However, we fully appreciate that DUX4 is ‘easier to detect’ in vitro versus in vivo.

Regardless, anti-DUX4 therapy is currently the most heavily funded approach to FSHD treatment, and all anti-DUX4 therapies currently under consideration were first investigated in the myoblast in vitro setting. Thus, the aim of our model is to provide an additional level of in silico screening for anti-DUX4 therapy, before progressing to in vitro investigation.

Full consideration of DUX4 expression in vivo (while an exciting prospect), would require significantly more data than is currently available (relating to inflammation, vascularity and other systemic factors) and a highly complex model which is beyond the remit of this investigation.

We will clarify in the manuscript that our model applies in vitro, highlighting differences with in vivo and emphasising that the model is limited for understanding DUX4 expression in FSHD muscle biopsies.

We will also change the title to emphasise this point to: ‘In silico FSHD muscle cell culture for modelling DUX4 dynamics and predicting the impact of therapy’.

A second reason for caution in extrapolating correlates from the in vitro model to the disease process in muscle tissue is that in vivo there is a continual source of replacement cells, as the authors have shown in a previous study. Have the authors attempted to model a situation in which new cells are provided into the system at some rate, related to the amount of death occurring at different times? Although the authors mention that the static cell number is a limitation of the model, it would be valuable to revisit or explore this idea in the Discussion section, if only to provide the reader with a more pragmatic perspective.

We are focused specifically on modelling the in vitro differentiated myogenic cell setting to provide an in silico pre-screening tool for assessing anti-DUX4 therapy, rather than attempting to model the full pathology in vivo, which given data limitations, is beyond the scope of our study. As the reviewer notes, we highlight this limitation relating to proliferation when we introduce the model in the results stating: ‘Cells are assumed not to proliferate over the evolution of the model. We restrict applications to differentiating cells which have exited the cell cycle.’

As the reviewer likely appreciates, DUX4 expression in proliferating cells differs significantly from differentiated cells. The single nuclear and single cell RNA-seq data we employ to derive the parameters underlying DUX4 and DUX4 target transcription rates are from non-proliferating myocytes differentiated for 3 days. Hence it is not appropriate to include proliferation in this model, as the transcription rates we use will not be accurate for proliferating cells.

Introducing proliferation will also require us to estimate the proliferation rate of FSHD myoblasts, and how it is impacted by DUX4 expression, as well as how DUX4/target transcription rates change as the proliferating cells differentiate. Though this is an interesting application there is not sufficient data available to produce this wider scale model at present.

We thus restrict application of our model to the non-proliferating differentiated setting, which can be easily accessed in vitro to screen anti-DUX4 therapy.

We appreciate the observation that in vivo, there will be addition of new cells during muscle regeneration, and will include this in discussion in the updated manuscript. We will also expand on our discussion of what data would be required to include proliferation in our model in the revised manuscript.

The second part of the paper models presence of DUX4 in nuclei based on diffusion from expressing to non-expressing nuclei, and characterizes this as the activity of "an infectious agent, able to spread from one nucleus to another by adapting epidemiological compartment models". The relationship to an infection process is probably not the ideal way to characterize this process, because an infection implies the setting up of new sites of production of the agent, DUX4, where what is really happening is that DUX4 diffusing into these other nuclei isn't leading to more DUX4 production, it is just diffusing into nearby nuclei and accumulating there. Unless I am misunderstanding, the authors are simply showing that a larger number of nuclei will be positive in a system in which cells are fusion products having many nuclei than in a system in which all nuclei are isolated within their own cells.

The term ‘infectious agent’ is only an analogy and implies some preconceptions. The reviewer interprets our infectious agent model as: ‘an infection implies the setting up of new sites of production of the agent, DUX4, where what is really happening is that DUX4 diffusing into these other nuclei isn't leading to more DUX4 production, it is just diffusing into nearby nuclei and accumulating there. Our bespoke model is not based on a direct mapping of e.g., viral infection to our setting. In our case, the ‘infectious agent’ DUX4 causes harm but does not replicate. DUX4 protein is a transcription factor and thus activates the expression of target genes (not including DUX4). In our model, DUX4 can be seen as the ‘infectious agent’, and expression of DUX4 target genes can be seen as the ‘infection’, which leads to cell death. DUX4 can either stay in the ‘infected’ cell until it dies or diffuse to another cell to spread the ‘infection’.

The reviewer interprets the results of our model as ‘simply showing that a larger number of nuclei will be [DUX4] positive in a system in which cells are fusion products having many nuclei than in a system in which all nuclei are isolated within their own cells’.

In fact, we make several observations: firstly we compare single cell RNA-seq of unfused myocytes to single nuclear RNA-seq of fused muti-nucleated myotubes and find that the latter has a greater proportion of cells expressing DUX4 target genes (i.e. more infection). The proportion of DUX4 positive cells (i.e. the amount of infectious agent produced) is similar in the two settings. We posit that this difference in DUX4 target gene positive cells (infection) may be due to DUX4 protein diffusing between cells in the myotube syncytium and activating the targets in neighbouring nuclei (i.e., greater mobility of the infectious agent/loss of quarantine). Note that there are many other possibilities, such as fusion causing an increase in DUX4 transcription/mRNA stability etc.

We find that by introducing a DUX4 protein diffusion term (loss of quarantine) into our model we can completely explain the difference between DUX4 target gene expression (infection) in real data of unfused versus fused myonuclei, despite identical levels of DUX4 (infectious agent). This is a novel finding providing evidence for DUX4 protein diffusion in syncytial myonuclei, which represents an often overlooked therapeutic target/consideration in FSHD. We also provide the first explicit quantification of this diffusion rate via our model as well as a framework for investigators to understand how modifying this parameter can impact DUX4 induced myotoxicity.

We will better define our ‘infectious agent’ analogy and clarify our interpretation in the updated manuscript.

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

An in silico FSHD muscle fibre for modelling DUX4 dynamics

Summary

This paper studies and mathematically models the stochastic process of presence or absence of DUX4 expression in individual myocytes in FSHD, which underlies the very small proportion of individual cells which do express DUX4. The paper is valuable in providing a mathematical basis for modelling this, and hence a basis by which to study the effects of other factors or potential therapeutic interventions which may influence the overall proportion of cells which do express.

We thank the reviewer for the kind comments on our manuscript and recognition of its value to design/screening of potential therapeutics for FSHD.

To understand the paper fully, requires a familiarity with mathematical and statistical reasoning which will be beyond many potential readers, including this reviewer, but I hope that the following specific comments may still be helpful.

We appreciate the reviewer’s candour regarding their understanding of the more advanced mathematical aspects the paper. Interdisciplinary work such as ours may introduce concepts which are unfamiliar to some readers.

As the reviewer will appreciate, understanding a complex process like DUX4 expression requires new and more sophisticated approaches, having largely eluded the conventional approaches to date. The main mathematical tools employed in our work derive from the well-established stochastic gene expression and epidemiological compartment models. We have endeavoured to make the mathematics self-contained to facilitate understanding and will edit to further improve accessibility.

Major Comments

1. A potential major concern is that the modelling seems to be based largely on combined data from 27 DUX-4 positive myocytes out of 5133 myocytes from 4 FSHD patients described by Van Den Heuvel et al, 2019 (ref 17), but with 2 being FSHD1 and 2 FSHD2, and without the 2 patients within each FSHD type being matched for D4Z4 fragment length (RU = 'residual units'), which can be expected to be a major influencing factor on the threshold for expression in any one myocyte upon which the stochastic process acts. Thus, one can expect lower RU number to show a greater proportion of expressing cells within FSHD1, and to some extent similarly within FSHD2 (although in FSHD2, different SMCHD1 mutations will also have different 'strengths'). The 4 patients were : 2x FSHD1 (3RU ; 6RU); 2x FSHD2 (12RU ; 18 RU). In the paper of Den Heuvel et al it is evident that the DUX4-positive cell count differs between these 4 cell lines very much as might be anticipated from their D4Z4 RU number. Thus (Figure 2 in that paper) patient 1.1 (3RU) has 12 positive cells, whereas patient 1.2 (6RU) has only 2 positive cells. Patient 2.1 (SMCHD1 exon 37 mutation, and 12 RU) has 11 positive cells, whereas Patient 2.2 (SMCHD1 exon 10 mutation, and 18 RU) has only 2 positive cells. Therefore, if the validity and accuracy of the modelling here would be affected by genetic heterogeneity, the authors should present their calculations and analyses based only on the 2 patients who account for 23/27 of the positive cells, and furthermore, that the calculations and modelling are performed and presented for those 2 patients separately.

The reviewer raises the important point that 2/5 of the parameters of our model were derived from the single cell RNA-seq data set of van den Heuvel et al., which comprises 4 FSHD patients of various genotype. The reviewer remarks that the number of DUX4 positive cells reported for each patient in the van den Heuvel data, differs according to genotype, in line with the known inverse association between DUX4 expression and D4Z4 short allele length. Figure 2E in van den Heuvel et al., to which the reviewer refers, does not present absolute DUX4+ cell counts, but rather ‘DUX4 affected’ cell counts, based on the expression of 67 DUX4 target genes. The numbers in that figure thus differ from ours. We caution that the number of ‘DUX4 affected’ cell counts must be normalised by the total number of cells assayed per patient, to obtain a ‘DUX4 affected’ proportion for each patient. However, we appreciate the reviewers point.

A concern is raised that we have pooled all 4 FSHD patients to derive our parameter estimates, essentially averaging over genotypes. The two parameters we derived from this data are ‘the average DUX4 transcription rate, and ‘the average DUX4 target gene transcription rate.

The average DUX4 target gene transcription rate was derived from consideration of DUX4 target gene expression in the 27 FSHD cells already expressing DUX4 mRNA. Since the capacity of DUX4 to activate its target genes is not known to depend on genotype, we feel it is appropriate to pool the patients to estimate this parameter.

For the average DUX4 transcription rate however, we do appreciate the reviewer’s concern that patient genotype may impact the estimate. The reason for pooling all patients is partly statistical. As there are so few cells expressing DUX4 in each patient, the gradient descent algorithm we employed to estimate the 3 rate parameters for the promoter switching model of DUX4, may fail to converge for every patient separately. By pooling patients, we obtained an estimate that may not be true for an individual but represented the ‘average’ transcription rate of DUX4 in these 4 FSHD patients, hence our careful use of wording when defining the average DUX4 transcription rate parameter.

Our in silico model of DUX4 expression is proposed as a pre- in vitro screening tool to guide anti-DUX4 therapy for the general population, not a digital twin of a single FSHD patient, and so an average DUX4 transcription rate derived from pooling available data is optimal.

This average measure is not irrelevant in light of genetic variation and in fact may be more informative for anti-DUX4 therapeutics intended for a general population, but less informative for genotype stratified therapeutic design. Genotypic stratification by D4Z4 repeat length was not performed in recent FSHD clinical trials for anti-DUX4 therapy (e.g., losmapimod: NCT04003974), which recruited patients with all pathogenic D4Z4 repeat lengths, motivating an average over genotype design in the first instance.

We will elaborate in detail on the reasons for patient pooling throughout the manuscript.

We will attempt the analysis the reviewer suggests i.e. ‘the authors should present their calculations and analyses based only on the 2 patients who account for 23/27 of the positive cells…separately’. However, this is conditional on algorithm convergence given the smaller number of cells, and we must caveat that in doing so we will have eliminated 2 patients purely on the basis of low DUX4 expression and so will over-estimate the average DUX4 transcription rate for FSHD patients in general.

In order to be of value for assessing potential interventions more widely in FSHD, the authors would then need, in further publications, to extend their analysis similarly to genetically homogeneous myocyte sets with other RU number.

We agree with the reviewer that for genotypically stratified therapeutic design, based on D4Z4 short allele length, personalised or genotype specific estimates of the DUX4 transcription rate should be calculated. However, as the reviewer clearly appreciates, developing such FSHD digital twins, though exciting, would require a much larger library of single cell RNA-seq datasets describing FSHD patients of various genotypes than is currently available.

We will explore this in an updated discussion.

Specific points.

Introduction 4th paragraph: - 'Single cell and single nuclear transcriptomic studies find only 0.5-3.8% of in vitro differentiated FSHD myonuclei express DUX4 transcript16,17. Immunolabelling studies only detect DUX4 protein in between 0.1-5% of FSHD myonuclei18,19.' The authors should comment whether there is any evidence from the data in these references, or from other publications, regarding differences in these proportions by FSHD diagnosis (I or II) or by D4Z4 residual number?.

We will expand discussion in the updated manuscript of the result (explained by the reviewer) that the number of DUX4 affected cells found in the single cell RNA-seq dataset of van den Heuvel may correlate with genotype. We will contrast this with the observation that immortalised isogenic FSHD myoblast clones isolated from a single FSHD patient can also have a very wide range of DUX4 expression (e.g., Krom et al., doi: 10.1016/j.ajpath.2012.07.007). This will highlight the mix of poorly understood genetic and epigenetic factors impacting DUX4 expression and further motivate an average model in the first instance.

Introduction 5th paragraph: - Importantly, a recent phase 2b clinical trial of the DUX4 inhibitor losmapimod failed to reach its primary endpoint of reduced DUX4 target gene expression in patient muscle, despite improvement in functional outcomes23. Please state here whether or not there was any evidence that DUX4 expression itself was reduced in this trial. It would help if the authors could also add here an interpretive comment as to whether this indicates that the current presumed target genes for DUX4 are not in fact the ones important for FSHD, or whether their expression might be retained from compensatory upregulation by other upstream regulators. The authors should expand briefly also in the introduction on the evidence for toxicity of target gene expression.

We are very happy to include a discussion of these points, which we explore in considerable detail in our recent review of DUX4 in FSHD (Banerji and Zammit 2021, https://doi.org/10.15252/emmm.202013695). Briefly, the losmapimod trial did not publish data showing they measured DUX4, as Reviewer #1 pointed out DUX4 is highly difficult to detect in patient muscle biopsies and dynamic change following anti-DUX4 therapy has certainly never been observed. Expression of four DUX4 target genes was intended as a surrogate measure of DUX4 activity, reasons for its lack of change in this trial is very much a topic of open debate, which we will outline in an updated manuscript.

Results 2nd paragraph: - We first describe the compartment model. FSHD single myocytes can express DUX4 and therefore DUX4 target genes; DUX4 target gene expression leads to cell death19,27,28. Please indicate whether the evidence in these studies is independent of DUX4. Ie. Is there independent evidence that it is definitely the target gene expression, rather than DUX4 expression per se, that leads to cell death, especially if losmapimod improves function by inhibiting DUX4, but has no impact on these target genes.

This is an important point raised by the reviewer and one that we have rigorously investigated (Knopp et al., 2016, https://doi.org/10.1242/jcs.180372).

The c-terminus of DUX4 is a potent transcriptional activator and is required for DUX4 target gene activation, while an inverted centromeric D4Z4 repeat unit encodes a version of DUX4 lacking the c-terminus, named DUX4c. We constructed a library of DUX4 expression constructs encoding: DUX4, DUX4c, tMALDUX4 (DUX4 with the c-terminus removed), DUX4-VP16 (DUX4 with the c-terminus replaced by the VP16 transactivation domain) and DUX4-ERD (DUX4 with the c-terminus replaced by the engrailed repressor domain). In a series of experiments outlined in Knopp et al., we clearly demonstrated that only DUX4 and DUX4-VP16 could activate DUX4 targets in C2C12 murine myoblasts and that only these constructs could induce apoptosis. Our prior work provides this clear link between DUX4 target gene expression and cell death. We will include discussion of this point in the updated manuscript.

Results 2nd paragraph Item 4 : - 𝑅(𝑡) - a resigned state where the cell expresses no DUX4 mRNA but does express DUX4 target mRNA (DUX4 -ve/Target gene +ve: i.e. a historically DUX4 mRNA expressing cell) Since the mRNA is from the target genes, why should it be produced only in response to DUX4 expression ? ie. Please indicate the evidence to say that these must be 'historically DUX4 mRNA expressing cells', rather than target gene expression in response to other factors, or even autonomously.

The reviewer raises the point that the 8 DUX4 target genes we investigate may be expressed in a DUX4 independent manner, making DUX4 target gene +ve, DUX4 -ve cells, potentially not historic DUX4 expressing cells. We selected these 8 genes as they are confirmed DUX4 targets by ChIP-seq and have been identified as upregulated by DUX4 in every transcriptomic analysis of DUX4 over-expressing human myoblasts (Banerji and Zammit 2021, https://doi.org/10.15252/emmm.202013695). We provide further evidence for these genes indicating historic DUX4 in these cells: first, we never find a single transcript for any of these 8 target genes in 1914 single myocytes and 77 single nuclei from control individuals, suggesting that their expression requires an FSHD genotype (and thus likely DUX4 expression). Second, our investigation of these genes in the single cell RNA-seq data set of van den Heuvel demonstrated that in the presence of DUX4, expression of all 8 genes significantly increase (Figure 3D), indicating activation by DUX4.

While this is evidence that the 8 targets are specific to DUX4, we accept that it is not 100% conclusive and these genes may also be induced by some other factor related to the FSHD genotype. We will thus highlight this possibility of other modes of expression in the discussion.

Results - Estimating the kinetics of DUX4 mRNA - 2nd paragraph: - DUX4 expression was induced with 250 ng/ml doxycycline for 7 hours .... Might some of the laboratory detail here be better placed in the 'Methods' section ?

We will move some methodological detail to the methods as suggested.

Results - Estimating the kinetics of DUX4 mRNA - 4th paragraph: - '...from 4 FSHD patients (2 FSHD1 and 2 FSHD2).' Please give additional information about these patients - ie. Age, sex, severity (or age-onset), and genetic results - perhaps best done as a Table in this paper rather than only by reference back to the Van den Heuvel et al. paper (see main comment 1 above).

We will provide this information in a table as the reviewer suggests.

Results - Estimating kinetics of DUX4 target activation - 2nd paragraph: - '8 DUX4 target genes......expression was restricted to FSHD cells/nuclei and never observed in controls.' So are these genes normally only expressed in the zygote , rather than post-birth (except perhaps the testis)? The authors should comment on this.

The 8 DUX4 target genes are not well characterised in terms of their expression pattern in other tissues. We are confident that in our data sets they are not expressed in healthy myocytes and myotubes, but it is difficult to expand confidently on other tissues due to lack of data. We will include comment on what is known about their expression pattern during zygotic genome activation and in testicular tissue, but this cannot be an exhaustive description of their expression. For example, DUX4 has been detected in keratinocytes, osteoblasts and lymphocytes, but the target genes have not been characterised in such settings. We still have much to learn about the expression of these genes outside of the muscle setting.

Results - Estimating kinetics of DUX4 target activation - 4nd paragraph: - In the presence of DUX4 mRNA the proportion of time the promoters of the 8 DUX4 target genes remained in the active state,....significantly increased. It would be interesting to know if any of these temporal dynamic descriptors of DUX4 target promoters differ consistently between sample II-2 and II-1 and in the same direction between I-2 and I-1. ie. Might the same factors (eg. D4Z4 copy number) which may affect presence/absence of DUX4 also affect other cellular measures of DUX 4 ?

The reviewer raises an interesting point about genetic modifiers of DUX4 transcriptional activation. As discussed in response to point 1, the DUX4 target gene transcription rate is derived from the 27 cells expressing DUX4. As there are so few cells expressing DUX4 in each patient (sometimes We will attempt analysis of the two patients which provide the majority of DUX4 positive cells separately, as the reviewer requested in point 1. If successful, we will investigate whether the derived DUX4 target gene expression parameters differ between these two patients as the reviewer suggests. While this limited data will not permit anything definitive about impact of D4Z4 repeat length on DUX4 target gene expression, it may highlight the existence of inter-individual differences in such parameters.

Results - Compartment model simulation - 4th paragraph: - '...so that after 10 days 26.3% of cells had died.' The authors need to give data here also for controls. Ie. What proportion of cells die after 10 days in controls ?

As mentioned in the introduction of our model, we only model DUX4 dependent cell death. As control cells do not express DUX4 they cannot experience cell death by this mechanism. The reviewer can therefore interpret this result as excess death due to DUX4, i.e., the 26.3% of FSHD cells die which would not have died in control cells. We will clarify this point in an updated manuscript.

Discussion - 1st paragraph: - ' However, DUX4 expression in FSHD muscle demonstrates a complex dynamic with DUX4 mRNA, protein and target gene accumulation all difficult to detect2,21. Understanding this complex dynamic is essential to the construction of optimal therapy' So, it presumably follows that comparison by known genetic or demographic modifiers of disease severity (such as D4Z4 copy number) could be of fundamental importance in interpreting this dynamic and hence the results of clinical trials) (see Main point 1, and point 7).

This point appears to generally be a reiteration of the reviewer’s initial concerns on genotypic variation in the van den Heuvel et al., data set. We have addressed these concerns in response to points 1, 2 and 9.

Discussion - 6th paragraph: - 'Our model predicts

We will update the manuscript to amend to ‘DUX4 target gene mRNA positive’. The reviewer again raises the concern of genotypic variability, we will of course discuss this and perform additional analysis as detailed above (points 1, 2, 9 and 11).

Minor comments

Methods - scRNAseq and snRNAseq data - 2nd paragraph: - '..2 sperate patients...' Typo. to correct.

We thank the reviewer and will address this typo.

Methods - Hypothetical scenarios for raising 𝑬[𝒎] under the promoter-switching model- 1st paragraph: - '...constant in the second...' needs punctuation.

We thank the reviewer and will address this typo.

Methods - Cellular Automaton Model - 3rd paragraph: - '...according the non-syncytial...' Typo : '...according to...'

We thank the reviewer and will address this typo.

Methods - Genetic algorithms - 3rd paragraph: - '...elitism of 5%, mutation probability 0.1, crossover probability 0.8,...' Should these parameters be given in the same format to avoid any confusion - ie. all as probability, or all as percentages ?

We thank the reviewer and will change all parameters and probabilities.

Reviewer #2 (Significance (Required)):

Please see comments in the 'Evidence, reproducibility and clarity' box above.

This paper is valuable in providing a mathematical basis for modelling the stochastic process of presence or absence of DUX4 expression in individual myocytes in FSHD (facioscapulohumeral muscular dystrophy), and hence a basis by which to study the effects of other factors or potential therapeutic interventions which may influence the overall proportion of muscle cells which do express DUX4, and hence succumb to its toxicity.

This is a specialist field and the nature of presentation of the mathematical modelling in the paper will restrict accessibility to only a very few researchers in this field.

We appreciate that introducing concepts from different fields such as mathematics can present a challenge to some readers. However, new methods and interdisciplinary science are essential to understand complex disorders and this is even more so in complex rare diseases such as FSHD, where diversity in expertise is important. ‘Stochastic’ has been used to describe DUX4’s expression pattern for many years and is commonly used in many FSHD papers and conferences. We have introduced concepts from the well established field of ‘stochastic gene expression’ to understand DUX4 expression in FSHD. The methods are different from conventional approaches used in FSHD, but are arguably the most natural framework in which to understand DUX4.

We have provided user-friendly programs that can be used to estimate the effects on DUX4 parameters of multiple variables to make the paper useful, even if some of the more advanced mathematics is challenging. We will also further edit to increase accessibility.

There appears to be a possible major problem in the way that the paper has combined data from patients with different genetic forms of FSHD, and different likely genetic severities. It would seem better to analyse the data separately for each of these to allow a more homogenous platform on which to construct a model.

That we pooled data from 4 FSHD patients, to derive estimates of 2 of the 5 parameters of our model is the major (non-editorial) comment about our study, and is raised in various versions in points 1, 2, 9, 11 and 12 of reviewer 2’s response. We have explained above that the reason for pooling is partly statistical, due to dataset size limitation. Considering each patient separately may prevent convergence of a gradient descent algorithm for parameter estimation. We again emphasise that pooling 4 FSHD patients, while less personalised, does not make our estimate of the average DUX4 transcription rate in FSHD patients invalid. It is simply an average level across our 4 patients, perhaps higher than for some patients but lower than for others.

As commented, we will repeat the analysis separately for the 2 patients who have larger numbers of DUX4 positive cells and report the findings, with the caveat that these estimates will, by definition, be over-estimates of the true value of the average DUX4 transcription rate for the FSHD population.

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Referee #2

Evidence, reproducibility and clarity

An in silico FSHD muscle fibre for modelling DUX4 dynamics

Summary

This paper studies and mathematically models the stochastic process of presence or absence of DUX4 expression in individual myocytes in FSHD, which underlies the very small proportion of individual cells which do express DUX4. The paper is valuable in providing a mathematical basis for modelling this, and hence a basis by which to study the effects of other factors or potential therapeutic interventions which may influence the overall proportion of cells which do express. To understand the paper fully, requires a familiarity with mathematical and statistical reasoning which will be beyond many potential readers, including this reviewer, but I hope that the following specific comments may still be helpful.

Major Comments

1. A potential major concern is that the modelling seems to be based largely on combined data from 27 DUX-4 positive myocytes out of 5133 myocytes from 4 FSHD patients described by Van Den Heuvel et al, 2019 (ref 17), but with 2 being FSHD1 and 2 FSHD2, and without the 2 patients within each FSHD type being matched for D4Z4 fragment length (RU = 'residual units'), which can be expected to be a major influencing factor on the threshold for expression in any one myocyte upon which the stochastic process acts. Thus, one can expect lower RU number to show a greater proportion of expressing cells within FSHD1, and to some extent similarly within FSHD2 (although in FSHD2, different SMCHD1 mutations will also have different 'strengths'). The 4 patients were : 2x FSHD1 (3RU ; 6RU); 2x FSHD2 (12RU ; 18 RU). In the paper of Den Heuvel et al it is evident that the DUX4-positive cell count differs between these 4 cell lines very much as might be anticipated from their D4Z4 RU number. Thus (Figure 2 in that paper) patient 1.1 (3RU) has 12 positive cells, whereas patient 1.2 (6RU) has only 2 positive cells. Patient 2.1 (SMCHD1 exon 37 mutation, and 12 RU) has 11 positive cells, whereas Patient 2.2 (SMCHD1 exon 10 mutation, and 18 RU) has only 2 positive cells. Therefore, if the validity and accuracy of the modelling here would be affected by genetic heterogeneity, the authors should present their calculations and analyses based only on the 2 patients who account for 23/27 of the positive cells, and furthermore, that the calculations and modelling are performed and presented for those 2 patients separately. In order to be of value for assessing potential interventions more widely in FSHD, the authors would then need, in further publications, to extend their analysis similarly to genetically homogeneous myocyte sets with other RU number.

Specific points.

1. Introduction 4th paragraph: - 'Single cell and single nuclear transcriptomic studies find only 0.5-3.8% of in vitro differentiated FSHD myonuclei express DUX4 transcript16,17. Immunolabelling studies only detect DUX4 protein in between 0.1-5% of FSHD myonuclei18,19.' The authors should comment whether there is any evidence from the data in these references, or from other publications, regarding differences in these proportions by FSHD diagnosis (I or II) or by D4Z4 residual number?.
2. Introduction 5th paragraph: - Importantly, a recent phase 2b clinical trial of the DUX4 inhibitor losmapimod failed to reach its primary endpoint of reduced DUX4 target gene expression in patient muscle, despite improvement in functional outcomes23. Please state here whether or not there was any evidence that DUX4 expression itself was reduced in this trial. It would help if the authors could also add here an interpretive comment as to whether this indicates that the current presumed target genes for DUX4 are not in fact the ones important for FSHD, or whether their expression might be retained from compensatory upregulation by other upstream regulators. The authors should expand briefly also in the introduction on the evidence for toxicity of target gene expression.
3. Results 2nd paragraph: - We first describe the compartment model. FSHD single myocytes can express DUX4 and therefore DUX4 target genes; DUX4 target gene expression leads to cell death19,27,28. Please indicate whether the evidence in these studies is independent of DUX4. Ie. Is there independent evidence that it is definitely the target gene expression, rather than DUX4 expression per se, that leads to cell death, especially if losmapimod improves function by inhibiting DUX4, but has no impact on these target genes.
4. Results 2nd paragraph Item 4 : - 𝑅(𝑡) - a resigned state where the cell expresses no DUX4 mRNA but does express DUX4 target mRNA (DUX4 -ve/Target gene +ve: i.e. a historically DUX4 mRNA expressing cell) Since the mRNA is from the target genes, why should it be produced only in response to DUX4 expression ? ie. Please indicate the evidence to say that these must be 'historically DUX4 mRNA expressing cells', rather than target gene expression in response to other factors, or even autonomously.
5. Results - Estimating the kinetics of DUX4 mRNA - 2nd paragraph: - DUX4 expression was induced with 250 ng/ml doxycycline for 7 hours .... Might some of the laboratory detail here be better placed in the 'Methods' section ?
6. Results - Estimating the kinetics of DUX4 mRNA - 4th paragraph: - '...from 4 FSHD patients (2 FSHD1 and 2 FSHD2).' Please give additional information about these patients - ie. Age, sex, severity (or age-onset), and genetic results - perhaps best done as a Table in this paper rather than only by reference back to the Van den Heuvel et al. paper (see main comment 1 above).
7. Results - Estimating kinetics of DUX4 target activation - 2nd paragraph: - '8 DUX4 target genes......expression was restricted to FSHD cells/nuclei and never observed in controls.' So are these genes normally only expressed in the zygote , rather than post-birth (except perhaps the testis)? The authors should comment on this.
8. Results - Estimating kinetics of DUX4 target activation - 4nd paragraph: - In the presence of DUX4 mRNA the proportion of time the promoters of the 8 DUX4 target genes remained in the active state,....significantly increased. It would be interesting to know if any of these temporal dynamic descriptors of DUX4 target promoters differ consistently between sample II-2 and II-1 and in the same direction between I-2 and I-1. ie. Might the same factors (eg. D4Z4 copy number) which may affect presence/absence of DUX4 also affect other cellular measures of DUX 4 ?
9. Results - Compartment model simulation - 4th paragraph: - '...so that after 10 days 26.3% of cells had died.' The authors need to give data here also for controls. Ie. What proportion of cells die after 10 days in controls ?
10. Discussion - 1st paragraph: - ' However, DUX4 expression in FSHD muscle demonstrates a complex dynamic with DUX4 mRNA, protein and target gene accumulation all difficult to detect2,21. Understanding this complex dynamic is essential to the construction of optimal therapy' So, it presumably follows that comparison by known genetic or demographic modifiers of disease severity (such as D4Z4 copy number) could be of fundamental importance in interpreting this dynamic and hence the results of clinical trials) (see Main point 1, and point 7).
11. Discussion - 6th paragraph: - 'Our model predicts <2.9% of single FSHD myocytes will be DUX4 target gene positive at any given time...' Should this be: '...DUX4 target gene-mRNA positive' . Also, does this 2.9%-figure differ between individual patients, and therefore might generally differ between FSHD1 and 2, and between different D4Z4 genetic categories ?; as this will matter for different individual patients.

Minor comments

1. Methods - scRNAseq and snRNAseq data - 2nd paragraph: - '..2 sperate patients...' Typo. to correct.
2. Methods - Hypothetical scenarios for raising 𝑬[𝒎] under the promoter-switching model- 1st paragraph: - '...constant in the second...' needs punctuation.
3. Methods - Cellular Automaton Model - 3rd paragraph: - '...according the non-syncytial...' Typo : '...according to...'
4. Methods - Genetic algorithms - 3rd paragraph: - '...elitism of 5%, mutation probability 0.1, crossover probability 0.8,...' Should these parameters be given in the same format to avoid any confusion - ie. all as probability, or all as percentages ?

Significance

Please see comments in the 'Evidence, reproducibility and clarity' box above.

This paper is valuable in providing a mathematical basis for modelling the stochastic process of presence or absence of DUX4 expression in individual myocytes in FSHD (facioscapulohumeral muscular dystrophy), and hence a basis by which to study the effects of other factors or potential therapeutic interventions which may influence the overall proportion of muscle cells which do express DUX4, and hence succumb to its toxicity.

This is a specialist field and the nature of presentation of the mathematical modelling in the paper will restrict accessibility to only a very few researchers in this field. There appears to be a possible major problem in the way that the paper has combined data from patients with different genetic forms of FSHD, and different likely genetic severities. It would seem better to analyse the data separately for each of these to allow a more homogenous platform on which to construct a model.

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Referee #1

Evidence, reproducibility and clarity

Banerji and colleagues measure DUX4 and target gene expression over a time course in doxycycline-inducible myoblasts to estimate kinetic parameters and rates underlying the transition of non-expressing cells through DUX4-expressing cells to cell death. They then use these parameters to model the rate of appearance of DUX4+ cells, DUX4 target gene expression, etc. in cells from FSHD patients, and derive a model that predicts that over 100 days around one fourth of cells die while less than 1% of cells express DUX4 or its target genes at any given time. This is somewhat similar to what is seen in FSHD patients, where DUX4 expression is infrequent in cultured cells, while patients eventually have substantial muscle loss. The experiments are well-designed and explained clearly.

Significance

The primary significance of this study is that the field has a sense that the damage seen in patient muscle is not congruent with the low expression of DUX4 in patients, and the model showing many cells dying with only a few cells expressing DUX4 at any given time suggests that overall damage can be greater than that observed in any particular snapshot.

However, it is important not to conflate low frequencies of DUX4+ nuclei in cultured myoblasts with "DUX4 being difficult to detect" as in p 12, Discussion. DUX4 is difficult to detect, indeed basically not detected, in muscle biopsy specimens, but in cells in vitro, DUX4 is fairly easy to detect, albeit in quite low numbers of cells. Since the study evaluates cells in vitro, it is important to make clear that the situation in vivo is qualitatively different from that seen in vitro, namely DUX4 not being detected, and the authors should clarify this importance difference.

A second reason for caution in extrapolating correlates from the in vitro model to the disease process in muscle tissue is that in vivo there is a continual source of replacement cells, as the authors have shown in a previous study. Have the authors attempted to model a situation in which new cells are provided into the system at some rate, related to the amount of death occurring at different times? Although the authors mention that the static cell number is a limitation of the model, it would be valuable to revisit or explore this idea in the Discussion section, if only to provide the reader with a more pragmatic perspective.

The second part of the paper models presence of DUX4 in nuclei based on diffusion from expressing to non-expressing nuclei, and characterizes this as the activity of "an infectious agent, able to spread from one nucleus to another by adapting epidemiological compartment models". The relationship to an infection process is probably not the ideal way to characterize this process, because an infection implies the setting up of new sites of production of the agent, DUX4, where what is really happening is that DUX4 diffusing into these other nuclei isn't leading to more DUX4 production, it is just diffusing into nearby nuclei and accumulating there. Unless I am misunderstanding, the authors are simply showing that a larger number of nuclei will be positive in a system in which cells are fusion products having many nuclei than in a system in which all nuclei are isolated within their own cells.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript "Compaction of Drosophila histoblasts in a crowded epidermis is driven by buckling of their apical junctions", Rigato et al. study the behaviour of histoblasts during the third larval stage of Drosophila. Histoblasts are the progenitor cells of the adult abdominal epidermis. They are produced in the embryo and lie between the larval epithelial cells (LECs) in the larva before abdominal morphogenesis takes place during metamorphosis. The authors use this system to explore the role of forces during epithelial development.

The authors show that histoblasts undergo a dramatic change in morphology between 90 and 115 h AEL (after egg laying), with adherens junctions changing from a straight to a highly folded appearance. They also show that, during this time, histoblast volume is increasing. Furthermore, both junctional actin and junctional myosin II levels decrease over time. Interestingly, reduction of contractility (Rok-RNAi) and increase of contractility (overexpression of Rok constitutively active) in both histoblasts or LECs has no significant effect on histoblast behaviour.

The authors hypothesise that growth of histoblasts and LECs lead to a compression of the histoblasts, which causes the buckling of the histoblast junctions. To test this hypothesis, they perform three experiments and show that (1) overexpression of a dominant negative form of insulin receptor in LECs leads to a non-autonomous effect on histoblast cell size, (2) overexpression of a dominant negative Rab11 in histoblasts interferes with histoblast buckling, and (3) overexpression of String in histoblasts induces histoblast division and reduces histoblasts buckling.

Major comments

As mentioned in the significance box below, a major weakness of the manuscript is that the presented data does not sufficiently support the hypothesis that histoblasts are compressed by the LECs: Firstly, the manuscript does not provide evidence that the LECs expand and compress the histoblasts. Such an analysis needs to be included.

Secondly, the authors present two pieces of evidence to support their hypothesis, which are not very convincing:

1. In line 147ff, the authors imply that their experiments in Fig. S3D (Rok alterations in LECs) support a 'mechanical tug of war'. However, the authors point out that their analysis is not statistically significant, so their data does not actually support their hypothesis.
2. The authors show that overexpression of a dominant negative form of insulin receptor results in a non-autonomous increase in apical histoblast cell area. Here, I wonder to which extent the change in histoblast cell size might be due to a change in histoblast cell number per nest rather than a change in LEC pressure. Furthermore, they state that junctions are straighter, however, it would be good to see a statistical analysis here. Importantly, the conclusion of the experiment is not very convincing as it has not been shown that LECs are smaller, or grow slower, or exert less pressure on the histoblasts due to the performed genetic manipulation. So, the reason for the observed histoblast phenotype is unclear. This needs to be explored further.

Thirdly, there are a few observations, which further challenge the author's hypothesis:

1. Buckling appears to happen in the plane of the tissue, but should it not happen orthogonal to it, as shown in the scheme in Fig. 2J, if LEC compression was the cause of buckling?
2. In Fig. 1D, one can see buckling at interfaces between histoblasts and LECs - does this not suggest that histoblasts push against LECs (and that in that area LECs are not pushing against histoblasts)? I think this observation is very interesting and a more detailed analysis of this phenomenon at histoblast-LEC junctions could be included in the manuscript.

Based on the above point, there are various sections of the manuscript that need to be adjusted in my opinion, for example:

• Line 190ff. 'Combined, these perturbation experiments provide strong evidence that junctional buckling of the histoblasts is the result of a imbalance between the addition of junctional material in the histoblasts and mechanical constraints from the overcrowding of the epidermis.' - There is not sufficient evidence for this statement with respect to overcrowding. The presented data does however show convincingly that junctional remodelling is needed for buckling.
• Line 201 ff. 'This experiments confirms that junctional buckling is a result of the combined overcrowding and absence of divisions.' - The presented experiments do not provide sufficient evidence for this statement. The presented data does however show convincingly that inducing cell division leads to less buckling. I wonder whether this result is counterintuitive with respect to the authors' compression hypothesis, as if increased compression from LECs would lead to buckling would then not also increased pushing by neighbouring histoblast lead to buckling?
• Line 235 ff. 'We investigated the formation of histoblast junctional folding and found that it is a non-autonomous transition originated by the competition for space of the two cell populations.' - This needs to be rephrased are there is not sufficient evidence for this hypothesis.

Further major comments

Line 56: More details about which histoblasts were imaged would help the reader understanding the experiments better. - Which abdominal segment was imaged? - The authors state that they have imaged the "dorsal posterior nest, which has the largest number of cells (15-17)" - is this correct? In later stages, the anterior dorsal nest is larger than the posterior dorsal nest. - Do the different abdominal segments have different histoblast numbers/ histoblast nest sizes? - I think that it would be helpful to show the behaviour of the ventral nest - the authors' hypothesis suggests that all histoblasts behave similarly and the buckling should be observed in all nests.

In the figures, the authors do not clearly present the statistics done. Here, giving the p-values in the graphs would be very helpful. There are instances where it is not sufficiently clear whether the authors present a significant finding or merely a non-significant tendency (e.g. in Fig. 6).

Fig. 4CD. I do not agree with the authors' interpretation that Sqh::GFP is lost from the junctions. The figure shows that levels are reduced. Also, in line 157, it might be better to use 'reduction of junctional myosin' rather than 'loss of junctional myosin'.

With a few exceptions, the authors do not show the fluorescent marker used in their experiments to report where and how strong the Gal4 is expressed, which they use to drive their RNAi or dominant negative constructs. For example, in Fig. 5, they say that they have co-expressed a cytosolic GFP, but they do not show it. To have this kind of information would be very useful when interpreting the data.

In line 171 and in other places the authors talk about 'plastic remodelling'. It would be helpful if the authors could explain in more detail what they mean by this.

Minor comments

In my opinion, the paragraph about the 'Qualitative model of junctional buckling' would be better placed in the discussion.

Fig. 6. Why was the insulin receptor experiment done in white pupa and not in wandering L3 larvae? This makes comparison of data more difficult.

I wonder when are histoblasts stopping to show the buckling? I assume that it must be before the beginning of abdominal morphogenesis, as at the beginning of LEC replacement, the junctions are straight again?

What is the morphology of histoblasts in late L1- and late L2-stage larvae? Potentially there might also be crowding during those stages?

Significance

The observations by Rigato et al. are very interesting. They present a novel model system that enables the study of the interaction of two epithelial cell types, which do not divide. The fact that the presented data suggests that neither histoblasts nor LECs are under tension, makes this an extremely interesting novel system to explore the forces involved. The presented results provide some interesting insights into histoblast biology. However, a major weakness of the manuscript is that the proposed hypothesis of histoblast compression by larval epithelial cells is not sufficiently supported by the results. So apart from the very interesting observations, the manuscript lacks insight into the mechanistical basis of junctional buckling.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Rigato and colleagues uses live imaging of Drosophila larvae to describe the transition of histoblasts from a configuration where junctions are straight to a configuration where junctions are folded. The emergence of folded junctions is concomitant with a decreasing apical area, an increasing cell height and decreasing junctional Myosin. Expression of Rab11-DN (reducing apical junctional trafficking) or Cdc25 (inducing proliferation) in histoblasts abolishes the emergence of folded junctions. Expression of InRdn (impairing insulin receptor pathway) in surrounding larval epidermal cells (LEC) is claimed to also reduce folding (but see comment below). Finally, laser ablation of folded junctions reveals little or no relaxation, indicating that these junctions are not under tension. The authors propose a model in which growth mediated cellular crowding generates mechanical constrain on histoblasts which, in conjunction with decreased junctional Myosin II, leads to the buckling of junctions.

• The authors convincingly show that histoblast junctions buckle/fold during late larval stages. They also convincingly show that junctional folding requires Rab11 and is counteracted by cell proliferation. The conclusion that folded junctions are not under tension is less well supported. Ablation of folded junction results in little/no relaxation (Fig. 5), however, the positive control (late pupal histoblasts) did not show obvious relaxation either (in absence of quantification).
• The key conclusion that growth mediated crowding drives junctional folding is not well supported. The authors claim that InRdn expression in LECs leads to straight junctions of histoblast, but that is not obvious from Fig. 6F. Additional experiments will be required to further test the role of growth mediated crowding in junctional folding. For example, the authors should further attempt to modulate growth of LECs and/or test whether overgrowth of larvae to increase epidermal surface (e.g. lgl mutants) would influence junctional folding. These experiments will likely require several months.
• Moreover, the authors provide a 'qualitative model' (Fig. 7). The paper would be strengthened by providing a model based simulation (best based on quantitative data derived from experiment (e.g. growth rates of histoblasts, LECs, etc.) of the transition of straight to folded junctions. This simulation would test whether the authors' hypothesized mechanism is feasible. This work will likely require several months.
• Statistical analysis to reveal significances between datasets is missing throughout the manuscript. The number of biological replicates (i.e. larvae) for each experiment are not provided. The authors should provide statistical analysis.
• Fig. 3/S3. The authors observe a correlation between junctional folding and a decrease in F-actin and Myosin on junctions. However, changes in Rok activity do not alter junctional folding. It is therefore unclear whether the decrease of F-actin and Myosin is a cause or consequence of junctional folding. The authors should tune down their conclusion that junctions 'soften' through a loss of cytoskeletal components.
• Fig. 5. The laser ablation experiments require a quantitative analysis.
• Fig. 6. A control image (Cad;mKate without perturbation) is missing.
• Fig. 7. The junctional buckling model is a hypothesis by the authors to explain their observation; it is not data and therefore should be removed from the 'results' section of the manuscript.

Significance

The manuscript describes an interesting behaviour of cells to change their junctional morphology from straight to folded during development. Previous work has mainly described epithelia as networks of cell junctions under tension. This work advances our understanding by providing evidence that epithelia can also be in a configuration that is not based on tension. The authors provide evidence that this cell configuration requires the absence of cell proliferation and trafficking of junctional material. However, beyond this, the mechanisms that drive epithelia into this configuration remain somewhat unclear. The manuscript would therefore likely target a specialist audience

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the answers and strategy plans are details in the "revision plan" document attached to this submission

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Referee #3

Evidence, reproducibility and clarity

This study used metabolomic, lipidomic and proteomic approaches to analyze the metabolic changes induced by the p.S59L CHCHD10 mutation in mice. Altered metabolism in plasma and heart of Chchd10S59L/+ mice was seen compared to their wild type littermate. In plasma, levels of phospholipids were decreased while those of carnitine derivatives and most of amino acids were increased. In cardiac tissue, Chchd10S59L/+ mice showed a decreased Oxidative Phosphorylation (OXPHOS) and -oxidation proteins levels as well as tricarboxylic acid cycle (TCA) intermediates and carnitine pathway metabolism. In parallel, lipidomics analysis revealed changes in the lipidome, including triglycerides, cardiolipin and phospholipids. Consistent with this energetic deficiency in cardiac tissue, the authors show that L-acetylcarnitine supplementation improved the mitochondrial network length in IPS-derived cardiomyocytes from a patient carrying the CHCHD10S59L/+ mutation. It is concluded that L-acetylcarnitine may restore mitochondrial function in CHCHD10-related disease, due to the reduction in energy deficit that could be compensated by carnitine metabolic pathways.

General Comments:

The authors had already generated knock-in (KI) mice (Chchd10S59L/+) that developed cardiomyopathy associated with morphological abnormalities of mitochondria, severe oxidative phosphorylation (OXPHOS) deficiency and multiple defects of respiratory chain (RC) complexes activity in several tissues in the late stage. Other authors have seen similar results (see reference 5) As a result, the insights provided by this further metabolomic, lipidomic and proteomic analysis are not that novel. The data is very descriptive and often the authors misinterpret the meaning of the results. Addionally the alterations in either metabolites or proteins have in their mutant CHCHD10S59L/+ mice are often not accurately represented.

The administration of acetylcarnitine to reverse the metabolic defects in iPSC cardiomyoctytes derived from a from a patient carrying the CHCHD10S59L/+ mutation is interesting, although the authors did not determine if similar benefits of acetylcarnitine were seen in the CHCHD10S59L/+ mice. They msy also misinterprete the role of acetylcarnitine in the mitochondria.

Specific Comments:

1. The authors point out that in vitro and in vivo studies have shown that CHCHD10 mutants cause disease by a toxic gain-of-function mechanism rather than by loss of function. It would be helpful to better define what the effects of the CHCHD10S59L/+ mutation represent.
2. An aim of this study is to understand how mitochondria dysfunction associated with CHCHD10 mutations triggers altered global metabolism. This aim has not really be accomplished in this study.
3. Figure 3C: The authors put FABP3 in the ß-oxidation pathwayt. This is incorrect. The figure also does not highlight the ketothiolase (ACAT1)? It is also not clear why CPT1 and CPT2 are place together. There is a carnitine translocase situation in between these enzymes. It should also be pointed out that CACT also has role in long chain acylcarnitine translocation.
4. The main direction of CrAT in heart mitochondria is the conversion of acetyl CoA to acetylcarnitine. This is not recognized by the authors, and has important implications on interpreting acetylcarnitine therapy.
5. Figure 3C: Why is just palmitate and linoeate shown as fatty acids?
6. The authors state that "In cells, long-chain fatty acids dependent on esterification with L-carnitine to form acetyl-carnitine for transport from the cytoplasm to the mitochondrial matrix for oxidation and energy production." This sentence should be re-written and broken down into clearer statements.
7. The authors state that "In lipid biosynthesis pathway, protein levels of long-chain- fatty-acid-CoA ligase 1 (Acsl1; as well as mRNA expression) and mitochondrial short-chain specific acyl-CoA dehydrogenase (Acads) in the hearts of symptomatic Chchd10S59L/+ mice were significantly lower than those of age-matched wild-type mice (with adjusted p < 0.05) (Fig. 3A-B). Please clarify and re-write this sentence. ACSl1 is primarily producing acyl CoA for CPT1. ACADs is involved in ß-oxidation of short chain fatty acids. Not lipid biosynthesis.
8. The authors state that "With respect to the lipid oxidation, protein levels of carnitine-O-Acetyltransferase (Crat; as well as mRNA expression)...". Crat is not involved in fatty acid oxidation.
9. It is stated that "Interestingly, a significant decrease in fatty acid biosynthesis intermediates, such as malonate and ethyl-malonate, was also observed in the hearts of symptomatic Chchd10S59L/+ mice (Fig. 3C). I do no not see this data. Where is this?
10. Figure 3G.; Data is incomplete
11. The authors state that: Altogether, those results suggest that CHCHD10S59L mutation induces branched-chain amino acids catabolic defects and increased non-essential amino acids synthesis which may contribute to the elevated levels of amino acids metabolites observed in plasma and heart of Chchd10S59L/+ mice (Fig.1D, Fig. 3D)." It should be recognized that the heart is a small contributor to plasma amino acid changes.
12. The IPSC-derived cardiomyocytes seem to involve one single patient.
13. The authors state that "As highlighted by our study, Chchd10S59L/+ mice displayed markedly elevated levels of cholesteryl esters, some glycerophospholipids and reduced concentrations of triglycerides species in heart at the symptomatic stage. Changes in cholesteryl ester metabolism were not reflected in changes in plasma total cholesterol pools (Table 1). In other words, it appears that cholesterol synthesis is upregulated but does not result in accumulation of free cholesterol." It should be recognized that the heart is not really involved in the synthesis of cholesterol.
14. The authors state that "Increased plasma concentrations of acylcarnitines (which formed from carnitine and acyl-CoAs) are suggested as a marker of metabolism disorders related to cardiovascular diseases [43, 44]. Based on our data and the literature, we suggested that targeting carnitine metabolism pathway could counterbalance the metabolic disturbances, ameliorate mitochondrial functions, and therefore delay CHCHD10-related disease progression." This statement is not supported by data. There are also inaccuracies with the discussion of metabolism.

Significance

The authors had already generated knock-in (KI) mice (Chchd10S59L/+) that developed cardiomyopathy associated with morphological abnormalities of mitochondria, severe oxidative phosphorylation (OXPHOS) deficiency and multiple defects of respiratory chain (RC) complexes activity. Other authors have seen similar results (see reference 5) As a result, the insights provided by this further metabolomic, lipidomic and proteomic analysis are not that novel. The data is very descriptive and often the authors misinterpret the meaning of the results. They often misrepresent the significance of the observed alterations in metabolites and proteins in their mutant CHCHD10S59L/+ mice.

The administration of acetylcarnitine to reverse the metabolic defects in iPSC cardiomyoctytes derived from a from a patient carrying the CHCHD10S59L/+ mutation is interesting, although the authors did not determine if similar benefits of acetylcarnitine were seen in the CHCHD10S59L/+ mice.

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Referee #2

Evidence, reproducibility and clarity

This manuscript by Madji Hounoum et al presents a study of the metabolic changes associated with the p.S59L CHCHD10 mutation that underlies a toxic gain-of-function in a number of disease phenotypes, including ALS and mitochondrial myopathy. The authors have used a multi-omics approach in their mouse model to demonstrate significant decreases in proteins required for oxidative phosphorylation (OXPHOS) and -oxidation, as well as metabolites involved in the Kreb's cycle and carnitine metabolism, along with significant changes in lipid profiles. The authors also provide evidence that L-acetylcarnitine appears to ameliorate mitochondrial function.

Major comments:

The manuscript is, in general, well-written and 1. The conclusions drawn are mostly justified on the basis of the data presented (see comment 2). 2. The authors have used a multi-omics approach, which is very powerful in allowing a cell-(or organism-) wide analysis of multiple systems. However, the key drawback for the proteomics approach is that low abundance proteins, such as the OXPHOS proteins, are often not accurately assessed. In this regard, it seems critical that the authors provide further evidence for the changes suggested by the heatmaps - or justify that the technical approach they have taken can account for low abundance. Western blotting would fit the bill nicely - there are antibodies available to many of the OXPHOS complex subunits. 3. There is a very rich and deep literature, particularly from the '80s and '90s, regarding the metabolic disturbances identified in mitochondrial disease patients, both for OXPHOS and fatty acid oxidation defects, that should be cited. In addition, the literature delineating the mitochondrial 'cofactor cocktail' should at least be mentioned.

Minor comments:

1. Some attention to grammar would help clarify the meaning for the reader. Some sentences are incomplete.
2. The authors are encouraged to check their references, as a number are incomplete.
3. Adding the scale to the scale bar for the MitoTracker Red confocal images would be handy. The right-most micrographs of the S59L/+ cells (Fig 4C) are not nearly as convincing as the middle pair of micrographs - and detract from the message. In addition, it would be nice to have the authors comment on the very intense staining of the mitochondria in the S59L/+ cardiomyocytes in panel A of Figure 4.
4. The final paragraph of the Discussion is not as impactful as it could be. Specifically, referencing the mitochondrial disease literature would aloow a more fulsome discussion as to the mechanism underlying the action of ALCAR i.e. is the anti-oxidant function most important? Or is it simply it's action as a metabolite?

Significance

This study is an important contribution to the literature examining neurodegenerative diseases, such as ALS, that remain poorly understood. While the advance seems incremental, especially in light of the many studies in recent years that have document mitochondrial dysfunction in the mutant mice, the ALCAR treatment is novel and worthy of dissemination. The audience that this manuscript would interest would span from neurologists and other clinicians, such as medical geneticists, to basic biomedical researchers in both academic and industry.

Reviewer area of expertise: mitochondrial function in health and disease; insufficient expertise to evaluate the details of the 'omics' methodologies.

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Referee #1

Evidence, reproducibility and clarity

Summary

In the manuscript titled "Multiomics study of CHCHD10 S59L -related disease reveals energy metabolism downregulation: OXPHOS and beta-oxidation deficiencies associated with lipids alterations," Hounoum et al. performed metabolomics, lipidomics, and proteomics analyses of symptomatic and presymptomatic CHCHD10 S59L murine hearts. The results are largely descriptive. Levels of carnitine acyltransferases and carnitine palmitoyltransferase 2 are lower in S59L hearts. Many proteins of lipid biosynthesis and beta-oxidation pathways are also reduced. Authors concluded that utilization of fatty acids for energy production is deficient in the mutants. They treated CHCHD10 S59L iPS-derived cardiomyocytes with acetyl-L-carnitine and observed a rescue of mitochondrial length and cristae organization in vitro. This paper contains a few grammatical, structural, and organizational issues. The paper confirms several findings recently published by Sayles et al. (Cell Reports, 2022) while providing some additional information through lipidomics analysis in the hearts and metabolomics analysis in the plasma. Overall, it is largely descriptive apart from an experiment in the figure 3 in which the authors use iPSC-derived cardiomyocytes to show partial rescue of a mitochondrial morphology defect following acetyl-L-carnitine supplementation. However, these data seem preliminary and the correlation between the iPSC and mouse model is not established. Overall, these data are likely to be of interest to the immediate field but lack novelty for a general audience.

Major comments

1. In Figure 3, authors treated CHCHD10 S59L iPSC-derived cardiomyocytes with acetyl-L-carnitine for 72 hours and observed a rescue of mitochondrial length and cristae organization. However, it is not established that the defect in mitochondrial length is important to the pathology in the mouse heart. A rescue of the mitochondrial functional defects would have greater relevance. Thus, the relevance of rescuing this phenotype is not clear. It is not clear a priori whether increasing acetyl-L-carnitine levels would be protective. While certainly there is evidence for downregulation of fatty acid beta-oxidation as well as carnitine-related metabolites, if this a compensatory response to an OXPHOS defect, increasing acetyl-L-carnitine levels could make the cardiomyopathy worse. Supporting this alternative interpretation, levels of carnitine derivatives are already higher in mutant plasma. Thus, it is important to demonstrate this rigorously and in the most appropriate model. For instance, showing rescue of cardiomyopathy in the in vivo mouse model would be stronger than showing rescue of a minor defect in mitochondrial morphology in iPSC derived cardiomyocytes. Additionally, the EM images provided in the paper are in low magnification, and cristae structures are not clear. Quantifications of the cristae defects are not provided. Authors also do not provide any information regarding how they quantify mitochondrial length. Mitochondria is a 3D structure. The length of mitochondria on a 2D transmission EM image may not represent the true length of the mitochondria.

Minor comments

1. Title. Recommend changing "lipids alterations" to "lipid alterations."
2. Page 7, line 13, 14, and 23. Some characters appear as squares.
3. Page 12, line 8. Recommend changing "After 72 treated with Acetyl-L-carnitine" to "After treated with acetyl-L-carnitine for 72 hours."
4. Page 14. It is unclear what the significance of the electrolyte measurements is. A brief discussion of the implications should be added.
5. Page 15, last sentence. Consider rephrasing the sentence "Very interestingly, most of the amino acids detected in the plasma were increased in CHCHD10 S59L/+ mice at both stages, as well as carnitine, O-acetyl-L-carnitine and deoxycarnitine increased at symptomatic stage."
6. Page 16, line 10. "Longitudinal proteomics" implies that the pre-symptomatic and symptomatic heart samples came from the same subjects. If this is not the case, I would recommend deleting the word "longitudinal."
7. Page 17, line 14. Authors show changes in levels of proteins and metabolites in beta-oxidation, but there is no evidence presented to show that beta-oxidation is impaired in CHCHD10 S59L/+ hearts.
8. Page 17, line 19. Please indicate where Cyb5r1 levels are shown.
9. Page 18, line 15. Please indicate where Acss1 levels are shown.
10. Page 18, line 10. Abbreviation Plaa has already been spelled out in line 2.
11. Page 18, line 10, it is stated here that Plaa levels are lower in the CHCHD10 S59L KO mice but 3 sentences earlier and on the figure they are higher. Please resolve this discrepancy.
12. Page 18, line 18. Recommend changing "lipids alterations" to "lipid alterations."
13. Are enzymes involved in amino acid metabolism (PHGDH, PSAT1, and ASNS) upregulated in your proteomics analysis?
14. Page 20, line 6. Recommend changing "Catabolic enzymes are mainly located in the mitochondrial matrix such as the branched-chain alpha-keto dehydrogenase complex (Bckdha, Bckdhb) and branched-chain-amino-acid aminotransferase (Bcat2)" to "Catabolic enzymes, such as the branched-chain alpha-keto dehydrogenase complex (Bckdha, Bckdhb) and branched-chain-amino-acid aminotransferase (Bcat2), are mainly located in the mitochondrial matrix."
15. Page 20, line 12. The authors interpret the increase in non-essential amino acid synthesis as a consequence of the changes in branch-chain amino acids. An alternative interpretation is that this is a consequence of the mitochondrial integrated stress response, which has been demonstrated to be upregulated in symptomatic CHCHCD10 S59L hearts. Specifically, expression of proline synthesis, serine metabolism, and asparagine synthesis enzymes is increased in a manner that depends on the OMA1-DELE1-eIF2alpha signaling axis. This alternative explanation for these results should be discussed and the relevant studies cited (PMID: 35700042 and 32338760).
16. Page 22, line 12. An alternative explanation for lower creatinine levels is decreased body weight in the CHCHD10 S59L mice. This should be discussed in addition to noting changes in creatinine in ALS. Additionally, it seems more likely that these changes in the CHCHD10 S59L mouse relate more to the myopathy/cardiomyopathy in this model than motor neuron disease, which is not a prominent feature in the model.
17. Page 23, line 10. Recommend showing Uchl1 levels in Results and/or Figures.
18. Page 23, line 15. Myh6 levels are unchanged or decreased not increased in cardiomyopathy.
19. Page 24, line 17. Recommend changing "mitochondrial dysfunctional" to "mitochondrial dysfunction."
20. Page 27, line 10. Recommend changing "ameliorate mitochondrial function" to "ameliorate mitochondrial dysfunction."
21. Page 31, line 22. Formatting error.
22. Figure and Figure legend 3D. Recommend changing "not regulated" and "not modulated" to "unchanged" or "not significantly different."
23. Figure 4C and 4D. Please provide images of higher magnification to clearly show mitochondrial network and cristae. Please provide quantification for 4C.

Significance

This paper contains a few grammatical, structural, and organizational issues. The paper confirms several findings recently published by Sayles et al. (Cell Reports, 2022) while providing some additional information through lipidomics analysis in the hearts and metabolomics analysis in the plasma. Overall, it is largely descriptive apart from an experiment in the figure 3 in which the authors use iPSC-derived cardiomyocytes to show partial rescue of a mitochondrial morphology defect following acetyl-L-carnitine supplementation. However, these data seem preliminary and the correlation between the iPSC and mouse model is not established. Overall, these data are likely to be of interest to the immediate field but lack novelty for a general audience.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

The study "Mesenchymal stem cell models reveal critical role of Myc as early molecular event in osteosarcomagenesis" by Akkawi et al shows that BM-MSCs are a foundational tool for study of osteosarcoma. And authors identified Myc and its targets as early molecular events in osteosarcoma formation, using BM-MSCs knocked out of both p53 and Wwox. Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings.

Response: We thank the reviewer for the kind words regarding our manuscript. Our study revealed that combined deletion of WWOX and p53, two known tumor suppressors in OS, results in early transformation of BM-MSCs that mediates upregulation of Myc. We believe that this notion is original and has not been described before. In addition, we provide evidence that this genetic manipulation has an advantage over p53 deletion alone. More details are provided below.

1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref. 21).

Response: We agree with the reviewer about this notion/fact and we indeed cited this reference. It should be noted that in this study the cell of origin of osteosarcoma was proposed to be BM-MSCs-derived osteogenic progenitors when p53 and Rb are co-deleted. Interestingly, this study did not show whether p53-/- BM-MSCs-derived osteogenic progenitors are able to form osteosarcoma or any other type of sarcomas. In our model BM-MSCs p53-KO alone were not able to develop osteosarcoma tumors as well, even when injected intratibially. Moreover, we showed that co-deletion of Wwox and p53 in BM-MSCs-derived osteogenic progenitors have the ability to form OS at earlier stages at the time point when deletion of p53 alone is still not enough to induce osteosarcomagenesis. Based on our findings and those in the literature, we believe that this combined genetic perturbation is critical for initiation of OS.

1. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).

Response: It is well known that Myc is a potent oncogene and overexpressed in osteosarcoma (ref. 12, 13, 28 and ref. 29) as well as in many other human malignancies. The referred study (PMID: 12098700; ref 32) which expressed Myc in lymphocytes based their analysis of Myc contribution at the tumor stage (at the time of tumor detection). Our study, on the contrary, shows that upregulation of Myc is an early event in osteogenic-committed BM-MSCs deficient for Wwox and p53 in tumor-free mice. This notion indicates that combined deletion of two important tumor suppressors, WWOX and p53, promotes osteosarcoma through early changes in Myc levels. Our data further show that p53 deletion alone is dispensable for this change to happen at early stages further highlighting the significance of our findings. The referred paper was cited and discussed in the discussion section (ref 32).

Our data further shed light on WWOX as a key determinant of Myc function placing it as an upstream regulator. To further proof this, we propose in our revision plan to knockout WWOX in yBM SKO cells and/or restore WWOX in yBM DKO cells and determine consequences on Myc levels and activity as well as on tumorigenesis. In addition, we shall perform a ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

1. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).

Response: This is a very relevant new study that we regrettably missed to cite so we are grateful for the reviewer to bring this up. In our revised version, we will be citing this important reference and discussing it (ref 33). Although this article shows that p53 deficiency promotes osteosarcomagenesis mediated through oncogenic Myc, which we also showed in our study (figure 6C, D), but the authors did not validate the tumorigenic ability of these cells at this earlier stage of osteosarcomagenesis. The later was showed in our study by injecting yBM-MSCs deficient for p53 intratibially into immunocompromised mice and showed that they lack tumorigenic ability although they display a mild upregulation of Myc (figure S5). On the other hand, co-deletion of Wwox and p53 at this earlier stage resulted in even higher levels of Myc and inducing osteosarcomagenesis at this earlier stage. Therefore, our study provides unforeseen effect of genetic perturbation that promotes OS initiation.

First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.

Response: Thank you for this comment, we will cite and discuss these valuable studies in our revised version.

The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism Some additional analysis would be needed to mention it

Response: As addressed above, our revision plan will include:

1. Depleting WWOX in yBM SKO and/or restoring WWOX in yBM DKO cells to further prove the tumor suppressor function of WWOX.
2. Performing ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

In the first place, Myc is upregulated in the absence of both p53 and Wwox, compared in only p53-null situation? Western blotting would be better to show it

Response: In our revised version we will add a western blotting showing the upregulation of Myc in DKO (WWOX, p53) compared to SKO (p53) yBM cells; This is already added in new Figure 5S C.

1. In Figure 1D, should separate each panel so that it is clearly visible. What is the blue-colored fluorescence, DAPI? If so, why don't tdTomato positive cells overlap with blue (Figs, 1D, 2C, 4C, 4E?

Response: In our revised version we added more precise images in all Figures indicating the overlap between DAPI (blue) and tdTomato (Red).

1. Why was MCM7 chosen among the Myc targets (S Figure 3)? What is the rationale for this?

Response: Thank you for this important notion. MCM7 is part of the MCMs protein family, that plays and essential role as a helicase and organizing center in DNA replication initiation. Moreover, several studies show the upregulation of MCM7 in several types of cancers among which is osteosarcoma, as cited in our manuscript (Ref. 14, 15, 16). MCM7 is also a direct target of Myc and has been shown to be a druggable target, by SVA as has been presented in Fig 7. Altogether, these facts and observations made us exploring its significance in our mode. In our revision plan, we will also explore other Myc targets through performing ChIP-seq on DKO cells.

In Figure 5 legend, what does "yBM cells (1.5, 4-months) (n=6)" mean? yBM cells (1.5-months) (n=3) and yBM cells (4-months) (n=3)?

Response: Thank you for this notion. yBM, at age of 1.5 months or 4-months were collected from tumor free mice and analyzed. In our revised paper, we updated and clarified this in the Figure 5 legend.

1. In Figure 7B, is there a correlation between MCM7 and Myc protein expression levels?

Response: Thank you for this comment. In our revised version we added a western blot analysis showing the upregulation of both Myc and MCM7 in yBM-DKO compared to SKO cells; new Figure S5 C. (did the mean 7B upper panne, if so, we have to add this in the updated version).

Also, do MCM7 and Myc immunopositivites overlap in Figure 7G?

Response: In our revised version we will perform Myc IHC on same tumor sections. In the meanwhile, we added a western blot analysis showing the inhibitory effect of Simvastatin on both MCM7 and Myc in vitro (new Fig 7B, lower panel, re-blotted for Myc).

In S Figure 4C, what is 'PC'? What sample was loaded?

Response: PC refers to positive control for p53 that was used which was in this case HEPG2 cells treated with Nutlin to stabilize p53. p53 antibody used in this plot (IC12-Cell signaling) detects both human and mouse p53. A note was added to Figure legend.

1. In S Figure 2A, what does 'US' (BM-US) mean? In S Figure 4F, what does 'US' and 'S' (Direct US and Direct S) mean?

Response: Thank you for this notion. We apologize for not clearly defined these symbols. In our revised version we added clarifications in the legends of these figures. The symbols are as follow: US: unstained BM-control, S: stained BM, Direct: directly collected BM and checked with FACS before culturing.

1. Overall, this manuscript, there are too many symbols and it is cumbersome. Ex, in S Figure 3, yBM_DKO, Tum_DKO, DKOT, DKO-BMT, etc. All figures should be consistent with the same notation.

Response: We apologize for this in consistency in using too many symbols. In our revised version we will provide a table with all the symbols that should be consistent all over the manuscript

Reviewer #1 (Significance):

Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings, such as;

1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref.21).
2. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).
3. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).

First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.

Response: Thank you for your valuable comments, and as we mentioned these important studies were and will be cited and discussed properly in our revised version.

The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism.

Response: As stated in our response above, we argue that our observations showing very early transformation of BM-MSCs in combined genetic perturbation of WWOX and p53 is novel. In our revision plan we propose to perform additional ex vivo experiments to prove this notion by performing WWOX deletion in SKO-yBM cells and WWOX restoration in DKO-yBM cells and test consequences on Myc levels/activity and tumorigenicity. To further shed light on the mechanistic outcome of WWOX action in this context, we shall perform Myc ChIP and ChIP seq assays in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc [follow up of Fig-I shown above]. These experiments should further strengthen our findings.

Reviewer #2 (Evidence, reproducibility and clarity):

In current study, the authors established a mouse model with tdTomato expression under the OSX-controlled double deletions of Wwox and Trp53. Such mouse strain gives a great platform to study the OS development and therapeutic potential. Experiments are clear and convincing. Results are well presented.

Response: We thank the reviewer for the kind words regarding our manuscript and acknowledging its clarity and validity.

To better improve this study, few minor suggestions regarding the data are as following:

1. Some of the legends on figures are too small to read, or in low quality. please change these labels.

Response: We thank the reviewer for this notion. We apologize for the low quality and inconvenience. In our revised version, we shall provide an improved resolution of legends.

1. For Figure 7C and D, from 7C, the control WT BM showed clear resistance to the SVA treatment, but in 7D, there is almost no cells in the WT BM group. Data of this group might be missed?

Response: Thank you for this comment. Cre+WT BM cells (shown in 7D) were unable to form colonies as shown previously in figures 2B and 4B. Fig 7C refers to sensitivity to SVA using MTT assay.

1. For figure 7G, the difference among MCM7 IHC staining of two groups didn't show as much as the statistical analysis in the right panel. Authors may consider using MCM7 western blot to check its levels after SVA treat.

Response: Thank you for this notion. We updated the Figure showing a more representative image (new Figure 7G).

Reviewer #2 (Significance):

This study uses a transgenic mouse model with tdTomato expressed in combination of loss of p53 and Wwox under the OSX lineage to study early initiation of OS. They found only DKO bone marrow cells can form OS in a subsequent orthotopic mouse model, but not the p53 single KO cells. After compare the RNA-seq from these different cell population, they identified the Myc pathway is the key player to promote OS development, especially the MCM7. Moreover, they tested SVA in treating these BM cells and reveal a therapeutic potential. This animal model is a good platform to study OS, especially at the early stage. Most results are clear and convincing. With the identification of Myc pathway, they further tested the SVA effects on treating these DKO BM. This is an important study and provided meaningful information to the OS, even broad cancer research community.

Response: We thank the reviewer for his/her supporting comments, and acknowledging the importance of our study.

However, the significance, or novelty of this work is not sufficient. For instance, SKO BM won't form tumor in the IT injection assays compare to the DKO BM groups, therefore, the involvement of Wwox during the OS tumorigenesis is clear. However, authors didn't explore any potential mechanisms of Wwox function or related signaling behind this observation

Response: We thank the reviewer for this very important comment. As mentioned previously, response to reviewer 1, our results indicate that combined deletion of two important known tumor suppressors, WWOX and p53, promotes osteosarcoma through early changes in Myc levels. Our data further show that p53 deletion alone is dispensable for this change to happen at early stages further highlighting the significance of our findings. In our revision plan we will do knockout of WWOX in yBM SKO and restore WWOX in yBM DKO cells. In addition, we are currently working to perform ChIP assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

And the RNA-seq analysis mostly focus on c-Myc pathway and its downstream targets. Given the well-known relationship of p53, c-myc even RB in the OS, it will be more interesting and attractive to see a clear mechanism of Wwox in this context.

Response: We thank the reviewer for this suggestion. Indeed, combined deletion of WWOX and p53 resulted in alteration of key cellular pathways involved in OS development. Due to the capacity of the work, we focused here on this important notion showing very early upregulation of Myc in BM-MSCs isolated from DKO cells, but not from SKO cells. Future work can expand use of this model to address relationship with other key pathways and genes.

Second, since authors took effort to generate this Tomato-DKO mice, it could be clearer if they isolate tdTomato positive cells instead of a mixture of BM, culture them, differentiate them, and perform more assays using these cells. In this way, it will give better clean background for all assays, and may be able to find novel effectors during this OS progression process.

Response: Thank you for this important suggestion. BM-MSCs cells collected directly from the mouse (Tom+, Sca1+, CD45-) represents a very small and minute population and was used to be cultured for enrichment as was done in previous studies (ref. 33 and 34). So direct collecting this small population and injecting directly to immunocompromised mice is not feasible. Moreover, further validation of the cultured cells used in our study confirmed their mesenchymal identity. We however, propose to try performing in vitro tumorigenic assays on these sorted cells. In our revision plan we suggest performing colony formation assays and soft agar assays to address tumorigenicity of these cells.

Third, within the text, authors tried to use OB differentiation and some other assays to discuss the OS origin cells, MSC or OB; but didn't get a preferred conclusion. It could be possible to better understand this process with the single cell RNA-seq using these BM from different mice or at different ages

Response: Thank you for this important point. According to our results we can conclude that BM-MSCs committed to the osteoblast lineage are supposed to be the cell of origin for OS and will be clearly emphasized in our revised version. Preforming a single cell RNA-seq is beyond the scope of this study and can be explored in future studies.

In general, this is a clean, straightforward study, and they established a very useful model to study OS. But the mechanisms merit is somewhere short

Response: Thank you for the kind words, as proposed previously our plan to further investigate the mechanism of WWOX regulatory effect on Myc will be addressed using the in vitro assays of WWOX deletion/restoration to SKO/DKO-yBM cells respectively. Moreover, ChIP-seq assay for Myc in yBM DKO and compare it to yBM SKO cells to show whether WWOX deletion is indeed results in enhanced chromatin accessibility of Myc.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary: The work describes analysis of an Osx1-Cre p53fl/fl Wwoxfl/fl mouse model compared to an Osx1-Cre p53fl/fl model. The authors have assessed the osteosarcoma inducing potential of cells within the bone marrow by including a tdTom reporter of Cre expression. They conclude that bone marrow mesenchymal cells can give rise to osteosarcoma when transplanted. Based on transcriptomics they contend that upregulation of Myc is important and its target MCM7 can be targeted with simvastatin.

Major comments: -

Are the key conclusions convincing?

The authors can generate tumors in immunocompromised mice upon injecting cells derived from bone marrow flushed. This is not surprising given the data available now about expression of Osx1-Cre

Response: Thank you for this notion. In our study we showed that yBM cells harboring the deletion of two known important tumor suppressor genes WWOX and p53 collected from tumor free mice are tumorigenic compared to SKO p53 at this earlier stage. This is a novel notion that has not been presented previously. In our revised version, we propose to further study the mechanism of WWOX action on Myc accessibility.

The analysis of MSCs lacks detail and needs significant more improvement and assessment by FACS using the well-defined criteria for mesenchymal cells that have been developed

Response: Thank you for this comment. In our revised version we will add another marker (CD11b), in addition to the used markers (CD45/Sca-1 and CD29) which are all well known to define MSCs as mentioned in Ref. 21, 33, 34.

It is not clear to me from the available information if the authors have used bone marrow cells that were flushed and immediately transplanted or if all cells transplanted have been placed in culture first, adherent cells expanded in culture media favoring survival and proliferation of non-hematopoietic cells and then transplanted- this is important to clarify explicitly as it is important to the significance of the study. If these cells are all used after culture, then the novelty of these studies are questionable as it was demonstrated previously that similar types of cells give rise to OS when transplanted (PMID: 18697945).

Response: Thank you for this important point. In the referred reference (PMID: 18697945, ref 34) authors used p53/Rb Cre- stromal cells that were cultured in vitro then infected with Ad-Cre and then injected subcutaneously in immunocompromised mice. In our study, we provide evidence that genetically manipulated young BM-MSCs (for Wwox/p53) are tumorigenic when injected intratibially, a more relevant niche for these cells, and this involved upregulation of Myc. In our revised version, we shall provide more mechanistic insights on the functional relationship of WWOX and Myc. Using BM-MSCs cells that are directly collected from the mouse (Tom+, Sca1+, CD45-) is not feasible due to very low percentage of cells which has been also previously reported by many groups (Ref 34). In our revision plan, we also propose to try performing in vitro tumorigenic assays on sorted cells.

Moreover, in our study we validated that the deletion of p53 alone at this earlier stage is not enough to induce osteosarcomagenesis (which was not shown previously) suggesting additional hits are required for OS formation. Importantly, co-deletion of Wwox and p53 using the same Cre line resulted in the upregulation/higher levels of Myc that promotes osteosarcomagenesis at this earlier stage.

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

I think overall the authors are appropriately cautious in interpretation. The points raised above regarding the nature of the cells would need clarification and then the claims reassessed however.

Response: Thank you for this acknowledgment. According to our results we can conclude that BM-MSCs committed to the osteoblast lineage could be the cell of origin for OS and this will be clearly emphasized in the discussion of our revised version.

Claims re "metastatic potential" should be significantly reconsidered - the authors present (motility assays) which should be referred to as motility assays. The injection of cells intravenously is a lodgment assay of cells in the venous circulation and does not equate to the process a cancer cell must undergo and survive to metastasis from a primary tumor in an immune competent environment. The claims around these assays should be significantly reconsidered.

Response: Thank you for this important comment. In our revision plan we will check the lungs of IT injected mice for the presence of lung metastatic nodules (tdTomato positive cells in the lungs).

• 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.

It is not explained or justified what the control cohort in Fig 7F is significantly smaller than the treated cohort. This will affect the statistical analysis and interpretation. There is no statement regarding blinding/randomization (or not) in the in vivo simvastatin experiment - this needs to be added.

Response: In our revised version we clarified in the materials and methods section clearly the randomization of the mice selection for each group and number of mice in each group.

The authors should include discussion that these are relatively long latency OS models compared to p53/pRb compound mutants and contrast with previous data from these models where in vitro cultured cells did give rise to OS in vivo after Cre treatment.

Response: Thank you for this suggestion. In our revised paper, we will include the latency of OS in our model and compare them to p53/Rb, and emphasize that our model tested the tumorigenic ability of SKO yBM cells and showed that they are unable to form osteosarcoma tumors at this early stage compared to DKO yBM cells.

• 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 a patient could to be treated based on these data, then the extra experiments to provide a robust preclinical dataset should be provided otherwise significant caution should be stated.

Response: Our study provides a proof-of-concept showing that our model can be used to screen for drugs that could inhibit OS development. The inhibitory potential of SVA affecting the progression of OS for clinical assessment would certainly need further investigation that goes beyond the scope of this paper.

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

See comments regarding cells used for injection and blinding. Need to more clearly describe the cells being injected and what was done to them from isolation to injection.

Response: Thank you for this comment. In our revised version we will add clearly in the materials and methods section the protocol of cell isolation and injection, randomization of the mice selection for each group and number of mice in each group.

• Are the experiments adequately replicated and statistical analysis adequate? Unclear if appropriate experiment completed in Fig 7F as no justification of different sample sizes is provided nor a statement reblinding/randomization.

Response: Thank you for this point. In our revised version we will add clearly in the materials and methods section the randomization of the mice selection for each group and number of mice in each group.

Minor comments:

• Specific experimental issues that are easily addressable.

The Osx1-Cre expresses a Cre:GFP fusion - the authors should correlate the GFP and tomato signal.

Response: We thank the reviewer for this point. We shall perform validation analysis in our revised plan.

Fig 2A- if 50% of the bone marrow is tdTom positive this is not evident in the image in Fig 1D. Quantify the images in Fig 1D. The tumor images in Fig 2C appear to have only sporadic tdTom positive cells - the authors should explain this further.

Response: Thank you for this notion, Figure 2A represents BM collected from a tumor bearing mouse which has a higher percentage of tomato positive BM cells compared to tumor free mouse (Fig 1D). Fig 2C is updated

Need statistics added to all GSEA plots.

Response: Thank you for this comment, statistics will be added in our revised version.

4C is very different than 2C - 4C is more consistent with the levels of tomato stated

Response: Thank you for this notion, the images were updated in our revised version

• Are prior studies referenced appropriately?

This seems appropriate

Response: Thank you for this comment.

• Are the text and figures clear and accurate?

Figures are not ideal and could be improved in terms of >clarity and text size

Response: We apologize for the low quality. In our revised version, we shall provide an improved resolution and accuracy.

Several sections of text are either contradictory or questionably accurate:

page 3: Molecular studies of OS are significantly hindered by its genetic complexity and chromosomal instability, which precludes the identification of a single recurrent event associated with OS. Contrasts with the following text: Page 18 - p53 has been extensively shown to play a central role in OS development in both human and mouse models. The data from sequencing of human OS and mouse models and canine data all point to p53 loss as being a central event in OS.

Response: We apologize for this inaccurate statement. In our revised paper, we revised this statement to reflect the common event of p53 deletion in OS and its significance in osteosarcomagenesis.

Page 18: High genetic heterogeneity and chromosomal instability limit the early diagnosis of OS and lead to lung metastases and a worse prognosis. I don't understand this statement given that intratumor characteristics are not a determinant of early diagnosis - the patient being aware they have an issue and the clinical follow up determine the rate of diagnosis (and access to healthcare).

Response: We shall revise this statement to reflect the genetic heterogeneity of OS tumors.

Page 21: Consistent with their roles as tumor suppressor genes, the combined deletion of WWOX and p53 in Osx1-positive progenitor cells resulted in their transformation and growth advantage. Didn't the authors reach this conclusion from their previous work published in 2016? –

Response: Thank you for this notion. In our previous paper, we provided evidence that WWOX and p53 loss contributes to more aggressive OS tumor formation. In the current study, we provide evidence about early events contributes to osteosarcomagenesis using our Wwox/p53 model.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

improve figure clarity and text sizes

Response: We thank the reviewer for this notion; in our revised version, we shall provide an improved resolution, accuracy and clarity.

Reviewer #3 (Significance):

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

I think this is largely an incremental study which largely confirms previous studies.

Response: Although our results are consistent with some previously noted observations, we clearly provide unforeseen evidence that links Wwox/p53 in early osteosarcomagenesis and suggest that p53 deletion alone is not enough at this stage; other hits are required as in Wwox/p53 or Rb/p53. The mechanism of how DKO cells (Wwox/p53) results in Myc upregulation is also novel and will be further tested in our revised submission.

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

The study is a modest advance over the existing literature.

Response: We believe that with our revision plan, our paper will provide a significant advancement in the research in osteosarcomagenesis.

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

This would be relevant to basic sarcoma researchers.

• 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.

Generated multiple murine models of OS and characterized and applied them for biological understanding and preclinical studies.

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Referee #3

Evidence, reproducibility and clarity

Summary: The work describes analysis of an Osx1-Cre p53fl/fl Wwoxfl/fl mouse model compared to an Osx1-Cre p53fl/fl model. The authors have assessed the osteosarcoma inducing potential of cells within the bone marrow by including a tdTom reporter of Cre expression. The conclude that bone marrow mesenchymal cells can give rise to osteosarcoma when transplanted. Based on transcriptomics they contend that upregulation of Myc is important and its target MCM7 can be targeted with simvastatin.

Major comments:

• Are the key conclusions convincing?

The authors can generate tumors in immunocompromised mice upon injecting cells derived from bone marrow flushed. This is not surprising given the data available now about expression of Osx1-Cre. The analysis of MSCs lacks detail and needs significant more improvement and assessment by FACS using the well-defined criteria for mesenchymal cells that have been developed.

It is not clear to me from the available information if the authors have used bone marrow cells that were flushed and immediately transplanted or if all cells transplanted have been placed in culture first, adherent cells expanded in culture media favouring survival and proliferation of non-hematopoietic cells and then transplanted - this is important to clarify explicitly as it is important to the significance of the study. If these cells are all used after culture then the novelty of these studies are questionable as it was demonstrated previously that similar types of cells give rise to OS when transplanted (PMID: 18697945).<br /> - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

I think overall the authors are appropriately cautious in interpretation. The points raised above regarding the nature of the cells would need clarification and then the claims reassessed however.

Claims re "metastatic potential" should be significantly reconsidered - the authors present motility assays which should be referred to as motility assays. The injection of cells intravenously is a lodgement assay of cells in the venous circulation and does not equate to the process a cancer cell must undergo and survive to metastasise from a primary tumour in an immune competent environment. The claims around these assays should be significantly reconsidered.<br /> - 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.

It is not explained or justified what the control cohort in Fig 7F is significantly smaller than the treated cohort. This will affect the statistical analysis and interpretation. There is no statement regarding blinding/randomisation (or not) in the in vivo simvastatin experiment - this needs to be added.

The authors should include discussion that these are relatively long latency OS models compared to p53/pRb compound mutants and contrast with previous data from these models where in vitro cultured cells did give rise to OS in vivo after Cre treatment.<br /> - 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 a patient could to be treated based on these data then the extra experiments to provide a robust preclinical dataset should be provided otherwise significant caution should be stated.<br /> - Are the data and the methods presented in such a way that they can be reproduced?

See comments regarding cells used for injection and blinding.

Need to more clearly describe the cells being injected and what was done to them from isolation to injection.<br /> - Are the experiments adequately replicated and statistical analysis adequate?

Unclear if appropriate experiment completed in Fig 7F as no justification of different sample sizes is provided nor a statement re blinding/randomisation.

Minor comments:

• Specific experimental issues that are easily addressable.

The Osx1-Cre expresses a Cre:GFP fusion - the authors should correlate the GFP and tomato signal.

Fig 2A- if 50% of the bone marrow is tdTom positive this is not evident in the image in Fig 1D. Quantify the images in Fig 1D.

The tumor images in Fig 2C appear to have only sporadic tdTom positive cells - the authors should explain this further.

Need statistics added to all GSEA plots

Panel 4C is very different than 2C - 4C is more consistent with the levels of tomato stated<br /> - Are prior studies referenced appropriately?

This seems appropriate<br /> - Are the text and figures clear and accurate?

Figures are not ideal and could be improved in terms of clarity and text sizes

Several sections of text are either contradictory or questionably accurate:<br /> page 3: Molecular studies of OS are significantly hindered by its genetic complexity and chromosomal instability, which precludes the identification of a single recurrent event associated with OS.<br /> Contrasts with the following text:<br /> Page 18 - p53 has been extensively shown to play a central role in OS development in both human and mouse models.

The data from sequencing of human OS and mouse models and canine data all point to p53 loss as being a central event in OS.

Page 18: High genetic heterogeneity and chromosomal instability limit the early diagnosis of<br /> OS and lead to lung metastases and a worse prognosis.<br /> I don't understand this statement given that intratumor characteristics are not a determinant of early diagnosis - the patient being aware they have an issue and the clinical follow up determine the rate of diagnosis (and access to healthcare).

Page 21: Consistent with their roles as tumor suppressor genes, the combined deletion of<br /> WWOX and p53 in Osx1-positive progenitor cells resulted in their transformation<br /> and growth advantage.

Didn't the authors reach this conclusion from their previous work published in 2016?<br /> - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

improve figure clarity and text sizes

Significance

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

I think this is largely an incremental study which largely confirms previous studies.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

The study is a modest advance over the existing literature.<br /> - State what audience might be interested in and influenced by the reported findings.

This would be relevant to basic sarcoma researchers.<br /> - 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.

Generated multiple murine models of OS and characterized and applied them for biological understanding and preclinical studies.

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Referee #2

Evidence, reproducibility and clarity

In current study, the authors established a mouse model with tdTomato expression under the OSX-controlled double deletions of Wwox and Trp53. Such mouse strain gives a great platform to study the OS development and therapeutic potential. Experiments are clear and convincing. Results are well presented.

To better improve this study, few minor suggestions regarding the data are as following,

1. Some of the legends on figures are too small to read, or in low quality. please change these labels.
2. For Figure 7C and D, from 7C, The control WT BM showed clear resistance to the SVA treatment, but in 7D, there is almost no cells in the WT BM group. Data of this group might be missed?
3. For figure 7G, the difference among MCM7 IHC staining of two groups didn't show as much as the statistic analysis in the right panel. Authors may consider using MCM7 western blot to check its levels after SVA treat.

Significance

This study use a transgenic mouse model with tdTomato expressed in combination of loss of p53 and Wwox under the OSX lineage to study early initiation of OS. They found only DKO bone marrow cells can form OS in a subsequent orthotopic mouse model, but not the p53 single KO cells. After compare the RNA-seq from these different cell population, they identified the Myc pathway is the key player to promote OS development, especially the MCM7. Moreover, they tested SVA in treating these BM cells and reveal a therapeutic potential.

This animal model is a good platform to study OS, especially at the early stage. Most results are clear and convincing. With the identification of Myc pathway, they further tested the SVA effects on treating these DKO BM. This is an important study and provided meaningful information to the OS, even broad cancer research community. However, the significance, or novelty of this work is not sufficient.

For instance, SKO BM won't form tumor in the IT injection assays compare to the DKO BM groups, therefore, the involvement of Wwox during the OS tumorigenesis is clear. However, authors didn't explore any potential mechanisms of Wwox function or related signaling behind this observation. And the RNA-seq analysis mostly focus on c-Myc pathway and its downstream targets. Given the well known relationship of p53, c-myc even RB in the OS, it will be more interesting and attractive to see a clear mechanisms of Wwox in this context.

Second, since authors took effort to generate this Tomato-DKO mice, it could be more clear if they isolate tdTomato positive cells instead of a mixture of BM, culture them, differentiate them, and perform more assays using these cells. In this way, it will give better clean background for all assays, and may be able to find novel effectors during this OS progression process.

Third, within the text, authors tried to use OB differentiation and some other assays to discuss the OS origin cells, MSC or OB; but didn't get a preferred conclusion. It could be possible to better understand this process with the single cell RNA-seq using these BM from different mice or at different ages.

In general, this is a clean, straightforward study, and they established a very useful model to study OS. But the mechanisms merit is somewhere short.

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Referee #1

Evidence, reproducibility and clarity

The study "Mesenchymal stem cell models reveal critical role of Myc as early molecular event in osteosarcomagenesis" by Akkawi et al shows that BM-MSCs are a foundational tool for study of osteosarcoma. And authors identified Myc and its targets as early molecular events in osteosarcoma formation, using BM-MSCs knocked out of both p53 and Wwox. Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings, such as;

1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref.19).
2. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).
3. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).<br /> First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.

The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism. Some additional analysis would be needed to mention it. In the first place, Myc is upregulated in the absence of both p53 and Wwox, compared in only p53-null situation? Western blotting would be better to show it.

1. In Figure 1D, should separate each panel so that it is clearly visible. What is the blue-colored fluorescence, DAPI? If so, why don't tdTomoto positive cells overlap with blue (Figs, 1D, 2C, 4C, 4E?
2. Why was MCM7 chosen among the Myc targets (S Figure 3)? What is the rationale for this?
3. In Figure 5 legend, what does "yBM cells (1.5, 4-months)(n=6)" mean? yBM cells (1.5-months)(n=3) and yBM cells (4-months)(n=3)?
4. In Figure 7B, is there a correlation between MCM7 and Myc protein expression levels? Also, do MCM7 and Myc immunopositivites overlap in Figure 7G?
5. In S Figure 4C, what is 'PC'? What sample was loaded?
6. In S Figure 2A, what does 'US' (BM-US) mean? In S Figure 4F, what does 'US' and 'S' (Direct US and Direct S) mean?
7. Overall this manuscript, there are too many symbols and it is cumbersome. Ex, in S Figure 3, yBM_DKO, Tum_DKO, DKOT, DKO-BMT, etc. All figures should be consistent with the same notation.

Significance

Although the quality and validity of the data presented in this manuscript are not in question, this reviewer has doubts about the novelty of the findings in this current version of manuscript. It does not clearly indicate what is different from previous findings, such as;

1. It has already been reported that p53-/- BM-MSCs are the cells of origin in osteosarcoma development (ref.19).
2. Indispensability of Myc in osteosarcomagenesis was revealed (PMID: 12098700).
3. Osteosarcoma formation of p53flox-OsxCre mice was rescued by Myc-depletion and BM-MSCs from p53flox-OsxCre was shown to upregulate Myc (PMID: 34803166).<br /> First of all, the authors need to cite the above papers and compare their findings with them to better clarify the value of their findings.<br /> The only one clear finding would be that Wwox functions as a tumor suppressor by repressing Myc function in the absence of p53, but unfortunately, no data have been presented on the mechanism.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:<br /> Plasmodium sporozoites rely on gliding motility to invade the salivary glands of the mosquito vector where they reside until being inoculated into the vertebrate host. Once in the vertebrate host, sporozoites again have to glide to pass through liver cells and reach the final host cell before exo-erythrocytic replication occurs. In sporozoites, thrombospondin-related anonymous protein (TRAP) is the key adhesin, linking the molecular motor to the substrate. TRAP and TRAP-like proteins contain on the extracellular side an inserted (I) domain that is responsible for substrate binding. The I domain can be in an open or closed state which can be fixed by mutating specific amino acids. Braumann et al. then assessed the effect of these states on gliding motility and infection. Whilst no effect was seen for both mutants with closed or open states until the production of haemolymph sporozoites, the number of salivary gland sporozoites were highly reduced in both mutants. The closed mutant was not able to be transmitted from mosquitoes to mice whilst the open mutant is severely impacted in transmission. Gliding of both mutant is highly impaired. Some of these phenotypes could be reverse by adding a reducing agent.

Major comments:<br /> This is an elegant study with exhaustive experiments to address the research questions. One of the things I wondered about is the effect of exchanging amino acids. As far as I understood, structural information of integrins from other organisms informed the authors about positions that should be mutated and about the impact (or loss of it) on the 3D structure of the domain. Would it not be useful to run a structure prediction programme such as Alphafold on all the mutants to at least confirm stability of the domain structure upon mutation in silico?_

A: Thanks for this suggestion, we added the crystal structure models of P. falciparum and P. vivax as well as the Alphafold models of the open and closed state of P. berghei TRAP as new panels in Figure 1, added some corresponding text to the results, materials and methods, and the figure legend.

Some of the concentrations of reducing agents tested are very high. Can the authors be sure that the parasites are not dead?

A: We used sporozoites expressing GFP in the cytosol and did not see loss of fluorescence. While this does not rule out that the parasites are not alive anymore, it shows that the plasma membrane was not compromised. Importantly, when using the more moderate concentrations that rescued motility, wild type parasites were also moving, showing that the parasites were alive.

Minor comments:<br /> -Please make sure that punctuation is correct (e.g. missing commas) and that there are no other typing errors.

A: We carefully read and edited the manuscript again and hopefully corrected all those errors.

• Line 50: This sentence may imply mechanical rupture of oocysts rather than active egress of the sporozoite. Please re-phrase.

A: rephrased, it now reads “egress” instead of “burst”. (now line 53)

• Line 82: Do you mean a complexed Mg2+ ion?

A: Yes, this sentence was revised. (now line 99)

• The introduction stops rather abruptly. Maybe a sentence of the significance of this study could be added.

A: we added the following: “Our results suggest that active change between the open and closed conformation of the TRAP I domain is essential for sporozoite invasion of salivary glands and transmission from mosquitoes to mammals.” (line 122-125).

• Line 361: 20 million parasites each? Please re-phrase to make it clearer.

A: done, it now reads: “…into each of two naïve mice…”.(now line 517)

• Line 363: Field of view needs to defined: What is the magnification used to observe exflagellation?

A: done, it now reads: “as observed with a Zeiss Axiostar using a 100x objective lens – revealing 300-400 red blood cells per field of view) – now line 519-521.”

• Materials and Methods: Please write out reagent names for the first time.

A: done, e.g. “phosphate buffered saline” for PBS; line 540; Roswell Park Memorial Institute medium 1640 for RPMI; line 420; as well as for DTT and TCEP (lines 560, 561), BSA (line 565), etc

Figures:<br /> - Figure 1A is very small. The label font of the whole figure 1 is often too small.

A: We intentionally kept Figure 1A small as most readers will be familiar with the life cycle but increased font size as much as possible throughout the figure including Figure 1B, which we needed to shrink in the new design to accommodate the Alphafold structures.

• Figure 1D: Please list the components in the figure legend, e.g. blue: substrate etc.

A: done, now lines 722-725

• Figure 3: The figure title should be changed as there is no complete failure of sporozoites with fixed I-domains to invade salivary glands.

A: done, it now reads: “…show strong reduction in salivary gland invasion”, line 790-791

• Figure 5: for clarity, it would be easier if Figure C would not be squeezed into the legends of A and B.

A: We agree this is a tricky arrangement, but when composing the figure, we tried many different ways, which were all unsatisfactory too, leading to either too awkward an arrangement or too much white between the panels. We hence, despite anticipating this critique, decided on this arrangement and would hence like to keep it. However, we changed the color of the box around C so that it is marked more differently.

Reviewer #1 (Significance):

This study addresses in detail the mechanism of Plasmodium TRAP I domain in gliding motility and transmission. It builds on work from many years and groups including their own and contributes to deepen our understanding of TRAP-family proteins, gliding motility in Plasmodium sporozoites and maybe even in other Plasmodium stages or other Apicomplexan parasites.

This work is of interest to researchers in the field of Plasmodium mosquito stages and transmission as well as scientist who work on apicomplexan gliding motility and transmission.

I have previously worked on Plasmodium mosquito stages, but currently work on Toxoplasma_ gondii.

A: Thank you for your appreciation of our study, the mechanistic insights it gives into gliding and transmission, and the helpful critique.

Reviewer #2 (Evidence, reproducibility and clarity):

This work seeks to investigate the effects on sporozoite motility of hypothesized conformational changes in TRAP's integrin domain. Previous structural studies suggested that the Integrin-like domain of TRAP assumes 'open' and 'closed' conformations induced by ligand binding. Here the authors hypothesize that TRAP's ability to dynamically switch between these states is crucial for sporozoite motility. The hypothesis is interesting and the authors elected to test it by 'fixing' TRAP in constitutively 'open' and 'closed' conformations by introducing Cys residues that are presumed to form disulphide bonds resulting in these states.

Major Comments<br /> The approach is creative. The data that are presented are of high quality with adequate reproducibility. However, as written the manuscript does not provide a rationale for the specific substitutions they chose, how these substitutions are expected to lead to 'open' and 'closed' states, and how they mimic the natural route of TRAP transitioning between the 'open' and 'closed' conformations._

A: We now add a more detailed rationale at the very start of the results section, which now reads: “We used closed and open structures of TRAP from P. falciparum and P. vivax, respectively (Song et al., 2012), to design cysteine mutations in P. berghei (Fig. 1E-J). The TRAP I domain of P. berghei is 42 and 48% identical in sequence to those of P. vivax and P. falciparum, respectively; lower identity was used for successful introduction of a disulfide into integrin αI domains (Shimaoka et al., 2001; Shimaoka et al., 2003). Between the open and closed TRAP I domain conformations, the I domain α7-helix pistons 9 Å relative to the neighboring β6-strand; therefore, disulfides introduced between these two structural elements can stabilize one state over the other, as previously pioneered in integrin I domains. β6-strand and α7-helix residues with Cβ atoms within 5 Å of one another in structures of the two states were chosen for mutation to cysteine (Fig. 1G&H). P. falciparum TRAP-Fc fusions with homologous cysteine mutations were well expressed in mammalian cells (Koksal et al,, 2013). Using the TRAP sequence alignment (Fig. 1F), homologous residues in P. berghei TRAP were mutated to cysteine to stabilize the closed conformation (Ser-210 and Phe-224 in the S210C/F224C mutation) and the open conformation (Ser-210 and Gln-216 in the S210C/Q216C mutation). AlphaFold (Mirdita et al 2022) predicted that the P. berghei TRAP I domains were well folded and assumed the desired conformations with formation of the mutationally introduced disulfide bonds (Fig. 1I&J). The I domain of P. berghei is 42 and 48% identical in sequence to those of P. vivax and P. falciparum in which the open and closed conformations are defined, assuring highly confident modeling (Song et al., 2012). Cysteines were substituted in positions that were sufficiently close in one conformation to form a disulfide but not in the alternative conformation..” lines 130-202 and also provided a detailed view of the structures in Figure 1.

Furthermore, there is no biochemical, biophysical or modeling data demonstrating that the introduced mutations impact the folding/unfolding of TRAP's I domain in the manner hypothesized. Therefore, it is difficult to interpret subsequent phenotypic data from the two mutant lines. While mutant parasites display defects in gliding motility, these defects are unexpected, perhaps pointing to alternative explanations - such as aberrant inter- or intra-molecular disulphide bonding in TRAP's extracellular domain.

A: The ability of such mutations to give biologically meaningful results has already been demonstrated in integrin I domains, where ligand-binding affinity was measured. We now describe the rationale in more detail, and demonstrate that the cysteines are recognized by Alphafold to yield well-folded domains that are stabilized in the desired conformations. These are presented as new panels in Figure 1.

About 10% of mutant sporozoites assumed to be in a constitutively 'closed' conformation display gliding motility and they move faster that WT. Yet this mutant did not cause patency in mice when introduced either via mosquito bite or IV injection. In contrast, the mutant assumed to be in an 'open' conformation displayed no motility in vitro but was able to infect mice (albeit at a significantly reduced rate compared to controls). Presumably, these data suggest that the in vitro gliding motility assays used are insufficient for testing the effect of these mutations on motility that is relevant in vivo. The model is that TRAP's interaction with extracellular ligands stabilizes its 'open' position. This suggests that motility assays conducted with extracellular matrix eg Matrigel are a more appropriate test of motility.

A: The reviewer raises interesting points. There are several types of sporozoite motility assays available, 2D on glass, 2D on ligands (e.g. Matrigel), 3D in polyacrylamide gels or Matrigel, and 3D in the skin. All of these have a number of advantages and disadvantages. 2D on glass is clearly the easiest, 3D in skin, clearly the most complex one. The advantage of the 2D on glass assay is that it reveals even tiny defects on motility that cannot be revealed in 3D assays as the enclosure of the parasite in a 3D matrix compensates for several defects, especially those in adhesion that are most relevant for our study here (see also e.g. Bane et al. Plos Path 2016, Ripp et al., EMBO Mol Med 2021). Hence, we believe that for understanding the role of adhesion in motility the 2D assay is highly valuable because it represents a minimal system. In contrast, the complex 3D environments, are closer to the natural situation but are less useful in our specific case. The fact that the mutants are severely limited in vivo (in the mosquito where they are blocked at the 2D surface of the salivary gland) suggests that the assays are highly relevant. We take this comment as a motivation to write a review/opinion like paper in the near future but feel it would distract if added here to e.g. the discussion.

Experiments with DTT are difficult to interpret since there is once again no biochemical evidence that this treatment leads to a change in conformation of TRAP. DTT's effect on motility of the mutant could be non-specific. Overall, conclusions that are presented need to be supported by more data.

A: We are aware of this and hence already discussed these potential shortcomings of our study in the discussion. Importantly: there is no known specific ligand to the TRAP I domain. Hence, we believe that biochemical tests would not justify the effort it would take to express the different domains.

Reviewer #2 (Significance):

Sporozoite motility is a prerequisite for infection by Plasmodium of the liver. A better mechanistic understanding of this process is significant for our understanding of the first step of malaria infection. TRAP is the major adhesin on the sporozoite surface and its loss abrogates sporozoite motility. The authors are to be commended for undertaking a challenging study.

A: We thank the reviewer for appreciating our efforts, the advance provided, and for the constructive critique.

Reviewer #3 (Evidence, reproducibility and clarity):

This paper builds on previous structural studies from the Springer lab, which show that the I-domain of the malaria sporozoite surface protein TRAP can switch between two conformations ("open" and "closed"), similar to what happens with the I domain of integrins. In the current study the authors explore whether the open and closed conformations of TRAP are important for sporozoite migration and infectivity by generating parasites expressing each form locked in place using introduced disulfides. Locking TRAP into either form inhibits sporozoite gliding, invasion of the mosquito salivary glands and transmission from mosquito to mouse. The addition of low levels of a reducing agent can partially rescue these effects with the open form, which the authors suggest points to a requirement for TRAP to undergo a switch between its open and closed states to support these key processes in the parasite's life cycle.

The paper is well written and the data and methods are clearly presented.

Major Comments:

Lines 109-115: The entire paper relies on the premise that the introduced disulfides lock the I domain of TRAP in either the open or closed conformation. In the absence of experimental proof that this is the case, it would be helpful to the reader to have more detail on how this can be confidently inferred from the previous work on integrins -- perhaps as a supplementary figure._

A: We now added a statement on how the positions for the cysteines were selected and also a figure on structures and Alphafold prediction as also suggested by reviewers 1 and 2 as panels in Figure 1.

The partial rescue of motility in the S210C/Q216C parasites by 50-100mM DTT is a VERY indirect approach to testing whether the conformational switch between the open and closed states is functionally important. The authors are appropriately reserved in their conclusions, but wildtype parasites are ultimately a better (less confounded) comparator; the authors may want to stress this more._

A: Thanks for appreciating our efforts to navigate the limitations of our experimental system. We have changed our wording (e.g. replaced “fixed state” with “stabilized state” in the discussion) and added cautionary statements, which now read “While DTT clearly does not only act on TRAP, these data nevertheless suggest that…” (lines 353-354); “While not providing final proof, this phenotype indicates…” (lines 371); “…antibodies that stabilize… could contribute to stopping parasite migration” (line 415)

The implications of the current results for the authors' previous stick-and-slip model of sporozoite motility should be discussed.

A: We added a few sentences including two references on this in the discussion: “Sporozoites glide in a stick-slip fashion…” (lines 417-422)

Minor Comments:

The authors are experienced in the use reflection interference contrast microscopy to visualize attachment of the parasite to the substrate. Did they do any RICM on the mutants? Although not necessary for the paper, this would be a good way to support the interpretation of some of their results (eg, lines 185-190)._

A: We indeed hoped that the mutants would allow us to exploit these techniques as this would give much insight into the mode of migration. However, all our previous work on RICM was done with salivary gland derived sporozoites, which are robustly gliding. In contrast, those derived from the hemolymph or oocysts do not glide very well. While we can quantitatively image them at low magnification (large field of view) settings, we have consistently failed to get sufficient data on most of our advanced assays that require single sporozoite imaging such as RICM, TIRFM, and laser tweezer experiments. This, unfortunately, is a real limitation of the system as most interesting mutants (see also e.g. recent paper by Yee et al., Plos Pathogens 2022) are not entering the salivary glands.

Line 127: Figure 2A does not show "no growth difference to wild type mice"

A: we split the sentence in two with Figure 2A now only referring to mosquito infections (now line 212)

Line 154: "higher, but not significantly higher" - if the data don't meet the significance threshold being used, they cannot be called higher

A: Yes, the thing here is that we always got more from the open mutant and hence could do experiments but indeed the stats showed it not to be significant. We changed the sentence to read “…was not significantly higher…” (line 242)

Line 186: "showed nearly no floating parasites compared to the controls S210C and cFluo" - cFluo and S210C/Q216C show the same amount of floaters in Fig 5B. Also, S210C/Q216C HLS show similar levels of floaters to both controls in 2A.

A: Thanks for spotting this mixup, we changed it to: “Interestingly, hemolymph sporozoites expressing the closed I domain (S210CF224C) showed more floating parasites suggesting reduced adhesion. Intriguingly, the few moving sporozoites of this mutant were also significantly faster than the corresponding controls.” (lines 296-299). We also pick up on this observation in the discussion in the new paragraph on stick-slip motility, lines 414-422) which links back to the Munter et al paper cited in line 303. This is in line with observations in mammalian cells where reduced ligand density also leads to less adhesion and faster migration.

Fig S2 defines unproductive gliding to include waving, but Figs. 5 and S3 scores waving as a separate category

A: We define it as unproductive motility, which we now changed into “unproductive movement” in the hope to be less misleading, clearly “lazy gliding” is motility while waving is no motility but some type of movement.

Fig S3B and Fig 5D,E appear to be the same data presented twice

A: Correct, we now state this explicitly in the figure legend to Figure S3B (lines 958-960) where they are reproduced together with the matching controls

Line 304: "the inability of sporozoites with the TRAP I domain to migrate" (?)

A: “conformationally stabilized” was missing and has now been included (line 453)

Line 226: please explain what the + > ++++ qualitative descriptors signify in the tables and how they were scored

A: Now added in the legends to the table, lines 885-887 and 892, respectively.

Lines 282, 312: the authors should mention here the extensive work that has been done on efficacy of viral vaccines directed against a particular conformational state of the immunogen

A: We were naturally tempted to do this but feel that this could hype the study more than is justified by our data.

Fig 2B: The labels above the lanes are incomplete and therefore confusing; suggest sticking to the same nomenclature as in 2A

A: Modified as suggested

Typographical errors on lines: 58 (space missing), 80-81 (parentheses), 155 (Figure misspelled), 188 (expressing the closed (S210C/F224C)), 306 (comma after both)

A: Thanks, these were corrected.

CROSS-CONSULTATION COMMENTS<br /> Since all three reviewers questioned whether the introduced disulfides would have the assumed effects on I domain structure, the authors should provide a stronger rationale for this assumption or -- as suggested by reviewer 2 -- some actual data to support it. Reviewer 2's comment about the extracellular ligands available for the parasite to bind to in the gliding assay and whether this could influence the outcome (and relevance to what occurs in vivo) also deserves consideration.

A: We have now added Alphafold predictions of the mutants (Figure 1) and also described better how we selected the mutations based on extensive work by the Springer lab (see newly added lines 130-202). We appreciate the idea of using extracellular ligands, however, all our previous assays (e.g. Klug et al. eLife 2020 and Perschmann et al., Nano Letter 2011) suggest that the parasites move on a large variety of surfaces at the same rate.

Reviewer #3 (Significance):

This is an elegant study that contributes important new information to our understanding of apicomplexan parasite motility and the function of the TRAP protein. The results will be of significant interest to those who study parasite motility, and likely also to those who study the role of integrins in cellular adhesion and signaling. The data nicely connect what was previously known about conformational changes in integrins with parasite adhesion to the substrate and motility.

I have expertise in the area of parasite motility._

A: We thank the reviewer for the constructive comments and appreciation of our work

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Referee #3

Evidence, reproducibility and clarity

This paper builds on previous structural studies from the Springer lab, which show that the I-domain of the malaria sporozoite surface protein TRAP can switch between two conformations ("open" and "closed"), similar to what happens with the I domain of integrins. In the current study the authors explore whether the open and closed conformations of TRAP are important for sporozoite migration and infectivity by generating parasites expressing each form locked in place using introduced disulfides. Locking TRAP into either form inhibits sporozoite gliding, invasion of the mosquito salivary glands and transmission from mosquito to mouse. The addition of low levels of a reducing agent can partially rescue these effects with the open form, which the authors suggest points to a requirement for TRAP to undergo a switch between its open and closed states to support these key processes in the parasite's life cycle.

The paper is well written and the data and methods are clearly presented.

Major Comments:

Lines 109-115: The entire paper relies on the premise that the introduced disulfides lock the I domain of TRAP in either the open or closed conformation. In the absence of experimental proof that this is the case, it would be helpful to the reader to have more detail on how this can be confidently inferred from the previous work on integrins -- perhaps as a supplementary figure.

The partial rescue of motility in the S210C/Q216C parasites by 50-100mM DTT is a VERY indirect approach to testing whether the conformational switch between the open and closed states is functionally important. The authors are appropriately reserved in their conclusions, but wildtype parasites are ultimately a better (less confounded) comparator; the authors may want to stress this more.

The implications of the current results for the authors' previous stick-and-slip model of sporozoite motility should be discussed.

Minor Comments:

The authors are experienced in the use reflection interference contrast microscopy to visualize attachment of the parasite to the substrate. Did they do any RICM on the mutants? Although not necessary for the paper, this would be a good way to support the interpretation of some of their results (eg, lines 185-190).

Line 127: Figure 2A does not show "no growth difference to wild type mice"

Line 154: "higher, but not significantly higher" - if the data don't meet the significance threshold being used, they cannot be called higher

Line 186: "showed nearly no floating parasites compared to the controls S210C and cFluo" - cFluo and S210C/Q216C show the same amount of floaters in Fig 5B. Also, S210C/Q216C HLS show similar levels of floaters to both controls in 2A.

Fig S2 defines unproductive gliding to include waving, but Figs. 5 and S3 scores waving as a separate category

Fig S3B and Fig 5D,E appear to be the same data presented twice

Line 304: "the inability of sporozoites with the TRAP I domain to migrate" (?)

Line 226: please explain what the + > ++++ qualitative descriptors signify in the tables and how they were scored

Lines 282, 312: the authors should mention here the extensive work that has been done on efficacy of viral vaccines directed against a particular conformational state of the immunogen

Fig 2B: The labels above the lanes are incomplete and therefore confusing; suggest sticking to the same nomenclature as in 2A

Typographical errors on lines: 58 (space missing), 80-81 (parentheses), 155 (Figure misspelled), 188 (expressing the closed (S210C/F224C)), 306 (comma after both)

Referees cross-commenting

Since all three reviewers questioned whether the introduced disulfides would have the assumed effects on I domain structure, the authors should provide a stronger rationale for this assumption or -- as suggested by reviewer 2 -- some actual data to support it. Reviewer 2's comment about the extracellular ligands available for the parasite to bind to in the gliding assay and whether this could influence the outcome (and relevance to what occurs in vivo) also deserves consideration.

Significance

This is an elegant study that contributes important new information to our understanding of apicomplexan parasite motility and the function of the TRAP protein. The results will be of significant interest to those who study parasite motility, and likely also to those who study the role of integrins in cellular adhesion and signaling. The data nicely connect what was previously known about conformational changes in integrins with parasite adhesion to the substrate and motility.

I have expertise in the area of parasite motility.

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Referee #2

Evidence, reproducibility and clarity

This work seeks to investigate the effects on sporozoite motility of hypothesized conformational changes in TRAP's integrin domain. Previous structural studies suggested that the Integrin-like domain of TRAP assumes 'open' and 'closed' conformations induced by ligand binding. Here the authors hypothesize that TRAP's ability to dynamically switch between these states is crucial for sporozoite motility. The hypothesis is interesting and the authors elected to test it by 'fixing' TRAP in constitutively 'open' and 'closed' conformations by introducing Cys residues that are presumed to form disulphide bonds resulting in these states.

Major Comments

The approach is creative. The data that are presented are of high quality with adequate reproducibility. However, as written the manuscript does not provide a rationale for the specific substitutions they chose, how these substitutions are expected to lead to 'open' and 'closed' states, and how they mimic the natural route of TRAP transitioning between the 'open' and 'closed' conformations.

Furthermore, there is no biochemical, biophysical or modeling data demonstrating that the introduced mutations impact the folding/unfolding of TRAP's I domain in the manner hypothesized. Therefore, it is difficult to interpret subsequent phenotypic data from the two mutant lines. While mutant parasites display defects in gliding motility, these defects are unexpected, perhaps pointing to alternative explanations - such as aberrant inter- or intra-molecular disulphide bonding in TRAP's extracellular domain.

About 10% of mutant sporozoites assumed to be in a constitutively 'closed' conformation display gliding motility and they move faster that WT. Yet this mutant did not cause patency in mice when introduced either via mosquito bite or IV injection. In contrast, the mutant assumed to be in an 'open' conformation displayed no motility in vitro but was able to infect mice (albeit at significantly reduced compared to controls). Presumably these data suggest that the in vitro gliding motility assays used are insufficient for testing the effect of these mutations on motility that is relevant in vivo. The model is that TRAP's interaction with extracellular ligands stabilizes its 'open' position. This suggests that motility assays conducted with extracellular matrix eg Matrigel are a more appropriate test of motility.

Experiments with DTT are difficult to interpret since there is once again no biochemical evidence that this treatment leads to a change in conformation of TRAP. DTT's effect on motility of the mutant could be non-specific. Overall, conclusions that are presented need to be supported by more data.

Significance

Sporozoite motility is a prerequisite for infection by Plasmodium of the liver. A better mechanistic understanding of this process is significant for our understanding of the first step of malaria infection. TRAP is the major adhesin on the sporozoite surface and its loss abrogates sporozoite motility. The authors are to be commended for undertaking a challenging study.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Plasmodium sporozoites rely on gliding motility to invade the salivary glands of the mosquito vector where they reside until being inoculated into the vertebrate host. Once in the vertebrate host, sporozoites again have to glide to pass through liver cells and reach the final host cell before exo-erythrocytic replication occurs. In sporozoites, thrombospondin-related anonymous protein (TRAP) is the key adhesin, linking the molecular motor to the substrate. TRAP and TRAP-like proteins contain on the extracellular side an inserted (I) domain that is responsible for substrate binding. The I domain can be in an open or closed state which can be fixed by mutating specific amino acids. Baumann et al. then assessed the effect of these states on gliding motility and infection. Whilst no effect was seen for both mutants with closed or open states until the production of haemolymph sporozoites, the number of salivary gland sporozoites were highly reduced in both mutants. The closed mutant was not able to be transmitted from mosquitoes to mice whilst the open mutant is severely impacted in transmission. Gliding of both mutant is highly impaired. Some of these phenotypes could be reverse by adding a reducing agent.

Major comments:

This is an elegant study with exhaustive experiments to address the research questions. One of the things I wondered about is the effect of exchanging amino acids. As far as I understood, structural information of integrins from other organisms informed the authors about positions that should be mutated and about the impact (or loss of it) on the 3D structure of the domain. Would it not be useful to run a structure prediction programme such as Alphafold on all the mutants to at least confirm stability of the domain structure upon mutation in silico?

Some of the concentrations of reducing agents tested are very high. Can the authors be sure that the parasites are not dead?

Minor comments:

• Please make sure that punctuation is correct (e.g. missing commas) and that there are no other typing errors.
• Line 50: This sentence may imply mechanical rupture of oocysts rather than active egress of the sporozoite. Please re-phrase.
• Line 82: Do you mean a complexed Mg2+ ion?
• The introduction stops rather abruptly. Maybe a sentence of the significance of this study could be added.
• Line 361: 20 million parasites each? Please re-phrase to make it clearer.
• Line 363: Field of view needs to defined: What is the magnification used to observe exflagellation?
• Materials and Methods: Please write out reagent names for the first time.

Figures:

• Figure 1A is very small. The label font of the whole figure 1 is often too small.
• Figure 1D: Please list the components in the figure legend, e.g. blue: substrate etc.
• Figure 3: The figure title should be changed as there is no complete failure of sporozoites with fixed I-domains to invade salivary glands.
• Figure 5: for clarity, it would be easier if Figure C would not be squeezed into the legends of A and B.

Significance

This study addresses in detail the mechanism of Plasmodium TRAP I domain in gliding motility and transmission. It builds on work from many years and groups including their own and contributes to deepen our understanding of TRAP-family proteins, gliding motility in Plasmodium sporozoites and maybe even in other Plasmodium stages or other Apicomplexan parasites.

This work is of interest to researchers in the field of Plasmodium mosquito stages and transmission as well as scientist who work on apicomplexan gliding motility and transmission.

I have previously worked on Plasmodium mosquito stages, but currently work on Toxoplasma gondii.

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Reply to the reviewers

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

The Kim et al. paper titled "PRMT5 links lipid metabolism to contractile function of skeletal muscles" reports how the arginine methyltransferase PRMT5 affects lipid metabolism in myofibers by stabilizing the mSREBP1 protein and repressing the expression of the PNPLA2 gene. The genetic deletion of PRMT5 in muscle results in the loss of lipid droplets in myofibers and a loss in muscle strength. Additionally, there is a change in muscle fiber types, moving from an oxidative state to a more glycolytic one. While the authors present compelling data on PRMT5's role in muscle metabolism, there are some concerns on the mouse model used and the sequencing data.

Major concerns:

1. The mouse model used in this study is PRMT5fl/fl , Myl1cre in order to genetically delete PRMT5 in skeletal muscle. While there is no issue with the KO mice, the WT mice are PRMT5fl/fl , Myl1+ which is not an acceptable control. It is known that Cre itself can have a phenotype, and additionally Myl1 is very highly expressed. Thereby, there is a large amount of Cre in the KO mice, but none in the WT which may contribute to the differences seen between WT and KO mice. The appropriate WT control is PRMT5+/+ Myl1cre and the experiments would need to be repeated using this mouse genotype as the WT.

2. We appreciated the comment and have analyzed many mice from the various control groups (WT, Myl1Cre, Prmt5f/f, and Myl1Cre/Prmt5f/+). Long story short, we have maintained and used Myl1Cre for multiple projects and in several previous publications (PMID: 25794679; PMID: 27644105), and never observed a phenotype of the Myl1Cre In the current study during the development of the Myl1Cre-Prmt5KO mouse model, we had to first breed Myl1Cre with Prmt5f/f to generate the Myl1Cre/Prmt5f/+ mice, and then breed Myl1Cre/Prmt5f/+ with Prmt5f/fmice to generate the Myl1Cre/Prmt5f/f mice. During the breeding we had generated many Myl1Cre/Prmt5f/+ mice (at least 10). We only observed phenotypes in the Myl1Cre/Prmt5f/f mice but not in the Myl1Cre/Prmt5f/+ (heterozygous KO) mice. In line with our observation, no phenotypes were described in the original report on the generation of the Myl1Cre mice by Steve J. Burden and his colleagues (Bothe, Genesis, 2000, PMID: 10686620). Also, in consistent to our choice of the floxed mice as control, Pereira et al (EMBO Molecular Medicine, 2020, PMC7005622 ) used the Ndufsf/+/Myl1Cre-/- or Ndufsf/f/Myl1Cre-/- as control for their Ndufs3f/f/Myl1Cre+/- KO mice.

3. Given the situation, we trust that the reviewer will agree that the Prmt5f/f mice are appropriate controls and repeating all the experiments with a new control model will not only require years of work but also violate IACUC’s and NIH’s 3R policy in reducing unnecessary use of animals. We added a sentence to state that the Prmt5f/f are phenotypically identical to Myl1Cre/Prmt5f/+ mice in the revised manuscript.

In the scRNA-Seq the authors claim that PRMT5 is not expressed in quiescent muscle stem cells. However, the data set that is used only has approximately 250 muscle stem cells, which would not provide much coverage. It would be necessary to validate this claim by using other data sets, such as Tabula Muris or publicly available bulk RNA-Seq.

• As suggested, we queried Tabula Muris on Prmt5 expression in skeletal muscles based on scRNA-seq. The results showed that Prmt5 is expressed at very low levels in various mononuclear cell populations in the skeletal muscle. Specifically, only 8% of satellite cells had detectable levels of Prmt5, while 92% satellite cells had no detectable levels of Prmt5 (Table 1). We included the results in the revised Supplemental Table S1.

The ChIP-Seq data shown was performed on 3T3-L1 cells and is not appropriate for a muscle paper. The ChIP-Seq must be performed on muscle cells in order to confirm their conclusion.

• We trust that the reviewer understand that ChIP-seq only represents a discovery tool that needs to be experimentally validated. Although the ChIP-Seq data and identification of the PRMT5 binding peak at the Pnpla2 gene was based on 3T3-L1 cells, we have validated enrichment of PRMT5 on the potential binding region through ChIP-qPCR experiments. Repeating the ChIP-seq on muscle cells will not add additional support to the conclusion.

The authors claim that loss of PRMT5 leads to a gradual loss of muscle fiber size but has no effect on myogenesis. The evidence to support that claim is shallow, being based solely on CSA and total number of myofibers, along with a loss of lean body mass. To confirm this statement, it would be best to quantify the CSA and # of myofibers in EDL and TA at P7 and P21. Further, a regeneration assay would also demonstrate if myogenesis is compromised or not.

• Thank you for this suggestion. As Myl1Cre is only expressed in post-differentiation myocytes and myofibers (Bi et al, 2016, eLife, PMID: 27644105), we do not expect the Myl1Cre/Prmt5f/f to impact muscle development. Nevertheless, we now provide data on analysis of muscles at P7 and P21, as well postnatal muscle regeneration. The results are included in Supplementary Fig S2.

The data presented shows that there is fiber type switch from oxidative to glycolytic, along with a decrease in muscle strength in the PRMT5 KO mice. This seems counterintuitive to what is known in the field as glycolytic fibers are viewed as being capable of generating more force than oxidative, while having less endurance. The authors should clarify this point and elaborate more on their conclusion that the loss of strength is due to an altered metabolism.

• We agree with the reviewer that an increased abundance of glycolytic myofibers should increase muscle force under normal conditions. However, the increased glycolytic fibers in the Prmt5 mKO mice is associated with metabolic deficiencies. These fibers are atrophic (smaller than control fibers of the same myosin type), devoid of lipid droplets and had less force production. We added the following sentences to the discussion in this matter. Minor concerns:

• The term myocyte is not the most accurate word for describing skeletal muscle. Myofibers would be best for fully differentiated muscle, myotubes for in vitro differentiation and myocytes should be reserved for differentiated cells that have not fused yet (1-2 nuclei/cell). I would recommend changing all mentions of myocyte to myofiber and changing "myocyte specific" with regards to the mouse models as "skeletal muscle specific"

• Thank you for your suggestion. We updated the nomenclature accordingly.

Figure 1 Panel F and G would be clearer if they were labelled as 2 months old mice

We edited the Figure 1F, and 1G to include the mouse age. 3. Figure 2 Panel E, G and H the control line appears to be missing error bars

Error bars were in the original panels, but the variation was very small, making it hard to visualize the error bars. 4. Figure 2 legend should specify that these mice are 2 months old for the sake of clarity

We added mouse age to the Figure 2 legend. 5. Figure 3 Panel G and H are not informative and are misleading. The supplemental panels show that when normalized to body mass there is no difference in O2 consumption or CO2 production. These should be replaced with the supplemental panels

We reformatted the figure as suggested.

Figure 4 Panel E and F, are these separate experiments? The values do not match between the 2 panels. If these are separate it should be made more clear. a. Error bars appear to be missing in Panel E

Error bars were included (as it was obvious after FCCP) but the error bars are small for the rest of the time points. b. As this is an experiment where the state of the cell is incredibly important (metabolism of myoblast is much different to myotube), the authors must demonstrate that there is no defect in in vitro differentiation by showing the fusion index assay for these cells and a representative image of the myotubes.

The differentiation of the KO cells was normal as Myl1cre is only expressed after differentiation. we isolated myoblasts from WT and Prmt5MKO mice and differentiated for 3 days to stain MyoG and MF20 to measure fusion index. However, we did not see any change in fusion index from differentiated myotubes (Supplementary Fig. S4A)

c.The authors should mention in the legend that these are 3 DM myotubes

Figure legends are updated.

1. Figure 5 Panel G, a qPCR to confirm the O/E of the PRMT5 and SREBP1a in the test samples would be necessary.

We added the PRMT5 and SREBP1 O/E data (Supplementary Fig S5C, D). a. The double O/E cells are marked as significantly different, but it is unclear to which group they are significantly different to.

We edited the figure to highlight the comparison. 8. Figure 7 Panel B, body weight would be more informative in g rather than percentage.

We reformatted the figure as suggested.

It would be interesting to test whether the KO of Pnpla2 also rescues the fiber type switch.

We added Fiber type staining in dKO muscles. Based on quantification in Sol and EDL muscles, increased Type IIB (glycolytic myofibers) in Prmt5MKO mice was significantly decreased in Prmt5/Pnpla2MKO mice (Supplementary Fig S6D-F).

Reviewer #1 (Significance (Required)):

Overall the manuscript provides insight into the role of PRMT5 in regulating the metabolism of skeletal muscle. While the paper provides some interesting data, it is severely hampered by the improper WT controls lacking the cre alleles. In order for the data to be reliable, all of the WT samples will need to be replaced with PRMT5+/+ Myl1 cre mice. There are other papers looking at the role of PRMT5 in skeletal muscle, however these are more focused on muscle stem cells than the myofibers. Therefore this paper does fill in a gap with regards to the metabolic role of PRMT5 and how this can affect skeletal muscle function. This paper would most likely be of interest to a specialized audience, mostly those in the skeletal muscle field.

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

In the manuscript "PRMT5 links lipid metabolism to contractile function of skeletal muscles", Kim et al., describe the role for the arginine methyl transferase PRMT5 in maintaining skeletal muscle homeostasis. The authors showed that myocyte-specific deletion of PRTM5 results in the loss of muscle mass, reduced motor-performance, a fiber type switch from oxidative to glycolytic fibers along with reduced lipid content in the myofibers. The authors reasoned that absence of PRMT5 results in reduced methylation and stability of mSREBP1 and an increase in the expression of adipose triglyceride lipase (ATGL).

Major Comments

1. Experiments looking at the interaction between PRMT5 and mSREBP1a using overexpressed proteins are used to conclude that PRMT5 methylates SREBP1a. While the results are consistent with these conclusions, the finding remains correlative. Methylation assays using purified PRMT5 and SREBP1a would be required to make a definite conclusion that PRMT5 methylates SREBP1a. These should be performed. In the absence of such data, the authors would need to adjust their conclusions to say that in the presence of PRMT5, SREBP1a becomes methylated, and that it remains to be determined if this is directly mediated by PRMT5.

[response] We thank the reviewer for this comment and wished we could perform in vitro methylation assay to address whether SREBP1a is directly methylated by PRMT5. However, our co-author (Dr. Changdeng Hu) who is an expert of PRMT5 biochemistry unfortunately died recently, hampering the validation of SREBP1 as PRMT5 substrate. We would also like to mention that several other studies have reported SREBP1 as substrate of PRMT5 in cancer cells (Liu et al, 2016, Cancer Research, https://doi.org/10.1158/0008-5472.CAN-15-1766 ).We cited and discussed the paper in the manuscript. Our new results using enzymatic inhibitor of PRMT5 (BLL3.3) further supports that PRMT5 mediates methylation of SREBP1 (Supplementary Fig. S5A).

Stability studies in figure 5E have been performed in HEK293 cells using over-expressed proteins. While these results show that PRMT5 protects SREBP1a from degradation, the significance of this in muscle is less clear. Western blots of these proteins in C2C12 cells show that the over expression of PRMT5 does not stabilize SREBP1a (Flag) despite the appearance of increased methylation. To solidify the concept that PRMT5-dependent methylation of SREBP1 leads to its stabilization, cycloheximide experiments should be performed in myotubes generated in culture from the PRMT5mko mice. The stability of the endogenous SREBP1a gene could be monitored using antibodies.

[response] We performed that study as suggested. Consistent with the notion that PRMT5 stabilizes SREBP1, we found extremely low levels of SREBP1 proteins (nearly undetectable) in Prmt5 KO myotubes, in contrast to the robust expression of SREBP1 in WT myotubes (Supplementary Fig. S5B). This extremely low levels of SREBP1 precludes us from examining degradation after cycloheximide treatment

The measurement of methylation levels of SREBP1a are complicated by the fact that the protein levels are destabilized in the absence of PRMT5. The authors should use a PRMT5 inhibitor experiments in complement to these overexpression studies to measure the relative methylation of mSREBP1a (SMY10/mSREBP1).

[response] We used PRMT5 inhibitor, BLL 3.3, to support that mSREBP1a methylation is mediated by PRMT5 (Supplementary Fig. S5A).

Minor Comments 4. In figure 1H, there are more nuclei surrounding the myofibers. The authors should document the number of PAX7 cells per myofiber in the Control and Prmt5MKO mouse strains as it helps understand the additive effect of change in PAX7 cells during muscle atrophy.

[response] We quantified the number of Pax7+ cells in muscles and myofibers of WT and KO mice (Supplementary S2D,E).

In figure 5, the legend title mentioned ATGL. However the stability or methylation results of ATGL are not presented anywhere in the manuscript. Only in figure 6 did the authors show the differential expression of ATGL in the presence and absence of PRMT5. This title for Figure 5 should corrected.

[response] Thanks for clarifying. We fixed title of Figure 5 in the manuscript.

For better representation, the authors should consider moving the western blot panel (S3A) and lipid droplet staining data (S3E) to main figures and some of the data on force generation and body weights to supplementary. [response] We updated the figures as suggested.

Reviewer #2 (Significance (Required)):

This is an interesting manuscript that provides novel insight into the role for PRMT5 in muscle homeostasis. While previous studies have looked at the PRMT5 from a transcriptional standpoint during muscle differentiation, this work shows a role for PRMT5 in controlling the metabolic state of myofibers through transcriptional and potentially non-transcriptional mechanisms. Identification of the transcriptional regulation of Pnpla2 gene by PRMT5 is confirmed by mouse rescue experiments with the double KO mice. However the role for non-transcriptional control of SREBP1a stability through methylation by PRMT5 is not as clearly established. To strengthen this aspect of the manuscript, additional experiments are needed.

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

The lipid droplets represent an energy store and central hub of lipid metabolism in cells. In the skeletal muscle, abundant lipid droplets are present in myofibers (especially in oxidative Type 1 and IIA myofibers) and thought to play a role in supplying energy through fatty acid oxidation (FAO) to power muscle contraction. Increased abundance of lipid droplets in myofibers is associated with poor muscle function and insulin resistance in patients of type 2 diabetes. Paradoxically, myofibers of trained athletes also contain higher than normal levels of lipid droplets but with better contractile function and insulin sensitivity. The molecular mechanism underlying this "Athlete's Paradox" has been unclear, due to the lack of understanding of what controls biogenesis and metabolism of lipid droplets in the myofiber.

In this manuscript, Kuang and colleague provide compelling evidence to support a key role of PRMT5 in maintaining lipid droplets in the myofiber. They generated a conditional knockout mouse model to disrupt the Prmt5 gene in the myofibers. This leads to an astonishing depletion of lipid droplets in myofibers. The Prmt5 null myofibers also exhibited poor contractile function and classical signs of atrophy. To determine if the depletion of lipid droplets drives muscle atrophy or if muscle atrophy drives depletion of lipid droplets, the authors introduced a secondary lesion (ATGL-KO) in the Prmt5 null myofibers that would preserve the lipid droplets by preventing ATGL-mediated lipolysis. This restored lipid droplet content and largely rescued muscle contractile function, demonstrating that depletion of lipid droplets drives muscle atrophy and impairs muscle contractile function. The authors also performed a series of biochemical and molecular biology assays to show that PRMT5 stabilizes SREBP1a through dimethylation, enhancing the activity of this master transcriptional regulator of de novo lipogenesis. PRMT5 also methylates H4R3, and the methylated H4R3 represses the transcription of Pnpla2 (ATGL - which controls lipolysis), therefore inhibiting degradation of lipid droplets. These results together illustrate the dual role of PRMT5 in promoting lipid biogenesis and inhibiting lipid droplet degradation.

Reviewer #3 (Significance (Required)):

Collectively, this study identify PRMT5 as a key regulator of lipid metabolism in the muscle and establish a causal relationship between lipid droplet and muscle contractile function, and point to scarce lipid droplets as a driver of muscle atrophy. PRMT5 has previously been reported to regulate early myogenesis but its role in post-fusion myofibers has never been reported, therefore the conceptual novelty of this study is high. There are couple of minor points authors should consider: 1) While muscle-specific Prmt5-KO mice show reduced muscle mass, it is not clear whether this is a developmental effect or muscle atrophy. Authors should measure a few markers of muscle atrophy such as Atrogin1 and MuRF1 and overall levels of ubiquitination. Alternatively, authors can also subject the mice to conditions of muscle atrophy such as starvation and measure how various markers of atrophy are affected.

[response] Thank you for this suggestion. As the Myl1Cre is only expressed in post-differentiation myocytes and myotubes/myofibers, we did see any developmental defects during postnatal myogenesis (Supplementary Fig S2A-C). We also checked Atrogin-1 and MuRF1 in WT and KO muscle tissues based on your advice, but we did not find any significant difference (Supplementary Fig S1F). These two genes are muscle-specific E3 ubiquitin ligases involved in protein degradation by the ubiquitin proteasome pathway. This finding is consistent with the idea that there are multiple mechanisms that can lead to muscle atrophy, and our study clearly elaborates that dysregulation of lipid catabolism by PRMT5 is the main pathway associated with muscle wasting.

2) Please show representative EcoMRI images for body composition analysis.

[response] Thank you for your comments, but the EcoMRI equipment does not provide images of body composition. It only provides data of lean mass, fat mass and water in gram. We presented those data in the manuscript.

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Referee #3

Evidence, reproducibility and clarity

The lipid droplets represent an energy store and central hub of lipid metabolism in cells. In the skeletal muscle, abundant lipid droplets are present in myofibers (especially in oxidative Type 1 and IIA myofibers) and thought to play a role in supplying energy through fatty acid oxidation (FAO) to power muscle contraction. Increased abundance of lipid droplets in myofibers is associated with poor muscle function and insulin resistance in patients of type 2 diabetes. Paradoxically, myofibers of trained athletes also contain higher than normal levels of lipid droplets but with better contractile function and insulin sensitivity. The molecular mechanism underlying this "Athlete's Paradox" has been unclear, due to the lack of understanding of what controls biogenesis and metabolism of lipid droplets in the myofiber.

In this manuscript, Kuang and colleague provide compelling evidence to support a key role of PRMT5 in maintaining lipid droplets in the myofiber. They generated a conditional knockout mouse model to disrupt the Prmt5 gene in the myofibers. This leads to an astonishing depletion of lipid droplets in myofibers. The Prmt5 null myofibers also exhibited poor contractile function and classical signs of atrophy. To determine if the depletion of lipid droplets drives muscle atrophy or if muscle atrophy drives depletion of lipid droplets, the authors introduced a secondary lesion (ATGL-KO) in the Prmt5 null myofibers that would preserve the lipid droplets by preventing ATGL-mediated lipolysis. This restored lipid droplet content and largely rescued muscle contractile function, demonstrating that depletion of lipid droplets drives muscle atrophy and impairs muscle contractile function. The authors also performed a series of biochemical and molecular biology assays to show that PRMT5 stabilizes SREBP1a through dimethylation, enhancing the activity of this master transcriptional regulator of de novo lipogenesis. PRMT5 also methylates H4R3, and the methylated H4R3 represses the transcription of Pnpla2 (ATGL - which controls lipolysis), therefore inhibiting degradation of lipid droplets. These results together illustrate the dual role of PRMT5 in promoting lipid biogenesis and inhibiting lipid droplet degradation.

Significance

Collectively, this study identify PRMT5 as a key regulator of lipid metabolism in the muscle and establish a causal relationship between lipid droplet and muscle contractile function, and point to scarce lipid droplets as a driver of muscle atrophy. PRMT5 has previously been reported to regulate early myogenesis but its role in post-fusion myofibers has never been reported, therefore the conceptual novelty of this study is high.

There are couple of minor points authors should consider:

1. While muscle-specific Prmt5-KO mice show reduced muscle mass, it is not clear whether this is a developmental effect or muscle atrophy. Authors should measure a few markers of muscle atrophy such as Atrogin1 and MuRF1 and overall levels of ubiquitination. Alternatively, authors can also subject the mice to conditions of muscle atrophy such as starvation and measure how various markers of atrophy are affected.
2. Please show representative EcoMRI images for body composition analysis.

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Referee #2

Evidence, reproducibility and clarity

In the manuscript "PRMT5 links lipid metabolism to contractile function of skeletal muscles", Kim et al., describe the role for the arginine methyl transferase PRMT5 in maintaining skeletal muscle homeostasis. The authors showed that myocyte-specific deletion of PRTM5 results in the loss of muscle mass, reduced motor-performance, a fiber type switch from oxidative to glycolytic fibers along with reduced lipid content in the myofibers. The authors reasoned that absence of PRMT5 results in reduced methylation and stability of mSREBP1 and an increase in the expression of adipose triglyceride lipase (ATGL).

Major Comments

1. Experiments looking at the interaction between PRMT5 and mSREBP1a using overexpressed proteins are used to conclude that PRMT5 methylates SREBP1a. While the results are consistent with these conclusions, the finding remains correlative. Methylation assays using purified PRMT5 and SREBP1a would be required to make a definite conclusion that PRMT5 methylates SREBP1a. These should be performed. In the absence of such data, the authors would need to adjust their conclusions to say that in the presence of PRMT5, SREBP1a becomes methylated, and that it remains to be determined if this is directly mediated by PRMT5.
2. Stability studies in figure 5E have been performed in HEK293 cells using over-expressed proteins. While these results show that PRMT5 protects SREBP1a from degradation, the significance of this in muscle is less clear. Western blots of these proteins in C2C12 cells show that the over expression of PRMT5 does not stabilize SREBP1a (Flag) despite the appearance of increased methylation. To solidify the concept that PRMT5-dependent methylation of SREBP1 leads to its stabilization, cycloheximide experiments should be performed in myotubes generated in culture from the PRMT5mko mice. The stability of the endogenous SREBP1a gene could be monitored using antibodies.
3. The measurement of methylation levels of SREBP1a are complicated by the fact that the protein levels are destabilized in the absence of PRMT5. The authors should use a PRMT5 inhibitor experiments in complement to these overexpression studies to measure the relative methylation of mSREBP1a (SMY10/mSREBP1).

Minor Comments

1. In figure 1H, there are more nuclei surrounding the myofibers. The authors should document the number of PAX7 cells per myofiber in the Control and Prmt5MKO mouse strains as it helps understand the additive effect of change in PAX7 cells during muscle atrophy.
2. In figure 5, the legend title mentioned ATGL. However the stability or methylation results of ATGL are not presented anywhere in the manuscript. Only in figure 6 did the authors show the differential expression of ATGL in the presence and absence of PRMT5. This title for Figure 5 should corrected.
3. For better representation, the authors should consider moving the western blot panel (S3A) and lipid droplet staining data (S3E) to main figures and some of the data on force generation and body weights to supplementary.

Significance

This is an interesting manuscript that provides novel insight into the role for PRMT5 in muscle homeostasis. While previous studies have looked at the PRMT5 from a transcriptional standpoint during muscle differentiation, this work shows a role for PRMT5 in controlling the metabolic state of myofibers through transcriptional and potentially non-transcriptional mechanisms. Identification of the transcriptional regulation of Pnpla2 gene by PRMT5 is confirmed by mouse rescue experiments with the double KO mice. However the role for non-transcriptional control of SREBP1a stability through methylation by PRMT5 is not as clearly established. To strengthen this aspect of the manuscript, additional experiments are needed.

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Referee #1

Evidence, reproducibility and clarity

The Kim et al. paper titled "PRMT5 links lipid metabolism to contractile function of skeletal muscles" reports how the arginine methyltransferase PRMT5 affects lipid metabolism in myofibers by stabilizing the mSREBP1 protein and repressing the expression of the PNPLA2 gene. The genetic deletion of PRMT5 in muscle results in the loss of lipid droplets in myofibers and a loss in muscle strength. Additionally, there is a change in muscle fiber types, moving from an oxidative state to a more glycolytic one. While the authors present compelling data on PRMT5's role in muscle metabolism, there are some concerns on the mouse model used and the sequencing data.

Major concerns:

1. The mouse model used in this study is PRMT5fl/fl , Myl1cre in order to genetically delete PRMT5 in skeletal muscle. While there is no issue with the KO mice, the WT mice are PRMT5fl/fl , Myl1+ which is not an acceptable control. It is known that Cre itself can have a phenotype, and additionally Myl1 is very highly expressed. Thereby, there is a large amount of Cre in the KO mice, but none in the WT which may contribute to the differences seen between WT and KO mice. The appropriate WT control is PRMT5+/+ Myl1cre and the experiments would need to be repeated using this mouse genotype as the WT.
2. In the scRNA-Seq the authors claim that PRMT5 is not expressed in quiescent muscle stem cells. However, the data set that is used only has approximately 250 muscle stem cells, which would not provide much coverage. It would be necessary to validate this claim by using other data sets, such as Tabula Muris or publicly available bulk RNA-Seq.
3. The ChIP-Seq data shown was performed on 3T3-L1 cells and is not appropriate for a muscle paper. The ChIP-Seq must be performed on muscle cells in order to confirm their conclusion.
4. The authors claim that loss of PRMT5 leads to a gradual loss of muscle fiber size but has no effect on myogenesis. The evidence to support that claim is shallow, being based solely on CSA and total number of myofibers, along with a loss of lean body mass. To confirm this statement, it would be best to quantify the CSA and # of myofibers in EDL and TA at P7 and P21. Further, a regeneration assay would also demonstrate if myogenesis is compromised or not.
5. The data presented shows that there is fiber type switch from oxidative to glycolytic, along with a decrease in muscle strength in the PRMT5 KO mice. This seems counterintuitive to what is known in the field as glycolytic fibers are viewed as being capable of generating more force than oxidative, while having less endurance. The authors should clarify this point and elaborate more on their conclusion that the loss of strength is due to an altered metabolism.

Minor concerns:

1. The term myocyte is not the most accurate word for describing skeletal muscle. Myofibers would be best for fully differentiated muscle, myotubes for in vitro differentiation and myocytes should be reserved for differentiated cells that have not fused yet (1-2 nuclei/cell). I would recommend changing all mentions of myocyte to myofiber and changing "myocyte specific" with regards to the mouse models as "skeletal muscle specific"
2. Figure 1 Panel F and G would be clearer if they were labelled as 2 months old mice
3. Figure 2 Panel E, G and H the control line appears to be missing error bars
4. Figure 2 legend should specify that these mice are 2 months old for the sake of clarity
5. Figure 3 Panel G and H are not informative and are misleading. The supplemental panels show that when normalized to body mass there is no difference in O2 consumption or CO2 production. These should be replaced with the supplemental panels
6. Figure 4 Panel E and F, are these separate experiments? The values do not match between the 2 panels. If these are separate it should be made more clear.
   • a. Error bars appear to be missing in Panel E
   • b. As this is an experiment where the state of the cell is incredibly important (metabolism of myoblast is much different to myotube), the authors must demonstrate that there is no defect in in vitro differentiation by showing the fusion index assay for these cells and a representative image of the myotubes.
   • c. The authors should mention in the legend that these are 3 DM myotubes
7. Figure 5 Panel G, a qPCR to confirm the O/E of the PRMT5 and SREBP1a in the test samples would be necessary.
   • a. The double O/E cells are marked as significantly different, but it is unclear to which group they are significantly different to.
8. Figure 7 Panel B, body weight would be more informative in g rather than percentage.
9. It would be interesting to test whether the KO of Pnpla2 also rescues the fiber type switch.

Significance

Overall the manuscript provides insight into the role of PRMT5 in regulating the metabolism of skeletal muscle. While the paper provides some interesting data, it is severely hampered by the improper WT controls lacking the cre alleles. In order for the data to be reliable, all of the WT samples will need to be replaced with PRMT5+/+ Myl1 cre mice. There are other papers looking at the role of PRMT5 in skeletal muscle, however these are more focused on muscle stem cells than the myofibers. Therefore this paper does fill in a gap with regards to the metabolic role of PRMT5 and how this can affect skeletal muscle function. This paper would most likely be of interest to a specialized audience, mostly those in the skeletal muscle field.

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Reply to the reviewers

1. General Statements [optional]

We thank the reviewers for their comments and very helpful suggestions to improve the manuscript. All the reviewers address that further confirmation of the causality of activity-induced AMPK activation and AMPK-induced mitochondrial fission and mitophagy regulating dendritic outgrowth in immature neurons would strengthen the significance of this study. We believe that this is the first study demonstrating that AMPK mediates activity-dependent dendritic outgrowth of immature neurons, and that regulation of mitophagy is critical for dendrite development.

We can perform most of the experimentations and corrections requested by the reviewers. We have already made several revisions and are currently working on additional experiments. All experiments will be finished in several weeks and we expect to submit a full revision by the due date.

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 #1-1.- MMP alone is not a good indicator of mitochondrial health. For instance, ATPase inhibitor causes increase in MMP and complex I inhibition diminish MMP and in both cases mitochondrial function is impaired. On the other hand, authors use increased flickering and mitochondrial ROS production as an indicator of enhanced respiration but they could also be used as indicators of mitochondrial dysfunction. Other assays, such as oxygen consumption, are needed to assess the mitochondrial function.”

*Related comments by Reviewer #2-C. In figure 6 it is unclear what is the significance of the TMRM "flickering" parameter quantified and the difference between the control and knockdown condition is small on average. *

Increase in TMRM flickering and mitochondrial ROS production, which we used as indicators of enhanced mitochondrial respiration, can certainly also be caused by mitochondrial dysfunction. We think it difficult to adopt an oxygen consumption assay in our system, as the transfection efficiency in the primary hippocampal culture is low (~10%). Instead, we plan to assess the mitochondrial function in control and AMPK deficient cells by using an ATP FRET sensor targeted to mitochondria (Mito-ATeam, Imamura et al., PNAS, 2009; Yoshida et al., Methods Mol Biol 2017). Mito-ATeam will be transfected in neurons to compare mitochondrial ATP synthesis in control and AMPK deficient neurons.

*Reviewer #1-2.- It would be interesting to show a better characterization of the mitophagy flux and to test whether pharmacological or genetic stimulation of mitophagy could revert the effect of AMPK KD on dendritic outgrowth, ultimately linking AMPK, mitophagy and dendritic outgrowth. The latter experiments may be challenging but not impossible, for example see (PMID: 27760312). *

We understand that it is important to demonstrate more strongly the correlation of the AMPK-induced peripheral fission and subsequent mitophagy of fragmented mitochondria with dendritic outgrowth. We will attempt the suggested experiment to see if induction of autophagy could revert the dendritic hypoplasia by AMPK KD. However, because AMPK deficiency generates elongated mitochondria defective in fission rather than fragmented mitochondria that are failed to undergo mitophagy, we doubt that activating mitophagy will properly remove damaged mitochondria.

In parallel to the above experiments, we currently analyze if inhibition of mitochondrial fission or mitophagy would phenocopy the hypoplastic dendrites of AMPK-deficient neurons, and if the activation of fission would rescue the phenotypes of AMPK KD, to strengthen the causality of AMPK-dependent fission, autophagy and dendrite outgrowth. So far we have observed that inhibition of mitochondrial fission by MFF knockdown or inhibition of autophagy by bafilomycin treatment strongly suppress dendrite outgrowth. MFF knockdown also leads to the elongation of mitochondria with decreased association of p62-puncta, strikingly reminiscent of AMPK-deficient neurons. Please see attached figures. Completed analyses will be included in the full revision.

*Reviewer #1-4.- Results clearly indicate that AMPK enhances mitochondrial fission, and that AMPK is necessary for proper dendritic outgrowth. However, as indicated, the role of AMPK-dependent mitochondrial fission in promoting dendritic growth is not well demonstrated. A possible, and not very difficult experiment, would be the expression of non-phosphorylable MFF S155/172 mutant (perhaps is also needed to knock down the endogenous MFF). Use of this mutant would abolish AMPK-dependent mitochondrial fission while preserving its other functions. *

Related comments by Reviewer #3-3. The authors could further confirm the claim by examining how mutations in Mff and ULK2 which cannot be phosphorylated by AMPK can rescue defects in mitochondrial fission and spine density.

We will examine if the expression of non-phosphorylable MFF S155/172 mutant would cause defective autophagy and dendritic arbor growth similarly to AMPK KD neurons. In addition, we will test whether MFF S155/172 mutant would inhibit activity-induced mitochondrial fission to strengthen the link between activity-AMPK-MFF-autophagy axis and dendritic outgrowth.

*Reviewer #1-Minor 2.- It is intriguing that as shown in Fig. 2A, rather than an increase in pAMPK/AMPK at DIV5 seems there is less phosphorylation despite FRET analysis indicate more AMPK activation. On the other hand, most of the blots in Fig. 6 seem to be overexposed. *

The exposure time of WB in Fig. 2A was adjusted so that all lanes can be compared. We will fix the exposure time.

Reviewer#2-A. Most of the evidence on the role of AMPKa2 relies on a shRNA-based strategy. The authors have performed this approach with the best practice, including selecting 2 shRNA plasmids for each gene, and performing a rescue experiment with shRNA -resistant cDNA. Yet, it is critical to provide stronger evidence with all the tools available to demonstrate the role of AMPKa2 in dendritic development. This is especially important because the effect reported by the authors is a transient effect: indeed, dendritic development appears abnormal in very young neurons (P5) but largely normal afterwards (P10). Hence one cannot discard a non specific effect on cell viability or sampling effect. The number of neurons counted is fairly low (about 30 neurons per condition) and it is not clear if they come from several independent cultures. It is known that plasmid preparation can impact cell viability and performing the experiment with only one batch of plasmid prep could explain why one plasmid would produce a short-lived effect on cell morphology. Two shRNA constructs are presented in figure S2A but only one is used for morphological experiments quantified in S2D-E with again a very low N number. The specific experiments I would recommend would be to increase the N: at least 25-30 neurons counted per culture, 3 independent cultures, and presenting the results of the two shRNA plasmids for both AMPKa1 and AMPKa2. Furthermore, the immunofluorescence validation of knockdown provided in figure S2B is not really convincing, a nuclear marker is lacking to determine where cells are (it seems that many cells are present in the image, maybe some of them with low AMPKa2 expression as well). A quantification should be provided as well as evidence for shRNA #1 and #2. *

*

We thank the reviewer for valuable suggestions to improve our manuscript. All the knockdown analyses were done from three independent experiments using different mouse litters and multiple batches of plasmid prep. N number was low because of a low transfection efficiency in the primary culture. We will repeat experiments and increase the sample number. We will also present results of the two shRNA constructs. We will redo the immunofluorescence for validation of shRNA knockdown and replace Extended Data Fig. 2B which was pointed out as not being clear.

*Reviewer #2-C. The observation, in vivo, that dendritic development is normal at P10 is intriguing but this reconciles the observation of altered dendritic development with previous studies demonstrating that AMPK knockout has little effect on brain development, as well as previous studies (Mairet-Coello et al. Neuron 2013, Lee et al. Nat commun 2022) targeting AMPKa2 in the hippocampus of AD mouse models by in utero electroporation. This is a critical aspect of the paper and as stated in the discussion, the previous studies only looked at the end product (neuronal morphology appears normal after development) but not the process of neuronal development and maturation. The in vitro experiment offer the possibility to study dendritic development over time in the same population of neurons, either through selected time points, or through time lapse imaging. This would strengthen one of the most original aspect of this work. *

We thank the reviewer for an important suggestion. We will analyze if dendritic morphology and mitochondria would recover in later stages in culture. However, the dendritic growth defects in AMPK KD neurons are apparently more severe in culture and our preliminary results have shown that dendritic growth defects and mitochondrial elongation persist until 10DIV. We anticipate that AMPK deficiency is complemented by certain compensation mechanisms in vivo that are not present in culture, such as chemical signals or synaptic inputs from correct afferents. We will confirm the recovery of dendritic outgrowth in vivo using an AMPK alpha2 knockout mouse. We will include the results in vivo and in vitro in the revised manuscript.

* The authors use a FRET probe to witness AMPK activity, and this part raises a lot of questions. A lot of the signal matches the regularly spaced activity peaks suggesting that FRET response is a coincidence detector of calcium waves. Hence, is the FRET signal influenced by intracellular calcium concentration, or changes in pH? To address this question, the proper control would be to use a FRET biosensor with a mutated AMPK phosphorylation site and demonstrating the absence of response to calcium waves. *

We think it unlikely that the FRET probe detects calcium concentration or pH change, as its kinetics and timing are different from calcium spikes. For confirmation, we will examine a FRET probe lacking phosphorylation sites to negate that calcium waves directly activate the FRET probe.

* Also, the parameter used for quantification is a so-called "number of FRET peaks over 3 minutes" for which the biological significance is unknown. On average there are 1-2 such "peaks" in control conditions (figure 4). These peaks have low amplitude, sometimes around 0.05-0.1 of the YFP/CFP ratio, which is about what is expected even in AMPKa2 knockdown cells (figure S4C). Are there changes in the baseline of FRET signal? *

We monitor FRET at 3-5 sec intervals and is set to 3 minutes due to gradual photobleaching. Although the event frequency is 0-4 times per 3 minutes observation, it is nearly absent in AMPK KD (1 small peak in 3 cells out of 40 cells) or activity deprivation, which we consider a significant difference. We have replaced Figure 4B, 4D, 4I, 4J andExtended Data Fig.4E. The basal FRET signal is lowered in AMPK KD cells, but also varies depending on the expression level of the probe. For comparision of the results shown in Figure 4 and Extended Data Fig.4, we have changed the y-axis to the normalized FRET signal {FRET/FRETbaseline} and jRGECO signals (DF/F0) in Fig. 4F, Extended Data Fig.4C, 4D.

*

*

*Finally, given that calcium peaks and AMPK activity peaks overlap, one key observation is the continued presence of calcium peaks upon AMPKa2 knockdown in figure S4D. Yet, the scale for jRGECO1 intensity in figure S4D differs from the scale in figure 4, making it difficult to interpret. It seems that on average the delta (peak-baseline) is 2000 in wild-type cells (figure 4), compared to 500 in AMPKa2 knockdown cells, which suggests a strong reduction in calcium signal amplitude upon knockdown of AMPK. This should be clarified to demonstrate that the FRET probe peaks are really due to AMPK activity. Also, the effect of STO-609 should be added to this figure. *

We think that the presence of calcium transients in AMPK KD cells supports our conclusion that AMPK is downstream of calcium signaling. The amplitude of calcium spikes was actually lowered in AMPK KD cells. We think it is due to the reduction of the cell size and complexity in KD cells. To negate that AMPK inhibition affects calcium influx, we will examine if acute inhibition by an AMPK inhibitor will suppress only FRET signals but not calcium waves. In addition, we will monitor calcium waves and FRET signals in neurons treated with STO-609 or AICAR. STO-609 and AICAR should decrease and increase FRET signals without affecting calcium influx.

• Other comments by Reviewer #2*
• Similarly, the number of events in figure 5F-G is really low. Is a difference between 0.02 in the control group and 0.01 in the knockdown group physiologically relevant?*

Since p62 puncta contact only a small mitochondrial region, the overlap area of mitochondria with p62 in the total mitochondrial area is small. We will analyze the number of p62 puncta associated with mitochondria per unit dendritic area.

1. • Lines 339-350, the authors discuss about a putative regulatory loop involving AMPK dephosphorylation. Since this part of the discussion is based on the FRET signal, the authors should consider if an alternative explanation could be the kinetics of the biosensor dephosphorylation.* We will revise Discussion to argue about alternative possibilities of dynamic oscillation of the FRET signal when we get data from the above experiments.

*In terms of significance, I would have two major criticisms. The first is that it appears that many of the findings by the authors are redundant with observations of the roles of CAMKK2-AMPK-MFF-ULK1 in AD model mice, see for example the work by Polleux (Mairet-Coello et al. Neuron 2013, Lee et al., Nat commun 2022). As said above, my opinion is that the paper should put more emphasis on the transient effect of AMPK, which would be a novel observation and, as the authors rightfully discuss, a phenotype potentially overlooked in previous studies of AMPK KO mice. The second is that many points in the discussion seem to be over reached and are not entirely supported by the data. As an example lines 298-299 "leading to mitochondrial dysfunction with low respiratory activity" (not addressed in this manuscript), lines 312-313 "multiple signatures of mitochondrial dysfunction such as reduced delta-Psi-m and ROS production" (biological significance of these parameters?), lines 332-334 "AMPK phosphorylation dynamically oscillates in dendrites, depending on Ca2+ influx and CAMKK2 activity, while it is independent of LKB1" (the authors don't study AMPK phosphorylation, and the experimental data has many limitations that need addressing), etc. *

We thank reviewer’s guidance. We think this is the first study showing AMPK function in dendritic arbor growth in immature neurons before synaptogenesis. We will rewrite the manuscript to emphasize that neuronal activity in immature neurons regulates dendrite formation via AMPK in a short time window during brain development. Discussion will be revised according to the data of the ongoing additional experiments.

Reviewer#3-1. All these studies are done in invitro neuronal culture modal with transfection of ShRNAs to Knockdown AMPK. An alternative possibility is that authors could use an AMPK Conditional Knockout mouse models Conditional deletion of (AMPKα1/α2 (AMPKα1−/−; AMPKα2F/F; Emx1-Cre) derived neurons for this study.

We showed the effect of AMPK knockdown in hippocampal neurons in culture and in vivo (Fig. 2). For validation, we also examined CRISPR interference (Extended data Fig.2). We will examine in vivo phenotypes in pyramidal neurons in AMPK alpha2 knockout mice to further validate our observation.

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

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*Reviewer #1-1.- Authors use mitochondrial membrane potential (MMP), MMP flickering and mitochondrial ROS production as indicators of mitochondrial function, but this is not convincing. To analyze MMP, authors use TMRM fluorescence normalized by mitochondria area. This is not correct, using this strategy would mean that a symmetric fission would instantly double MMP and fusion would half MMP. The analysis must be made by tracing ROIs of the same surface in different mitochondria and determining TMRM fluorescence in these ROIs. *

We have reanalyzed TMRM fluorescence using the method indicated. As a result, TMRM fluorescence show a slight but significant decrease (p=0.0071) in AMPK KD cells. Extended Data Fig. 5C has been replaced accordingly. We thank the Reviewer for kind guidance!

Reviewer #1-3.- The authors treat neurons with glutamate to support the view that synaptic activity activates AMPK and promotes mitochondrial fission. However, the concentration used (100 mM) may be excitotoxic. Synaptic activity can be induced by electric field stimulation, although this require equipment that may not be available in the authors' lab. Another alternative is network disinhibition with bicuculine or to use lower concentrations of glutamate. In any case, since neurons are immature and may respond differently from mature neurons, it would be worth to verify synaptic activity by analyzing Ca2+ transients.

*Reviewer #1-Minor 3.- It is necessary more explanation about spontaneous Ca2+ transients in immature cultures. What percentage of neuros experience it? Is it synchronized? *

*Related comments by Reviewer#2-D. It is well established and thus not surprising that AMPK activity increases in response to synaptic activity. It is more surprising to witness such an effect of activity in very immature neurons, where presumably synapses are sparse and not well developed. For example dendritic segments in Figure 1E and 3A don't have dendritic spines. Western-blot and/or immunofluorescence of synaptic markers with comparison to fully mature neurons would complete figure 1 and make the case whether the reported effects are marginal or a strong driver for dendritic development and AMPK regulation. *

We thank the reviewers’ point that we failed to emphasize in the original manuscript.

We focus on AMPK function during activity-dependent dendritic outgrowth in immature neurons before the onset of synaptogenesis. It has been shown that synaptogenesis occurs in dissociated hippocampal cultures between 7-12 DIV (eg, Renger et al., Neuron 2001) and that developing dendrites at 5 DIV are activated by ambient glutamate which is spontaneously released from nearby immature axon terminals and undergo spontaneous Ca2+ transients, and this non-synaptic activity is important for dendritic outgrowth (Andreae and Burrone, Cell Rep 2015). We have observed that Ca2+ transients in individual neurons are variable in frequency and magnitude and are not synchronized in consistent with previous studies. We have performed immunofluorescence with a synaptic marker PSD95 and confirmed that dendritic spines are not yet differentiated and PSD95 is sparsely distributed along the dendritic shaft in DIV5 hippocampal neurons. We describe the nature of Ca2+ transients in the Results more clearly and provide high magnified images and immunofluorescence with a synaptic marker PSD95 of the neurons at DIV5 and DIV13 as a new Fig. 1A. We believe that this is the first indication of AMPK function in non-synaptic neuronal activity during dendritogenesis.

We have observed induction of mitochondrial fission in neurons treated with 1 µM glutamate. Extended Data Fig. 1E has been replaced accordingly. Since GABA is known to induce depolarization in immature neurons (Soriano et al., PNAS 2008), we would like to exclude bicuculine treatment from this analysis.

*Reviewer #1-5.- The statistical analysis seems appropriate, but it is confusing that sometimes non-parametric and sometimes parametric tests are used. It is not indicated which test is used to determine normality since the methods section lacks a statistical analysis section.

*

We have revised Methods and have described statistical analysis in detail.

*Reviewer #1-Minor 1.- Authors should double check the analysis shown in Fig. 1A. As it is shown, Ca2+ transients are 2-3% higher than basal, when the video shown in video 1 seem to indicate much more. *

Thank you for pointing this out. In the original version, the percentile change was erroneously measured across the entire visual field, including areas without neurons. We have replaced Fig. 1B (original Fig. 1A) with reanalyzed data in the proximal region of the apical dendrite.

*Reviewer #1-Minor 4.- It is interesting that AMPK KD in vivo impairs dendritic architecture at P5, however at P10 the defect seem to be somehow compensated. This result apparently detracts from the relevance of the findings, however last year was published a paper in which in an animal model of Huntington's disease dendritic architecture is delayed during the first week but normalizes thereafter. Despite later normalization in dendritic architecture, this early defect in maturation has effects in adulthood as pharmacological restoration of arborization during the neonatal period suppresses some phenotypes observed in adulthood (PMID: 36137051). I believe that discussing this paper would help the reader to recognize the potential relevance of the findings. *

*Related comments by Reviewer #2: Nonetheless let aside the technical concern, if their findings hold true, this is an intriguing mechanism. There are interesting parallels to be made with observations of altered morphology and excitability of neurons in Huntington's disease model mice during the first postnatal week. These changes spontaneously reverts and are undetectable in the second week (Braz et al. Science 2022). Thus, precedent suggests that indeed dendritic development can take a slow course, and this study also suggests that this is important later since normalization of abnormal excitability during the first week in HTT mice prevents some of the phenotypes later in life. Here again, an interesting parallel could be made with the known role of AMPK in synaptic loss in AD models. *

We thank the reviewers for the supportive comments. We will refer this paper and discuss about potential significance of the transient defects in early dendrite morphology in AMPK deficient neurons.

*Reviewer#2-B. The Crispr method lacks validation which should be provided somehow. The drug-based experiment relies on compound C, a notoriously non specific AMPK inhibitor (see for example Bain et al. Biochem J 2007, or Vogt et al. Cell Signal 2011). Data obtained with Compound C is hard to interpret given the number of kinases that are affected by the drug and should be removed from the manuscript. *

We have added immunofluorescence images for validation of AMPK deletion by CRISPRi (Extended Data Fig. 2F).

We think the results of Compound C treatment support our conclusion in combination with KD and CRISPRi, but will delete the results in accordance with this comment.

• Other comments by Reviewer#2*
• Figure 5A-C relies on the quantification of fission events that appear very rare (0.4 event per 20 minutes). The difference between the two groups is between 0.1 and 0.2 events on average. Since this was quantified on a fairly low number of cells (N=14), it is hard to appreciate exactly how many events have been observed and the actual physiological relevance. Furthermore individual datapoints should be added to the figure to estimate variability.*

The number of fission events was counted in mitochondria in a unit length of dendrite of similar diameter, and normalized by the number of mitochondria. The values were thus small as they represent average number of events in one mitochondrion in 20 minutes. We have replaced the Fig. 1K, 3F, 5B and 5C to show the number of fission events in mitochondria included in a unit length of dendrites of similar diameter. Individual data points have been included.

Reviewer #3-4. Authors showed activity-dependent calcium signaling controls mitochondrial homeostasis and dendritic outgrowth via AMP-activated protein kinase (AMPK) in developing hippocampal neurons do the cortical mitochondria respond the same way as the hippocampal neurons?

Thank you for the comment. As pyramidal neurons in the cerebral cortex and hippocampus are basically the same origin, it is likely that they share the same signaling. We use hippocampal neurons in this study to perform quantitative analysis of dendritic morphology in the same type of neurons. Primary cultures of cortical neurons contain multiple different cell types, making it difficult to analyze the same cell type.

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.

*Reviewer #1: If activity is observed in only a portion of the neurons, taking advantage of the stablished long-term live imaging protocol in the authors' lab, it would be interesting to study in the same culture whether neurons that experience spontaneous activity develop more than those that do not. *

We prefer not to carry out this analysis, as activity-dependent dendritic growth has already been well described in previous papers. It will take considerable time to observe the number of neurons for analysis of correlation between Ca2+ transients and dendrite morphology. We would like to focus our effort to demonstrate AMPK signaling during activity-dependent dendritic growth.

Reviewer#3-2. Another technical issue here, most of the experiments are carried out on Neurobasal media, which has a lot of glucose plus substitution of glutamax might be not the perfect conditions for AMPK. Authors could not obtain evidence supporting the regulation of mitochondria biogenesis by PGC1α phosphorylation and expression. This surprise me, if you could reduce the glucose concentration if might change.

We observed little or no changes in phosphorylation of PGC1alpha by enhancing or suppressing neuronal activity or AMPK activity. As mitochondrial biogenesis is very active in growing neurons, we surmise that PGC1alpha and mitochondrial biogenesis is regulated by multiple mechanisms during neuronal differentiation and AMPK activation/inhibition might not induce visible changes. We agree the reviewer that there is room to seek the conditions under which changes in PGC1alpha can be detected, but we do not see why Neurobasal plus glutamax is not suitable for this study. Multiple papers studying AMPK function in cultured neurons use similar culture media (Sample et al., Mol. Cell. Biol., 2015; Muraleedharan et al., Cell. Rep., 2020; Lee et al., Nat. Commun., 2022). We might see PGC1 phosphorylation by glucose deprivation, as it decreases glycolysis-derived ATP and thereby activates AMPK. Since we focus on AMPK activation by calcium signals, we are afraid that it would be difficult to distinguish AMPK activation by ATP deficiency or calcium signaling in glucose deficient condition. In addition, glucose deprivation would affect neuronal activity (which consumes large amount of ATP) and neuronal differentiation including dendritic outgrowth.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Hatsuda and colleagues builds on previous work from their group and others, to further investigate the activity-dependent dendritic arbor development and AMPK-dependent mitochondrial quality control. It recently been illustrated that over-activation of the CAMKK2-AMPK kinase dyad mediates synaptic loss through coordinated phosphorylation of MFF-dependent mitochondrial fission and ULK2-dependent mitophagy (Aβ42 oligomers trigger synaptic loss through CAMKK2-AMPK-dependent effectors coordinating mitochondrial fission and mitophagy). Though this is progress is modest, present study showed neuronal activity induces activation of two downstream effectors of AMPK, MFF and ULK1, which are the key regulators of mitochondrial fission and mitophagy.

Major Concerns:

1. All these studies are done in invitro neuronal culture modal with transfection of ShRNAs to Knockdown AMPK. An alternative possibility is that authors could use an AMPK Conditional Knockout mouse models Conditional deletion of (AMPKα1/α2 (AMPKα1−/−; AMPKα2F/F; Emx1-Cre) derived neurons for this study.
2. Another technical issue here, most of the experiments are carried out on Neurobasal media, which has a lot of glucose plus substitution of glutamax might be not the perfect conditions for AMPK. Authors could not obtain evidence supporting the regulation of mitochondria biogenesis by PGC1α phosphorylation and expression. This surprise me, if you could reduce the glucose concentration if might change.
3. The authors could further confirm the claim by examining how mutations in Mff and ULK2 which cannot be phosphorylated by AMPK can rescue defects in mitochondrial fission and spine density.
4. Authors showed activity-dependent calcium signaling controls mitochondrial homeostasis and dendritic outgrowth via AMP-activated protein kinase (AMPK) in developing hippocampal neurons do the cortical mitochondria respond the same way as the hippocampal neurons?

Significance

It recently been illustrated that over-activation of the CAMKK2-AMPK kinase dyad mediates synaptic loss through coordinated phosphorylation of MFF-dependent mitochondrial fission and ULK2-dependent mitophagy (Aβ42 oligomers trigger synaptic loss through CAMKK2-AMPK-dependent effectors coordinating mitochondrial fission and mitophagy). Though this is progress is modest, present study showed neuronal activity induces activation of two downstream effectors of AMPK, MFF and ULK1, which are the key regulators of mitochondrial fission and mitophagy.

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Referee #2

Evidence, reproducibility and clarity

The manuscript 'Calcium signals tune AMPK activity and mitochondrial homeostasis in dendrites of developing neurons' by Hatsuda and collaborators aims at studying the interrelationship between neuronal activity, mitochondria dynamics (ie. fusion and fission mechanisms) and dendritic development. The authors provide evidence linking activity-dependent activation of a CAMKK2-AMPK pathway and the regulation of mitochondria fission and autophagy. Based on the literature, they focus on the roles of the mitochondria fission factor MFF and the autophagy regulator ULK1, both previously known targets of AMPK.

This work parallels previous observations in the context of Alzheimer's disease and as such the discovery of a molecular link between CAMKK2-AMPK, MFF/ULK1 and the regulation of dendritic mitochondria is not surprising. The change of biological context raises interesting question although the relevance of these observations is not addressed in this manuscript.

As a general comment, the work is well structured, reads easily. Iconography and figures organization are good. Major criticisms would concern the tools used to study AMPK and challenge some of the observations, as such I believe these are essential to address to validate the findings.

Major comments

• A. Most of the evidence on the role of AMPKa2 relies on a shRNA-based strategy. The authors have performed this approach with the best practice, including selecting 2 shRNA plasmids for each gene, and performing a rescue experiment with shRNA -resistant cDNA. Yet, it is critical to provide stronger evidence with all the tools available to demonstrate the role of AMPKa2 in dendritic development. This is especially important because the effect reported by the authors is a transient effect: indeed, dendritic development appears abnormal in very young neurons (P5) but largely normal afterwards (P10). Hence one cannot discard a non specific effect on cell viability or sampling effect. The number of neurons counted is fairly low (about 30 neurons per condition) and it is not clear if they come from several independent cultures. It is known that plasmid preparation can impact cell viability and performing the experiment with only one batch of plasmid prep could explain why one plasmid would produce a short-lived effect on cell morphology. Two shRNA constructs are presented in figure S2A but only one is used for morphological experiments quantified in S2D-E with again a very low N number. The specific experiments I would recommend would be to increase the N: at least 25-30 neurons counted per culture, 3 independent cultures, and presenting the results of the two shRNA plasmids for both AMPKa1 and AMPKa2. Furthermore, the immunofluorescence validation of knockdown provided in figure S2B is not really convincing, a nuclear marker is lacking to determine where cells are (it seems that many cells are present in the image, maybe some of them with low AMPKa2 expression as well). A quantification should be provided as well as evidence for shRNA #1 and #2.
• B. To complete the shRNA-based experiments, the authors use a single cell Crispr approach, as well as a pharmacological approach. The Crispr method lacks validation which should be provided somehow. The drug-based experiment relies on compound C, a notoriously non specific AMPK inhibitor (see for example Bain et al. Biochem J 2007, or Vogt et al. Cell Signal 2011). Data obtained with Compound C is hard to interpret given the number of kinases that are affected by the drug and should be removed from the manuscript.
• C. The observation, in vivo, that dendritic development is normal at P10 is intriguing but this reconciles the observation of altered dendritic development with previous studies demonstrating that AMPK knockout has little effect on brain development, as well as previous studies (Mairet-Coello et al. Neuron 2013, Lee et al. Nat commun 2022) targeting AMPKa2 in the hippocampus of AD mouse models by in utero electroporation. This is a critical aspect of the paper and as stated in the discussion, the previous studies only looked at the end product (neuronal morphology appears normal after development) but not the process of neuronal development and maturation. The in vitro experiment offer the possibility to study dendritic development over time in the same population of neurons, either through selected time points, or through time lapse imaging. This would strengthen one of the most original aspect of this work.
• D. It is well established and thus not surprising that AMPK activity increases in response to synaptic activity. It is more surprising to witness such an effect of activity in very immature neurons, where presumably synapses are sparse and not well developed. For example dendritic segments in Figure 1E and 3A don't have dendritic spines. Western-blot and/or immunofluorescence of synaptic markers with comparison to fully mature neurons would complete figure 1 and make the case whether the reported effects are marginal or a strong driver for dendritic development and AMPK regulation. Furthermore the authors use a FRET probe to witness AMPK activity, and this part raises a lot of questions. A lot of the signal matches the regularly spaced activity peaks suggesting that FRET response is a coincidence detector of calcium waves. Hence, is the FRET signal influenced by intracellular calcium concentration, or changes in pH? To address this question, the proper control would be to use a FRET biosensor with a mutated AMPK phosphorylation site and demonstrating the absence of response to calcium waves. Also, the parameter used for quantification is a so-called "number of FRET peaks over 3 minutes" for which the biological significance is unknown. On average there are 1-2 such "peaks" in control conditions (figure 4). These peaks have low amplitude, sometimes around 0.05-0.1 of the YFP/CFP ratio, which is about what is expected even in AMPKa2 knockdown cells (figure S4C). Are there changes in the baseline of FRET signal? Finally, given that calcium peaks and AMPK activity peaks overlap, one key observation is the continued presence of calcium peaks upon AMPKa2 knockdown in figure S4D. Yet, the scale for jRGECO1 intensity in figure S4D differs from the scale in figure 4, making it difficult to interpret. It seems that on average the delta (peak-baseline) is 2000 in wild-type cells (figure 4), compared to 500 in AMPKa2 knockdown cells, which suggests a strong reduction in calcium signal amplitude upon knockdown of AMPK. This should be clarified to demonstrate that the FRET probe peaks are really due to AMPK activity. Also, the effect of STO-609 should be added to this figure.

Other comments

• A. Figure 5A-C relies on the quantification of fission events that appear very rare (0.4 event per 20 minutes). The difference between the two groups is between 0.1 and 0.2 events on average. Since this was quantified on a fairly low number of cells (N=14), it is hard to appreciate exactly how many events have been observed and the actual physiological relevance. Furthermore individual datapoints should be added to the figure to estimate variability.
• B. Similarly, the number of events in figure 5F-G is really low. Is a difference between 0.02 in the control group and 0.01 in the knockdown group physiologically relevant?
• C. In figure 6 it is unclear what is the significance of the TMRM "flickering" parameter quantified and the difference between the control and knockdown condition is small on average. Rather, the data presented in figure S5 would suggest that there is no difference in TMRM signal.
• D. Lines 339-350, the authors discuss about a putative regulatory loop involving AMPK dephosphorylation. Since this part of the discussion is based on the FRET signal, the authors should consider if an alternative explanation could be the kinetics of the biosensor dephosphorylation.

Significance

The manuscript by Hatsuda and collaborator studies the roles of neuronal AMPK in the development of hippocampal neurons, specifically the authors describe a transient effect on dendritic development. To this reviewer's opinion, this is the major findings of this paper. Although the physiological implications of this finding are unknown, this is beyond the scope of this paper.

Yet in terms of significance, I would have two major criticisms. The first is that it appears that many of the findings by the authors are redundant with observations of the roles of CAMKK2-AMPK-MFF-ULK1 in AD model mice, see for example the work by Polleux (Mairet-Coello et al. Neuron 2013, Lee et al., Nat commun 2022). As said above, my opinion is that the paper should put more emphasis on the transient effect of AMPK, which would be a novel observation and, as the authors rightfully discuss, a phenotype potentially overlooked in previous studies of AMPK KO mice. The second is that many points in the discussion seem to be over reached and are not entirely supported by the data. As an example lines 298-299 "leading to mitochondrial dysfunction with low respiratory activity" (not addressed in this manuscript), lines 312-313 "multiple signatures of mitochondrial dysfunction such as reduced delta-Psi-m and ROS production" (biological significance of these parameters?), lines 332-334 "AMPK phosphorylation dynamically oscillates in dendrites, depending on Ca2+ influx and CAMKK2 activity, while it is independent of LKB1" (the authors don't study AMPK phosphorylation, and the experimental data has many limitations that need addressing), etc.

Nonetheless let aside the technical concern, if their findings hold true, this is an intriguing mechanism. There are interesting parallels to be made with observations of altered morphology and excitability of neurons in Huntington's disease model mice during the first postnatal week. These changes spontaneously reverts and are undetectable in the second week (Braz et al. Science 2022). Thus, precedent suggests that indeed dendritic development can take a slow course, and this study also suggests that this is important later since normalization of abnormal excitability during the first week in HTT mice prevents some of the phenotypes later in life. Here again, an interesting parallel could be made with the known role of AMPK in synaptic loss in AD models.

Reviewer expertise: I have expertise in neuronal metabolism

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Hatsuda et al study the role of synaptic activity-dependent Ca2+ signals in activating AMPK and regulating mitochondrial homeostasis and dendritic outgrowth in developing neurons. Using cultures of immature primary hippocampal neurons, the authors found that Ca2+ transients activate AMPK, which regulates mitochondrial fission and dendritic growth. AMPK KD and pharmacological manipulation of AMPK activity confirmed a role of AMPK in mitochondrial morphology that correlated with impaired mitophagy and mitochondrial function. These findings led the authors to conclude that activity-dependent activation of AMPK promotes mitochondrial fission, which facilitates removal of dysfunctional mitochondria and thus contributes to maintaining a healthy mitochondrial pool that is necessary in the intense energetic effort that requires dendritic outgrowth.

Major comments:

Meanwhile the activation of AMPK and its role in mitochondrial fission and dendritic outgrowth is in general well demonstrated, the conclusion that AMPK is necessary for proper dendritic outgrowth because by promoting asymmetric mitochondrial fission facilitates mitophagy of dysfunctional mitochondria and thus ensures adequate generation of energy for dendritic outgrowth still seems preliminary.

1. Authors use mitochondrial membrane potential (MMP), MMP flickering and mitochondrial ROS production as indicators of mitochondrial function, but this is not convincing. To analyze MMP, authors use TMRM fluorescence normalized by mitochondria area. This is not correct, using this strategy would mean that a symmetric fission would instantly double MMP and fusion would half MMP. The analysis must be made by tracing ROIs of the same surface in different mitochondria and determining TMRM fluorescence in these ROIs. But even in the case that there were changes in MMP, that it does not seem to be the case, MMP alone is not a good indicator of mitochondrial health. For instance, ATPase inhibitor causes increase in MMP and complex I inhibition diminish MMP and in both cases mitochondrial function is impaired. On the other hand, authors use increased flickering and mitochondrial ROS production as an indicator of enhanced respiration but they could also be used as indicators of mitochondrial dysfunction. Other assays, such as oxygen consumption, are needed to assess the mitochondrial function.
2. It would be interesting to show a better characterization of the mitophagy flux and to test whether pharmacological or genetic stimulation of mitophagy could revert the effect of AMPK KD on dendritic outgrowth, ultimately linking AMPK, mitophagy and dendritic outgrowth. The latter experiments may be challenging but not impossible, for example see (PMID: 27760312).
3. The authors treat neurons with glutamate to support the view that synaptic activity activates AMPK and promotes mitochondrial fission. However, the concentration used (100 M) may be excitotoxic. Synaptic activity can be induced by electric field stimulation, although this require equipment that may not be available in the authors' lab. Another alternative is network disinhibition with bicuculine or to use lower concentrations of glutamate. In any case, since neurons are immature and may respond differently from mature neurons, it would be worth to verify synaptic activity by analyzing Ca2+ transients.
4. Results clearly indicate that AMPK enhances mitochondrial fission, as previously reported, and that AMPK is necessary for proper dendritic outgrowth. However, as indicated, the role of AMPK-dependent mitochondrial fission in promoting dendritic growth is not well demonstrated. For example, AMPK could regulate dendritic outgrowth through its role on cytoskeletal dynamics. A possible, and not very difficult experiment, would be the expression of non-phosphorylable MFF S155/172 mutant (perhaps is also needed to knock down the endogenous MFF). Use of this mutant would abolish AMPK-dependent mitochondrial fission while preserving its other functions.
5. The statistical analysis seems appropriate, but it is confusing that sometimes non-parametric and sometimes parametric tests are used. It is not indicated which test is used to determine normality since the methods section lacks a statistical analysis section.

Minor comments:

1. Authors should double check the analysis shown in Fig. 1A. As it is shown, Ca2+ transients are 2-3% higher than basal, when the video shown in video 1 seem to indicate much more.
2. It is intriguing that as shown in Fig. 2A, rather than an increase in pAMPK/AMPK at DIV5 seems there is less phosphorylation despite FRET analysis indicate more AMPK activation. On the other hand, most of the blots in Fig. 6 seem to be overexposed.
3. It is necessary more explanation about spontaneous Ca2+ transients in immature cultures. What percentage of neuros experience it? Is it synchronized? If activity is observed in only a portion of the neurons, taking advantage of the stablished long-term live imaging protocol in the authors' lab, it would be interesting to study in the same culture whether neurons that experience spontaneous activity develop more than those that do not.
4. It is interesting that AMPK KD in vivo impairs dendritic architecture at P5, however at P10 the defect seem to be somehow compensated. This result apparently detracts from the relevance of the findings, however last year was published a paper in which in an animal model of Huntington's disease dendritic architecture is delayed during the first week but normalizes thereafter. Despite later normalization in dendritic architecture, this early defect in maturation has effects in adulthood as pharmacological restoration of arborization during the neonatal period suppresses some phenotypes observed in adulthood (PMID: 36137051). I believe that discussing this paper, and others with similar message (if they exist, I do not know), would help the reader to recognize the potential relevance of the findings.

I believe that all the proposed experiments would strongly help to support the claims of the paper and are perfectly feasible during the time given for a revision and economically affordable.

Significance

As authors already cite in their work, several groups have shown that mitochondrial fission is necessary for proper dendritic growth. Here, the authors have proposed that synaptic activity in immature neurons induce mitochondrial fission via AMPK activation and subsequent MFF phosphorylation. Many of the findings here are confirmations of previous work, for instance activity-dependent activation of AMPK (PMID: 25698741, PMID: 27012879) and induction of mitochondrial fission by AMPK via MFF phosphorylation (PMID: 26816379). The novelty of the work is in studying these processes in immature neurons and how this affects dendritic growth, which is of interest for cellular neuroscientist, but I do not think it represents a conceptual breakthrough. A more detailed understanding of the mechanism proposed, i.e. mitochondrial fission facilities removal of dysfunctional mitochondria to maintain the high energy demands of growing dendrites, would greatly enhance the significance of the study.

This reviewer specializes in neuronal cell biology, specifically the study of the mechanisms by which mitochondrial function regulates aspects of neuronal physiology, including neuritic outgrowth.

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Reply to the reviewers

Point-by-point response to reviewer comments

General statement

Several studies have previously demonstrated functional links between the death receptors (DR) TRAIL-R1/2 and the Unfolded protein response (UPR). In this manuscript, we describe the previously unrecognized IRE1-dependent dual regulation of the expression of another DR, CD95, and CD95L-induced cell death. Our work therefore adds to the current knowledge on the functional links existing between UPR and DR signaling and provides novel mechanistic insights on a dual regulation involving both transcriptional and post-transcriptional control of the expression of CD95 mRNA expression by IRE1. To demonstrate this, we have used both genetic (overexpression of XBP1s or dominant-negative forms of IRE1) and pharmacologic (IRE1 RNase inhibitor) approaches and cellular models of glioblastoma (GB) and triple-negative breast cancer (TNBC). We show that IRE1 RNase activity promotes CD95 expression and CD95-mediated cell death via the transcription factor XBP1s whilst IRE1 RNase limits CD95 expression and cell death via its ability to cleave RNAs (through RIDD, for Regulated IRE1-dependent decay of RNAs, activity). Furthermore, we report that IRE1-mediated control of CD95 expression is active in vivo, using a model of CD95-mediated fulminant hepatitis in mice. Lastly, we correlate these results to the pathology by showing that CD95 expression is decreased in RIDD high or XBP1s low human GB and TNBC tumors.

We thank the reviewers for their fair assessment of our manuscript and for their insightful comments. Below, we describe the experiments we plan to carry out to address the reviewers’ comments.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary: Here the authors argue that IRE1 activation has opposite effects on Fas/CD95 expression/stability in a number of contexts, via either RIDD-dependent degradation of Fas mRNA or XBP-1-mediated induction of Fas expression, which led to either increased or decreased sensitivity to Fas-induced apoptosis in a number of settings. Major issues: The study is somewhat preliminary and inconclusive in that it is not clear why the RIDD function of XBP-1 appears to predominate in vitro in the cell lines examined, leading to modest increases in Fas expression levels (Figure 1) when IRE1 DN versus IRE1 WT constructs are overexpressed, which is at odds with the latter part of the paper which suggests that inhibition of RIDD led to reduced Fas expression levels. However, this could be due to supraphysiological levels of IRE1 being expressed under overexpression conditions, leading to confounding results. Similarly, when XBP-1s is overexpressed in vitro (Figure 5) the modest increases in CD95/Fas expression and sensitization to Fas-induced cell death may not be fully representative of what would be observed at physiological levels of XBP-1s activation. The in vivo results obtained using an IRE1 RNase inhibitor (MKC8866) contradict the earlier part of the study (as Fas levels decreased and there was protection from Fas-induced liver toxicity) and this could be due to a multitude of reasons. There is no doubt that impacting on IRE1 activity has interesting effects on CD95/Fas expression, which can be up- or -down-regulated, with consequences for cell death induced via engagement of the latter receptor, however, the manuscript does not offer a lot of clarity on which outcome is the predominant one in the context of engagement of the UPR. I have the following suggestions for improvement.

We thank the reviewer for this overall positive assessment.

1. The authors should induce ER stress using Thaps, Brefeldin A and Tunicamycin, and explore the effects of doing this on Fas expression levels in the context of silencing endogenous IRE1, XBP-1 and PERK.

We do agree with this reviewer that the proposed experiments might further highlight which of the IRE1-dependent control of CD95 expression dominates upon ER stress induction. Therefore, we will perform the requested experiment in the various cell lines already used in the manuscript.

We propose to evaluate the expression of CD95 (at the mRNA and total protein levels) under ER stress induction (by different ER stressors) upon knock-down of IRE1 or XBP1. Other DRs (TRAIL-R1 and 2) have been shown to be induced by PERK activation and it is also demonstrated that PERK and IRE1 signaling pathways coregulate each other. As such, we also propose to assess whether PERK could also control CD95 expression in this setting.

1. The authors should explore the effects of silencing of IRE1, XBP-1 and PERK on constitutive Fas expression and the outcome of Fas/CD95-induced apoptosis in cells not experiencing an overt activation of the UPR (i.e. in the absence of Thaps, Brefeldin A or other UPR inducer).

We thank the reviewer for their suggestion and will perform the requested experiments as proposed.

1. The specificity of MKC8866 at the concentration used (30 uM) is unclear. What effect does MKCC have on sensitivity towards Fas-induced apoptosis, similar to the type of experimental set up presented in Figure 5A, 5B?

Regarding the specificity of MKC8866, this drug has been optimized and refined from a family of IRE1-specific endoribonuclease inhibitors initially obtained from a chemical library screen [1-3]. This salicylaldehyde analog has already shown to be effective in multiple cancer models including breast [4, 5] and prostate [2] cancers. We have recently demonstrated its efficacy in a GB mouse model [6]. It is therefore a widely used IRE1 inhibitor, including in the dose range 10-30 mM used in this study (e.g [4, 5]). We therefore do not think it is in the scope of this manuscript to re-assess it specificity. However, we will aim at testing an additional IRE1 inhibitor to assess whether similar effects can be observed on CD95 expression in cells. To do so, we propose to use a novel IRE1 kinase inhibitor developed in the laboratory (DOI: 10.26434/chemrxiv-2022-2ld35 – Accepted iScience) and shown to efficiently blunt IRE1 activity in GB. As also suggested by the reviewer, we will assess whether the use of MKC-8866 can affect CD95L-induced cell death in cell lines.

1. Similarly, what effects does MKC8866 (at 30 uM) have on key Fas pathway determinants, such as Fas, FLIPL, FLIPs, Caspase-8, FADD, RIPK1, A20, CYLD, cIAP-1, cIAP-2 and Bid? There are many points at which MKC8866 could influence the outcome of Fas receptor engagement beyond the receptor itself.

In the present manuscript, we have shown that MKC-8866 reduces CD95 expression in mouse liver (IHC depicted in Figure 4B and S3B) in vivo and that, when used at 30 mM in vitro, it prevents the loss of CD95 expression induced by tunicamycin or thapsigargin in U87 cells (Fig 1C-F). We do agree with the reviewer that IRE1 may impact CD95-induced cell death beyond modulating CD95 expression, as also already discussed in the present manuscript. Therefore, and as suggested, we will assess whether MKC-8866, used at 30 mM, also impacts on the basal cellular expression of the various components of CD95 signaling mentioned by this reviewer.

Minor issues:

1. For the Fas mRNA cleavage experiments presented in Figure 2, there are no irrelevant control mRNAs to allow the reader to judge whether the effects presented are specific to Fas mRNA or are commonly observed for many mRNAs at these amounts of IRE1 (1 ug, 0.5 ug, which appear high).

The expression of Fas mRNA was already normalized to GAPDH (which does not seem to vary upon incubation with IRE1). We nevertheless will test the expression of additional “irrelevant” RNAs as suggested by the reviewer.

Reviewer #1 (Significance (Required)): General assessment: this is an interesting study, as there is little knowledge currently concerning how the UPR influences Fas expression or Fas-dependent outcomes. However, the impact of this work is limited by the overexpression approaches used, which could produce artifactual results, as well as the contradictory message of the study.

Although we think that the message of the manuscript is indeed complex, the work presented herein does not rely exclusively on overexpression approaches as our genetic-based results are also comforted by the use of pharmacologic inhibitors of IRE1.

Advance: the advance reported here is relatively modest and limited in scope due to the inconclusive nature of the data presented.

Audience: this study will be of interests to specialists in the UPR and cell death communities.

We thank the reviewer for acknowledging the overall novelty of our work. We do hope that the experiments proposed will address her/his remaining concerns.

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

The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress. The study is performed on the high level and supported by all necessary controls. They find the connection between IRE1 and CD95 and show that it might play a role in Cd95 signaling and attenuate CD95-mediated cell death.

Further, the correlation between CD95 expression and IRE is found in tumors. Importantly the authors find out the connection between XBP1 and CD95 expression, which was not reported to date. Hence, it is a very important and highly essential piece of research.

We thank the reviewer for these very positive comments and the acknowledging of the novelty and importance of our study.

However, I would like to clarify the several issues:

1: Figure 1. Tunicamycin obviously leads to deglycosylation of CD95, which is indicated by the appearance of 35 Kda band. This should be highlighted and commented.

We agree, this will be commented on in the text.

1. Figure 2c, d. The piece of mRNA structure, which is synthesized, might have the different secondary structure and might be not cleaved by IRE, accordingly. More detailed comments have to be provided in this regard.

The model depicted in Figure 2B is a predicted computational secondary structure of CD95 mRNA. In the experiments performed in Figure 2A, C and D the mRNA was extracted from U87 cells prior to incubation with recombinant IRE1 and the resulting products analyzed using RT-qPCR with primers flanking different portions of the CD95 mRNA sequence. For Figures 2C and D, the primers used flank the two sites which were predicted to be cleaved by IRE1 based on previous work from our lab [7]. Even though we cannot exclude that additional sites can be targeted beyond these two, the fact that the amplification of CD95 sequence is reduced in samples pre-incubated with recombinant IRE1 strongly suggests that IRE1 is indeed able to cleave CD95 mRNA in these regions in vitro. We will modify the main text to further explain this point.

1. Figure 3. Caspase-8-3 western blots show beautiful effects but did authors see some effects further downstream, eg on PARP1 cleavage? Was cell death (not viability) measured as well? Can you comment on this?

This is absolutely right, we will test PARP-1 cleavage in this setting as suggested. Given the morphology of the cells we observed in the viability experiments, we would expect a similar trend using cell death assays. However, we do agree with the reviewer that this should be proven experimentally, so we will perform these experiments again using cell death assays as a read out.

1. Did the authors looked at the DISC assembly? Did they detect some differences there?

No, we did not. We would expect some difference given the impact we have observed on CD95 expression, caspase-8 activation and cell death of expressing dominant negative forms of IRE1, but this of course needs to be actually tested. We are in the process of optimizing CD95 DISC experiments in our lab and we therefore hope to be able to address this reviewer’s comment in a revised version of the manuscript.

Reviewer #2 (Significance (Required)):

This is an excellent study. The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress. The study is performed on the high level and supported by all necessary controls. This is an important advance for the death receptor field.

Thank you again for these very positive comments and your insightful appreciation of our work.

References

1. Volkmann, K., Lucas, J. L., Vuga, D., Wang, X., Brumm, D., Stiles, C., Kriebel, D., Der-Sarkissian, A., Krishnan, K., Schweitzer, C., Liu, Z., Malyankar, U. M., Chiovitti, D., Canny, M., Durocher, D., Sicheri, F. & Patterson, J. B. (2011) Potent and selective inhibitors of the inositol-requiring enzyme 1 endoribonuclease, J Biol Chem. 286, 12743-55.
2. Sheng, X., Nenseth, H. Z., Qu, S., Kuzu, O. F., Frahnow, T., Simon, L., Greene, S., Zeng, Q., Fazli, L., Rennie, P. S., Mills, I. G., Danielsen, H., Theis, F., Patterson, J. B., Jin, Y. & Saatcioglu, F. (2019) IRE1α-XBP1s pathway promotes prostate cancer by activating c-MYC signaling, Nat Commun. 10, 323.
3. Langlais, T., Pelizzari-Raymundo, D., Mahdizadeh, S. J., Gouault, N., Carreaux, F., Chevet, E., Eriksson, L. A. & Guillory, X. (2021) Structural and molecular bases to IRE1 activity modulation, Biochem J. 478, 2953-2975.
4. Logue, S. E., McGrath, E. P., Cleary, P., Greene, S., Mnich, K., Almanza, A., Chevet, E., Dwyer, R. M., Oommen, A., Legembre, P., Godey, F., Madden, E. C., Leuzzi, B., Obacz, J., Zeng, Q., Patterson, J. B., Jager, R., Gorman, A. M. & Samali, A. (2018) Inhibition of IRE1 RNase activity modulates the tumor cell secretome and enhances response to chemotherapy, Nat Commun. 9, 3267.
5. Almanza, A., Mnich, K., Blomme, A., Robinson, C. M., Rodriguez-Blanco, G., Kierszniowska, S., McGrath, E. P., Le Gallo, M., Pilalis, E., Swinnen, J. V., Chatziioannou, A., Chevet, E., Gorman, A. M. & Samali, A. (2022) Regulated IRE1α-dependent decay (RIDD)-mediated reprograming of lipid metabolism in cancer, Nat Commun. 13, 2493.
6. Le Reste, P. J., Pineau, R., Voutetakis, K., Samal, J., Jégou, G., Lhomond, S., Gorman, A. M., Samali, A., Patterson, J. B., Zeng, Q., Pandit, A., Aubry, M., Soriano, N., Etcheverry, A., Chatziioannou, A., Mosser, J., Avril, T. & Chevet, E. (2020) Local intracerebral Inhibition of IRE1 by MKC8866 sensitizes glioblastoma to irradiation/chemotherapy in vivo, 841296.
7. Voutetakis, K. D., D.; Vlachavas, E-I., Leonidas, DD.; Chevet, E.; Chatzioannou, A. (In preparation) RNA sequence motif and structure in IRE1-mediated cleavage.

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Referee #2

Evidence, reproducibility and clarity

The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress.The study is performed on the high level and supported by all necessary controls. They find the connection between IRE1 and CD95 and show that it might play a role in Cd95 signaling and attenuate CD95-mediated cell death.

Further, the correlation between CD95 expression and IRE is found in tumors. Importantly the authors find out the connection between XBP1 and CD95 expression, which was not reported to date. Hence, it is a very important and highly essential piece of research.

However, I would like to clarify the several issues:

1: Figure 1. Tunicamycin obviously leads to deglycosylation of CD95, which is indicated by the appearance of 35 Kda band. This should be highlighted and commented. 2.Figure 2c, d. The piece of mRNA structure, which is synthesized, might have the different secondary structure and might be not cleaved by IRE, accordingly. More detailed comments have to be provided in this regard. 3. Figure 3. Caspase-8-3 western blots show beautiful effects but did authors see some effects further downstream, eg on PARP1 cleavage? Was cell death ( not viability) measured as well? Can you comment on this? 4. Did the authors looked at the DISC assembly? Did they detect some differences there?

Significance

This is an excellent study. The authors address here for the first time the connection between CD95, which is known as Fas, and ER stress. The role of another DR, TRAIL-R2 has been already reported, but this is the first study uncovering the link between Cd95 system and ER stress.The study is performed on the high level and supported by all necessary controls. This is an important advance for the death receptor field.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Here the authors argue that IRE1 activation has opposite effects on Fas/CD95 expression/stability in a number of contexts, via either RIDD-dependent degradation of Fas mRNA or XBP-1-mediated induction of Fas expression, which led to either increased or decreased sensitivity to Fas-induced apoptosis in a number of settings.

Major issues:

The study is somewhat preliminary and inconclusive in that it is not clear why the RIDD function of XBP-1 appears to predominate in vitro in the cell lines examined, leading to modest increases in Fas expression levels (Figure 1) when IRE1 DN versus IRE1 WT constructs are overexpressed, which is at odds with the latter part of the paper which suggests that inhibition of RIDD led to reduced Fas expression levels. However, this could be due to supraphysiological levels of IRE1 being expressed under overexpression conditions, leading to confounding results. Similarly, when XBP-1s is overexpressed in vitro (Figure 5) the modest increases in CD95/Fas expression and sensitization to Fas-induced cell death may not be fully representative of what would be observed at physiological levels of XBP-1s activation. The in vivo results obtained using an IRE1 RNase inhibitor (MKC8866) contradict the earlier part of the study (as Fas levels decreased and there was protection from Fas-induced liver toxicity) and this could be due to a multitude of reasons. There is no doubt that impacting on IRE1 activity has interesting effects on CD95/Fas expression, which can be up- or -down-regulated, with consequences for cell death induced via engagement of the latter receptor, however, the manuscript does not offer a lot of clarity on which outcome is the predominant one in the context of engagement of the UPR. I have the following suggestions for improvement.

1. The authors should induce ER stress using Thaps, Brefeldin A and Tunicamycin, and explore the effects of doing this on Fas expression levels in the context of silencing endogenous IRE1, XBP-1 and PERK.
2. The authors should explore the effects of silencing of IRE1, XBP-1 and PERK on constitutive Fas expression and the outcome of Fas/CD95-induced apoptosis in cells not experiencing an overt activation of the UPR (i.e. in the absence of Thaps, Brefeldin A or other UPR inducer).
3. The specificity of MKC8866 at the concentration used (30 uM) is unclear. What effect does MKCC have on sensitivity towards Fas-induced apoptosis, similar to the type of experimental set up presented in Figure 5A, 5B?
4. Similarly, what effects does MKC8866 (at 30 uM) have on key Fas pathway determinants, such as Fas, FLIPL, FLIPs, Caspase-8, FADD, RIPK1, A20, CYLD, cIAP-1, cIAP-2 and Bid? There are many points at which MKC8866 could influence the outcome of Fas receptor engagement beyond the receptor itself

Minor issues:

1. For the Fas mRNA cleavage experiments presented in Figure 2, there are no irrelevant control mRNAs to allow the reader to judge whether the effects presented are specific to Fas mRNA or are commonly observed for many mRNAs at these amounts of IRE1 (1 ug, 0.5 ug, which appear high).

Significance

General assessment: this is an interesting study, as there is little knowledge currently concerning how the UPR influences Fas expression or Fas-dependent outcomes. However, the impact of this work is limited by the overexpression approaches used, which could produce artifactual results, as well as the contradictory message of the study.

Advance: the advance reported here is relatively modest and limited in scope due to the inconclusive nature of the data presented.

Audience: this study will be of interests to specialists in the UPR and cell death communities.

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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

Overview:

This manuscript addresses the emerging nexus linking the machinery associated with clathrin endocytosis (clathrin-coated pits; CCPs), flat clathrin lattices (FCLs) and the recently discovered Reticular Adhesions (RAs). This is timely work, reflecting recent foci on the relationship between these structures and systems. Initially clearly identifying reductions in FCL and RA formation on fibronectin, the authors sought to clarify the mechanisms that suppress or prevent FCL / RA formation on this matrix. Knock-down of integrin avb5 (core RA component) suppressed both RA and FCL formation, suggesting a dependence of FCLs on this integrin. This was supported by acute avb5 inhibition via cilengitide (avb5 and avb3 inhibitor) which caused disassembly of existing RAs and FCLs.

Notably, the inverse relationship also appears true, with suppression of core clathrin endocytic machinery (AP2 complex components) being sufficient to greatly deplete RA formation. Supporting this finding, overexpression of a dominant negative-acting protein fragment (AP180 c-terminal fragment) blocked both AP2 localisation to the plasma membrane and RA formation.

To unmix this bi-directional dependence further, the authors used acute cilengitide treatment followed by washout and post-washout incubation to first deplete RAs (cillengitide) and then allow monitoring of en masse RA formation after cilengitide washout. This is an effective experiment, however, the analysis would benefit from greater depth, particularly relating to the order of events. Analysis of this aspect seems central to the thrust of the paper, and some statistical analysis of either static co-occurrence or dynamic ordering in large numbers of FCL / RA structures (i.e. hundreds) would be of value.

The authors next focused on the observation that fibronectin suppressed both FCL and RA structures, by assessing the role of fibronectin-receptor integrin b1. Acute antibody mediated integrin b1 inhibition (mab13) and integrin b1 knock down both confirmed that in cells on fibronectin, suppression of integrin b1 is sufficient to permit massive upregulation of both FCL and RA formation. This is surprising and very interesting. It raises questions about the actual ECM requirements for avb5-mediated reticular adhesion formation. It would seem that fibronectin per se can support very efficient RA / FCL formation, but that normally concurrent integrin b1 activities would suppress this. Given this implication, it would be especially important to clarify the purity of FN ECM coating (as explored in questions 1-3 below) at the time of imaging. The discussion addresses a number of topics, and proposes a mechanistic model to explain the results presented. I don't find the mechanism very convincing, as the directionality of the dependence between FCLs and RAs is not clearly delineated by the experiments presented, in my opinion. That there is co-dependence is convincingly shown, but whether there is directionality, and what order of events underpins FCL then RA or RA then FCL formation, is unclear. Nonetheless, the evidence presented does generally support the idea of a shift in the way we consider the role of endocytic machinery in adhesion regulation, from a disassembly only related function to additional functions associated with adhesion formation and maintenance. Ideally, the mechanisms around this new assembly / maintenance function would be further delineated here, but regardless, this work does point in the direction of important new questions in this area. Further discussion about the potential role of this interdependent regulatory process in, for example, mitosis, seems unwarranted and should probably be removed.

Questions:

1. A technical question on the replating experiments onto specific matrix proteins; after coating surfaces with the purified ECM components or controls, what media were the cells replated in? Ideally this should be serum-free media, to ensure that the ECM components of FBS / FCS are not immediately added to the purified ECM components. This should be clarified in the methods.
2. Related to above, I cannot see how long cells were plated onto the different ECM conditions. This would be relevant to know and should be clarified because cells will secrete ECM over time and thus the purity of the ECM components addressed is dependent on the length of time cells are incubated and imaged for after attachment.
3. Similarly, it is noted that cells plated on 'glass' support RA formation. It should be clarified what ECM component is then actually responsible for cell adhesion and adhesion complex (RA, FA or other) formation
   • since this requires an ECM component of some type. Presumably, 'on glass' means whatever ECM is either derived from the media used during cell attachment / incubation (if that media contains serum, which is vitronectin rich), or whatever ECM is secreted by the cells themselves over the attachment / incubation period prior to imaging.
4. In the cilengitide washout experiments, the evidence shown in figures seems to suggest that AP2-positive FCLs form in locations where avb5 (probably RAs) are already present, whereas avb5 positive structures do not form from AP2-only structures. Statistical analysis of this pattern (i.e. which protein is present first) would be valuable to address directionality. Notably, in Figure 3G, it appears that avb5 is present and increasing prior to the subsequent arrival of AP2.
   • a. I would suggest that the cilengitide experimental results(3E-G) be shown in a separate figure from the endocytosis inhibition results (3A-D).
5. The integrin b1 inhibition and knock down results are clear and interesting. Clarifying the ECM components present during these experiments would be valuable to interpretation of the paper.

Significance

see above

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Referee #2

Evidence, reproducibility and clarity

Summary:

Hakanpää and colleagues report on the relationship between flat clathrin lattices (FCLs) and reticular adhesions, with FCLs being preposed to nucleate reticular adhesions. Overall the experimental work is high quality and the data are generally well presented.

Major comments:

The Introduction is very brief and doesn't cover the information required to understand the paper. There are three cellular structures to be understood: focal adhesions, reticular adhesions and FCLs. The intro jumps straight into the FCLs (the paper is written from a FCL point of view) but there is no information about the other two structures really particularly the differences between them. Furthermore, there is nothing in the intro about cellular adhesion or why this is even worth studying. The authors should fully revise this Intro, there is much room for improvement!

Fig 4D I could not understand this plot and the legend did not describe it properly. What are the units of FCL frequency? I guess it is FCLs per some distance (10µm?), the images need a scale bar. OK, I read the description in the methods and now I see it is the proportion of total CCSs that are FCLs; so frequency is the wrong term. The legend says that there were 32 videos and there are 32 points on the plot but what we need to know is: where n = 1 cell, what did the FCL frequency do over time? A line is drawn on the graph, no info on what the line is and the fit is poor (R2 < 0.5). The authors should take their series of data points from individual cells and fit curves to each and describe the summary statistics of the parameters of these fits OR average the data and fit to that, using the 1 SD of the data for weighting the fit. Probably more data is required to do any meaningful fitting here. To my eye it looks like FCL frequency goes from 0.3 to a plateau of 0.5 at 10 min and that more timepoints between 0 and 10 min would have been useful.

Fig 3F/G is a nice expt. It looks as though the ITGB5 signal is already creeping up when the AP2 arrives. I agree that they accelerate together, but the prior accumulation of integrin is at odds with the conclusion that AP2/clathrin nucleates the adhesion. This experiment is missing two controls: what is the behaviour of ITGB5 in AP2 negative regions? What happens to both signals in the continued presence of cilengitide? These controls are needed to conclude that AP2 is nucleating the adhesion.

Minor comments:

Fig 6 - several typos - "adaptor proteins" "engagement" "containing"

Significance

Previously, endocytosis (clathrin-mediated) was thought to decrease cellular adhesion by removal of integrins. This paper suggests that the same machinery can be used to build adhesions. This is a surprising conclusion that will be of interest to many cell biologists; the topic of clathrin and adhesion is being actively explored by several labs either from the adhesion or the endocytosis sides. I have been following this topic from a distance and don't know the details of all the published papers, but this paper does seem to add something new over the recent work from Taraska, Sonnenberg, Montagnac, Strömblad.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Hakanpää et al explored the connection between flat clathrin lattices and reticular adhesions and the regulatory mechanism underlying the formation of these two structures in the U2OS cells. The author provided evidence that the composition of the extracellular matrix plays critical roles in their formation and concluded that fibronectin and its receptor, β1 integrin, inhibit the assembly of FCLs and RAs. The author depleted several components of the clathrin mediated endocytosis machinery and could show that it blocked the formation of reticular adhesions.

Major comments

1. In Fig. 1, the author plated U2OS cells on surfaces coated with different ECM components and then measured the frequency of FCLs. How long were the cells allowed to attach to the surfaces before they were imaged? Were the cells serum-starved before seeding? Given the fact that cells attached well even on BSA-coated dishes, I guess the cells were allow to attach and spread for at least overnight. In this case, the ECM components (vitronectin is very abundant in the serum in a concentration of 200-400 ug/mL, fibronectin is another one) in the culture medium would coat the glass surface and this has profound effects on the adhesion status of the cells. Thus, more details of this experiment need to be included and more attention should be pay regarding the data interpretation. In Fig. 1C, it seems like the mere glass surface induced the most FCL formation. However, if the cells are grown on the glass overnight or for days, the major component of the surface would actually be vitronectin and fibronectin (maybe more) rather than glass, thus it is not accurate to say that 'VTN reduced FCL frequency to some extent compared to glass' (Line 67).
2. Line 91-93. It is not accurate to claim that the reticular adhesions are the only type of cellular adhesion maintained during mitosis. In several studies, active integrin β1 are found along the retraction fibers (Dix et al, Dev Cell, 2018; Chen et al, NCB, 2022). The importance of αVβ5 integrin in the spatial memory during mitosis was only shown in the in vitro cell culture. In fact, mice lacking αVβ5 integrin or its ligand vitronectin are both viable and show no major defects during embryonic development (Zheng et al, PNAS, 1995; Huang et al, Mol Cell Biol, 2000), suggesting reticular adhesions are dispensable in cell division in vivo. I advise to change it into'RAs are composed of αVβ5 integrin and are maintained during mitosis in culture'.
3. It has been shown that fibronectin and laminin coating inhibit formation of reticular adhesions (Lock et al, NCB, 2018, Fig. S7). This study should be cited in Fig. 1. I suggest to also include laminin in Fig. 1C to make the list of ECM components more complete.
4. Fig. 5C, the fluorescent intensity of αVβ5 integrin is increased dramatically when integrin β1 was depleted compared to the control shRNA. Although the images were collected in the TIRF mode, it is important to measure αVβ5 and β1 integrin level by immunoblot to confirm the knockdown efficiency of β1 integrin and exclude the possibility that the increase of RA formation is not due to the compensation by upregulation of αVβ5.
5. Antibody-blocking or depletion of β1 integrin both lead to accumulation of FCL and RA formation, indicating that the activation of β1 integrin is critical in the inhibition of FCL and RA. Activation of β1 integrin depends on talin and kindlin, which bind β5 integrin with a much lower affinity. Would depletion of talin or kindlin cause FCL and RA formation similar to inhibition of β1 integrin?

Minor comments

1. In most of the quantifications, only the number of the cells measured were mentioned in the legend. The number of replicate experiments is missing. It should be included in the legend as well.
2. A recent study (https://doi.org/10.1242/jcs.259465) demonstrated the molecular mechanism underlying the localization of αVβ5 integrin in flat clathrin lattices. It should be mentioned in the introduction.

Significance

Although it is not novel that FCLs and RAs share same localization and might actually be the different parts of the same structure (Zuidema et al, JCS, 2022), the observation that inhibiting β1 integrin stimulates FCL and RA assembly is interesting as it indicates the counter-balance between the αVβ5 and α5β1 integrins. It is a pity that the author did not dig deeper into the mechanism underlying this interesting finding, which should greatly increase the impact of this study.

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Reply to the reviewers

Reviewer 1:

We would like to thank you for taking the time to review our manuscript. Your thoughtful and insightful comments have greatly improved the quality of our work. We appreciate your thoroughness in evaluating our study and providing valuable feedback.

Your constructive criticism and suggestions have helped us identify areas that needed further clarification and improvement, and we are grateful for your efforts in guiding us towards a stronger manuscript.

Thank you again for your time and expertise in reviewing our work. We hope that you find our revisions satisfactory and look forward to hearing your thoughts on the revised manuscript.

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

In this manuscript by Sharma and colleagues, the authors investigate the transcriptional regulation of the TAL1 isoforms - that derive from differential promoter usage and/or alternative splicing - and the contribution of TAL1 long and TAL1 short protein isoforms in normal haematopoietic development and disease.

The study suggests that TAL1 transcript isoforms are fine-tuned regulated. By using CRISPR/Cas9 techniques, the authors show that the enhancer -8 (MuTE) and enhancer -60 differentially regulate the TAL1 isoforms. Whether the remaining enhancers at the TAL1 locus (see Zhou Y et al, Blood 2013) also differentially regulate TAL1 transcription remains to be elucidated.

The authors found that TAL1 short isoform interacts strongly with T-cell specific transcription factors such as TCF3 and TCF12, as compared to TAL1 long isoform. TAL1 short shows an apoptotic transcription signature and it fails in rescuing cell growth as compared to TAL1 long in T-ALL. In addition, TAL1 short promotes erythropoiesis.

Lastly, the authors suggest that altering TAL1 long and TAL1 short protein isoforms ratio could have a potential therapeutic application in disease, but further studies are needed. *

We would like to thank you for your time and effort in reviewing our manuscript. Your constructive feedback and insightful comments have been immensely valuable in improving the quality of our work. Your expertise in the field has undoubtedly contributed to the credibility and accuracy of this research. In addition, your dedication and attention to detail have been instrumental in shaping the final version of the manuscript.

* I have a number of comments: Figure 1 It was not mentioned that MOLT4 cells also have MuTE. Do Jurkat and MOLT4 share a similar profile in terms of TAL1 transcript isoforms? It would have been very interesting to see whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells (e.g RPMI-8402). In these cells, TAL1 activation results from a deletion that fuses the 5' non-coding region of SIL with TAL1. *

Thank you for your comment. We apologize for the confusion regarding the MOLT4 cells in our analysis. We have now updated the manuscript to explicitly mention the presence of MuTE in MOLT4 cells (Line 127). Additionally, we agree that it would be interesting to investigate whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells, such as RPMI-8402. To address this point, we have included the CCRF-CEM cell line that harbors the SIL-TAL1 recombination in our analysis. We have updated the manuscript with these new findings (Fig. 1C&D and S1A&B). Thank you for bringing this to our attention.

Figure 2 * It is not very clear how the expression of the short isoform delta exon 3 is quantified. Detailed information and a schematic of the primer location could be helpful. *

Thank you for your comment. We apologize for any confusion regarding the quantification of the expression of the short isoform (delta exon 3). The detailed information and schematic of the primer location can be found in Supplementary Figure 2B. We have included the location of each primer used in real-time PCR analysis for the quantification of all TAL1 isoforms. We hope this additional information will address your concerns.

* The results on Figure 2 derive from complex Cas9/CRISPR experiments. A schematic representation showing the location of the following elements is missing: CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer. *

We agree that providing a schematic representation of the Cas9/CRISPR experiments would be helpful for better understanding the data in Figure 2. We have now included a detailed schematic of the location of the CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer in Supplementary Figure 2E. We believe this new figure will provide a clearer overview of the experiments performed and will aid in the interpretation of the results.

* Are the levels of dCas9-p300 WT and dCas9-p300 MUT comparable in transfected HEK 293 cells? Were those possibly measured by qPCR or Western Blot? Why the authors chose to use 293T cells for the CTCF del as the enhancer usage around the locus must be so different from haematopoietic cells. *

Thank you for your question. We have added Western Blot analysis to compare the levels of dCas9-p300 WT and dCas9-p300 MUT in transfected HEK293T cells, as suggested. The results are presented in Supp. Fig. S2H.

Regarding the choice of HEK293T cells for the CTCF deletion experiment, we selected this cell line for its low expression of TAL1, which contributes to a high dynamic range when tethering p300 core to a closed chromatin region. We have added a clarification of our rationale for using HEK293T cells in the revised manuscript (Lines 177-8). Thank you for your valuable feedback.

* Is CPT - camptothecin? A control gene that is sensitive to CPT treatment would ensure the inhibitor is working. *

Thank you for your comment. Indeed, CPT stands for camptothecin, and this information is already included in the methods section. We have also added this information to the results section (Line 221) to make it clearer.

Regarding the suggestion to use a control gene sensitive to CPT treatment, we agree that this could be a useful addition to our experimental design. To address this, we have quantified the amount of TAL1 transcript to an endogenous control which is not transcribed by RNA Polymerase II (RNAPII) (18s rRNA). As a positive control, we compared Cyclo A, our endogenous control, to 18s rRNA and observed a reduction (Supp. Fig. S2K). This allows us to confidently conclude that the inhibitor is working as intended.

Thank you for bringing up this point, and we hope that our response addresses your concern.

*

In supplementary Figure 2D, the reduction in expression in Jurkat Del-12 is restricted to TSS2. There is no reduction in TAL1 TSS1 and TAL1 TSS4 (this is not clear from the result description section). As seen, these isoforms are upregulated and that could suggest a compensatory mechanism mediated by alternative promoter activation. The fact that Jurkat Del-12 express TAL1 from MSCV-TAL1 could also suggest that TSS1 and TSS4 are upregulated by TAL1 or indirectly, by other members of the TAL/LMO complex (see Sanda T et al, Cancer Cell 2012) *

Certainly, we appreciate your feedback. Supplementary Figure 2D indeed shows that the MuTE enhancer has a differential effect on the promoters, and we have now included this in the text of the manuscript. Regarding the TAL1-long isoform, while MSCV-TAL1 in the Jurkat Del-12 cell line does give rise to this isoform, our results from Figure 3A did not find TAL1-long to have a differential effect on TAL1 promoters. It is important to note that the experiment conducted was an exogenous construct in HEK293T cells, which has its limitations. Thus, the speculation that TAL1-long drives the result in supplementary Figure 2D is possible, and we have added this to the text. Thank you for bringing up this important point (Lines 167-9).

Figure 3 * A. Are the levels of TAL1 short cDNA and TAL1 long cDNA comparable in the co-transfection luciferase experiments? The overexpression of the isoforms does not reflect the endogenous expression levels in cell lines where one of the isoforms is more predominantly expressed (e.g Jurkat cells express low levels of TAL1 short). *

Thank you for your comment. To address your concern, we have added real time (Supp. Fig. S3A) as well as Western blot in a new figure (Supp. Fig. S3B) to show that the levels of TAL1-short and TAL1-long cDNA are comparable in the co-transfection luciferase experiments. Additionally, we observed a very low amount of endogenous TAL1 isoforms in the cell line (Supp. Fig. S3A&B), which was below detection using these methods. This suggests that the effect of the endogenous TAL1 in this cell line is low. We appreciate your feedback, and we hope this additional information addresses your concern.

* Figure 4 Are the levels of flag-TAL1 long and flag-TAL1 short comparable? The levels of expression could explain the low intensity signal for TAL1 long. *

Thank you for your insightful comment. Indeed, the issue of isoform quantification is critical in understanding the functional differences between TAL1-short and TAL1-long. To address this concern, we performed careful quantification of the isoforms and made sure that the amount was equal or slightly in favor of TAL1-long before conducting the experiments in this manuscript. We have also added a Western blot in Supp. Fig. S3A and real time in Supp. Fig. S3B showing the similar amount of the two isoforms. Furthermore, in Figure 4A, we provided the amount of each isoform in the input section, showing a higher amount of TAL1-long. This strengthens our result, which shows that TAL1-short binds stronger to TCF-3 and 12. Protein levels for ChIP-seq experiment (Fig. 4B-H) is now in Supp. Fig. S4B. We thank you for bringing up this important point, and we hope that our additional data and clarifications have addressed your concern

*Is there any reason for not performing a depletion of endogenous TAL1 prior to the ChIP seq flag experiment? *

Thank you for your comment. In our experience, infecting Jurkat cells with shRNA or an expressing vector systems can induce some cellular stress, and we did not want to add additional stress to the cells by depleting endogenous TAL1. Since we immunoprecipitated using a Flag-tagged protein, we did not see a need to deplete the endogenous TAL1 protein. However, in our RNA-seq experiment, depletion of endogenous TAL1 was critical, and we have added this additional step in this experiment.

* Could the authors speculate about MAF motif enrichment in both isoforms and not in TAL1-total? *

Thank you for bringing up this interesting point. It is worth noting that while all ChIP-seq experiments were performed in Jurkat cells, not all of them were conducted by us. In particular, ChIP-seq of TAL1 total was performed by Sanda et al., 2012, using an endogenous antibody against both isoforms, whereas we conducted ChIP-seq for TAL1-short and TAL1-long using a FLAG tag antibody in cells expressing each of the isoforms. Therefore, the different conditions of these experiments may have contributed to the observed MAF motif enrichment in both isoforms and not in TAL1-total. While we cannot provide a definitive explanation, we speculate that the overexpression of the isoforms or the presence of the FLAG tag may have facilitated the detection of the MAF motif. We have added this discussion to the manuscript to acknowledge and address this interesting observation (Lines: 307-8).

1. Sanda et al., Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia. Cancer Cell 22, 209-221 (2012).

   * Do TAL1 long and TAL1 short recognise the same DNA motif? *

This is indeed a very interesting question but a difficult one to answer since TAL1 does not bind to the DNA alone but in a complex. In this situation, the ChIP-seq de-novo binding results suggest motifs that could be recognized by TAL1 or any of its complex partners. Using previous data, TAL1’s binding motif is CAGNTG (Hsu et al., 1994), while this motif was not identified in our analysis of the TAL1-total or FLAG-TAL1-long ChIP-seq results, we did, however, identify this sequence in FLAG-TAL1-short ChIP-seq results (p value=1e-93). We predict that this discrepancy is due to the complex nature of transcription factors binding and the fact that the ChIP-seq results were not all done in the same way. We have now added this to the discussion (Lines: 419-25).

1. L. Hsu et al., Preferred sequences for DNA recognition by the TAL1 helix-loop-helix proteins. Mol Cell Biol 14, 1256-1265 (1994).

* Figure 6 In A and B, are the levels of flag-TAL1 long and flag-TAL1 short in transduced K562 comparable? In C and D, are the TAL1 levels reduced at the protein level?*

Thank you for your question. To answer your question, we added Western Blot analysis to show the comparable levels of flag-TAL1-long and flag-TAL1-short in transduced K562 cells (Supp. Fig. S6C). In Figure 6C and D, we also added Western Blot analysis to show the reduction in TAL1 protein levels upon shRNA-mediated knockdown(Supp. Fig. S6B).

* Minor points: Figure 1 A. Include a scale bar *

To address this, we included coordinates of the components of the gene marked in the figure.

* C. Loading control such as GAPDH is missing in the Western Blot. Are CUTLL cells the same as CUTTL-1? *

We added loading controls as requested now supplementary Fig. 1C, S2C, S3A, S4B, S6B&C. Yes, CUTLL is the same as CUTLL-1 we have now fixed this in the text (Line 120).

D. Adjust scale of the CHIP seq tracks in K562 cells in order to see the peak summit. *Include genome build *

Thank you for your comment. We have adjusted the scale of the ChIP-seq tracks in K562 cells as suggested to improve the visualization of the peak summit. However, one of the peaks still had a much higher signal and the summit is still missing from this particular peak. To address this, we have added a new figure in the supp. Fig. S1C materials where we adjusted the peak to show the summit. Please note that in this track, the chromatin structure at the enhancers is missing, and therefore, we did not include it in the main figure. Thank you for bringing this to our attention.

We have added a genome build hg19 to the figure legend.

* In supplementary Figure 1B, the symbol scheme is not clear *

Thank you for this note, we have replaced the figure and added text to make it clearer.

* Figure 2 A & C. Remove 'amount' from the Y axis. Is the total mRNA amount calculated as % of the reference genes? It could be specified on the y axis or figure legend. *

We have removed the word "amount" from the Y axis as requested. Total mRNA amount is normalized relative to the reference genes (∆∆Cq) by Bio-Rad's CFX Maestro software (version 2.3) according to the formula:

where:

• RQ = Relative Quantity of a sample
• Ref = Reference target in a run that includes one or more reference targets in each sample
• GOI = Gene of interest (one target)

* In supplementary Figure 2C, a loading control is missing.*

We have added alpha-tubulin to this figure.

* Figures 4, 5 and 6 Size of the figures should be increased. *

We have increased the figure size as suggested. *

Reviewer #1 (Significance (Required)): The study from Sharma and colleagues is novel and it extends the knowledge on TAL1 regulation and the role of TAL1 in development and disease. Although the study suggests that there is a correlation between enhancers, chromatin mark deposition at exons and regulation of alternative splicing, the mechanistic link is not fully elucidated.*

To further elucidate the mechanistic link between the MuTE enhancer, broad H3K4me3 modification spanning 7.5 Kbp from TAL1 promoter 1 to promoter 5 (as shown in Fig. 1D), and alternative splicing, we conducted experiments where we manipulated KMT2B, a component of the SET1/COMPASS complexes responsible for methylating H3K4. Our findings indicate that silencing KMT2B in Jurkat cells led to a significant 30% increase in TAL1-∆Ex3 (Fig. 2H and Supp. Fig. S2I&J). These results contribute to a more comprehensive understanding of the molecular mechanisms underlying TAL1 alternative splicing regulation.

The findings on TAL1 short protein are interesting but the data on TAL1 long lacks some refinement so then robust conclusions can be drawn. * The experimental data lacks a few controls. The text is clear and prior studies could be better referenced. *

We have made an effort to better reference out manuscript.

* As TAL1 is a very crucial transcription factor oncogene in T-ALL, the study is important as it addresses a very relevant question in the field that is the regulation of the transcription of TAL1 and the functional relevance of both TAL1 short and TAL1 long isoforms. *

Reviewer 2: *

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

Summary: Sharma et al. thoroughly characterized the regulation of TAL1 by mapping the use of its five promoters and enhancers, which together transcribe five transcripts, coding for two protein isoforms. For that purpose the authors used few cell lines: Jurkat as a T-ALL cell line, chronic myeloid leukemia (CML) cell line K562 and HEK293T with low TAL1 expression, as well as CutLL and MOLT4. They profiled the chromatin marks H3K27ac and H3K4me3 at the TAL1 locus, and show that when a the -8 enhancer is compromised tha chromatin marks change, and not only the expression level of TAL1 is reduced, the level of exon 3 skipping is increased. When the -60 enhancr was activated, TAL1 expression increased, and exon 3 skipping was reduced. Those findings indicate that in tal1, transcription and alternative splicing are co-regulated, independent of RNAPII. The authors also show that as an autoregulator, TAL1-short has a preference to TSS1-3 of TAL1, which is not shared by TAL1-long, and that each of the 5' UTR affect Tal1 expression differently. TAL1-short binds E-proteins more strongly than TAL1-long, binds many more sites than TAL1-long and stronger, and each isoform has unique set of targets. Finally, the authors set to identify the different functions of the TAL1 isoforms, and showed that Tal1-short slows cell growth and leads to TAL1-short but not TAL1-long leads to exhaustion of hematopoietic stem cells and promotes differentiation into erythroids. This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

Thank you for taking the time to carefully review our manuscript on the regulation and function of TAL1 isoforms. We appreciate your positive feedback on our comprehensive characterization of TAL1 regulation using chromatin profiling and isoform-specific ChIP-seq. We are glad that you found our findings on the co-regulation of transcription and alternative splicing, as well as the isoform-specific functions of TAL1, to be of interest.

We also appreciate your suggestions to improve the readability of the manuscript and have made the necessary revisions accordingly. Your feedback has been invaluable in strengthening the quality of our work, and we are grateful for your contribution to the scientific community.

* Minor comments: Add explicitly the motivation for choosing the cell line in each part. *

We have added motivation (Lines: 157-8, 177-8, 192-194, 235-6 text that was on the previous version: 192-194, 379-80).

* Figure 1 - Consider marking the promoter numbers and the enhancers names in the same names as in text (-8,-60 etc.), to make it easier for the readers to understand which enhancers is being discussed. *

This in a very important point. We have added the numbering to Figure 1D and Supp. Fig. S2A, B & E.

*P5, P18 - ProtParam is only a prediction tool, and does not supply an experimental measurement, as may be assumed from text. Please rephrase accordingly. *

The words “prediction tool” were added in the indicated paragraphs (Lines 115 and 427).

* Figure 2B/D - y axis label unclear, not explained in text. In accordance, unclear if the change is in the amount of RNA, or the ratio between the long and short variants. *

Thank you for this comment. We greatly appreciate your feedback and suggestions. To make our calculations, which are the norm in the splicing field, clearer, we have now added text to Figure 4 and provided more detailed explanations in lines 670-73. We hope that these modifications will improve the clarity and comprehensiveness of our manuscript.

*Consider removing the bars and increasing the dots, to make the graphs cleaner. *

We removed the bars throughout the manuscript for a cleaner look.

* P8 - The term '5C' may require more explanation, depending on target audience. *

We have added text to explain the technique (Lines 179-81).

* Figure 3 - the trend is that TAL1-short promotes transcription from all five TSSs. However, only in TSS1-3 is the difference significant, but the difference between the long and short forms is not significant. It is unclear if "The mean of three independent experiments done with three replicates" means overall there are three replicates per condition or nine. Please rephrase to clarify. *

Thank you for your comment. To clarify, we want to state that each biological experiment was done in three technical replicates, resulting in a total of nine replicates for each condition. We apologize for any confusion and have now rephrased to: The mean was calculated from three independent biological experiments, each performed with three technical replicates (Lines: 696 and 699).

*Fig 4 A - it seems that many of the sites bound by Tal1 total are not bind by either Tal1-short or Tal1-long. Indeed very little overlap between Tal1-short and Tal-1-total is seen in Fig 4I as well. It seems Tal1-long has very few peaks. Consider adding a discussion of possible reasons. *

We agree that these findings are noteworthy and warrant further discussion. We added text to the discussion section to explore potential reasons for these observations (Lines 416-25).

* Fig 4c - it is hard to distinguish the different lines. Consider a more clear visualization. Also, some text is in a font size too small to read. *

We have changed the format of the figure and took out the input data from the main figure to help the visualization. The input data appear in the Supp. Fig. S4C.

* Fig 4 D-H - will be useful to see the numbers, not just the % divided by %. *

A table with the specific numbers can be found in Supp Figure 4F-J.

* Fig 4 legend - 'I&L' possibly means 'I-L'. P14 - refer to where the results of the 'validation using real-time PCR' are shown. P16 - symbol replaced by an empty rectangle 20 􀀀M *

Thank you for these valuable comments, we have fixed/added these in the manuscript.

* Figure 6D - Y axis value seem strange (fold change relative to day 0 should be 1 at day 0). Consider different Y axis label for C and D to clarify. *

Thank you for this comment, we have changed the y-axis to: Fold-change relative to day 1.

* P18 - It is unclear which "two isoforms with posttranslational modifications which affected the migration rate of the protein (Fig. 1C)" were shown. Only two isoforms are mentioned throughout the paper. *

We have added text to clarify we are referring to TAL1-short and long (Lines 409-10).

*

P18 - "Our ChIP-seq results suggest that the isoforms bind at the same location (Fig. 4B)." - in 4B it seems most of TAL1-short bound positions are not bound by TAL1 long. Please clarify. *

* Worth mentioning that the Total TAL1 is taken from Jurkat cells but from a different experiment. * We have changed the statement and added the text referring to the experiments done independently (Lines 422-3).

*

Reviewer #2 (Significance (Required)): This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability. *

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Referee #2

Evidence, reproducibility and clarity

Summary:

Sharma et al. thoroughly characterized the regulation of TAL1 by mapping the use of its five promoters and enhancers, which together transcribe five transcripts, coding for two protein isoforms. For that purpose the authors used few cell lines: Jurkat as a T-ALL cell line, chronic myeloid leukemia (CML) cell line K562 and HEK293T with low TAL1 expression, as well as CutLL and MOLT4.

They profiled the chromatin marks H3K27ac and H3K4me3 at the TAL1 locus, and show that when a the -8 enhancer is compromised tha chromatin marks change, and not only the expression level of TAL1 is reduced, the level of exon 3 skipping is increased. When the -60 enhancr was activated, TAL1 expression increased, and exon 3 skipping was reduced. Those findings indicate that in tal1, transcription and alternative splicing are co-regulated, independent of RNAPII. The authors also show that as an autoregulator, TAL1-short has a preference to TSS1-3 of TAL1, which is not shared by TAL1-long, and that each of the 5' UTR affect Tal1 expression differently. TAL1-short binds E-proteins more strongly than TAL1-long, binds many more sites than TAL1-long and stronger, and each isoform has unique set of targets.

Finally, the authors set to identify the different functions of the TAL1 isoforms, and showed that Tal1-short slows cell growth and leads to TAL1-short but not TAL1-long leads to exhaustion of hematopoietic stem cells and promotes differentiation into erythroids.

This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability.

Minor comments:

Add explicitly the motivation for choosing the cell line in each part.

Figure 1 - Consider marking the promoter numbers and the enhancers names in the same names as in text (-8,-60 etc.), to make it easier for the readers to understand which enhancers is being discussed.

P5, P18 - ProtParam is only a prediction tool, and does not supply an experimental measurement, as may be assumed from text. Please rephrase accordingly.

Figure 2B/D - y axis label unclear, not explained in text. In accordance, unclear if the change is in the amount of RNA, or the ratio between the long and short variants. Consider removing the bars and increasing the dots, to make the graphs cleaner.

P8 - The term '5C' may require more explanation, depending on target audience.

Figure 3 - the trend is that TAL1-short promotes transcription from all five TSSs. However, only in TSS1-3 is the difference significant, but the difference between the long and short forms is not significant. It is unclear if "The mean of three independent experiments done with three replicates" means overall there are three replicates per condition or nine. Please rephrase to clarify.

Fig 4 A - it seems that many of the sites bound by Tal1 total are not bind by either Tal1-short or Tal1-long. Indeed very little overlap between Tal1-short and Tal-1-total is seen in Fig 4I as well. It seems Tal1-long has very few peaks. Consider adding a discussion of possible reasons.

Fig 4c - it is hard to distinguish the different lines. Consider a more clear visualization. Also, some text is in a font size too small to read.

Fig 4 D-H - will be useful to see the numbers, not just the % divided by %.

Fig 4 legend - 'I&L' possibly means 'I-L'.

P14 - refer to where the results of the 'validation using real-time PCR' are shown.

P16 - symbol replaced by an empty rectangle 20 􀀀M

Figure 6D - Y axis value seem strange (fold change relative to day 0 should be 1 at day 0). Consider different Y axis label for C and D to clarify.

P18 - It is unclear which "two isoforms with posttranslational modifications which affected the migration rate of the protein (Fig. 1C)" were shown. Only two isoforms are mentioned throughout the paper.

P18 - "Our ChIP-seq results suggest that the isoforms bind at the same location (Fig. 4B)." - in 4B it seems most of TAL1-short bound positions are not bound by TAL1 long. Please clarify. Worth mentioning that the Total TAL1 is taken from Jurkat cells but from a different experiment.

Significance

This paper used for the first time TAL1 isoform specific ChIP-seq, which enable accurate definition of isoform-specific targets in Jurkat cells. They demonstrated an interaction between choice of TSS and alternative splicing, and isoform specific functions. Given the clinical importance of TAL1 and the meticulous work performed to characterize its isoform specific regulation and function, I find this manuscript of interest, and only have minor suggestions to improve readability.

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

In this manuscript by Sharma and colleagues, the authors investigate the transcriptional regulation of the TAL1 isoforms - that derive from differential promoter usage and/or alternative splicing - and the contribution of TAL1 long and TAL1 short protein isoforms in normal haematopoietic development and disease.

The study suggests that TAL1 transcript isoforms are fine-tuned regulated. By using CRISPR/Cas9 techniques, the authors show that the enhancer -8 (MuTE) and enhancer -60 differentially regulate the TAL1 isoforms. Whether the remaining enhancers at the TAL1 locus (see Zhou Y et al, Blood 2013) also differentially regulate TAL1 transcription remains to be elucidated.

The authors found that TAL1 short isoform interacts strongly with T-cell specific transcription factors such as TCF3 and TCF12, as compared to TAL1 long isoform. TAL1 short shows an apoptotic transcription signature and it fails in rescuing cell growth as compared to TAL1 long in T-ALL. In addition, TAL1 short promotes erythropoiesis.

Lastly, the authors suggest that altering TAL1 long and TAL1 short protein isoforms ratio could have a potential therapeutic application in disease, but further studies are needed.

I have a number of comments:

Figure 1

It was not mentioned that MOLT4 cells also have MuTE. Do Jurkat and MOLT4 share a similar profile in terms of TAL1 transcript isoforms? It would have been very interesting to see whether the TAL1 transcript isoforms are similar in SIL-TAL1+ cells (e.g RPMI-8402). In these cells, TAL1 activation results from a deletion that fuses the 5' non-coding region of SIL with TAL1.

Figure 2

It is not very clear how the expression of the short isoform delta exon 3 is quantified. Detailed information and a schematic of the primer location could be helpful.

The results on Figure 2 derive from complex Cas9/CRISPR experiments. A schematic representation showing the location of the following elements is missing: CTCF sites, CTCF gRNA target region, dCas9-p300 gRNA target region and -60 enhancer. Are the levels of dCas9-p300 WT and dCas9-p300 MUT comparable in transfected HEK 293 cells? Were those possibly measured by qPCR or Western Blot? Why the authors chose to use 293T cells for the CTCF del as the enhancer usage around the locus must be so different from haematopoietic cells.

Is CPT - camptothecin? A control gene that is sensitive to CPT treatment would ensure the inhibitor is working.

In supplementary Figure 2D, the reduction in expression in Jurkat Del-12 is restricted to TSS2. There is no reduction in TAL1 TSS1 and TAL1 TSS4 (this is not clear from the result description section). As seen, these isoforms are upregulated and that could suggest a compensatory mechanism mediated by alternative promoter activation. The fact that Jurkat Del-12 express TAL1 from MSCV-TAL1 could also suggest that TSS1 and TSS4 are upregulated by TAL1 or indirectly, by other members of the TAL/LMO complex (see Sanda T et al, Cancer Cell 2012)

Figure 3

A. Are the levels of TAL1 short cDNA and TAL1 long cDNA comparable in the co-transfection luciferase experiments? The overexpression of the isoforms does not reflect the endogenous expression levels in cell lines where one of the isoforms is more predominantly expressed (e.g Jurkat cells express low levels of TAL1 short).

Figure 4

Are the levels of flag-TAL1 long and flag-TAL1 short comparable? The levels of expression could explain the low intensity signal for TAL1 long. Is there any reason for not performing a depletion of endogenous TAL1 prior to the ChIP seq flag experiment? Could the authors speculate about MAF motif enrichment in both isoforms and not in TAL1-total? Do TAL1 long and TAL1 short recognise the same DNA motif?

Figure 6

In A and B, are the levels of flag-TAL1 long and flag-TAL1 short in transduced K562 comparable? In C and D, are the TAL1 levels reduced at the protein level?

Minor points:

Figure 1

A. Include a scale bar C. Loading control such as GAPDH is missing in the Western Blot. Are CUTLL cells the same as CUTTL-1? D. Adjust scale of the CHIP seq tracks in K562 cells in order to see the peak summit. Include genome build In supplementary Figure 1B, the symbol scheme is not clear

Figure 2

A & C. Remove 'amount' from the Y axis. Is the total mRNA amount calculated as % of the reference genes? It could be specified on the y axis or figure legend.

In supplementary Figure 2C, a loading control is missing.

Figures 4, 5 and 6

Size of the figures should be increased.

Significance

The study from Sharma and colleagues is novel and it extends the knowledge on TAL1 regulation and the role of TAL1 in development and disease. Although the study suggests that there is a correlation between enhancers, chromatin mark deposition at exons and regulation of alternative splicing, the mechanistic link is not fully elucidated. The findings on TAL1 short protein are interesting but the data on TAL1 long lacks some refinement so then robust conclusions can be drawn.

The experimental data lacks a few controls. The text is clear and prior studies could be better referenced.

As TAL1 is a very crucial transcription factor oncogene in T-ALL, the study is important as it addresses a very relevant question in the field that is the regulation of the transcription of TAL1 and the functional relevance of both TAL1 short and TAL1 long isoforms.

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Reply to the reviewers

We would like to thank the reviewers for their extensive review of our manuscript and constructive criticism. We have attempted to address the points raised in the reviewer's comments and have performed additional experiments and have edited the text of the manuscript to explain these points. Please see below, our point-by-point response to the reviewer’s comments. In the submitted revised manuscript, some figure numbers have changed from the prior reviewed version.

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

In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

Reviewer #1 (Significance (Required)):

Figure 1 (plus related Supplemental figures): • There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?

According to Flybase, Mrj has 8 predicted isoforms of which four are of 259 amino acids (PA, PB, PC, and PD), 3 are of 346 amino acids (PE, PG, and PH) and one is of 208 amino acids (PF) length (Supplementary data 1). We isolated RNA from flyheads and used this in RT-PCR experiments to check which Mrj isoforms express in the brain. Since both the 346 amino acid (1038 nucleotide long) and 259 amino acids (777 nucleotides long) form, which we refer to as the long and middle isoform, has the same N and C terminal sequences we used the same primer pair for this, but on RT-PCR the only amplicon we got corresponds to the 259 amino acid form. For the 208 amino acids (624 nucleotides long) form we designed a separate forward primer and attempted to amplify this using RT-PCR but were unable to detect this isoform also. This data is now presented in Supplemental Figure 4B. Since the only isoform detected from fly head cDNA corresponded to the 259 amino acid form, we think this is the predominant isoform of Mrj expressing in Drosophila and this is what is in our DnaJ library and what we have used in all our experiments here. This is also the same isoform described in previous papers on Drosophila Mrj (Fayazi et al, 2006; Li et al, 2016b). For this 259 amino acid Mrj isoform, we see its expression in both the nucleus and cytoplasm (Supplemental Figure 4C). As the long 346 AA fragment was undetectable in the brain, it was not feasible to address the reviewer’s point of using the long and short forms of Mrj for IP with Orb2. However, we have performed IP of human CPEB2 (hCPEB2) with the long and short isoforms of human DnaJB6 and have detected interaction of hCPEB2 with both the long and short isoforms of DnaJB6 (Supplemental Figure 6E).

• I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.

We have performed BlastP analysis of CG4164, CG9828, CG7130, DroJ2, and Tpr2 protein sequences against Human proteins. Based on this we have listed the highest-ranking candidate identified here for each of these genes.

Drosophila Gene

Human gene

Query cover

Percent identity

E value

CG4164

dnaJ homolog subfamily B member 11 isoform 1

98 %

62.96%

2e-150

CG9828

dnaJ homolog subfamily A member 2

92%

39.41%

3e-84

CG7130

dnaJ homolog subfamily B member 4 isoform d

56%

69.44%

2e-30

Tpr2

dnaJ homolog subfamily C member 7 isoform 1

93%

46.22%

6e-139

DroJ2

dnaJ homolog subfamily A member 4 isoform 2

98%

60.60%

2e-169

In the context of the chimeric Sup35-based assay where Orb2A’s Prion-like domain (PrD) is coupled with the C-terminal domain of Sup35, the only protein which could convert Orb2A PrD-Sup35 C from its non-prion state to prion state was Mrj. Within the limitations of this heterologous-system based assay, the other 5 DnaJ domain proteins as well as the Hsp70’s were unable to convert the Orb2A PrD from its non-prion to prion-like state. What these other 5 interacting JDP proteins are doing through their interaction with Orb2A and if they are even expressing in the Orb2 relevant neurons will need to be tested separately and will be the subject of our future studies.

• The data in panels H, I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

On the reviewer’s point to address what is the actual physicochemical change occurring here, we will need to develop methods to purify the Orb2 oligomers in significant quantities to examine and distinguish if they are of gel or real amyloid-like nature. Currently, within the limitations of our ongoing work, this has not been possible for us to do and we can attempt to address this in our future work. Cryo-EM derived structure of endogenous Orb2 oligomers purified from a fly head extract from 3 million fly heads, made in the TritonX-100 and NP-40 containing buffer, the same buffer as what we have used here for the first soluble fraction, showed these oligomers as amyloids (Hervas et al, 2020). If the oligomers extracted using 0.1% and 2% SDS are structurally and physicochemically different, within the limitations of our current work, had not been possible to address.

The other point raised by the reviewer is, if in the absence of Mrj (in the context of Figure 4 of our previously submitted manuscript), a part of Orb2 is not solubilized due to us using a lower 0.1% SDS for extraction. To address this, we attempted to see how much of leftover Orb2 is remaining in the pellet after extraction with 0.1 % SDS. Towards this, according to the reviewers’ suggestion, we used a higher 2% SDS containing buffer to resuspend the leftover pellet after 0.1% SDS extraction, and post solubilisation ran all the fractions in SDD-AGE. We did this experiment with both wild-type and Mrj knockout fly heads. Under these different extractions, we first observed while there is more Orb2 in the soluble fraction (Triton X-100 extracted) of Mrj knockout, this amount is reduced in both the 0.1% SDS solubilized and 2% SDS solubilized fractions. So, even though there is leftover Orb2 after 0.1% SDS extraction, which can be extracted using 2% SDS, this amount is reduced in Mrj knockout. The other observation here is the Orb2 extracted using 2% SDS shows a longer smear in comparison to the 0.1% SDS extracted form suggesting a possibility of more and higher-sized oligomers present in this fraction. Since we do not have the exact physicochemical characterization of these oligomers detected with 0.1% and 2% SDS-containing buffer, we are not differentiating them by using the terms gels and real amyloids, but refer to them as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. Overall, our observations here suggest in absence of Mrj, both of these kinds of Orb2 oligomers are decreased and so Mrj is most likely promoting the formation of Orb2 oligomers. It is possible that the 0.1% SDS soluble Orb2 oligomers gradually accumulate and undergo a further transition to the 2% SDS soluble Orb2 oligomers, so if in absence of Mrj, the formation of the 0.1% SDS soluble Orb2 oligomers is decreased, the next step of formation of 2% SDS soluble Orb2 oligomers also be decreased. This data is now presented in Figure 5H, I and J).

On the other possibility raised by the reviewer that Mrj can prevent the oligomeric state of Orb2 to become an irreversible non-functional amyloid, we think it is still possible for Mrj to do this but this could not be tested under the present conditions.

• It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.

We have now changed mentions of human Mrj to DNAJB6. We apologize for missing the Hageman et al 2010 reference and have now cited this reference in the context of Mrj oligomerization.

• It is striking to see that Mrj co-Ips with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.

In IP experiments we notice Mrj interacts with Hsp70Aa and Hsc70-4 but not with Hsc70-1 and Hsc70Cb. In our previously submitted manuscript, we realized we made a typo on the figure, where we referred to Hsp70Aa as Hsc70Aa. We have corrected this in the current revised manuscript. On the crosslinking point raised by the reviewer, we reviewed the published literature for studies of immunoprecipitation experiments which showed an interaction between DnaJB6 and Hsp70. We noted while one of the papers (Kakkar et al, 2016) report the use of a crosslinker in the experiment which showed an interaction between GFP-Hsp70 and V5-DnaJB6, in another two papers the interaction between endogenous Mrj and endogenous Hsp/c70 (Izawa et al, 2000) and Flag-Hsp70 and GFP-DnaJB6 (Bengoechea et al, 2020) could be detected without using any crosslinker. Our observations of detecting the interaction of Mrj with Hsp70Aa and Hsc70-4 in the absence of a crosslinker are thus similar to the observations reported by (Izawa et al, 2000; Bengoechea et al, 2020).

On the point of if Mrj is a substrate for Hsp70aa and Hsc70-4 and not a co-chaperone, we feel in the context of this manuscript, since we are focussing on the role of Mrj in the regulation of oligomerization of Orb2 and in memory, the experiment with HPD motif mutant is probably not necessary here. However, if the reviewers suggest this experiment to be essential, we can attempt this experiment by making this HPD motif mutant.

• The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

Figure 2 and Supplemental Figure 4 discuss the effect of Mrj on Htt aggregation. We have used 2 different Htt constructs here. For Figure 2I, we used only Exon1 of Htt with the poly Q repeats. Here in SDD-AGE, for the control lane, we see the Htt oligomers as a smear for the control. For Mrj, we see only a small band at the bottom which can be interpreted most likely as either a monomer or as small oligomers since we do not observe any smear here. However, for the 588 amino acid fragment of HttQ138 in the SDD-AGE we do not see a difference in the length of the smear but in the intensity of the smear of the Htt oligomers (Supplemental Figure 4E). Based on this we are suggesting in presence of Mrj, there are lesser Htt oligomers. On the point of Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid-like state, our experiments with the Mrj knockout show reduced Orb2 oligomers (both for 0.1% and 2% SDS soluble forms), suggesting Mrj plays a role in the conversion of Orb2 to the oligomeric state. If Mrj inhibits the conversion of oligomers to a more amyloid-like state, this is possible but we couldn’t test this hypothesis here. However, for Htt amyloid aggregates, previous works done by other labs with DnaJB6 as well as our experiments with Mrj suggest this as a likely possibility.

Figure 3: • The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.

We agree with the reviewer’s point that DNAJB6 mutant mice have a problem with placental development. However, one of the papers cited here (Watson et al, 2009) suggests DNAJB6 also plays a crucial role in the development of the embryo independent of the placenta development defect. The mammalian DNAJB6 mutant embryos generated using the tetraploid complementation method show severe neural defects including exencephaly, defect in neural tube closure, reduced neural tube size, and thinner neuroepithelium. Due to these features seen in the mice knockout, and the lack of such developmental defects in the Drosophila knockout, we interpreted our findings in Drosophila as significantly different from the mammals.

• It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

We agree with the reviewer and have added this suggestion in the discussion that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Figure 4:

• IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?

In the previously submitted manuscript, we have used HA-tagged Mrj (not V5) for checking the interaction with full-length Orb2B tagged with GFP. This was done with the goal of using the same Mrj-HA construct as that used in the initial Orb2A immunoprecipitation experiment. Since this has raised some concern as in the IPs to check for interaction between truncated Orb2A constructs (Orb2A325-GFP and Orb2AD162-GFP) and Mrj (Mrj-RFP), we used a different GFP and RFP tag combination. To address this, we have now added the same tag combinations for the IPs (Mrj-RFP with Orb2A-GFP and Orb2B-GFP). In these immunoprecipitation experiments where Mrj-RFP was pulled down using RFP Trap beads, we were able to detect positive interaction with GFP-tagged Orb2A and Orb2B. This data is now added in Figure 4F and 4I. We also have added the IP data for interaction between Mrj-HA and untagged Orb2B in Figure 4K, similar to the combination of initial experiment between Mrj-HA and untagged Orb2A.

• In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).

We have now performed IP from wild-type fly heads using anti Mrj antibody and ran the immunoprecipitate in SDS-PAGE and SDD-AGE followed by immunoblotting them with anti-Orb2 antibody. Our experiments suggest that immunoprecipitating endogenous Mrj brings down both the monomeric and oligomeric forms of Orb2. This data is now added in Figure 4L, M and N.

• This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).

We have addressed this in response to the reviewer’s comments on Figure 1. We find in both wild type and Mrj knockout fly heads, there are Orb2 oligomers that can be detected using 0.1% SDS extraction and with further extraction with 2% SDS. The 2% SDS soluble Orb2 oligomers were previously insoluble during 0.1% SDS-based extraction. However, the amounts of both of these oligomers are reduced in Mrj knockout fly heads. Since we do not have the physicochemical characterization of both of these kinds of oligomers, we are not using the terms gel or solid state here but referring to these oligomers as 0.1% SDS soluble Orb2 oligomers and 2% SDS soluble Orb2 oligomers. We speculate that the 0.1% SDS soluble Orb2 oligomers over time transition to the 2% SDS soluble Orb2 oligomers. As in the absence of Mrj in the knockout flies, both the 0.1% SDS soluble and 2% SDS soluble Orb2 oligomers are decreased, this suggests Mrj is promoting the formation of Orb2 oligomers. On the reviewer’s point, if Mrj can prevent the transition from 0.1% SDS soluble to 2% SDS soluble Orb2 oligomers, we think it is possible for Mrj to both promote oligomerization of Orb2 as well as prevent it from forming bigger non-functional oligomers, but the second point is not tested here. The relevant data is now presented in Figure 5H, I and J.

• I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

Based on the reviewer’s suggestion we have now changed the sentence to Mrj is probably regulating the oligomeric status of Orb2

Figure 5:

• To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?

On driving Mrj RNAi with Elav Gal4, we did not detect any Mrj in the western. We attempted to address the glial contribution towards Mrj’s expression we used a Glia-specific driver Repo Gal4 line to drive the control and Mrj RNAi line and performed a western blot using fly head lysate with anti-Mrj antibody. In this experiment, we did not observe any difference in Mrj levels between the two sets. As the Mrj antibody raised by us works in western blots but not in immunostainings, we could not do a colocalization analysis with a glial marker. However, we used the Mrj knockout Gal4 line to drive NLS-GFP and performed immunostainings of these flies with a glial marker anti-Repo antibody. Here we see two kinds of cells in the brain, one which have GFP but no Repo and the other where both are present together. This suggest that while Glial cells have Mrj but probably majority of Mrj in the brain comes from the neurons. We also found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia. This coupled with the catalytic nature of RNAi probably creates an effective knockdown of Mrj as seen in the western blot. This data is now added in Supplementary Figure 5G and H.

• Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

We have now performed the memory experiments with Mrj knockout flies. Our experiments show at 16 and 24-hour time points Mrj knockout flies have significantly reduced memory in comparison to the control wildtype. This data is now added in Figure 6B.

Figure 6:

The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

We have now performed the reverse co-IP between Rpl18-Flag and Mrj-HA using anti-HA antibody and could detect an interaction between the two. This data is now added in Supplementary Figure 6A.

To address if Mrj is a self-oligomerizing protein that can migrate to heavier polysome fractions due to its size, we have loaded recombinant Mrj on an identical sucrose gradient as we use for polysome analysis. Post ultra-centrifugation we fractionated the gradients and checked if Mrj can be detected in the fraction numbers where polysomes are present. In this experiment, we could not detect recombinant Mrj in the heavier polysome fractions (data presented in Supplementary Figure 6B). Overall, our observations of Mrj-Rpl18 IPs, the presence of cellularly expressed Mrj in polysome fractions, and the absence of recombinant Mrj from these fractions, suggest a likelihood of Mrj’s association with the translating ribosomes.

On the reviewer’s point of us concluding Mrj may interact with nascent Orb2 chains, we have not mentioned this possibility in the manuscript as we don’t have any evidence to suggest this. We have also added a sentence: This indicates that in presence of Mrj, the association of Orb2A with the translating ribosomes is enhanced, however, if this is a consequence of increased Orb2A oligomers due to Mrj or caused by interaction between polysome-associated Orb2A and Mrj will need to be tested in future.

Based on these above-mentioned points, we hope we can keep the data and conclusions of this section.

Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

We have addressed the comments on Figures 1 and 4 earlier. We have also added new memory experiment’s data with the Mrj knockout in Figure 6.

We have attempted to put the observations with Drosophila Mrj in perspective to observations in Thiruvalluvan et al, on human DnaJB6 in the discussions as follows:

Can our observation in Drosophila also be relevant for higher mammals? We tested this with human DnaJB6 and CPEB2. In mice CPEB2 knockout exhibited impaired hippocampus-dependent memory (Lu et al, 2017), so like Drosophila Orb2, its mammalian homolog CPEB2 is also a regulator of long-term memory. In immunoprecipitation assay we could detect an interaction between human CPEB2 and human DnaJB6, suggesting the feasibility for DnaJB6 to play a similar role to Drosophila Mrj in mammals. However, as the human DnaJB6 level was observed to undergo a reduction in transitioning from ES cells to neurons, (Thiruvalluvan et al, 2020) how this can be reconciled with its possible role in the regulation of memory. We speculate, such a reduction if is happening in the brain will occur in a highly regulatable manner to allow precise control over CPEB2 oligomerization only in specific neurons where it is needed and the reduced levels of DnaJB6 is probably sufficient to aid CPEB oligomerization Alternatively, there may be additional chaperones that may function in a stage-specific manner and be able to compensate for the decline in levels of DNAJB6.

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

Summary: The manuscript describes the role of the Hsp40 family protein Mrj in the prion-like oligomerization of Orb2. The authors demonstrate that Mrj promotes the oligomerization of Orb2, while a loss in Mrj diminishes the extent of Orb2 oligomerization. They observe that while Mrj is not an essential gene, a loss in Mrj causes deficiencies in the consolidation of long-term memory. Further, they demonstrate that Mrj associates with polysomes and increases the association of Orb2 with polysomes.

Major comments: None

Minor comments:

1. In the section describing the chaperone properties of Mrj in clearing Htt aggregates (Fig 2), the legend describes that "Mrj-HA constructs are more efficient in decreasing Htt aggregation compared to Mrj-RFP". It would be helpful to add Mrj-RFP to the quantification in Fig 2G to know exactly the difference in efficiency. Is there an explanation for why the 2 constructs behave differently?

We have added the quantitation of Htt aggregates in presence of Mrj-RFP in the revised version (Data presented in Figure 2G). While the efficiency of Mrj-RFP to decrease Htt aggregates is significantly less in comparison to Mrj-HA, it is still significantly better in comparison to the control CG7133-HA construct. It is possible, due to the tagging of Mrj with a larger tag (RFP), this reduces its ability to decrease the Htt aggregates in comparison to the construct where Mrj is tagged with a much smaller HA tag.

Figs A, B, C, G need to have quantification of the percentage of colocalization with details about the number of cells quantified for each experiment.

We have now added the intensity profile images and colocalization quantitation (pearson’s coefficient) in the Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from at 4-6 cells.

In Fig 6 B, C, F, G it would be helpful to label the 40S, 60S and 80S peaks in the A 254 trace.

We have now labeled the 80S, and polysome peaks in the Figure 7B, C, F and G. We could not separate the 40S and 60S peaks in the A254 trace.

It's interesting that Mrj has opposing functions with regard to aggregation when comparing huntingtin with Orb2. From the literature presented in the discussion, it appears as though chaperones including Mrj have an anti-aggregation role for prions. It would be helpful to have more discussion around why, in the case of Orb2, this is different. The discussion states that "The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2's oligomerization is the yeast Jjj2 protein" - this point needs elaboration, as well as a reference.

In the discussions section we have now added the following speculations on this:

One question here is why Mrj behaves differently with Orb2 in comparison to other amyloids. Orb2 differs from other pathogenic amyloids in its extremely transient existence in the toxic intermediate form (Hervás et al, 2016). For the pathogenic amyloids, since they exist in the toxic intermediate form for longer, Mrj probably gets more time to act and prevent or delay them from forming larger aggregates. For Orb2, Mrj may help to quickly transition it from the toxic intermediate state, thereby helping this state to be transient instead of longer. An alternate possibility is post-transition from the toxic intermediate state, Mrj stabilizes these Orb2 oligomers and prevents them from forming larger aggregates. This can be through Mrj interacting with Orb2 oligomers and blocking its surface thereby preventing more Orb2 from assembling over it. Another difference between the Orb2 oligomeric amyloids and the pathogenic amyloids is in the nature of their amyloid core. For the pathogenic amyloids, this core is hydrophobic devoid of any water molecules, however for Orb2, the core is hydrophilic. This raises another possibility that if the Orb2 oligomers go beyond a certain critical size, Mrj can destabilize these larger Orb2 aggregates by targeting its hydrophilic core.

On the Jjj2 point raised by the reviewer, we have added the (Li et al, 2016a) reference now and elaborated as:

The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2’s oligomerization is the yeast Jjj2 protein. In Jjj2 knockout yeast strain, Orb2A mainly exists in the non-prion state, whereas on Jjj2 overexpression the non-prion state could be converted to a prion-like state. In S2 cells coexpression of Jjj2 with Orb2A lead to an increase in Orb2 oligomerization (Li et al, 2016a). However, Jjj2 differs from Mrj, as when it is expressed in S2 cells, we do not detect it to be present in the polysome fractions.

The Jjj2 polysome data is now presented in Supplementary Figure 6C.

Reviewer #2 (Significance (Required)):

General assessment:

Overall, the work is clearly described and the manuscript is very well-written. The motivation behind the study and its importance are well-explained. I only have minor comments and suggestions to improve the clarity of the work. The study newly describes the interaction between the chaperone Mrj and the translation regulator Orb2. The experiments that the screen for proteins that interact with Orb2 and promote its oligomerization are very thorough. The experiments that delve into the role of Mrj in protein synthesis are a good start, and need to be explored further, but that is beyond the scope of this study.

Advance: The study describes a new interaction between the chaperone Mrj and the translation regulator Orb2. The study is helpful in expanding our knowledge of prion regulators as well factors that affect memory acquisition and consolidation.

Audience: This paper will be of most interest to basic researchers.

My expertise is in Drosophila genetics and neuronal injury.

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

The manuscript submitted by Desai et al. identifies a chaperone of the Hsp40 family (Mrj) that binds Orb2 and modulates its oligomerization, which is critical for Orb2 function in learning and memory in Drosophila. Orb2 are proteins with prion-like properties whose oligomerization is critical for their function in the storage of memories. The main contribution of the article is the screen of Hsp40 and Hsp70-family proteins that bind Orb2. The authors show IP results for all the candidates tested, including those that bind Fig. 1) and those that don't (Supp Fig 3). There is also a figure devoted to examining the interaction of Mrj with polyglutamine models (Htt). They also generate a KO mutant that is viable and shows no gross defects or protein aggregation. Lastly, they show that the silencing of Mrj in the mushroom body gamma neurons results in weaker memories in a courtship paradigm. Although the data is consistent and generally supportive of the hypothesis, key details are missing in several areas, including controls. Additionally, the interpretation of some results leaves room for debate. Overall, this is an ambitious article that needs additional work before publication.

Specific comments:

1. General concern over the interpretation of IP experiments and colocalization. These experiments don't necessarily reflect direct interactions. They are consistent with direct interaction but not the only explanation for a positive IP or colocalization.

This paper is centred on the interaction between Orb2A and Mrj, which we have detected using immunoprecipitation. The reviewer’s concern here is, this experiment is not able to distinguish if this can be a direct protein-protein interaction or if the two proteins are part of a complex.

To address this concern we have used purified recombinant protein-based pulldowns. Our experiments with purified protein pulldowns (GST tagged Mrj from E.coli with Orb2A from E.coli or Orb2A-GFP from Sf9 cells) suggest Orb2A and Mrj can directly interact amongst themselves. This data is now presented in Figure 1J and K.

The Huntingtin section has a few concerns. The IF doesn't show all controls and the quantification is not well done in terms of what is relevant. A major problem is the interpretation of Fig 2F. The idea is that Mrj prevents the aggregation of Htt, which is the opposite of what is observed with Orb2. The panel actually shows a large Htt aggregate instead of multiple small aggregates. This has been reported before in Drosophila and other systems with different polyQ models. Mrj and other Hsp40 and Hsp70 proteins modify Htt aggregation, but in an unexpected way. This affects the model shown in Fig. 6H. Lastly, Fig 2H and 2I show very different level of total Htt.

In Figure 2F of the previously submitted manuscript, we have shown representative images of HttQ103-GFP cells coexpressing with a control DnaJ protein CG7133-HA and Mrj-HA. In Figure 2G we quantitated the number of cells showing aggregates within the population of doubly transfected cells. On the reviewer’s point of figure 2F showing large Htt aggregates instead of multiple small aggregates, we do not see a large Htt aggregate in presence of Mrj in this figure, the pattern looks diffused here and very different from the control CG7133 where the aggregates are seen. We have performed the same experiment with a different Htt construct (588 amino acids long fragment) tagged with RFP, and here also we notice in presence of Mrj, the aggregates are decreased and the expression pattern looks diffused (Supplementary Figure 4E, 4F).

If the comment on large Htt aggregates in presence of Mrj is concerning figure 2E, here we show Mrj-RFP to colocalize with the Htt aggregates. Here, even though Mrj-RFP colocalizes with Htt aggregates, it rescues the Htt aggregation phenotype as in comparison to the control CG7133, the number of cells with Htt aggregates is still significantly less here. We have added this quantitation of rescue by Mrj-RFP in the revised manuscript now. The observation of colocalization of Mrj-RFP with Htt aggregates is similar to previous reports of chaperones rescuing Htt aggregation and yet showing colocalization with the aggregates. Both Hdj-2 and Hsc70 suppress Htt aggregation and yet were observed to colocalize with Htt aggregates in the cell line model as well as in nuclear inclusions in the brain (Jana et al, 2000). In a nematode model of Htt aggregation, DNJ-13 (DnaJB-1), HSP-1 (Hsc70), and HSP-11 (Apg-2) were shown to colocalize with Htt aggregates and yet decrease the Htt aggregation (Scior et al, 2018). Hsp70 was also found to colocalize with Htt aggregates in Hela cells (Kim et al, 2002).

Regarding Figures 2H and 2I, while figure 2H is of an SDS-PAGE to show no difference in the levels of monomeric HttQ103 (marked with *) in presence of Mrj and the control CG7133, figure 2I is for the same samples ran in an SDD-AGE where reduced amount of Htt oligomers as seen with the absence of a smear in presence of Mrj. The apparent difference in Htt levels between 2H and 2I is due to the detection of Htt aggregates/oligomers in the SDD-AGE which are unable to enter the SDS-PAGE and hence undetected. In Supplementary Figure 4E, similar experiments were done with the longer Htt588 fragment and here we notice in the SDD-AGE reduced intensity of the smear made up of Htt oligomers, again suggesting a reduction in Htt aggregates. Thus our results are not in contradiction to previous studies where Mrj was found to rescue Htt aggregate-associated toxicity.

Endogenous expression of Mrj using Gal4 line: where else is it expressed in the brain / head and in muscle. Fig 3G shows no muscle abnormalities but no evidence is shown for muscle expression. It is nice that Fig 3E and F show no abnormal aggregates in the Mrj mutant, but this would be maybe more interesting if flies were subjected to some form of stress.

We have now added images of the brain and muscles to show the expression pattern of Mrj. Using Mrj Gal4 line and UAS- CD8GFP, we noticed enriched expression in the optic lobes, mushroom body, and olfactory lobes. We also noticed GFP expression in the larval muscles and neuromuscular junction synaptic boutons. This data is now presented in Supplementary Figure 5C, D, E and F.

On the reviewer’s point of subjecting the Mrj KO flies to some form of stress, we have not performed this. We have added in the discussions a note of caution, that loss of Mrj may still result in a protein aggregation-related disease phenotype, probably under a sensitized condition of certain stresses which is not tested in this manuscript.

Fig. 5B shows no Mrj detectable from head homogenates in flies silencing Mrj in neurons with Elav-Gal4. It would be nice if they could show that ONLY neurons express Mrj in the head. Also noted, Elav-Gal4 is a weak driver, so it is surprising that it can generate such robust loss of Mrj protein

We have used an X chromosome Elav Gal4 driver to drive the UAS-Mrj RNAi line and here we could not detect Mrj in the western. To address the reviewer’s point on the glial contribution towards expression of Mrj, we used a Glial driver Repo Gal4 to drive Mrj RNAi. In this experiment, we did not detect any difference in Mrj levels between the control and the Mrj RNAi line (presented now in Supplementary Figure 5G). We also used the Mrj knockout Gal4 line to drive NLS-GFP and immunostained these using a glial marker anti-Repo antibody. Here, we were able to detect cells colabelled by GFP as well as Repo, suggesting Mrj is likely to be present in the glial cells (presented now in Supplementary Figure 5H). We also looked in the literature and found a reference where it was shown that Elav protein as well as Elav Gal4 at earlier stages of development expresses in neuroblasts and in all Glia (Berger et al, 2007). So, another possibility is when we are driving Mrj RNAi using Elav Gal4, this knocks down Mrj in both the neurons as well as in the glia.

Fig 4-Colocalization of Orb2 with Mrj lacks controls. The quantification could describe other phenomena because the colocalization is robust but the numbers shown describe something else.

We have now added the intensity profile and colocalization quantitation (pearson’s coefficient) in Supplemental Figure 5A and B. This quantitation is done from multiple ROI’s taken from 4-6 cells. Also, to suggest the interaction of Orb2 isoforms with Mrj, we are not depending on colocalization alone and have used immunoprecipitation experiments to support our observations.

Fly behavior. The results shown for Mrj RNAi alleles is fine but it would be more robust if this was validated with the KO line AND rescued with Mrj overexpression.

We have now performed memory assays with the Mrj knockout. Our experiments showed Mrj knockouts to show significantly decreased memory in comparison to wild-type flies at 16 and 24-hour time points (presented in Figure 6B). We have not been able to make an Mrj Knockout-UAS Mrj recombinant fly, most likely due to the closeness of the two with respect to their genomic location in second chromosome.

Minor comments:

Please, revise minor errors, there are several examples of words together without a space.

We have identified the words without space and have corrected them now.

Intro: describe the use of functional prions. Starting the paragraph with this sentence and then explaining what prion diseases are is a little confusing. Also "prion proteins" can be confusing because the term refers to PrP, the protein found in prions.

We have now altered the introduction and have described functional prions.

Results, second subtitle in page 5. This sentence is quite confusing using prion-like twice

We have now changed the heading to “Drosophila Mrj converts Orb2A from non-prion to a prion-like state.”

Page 6: "conversion from non-prion to prion-like form...". This can be presented differently. Prion-like properties are intrinsic, proteins don't change from non-prion to prion-like. They may be oligomeric or monomeric or highly aggregated but the prion-like property doesn't change

We agree with the reviewer's point of Prion-like properties are intrinsic, but the protein might or might not exist in the prion-like state or confirmation. When we are using the term conversion from non-prion to prion-like form we mean to suggest a conformational conversion leading to the eventual formation of the oligomeric species. We also noted the terminology of non-prion to prion-like state change is used in several papers (Satpute-Krishnan & Serio, 2005; Sw & Yo, 2012; Uptain et al, 2001).

Scale bars and text are too small in several figures

We have now mentioned in the figure legends the size of the scale bars. For several images we have made the scale bars also larger.

Not sure why Fig 4C is supplemental, seems like an important piece of data.

We have kept this data in the supplemental data as we performed this experiment with recombinant protein which is tagged with 6X His and we are not sure if this high degree of oligomerization/aggregation of recombinant Mrj and further precipitation over time, happens inside the cells/ brain.

Intro to Mrj KO in page 7 is too long. Most of it belongs in the discussion

We have now moved the portions on mammalian DNAJB6 which were earlier in the results section to the discussions section.

Change red panels in IF to other color to make it easier for colorblind readers.

We have now changed the red panels to magenta. We apologize for our figures not being colorblind friendly earlier.

The discussion is a little diffuse by trying to compare Orb2 with mammalian prions and amyloids and yeast prions.

We looked into the functional prion data and couldn’t find much on chaperone mediated regulation of these. Also, we felt comparing with the amyloids and yeast prions brings out the contrast with respect to the Mrj mediated regulatory differences between the two.

Reviewer #3 (Significance (Required)):

This is a paper with a broad scope and approaches. The paper describes the role of Mrj in the oligomerization of Orb2 by protein biochemistry techniques and determine the role of loss of Mrj in the mushroom bodies in fly behavior.

The audience for this content is basic research and specialized. The role of Mrj in Orb2 aggregation and function sheds new light on the mechanisms regulating the function of this protein involved in a novel mechanism of learning and memory.

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Hervás R, Li L, Majumdar A, Fernández-Ramírez MDC, Unruh JR, Slaughter BD, Galera-Prat A, Santana E, Suzuki M, Nagai Y, et al (2016) Molecular Basis of Orb2 Amyloidogenesis and Blockade of Memory Consolidation. PLoS Biol 14: e1002361

Hervas R, Rau MJ, Park Y, Zhang W, Murzin AG, Fitzpatrick JAJ, Scheres SHW & Si K (2020) Cryo-EM structure of a neuronal functional amyloid implicated in memory persistence in Drosophila. Science 367: 1230–1234

Izawa I, Nishizawa M, Ohtakara K, Ohtsuka K, Inada H & Inagaki M (2000) Identification of Mrj, a DnaJ/Hsp40 family protein, as a keratin 8/18 filament regulatory protein. J Biol Chem 275: 34521–34527

Jana NR, Tanaka M, Wang G h & Nukina N (2000) Polyglutamine length-dependent interaction of Hsp40 and Hsp70 family chaperones with truncated N-terminal huntingtin: their role in suppression of aggregation and cellular toxicity. Hum Mol Genet 9: 2009–2018

Kakkar V, Månsson C, de Mattos EP, Bergink S, van der Zwaag M, van Waarde MAWH, Kloosterhuis NJ, Melki R, van Cruchten RTP, Al-Karadaghi S, et al (2016) The S/T-Rich Motif in the DNAJB6 Chaperone Delays Polyglutamine Aggregation and the Onset of Disease in a Mouse Model. Mol Cell 62: 272–283

Kim S, Nollen EAA, Kitagawa K, Bindokas VP & Morimoto RI (2002) Polyglutamine protein aggregates are dynamic. Nat Cell Biol 4: 826–831

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Li S, Zhang P, Freibaum BD, Kim NC, Kolaitis R-M, Molliex A, Kanagaraj AP, Yabe I, Tanino M, Tanaka S, et al (2016b) Genetic interaction of hnRNPA2B1 and DNAJB6 in a Drosophila model of multisystem proteinopathy. Hum Mol Genet 25: 936–950

Liebman SW & Chernoff YO (2012) Prions in yeast. Genetics 191: 1041–1072

Lu W-H, Yeh N-H & Huang Y-S (2017) CPEB2 Activates GRASP1 mRNA Translation and Promotes AMPA Receptor Surface Expression, Long-Term Potentiation, and Memory. Cell Rep 21: 1783–1794

Prusiner SB (2001) Neurodegenerative Diseases and Prions. New England Journal of Medicine 344: 1516–1526

Satpute-Krishnan P & Serio TR (2005) Prion protein remodelling confers an immediate phenotypic switch. Nature 437: 262–265

Scior A, Buntru A, Arnsburg K, Ast A, Iburg M, Juenemann K, Pigazzini ML, Mlody B, Puchkov D, Priller J, et al (2018) Complete suppression of Htt fibrilization and disaggregation of Htt fibrils by a trimeric chaperone complex. EMBO J 37: 282–299

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Referee #3

Evidence, reproducibility and clarity

The manuscript submitted by Desai et al. identifies a chaperone of the Hsp40 family (Mrj) that binds Orb2 and modulates its oligomerization, which is critical for Orb2 function in learning and memory in Drosophila. Orb2 are proteins with prion-like properties whose oligomerization is critical for their function in the storage of memories. The main contribution of the article is the screen of Hsp40 and Hsp70-family proteins that bind Orb2. The authors show IP results for all the candidates tested, including those that bind Fig. 1) and those that don't (Supp Fig 3). There is also a figure devoted to examining the interaction of Mrj with polyglutamine models (Htt). They also generate a KO mutant that is viable and shows no gross defects or protein aggregation. Lastly, they show that the silencing of Mrj in the mushroom body gamma neurons results in weaker memories in a courtship paradigm. Although the data is consistent and generally supportive of the hypothesis, key details are missing in several areas, including controls. Additionally, the interpretation of some results leaves room for debate. Overall, this is an ambitious article that needs additional work before publication.

Specific comments:

1. General concern over the interpretation of IP experiments and colocalization. These experiments don't necessarily reflect direct interactions. They are consistent with direct interaction but not the only explanation for a positive IP or colocalization.
2. The Huntingtin section has a few concerns. The IF doesn't show all controls and the quantification is not well done in terms of what is relevant. A major problem is the interpretation of Fig 2F. The idea is that Mrj prevents the aggregation of Htt, which is the opposite of what is observed with Orb2. The panel actually shows a large Htt aggregate instead of multiple small aggregates. This has been reported before in Drosophila and other systems with different polyQ models. Mrj and other Hsp40 and Hsp70 proteins modify Htt aggregation, but in an unexpected way. This affects the model shown in Fig. 6H. Lastly, Fig 2H and 2I show very different level of total Htt.
3. Endogenous expression of Mrj using Gal4 line: where else is it expressed in the brain / head and in muscle. Fig 3G shows no muscle abnormalities but no evidence is shown for muscle expression. It is nice that Fig 3E and F show no abnormal aggregates in the Mrj mutant, but this would be maybe more interesting if flies were subjected to some form of stress.
4. Fig. 5B shows no Mrj detectable from head homogenates in flies silencing Mrj in neurons with Elav-Gal4. It would be nice if they could show that ONLY neurons express Mrj in the head. Also noted, Elav-Gal4 is a weak driver, so it is surprising that it can generate such robust loss of Mrj protein
5. Fig 4-Colocalization of Orb2 with Mrj lacks controls. The quantification could describe other phenomena because the colocalization is robust but the numbers shown describe something else.
6. Fly behavior. The results shown for Mrj RNAi alleles is fine but it would be more robust if this was validated with the KO line AND rescued with Mrj overexpression.

Minor comments:

Please, revise minor errors, there are several examples of words together without a space.

Intro: describe the use of functional prions. Starting the paragraph with this sentence and then explaining what prion diseases are is a little confusing. Also "prion proteins" can be confusing because the term refers to PrP, the protein found in prions.

Results, second subtitle in page 5. This sentence is quite confusing using prion-like twice

Page 6: "conversion from non-prion to prion-like form...". This can be presented differently. Prion-like properties are intrinsic, proteins don't change from non-prion to prion-like. They may be oligomeric or monomeric or highly aggregated but the prion-like property doesn't change

Scale bars and text are too small in several figures

Not sure why Fig 4C is supplemental, seems like an important piece of data.

Intro to Mrj KO in page 7 is too long. Most of it belongs in the discussion

Change red panels in IF to other color to make it easier for colorblind readers.

The discussion is a little diffuse by trying to compare Orb2 with mammalian prions and amyloids and yeast prions.

Significance

This is a paper with a broad scope and approaches. The paper describes the role of Mrj in the oligomerization of Orb2 by protein biochemistry techniques and determine the role of loss of Mrj in the mushroom bodies in fly behavior.

The audience for this content is basic research and specialized. The role of Mrj in Orb2 aggregation and function sheds new light on the mechanisms regulating the function of this protein involved in a novel mechanism of learning and memory.

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:

The manuscript describes the role of the Hsp40 family protein Mrj in the prion-like oligomerization of Orb2. The authors demonstrate that Mrj promotes the oligomerization of Orb2, while a loss in Mrj diminishes the extent of Orb2 oligomerization. They observe that while Mrj is not an essential gene, a loss in Mrj causes deficiencies in the consolidation of long-term memory. Further, they demonstrate that Mrj associates with polysomes and increases the association of Orb2 with polysomes.

Major comments: None

Minor comments:

1. In the section describing the chaperone properties of Mrj in clearing Htt aggregates (Fig 2), the legend describes that "Mrj-HA constructs are more efficient in decreasing Htt aggregation compared to Mrj-RFP". It would be helpful to add Mrj-RFP to the quantification in Fig 2G to know exactly the difference in efficiency. Is there an explanation for why the 2 constructs behave differently?
2. Figs A, B, C, G need to have quantification of the percentage of colocalization with details about the number of cells quantified for each experiment.
3. In Fig 6 B, C, F, G it would be helpful to label the 40S, 60S and 80S peaks in the A 254 trace.
4. It's interesting that Mrj has opposing functions with regard to aggregation when comparing huntingtin with Orb2. From the literature presented in the discussion, it appears as though chaperones including Mrj have an anti-aggregation role for prions. It would be helpful to have more discussion around why, in the case of Orb2, this is different. The discussion states that "The only Hsp40 chaperone which was found similar to Mrj in increasing Orb2's oligomerization is the yeast Jjj2 protein" - this point needs elaboration, as well as a reference.

Significance

General assessment:

Overall, the work is clearly described and the manuscript is very well-written. The motivation behind the study and its importance are well-explained. I only have minor comments and suggestions to improve the clarity of the work. The study newly describes the interaction between the chaperone Mrj and the translation regulator Orb2. The experiments that the screen for proteins that interact with Orb2 and promote its oligomerization are very thorough. The experiments that delve into the role of Mrj in protein synthesis are a good start, and need to be explored further, but that is beyond the scope of this study.

Advance:

The study describes a new interaction between the chaperone Mrj and the translation regulator Orb2. The study is helpful in expanding our knowledge of prion regulators as well factors that affect memory acquisition and consolidation.

Audience:

This paper will be of most interest to basic researchers. My expertise is in Drosophila genetics and neuronal injury.

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Referee #1

Evidence, reproducibility and clarity

In this MS, Mrj - a member of the JDP family of Hsp70 co-chaperones was identified as a regulator of the conversion of Orb2A (the Dm ortholog of CPEB) to its prion-like form.

In drosophila, Mrj deletion does not cause any gross neurodevelopmental defect nor leads to detectable alterations in protein homeostasis. Loss of Mrj, however, does lead to altered Orb2 oligomerization. Consistent with a role of prion-like characteristics of Orb2 in memory consolidation, loss of Mrj results in a deficit in long-term memory.

Aside from the fact that there are some unclarities related to the physicochemical properties of Orb2 and how Mrj affects this precisely, the finding that a chaperone could be important for memory is an interesting observation, albeit not entirely novel.

In addition, there are several minor technical concerns and questions I have that I feel the authors should address, including a major one related to the actual approach used to demonstrate memory deficits upon loss of Mrj.

Significance

Figure 1 (plus related Supplemental figures):

• There seem to be two isoforms of Mrj (like what has been found for human DNAJB6). I find it striking to see that only (preferentially?) the shorter isoform interacts with Orb2. For DNAJB6, the long isoform is mainly related to an NLS and the presumed substrate binding is identical for both isoforms. If this is true for Dm-Mrj too, the authors could actually use this to demonstrate the specificity of their IPs where Orb2 is exclusively non-nuclear?
• I would be interested to know a bit more about the other 5 JDPs that are interactors with Orb2: are the human orthologs of those known? It is striking that these other 5 JDPs interact with Orb2 in Dm (in IPs) but have no impact on Sup35 prion behavior. Importantly, this does not imply they may not have impact on the prion-like behavior of other Dm substrates, including Dm-Orb2.
• The data in panels H,I indeed suggest that Mrj1 alters the (size of) the oligomers. It would be important to know what is the actual physicochemical change that is occurring here. The observed species are insoluble in 0.1 % TX100 but soluble in 0.1% SDS, which suggest they could be gels, but not real amyloids such as formed by the polyQ proteins that require much higher SDS concentrations (~2%) to be solubilized. This is relevant as Mrj1 reduces polyQ amyloidogenesis whereas is here is shown to enhance Orb2A oligomerization/gelidification. In the same context, it is striking to see that without Mrj the amount of Orb2A seems drastically reduced and I wonder whether this might be due to the fact that in the absence of Mrj a part of Orb2A is not recovered/solubilized due to its conversion for a gel to a solid/amyloid state? In other words: Mrj1 may not promote the prion state, but prevents that state to become an irreversible, non-functional amyloid?

Figure 2 (plus related Supplemental figures):

• It may be good for clarity to refer to the human Mrj as DNAJB6 according to the HUGO nomenclature. Also, the first evidence for its oligomerization was by Hageman et al 2010.
• It is striking to see that Mrj co-IPs with Hsp70AA, Hsp70-4 but not Hsp70Cb. The fact that interactions were detected without using crosslinking is also striking given the reported transient nature of J-domain-Hsp70 interactions Together, this may even suggest that Mrj-1 is recognized as a Hsp70 substrate (for Hsp70AA, Hsp70-4 but not Hsp70Cb) rather than as a co-chaperone. In fact, a variant of Mrj-1 with a mutation in the HPD motif should be used to exclude this option.
• The rest of these data reconfirm nicely that Mrj/DNAJB6 can suppress polyQ-Htt aggregation. Yet note that in this case the oligomers that enter the agarose gel are smaller, not bigger. This argues that Mrj is not an enhancer of oligomerization, but rather an inhibitor of the conversion of oligomers to a more amyloid like state.

Figure 3:

• The finding that knockout of DNAJB6 in mice is embryonic lethal is related to a problem with placental development and not embryonic development (Hunter et al, 1999; Watson et al, 2007, 2009, 2011) as well recognized by the authors. Therefore, the finding that deletion of Dm-Mrj has no developmental phenotype in Drosophila may not be that surprising.
• It is a bit more surprising that Mrj knockout flies showed no aggregation phenotype or muscle phenotype, especially knowing that DNAJB6 mutations are linked to human diseases associated with aggregation (again well recognized by the authors). However, most of these diseases are late-onset and the phenotype may require stress to be revealed. So, while important to this MS in terms of not being a confounder for the memory test, I would like to ask the authors to add a note of caution that their data do not exclude that loss of Mrj activity still may cause a protein aggregation-related disease phenotype. Yet, I also do think that for the main message of this MS, it is not required to further test this experimentally.

Figure 4:

• IPs were done with Orb2A as bite and clearly pulled down substantial amounts of GFP-tagged Mrj. For interactions with Orb2B, a V5-tagged Mrj was use and only a minor fraction was pulled down. Why were two different Mrj constructs used for Arb2A and Orb2b?
• In addition, I think it would be important what one would see when pulling on Mrj1, especially under non-denaturing conditions and what is the status of the Orb2 that is than found to be associated with Mrj (monomeric, oligomeric and what size).
• This also relates to my remark at figure 1 and the subsequent fractionation experiments they show here in which there is a slight (not very convincing) increase in the ratio of TX100-soluble and insoluble (0.1% SDS soluble) material. My question would be if there is a remaining fraction of 0.1% insoluble (2% soluble) Orb2 and how Mrj affects that? As stated before, this is (only) mechanistically relevant to understanding why there is less oligomers of Orb2 in terms of Mrj either promoting it or by preventing it to transfer from a gel to a solid state. The link to the memory data remains intriguing, irrespective of what is going on (but also on those data I do have several comments: see below).
• I also find the sentence that "Mrj is probably regulating the oligomerization of endogenous Orb2 in the brain" somewhat an overstatement. I would rather prefer to say that the data show that Mrj1 affects the oligomeric behavior/status of Orb2.

Figure 5:

• To my knowledge, the Elav driver regulates expression in all neurons, but not in glial cells that comprise a significant part of the fly heads/brain. The complete absence of Mrj in the fly-heads when using this driver is therefore somewhat surprising. Or do we need to conclude from this that glial cells normally already lack Mrj expression?
• Why not use these lines also for the memory test for confirmation? I understand the concerns of putative confounding effects of a full knockdown (which were however not reported), but now data rely only on the mushroom body-specific knockdown for the 201Y Gal4 line, for which the knockdown efficiency is not provided. But even more so, here a temperature shift (22oC-30oC) was required to activate the expression of the siRNA. For the effects of this shift alone no controls were provided. The functional memory data are nice and consistent with the hypothesis, but can it be excluded that the temperature shift (rather than the Mrj) knockdown has caused the memory defects? I think it is crucial to include the proper controls or use a different knockdown approach that does not require temperature shifts or even use the knockout flies.

Figure 6:

The finding of a co-IP between Rpl18 and Mrj (one-directional only) by no means suffices to conclude that Mrj may interact with nascent Orb2 chains here (which would be the relevant finding here). The fact that Mrj is a self-oligomerising protein (also in vitro, so irrespective of ribosomal associations!), and hence is found in all fractions in a sucrose gradient, also is not a very strong case for its specific interaction with polysomes. The finding that there is just more self-oligomerizing Orb2A co-sedimenting with polysomes in sucrose gradients neither is evidence for a direct effect of Mrj enhancing association of Orb2A with the translating ribosomes even though it would fit the hypothesis. So all in all, I think the data in this figure and non-conclusive and the related conclusions should be deleted.

Overall, provided that proper controls/additional data can be provided for the key experiments of memory consolidation, I find this an intriguing study that would point towards a role of a molecular chaperone in controlling memory functions via regulating the oligomeric status of a prion-like protein and that is worthwhile publishing in a good journal.

However, in terms of mechanistical interpretations, several points have to be reconsidered (see remarks on figure 1,4); this pertains especially to what is discussed on page 13. In addition, I'd like the authors to put their data into the perspective of the findings that in differentiated neurons DNAJB6 levels actually decline, not incline (Thiruvalluvan et al, 2020), which would be counterintuitive if these proteins are playing a role as suggested here in memory consolidation.

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Reply to the reviewers

We thank the reviewers for their time and for the helpful comments. We felt the reviews overall were fair and quite positive. All three reviewers felt the manuscript could be of broad, general interest, especially given the relevance of the protein (Pbp1/ataxin-2) to neurodegenerative conditions and stress granule biology. Reviewer 3 seems to have some doubt there could be specificity for the types of transcripts regulated by Pbp1, given prior studies of mammalian ataxin-2 which implicated 16,000+ mRNAs that could bind via PAR-CLIP experiments! However, our study shows the power of utilizing a simpler model organism and thinking about the metabolic state of cells for elucidating the function of this interesting protein. Although our demonstration of the specificity of Pbp1 for regulating Puf3-target mRNAs involved in mitochondrial biogenesis and mitochondrial function may be surprising to this reviewer, we have the utmost confidence in our data and feel the study represents a highly significant finding that will be of interest to many researchers.

1. Point-by-point description of the revisions

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

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

*In this manuscript, van de Poll et al. aim to further establish the function of poly(A) binding protein-binding protein 1 (Pbp1) and its relationship to the RNA-binding protein Puf3 in regulating the expression of mitochondrial proteins. This work builds upon a solid body of previous studies from this group regarding the function of Puf3 and the role of Pbp1 in regulating TORC1 signaling. Here, the authors show that Pbp1 has a physical and functional interaction with Puf3 and that ablation or disruption of Pbp1 eliminates this interaction and reduces the expression of some Puf3 target proteins. This work provides reliable data supporting a role for Pbp1 in the regulation of mitochondrial protein expression and that there is a clear functional interaction between Puf3 and Pbp1. Nonetheless, there are several issues that should be addressed before the manuscript is suitable for publication. *

*1.) The model put forward suggests that Pbp1 works to recruit Puf3 to the vicinity of mitochondria, where Puf3 can then promote the expression of its target mRNAs. In Figure 3A, however, deletion of Pbp1 has a stronger affect on the expression of COX2 than deletion of Puf3 alone, which would not be expected if the role of Pbp1 is to modulate Puf3 function. Similarly, the expression of COX2 is higher in the double deletion versus the Pbp1 single deletion. The authors should attempt to clarify this experimentally, or at least make mention of alternative mechanisms for Pbp1 which may be causing this. *

We have mentioned alternative mechanisms in the text. We suggest that Puf3-target mRNAs also can be translated through Tom20, in the absence of Puf3 (p. 8, ref 18). In single deletion strains lacking Pbp1 (but with Puf3 present), Puf3 may direct some of its target mRNAs to decay pathways, leading to lower Cox2 expression compared to double deletion strains that also lack Puf3. We performed qPCR analysis of several Puf3-target and other mRNAs in pbp1∆puf3∆ double deletion strains and some transcripts (e.g., COX17) would support this possibility (Fig S4).

* 2.) The authors mention that the increased pull-down of Puf3 with Pbp1 in respiratory conditions suggests that the Pbp1-Puf3 interaction is responsive to the cellular metabolic state (Figure 3B). However, the increase in Puf3 expression makes it difficult to compare the interactions between the two conditions. *

Yes this is correct, we stated this in the legend of Fig 4B as: “Increased amounts of Puf3 are associated with Pbp1 in respiratory conditions.” We clarified in the text that Puf3 expression increases in respiratory conditions and this likely explains the increased pull-down of Puf3 with Pbp1 (p. 10).

* 3.) The authors only look at a very small subset of Puf3 target mRNAs using qPCR when it would be much more informative and overall convincing to examine a larger amount using RNA-seq experiments. *

We conducted an RNA-seq experiment to compare transcriptomes of WT vs pbp1∆ cells in fermentative (YPD) vs respiratory (YPL) conditions and observed mRNAs with functions associated with mito-translation and mito-respiration (i.e., Puf3-targets) to be most differentially expressed – and majority of these are lower in abundance in pbp1∆ cells (new Fig 2).

* 4.) The authors consistently mention that Pbp1 function is helping to stabilize Puf3 target mRNAs. However, if the authors wish to prove this particular mode-of-action, more direct evidence should be provided, such as a pulse-chase experiment. Otherwise, other models allowing for increased mRNA abundance should be noted. *

Using thiolutin which is the standard for such expts, we measured mRNA half-lives of several Puf3-target and other mRNAs (COX17, COX10, POR1, ACT1) by qPCR in WT vs pbp1∆ cells. However, these data turned out to be difficult to interpret, as several “control” mRNAs exhibited different decay profiles in pbp1∆ vs WT cells, and their behavior was different in respiratory conditions compared to what was reported in common glucose media. Nonetheless, the data are included as Fig S2, and the important observation is that each of the Puf3-target mRNAs tested behaves similarly following thiolutin treatment, compared to non Puf3-target mRNAs. Given that Puf3-target mRNAs were more stable in pbp1∆ cells (compared to PGK1) following thiolutin treatment, we have deleted the term “stabilize” throughout the text. The exact fate of these mRNAs in normal vs pbp1∆ mutant cells will require more sophisticated investigation in future studies.

* 5.) Given the proposed model, one would expect Puf3 to have reduced mRNA binding upon deletion of Pbp1. It would be interesting to examine Puf3 mRNA binding, perhaps through cross-linking immunoprecipitation (CLIP), to see if this indeed is the case. This would provide further direct evidence that Pbp1 is functioning through Puf3 and facilitating its function. Similarly, the authors mention that Pbp1 contains putative RNA binding domains, however, they make no mention if these domains may contribute to its function in mitochondrial protein expression. *

We performed an RNA-IP experiment to test whether Puf3 has reduced binding to its target mRNAs in the absence of Pbp1. In new Fig 7, Puf3 is still able to bind its mRNA targets in the absence of Pbp1. However, the association of Pbp1 with these mRNAs is reduced in puf3∆ knockouts. Such results are perhaps expected as Puf3 has been shown to bind in a sequence-specific manner to a ~8 nt motif in the 3’UTR of its target mRNAs. However, it is unclear whether the Lsm / LsmAD domains of Pbp1 actually bind RNAs directly (hence our use of the term “putative”) - they may be involved in protein-protein interactions. Moreover, Fig 5 shows that deletion of both domains has no apparent effect on mitochondrial protein expression. We prefer to address the role of the Lsm and LsmAD domains of Pbp1 in a future study.

* Reviewer #1 (Significance (Required)):

Overall, this manuscript provides a modest advance, but one that could prove to have important implications for the field-especially if the Pbp1 findings prove relevant to its human ortholog, ataxin-2. The advance is limited by the robustness of the specific molecular model proposed and the extent to which the Pbp1-Puf3 relationship is examined on the gene-expression level.

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

In this manuscript, the authors found that the pbp1∆ mutant grew poorly on nonfermentable carbon source medium (YP Lactate medium) and that the pbp1∆ mutant had decreased amount of Cox2 protein, a cytochrome c oxidase. The pbp1∆ mutant also had decreased amounts of COX17, COX10, and MRP51 mRNAs. Since these mRNAs are target mRNAs of the RNA-binding protein Puf3, the authors next investigated the relationship between Pbp1 and Puf3. The analysis of the GFP reporter gene containing Puf3-binding sites in the 3' UTR showed that the levels of GFP reporter mRNA and protein were decreased in the pbp1∆ mutant strain. This reduction was dependent on the Puf3-binding site in the 3' UTR. Next, the authors examined the genetic interaction between the pbp1∆ and puf3∆ mutants, and found that the levels of Cox2 protein were reduced in the two mutants. Finally, the authors showed the interaction between Pbp1 and Puf3 by co-immunoprecipitation and determined the regions of Pbp1 and Puf3 required for the interaction. They also showed that these regions in Pbp1 and Puf3 proteins are also important for the regulation of Cox2 protein levels. The story is very clear and the data is reliable. However, the data from Western blotting should be shown quantitatively to make the results more reliable. Also, although the story is based on the reduction of Cox2 protein level, it would be better to discuss whether other proteins or mRNAs should be considered as well.

Major comments:

Figure 2C. Protein levels of GFP should be quantified and the data should be shown. *

These data have been quantified (now Fig 3C).

* Figure 3. Not only the Cox2 protein level but also mRNA levels of COX2, COX17, COX10, MRP51, etc in pbp1∆ mutant, puf3∆ mutant and pbp1∆ puf3∆ double mutant should be shown. Then the point of action of Pbp1 and Puf3 would become clearer. *

The mRNA levels have been determined by qPCR and are now in Fig S4.

Figure 4. * For the domain analysis of Pbp1 protein, showing differences in cell proliferation as in Figure 1B would indicate the physiological importance of the domain. *

Growth curves of the various Pbp1 domain deletions have been performed and shown in Fig 5D. They do support the physiological importance of the domain(s).

* Figure 4B. Quantification of the amount of co-immunoprecipitated proteins would indicate the strength of binding. *

These data have been quantified (now Fig 5B).

* Figure 4C. Protein levels should be quantified and the data presented. *

These data have been quantified (now Fig 5C).

* Line 153-8 The description of Line 153-8 is not appropriate for this position because it breaks up the flow of the story before and after. *

These text have been moved as requested.

* Minor comments:

Line 153 Isn't the following the first reference cited for Pbp1 is a negative regulator of TORC1? Transient sequestration of TORC1 into stress granules during heat stress Terunao Takahara 1, Tatsuya Maeda Mol Cell. 2012 Jul 27;47(2):242-52. doi: 10.1016/j.molcel.2012.05.019. Epub 2012 Jun 21.

Ref19. Ref 19 also shows that the pbp1∆ mutant strains grow poorly on the medium containing glycerol and lactate as carbon sources.

Overall, the gene is not italicized. *

These requested edits to the references and text have been made in the revised version of the manuscript.

* Reviewer #2 (Significance (Required)):

This manuscript analyzes the relationship between Pbp1 and Puf3 in yeast. Since these proteins are evolutionarily conserved from yeast to humans and are also associated with disease in humans, this reviewer believes this manuscript will be of interest to a wide audience. *

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

In this study, van de Poll et al. describe the involvement of a cytosolic RNA-binding protein (Pbp1) in the transcriptional regulation of mitochondrial biogenesis during the shift from fermentative to respiratory growth in budding yeast. Using the advantage of yeast genetics and molecular biology, they show that a number of mitochondrial transcripts and proteins are downregulated in pbp1Δ cells both under normal growth conditions and during respiratory shift. Since Puf3, another RNA-binding protein, is known to regulate the fate of these transcripts, they further investigate the interaction between Pbp1 and Puf3. Through a series of biochemical assays, they characterize the interaction between Pbp1 and Puf3 taking place through their low-complexity domains, which is suggested by authors to stabilize and promote the translation of Puf3-target transcripts.

As stated in the manuscript, Pbp1 is an evolutionarily conserved protein, encoded by ATXN2 gene in humans. Due to its involvement in multiple neurological disorders, it has been widely studied in mouse models and patient samples. The transcriptome profiles of Atxn2-KO mouse liver and cerebellum have been published (albeit having used a relatively older microarray technology for today's standards), revealing a prominent dysregulation of global translational machinery, and the ER-protein secretion pathway (Fittschen et al., 2015). These findings were followed by numerous studies showing dysregulations of distinct transcript pools under examination. Moreover, a PAR-CLIP study showed ATXN2 to associate with ~16.000 transcripts, 8000 of which depended on its interaction with PABPC1 (Yokoshi et al., 2014). In that study, ATXN2 was shown to preferentially bind to target transcripts on 3'-UTR AU-rich sequences. In addition to PABPC1, some other RNA-binding proteins, like TDP-43, were also shown to modulate the indirect interaction of ATXN2 with transcripts, while it also maintained direct interactions through its Lsm and LsmAD domains. Altogether, the mammalian data on ATXN2 thus far depicts it as an avid interactor of numerous RNA-binding proteins and countless transcripts, including microRNAs. The authors however have not cited or compared their findings with this vast array of mammalian literature (with the exception of two properly discussed papers in Discussion).

Considering its stress-responsive nature and association with RNP granules, it is plausible to assume that ATXN2/Pbp1 could regulate certain groups of transcripts in terms of their stability (in stress granules and p-bodies) or active translation under given environmental conditions and cellular state. The work of van de Poll et al. in this regard is an important step in expanding our knowledge about the many downstream effects of ATXN2/Pbp1. Yet, the following issues should be solved:

1) The pre-eminence of the proposed group of affected transcripts (i.e. those associated with mitochondrial biogenesis) has to be empirically established. Among all its interactions with other RNA-binding proteins, how important or dominant is the interaction of Pbp1 with Puf3 for mitochondrial biogenesis during the shift towards respiratory growth? Line 74 in the Results says "Analysis of a panel of nuclear- and mitochondrial-encoded mRNAs..."; how broad was this panel? how were the genes selected? Considering the fact that ATXN2/Pbp1 is associated with an immense number of transcripts, hand-picking a number of Puf3 targets and selectively analyzing their expression will surely give some significant dysregulations. Therefore, an unbiased transcriptomic survey is necessary to see all Pbp1-dependent dysregulations during respiratory shift. Since it's a process that requires heavy mitogenesis, one can assume that many dysregulations will concern mitochondrial factors. Indeed, proteome surveys in Atxn2-KO mouse tissues and pbp1Δ yeast (under fermentative growth and stress) point out to a strong mitochondrial dysregulation, so it raises hopes to see an even stronger Pbp1 impact during the respiratory shift in yeast. Then, bioinformatic analyses can reveal what proportion of those are Puf3 targets. If the authors' premise is valid, then the Puf3-targets will stand out in the transcriptome data, and give them an unbiased, solid and much stronger base for the following interaction analyses. They can then compare this dataset to the readily available mouse transcriptome from Atxn2-KO or polyQ-disease models, and strengthen their hypothesis about ATXN2/Pbp1 regulating mitochondrial biogenesis in association with Puf3.

The Results text about Figure 1 in its current state is an overstatement of the available data. Line 81-84 suggests mitochondrially encoded Cox2 levels are reduced because some mitoribosome subunits are Puf3 targets, but pbp1Δ itself is known to have altered mitochondrial membrane potential which negatively impacts mitochondrial import. So, the reduction of Cox2 levels (and potentially other mitochondrial-encoded proteins, it was never checked) may have nothing to do with Puf3, but rather be a direct consequence of reduced mitoribosome import in pbp1Δ. In order to make this statement, the total mitochondrial translation rates of WT and pbp1Δ strains would have to be compared, and if they are found the same, a selective effect on Puf3-target proteins has to be shown among many tested candidates. Same applies to line 86 "the specific decrease in Puf3-target mRNAs in pbp1Δ cells" referring to Figure 3C. This statement cannot be made without analyzing a larger group of transcripts including the targets of other RNA-binding proteins. The current data does not support any specific dysregulation of Puf3-targets, it just shows some Puf3 targets to be dysregulated, however without the knowledge of how many significant among all Puf3-targets, or how significant are Puf3-targets compared to others. An unbiased, high-throughput transcriptome data, and detailed bioinformatic analyses should replace Figure 1C. The high variation among replicates at 1h-3h-5h time points is also alarming, and puts the reproducibility of these experiments under question. *

As mentioned in the response to Reviewer 1, we performed an unbiased RNA-seq experiment comparing transcriptomes of WT vs pbp1∆ cells. The data are quite striking and strongly support our hypothesis (new Fig 2). To address the reviewer’s concern that pbp1∆ phenotypes may be due to altered mito membrane potential and mito protein import, we have performed an experiment to examine import of Cox4, which is a protein substrate that is commonly used for this purpose (note COX4 is not a Puf3-target mRNA). Steady-state Cox4 protein amounts are similar in WT vs pbp1∆ cells. Moreover, following treatment of cells with the uncoupler CCCP, there is more of the “pre”-processed Cox4 form in WT cells compared to pbp1∆ cells. These data would argue against the reviewer’s hypothesis and are included in the revised manuscript as Fig S1. Since every mitochondrial ribosomal subunit gene transcript is a target of Puf3 (PMID: 16254148) and therefore subject to regulation by Pbp1, we would argue that a defect in mito biogenesis (due to compromised translation of these mRNAs) precedes and may explain any subsequent defect in mito membrane potential. *

2) Stabilization of mRNAs The basal reduction in the mRNA levels of the reporter construct in pbp1Δ is a strong but not necessarily direct evidence of stabilization by Pbp1. mRNA half-life analyses (i.e. degradation curves) should be performed with desired targets to measure stability in WT and pbp1Δ strains. *

As mentioned above in the response to Reviewer 1, we performed mRNA half-life analyses for several transcripts in WT vs pbp1∆ strains using thiolutin treatment. The results are not straightforward to interpret as loss of Pbp1 led to a stabilization of Puf3-target mRNAs (relative to PGK1), however all Puf3-target mRNAs that we examined exhibited similar decay profiles. Thus, we deleted the term “stabilize” and further determination of the fate of these mRNAs in the absence of new transcription will require more careful and sophisticated experiments.*

3) Cox2 levels Regarding Line 125: "Cox2 protein levels, which are dependent on the translation of Puf3-target mRNAs": This may be generally true, but the data here suggests otherwise. pbp1Δ cells have completely diminished Cox2 levels, whereas puf3Δ have approx. 50% reduction. This means that Pbp1 is more important to maintain normal Cox2 levels, and does so independent of Puf3. In contrast, puf3Δ "rescues" some of the defect in pbp1Δ cells and increases Cox2 abundance to ~50%. As stated above, Cox2 reduction in pbp1Δ could be a direct consequence of mitochondrial membrane depolarization unrelated to Puf3, and could be accompanied by many other non-Puf3-targets being downregulated. Therefore, the authors should refrain from "Cox2 levels are dependent on the translation of Puf3-target mRNAs" statements throughout the text without an experimental proof in the context of pbp1Δ strain. *

See response to Reviewer 1. We believe that in the absence of Pbp1, Puf3 may now preferentially promote decay of various mito biogenesis transcripts, leading to apparently lower Cox2 levels. Moreover, per the results of our Cox4 experiment (Fig S1), we would respectfully disagree with the reviewer’s hypothesis. Nonetheless, we included an additional statement that mitochondrial membrane depolarization, as a consequence of reduced expression of numerous Puf3-targets, could also contribute to lower Cox2 abundance (p. 6 and 15). *

4) Pbp1-Puf3 interaction The authors state that Pbp1-Puf3 interaction is required for Puf3-target mRNA stabilization and translation. This suggests that Pbp1 stabilizes this pool of mRNAs because of its interaction with Puf3 primarily, not the mRNAs themselves. One general question while studying the interaction between two RNA-binding proteins is whether they interact in an RNA-dependent manner in vivo. The co-IP analyses show the interaction between Pbp1 and Puf3 to increase under respiratory shift as expected. However, in co-IPs from cell lysates, many RNA-binding proteins may seemingly interact due to their association with translation machinery at that given time. But this does not mean "direct" protein-protein interaction, just a co-existence around actively translating ribosomes. In order to ensure the direct interaction of these two proteins, the same co-IPs should be performed with/out RNase treatment of the lysate (many protocols available online). Only if Pbp1-Puf3 interaction persists in RNase+ samples, they can conclude a direct interaction. In addition, RNA-immunoprecipitation analyses should be performed in Pbp1-Flag and Pbp1-Flag/ puf3Δ strains to check if the association of Pbp1 with Puf3-target mRNAs indeed depends on Puf3. *

This is a good suggestion. We performed an experiment to test whether RNase treatment alters interaction between Pbp1 and Puf3 and there was minimal effect, supporting the hypothesis that the interaction may be direct. We also performed RNA-IP of Pbp1 in the presence of absence of Puf3 (new Fig 7). As the reviewer predicted, the RNA-IP enrichment of Puf3-target mRNAs was reduced in puf3∆ strains, suggesting that the association of Pbp1 with such transcripts depends on Puf3. It is known from work by others that Puf3 contains a PUF domain that enables sequence-specific binding to a motif in the 3’UTR of its target mRNAs, so these results are quite sensible.

* Minor comments: • Second sentence of the Abstract ("How mutations in its mammalian ortholog ataxin-2 are linked to neurodegenerative conditions remains unclear") is semantically incorrect. The term "linked" suggests an observed but uncharacterized effect of a genetic variation on a certain syndrome. Diseases can be linked to a chromosome or a locus without knowing the exact causative mutation. How the CAG repeat expansion mutations in ATXN2 are causative of SCA2, ALS or Parkinson-plus syndromes are very well known. One should also be careful with using "mutations" as a general term in the context of ATXN2, because there are certain variations in and around ATXN2 locus leading to a decrease in its activity and metabolic problems, which is far from its neurodegeneration-causing mutations. If the authors meant to state that the pathological mechanism is unclear by this sentence, that would also be a negligence of the extensive literature around this topic. Multiple disease models in mouse, Drosophila and C. elegans collectively point out to an RNA metabolism deficit, caused by toxic ATXN2 aggregates that sequestrate other RNA-binding proteins and their target transcripts. The specific downstream effects involve synaptic strength, Calcium-related action potentials, ER stress and cholesterol/sphingolipid synthesis. Therefore, this sentence could be rephrased to "PolyQ expansion mutations in its mammalian ortholog ataxin-2 lead to spinocerebellar dysfunction due to toxic protein aggregation." and simply avoid going into mechanistic details as it is not necessary for this manuscript.

• Lines 241-243: "Human sequencing" is also an incorrect term. Can be rephrased to "PolyQ expansion mutations in ATXN2 are associated with SCA2 and ALS". The references 22-24 are the first association of polyQ mutations with SCA2, however a reference for ALS is missing. Elden et al. Nature 2010 should be cited here.

• The authors should discuss the relevance of these findings to the mammalian ortholog of Pum3, namely PUM1/2. Afterall, it is also a very important conserved protein and well-studied in mammalian literature. *

These changes to the text and references have been made.

* Following a better characterization of the transcript pools selectively affected by Pbp1 (meaning a transcriptome survey), a graphical abstract sort of scheme could be useful in putting the findings in perspective and conveying the message.*

We decided not to include a graphical abstract at this time, since it is difficult for us to “picture” what is going on inside an Pbp1-containing RNP granule at this time. * Reviewer #3 (Significance (Required)):

The intricate experiments characterizing the nature of interaction between Pbp1 and Puf3 (Figures 3B, 4, 5, 6) are quite convincing. However, some fundamental questions remain especially regarding the primary rationale of studying Pbp1-Puf3 relationship and the breadth of some conclusions. The data are of general interest to a broad audience. The statistical tests in Figure 1 are of concern. The reviewer(s) has experience both with yeast molecular biology and with mammalian Atxn2 function. *

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

In this study, van de Poll et al. describe the involvement of a cytosolic RNA-binding protein (Pbp1) in the transcriptional regulation of mitochondrial biogenesis during the shift from fermentative to respiratory growth in budding yeast. Using the advantage of yeast genetics and molecular biology, they show that a number of mitochondrial transcripts and proteins are downregulated in pbp1Δ cells both under normal growth conditions and during respiratory shift. Since Puf3, another RNA-binding protein, is known to regulate the fate of these transcripts, they further investigate the interaction between Pbp1 and Puf3. Through a series of biochemical assays, they characterize the interaction between Pbp1 and Puf3 taking place through their low-complexity domains, which is suggested by authors to stabilize and promote the translation of Puf3-target transcripts.

As stated in the manuscript, Pbp1 is an evolutionarily conserved protein, encoded by ATXN2 gene in humans. Due to its involvement in multiple neurological disorders, it has been widely studied in mouse models and patient samples. The transcriptome profiles of Atxn2-KO mouse liver and cerebellum have been published (albeit having used a relatively older microarray technology for today's standards), revealing a prominent dysregulation of global translational machinery, and the ER-protein secretion pathway (Fittschen et al., 2015). These findings were followed by numerous studies showing dysregulations of distinct transcript pools under examination. Moreover, a PAR-CLIP study showed ATXN2 to associate with ~16.000 transcripts, 8000 of which depended on its interaction with PABPC1 (Yokoshi et al., 2014). In that study, ATXN2 was shown to preferentially bind to target transcripts on 3'-UTR AU-rich sequences. In addition to PABPC1, some other RNA-binding proteins, like TDP-43, were also shown to modulate the indirect interaction of ATXN2 with transcripts, while it also maintained direct interactions through its Lsm and LsmAD domains. Altogether, the mammalian data on ATXN2 thus far depicts it as an avid interactor of numerous RNA-binding proteins and countless transcripts, including microRNAs. The authors however have not cited or compared their findings with this vast array of mammalian literature (with the exception of two properly discussed papers in Discussion).

Considering its stress-responsive nature and association with RNP granules, it is plausible to assume that ATXN2/Pbp1 could regulate certain groups of transcripts in terms of their stability (in stress granules and p-bodies) or active translation under given environmental conditions and cellular state. The work of van de Poll et al. in this regard is an important step in expanding our knowledge about the many downstream effects of ATXN2/Pbp1. Yet, the following issues should be solved:

1. The pre-eminence of the proposed group of affected transcripts (i.e. those associated with mitochondrial biogenesis) has to be empirically established. Among all its interactions with other RNA-binding proteins, how important or dominant is the interaction of Pbp1 with Puf3 for mitochondrial biogenesis during the shift towards respiratory growth? Line 74 in the Results says "Analysis of a panel of nuclear- and mitochondrial-encoded mRNAs..."; how broad was this panel? how were the genes selected? Considering the fact that ATXN2/Pbp1 is associated with an immense number of transcripts, hand-picking a number of Puf3 targets and selectively analyzing their expression will surely give some significant dysregulations. Therefore, an unbiased transcriptomic survey is necessary to see all Pbp1-dependent dysregulations during respiratory shift. Since it's a process that requires heavy mitogenesis, one can assume that many dysregulations will concern mitochondrial factors. Indeed, proteome surveys in Atxn2-KO mouse tissues and pbp1Δ yeast (under fermentative growth and stress) point out to a strong mitochondrial dysregulation, so it raises hopes to see an even stronger Pbp1 impact during the respiratory shift in yeast. Then, bioinformatic analyses can reveal what proportion of those are Puf3 targets. If the authors' premise is valid, then the Puf3-targets will stand out in the transcriptome data, and give them an unbiased, solid and much stronger base for the following interaction analyses. They can then compare this dataset to the readily available mouse transcriptome from Atxn2-KO or polyQ-disease models, and strengthen their hypothesis about ATXN2/Pbp1 regulating mitochondrial biogenesis in association with Puf3.

The Results text about Figure 1 in its current state is an overstatement of the available data. Line 81-84 suggests mitochondrially encoded Cox2 levels are reduced because some mitoribosome subunits are Puf3 targets, but pbp1Δ itself is known to have altered mitochondrial membrane potential which negatively impacts mitochondrial import. So, the reduction of Cox2 levels (and potentially other mitochondrial-encoded proteins, it was never checked) may have nothing to do with Puf3, but rather be a direct consequence of reduced mitoribosome import in pbp1Δ. In order to make this statement, the total mitochondrial translation rates of WT and pbp1Δ strains would have to be compared, and if they are found the same, a selective effect on Puf3-target proteins has to be shown among many tested candidates. Same applies to line 86 "the specific decrease in Puf3-target mRNAs in pbp1Δ cells" referring to Figure 3C. This statement cannot be made without analyzing a larger group of transcripts including the targets of other RNA-binding proteins. The current data does not support any specific dysregulation of Puf3-targets, it just shows some Puf3 targets to be dysregulated, however without the knowledge of how many significant among all Puf3-targets, or how significant are Puf3-targets compared to others. An unbiased, high-throughput transcriptome data, and detailed bioinformatic analyses should replace Figure 1C. The high variation among replicates at 1h-3h-5h time points is also alarming, and puts the reproducibility of these experiments under question. 2. Stabilization of mRNAs The basal reduction in the mRNA levels of the reporter construct in pbp1Δ is a strong but not necessarily direct evidence of stabilization by Pbp1. mRNA half-life analyses (i.e. degradation curves) should be performed with desired targets to measure stability in WT and pbp1Δ strains. 3. Cox2 levels Regarding Line 125: "Cox2 protein levels, which are dependent on the translation of Puf3-target mRNAs": This may be generally true, but the data here suggests otherwise. pbp1Δ cells have completely diminished Cox2 levels, whereas puf3Δ have approx. 50% reduction. This means that Pbp1 is more important to maintain normal Cox2 levels, and does so independent of Puf3. In contrast, puf3Δ "rescues" some of the defect in pbp1Δ cells and increases Cox2 abundance to ~50%. As stated above, Cox2 reduction in pbp1Δ could be a direct consequence of mitochondrial membrane depolarization unrelated to Puf3, and could be accompanied by many other non-Puf3-targets being downregulated. Therefore, the authors should refrain from "Cox2 levels are dependent on the translation of Puf3-target mRNAs" statements throughout the text without an experimental proof in the context of pbp1Δ strain. 4. Pbp1-Puf3 interaction The authors state that Pbp1-Puf3 interaction is required for Puf3-target mRNA stabilization and translation. This suggests that Pbp1 stabilizes this pool of mRNAs because of its interaction with Puf3 primarily, not the mRNAs themselves. One general question while studying the interaction between two RNA-binding proteins is whether they interact in an RNA-dependent manner in vivo. The co-IP analyses show the interaction between Pbp1 and Puf3 to increase under respiratory shift as expected. However, in co-IPs from cell lysates, many RNA-binding proteins may seemingly interact due to their association with translation machinery at that given time. But this does not mean "direct" protein-protein interaction, just a co-existence around actively translating ribosomes. In order to ensure the direct interaction of these two proteins, the same co-IPs should be performed with/out RNase treatment of the lysate (many protocols available online). Only if Pbp1-Puf3 interaction persists in RNase+ samples, they can conclude a direct interaction. In addition, RNA-immunoprecipitation analyses should be performed in Pbp1-Flag and Pbp1-Flag/ puf3Δ strains to check if the association of Pbp1 with Puf3-target mRNAs indeed depends on Puf3.

Minor comments:

• Second sentence of the Abstract ("How mutations in its mammalian ortholog ataxin-2 are linked to neurodegenerative conditions remains unclear") is semantically incorrect. The term "linked" suggests an observed but uncharacterized effect of a genetic variation on a certain syndrome. Diseases can be linked to a chromosome or a locus without knowing the exact causative mutation. How the CAG repeat expansion mutations in ATXN2 are causative of SCA2, ALS or Parkinson-plus syndromes are very well known. One should also be careful with using "mutations" as a general term in the context of ATXN2, because there are certain variations in and around ATXN2 locus leading to a decrease in its activity and metabolic problems, which is far from its neurodegeneration-causing mutations. If the authors meant to state that the pathological mechanism is unclear by this sentence, that would also be a negligence of the extensive literature around this topic. Multiple disease models in mouse, Drosophila and C. elegans collectively point out to an RNA metabolism deficit, caused by toxic ATXN2 aggregates that sequestrate other RNA-binding proteins and their target transcripts. The specific downstream effects involve synaptic strength, Calcium-related action potentials, ER stress and cholesterol/sphingolipid synthesis. Therefore, this sentence could be rephrased to "PolyQ expansion mutations in its mammalian ortholog ataxin-2 lead to spinocerebellar dysfunction due to toxic protein aggregation." and simply avoid going into mechanistic details as it is not necessary for this manuscript.
• Lines 241-243: "Human sequencing" is also an incorrect term. Can be rephrased to "PolyQ expansion mutations in ATXN2 are associated with SCA2 and ALS". The references 22-24 are the first association of polyQ mutations with SCA2, however a reference for ALS is missing. Elden et al. Nature 2010 should be cited here.
• The authors should discuss the relevance of these findings to the mammalian ortholog of Pum3, namely PUM1/2. Afterall, it is also a very important conserved protein and well-studied in mammalian literature.
• Following a better characterization of the transcript pools selectively affected by Pbp1 (meaning a transcriptome survey), a graphical abstract sort of scheme could be useful in putting the findings in perspective and conveying the message.

Significance

The intricate experiments characterizing the nature of interaction between Pbp1 and Puf3 (Figures 3B, 4, 5, 6) are quite convincing. However, some fundamental questions remain especially regarding the primary rationale of studying Pbp1-Puf3 relationship and the breadth of some conclusions. The data are of general interest to a broad audience.<br /> The statistical tests in Figure 1 are of concern. The reviewer(s) has experience both with yeast molecular biology and with mammalian Atxn2 function.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors found that the pbp1∆ mutant grew poorly on nonfermentable carbon source medium (YP Lactate medium) and that the pbp1∆ mutant had decreased amount of Cox2 protein, a cytochrome c oxidase. The pbp1∆ mutant also had decreased amounts of COX17, COX10, and MRP51 mRNAs. Since these mRNAs are target mRNAs of the RNA-binding protein Puf3, the authors next investigated the relationship between Pbp1 and Puf3. The analysis of the GFP reporter gene containing Puf3-binding sites in the 3' UTR showed that the levels of GFP reporter mRNA and protein were decreased in the pbp1∆ mutant strain. This reduction was dependent on the Puf3-binding site in the 3' UTR. Next, the authors examined the genetic interaction between the pbp1∆ and puf3∆ mutants, and found that the levels of Cox2 protein were reduced in the two mutants. Finally, the authors showed the interaction between Pbp1 and Puf3 by co-immunoprecipitation and determined the regions of Pbp1 and Puf3 required for the interaction. They also showed that these regions in Pbp1 and Puf3 proteins are also important for the regulation of Cox2 protein levels.

The story is very clear and the data is reliable. However, the data from Western blotting should be shown quantitatively to make the results more reliable. Also, although the story is based on the reduction of Cox2 protein level, it would be better to discuss whether other proteins or mRNAs should be considered as well.

Major comments:

Figure 2C.

Protein levels of GFP should be quantified and the data should be shown.

Figure 3.

Not only the Cox2 protein level but also mRNA levels of COX2, COX17, COX10, MRP51, etc in pbp1∆ mutant, puf3∆ mutant and pbp1∆ puf3∆ double mutant should be shown. Then the point of action of Pbp1 and Puf3 would become clearer.

Figure 4.

For the domain analysis of Pbp1 protein, showing differences in cell proliferation as in Figure 1B would indicate the physiological importance of the domain.

Figure 4B.

Quantification of the amount of co-immunoprecipitated proteins would indicate the strength of binding.

Figure 4C.

Protein levels should be quantified and the data presented.

Line 153-8

The description of Line 153-8 is not appropriate for this position because it breaks up the flow of the story before and after.

Minor comments:

Line 153

Isn't the following the first reference cited for Pbp1 is a negative regulator of TORC1? Transient sequestration of TORC1 into stress granules during heat stress Terunao Takahara 1, Tatsuya Maeda Mol Cell. 2012 Jul 27;47(2):242-52. doi: 10.1016/j.molcel.2012.05.019. Epub 2012 Jun 21.

Ref19.

Ref 19 also shows that the pbp1∆ mutant strains grow poorly on the medium containing glycerol and lactate as carbon sources.

Overall, the gene is not italicized.

Significance

This manuscript analyzes the relationship between Pbp1 and Puf3 in yeast. Since these proteins are evolutionarily conserved from yeast to humans and are also associated with disease in humans, this reviewer believes this manuscript will be of interest to a wide audience.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, van de Poll et al. aim to further establish the function of poly(A) binding protein-binding protein 1 (Pbp1) and its relationship to the RNA-binding protein Puf3 in regulating the expression of mitochondrial proteins. This work builds upon a solid body of previous studies from this group regarding the function of Puf3 and the role of Pbp1 in regulating TORC1 signaling. Here, the authors show that Pbp1 has a physical and functional interaction with Puf3 and that ablation or disruption of Pbp1 eliminates this interaction and reduces the expression of some Puf3 target proteins. This work provides reliable data supporting a role for Pbp1 in the regulation of mitochondrial protein expression and that there is a clear functional interaction between Puf3 and Pbp1. Nonetheless, there are several issues that should be addressed before the manuscript is suitable for publication.

1. The model put forward suggests that Pbp1 works to recruit Puf3 to the vicinity of mitochondria, where Puf3 can then promote the expression of its target mRNAs. In Figure 3A, however, deletion of Pbp1 has a stronger affect on the expression of COX2 than deletion of Puf3 alone, which would not be expected if the role of Pbp1 is to modulate Puf3 function. Similarly, the expression of COX2 is higher in the double deletion versus the Pbp1 single deletion. The authors should attempt to clarify this experimentally, or at least make mention of alternative mechanisms for Pbp1 which may be causing this.
2. The authors mention that the increased pull-down of Puf3 with Pbp1 in respiratory conditions suggests that the Pbp1-Puf3 interaction is responsive to the cellular metabolic state (Figure 3B). However, the increase in Puf3 expression makes it difficult to compare the interactions between the two conditions.
3. The authors only look at a very small subset of Puf3 target mRNAs using qPCR when it would be much more informative and overall convincing to examine a larger amount using RNA-seq experiments.
4. The authors consistently mention that Pbp1 function is helping to stabilize Puf3 target mRNAs. However, if the authors wish to prove this particular mode-of-action, more direct evidence should be provided, such as a pulse-chase experiment. Otherwise, other models allowing for increased mRNA abundance should be noted.
5. Given the proposed model, one would expect Puf3 to have reduced mRNA binding upon deletion of Pbp1. It would be interesting to examine Puf3 mRNA binding, perhaps through cross-linking immunoprecipitation (CLIP), to see if this indeed is the case. This would provide further direct evidence that Pbp1 is functioning through Puf3 and facilitating its function. Similarly, the authors mention that Pbp1 contains putative RNA binding domains, however, they make no mention if these domains may contribute to its function in mitochondrial protein expression.

Significance

Overall, this manuscript provides a modest advance, but one that could prove to have important implications for the field-especially if the Pbp1 findings prove relevant to its human ortholog, ataxin-2. The advance is limited by the robustness of the specific molecular model proposed and the extent to which the Pbp1-Puf3 relationship is examined on the gene-expression level.

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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 manuscript by Naeli et al., presents study on effects of SARS-CoV-2 NSP2 protein on miRNA mediated translational repression. Authors show in multiple ways using reporter mRNAs with various miRNA sites that NSP2 protein stimulates miRNA mediated repression. The increase in miRNA repression is likely due to the interaction of NSP2 with either GIGYF2 or Argonaute protein directly thus making more stable repressive complex on mRNA. The manuscript is written clearly and methods provide enough details for reproducibility. Only major comment would be that authors could have tested multiple endogenous targets of miRNAs for extent of miRNA mediated expression in the presence of NSP2. This could be achieved using either western blots for known targets (looking at protein levels) or using targeted qRT-PCR or distribution of mRNAs in polysome fractions (mRNA translational repression. An alternative would be Ribo-Seq experiment.

Minor comment:

Line in introduction arguing: "The GIGYF2/4EHP complex is recruited by a variety of factors including miRNAs" should state miRISC instead of miRNAs.

Significance

General assessment: The study presents sold evidence that NSP2 protein interacts with miRISC complex and increases miRNA-mediated translational repression of reporter mRNAs. Multiple target sites for miR20, let7 and miR92 are tested as well as two different human cell lines (Hek293 and U87) which gives strength to study and reproducibility of the results. Focus on the endogenous miRNA targets in the presence or absence of the NSP2 protein would make study even stronger.

The advance of the study is more rigorous analyses of NSP2 protein effects on miRNA-mediated gene expression regulation with some novel mechanistic insights. The study will be of interest for specialized and basic research audience with potential impact on translational research.

My field of expertise covers mechanisms of gene expression regulation by miRNAs and RBPs as well as impact of mRNAs, nascent peptides and ribosomes on protein synthesis.

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Referee #2

Evidence, reproducibility and clarity

In this study, Naeli and collaborators propose a role for the SARS-CoV2 protein NSP2 in the miRNA-mediated translational repression. Their data show that NSP2 co-immunoprecipitate with the Argonaute AGO2 and the previously reported translation modulator GIGYF2. Using different reporters sensitive to miRNA repression, they show that overexpressing NSP2 enhances miRNA-mediated translational repression in different cell lines as well as one endogenous miRNA target CDK1 and thus without affecting the stability of targeted mRNAs. Interestingly, their data show that the effect of NSP2 depends on the number of miRNA binding sites suggesting that highly repressed mRNAs, due to the presence of several miRNA binding sites, are not affected by NSP2. Finally, using an AGO2 tethering reporter assay that does not rely on miRNA binding but needs two NSP2 interactors GIGYF2 and 4EHP, they demonstrate that overexpressing NSP2 still stimulates mRNA repression, suggesting that NSP2 can have a broader impact on miRISC-dependent silencing.

Overall, this is an interesting follow-up study from their previous paper (Xu et al., 2022) that have the potential to add another dimension to the modulation of mRNA translation by the SARS-CoV2 protein NSP2. But unfortunately, in its current form, the work falls short of supporting their claims appropriately and providing sufficient and relevant insights about the role of NSP2 in regulating the miRNA-mediate gene regulation. To overcome this, the authors should perform and add the following experiments to strengthen their study.

1. Interaction of NSP2 with the miRISC: With the data presented here, we can only conclude that NPS2 forms a complex with AGO2. Is their interaction direct or indirect? Is miRNA- and TNRC6-bound AGO2 (and thus miRISC) associated with NSP2? To address those important questions, the authors should perform NSP2 immunoprecipitations in GIGYF2 (and 4EHP) KO cell lines and monitor the presence of AGO2 and miRNAs in the immunoprecipitated complex. Those experiments will define the type of interaction NSP2 has with the miRISC (GIGYF2 dependent or not).
2. Along this line, is NSP2 action on miRNA-mediated gene regulation dependent or not of GIGYF2? Again, testing this by monitoring the repression of the different reporters upon NSP2 overexpression in the presence or not of GIGYF2 in cells will precise the contribution of NSP2 in miRNA-mediated gene silencing.

Besides these two sets of critical experiments that will define the relationship between NSP2 mRNA modulation and the microRNA pathway, it would be interesting for this study to readily test the effect of NSP2 on miRNA targets related to immune-regulatory processes and antiviral response. As the authors pointed out in their discussion, several mRNAs are involved in the control of genes found in those pathways. Demonstrating the contribution of NSP2 in the regulation of a few of them will strengthen their study's significance and interest by providing a possible mechanism at play during a SARS-CoV2 infection. Also, it would be interesting to test if the modulation of translation inhibition by NSP2 occurs in cells infected by the SARS-CoV2 virus. For instance, is NSP2 and miRISC interaction enhances during the viral infection? As the authors propose, NSP2 could also have improved antiviral microRNA repression, so it would be interesting to characterize this interplay in a relevant biological context.

Minor comment:

From the data presented in Figure S1A, it is impossible to conclude "that miR-20a represses the expression of the target mRNA in a GIGFY2-dependent manner" as its KO also affect the level/stability of 4EHP. Therefore, we cannot distinguish the contribution of GIGFY2 and 4EHP in this context as both protein levels decrease. The authors should tone down this statement on page 3.

Significance

With the addition of the proposed experiments, the revised study will better define the direct contribution of NSP2 with the miRISC. This work has the potential to provide another aspect of the mRNA translation modulation by the SARS-CoV2 protein NSP2 with the interesting angle of miRNA-mediated gene silencing.

As mentioned by the authors, another recent paper reports the potential impact of NSP2 on post-transcriptional silencing (Zou et al., iScience 2022). However, in contrast to the current study, this previous work did not directly test the interaction of NSP2 with the miRISC. Furthermore, it only used a single miRNA reporter (let-7) to support NSP2 contribution in miRNA-mediated gene silencing, which does not demonstrate the broader impact of NSP2 on this gene regulatory mechanism as tested in this current study. Upon revision, this study will provide more definitive proof of the involvement of NSP2 in miRNA-mediated gene regulation and thus will be of interest to experts in the miRNA field and scientists interested in understanding viruses/hosts interaction.

Although not essential, if the authors want to add data that addresses the function of NSP2 on this regulatory pathway in the context of viral infection (which seems feasible for this group), that will definitely increase the broader significance of their work.

I am an expert in molecular biology and molecular genetics, miRNA-mediated gene regulation, and small non-coding RNA biology.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript the authors examine whether SARS-CoV-2 protein, NSP2 mediate translation repression of cellular transcripts by increasing miRNA mediated suppression. The same authors previously demonstrated NSP2 interacts with GIGYF2 and this interaction suppresses the translation of the Ifnb mRNA. Here they extend this finding and using a series of reporters they illustrate that at least part of NSP2 translation suppression is mediated by increasing GIGYF2 mediated miRNA translation suppression. Overall the manuscript is clearly written and the experiments well executed.

Major comments:

Optional- The authors show that the ability of NSP2 to enhance miRNA-mediated repression is dependent on the extent of the initial repression and suggest this dependency on the initial repression levels may explain the discrepancy with published work showing NSP2 actually impairs (and not increase) microRNA-mediated silencing . Therefore, an important addition could be to examine what happen to translation in cells that express NSP2 and whether generally translation repression is significant in transcripts (native) that are enriched in miRNA binding site. This experiment will help to show NSP2 effect on native transcripts and potentially help to understand how much of these changes are likely explained by miRNAs.

Minor comments:

1. Personally I found the term repression fold quite confusing and unintuitive. Why not to present the actual measurements so that the control (blue bars) have high value and the red bars (representing repression) have lower values?
2. It seems inadequate to present on the same graph two values that are normalized to 1. For example, in figure 1D it will be important to show the mir20-Mut real values (at least mir20-Mut in NSP2 cells should not be normalized to 1). This will allow to show that the differences are indeed mediated by stronger translation repression of miR-20 WT luciferase in the presence of NSP2 and not by unexplained differences in mir20-mut luciferase expression. This is true for almost all the figures in the manuscript. Correspondingly, the statistical test should be two-factor ANOVA test examining if NSP2 expression significantly increase the difference between Luciferase miR20-WT and luciferase miR20-mut.

Significance

There is an urgent need for better molecular understanding of how SARS-CoV-2 proteins influence the machineries of the host cell.

This study investigates how NSP2 interaction with GIGYF2 mediate translation repression of cellular transcript. The authors also address the discrepancy with previously published work that showed NSP2 actually impairs (and not increase) microRNA-mediated silencing.

The paper would be of interest to RNA biologists and for molecular virologists that study SARS-CoV-2

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Reply to the reviewers

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

I summarise the major findings of the work below. In my opinion the range and application of approaches has provided a broad evidence base that, in general, supports the authors conclusions. However, there are, in my opinion, particular failures to utilise and communicate this evidence. The manuscript may be much improved with attention in the following areas. In each case I will give general criticism with a few examples, but the principals of my comments could be applied throughout the work.

1) Insufficient quantification. The investigation combines various sources of qualitative data (EM, fluorescence microscopy, western blotting) to generate a reasonably strong evidence base. However, the work is over-reliant on representative images and should include more quantification from repeat experiments. When there are multiple fluorescence micrographs with intensity changes (not necessarily just representative images) (e.g. Figure 1 or 2) the authors should consider making measurements of these. Also the VLP production assays, which are assessed by western blotting would particularly benefit from a quantitative assessment (either by densitometry or, if samples remain, ELISA/similar approach).

We have performed quantification of immunofluoresence, western blotting and VLP experiments from existing data. These quantification are presented in our revised manuscript. An overview of new quantification is shown below:

Data shown

Quantification now shown in

Method

Analysis

Figure 1A

Supp F1C

IF

HAE (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Figure 1D

Supp F1E

IF

HeLa+ACE2 (-/+ SARS-CoV-2 )

• Tetherin total fluorescence intensity

Figure 2C

Supp F2B

IF

A549+ACE2 (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Figure 2G

Supp F2D

IF

T84 (-/+ SARS-CoV-2)

• Tetherin total fluorescence intensity

Supp F4A

Supp F4B

IF

HeLa + ss-HA-Spike transients (-/+ HA stained cells) - Tetherin total fluorescence intensity

Figure 4D

Supp F4E

IF

HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

• Tetherin total fluorescence intensity

Figure 4F

Supp F4G

W blot

HeLa + TetOne ss-HA-Spike stables (-/+ Dox)

– Tetherin abundance

Figure 4G

Supp F4I

W blot – lysates

Spike VLP experiments

– tetherin abundance

Figure 4G

Supp F4J

W blot - VLPs

Spike VLP experiments

• N-FLAG abundance

Figure 6A

Supp F7A

W blot – lysates

ORF3a VLP experiments

– tetherin abundance

Figure 6A

Supp F7B

W blot - VLPs

ORF3a VLP experiments

• N-FLAG

For immunofluoresence anaysis, the mean, standard deviation, number of cells analysed and number of independent experiments are shown in the updated figure legends. Statistical analysis is also detailed in figure legends. Methods for the quantificaiton of fluoresence intensity is included in the Methods section.

Densitometry was performed on western blots and VLP experiments as suggested. The mean, standard devisation and number of independent expreiments analysed are expressed in figure legends. Methods for densityometry quantification is now included.

2) Insufficient explanation. I found some of the images and legends contained insufficient annotation and/or description for a non-expert reader to appreciate the result(s). Particularly if the authors want to draw attention to features in micrographs they should consider using more enlarged/inset images and annotations (e.g. arrows) to point out structures (e.g DMVs etc.). This short coming exacerbates the lack of quantification.

Additional detail has been provided to the figure legends, and we have updated several figures to draw attention to features in micrographs. Black arrowheads have been added to Figures 1E, 2D, 2H to highlight plasma membrane-associated virions, and asterisks to highlight DMVs in Figures 1E, 2D and Supplemental Figures 2C, 2E. Similarly, typical Golgi cisternae are highlighted by white arrowheads micrographs in Figure 2E. These figure legends have also been modified to highlight these additions.

3) Insufficient exploration of the data. I had a sense that some aspects of the data seem unconsidered or ignored, and the discussion lacks depth and reflection. For example the tetherin down-regulation apparent in Figures 1 and 2 is not really explained by the spike/ORF3a antagonism described later on, but this is not explicitly addressed.

We have made changes throughout the manuscript, but the discussion especially has been modified. We now discuss the ORF3a data in more depth, discuss possible mechanisms by which ORF3a alone enhances VLP release, and discuss our ORF7a data in context to previous reports.

The discussion has been updated to now include a better description of our data, and additional writing putting our work in to context with previously published work. See discussion section of revised manuscript.

Also, Figure 6 suggests that ORF3a results in high levels of incorporation of tetherin in to VLPs, but I don't think this is even described(?). The discussion should also include more comparison with previous studies on the relationship between SARS-2 and tetherin.

We have added a section to discuss how ORF3a may enhance VLP release,

‘We found that the expression of ORF3a enhanced VLP independently of its ability to relocalise tetherin (Figure 6A). This may be due to either the ability of ORF3a to induce Golgi fragmentation [38] which facilitates viral trafficking [39], or due to enhanced lysosomal exocytosis [37]. Tetherin was also found in VLPs upon co-expression with ORF3a (Figure 6A) which may also indicate to enhanced release via lysosomal exocytosis [37].

The secretion of lysosomal hydrolases has been reported upon expression of ORF3a [31] and whilst this may in-part be due to enhanced lysosome-plasma membrane fusion, our data highlights that ORF3a impairs the retrograde trafficking of CIMPR (Supplemental Figures 6B, 6F, 6G), which may similarly increase hydrolase secretion.’ – (Line 625-654).

The discussion has been developed to compare the relationship between SARS-CoV-2 and tetherin in previous studies,

‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

I have no minor comments on this draft of the manuscript.

Reviewer #1 (Significance (Required)):

Tetherin, encoded by the BST2 gene, is an antiviral restriction factor that inhibits the release of enveloped viruses by creating tethers between viral and host membranes. It also has a capacity for sensing and signalling viral infection. It is most widely understood in the context of HIV-1, however, there is evidence of restriction in a wide variety of enveloped viruses, many of which have evolved strategies for antagonising tetherin. This knowledge informs on viral interactions with the innate immune system, with implications for basic virology and translational research.

This study investigates tetherin in the context of SARS-CoV-2. The authors use a powerful collection of tools (live virus, gene knock out cells, recombinant viral and host expression systems) and a variety of approaches (microscopy, western blotting, infection assays), which is, itself, a strength. The study provides evidence to support a series of conclusions: I) BST2/tetherin restricts SARS-CoV-2 II) SARS-CoV-2 ablates tetherin expression III) spike protein can modestly down-regulate tetherin IV) ORF3A dysregulates tetherin localisation by altering retrograde trafficking. These conclusions are broadly supported by the data and this study make significant contributions to our understanding of SARS-CoV-2/tetherin interactions.

My enthusiasm is reduced by, in my opinion, a failure of the authors to fully quantify, explain and explore their data. I expect the manuscript could be significantly improved without further experimentation by strengthening these aspects.

This manuscript will be of interest to investigators in virology and/or cellular intrinsic immunity. Given the focus on SARS-CoV-2 it is possible/likely that it will find a slightly broader readership.

I have highly appropriate skills for evaluating this work being experienced in virology, SARS-CoV-2, cell biology and microscopy.

We wish to thank Reviewer #1 for their comments which have helped us to improve the quality of our revised manuscript.

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

BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

Major comments:- In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do

This text has now been edited,

‘The levels of tetherin in uninfected HAE cells is lower than observed in uninfected neighbours in infected wells demonstrating that infected HAE cells are able to generate IFN to act upon uninfected neighbouring cells, enhancing tetherin expression.’ - (Lines 163-172).

Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs.

The statement 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' has been removed. The micrographs in Figure 1E have been re-cropped, and image iii replaced with an image showing DMVs and budding virions. Plasma membrane-associated virions are highlighted by black arrowheads, DMVs by black asterisks, and intracellular virion by a white arrow.

Line 391, I can't see much change in CD63 distribution.

CD63 reproducibly appears clustered towards the nuclei in ORF3a expressing cells, whilst CD63 positive puncta are abundant in the periphery of mock cells. CD63 puncta are also larger, and the staining of CIMPR and VPS35 also appears to be associated with larger organelles. We have amended the text to now read,

‘Expression of ORF3a also disrupted the distribution of numerous endosome-related markers including CIMPR, VPS35, CD63, which all localised to larger and less peripheral puncta (Supplemental Figure 6B), and the mixing of early and late endosomal markers’ - (Line 469).

Quantification of the diameter of CD63 puncta indicate that they are larger in ORF3a expressing cells than in mock cells. Mock cells - 0.71μm (SD; 0.19), ORF3a - 1.15μm (SD;0.35). At least 75 organelles per sample, from 10 different cells. We have not included this data as we do not wish to labor this point but are happy to include this quantification if required to do so.

Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins?

We are careful in the manuscript not to claim that ORF7a has no effect on tetherin. Our data indicate that ‘ORF7a does not directly influence tetherin localisation, abundance, glycosylation or dimer formation’ - (Line 361-362).

We were unable to reproduce an effect of ORF7a on tetherin glycosylation. Our data conflicts with that presented by Taylor et al, 2015, where ORF7a impaired tetherin glycosylation and ORF7a localised to the plasma membrane in tetherin expressing cells. The experiments performed by Taylor et al used HEK293 cells and ectopically expressed tagged tetherin. The differences in results may be attributed to the differences between cell lines or due to differences between endogenous or ectopic / tagged tetherin.

The study by Taylor et al uses SARS-CoV-1 ORF7a-HA from Kopecky-Bromberg et al., 2007 (DOI: 1128/JVI.01782-06), where the -HA tag is positioned at the C-terminus. Our ORF7a-FLAG constructs have a C-terminal epitope tag. While we cannot exclude the possibility that tagged proteins may act differently from untagged ones, the differences between our findings and previous work appear unlikely to be due to epitope tags.

Our manuscript states that although we cannot find any effect of ORF7a on tetherin localisation, abundance, glycosylation, or dimer formation, we cannot exclude that ORF7a impacts tetherin by another mechanism. For example, ORF7a has been found to antagonise interferon responses. Tetherin is abundantly expressed in HeLa cells and expression does not require induction through interferon. None of our experiments above would be impacted by interferon antagonism yet this could impact other cell types besides infection in vivo. These possibilities may explain the reported differential impact of ORF7a by different labs. An addition comment has been added to the discussion to reflect this,

’We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a potently antagonises IFN signalling [38], which would impair tetherin induction in many cell types. - (Line 701-704).

Note - Reference 38 has been added to the manuscript – Xia et al., Cell Reports DOI: 10.1016/j.celrep.2020.108234

In the ORF screen, a number of the constructs are expressed at low level, is it possible they [the authors] are missing something?

Some of the ORFs expressed in the miniscreen appear poorly expressed. We accept that in the use of epitope tagged constructs expression levels of individual viral proteins may impact upon a successful screen. However, this screen was performed to identify any potential changes in tetherin abundance or localisation, and the screen did successfully identify ORF3a, which we were able to follow-up and verify.

Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

This paper had not been peer reviewed at the time of our initial submission. We have now included the following text,

‘SARS-CoV-2 ORF3a is an accessory protein that localises to and perturbs endosomes and lysosomes [29]. It may do so by acting either as a viroporin [30] or by interacting with, and possibly interfering with the function of VPS 39, a component of the HOPS complex which facilitates tethering of late endosomes or autophagosomes with lysosomes [29,31]. Given ORF3a likely impairs lysosome function, the observed increased….’ - (Lines 444-449).

Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

These experiments require more repeats to include error bars. Infection and plaque assay (Figure 3C, 3D) are currently ongoing and we plan to complete them in the next 6-8 weeks and include them in the finalised manuscript.

In the new experiments, infections will additionally be performed at MOI 0.01, in addition to the previous MOIs (1 and 5).

Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

We have altered the text to now say:

‘The ORF3a-mediated increase in tetherin abundance within endolysosomes could be due to defective lysosomal degradation.’ - (Line 475).

There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

Antibody uptake experiments in Figure 5H and Supp Figure 6E were performed and acquired on different days. Relatively low levels of signal are available in these antibody uptake experiments, and the disperse labelling seen in the mocks does not aid this.

Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

We have changed the blot in Figure 6A for one with clearer FLAG bands. Three independent experiments were performed for Figure 6A. Quantification of VLPs is now included in Supplemental Figure 7B.

We have changed ‘Bst2’ to ‘tetherin’ in all previous figures relating to protein; Figure 4G, Figure 6A, B, C.

We have no current information to compare ORF3a levels in these experiments versus in infected cells. We can investigate quantifying this if necessary.

My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

This is an interesting point, and as the reviewer concedes, this is tricky to answer. Expression of ORF3a causes the redistribution or remodeling of various organelles (Figures 1E, 2D, 2F, Supp Figures 2C, 2E, 3E, 6B, 6C, 6D). We have been unable to test the direct involvement of ERGIC, despite attempts with a number of commercial antibodies. Given the huge rearrangements of organelles during SARS-CoV-2 infection, it is unclear exactly what will happen to the distribution of ERGIC.

Minor comments: Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

Deleted.

Line 61, delete 'the'

Deleted.

Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

Deleted.

Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

We have now added the following text,

‘….liberating the viral nucleocapsid to the cytosol of the cell. Upon uncoating, the RNA genome is released into the host cytosol and replication-transcription complexes assemble to drive the replication of the viral genome and the expression of viral proteins. Coronaviruses modify host organelles to generate viral replication factories - so-called DMVs (double-membrane vesicles) that act as hubs for viral RNA synthesis [10]. SARS-CoV-2 viral budding occurs at ER-to-Golgi intermediate compartments (ERGIC) and newly formed viral particles traffic through secretory vesicles to the plasma membrane where they are released to the extracellular space.’ - (Lines 95-104).

Line 103, why are the neighbouring cells 'naive'?

‘naïve’ removed.

Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

Amended.

Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

Amended.

Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

This has been modified and now reads,

‘In order for tetherin to tether coronaviruses, tetherin must be incorporated in the virus envelope during budding which occurs in intracellular organelles.’ - (Lines 133-135).

Line 192, there is no EM data in Supplemental Fig 1C.

This has now been removed.

Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events.

This has been changed.

Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

These have been updated throughout the manuscript (Lines 183, 341, 3549, 356, 392, 509, 763, 1330, 1399).

Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

These are now changed to,

‘Transient transfection of cells with ss-HA-Spike caused a 32% decrease in tetherin as observed by immunofluorescence (Supplemental Figure 4A, 4B), with…’ – (Line 370).

‘To explore whether the Spike-induced tetherin downregulation altered virus release, we performed experiments with virus like particles (VLPs) in HEK293T …’ – (Line 399).

Line 379, OFR, should be ORF.

Yes, changed.

Line 448, 'Tetherin retains the ability' - did it ever loose it?

This has been rephrased to,

‘Tetherin has the ability to restrict a number of different enveloped viruses that bud at distinct organelles.’ - (Line 547).

Line 451, 'luminal' is confusing in this context.

This has been modified to,

‘Tetherin forms homodimers between opposing membranes (e.g., plasma membrane and viral envelope) that are linked via disulphide bonds.’ - (Line 549).

Line 453, the process of virus envelopment is likely to be more than a 'single step'

This now reads,

‘…virus during viral budding, which occurs in modified ERGIC organelles.’ - (Line 552).

Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

We have removed mention of mechanisms of tetherin antagonism by other viruses. The key point we wish to make here is that tetherin is lost from the budding compartment. This now reads,

‘Many enveloped viruses antagonise tetherin by altering its localisation and removing it from the respective site of virus budding.’ – (Line 552-553).

Line 472, what is meant by 'resting states'?

This should have been ‘in the absence of stimulation’ and have now been re-written,

‘Tetherin is an IFN-stimulated gene (ISG) [13], and many cell types express low levels of tetherin in the absence of stimulation.’ - (Line 577).

Line 1204, how were 'mock infected cells .......... infected'?

This has now been re-written,

‘Differentiated nasal primary human airway epithelial (HAE) cells were embedded to OCT….’ - (Line 1385).

Reviewer #2 (Significance (Required)):

This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

We wish to thank Reviewer #2 for their insightful comments and suggestions for improving this work.

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

Restriction factors are major barriers against viral infections. A prime example is Tetherin (aka BST2), which is able to physically tether budding virions to the plasma membrane preventing release of the infectious particles. Of note, tetherin has broad anti-viral activity and has been established as a crucial innate immune defense factor against HIV, IAV, SARS-CoV-2 and other important human pathogens. However, successful viruses like SARS-CoV-2 evolved strategies to counteract restriction factors and promote their replication. Important restriction factors, such as tetherin, may often be targeted by multiple viral strategies to ensure complete suppression of their anti-viral activities by the pathogen. Of note, it was previously published that the accessory protein ORF7a of SARS-CoV-2 binds to (Petrosino et al, Chemistry Europe, 2021) and antagonizes it (Martin-Sancho et al, Molecular Cell, 2021). Previous data on SARS-CoV also revealed that ORF7a promotes cleavage of tetherin (Taylor et al, 2015, J Virol). In this manuscript, the authors show that tetherin restricts SARS-CoV-2 by tethering virions to the plasma membrane and propose that tetherin is targeted by two proteins of SARS-CoV-2. Whereas the Spike protein promotes degradation of tetherin, the accessory protein ORF3a redirects tetherin away from newly forming SARS-CoV-2 virions. While the overall findings that both S and ORF3a are additionally targeting tetherin is both novel and intriguing, additional evidence is needed to support this. In addition, the authors show that in their experimental setups ORF7a does not induce cleavage of tetherin. This is in direct contrast to previously published data both on SARS-CoV(-1) and -2 (Taylor et al, 2015, J Virol; Petrosino et al, Chemistry Europe, 2021; Martin-Sancho et al, Molecular Cell, 2021). From my point of view that needs further experimental confirmation. While the authors state that the impact of Spike on tethrin is mild, the experiments should still allow the conclusion whether there is a (mild) effect or not. The mechanism of ORF3a is fortunately more robustly assessed and provides some novel insights. Unfortunately, the whole manuscript suffers from a striking lack of quantifications. In addition, it is not clear whether and how many times experiments were repeated to the same results. Overall, the data in this manuscript seem very speculative and preliminary and thus do not support the authors conclusions.

Major:

Much of the data seems like it was only done once. As I am sure that this is a writing issue, please clearly state how many times the individual assays were repeated, provide the quantification graphs and appropriate statistics. Some experiments may need additional quantification and confirmation by other methods to be convincing.

Quantification is provided throughout the revised manuscript. Figure legends have also been updated to provide information on quantification and statistical analysis.

For example, Figure 1A, C and D: Please quantify the levels of tetherin and use an alternative readout, e.g. Western blotting of infected cells.

Quantification has been performed and included in our revised manuscript in Supplemental Figures 1C, 1E. Tetherin is not shown in Figure 1C.

A table is provided (above) to highlight the additional quantification.

Figure 2A: Please quantify.

We are not sure we understand this point. The western blot shown in Figure 2A demonstrates the ectopic expression of ACE2 in our A549 cell line. A549 cells have been used by many labs to study SARS-CoV-2 infection, but express negligible ACE2.

Fig 3A: Please show and confirm successful tetherin KO in the cell lines that are used not only in microscopy.

A new blot is now shown in Figure 3A, including a blot demonstrating tetherin loss in both KO lines.

Figure 4C: Please quantify

Currently flow cytometry experiments have been performed twice each and this is now detailed in the figure legends. The data shown in each panel is representative and the data has been explored using analogous approaches. For example, Figure 4C is complemented by Figures 4A and 4B, Figures 4E is complemented by 4D and 4F. We do not feel that repeating these flow cytometry analysis will significantly improve the manuscript.

Figure 4D: Please quantify the effects are not obvious from the images provided.

Quantification is now provided in Supplemental Figure 4E.

Figure 4E, F Please provide a quantification of multiple independent repeats, the claimed differences are neither striking nor obvious.

Quantification of 4F is now provided in Supplemental Figure 4G. Tetherin levels were quantified to be reduced by 25% (SD: 8%) by addition of Doxycycline and induction of ss-HA-Spike. Information for quantification is provided in figure legends.

Figure 5A: Please quantify

These experiments have currently been performed twice and this is now described in the figure legends. Data shown is representative. We can perform one more repeat of these experiments to quantify if neccessary, but do not feel it will significantly alter the manuscript.

Figure 3C and D: At timepoint 0 the infection input levels are different. The initial infection levels have to be the same to draw the conclusion that tetherin KO affects virion release and not the initial infection efficiency. Can the authors either normalize or ensure that the initial infection is the same in all conditions and that variations in the initial infection efficiency do not correlated with the impact of tetherin on replication/release ? How often were those experiments repeated? Are the marginal differences in infectious titre significant? Overall the impact of tetherin on SARS-CoV-2 is very underwhelming but that may be due to efficient viral tetherin-counteraction strategies. Why is the phenotype inverted at 72 h?

Equal amounts of virus, as measured by plaque-forming units (PFU), were used for both HeLa cell lines and thus at 0 hpi the variation seen is within the parameters of the assay used. It remains possible that tetherin affects virus entry but this is unlikely and this assay was not designed to investigate that effect.

Growth curve assays are currently being repeated using an MOI of 0.01, 1 and 5. We are removing the 72 hpi sample from future experiments. At this time point, we find that the extensive cell death caused by viral replication (especially at higher MOIs) makes it difficult to accurately separate the released from intracellular fractions and conclusions cannot be accurately drawn from the data.

Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

Figure 4B and C: Can the authors provide an explanation why SARS-CoV ORF7a is not inducing cleavage/removes glycosylation of tetherin. To show that the assays work, an independent positive control needs to be included. The FACS data in C is unfortunately not quantified.

See above comments (Reviewer #2) regarding discussion on ORF7a. Additional text has been included to discuss ORF7a data,

‘SARS-CoV-1 ORF7a is reported to inhibit tetherin glycosylation and localise to the plasma membrane in the presence of tetherin [18]. We did not observe any difference in total tetherin levels, tetherin glycosylation, ability to form dimers, or surface tetherin upon expression of either SARS-CoV-1 or SARS-CoV-2 ORF7a (Figures 4A, 4B, 4C).

Others groups have demonstrated a role for ORF7a in sarbecovirus infection and both SARS-CoV-1 and SARS-CoV-2 virus lacking ORF7a show impaired virus replication in the presence of tetherin [18,41]. A direct interaction between SARS-CoV-1 ORF7a and SARS-CoV-2 ORF7a and tetherin have been described [18,41], although the precise mechanism(s) by which ORF7a antagonises tetherin remains enigmatic. We cannot exclude that ORF7a requires other viral proteins to antagonise tetherin, or that ORF7a antagonises tetherin via another mechanism. For example, ORF7a can potently antagonise IFN signalling [42] which would impair tetherin induction in many cell types.’ – (Line 667-704).

Fig 4G: The rationale and result of this experiment are not clear.

The rationale for Spike VLP experiments is explained at Line 403. Given that Spike caused a reproducible decrease in cellular tetherin, we examined whether this downregulation was sufficient to antagonise tetherin and increase VLP yield.

Fig 6: What is the benefit of doing the VLP assays as opposed to genuine virus experiments? To me it rather seems to be making the data unnecessarily complex. Again, no quantifications or repeats are provided.

VLPs are used to separate the budding and release process from the replication process of RNA viruses. VLPs have been used in a number of SARS-CoV (DOI: 1002/jmv.25518) and HIV-1 (DOI: https://doi.org/10.1186/1742-4690-7-51) studies to analyse the impact of tetherin (and tetherin mutants) on release.

VLP experiment quantification are now included throughout.

Minor: Fig 1D: How do the authors explain the mainly intracellular Spike staining?

We do not understand this point. Spike staining is intracellular, whether expressed alone or in the context of infected cells.

Please add statistical analyses on the data e.g. Fig. 3 C and D

Additional repeats of these experiments are in progress and will be included in the finalised manuscript.

Fig. 4B and F: Why do the annotated sizes of tetherin differ between the blots?

Figures 4B and 4F are run in non-reduced and reduced conditions respectively. In order to best show the dimer deficient C3A-Tetherin, blots are typically run in non-reduced conditions to exemplify dimer formation and to highlight any defects in dimer formation. The rest of the blots in the manuscript are run in denaturing conditions to aid blotting of other proteins. (Lines 957-958) and now (Lines 1356-1357).

Fig. 5A: What is ORF6a? Do the authors mean ORF6?

Yes, this has been changed.

An MOI of 1 is NOT considered a low or relevant MOI. Can the authors either rephrase or repeat experiments with an actual low or relevant MOI i.e. 0.01 ?

We are currently repeating these experiments and are including MOIs of 0.01, 1 and 5.

Why were the cell models switched between Figure 1 and 2 and essentially the same experiments repeated?

HeLa cells express high levels of tetherin at steady state, whilst A549 cells require IFN stimulation. HeLa cells demonstrate that tetherin downregulation occurs via an IFN-independent manner. A549 and T84 cells are more physiologically relevant cell types for SARS-CoV-2 infection. These points are stated in Lines 230 and 261.

The manuscript may benefit a lot from streamlining and removing unessential deviations from the main message (e.g. discussions why multistep/single step growth curves are used/not relevant; why are they shown if the authors conclude that a single step is not relevant?). The discussion is extremely lengthy and does not provide sufficient discussion of the presented data.

The multistep/single step growth curve text will be adapted, but it will be re-written after additional infection experiments.

We have removed from the Discussion a small section discussing ORF7a mutants, given that the emphasis of our manuscript is not on ORF7a.

We have also removed a small section describing the rearrangements of intracellular organelles by SARS-CoV-2 as it does not directly relate to the central message of our manuscript.

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

Reviewer #3 (Significance (Required)):

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

We thank Reviewer#3 for their comments and suggestions for improving this work.

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

Restriction factors are major barriers against viral infections. A prime example is Tetherin (aka BST2), which is able to physically tether budding virions to the plasma membrane preventing release of the infectious particles. Of note, tetherin has broad anti-viral activity and has been established as a crucial innate immune defense factor against HIV, IAV, SARS-CoV-2 and other important human pathogens. However, successful viruses like SARS-CoV-2 evolved strategies to counteract restriction factors and promote their replication. Important restriction factors, such as tetherin, may often be targeted by multiple viral strategies to ensure complete suppression of their anti-viral activities by the pathogen. Of note, it was previously published that the accessory protein ORF7a of SARS-CoV-2 binds to (Petrosino et al, Chemistry Europe, 2021) and antagonizes it (Martin-Sancho et al, Molecular Cell, 2021). Previous data on SARS-CoV also revealed that ORF7a promotes cleavage of tetherin (Taylor et al, 2015, J Virol).

In this manuscript, the authors show that tetherin restricts SARS-CoV-2 by tethering virions to the plasma membrane and propose that tetherin is targeted by two proteins of SARS-CoV-2. Whereas the Spike protein promotes degradation of tetherin, the accessory protein ORF3a redirects tetherin away from newly forming SARS-CoV-2 virions.

While the overall findings that both S and ORF3a are additionally targeting tetherin is both novel and intriguing, additional evidence is needed to support this. In addition, the authors show that in their experimental setups ORF7a does not induce cleavage of tetherin. This is in direct contrast to previously published data both on SARS-CoV(-1) and -2 (Taylor et al, 2015, J Virol; Petrosino et al, Chemistry Europe, 2021; Martin-Sancho et al, Molecular Cell, 2021). From my point of view that needs further experimental confirmation. While the authors state that the impact of Spike on tethrin is mild, the experiments should still allow the conclusion whether there is a (mild) effect or not. The mechanism of ORF3a is fortunately more robustly assessed and provides some novel insights. Unfortunately, the whole manuscript suffers from a striking lack of quantifications. In addition, it is not clear whether and how many times experiments were repeated to the same results. Overall, the data in this manuscript seem very speculative and preliminary and thus do not support the authors conclusions.

Major

Much of the data seems like it was only done once. As I am sure that this is a writing issue, please clearly state how many times the individual assays were repeated, provide the quantification graphs and appropriate statistics. Some experiments may need additional quantification and confirmation by other methods to be convincing. For example, Figure 1A, C and D: Please quantify the levels of tetherin and use an alternative readout, e.g. Western blotting of infected cells. Figure 2A: Please quantify. Fig 3A: Please show and confirm successful tetherin KO in the cell lines that are used not only in microscopy. Figure 4C: Please quantify. Figure 4D: Please quantify the effects are not obvious from the images provided. Figure 4E,F Please provide a quantification of multiple independent repeats, the claimed differences are neither striking nor obvious. Figure 5A: Please quantify.

Figure 3C and D: At timepoint 0 the infection input levels are different. The initial infection levels have to be the same to draw the conclusion that tetherin KO affects virion release and not the initial infection efficiency. Can the authors either normalize or ensure that the initial infection is the same in all conditions and that variations in the initial infection efficiency do not correlated with the impact of tetherin on replication/release ? How often were those experiments repeated? Are the marginal differences in infectious titre significant? Overall the impact of tetherin on SARS-CoV-2 is very underwhelming but that may be due to efficient viral tetherin-counteraction strategies. Why is the phenotype inverted at 72 h? Figure 4B and C: Can the authors provide an explanation why SARS-CoV ORF7a is not inducing cleavage/removes glycosylation of tetherin. To show that the assays work, an independent positive control needs to be included. The FACS data in C is unfortunately not quantified.

Fig 4G: The rationale and result of this experiment are not clear.

Fig 6: What is the benefit of doing the VLP assays as opposed to genuine virus experiments? To me it rather seems to be making the data unnecessarily complex. Again, no quantifications or repeats are provided.

Minor

Fig 1D: How do the authors explain the mainly intracellular Spike staining?

Please add statistical analyses on the data e.g. Fig. 3 C and D

Fig. 4B and F: Why do the annotated sizes of tetherin differ between the blots?

Fig. 5A: What is ORF6a? Do the authors mean ORF6?

An MOI of 1 is NOT considered a low or relevant MOI. Can the authors either rephrase or repeat experiments with an actual low or relevant MOI i.e. 0.01 ?

Why were the cell models switched between Figure 1 and 2 and essentially the same experiments repeated? The manuscript may benefit a lot from streamlining and removing unessential deviations from the main message (e.g. discussions why multistep/single step growth curves are used/not relevant; why are they shown if the authors conclude that a single step is not relevant?). The discussion is extremely lengthy and does not provide sufficient discussion of the presented data.

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin. My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

Significance

According to my opinion, the current manuscript does not provide significant advancement for the field. While the intention was to update and expand our existing knowledge about tetherin restriction by SARS-CoV-2, the experiments do not support this yet. However, the general premise and approach/concept of the manuscript would be appealing to a broader audience. I especially like the notion that multiple proteins of SARS-CoV-2 could synergistically counteract an important innate immune defense factor, tetherin.

My expertise is on SARS-CoV-2 and the interplay between the virus and host cell restriction factors.

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

BST2/tetherin can restrict the release and transmission of many enveloped viruses, including coronaviruses. In many cases, restricted viruses have developed mechanisms to abrogate tetherin-restriction by expressing proteins that antagonize tetherin; HIV-1 Vpu-mediated antagonism of tetherin restriction is a particularly well studied example. In this paper, Stewart et al. report their studies of the mechanism(s) underlying SARS-CoV-2 antagonism of tetherin restriction. They conclude that Orf3a is the primary virally encoded protein involved and that Orf3a manipulates endo-lysosomal trafficking to decrease tetherin cycling and divert the protein away from putative assembly sites.

Major comments:

In my view some of the claims made by the authors are not fully supported by the data. For example, the bystander effect discussed in line 162 may suggest that infected cells can produce IFN but does not 'indicate' that they do. Most of the EM images show part of a cell profile, so statements such as (line 192) 'virus containing tubulovesicular organelles were often polarised towards sites of significant surface-associated virus' should be backed up with appropriate images, or indicated as 'not shown', or removed (the observation is not so important for this story). Line 196, DMVs can't be seen in these micrographs. Line 391, I can't see much change in CD63 distribution.

Line 321, the authors show that ORF7a does not affect tetherin localization, abundance, glycosylation or dimer formation, but they don't show that it doesn't restrict SARS-CoV-2. Can they be sure that epitope tagging this molecule does not abrogate function (or the functions of any of the other tagged proteins for that matter), or that ORF7a works in conjunction with one of the other viral proteins? In the ORF screen, a number of the constructs are expressed at low level, is it possible they are missing something?

Line 376, the authors refer to ORF3a being a viroporin. A recent eLife paper (doi: 10.7554/eLife.84477; initially published in BioRxiv) refutes this claim and builds on other evidence that ORF3a interacts with the HOPS complex. The authors should at least mention this work, especially in the discussion, as it would seem to provide a molecular mechanism to support their conclusions.

Fig 3, the growth curves illustrated in Fig3 C and D do not have errors bars; how many times were these experiments repeated?

Line 396, the authors show increased co-localization with LAMP1. As LAMP1 is found in late endosomes as well as lysosomes, they cannot claim the redistributed tetherin is specifically in lysosomes.

There seems to be a marked difference in the anti-rb555 signal in the 'mock' cells in panels 5H and Suppl 6E. Is there a good reason for this, or does this indicate variability between experiments?

Fig 6a, why is there negligible VLP release from cells lacking BST2 and ORF3a-strep? How many times were these experiments performed? Is this a representative image? I think it confusing to refer to the same protein by two different names in the same figure (i.e. BST2 and tetherin). Do the authors know how the levels of ORF3a expressed in cells in these experiments compares to those seen in infected cells?

My final point is, perhaps, the trickiest to answer, but nevertheless needs to be considered. As far as we know, SARS-CoV-2 and at least some other coronaviruses, bud into organelles of the early secretory pathway, often considered to be ERGIC. In the experiments shown here the authors provide evidence that ORF3a can influence tetherin recycling, but the main way of showing this is through its increased association with endocytic organelles. Do the authors have any evidence that Orf3a reduces tetherin levels in the ERGIC or whether the tetherin cycling pathway(s) involve the ERGIC?

Minor comments:

Overall, the manuscript should be carefully edited to ensure the text reads clearly. A few examples of thing that need to be fixed are:-

Line 53, delete 'shell' its redundant and confusing when the authors have said coronaviruses have a membrane.

Line 61, delete 'the'

Line 72, delete 'enveloped'; coronaviruses already described as enveloped viruses (line 53)

Lines 93 - 100, lop-sided discussion of the viral life cycle; this paragraph is mostly about entry, which is not relevant to this paper, and does not really deal with the synthesis and assembly side of the cycle.

Line 103, why are the neighbouring cells 'naive'?

Line 112 - 113, delete last phrase; tetherin is described as an IFN stimulated gene in line 111; to be accurate, the beginning of the sentence should be 'Tetherin is expressed from a type 1 Interferon stimulated gene ...'

Line 118 - 119, should say 'For tetherin-restricted enveloped viruses' as not all enveloped viruses are restricted by tetherin.

Line 131, coronaviruses are not the only family of tetherin-restricted viruses that assemble on intracellular membranes, e.g. bunyaviruses.

Line 192, there is no EM data in Supplemental Fig 1C.

Line 251, 'a synchronous infection event' should be 'synchronous infection' as there will be multiple infection events

Page 13 (and elsewhere), unlike Southern, 'Western' should not have a capital letter, except at the start of a sentence.

Lines 330 and 352, can the authors quantitate S protein-induced reduction in cell surface tetherin rather than using the somewhat subjective 'mild'?

Line 379, OFR, should be ORF.

Line 448, 'Tetherin retains the ability' - did it ever loose it?

Line 451, 'luminal' is confusing in this context.

Line 453, the process of virus envelopment is likely to be more than a 'single step'

Line 457, in my view the notion that Vpu abrogation of tetherin restriction is just due to redistribution of tetherin to the TGN is somewhat simplistic and disregards a lot of other work.

Line 472, what is meant by 'resting states'?

Line 1204, how were 'mock infected cells .......... infected'?

Significance

This study builds on published work supporting the notion that SARS-Cov-2 ORF3a is an antagonist for the restriction factor tetherin. Importantly, it provides insights to the the mechanism of ORF3a mediated tetherin antagonism, specifically to ORF3a inhibits tetherin cycling, diverting the protein to lysosomes and away from compartment(s) where virions assemble. Overall, the authors provide good supporting evidence for these conclusions, however there are issues that the authors need to address.

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

I summarise the major findings of the work below. In my opinion the range and application of approaches has provided a broad evidence base that, in general, supports the authors conclusions. However, there are, in my opinion, particular failures to utilise and communicate this evidence. The manuscript may be much improved with attention in the following areas. In each case I will give general criticism with a few examples, but the principals of my comments could be applied throughout the work.

1. Insufficient quantification. The investigation combines various sources of qualitative data (EM, fluorescence microscopy, western blotting) to generate a reasonably strong evidence base. However, the work is over-reliant on representative images and should include more quantification from repeat experiments. When there are multiple fluorescence micrographs with intensity changes (not necessarily just representative images) (e.g. Figure 1 or 2) the authors should consider making measurements of these. Also the VLP production assays, which are assessed by western blotting would particularly benefit from a quantitative assessment (either by densitometry or, if samples remain, ELISA/similar approach).
2. Insufficient explanation. I found some of the images and legends contained insufficient annotation and/or description for a non-expert reader to appreciate the result(s). Particularly if the authors want to draw attention to features in micrographs they should consider using more enlarged/inset images and annotations (e.g. arrows) to point out structures (e.g DMVs etc.). This short coming exacerbates the lack of quantification.
3. Insufficient exploration of the data. I had a sense that some aspects of the data seem unconsidered or ignored, and the discussion lacks depth and reflection. For example the tetherin down-regulation apparent in Figures 1 and 2 is not really explained by the spike/ORF3a antagonism described later on, but this is not explicitly addressed. Also, Figure 6 suggests that ORF3a results in high levels of incorporation of tetherin in to VLPs, but I don't think this is even described(?). The discussion should also include more comparison with previous studies on the relationship between SARS-2 and tetherin.

I have no minor comments on this draft of the manuscript.

Significance

Tetherin, encoded by the BST2 gene, is an antiviral restriction factor that inhibits the release of enveloped viruses by creating tethers between viral and host membranes. It also has a capacity for sensing and signalling viral infection. It is most widely understood in the context of HIV-1, however, there is evidence of restriction in a wide variety of enveloped viruses, many of which have evolved strategies for antagonising tetherin. This knowledge informs on viral interactions with the innate immune system, with implications for basic virology and translational research.

This study investigates tetherin in the context of SARS-CoV-2. The authors use a powerful collection of tools (live virus, gene knock out cells, recombinant viral and host expression systems) and a variety of approaches (microscopy, western blotting, infection assays), which is, itself, a strength. The study provides evidence to support a series of conclusions: I) BST2/tetherin restricts SARS-CoV-2 II) SARS-CoV-2 ablates tetherin expression III) spike protein can modestly down-regulate tetherin IV) ORF3A dysregulates tetherin localisation by altering retrograde trafficking. These conclusions are broadly supported by the data and this study make significant contributions to our understanding of SARS-CoV-2/tetherin interactions.

My enthusiasm is reduced by, in my opinion, a failure of the authors to fully quantify, explain and explore their data. I expect the manuscript could be significantly improved without further experimentation by strengthening these aspects.

This manuscript will be of interest to investigators in virology and/or cellular intrinsic immunity. Given the focus on SARS-CoV-2 it is possible/likely that it will find a slightly broader readership.

I have highly appropriate skills for evaluating this work being experienced in virology, SARS-CoV-2, cell biology and microscopy.