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Referee #4

Evidence, reproducibility and clarity

Summary

The authors in this manuscript create in vitro degron models of DNMT1 as tools to investigate the roles and functions of DNA methylation in molecular and cellular processes. Degron models can directly target the tagged protein of interest leading to its degradation. When it comes to DNMT1, this system can bypass the use DNMT inhibitors, like DAC and GSK3685032 that can have secondary cytotoxic effects. More specifically, the authors create DNMT1 degron tagged models of two cell lines (DLD-1 and RPE1), as well as a DNMT1 degron tagged model of a DNMT3BKO DLD-1 cell line. These systems allowed the authors to investigate the passive demethylation occurring over consecutive cell divisions, and particularly the role of DNMT1 and DNMT3B and their cooperativity in maintaining DNA methylation levels and how this differs among different genomic regions. The authors characterise the cell fitness of the models they established when DNMT1 is degraded, and methylation levels are lost, and observe a reduction of fitness due to G1 arrest. Finally, the authors show that the loss of DNA methylation observed in these cells leads to reduced levels of heterochromatin (H3K9me3) as well as changes in chromatin and nuclear compartmentalization. Overall, the authors, show an appealing in vitro model that can directly target DNMT1, allowing for more delicate experiments that address the impact of DNA methylation levels in somatic cells, to de-convolute their exact roles from other epigenetic marks and cellular processes that are often correlated with.

Major comments

• The auxin degron system relies on the ectopic expression of OsTir1, which is described in materials and methods under 'Plasmids and Cell line generation'. However, OsTir1 expression is never addressed during the manuscript. Quantification of OsTir1 expression levels across the different cell lines is very important in order to more comprehensively characterise this system. This is especially when considering one of the key points of the authors is to establish these new in vitro models as a new tool to study DNA methylation dynamics in the field.
• The degron system requires an endogenous tag of the protein of interest. Specifically in this work, a tag including the mNGreen and the AID sequence are incorporated at the N-terminus of DNMT1. It is unlikely that there is major interference of the tag to protein function as the tagged cells for DLD-1 and RPE1 are both viable and demonstrate high methylation levels. However, the authors do not consider or discuss that the tag might interfere with the function of the protein at all. It would be useful if the authors compared the tagged cell lines (untreated) with wildtype controls for their methylation levels and/or DNMT1 expression and/or DNMT1 localisation with imaging. These experiments would better substantiate the use of untreated cells as 'wildtype' equivalents and contribute to the better characterisation of these systems as in vitro models.

Furthermore, DNMT1 can have different transcripts that begin from different sites. Do the authors consider whether the tag is included in all/most isoforms of DNMT1, or if there are any expressed without it? - The authors observe that DNMT1 is important for maintaining methylation levels as well as proper cell proliferation. They also observe that DNMT1 depletion does not lead to complete lethality as previously observed (Rhee et al., 2000 Nature, Chen et al., 2007 Nature Genetics). They hypothesise that this might be due to non-specific toxic effects (from CRE) and suggest that the degron system is better suited to bypass such toxicity effects. While this might be true and degron systems do provide a direct and acute protein depletion without non-specific toxicity, the authors do not discuss the implications p53 activity might have on the lack of lethality they observe. Omitting the role of p53 in hypomethylation models and drawing conclusions about toxicity effects between different systems can be misleading and should be corrected. Specifically, it has been shown that hypomethylation triggers p53 dependent apoptosis (Jackson-Grusby et al., 2001 Nature Genetics). The authors do acknowledge the difference in p53 activity when comparing between DLD-1 and RPE-1 DNMT1 depleted cells. The reduced proliferation of RPE-1 cells would suggest that irrespective to the degron system, viability depends on tolerance of each cell line to hypomethylation (whether this is p53 dependent or not). DLD-1 cells seem to have a single nucleotide variant in p53 (p.Ser241Phe (c.722C>T)) (Liu et al., 2006 PNAS), that could potentially explain their viability upon hypomethylation, although further work is required to conclusively suggest such interaction. Furthermore, DNA methylation levels and chromatin organisation of RPE-1 NADNMT1 cells are not characterised in the manuscript and is unclear why. - Figure 1D, 1E: The authors provide a Western blot of DNMT (1/3A/3B) across the established cell lines. While some effects like the degradation of DNMT1 based on the degron system or the KO of DNMT3B are convincing (and work well to validate the cell lines), the observation about upregulation of DNMT3B when DNMT1 is degraded, or levels of DNMT1 after wash out, are not as convincing when only showing one blot. This is especially when considering that the DNMTs might have cell cycle expression differences. Additional replicas of the western blot and quantification of bands across replicas, or qPCR to show upregulation of DNMT transcripts, or imaging (like figure S1E), would help make the claim of DNMT3B upregulation and DNMT1 recovery more convincing. - The authors show that during wash out (after stopping the IAA treatment), DNMT1 levels can recover slightly and show the methylation levels of specific sites (figure 2B). However, the authors do not make any characterisation of the global levels of methylation levels and their potential recovery (?) after wash out. This could be either done by imaging (like in figure 1F and 1G) or dot blot (like figure S2A) or mass-spec.

The authors note that recovery of DNMT1 after wash out is to a lesser extent in the NADNMT1/DNMT3B-/- background. The authors do not speculate why would this be. Past reports of degron tagged proteins show that after treatment endogenous protein levels can recover. Does this hint towards a viability issue of the line due to excessive hypomethylation? While difficult to prove it would be useful to speculate why this effect occurs. - The authors employ DNAme arrays to assess the DNA methylation loss after degradation of DNMT1 and study where in the genome this occurs. Specifically, the authors look on differentially methylated probes between treated/non treated samples and demonstrate their abundance over different genomic regions (figure 2E and S2 H, I, J, K). However, this way of visualising the data is a bit difficult to interpret as differences can be small. Furthermore, number of probes across the genome is not uniformly distributed, so it would be useful to include these numbers. It would be helpful if authors can provide genome browser snapshots with methylation levels and accompanying histone marks (from available data, Rokavec et al., 2017?) like done in figure 4F, S4B and S5C to show representative regions that showcase their observations. Coverage of the EPIC array will mean that these tracks will not have high coverage and thus gaps, and ideally one would need whole genome bisulfite data, however hopefully some snapshots can demonstrate locus specific changes better.

Considering the function of DNMT1 in remethylating the DNA after replication, one would assume that methylation is lost equally across the genome as a simplistic model. Of course, there are many reasons like secondary functions of DNMT1, DNMT3A/3B and TET activity etc that could alter this and provide biases over regions of the genome. The authors discuss this and note most probes show such loss (106,647 of 178,529). It would be useful for the authors to better describe where the rest of the probes (that do not lose the expected methylation, annotated as 'late') are located and speculate what mechanisms might be involved. This is partly addressed in figures S2H and J, but it is not immediately clear what distinguishes late regions from early. Genome tracks with methylation levels and histone tracks as mentioned above could provide examples of regions.

The authors briefly discuss the role of DNMT1 and DNMT3B in methylating specific regions and their cooperativity as well as the underexplored de novo activity of DNMT1. Based on their findings, can the authors draw any new mechanistic conclusions/observations about the activity of DNMT1 and/or DNMT3B and how it is directed? Are there any sequence signatures or histone mark profiles that could explain the hypomethylation or remethylation (after wash out) of specific loci? - The authors observe that 70% of DMPs display an increased methylation in the DNMT3BKO cell line compares to NADNMT1. The authors speculate that this is due to an 'uncontrolled activity' of DNMT1 in the absence of DNMT3B. The increased levels observed could be a clonal effect when generating the KO line. While including additional clonal lines can be a significant amount of work, the authors should acknowledge the effects of clonality in their findings when comparing between the cell lines used (that do not relate to the IAA treatments). - In figures 3D and S3D, the authors compare the viability between IAA treated cells as well as DAC and GSK3685032 and observe increased toxicity/lethality in the case of DAC and GSK3685032. It would be helpful for the authors to discuss the dosage and concentration they used for each drug and why. In order to compare the viability of cells between treatment of different drugs, one would expect dosages that lead to equivalent extents of hypomethylation. - The authors show in figure 3 that the cell lines used have major cell cycle defects, with pronounce G1 arrest, when treated with IAA. Then the authors proceed to perform HiC in treated and untreated sample in figure 4. Can cell cycle differences be cofounding in chromatin compartments and thus affect the data observed in HiC? - For figure 4F and G the authors note a global reduction of H3K9me3 levels after treatment. It would be helpful if the authors include assessment of global levels of H3K9me3 (for e.g. by WB) or ChIP qPCR on loci of interest or specify the use of spike-in in methods, as alterations in global levels of a mark can lead to skewed normalisation/quantifications between samples. Alternatively, comparing the peaks/domains of a mark (and whether they are conserved across cell lines) but not directly compare levels can provide a safer interpretation of the data. - For figures 4F and S5C different days of treatment are provided, with HiC and H3K9me3 being done after 10d of IAA and CpG methylation after 4d of IAA. It is not explained why this discrepancy in days of treatment has occurred, which can be misleading as 10d treated cells should have lower methylation levels from 4d treated cells.

Minor comments:

• Typo in introduction: germiline
• Introduction has some sentences that might need rewording. For example: 'Somatic DNAme domains are erased right after fertilization to establish a totipotent germiline epigenotype, deposited de novo during early development and undergo massive re-shaping during differentiation, lineage specification, and in response to external cues; then, they are maintained and inherited through cell divisions'. It would be good if this is broken into smaller sections as it is hard to follow.
• Introduction does not include the degron technologies and their advancement in the last couple of years. Considering the main point of the paper is to establish an in vitro tool to study DNA methylation based on degrons, it would be helpful to include some information about the technology in the introduction.
• Introduction does not include HiC technologies and the different compartments (A/B, and further subcategories) that the genome can be divided in by them. As the authors then proceed to use HiC data and perform such genome compartmentalisation, it would be helpful if this is addressed briefly at the introduction.
• The authors do not mention the DNMT3BKO strategy they employed. Specifically, the exact strategy should be listed under 'Plasmids and Cell line generation'. A genotyping PCR at supplementary (like figure S1B) could be added. A schematic like Supplementary Figure S1A would also be helpful, but not necessary.
• The duration and concentration of DAC and GSK368503 are not always indicated in figure legends.
• Figure 1C. Homozygous intensity of GFP is much more heterogeneous than the heterozygous levels. It would be interesting if authors could speculate why this is.
• Figure S1D, S1E: Quantification of imaging experiments is shown, however there is no representative images of the staining performed. Incorporate an example image of each staining would be helpful to accompany the quantifications.
• Typo: 106,647 ("early") of 178,529 probes
• Figure 2D: DNA methylation levels in somatic cell lines usually have a bimodal distribution with highly and lowly methylated regions, thus the representation of the data with a boxplot can be misleading.
• Figure 3E: The no. of colonies after IAA removal (from figure 3D) is not included, as suggested from the text.
• Figure S3E: Aneuploidy will be dependent on number of cell divisions so it would be helpful if authors specified how long after treatment the experiment was performed.
• Figure S4B typo: On top track blue compartment is annotated as DLD1-H, while I think it should be DLD1-B2/B3?
• It would be helpful if the authors include an example image of how the segmentation and quantifications for figure 4A and 4B-C were performed as a supplementary figure, demonstrating the area they consider as periphery.
• Figure 3B-C have no error bars and figure legend mentions N>15643 cells per condition. It would be helpful if the number of cells per condition is included in the legend and error bars are included in the figure.
• The authors note that there must be a cooperative effect of DNMT1 and DNMT3B in maintaining DNA methylation and that they observe a strong additive effect in cell survival in double DNMT1/3B depleted cells. These observations have already been observed in the past in HCT116 cells, so it would be useful to cite these papers along with their observations. For e.g. Rhee et al., 2002 Nature, Cai et al., 2017 Genome Research
• A degron tagged DNMT1 in HCT116 cells has already been shown at Onoda et al 2022 bioRxiv that would be good to reference. While the authors in this preprint do not perform any characterisation of methylation levels of the tagged line as in this work, it provides a similar in vitro model that is helpful to include.
• The effects of extensive hypomethylation due to the lack of DNMT activity and its effect in 3D genome integrity has also been shown in the best and would be helpful to mention. For e.g. Du et al., 2021 Cell Reports

Significance

The authors in this work generate and characterise an untransformed (DLD-1) and cancer (RPE-1) cell line model of DNMT1 with a degron tag, as well as DNMT3BKO line of DLD-1 with the degron tagged DNMT1. These in vitro degron models allow for acute deletion of DNMT1 and induced hypomethylation and can be valuable tools to study the effect of DNA methylation in other epigenetic marks and cellular processes. The authors demonstrate the role of DNMT1 and DNMT3B and their cooperativity in maintaining DNA methylation levels in these cells, as previously demonstrated in similar somatic cell models. They also characterise the fitness of these cell lines after DNMT1 degradation and note their viability over DAC and GSK3685032 treatments that can have secondary cytotoxic effects. However, the viability of the cells and the reasons of observed lethality in some systems is underexplored, with the extent of hypomethylation in each system not specified. Finally, the authors show that DNMT1 and DNMT3B impact heterochromatin and the loss of DNA methylation leads to changes in chromatin compartmentalization (with HiC), which have been observed before. While the DNA methylation levels and chromatin organisation of DLD-1 cells was investigated, the authors do not provide any characterisation of these in RPE-1 cells. Furthermore, it appears that RPE-1 cells show more pronounced cell cycle defects and reduced viability hinting towards p53 dependent apoptosis due to loss of methylation, something which is not extensively explored. These observations suggest that the viability of the DLD-1 cells is 'DLD-1 specific'/p53 dependent and not due to the degron system overall. Nevertheless, these in vitro tools will be highly valuable in the epigenetics and specifically DNA methylation fields and their more comprehensive characterisation and will be of high significance.

My field of expertise lies within DNA methylation mechanisms and have limited expertise in HiC experiments.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Scelfo et al. describes the establishment of an auxin-inducible degron system for depleting DNMT1 in human cancer and immortalized cell lines. Using this system, the authors show that lack of DNMT1 leads to a profound passive loss in 5mC that is enhanced in the context of DNMT3B knock-out. The decrease in 5mC is further associated with cell-cycle arrest in G1. In addition, they demonstrate through microscopy that the peripheral distribution of heterochromatin in the nucleus depends on DNA methylation. By running Hi-C analysis, the authors further show that specific chromatin domain interactions depend on DNMT1 levels while others depend on DNMT3B. Finally, restoring DNMT1 levels through auxin wash out, although with different kinetics, partially alleviates the above-mentioned effects of DNMT1 loss.

This is a well-designed study and in general the data support the conclusions that are drawn by the authors. I have one concern though regarding the description of the genomic distribution of DMPs (Page 11 of the text and Fig. S2H and S2I). Indeed, the authors indicate that "DNAme at active promoters was unaffected" but Fig. S2H and S2I show that, if I understood correctly how data are represented, 2.5 % (S2H) to 5 % (S2I) of DMPs are falling within the active promoter category. I agree that these are under-represented when compared to the % of CpGs falling in this category in the Epic array but one cannot say that no DNAme change is targeting these regions. A similar concern applies to the description of Fig. S2J,K. In addition, I found the authors could describe and justify in a more detailed way their choice of the two cell lines used in the present study (DLD1 and RPE cells). Also, it would be useful to have information on the cell cycle duration in these cells in order to be able to fully interpret the impact of DNMT1 loss on cell cycle in the time frame used here. Finally, information regarding the cell lines used to acquire data is missing in a number of figure legends. This is the case for instance for Fig. 1B,D,E,F,G, Fig. 3A and Fig. S3E in which it is not specified whether the authors used RPE or DLD1 cells.

Significance

The novel system described here will certainly be of interest to researcher in the field of DNA methylation and chromatin organization. This article presents convincing and original data showing that DNMT1 levels can be reversibly down-regulated through the auxin-inducible degron thus providing opportunities to study the effects of DNA methylation loss on chromatin organization without the drawbacks usually observed in long-term KO experiments or treatment with toxic DNMT inhibitors. Example of such data obtained with the degron system are convincingly showing that peripheral heterochromatin relies on DNA methylation by DNMT1 and that interaction of heterochromatic domains also depend on DNMT1 activity. Another original finding is that the spatial organization of different silent chromatin domains can also depend on DNMT3B activity, independently of DNMT1. The fact that DNMT1 levels can be restored after wash out of auxin medium is probably one of the most interesting aspects of this study since it allows to run assays that are not possible in the context of DNMT KO experiments. Using this strategy, the authors demonstrated that, concomitant to an increase in DNA methylation, heterochromatin relocates to the periphery of the nucleus and that DLD1-BA compartmentalization is restored. In this respect, the authors observed that compartmentalization of B4 is rapid and near complete at a time when DNA methylation recovery is still partial, suggesting that DNMT1 could have catalytic-independent roles in this process. Although this is possible, another explanation could be that a partial re-methylation of DNA is sufficient for recovering homotypic interactions.

Regarding Hi-C data, similar results obtained with DNMT1/DNMT3B DKO and 5-aza-deoxycytidine-treated HCT116 cells were already described in a previous study from the authors (Spracklin et al. Nat Struct Mol Biol, 2023). However, differences in the reorganization of chromatin contacts after auxin treatment of DLD1 cells compared to 5-aza-dC or DKO HCT116 cells can be evidenced and are possibly linked to a difference in the organization of heterochromatin between DLD1 cells and HCT116, highlighting the usefulness of running these analyses in different cell types.

Although not really crucial in the context of the present study, information on the transcriptomic changes induced by DNMT1 loss could add some insights into the cellular state induced by auxin treatment. Indeed, cells are arrested in G1 and peripheral heterochromatin seems to undergo spatial rearrangement. This is reminiscent of senescence-associated processes and a loss of DNA methylation during replicative aging has already been documented. Especially, knock-down of DNMT1 is known to trigger premature senescence entry (Cruickshanks et al., Nat Cell Biol, 2013). Hence, a further characterization of the G1-arrested cells upon auxin treatment would clearly add some value to the manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this paper, Scelfo, A. et al. investigated the mechanisms underlying the cooperative maintenance of DNA methylation by DNMT1 and DNMT3B. Using a rapid degradation of DNMT1 by the auxin-inducible Degron system, which allows the assessment of reversible and time-dependent effects of DNMT1 loss with low cytotoxicity, the authors revealed a cooperative activity between DNMT1 and DNMT3B to maintain DNA methylation. Furthermore, they showed that gradual loss of DNA methylation is accompanied by progressive and reversible changes in heterochromatin abundance, compartmentalization, and peripheral localization. Collectively, this study provides a new cellular model to investigate the fundamental and biological role of DNA methylation and the molecular mechanism underlying its establishment and maintenance.

Important comments:

1. Are there any data showing no change in DNA methylation level of WT DLD-1 and DNMT NA DLD-1 cells?
2. In Fig. S2A and S2D, differences in global DNA methylation between NA-DNMT1-IAA-Day4 and DAC-treated cannot be determined from these images alone because the blot intensities appear similar. It is better to present the results in a more quantitative manner with appropriate statistical analysis.
3. In Figure 2D, it is better to present the results in a more quantitative manner with appropriate statistical analysis.
4. In Figures 3A and S3B, the authors show that DNMT1 depletion decreases the percentage of cells in S phase and increases the percentage of cells in G1 phase and sub-G1 phase in NA-DNMT1/DNMT3B KO. The authors postulate that the decrease in cell proliferation after DNMT1 depletion is due to activation of p53. Data demonstrating activation of the p53 pathway are needed.

Minor comments:

1. Regarding IAA-induced DNMT1 degradation, the authors should provide complete DNMT1 blots to show that no additional isoforms are present.
2. In Fig. S1C. Molecular weight was not labeled in the immunoblot.
3. In Fig. S2B. NeonGreen-IAA 10days images appear to be exposed for different lengths of time.
4. In Fig. 4B-C, S1E, S2E. It is better to present the results in a more quantitative manner with appropriate statistical analysis.
5. Contrary to Fig. S2A, the PCA analysis does not seem to show any difference between NA-DNMT1-IAA Day2 and Day4.

Significance

Collectively, this study provides a new cellular model to investigate the fundamental and biological role of DNA methylation and the molecular mechanism underlying its establishment and maintenance.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Scelfo et al. describe how different DNMTs cooperate to maintain DNA methylation and the impact of decreased DNA methylation on chromatin structure. Toward this, they established an inducible degradation system for DNMT1 in untransformed and cancer cell models. The experiments revealed that DNMT1 and DNMT3B are required to maintain and control DNA methylation patterns throughout the genome. The authors also demonstrate that heterochromatic regions are highly susceptible to DNA demethylation, with loss of their localization to the nuclear periphery and disappearance of their compartmentalization patterns. Together, this work will allow better temporal resolution analysis of DNA methylation abnormalities and will be useful for clarifying the role of DNA methylation and its regulatory mechanism.

Major comments:

1. In Figure 2G, the authors report increased DNA methylation at selected loci in the absence of DNMT3B and suggest a compensatory role for DNMT1 in de novo methylation, as this increased DNA methylation is lost upon DNMT1 depletion. However, how do the authors rule out the possibility that this methylation is catalyzed by DNMT3A and maintained by DNMT1?
2. In Figure 3, the authors demonstrate that DNMT1 depletion leads to cell cycle arrest at G1. Since p53-proficient RPE-1 cells showed faster G1 arrest (Figure S3B), the authors suggest that DNMT1 depletion activates the cell cycle checkpoint. The authors might want to check p53, p16, and p21 levels in line with their suggestion.
3. Figure 4F and 4G demonstrate a global reduction of H3K9me3 levels upon DNMT1 depletion. Is a similar effect seen with H3K27me3?

Significance

Understanding how DNMTs regulate chromatin structure and cell fitness is critical to understand better how DNA methylation impact cell fate and function.

Strength: This work established an inducible DNMT1-degradation system with reversibility, temporal control, and low toxicity.

Weakness: this work is merely descriptive and preliminary to understand the observed phenotypes clearly.

Advance: Although the presented experiments are accurate and well-designed, it has already been reported that DNMT1 and DNMT3A/3B cooperate to maintain DNA methylation patterns and that reduced DNA methylation leads to the disruption of H3K9me3/HP1-enriched heterochromatin structure. In addition, the molecular understanding underlying these phonotypes remains unexplored. In its current form, the contributions of this study to the field will be limited.

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Reply to the reviewers

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Referee #3

Evidence, reproducibility and clarity

Rios-Szwed and co-authors show that the depletion of FAM111A results in faster replication speed, longer intra-origin distances, and less chromatin-bound RPA even without induction of replication stress in U2OS cells. Induction of replication stress in FAM111A-depleted cells results in blunted response with less DNA damage, decreased checkpoint activation and resistance to the replication-stress inducing agent, HU. They show that cells without FAM111A display lower levels of single stranded DNA after treatment.

In the second part, the authors show that FAM111A and FAM111B form a complex, although the similarities and differences of their functions are not explored in detail. From the little data shown, it looks like they might be working together in controlling amount of ssDNA. They find that both proteins are expected to have two conserved UBL domains, with one of them overlapping with ssDNA binding domain. Finally, the authors use overexpression of WT and mutant proteins to show that expression of WT and patient-derived mutant has increased level of DNA damage, increased levels of ssDNA, with and without DNA damage, and that the peptidase domain is necessary for the phenotypes.

The data from the first two figures are consistent with FAM111A being involved in regulation of single stranded DNA formation during normal replication and during replication stress. Unfortunately, the work gives no indication of the mechanism of such regulation. I am not convinced that the function has much to do with controlling origin activation (see below). The data from the last two figures is also descriptive. Until the substrates of FAM111A are identified, there will be no understanding of its true function and the data will continue to be descriptive.

Specific points:

Figure 1: The siFAM11A-2 has a stronger phenotype in growth assay but has very little change in levels of cells in G1. No complementation of the phenotype is given.

1D- there is no total RPA so it is unclear if there is no change in pRPA in relation to total RPA. Small differences will be missed without DNA damage and it would be helpful to use more sensitive assays to identify the reduction in ssDNA under unperturbed conditions.

1E- what does the data look like if the lengths of IdU are plotted? This would be a measure of speed of the ongoing forks. Generally, this would be better than the CldU measurement.

1F- the Inter-CldU distance increase could be secondary (indirect effect) of the increased replication speed

1G- It looks like there are many more data points in the siFAM11A-1 and many fewer in the siFAM111A-2. The increase in the MCM quantified in H is bigger with si2 even though the G1 distribution has less change than with si1. Consequently, these data are incolclusive.

1I- no plot is shown for si2 but it is quantified. It would be informative to see the plots for easy comparison.

Figure 2: This is the most interesting part of the paper and generally is well done. As mentioned above, I believe that the phenotype the authors see in Figure 1 is the same phenotype as seen here- less production of ssDNA but it is hard to see this under unperturbed conditions, thus more data should be gathered to test that.

Figure 3: shows novel findings but it is unclear how it relates to the rest of the paper except that it suggests that the paralogs may work together in the pathway that has been explored in Figure 1 and 2. The authors perform computational and predictive analysis that identifies two UBL domains in the FAM111A/B paralogs. The FAM111A UBL2 domain is known to bind ssDNA. The authors might test if the domain can also bind ssDNA in FAM111B and if FAM111B has similar ability to promote ssDNA formation

Figure 4: The human mutations provide some insight as to the requirement for functional peptidase activity for the function of the protein. The work would also be strengthened if a ssDNA binding mutant was made and tested given the authors interest in defining the UBL domains.

Not sure why they use a term "ssDNA exposure"? It implies a removal of something that was covering it which they certainly do not show. I would use ssDNA levels, maybe ssDNA production, formation?

Other points:

As QIBC is used throughout the paper, it would be nice to have a brief explanation of the technique when it is first introduced.

The authors write that the function of FAM111A in promoting ssDNA formation is "distinct from overcoming protein-DNA complexes ahead of the replisome by Top1 or PARP1". It is not clear to this reader how they have determined that they are not the result of the same mechanism as the phenotypes seem very related. I would clarify this point.

Since the authors are including patient mutations, more introduction to the diseases would be useful.

Referee cross-commenting

I have no further comments.

Significance

The findings add to the growing literature on the FAM111 proteins and will be of interest to scientists who are studying them and those interested in replication and replication stress response.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Rios-Szwed et al have investigated the role of FAM111A in DNA replication. Previous studies had identified that FAM111A suppresses DNA replication via an interaction with RFC and that hyperactive mutants induce apoptosis. Now, Rios-Szwed et al discovered that FAM111A knockdown affects inter origin distance without checkpoint induction. In particular, the firing of dormant origins when dNTPs are limiting is supressed and less ssDNA is produced. Although FAM111B is a strong interactor of FAM111A, no additive effect on DNA replication was detected when both proteins were depleted. On the other hand, overexpression or hyperactive mutants promote more gammaH2AX and ssDNA even in the presence of a caspase inhibitor, suggesting that the protease functions in ssDNA production prior to apoptosis.

Major comments:

Dormant origins are frequently inhibited by phosphatases - is there any evidence that phosphatases are the target of FAM111A. In this context I would suggest to blot for Treslin, as it is one of the first factors being recruited in a kinase dependent manner to the MCM2-7 complex.

Minor comments:

Abstract: Unclear why too much FAM111A causes cell death

Introduction: the R569H point mutant needs to be better introduced - e.g. explain where the mutation is localised or what it affects e.g. it is localised in the predicted peptidase domain

Figure 1A and 1D - are all the lanes shown originating from the same gel - if not please repeat.

Page 3 - I am not sure that in FAM111A depleted cells the DNA synthesis rate is reduced. Could it be, that just fewer cells are in S-phase.

Page 3 - It is stated: "In contrast, the inter-fork distance was slightly increased in FAM111A depleted cells (Fig. S1E)", however, the data but the data do not fully support this statement.

Figure 4C - the quantification of the last lane looks wrong. Is the average or the median? Please find information in the figure and methods section.

Question: If both FAM111A and FAM111B are overexpressed - is this better tolerated?

Is there a homologue in other species?

Referee cross-commenting

I agree with the other reviewers that the study has a descriptive nature. I guess this could be acceptable dependent on the journal choice.

Significance

In general, I really like the study as it establishes how initiation of DNA replication is affected by inhibition and activation of FAM111A. The work is done well and deserves to be seen in a good journal.

The study helps the field to move forward and will allow a more targeted search for specific protease targets. In this way it will help clinicians and also researchers.

My expertise is in initiation of DNA replication.

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Referee #1

Evidence, reproducibility and clarity

Rios-Szwed and colleagues investigate functions of FAM111A, a protease that Dr. Alabert has previously shown to localize at nascent DNA and promote PCNA loading. In this manuscript, the authors first describe that FAM111A facilitates efficient activation of replication origins by using DNA combing experiments and by analyzing chromatin loading of DNA replication proteins. Next, they show that FAM111A KO cells show reduced levels of ssDNA exposure after replication stress. Then the authors move on to show that the major FAM111A interactor is FAM111B, which they show to localize at nascent DNA and is epistatic to FAM111A in promoting DNA replication as well as RPA loading after replication stress. Finally, the authors show that unregulated FAM111A activity, either by overexpression of WT FAM111A or disease-associated mutants, causes extensive exposure of ssDNA.

Major comments

1. Fig. S1G: Actual inter-origin distances (distance between replication tracks in which a CldU track is flanked by IdU tracks on both sides) should be plotted to estimate the changes in origin firing frequencies. The results should be presented as inter-origin distances, not ratios between UCN-01-treated and untreated. The revised experiment should be included in the main figures as this is central to the conclusion, and statistics should be included.
2. The claim "FAM111A ... promotes DNA replication initiation of active and dormant origins" (page 4, line 4) is not fully supported by experiments. Does FAM111A localize at replication origins? Without direct evidence of FAM111A being present at replication origins, it remains possible that the changes in origin activity is secondary to the loss of FAM111A function at forks or something else.
3. Fig. S1G: If FAM111A's function to promote activation of dormant origins in response to UCN-01 is unrelated to the function of FAM111A at forks, it is expected to be independent of the PIP motif. Is it the case?
4. Fig. 2B: Increased survival after HU treatment might be secondary to reduced S-phase populations in FAM111A-depleted cells (Fig. 1C) as HU would affect only S-phase cells.
5. Fig. 2B-I: Similarly, the blunted response to replication stress in FAM111A depleted cells could be simply explained by reduced number of forks per cell as indicated by increased inter-fork distance (Fig. 1F). Similarly, the authors' group has previously reported reduced PCNA levels on chromatin (Alabert et al, 2014), suggesting that there are reduced number of active forks per nucleus.
6. Fig. 2H "FAM111A depletion reduced ssDNA exposure upon HU treatment (Fig. 2H, 2I)": The figure in Fig. 2H does not appear to be treated with FAM111A RNAi. If this is FAM111A RNAi cells, siControl cells need to be shown as a comparison.
7. Fig. 3B,C: The interaction between FAM111A and FAM111B needs to be validated by coimmunoprecipitation-WB of endogenous proteins.
8. Fig. 4A-C: Induction of DNA damage and apoptosis by FAM111A WT and disease mutants (including T338A that the authors claim unstudied) has been reported by Hoffman et al. and therefore not novel.
9. Fig. 4E: The increase in ssDNA intensities is mild and might not be biologically significant.
10. Fig. 4G: Cell cycle status needs to be assessed by FACS after treatment with each drug. Bleomycin might induce G1/S arrest if G1/S checkpoint is intact.
11. ssDNA exposure after FAM111A OE might not be because FAM111A has a function in promoting ssDNA exposure, but could be simply explained by replication fork stalling, for example, due to degradation of essential proteins as proposed before (Hoffman et al, 2020).
12. Page 8, line 17, "Altogether, these data revealed that unrestrained FAM111A peptidase activity leads to ssDNA exposure upstream of apoptosis.": Just because the caspase inhibitor did not block the ssDNA exposure, it does not mean ssDNA exposure is upstream of apoptosis - it could be happening in parallel and might be unrelated. A similar unsupported conclusion "ssDNA exposure is upstream of apoptosis" appears in other places: page 8, line 30; page 9, line 22.
13. Whether protease activity is necessary for the FAM111A function in regulation of origin activation and in ssDNA exposure is not addressed. Can the phenotypes of FAM111A KO cells be rescued by FAM111A WT but not an active site mutant?
14. Similarly, the authors need to test whether the PIP motif of FAM111A is required for the function of FAM111A at forks, such as promoting ssDNA exposure.

Minor comments

1. Page 2, Line 8, "FAM111A catalytic activity has not been shown in vitro": Protease activity of FAM111A has been shown using recombinant proteins in vitro by Hoffman et al, 2020.
2. Page 7, line 26, "T338A is a previously unstudied GCLEB patient mutation.": The T338A mutant was studied by Hoffman et al. and shown to have hyperactivity in vitro and to cause DNA damage when overexpressed in cells.

Referee cross-commenting

I feel that this study has problems even as a descriptive study. As I mentioned in my review, there are alternative explanations for their observations that the authors have not ruled out. If the authors remove all unsupported claims, then there is not much to conclude from this study. I am not saying their conclusions are wrong - I think this study is just premature.

Significance

This study could be of interest to the audience in DNA replication/DNA repair field and could be unveiling a new function of FAM111A in DNA replication. However, in the current form, this study appears to be a collection of loosely connected observations of FAM111A-manipulated cells without a clear message of what FAM111A does at replication forks and origins. Each observation appears to be loosely tied together with a keyword of ssDNA exposure, but how FAM111A regulates or changes ssDNA exposure is not addressed. The described phenotypes are potentially interesting, but for each observation there is an alternative explanation that could affect authors' interpretation. As outlined in my comments, lack of mechanism, lack of clear conclusion, and misinterpretation of some of the data led to this less enthusiastic review.

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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 entitled "A population instrinsic timer controls Hox gene expression and cell dispersion during progenitor addition to the body axis", Busby and colleagues investigate the topic of "cell type identity" in the context of body axis elongation in chick embryos. To this end, they performed heterochronic grafts from HH8 stage embryos to HH4 stage embryos and compared these to HH4 homochronic grafts. They found that HH8 grafts ingressed but were then delayed at a stage they termed cell dispersion. By scRNAseq this new cell state was characterized further. While HH8 cells adjusted their expression pattern to their surroundings, Hox gene expression was maintained as in the host developmental stage. Hox gene expression and collinearity of expression changes were also maintained when HH4 cells were grafted into HH8 embryos or cells were cultured ex vivo. Finally, the authors found differences in migration properties between HH4 and HH8 cells, when cultured ex vivo, with HH4 cells migrating faster than HH8.

This constitutes an elegant work to describe the existence of a "cell-intrinsic timer" that regulates cell identity and progressive body axis extension. Experiments and analysis have been performed adequately and conclusions have been drawn appropriately.

There are a rather minor comments I would suggest for further analysis, discussion and potentially experiments to further support this paper:

• A major finding is that grafted cells keep their Hox expression pattern, independent of whether it is from HH4 to HH8 or vice versa. Moreover, grafted HH8 cells pause at the cell dispersion stage and do not mix, unless grafted in very low cell populations. The authors conclude that Hox gene expression seems to be cell intrinsically regulated. However, for pausing of cells after ingression, I wonder if it is rather the difference to the neighbors than a cell-intrinsic effect that prevents the cells from dispersing. One possibility is that differences in adhesion could account for this, since sorting of cell populations based on differential expression of adhesion molecules has been observed in various model systems. This possibility is excluded here, since adhesion-related genes were not differentially expressed in their expression data. However, I would not exclude this possibility at this stage for the following reasons: 1. The authors detect different migration speeds for HH4 and HH8 cell clusters with HH4 cells migrating faster. Differential migration rate could indeed hint at differential adhesion and mechanical properties of the cells. 2. Hox genes have been shown to be upstream of and modulate adhesion molecules, which might be an interesting link. 3. So far, the authors have only analyzed expression of adhesion molecules at mRNA levels. However, the functional components are the adhesion proteins themselves. It might therefore be useful to stain embryos for some "obvious" candidate adhesion molecules, such as cadherins. If no further experiments are performed, then this should at least be discussed.
• The authors describe a new, intermediate stage, namely cell dispersion, in which HH8 MSP pause when grafted into HH4 embryos. They perform scRNAseq and GO term analyses to analyze these cells in more detail. The also perform gene set enrichment analysis. However, I am still wondering about the exact identity of these cells. What are they? What markers do they express? Do they upregulate certain signalling pathways? Etc. I would for instance be interested if there are differences in FGF or Wnt levels/ activities. It would be useful if the authors could analyze their scRNAseq data further in this regard.
• At several points in the manuscript, expression levels and patterns of HH4 and HH8 grafts are compared to each other. It does not become clear what the differences and similarities to the non-grafted cells of the same clusters are. Does grafting itself change the expression patterns?
• The authors found differences in cell cycle stages of HH4 and HH8 grafts. A more detailed discussion of this aspect would be useful rather than just excluding any cell cycle-related genes from the comparisons. Why could there be this difference? What effect could this have? Etc.

Optional: Other experiments that could increase the relevance of the work:

• As discussed by the authors, they specifically compare HH4 stage to HH8, which represents primitive streak stage and 4-somite stage, respectively. It would therefore be interesting to perform grafts from HH8 to later stages, such as HH10, or vice versa, when the process of somitogenesis is more similar. This could reveal if their findings are specific for pre to post node development or more general. However, this might be outside of the scope of this study.

Significance

General assessment: This study provides a systematic analysis of the interaction of embryonic cell clusters from different developmental stages. To this end, "classical" developmental biology techniques, i.e. grafting (complicated techniques that probably less and less people can perform nowadays), is combined with more modern ways of analysis, i.e. scRNAseq. This allows the authors to dissect the differential behaviour of hetero- and homochonic grafts. In the longer term this data can provide the basis for further in-depth mechanistic analyses, some of which could be added here already. The involvement of Hox genes in the control of developmental time is interesting and should be placed into context of our current knowledge. Here, Hox genes are rather used as readout of developmental time rather than active players.

Advance: How developmental time is maintained during embryonic development is a long-standing question in the field. This study provides conceptual advance in this question by describing a cell-intrinsic timer.

Audience: This study is relevant for developmental biologists in general, since it describes how developmental time can be kept by a cell-intrinsic timer, at least in early stages of somite formation in chick embryos.

My expertise: developmental biology, somitogenesis

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors investigate the importance of intrinsic and extrinsic factors in the timing of progenitor addition to the elongating primary body axis. During development, progenitor populations have to combine their self-renewal with the gradual contribution to the full length of the body axis. The mechanisms underlying the population dynamics that ensure the formation of a proportioned body plan remain poorly understood. By combining heterochronic (HH8 to HH4) and homochronic grafting (HH4 to HH4) of somitic progenitors with next generation sequencing and imaging, the authors observe that the older HH8 tissue shows intrinsic delays in migration and does not disperse within the surrounding mesodermal tissue after ingression through the primitive streak. This behavior correlates with intrinsic and tissue-specific differences in the expression of Hox genes but not with differences in the expression of cell adhesion/migration genes.

Overall, this study provides new data exploring how progenitors control their contribution to the body axis. By combining classic embryology techniques with single-cell sequencing, the authors describe novel cell states that might help understand the progenitor population dynamics. There are however a number of further analyses and experiments that should be performed to support the main claims of the manuscript.

Major comments:

1. The authors claim that grafted HH8 cells are paused after the ingression stage and before the dispersion stage. The grafted cells ingress through the primitive streak and then remain as a distinct cluster of cells that does not disperse throughout the mesoderm. This is in contrast with other observations where overexpression of late hox genes delays the cells at the point of ingression. The authors should better demonstrate that their grafts are actually ingressing and then stopping once in the mesoderm compartment. Figure 3B' shows grafted HH8 cells (GFP positive) present in the mesoderm (ME) compartment 3 h after grafting. It is surprising that a cluster of cells can ingress through the primitive streak in a short period of time and then remain paused. It would be helpful to have the equivalent figure right after grafting to assess the differences in the location of the HH8 GFP+ cells and potentially observe them while ingressing.
2. The authors describe a novel transcription state, namely clusters 6, 12 and 8 in Figure 2B, populated by HH8 cells 3 h after grafting. It is surprising that the UMAP looks very different between 0 h and 3 h in the HH8-HH4 grafts (Figure 2E and F). The authors should clarify where the HH4 (GFP negative) cells are present in Figure F, I. In the current figures, it looks as if both HH8 and HH4 cells changed completely their transcription profile in only 3 h and populated the central clusters (6, 12, 8). The authors claim that these central clusters are present in normal development and that cells rapidly transit through them. However, it is not clear whether this state happens before or after HH4. For example, the cells may be moving from right to left in UMAP_1 according to time (HH4 in the right, HH8 in the left and a central transient cluster). This would mean that in Figure 2F HH8 grafted cells are regressing to an earlier development state and not a new one. Including RNA velocity analysis could help clarify how the cells are changing their expression profiles.
3. Related to the previous point, the striking changes between 0 h and 3 h in the HH8-HH4 grafts (Figure 2E and F) may suggest an effect of the grafting procedure on the transcription profile of the cells. The authors should demonstrate that the grafting of cells does not have a huge impact on the transcriptome and that these changes are specific to the previously undescribed delayed state of HH8 cells. For this, they should include scRNA data of HH4-HH4 3 h. If grafting does not have a significant effect on the transcriptome, they should see GFP positive and negative cells in HH4-HH4 3 h remain intermixed.
4. The authors observe that when doing smaller heterochronic grafts, cells can disperse throughout the mesoderm. Nevertheless, the Hox gene expression does not change depending on the size of the grafts. This is in sharp contrast with their observations and claims done for big heterochronic grafts. The result is interesting as it demonstrates that the expression of Hox genes, but not dispersion, is cell intrinsic. However, the uncoupling of hox gene expression and cell dispersion requires further investigation. The authors should repeat the heterochronic grafting of Figure 1 using smaller grafts and check the contribution of grafted cells to the somites. If cells can readily disperse without delay, they might be able to contribute to all somites as observed with homochronic grafts. Similarly, the authors should repeat the explant spreading assay using smaller HH8 grafts and quantify whether differences in the migratory dynamics are observed. The authors already discuss the possibility that other factors apart from Hox expression might affect dispersion. Nevertheless, they should assess the importance of graft size in their experimental system. If smaller grafts maintain the expression profile but have a different capacity to contribute to the body axis, the initial observations might have been influenced by extrinsic factors of the graft (size, cell-to-cell contact, ECM...) and not by cell intrinsic properties (gene expression). This would change the conclusion of the work.

Minor comments:

1. It would be informative to have a better time-resolved description of the heterochronic graft behavior in Figure 1. For homochronic grafts, several timepoints are provided allowing the visualization of cells travelling through the body axis. For heterochronic grafts, by contrast, only an early and final timepoint are provided.
2. In Figure 4, the authors show that explants of HH4 and HH8 embryos have different migratory dynamics, with HH4 cells migrating faster than HH8. In Figure 4E, HH8 explants seem not to change their area for about 15 h and then start spreading. This indicates that there is a great delay in migration compared to HH4 explants. However, once they start spreading, it seems that the area starts to increase exponentially in a similar manner to what is observed for HH4 at earlier time points. It would be interesting to monitor the HH8 for a longer time to see the behaviors at later time points. HH8 explants may be just delayed, and once they start fully spreading, the speed may not be so different from the one of HH4 explants.
3. The authors conclude in Supp. Table 2 that HH8 and HH4 do not have different expressions of adhesion-related genes upon grafting. This observation is very important to understand the potential mechanism behind the different dispersion behaviors, and thus it should be included in the main figure.

Significance

The authors combine classic embryology with single-cell RNA-seq and imaging techniques to explore progenitor population dynamics during addition to the body axis. They conclude that the delayed contribution of older cells to axis formation correlates with the intrinsic expression of posterior hox genes. While the idea of intrinsic regulation of hox genes during axial specification is not conceptually new, the authors use modern techniques to describe with finer detail the progenitor population states. For this reason, this manuscript will be of interest to researchers in the development field who want to better understand the hox control and influence during axial elongation.

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Referee #1

Evidence, reproducibility and clarity

This manuscript describes the differential behavior of the epiblast region of chicken embryos containing the progenitor cells for the medial half of the somites (MSP) at HH4 (building the first 4-5 somites) and HH8 (building more caudal somites). Their approach combines grafting experiments with imaging, single cell and whole mount expression analyses of the grafts. The basic experiment involves the comparison of HH4 to HH4 homochronic grafts with HH8 to HH4 heterochronic grafts. They show that homochronic grafts undergo a dispersion stage after ingression through the primitive streak before they contribute to somites. The same region of HH8 embryos, when grafted into the MSP region of HH4 embryos, however, fail to undergo this dispersion and do not contribute to the first 4-5 somites. They also show that Hox gene expression follows the patterns observed in the grafted tissue, failing to acquire the expression profiles of the receiving host. The authors conclude that the MSP cells contain an internal timer involved in the regulation of their changing behavior as development proceeds.

The general findings reported in this manuscript are novel and can provide insights to further our understanding of the differences between formation of the first 4-5 somites and more caudal somites. However, I think that ADDITIONAL EXPERIMENTS are important to properly evaluate the data and the conclusions of this manuscript.

1. A control HH8 to HH8 homochronic graft to check the behavior of the grafted cells: do they disperse in their natural environment after ingression through the primitive streak or they are also paused as a distinctive cell cluster?
2. The reverse heterochronic grafting experiment, namely HH4 cells into HH8. Do HH4 cells maintain their dispersal behavior at the ectopic position, or they behave differently?

While the authors assessed the intrinsic properties of HH4 and HH8 tissue by incubating it on fibronectin, this experiment does not properly reproduce the environment of the embryonic region receiving the graft, which might be different at HH4 and HH8. The experiments I am suggesting take this variable into consideration and will therefore help assessing the possible involvement of the host tissue in the behavior of the grafts.

In addition to those experiments, a MORE EXTENSIVE ANALYSES of the already reported experiments could also improve the manuscript.

1. When the cells staying in the MSP region after homochronic HH4 grafts reach later stages (e.g. approaching HH8), do they keep dispersing as at earlier stages after ingression through the primitive streak or they remain as a distinct cluster? And does Hox gene expression within those grafts follow the same activation profile observed in the host cells as development proceeds?
2. In the experiment reported on fig. 5I, HH4 MSP grafted into HH8 embryos fail to activate Hox genes like Hoxa2, even after 6 hours of incubation. When these grafted embryos develop even further (for the period of time required for a HH4 embryo to reach the HH8 stage), do they activate Hoxa2 or Hoxa3 or they remain negative for these genes?
3. The differential GO terms between HH4 and HH8 tissue in cluster 6 include chromatin organization, DNA methylation and C5-methylation of cytosine. This suggests that epigenetic changes might be involved in the behavioral differences between the MSP of the two stages, which can affect many different processes involved in cell activity, including the activation of Hox genes.

SOME COMMENTS ON DATA INTERPRETATION.

1. It is clear that Hox gene expression in the grafts matches the profile of the donor tissue, indicating the existence of a Hox "timer". However, in my opinion, the authors place too much emphasis on the possible meaning of these observations in what concerns the differential behavior of the grafted cells. If they want to focus on Hox genes they should include some experiment testing their involvement in cell dispersal, either by misexpression or downregulation of specific genes (although there is plenty of information arguing against this possibility, maybe with the exception of that of Iimura and Pourquie, 2006; in this regard, the authors' own data already indicate that dispersion is independent of Hox gene expression).
2. The authors disregard the involvement of differential patterns of cell adhesion molecules as the origin of the differential behavior of HH4 and HH8 grafts in the HH4 context. However, in their data on supplementary fig. 2A there are several genes differentially expressed between the HH8 and HH4 cells (e.g. Ptk7, Spon1 or Nfasc) that could indeed play a role in the differential interaction between cells from the two embryonic stages. It might be interesting to perform HCR experiments with some of these factors to see if they are differentially expressed at the two embryonic stages. Also, although it might be somewhat far reaching, if differential expression is observed by HCR, it might be interesting to experimentally manipulate expression of the relevant gene (s) (misexpression or down-regulation, depending on the stage) to evaluate its/their potential functional relevance.

Minor points

1. The authors write that the HH8 specific clusters are 0, 4 and 6. However, I think that it is #7 and not #6 the one belonging to this group. I guess that this is typo, but becomes confusing, as a large part of the analysis of the single cell data is centered on cluster #6.
2. In the introduction the authors state that the first 4-5 somites do not develop ganglia, citing Lim et al 1987. I think that the way this is written is imprecise, as it sort of implies that more caudal somites develop ganglia (which would mean that the dorsal root ganglia are somite derivatives). However, somites at any level do not develop ganglia; the anterior half of their sclerotomes are permissive to migration of the neural crest that will eventually build the ganglia, something that seems not to happen in the more anterior somites.

A side note

Different alternative transcripts have been reported at least for Ptk7 and Nfasc. This might be relevant considering that another of the prominent differential GO terms identified in supplementary fig 2C is related to RNA splicing. Would different alternative transcripts for some of these genes be specifically associated with the cells from one of the embryonic stages?

Significance

It has been known for many decades that the first 4-5 somites of amniotes are different to the rest of the somites in several ways, from the structures they generate to the way they are generated or the gene regulatory networks controlling their morphogenesis. Much is known about how the posterior somites are generated and the mechanisms of their differentiation. Conversely, relatively little is known about the same processes in the most anterior somites. The work described in this manuscript shows that the progenitor cells from the epiblast that will contribute to the 4-5 first somites already behave different than those generating more caudal somites. Also, they show that progenitors generating more caudal somites are unable to contribute to the rostral somites. These two sets of observations show that the differences in the rostral and caudal somites are already present in their progenitors and that those features are quite stable within the cells, at least when they are kept as a group.

So far, the single cell analyses shown in this manuscript failed to provide clear hints to explain the different behavior of the two sets of progenitors. However, they represent an important resource to further explore this important biological question. The authors focus on Hox genes as potential regulators of the differential behavior of the HH4 and HH8 MSPs, I guess that prompted by the report by Iimura and Pourquie (2006) indicating the involvement of Hox genes in the migratory properties of the somite progenitors. However, there is plenty of information, mostly genetic studies in mice, indicating that Hox genes might have very little influence in the differential behavior of rostral and caudal somites. In this regard, expression does not mean causation. I think that this manuscript is interesting, most particularly for developmental biologists involved in understanding the mechanisms governing the basic layout of the vertebrate body plan.

Research in my laboratory also explores this type of biological questions, although using more genetic approaches and in a different model system, namely the mouse. I therefore consider myself in a position that allows a knowledgeable evaluation of this manuscript.

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Reply to the reviewers

We thank the four reviewers for their generally positive feedback on the manuscript. Below, we provide a point-by-point response to each reviewer.

We are performing new FCS and gradient measurements as suggested by the reviewers. We are confident we can have these completed within three months (accounting for the summer break).

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

*This manuscript reports a very thorough and careful study of the mobility of Bicoid in the early embryo, explored with single-point fluorescence correlation spectroscopy. Although previous groups have looked into this question in the past, the work presented here is novel and interesting because of the different Bicoid mutants and constructs the authors have examined, in particular with the goal of understanding the role of the protein DNA-binding homeodomain. The authors convincingly show that there is a significant increase in Bicoid dynamics from the anterior to the posterior region of the embryo, and that the homeodomain plays an important role in regulating the protein's dynamics. Their experiments are very well designed and carefully analyzed. The authors also modelled gradient formation to see whether this change in dynamics might play a role in setting the shape of the gradient. I am not sure I fully agree with their conclusion that it does, as mentioned in my comment below. However, it is an interesting discussion to have, and I think this paper makes a significant advance in our understanding of Bicoid's behavior in the early embryo. *

We thank the Reviewer for their positive comments and their suggestions for improving the manuscript. We will resolve the concerns raised by the reviewer with clarity in the revision. We will also add additional comment in the Discussion regarding the interpretation of our results.

*Major comments: *

• 1) Gradient profile quantification: Some of the conclusions made by the authors rely on the comparison between their model of gradient formation (as captured in the equations in lines 232 and 233) and the Bcd intensity profile measured in the embryos. Since the differences in gradient shape predicted by the different models are very small (see Fig. 3B, which is on a log scale and therefore emphasize small differences, and Fig. 3C), it is very important to understand how reliable the experimental concentration profiles are.*

This is a fair comment. It is worth noting that the key differences between the 1- and 2-component models are only apparent at large distances (and hence low concentrations) from the source.

We performed the quantification of the gradients in a manner similar to the Gregor lab, whereby the midsagittal plane is analysed. We used 488nm illumination (rather than 2-photon, as the Gregor lab does) so our measurements are likely noisier. However, we are not investigating the variability in the gradient here, but the mean extent. We currently correct background with a uniform subtraction, but we appreciate that is not the optimal method.

In the revised manuscript, we will repeat the above experiments using a 2-photon microscope. Further, we will image lines expressing His::mcherry without eGFP under the same imaging conditions to more accurately estimate the background signal. While we expect this to improve the data quality, we do not envisage significant change to the observed profiles based on prior experience.

At the moment, I do not find the evidence that [Bcd] concentration profile is more consistent with a 2-component diffusion model than a 1-component model very strong. A few comments related to this: * * 1a. Line 249, it is mentioned that: "observations ... incompatible with the SDD model". Which observations exactly are incompatible with the SDD model?

The key points are in the preceding paragraph. We will improve the model presentation in the Results and also include further contextualisation in the Discussion.

1b. In Fig. 3D, only the prediction of the 2-component model is shown. What would the simple 1-component diffusion model look like? Is it really incompatible with the data?

We agree with this comment and will provide the 1-component fit to the gradient profiles. We expect it to fit well for the anterior half of the embryo but fail at larger distances (as has been previously shown).

Regarding the FCS data, we also show one and two component fits. We will show the alternative fits – a 2 particle fit is clearly an improvement (see also related response to reviewer 2).

1c. Line 243: "The increased fraction in the fast form ... consistent with experimental observation of Bcd in the most posterior" (Mir et al.)". I am not sure how this is significant, since the simple model also predicts there will be Bcd in the posterior - the only difference is how much is there (as shown in Fig. 3C), and it's a very small difference.

The absolute differences are not large between the two models, but due to the observed clustering (Mir et al. 2018), even small differences can have very large effects. In the revision we will provide estimates of the actual concentration differences.

We are performing new experiments with the Fritzsche lab at Oxford to estimate if there is clustering of Bcd. We will also repeat our FCS experiments to validate our key conclusion of AP differences in diffusion of Bcd. These should be completed by the end of the summer.

1d. Since the difference between models is in the posterior region where Bcd concentration is very low, when comparing the models to the data the question of background subtraction is essential. How was the subtracted background (mentioned line 612) estimated?

See above response to the first comment.

1e. Along the same line, were the detectors on the Zeiss LSM analog or photon counting detectors, and how confident can we be that signal is exactly proportional to concentration?

We used PMTs and did not directly do photon counting. But the intensity is still proportional to the concentration. It is possible to estimate the absolute concentration value, e.g., Zhang et al., 2021 (https://doi.org/10.1016/j.bpj.2021.06.035). However, our main conclusions – especially regarding the spatially varying Bcd dynamics – are not dependent on this.

1f. Can the gradients created by the two Bcd mutants (FIg. 4B) be quantified as well, and are they any different from the original Bcd gradient?

We agree this would be useful. We will provide the gradient quantifications of the bcd mutants in the revision.

1e. What is the pink line in Figure 5C (I am assuming the green one is the same as in Fig. 3D)? It could be better to not use normalization here, or normalize everything respective to the eGFP::Bcd data to make comparison in relative concentrations in the posterior for different constructs more evident (also maybe different colors for the three different data sets would help clarity).

This is a fair comment, and we will create graphs with new data for better visualisation.

1f. Discussion, lines 402-403: Does the detailed shape of the Bcd in the posterior region matter at all, since the posterior is not a region where Bicoid is active, as far as we know? Could a varying Bcd dynamics have other consequences that would be more biologically relevant?

Bcd is now known to act at 70% EL (Singh et al., Cell Reports 2022). So, the gradient is relevant for a large extent of the embryo length, though it is not known if there is any effect in the most posterior region.

2) Model for gradient formation (lines 231-238): * * 2a. Whether the molecules of Bcd can change from their fast to slow form is never questioned. How do we know (or why might we suspect) they do exchange?

This is a good point. Within the nucleus, and based on our mutant data, we suspect the fast/slow forms correspond to unbound/bound DNA states.

In the cytoplasm, the dynamics are less clear. Bcd can bind to cytoskeletal elements (Cai et al., PLoS One 2017) as well as to Caudal mRNA. Therefore, it seems reasonable to have different effective dynamic modes – yet, how such switching occurs remains unclear.

Ultimately, our model approximates multiple dynamic modes that are integrated to drive Bcd motion. Including switching between states is a reasonable assumption based on what is known about cytoskeletal and protein dynamics, but we do not have a specific mechanism.

It is challenging to estimate a specific kon / koff rate, as the dynamic changes also depend on the diffusion – which itself is changing. For now, we believe our level of abstraction is appropriate given what is known about the system. It will be very interesting to explore the specific interactions underlying such behaviour in the future, but that is beyond this current manuscript.

2b. The values used in the model for alpha, beta_0 and rho_0 should be mentioned. Maybe having a table with all the parameters in the method section, or even in the supplementary section, would help. The exact values of alpha and beta matter, because if they are large (fast exchange) a single exponential gradient is to be expected, if they are 0 (no exchange) a double exponential gradient is to be expected, with intermediate behavior in between. Which case are we in here?

We agree and will add a more complete table in the revision.

3) Discussion about anomalous diffusion (lines 386-388): The 2-component model used by the authors to interpret their FCS data seems very well justified here (excellent fits with very small residuals). I agree with the authors' conclusion that "the dynamics of Bcd within the nucleus are more complicated than a simple model of bound versus unbound Bcd", but I don't see how that can lead to a diagnostic of anomalous diffusion instead. Maybe it is just a matter of exactly explaining what is meant by anomalous diffusion here (since this term is often used to mean different things). A more likely scenario I think, is that there are more than just two Bcd components in the system.

This is a good point, and we can’t easily differentiate two/multi- component fits from anomalous diffusion ones. This is a known problem. But we have recently shown in a collaboration with the Laurent Heliot lab (Furlan et al, Biophys J 2019), that anomalous diffusion is a good stable indicator of changes, even if it might not be the right model. We use anomalous diffusion as it stably predicts changes. We do not claim, however, that diffusion is anomalous. We will improve the discussion of these points in the revised manuscript.

4) Line 440 and after: What is the evidence that the transition between the two forms might vary non-linearly with Bcd concentration? How would that help adapt to different embryo sizes? It would be good to be more explicit here instead of just referring to another paper.

We will improve this discussion. The central point is that the action of Bicoid is unlikely to simply depend linearly on concentration as in that case the ratio of fast to slow forms would be constant across the embryo. Related to the above comment, it is important to emphasise that we are using a phenomenological model, not one based on a specific mechanism.

5) Since an important aspect of this work is the study of different Bcd constructs in vivo, it is important that these constructs are very clearly described, so the section on the generation of the fly lines (Methods) should be expanded. In particular: * * 5a. It seems that the eGFP:: NLS control used here was different from that first described in Ref. 64 (and used for FCS experiments in Ref. 30 and 36)? If so, what NLS sequence was used here, and precisely what type of eGFP was used (in particular, was the A206K mutation that prevents dimerization present in the eGFP used)? If it is the same construct as in Ref. 64, it should be mentioned explicitly. * * 5b. Were the mutant N51A and R54A lines gifts as well, or have they been described before? If so, previous publications should be referenced. If not, how the plasmid was introduced in the embryo should be briefly explained.

We agree and will expand on the fly lines in the revision.

6) Concentration calibration measurements (Methods Fig. 2, line 568 and on). It is well known that background noise is going to interfere with the measurement of N when the signal becomes equivalent to the background noise (Koppel 197, Phys Rev A 10:1938-1945, and for a recent discussion of this effect for morphogens in fly embryos: Zhang et al., 2021, Biophysical Journal 120,4230-4241). It is almost certain that in the low signal regions of the embryo (e.g. posterior cytoplasm) this is affecting the reported concentration, and should be at least acknowledged.

We agree with the reviewer. We will provide the SBR. We will also correct the N values based on the method followed in Zhang et al., 2021, Biophysical Journal 120,4230-4241.

*7) Reference 3 is mis-characterized in two different ways in the manuscript: * * 7a. Line 50: The conclusion in Ref. 3 was not that the gradient was due to a diffusive process, on the contrary Gregor et al. argued that Bcd was too slow to form such a long-range gradient by diffusion. Studies that do present data consistent with a morphogen gradient formation mechanism driven by diffusion are reference 5, reference 30, Zhou et al., Curr. Biol. 2012;22(8):668-75 and Müller et al., Science 336 (2012) 721-724. *

Gregor et al., do not argue against a diffusion process – indeed, they utilise a SDD model in their paper. However, they do extensively discuss how the predicted dynamics from the SDD model are not compatible with gradient formation as observed after n.c. 13. This problem was resolved to some degree by FCS measurements of Bcd (e.g., Dostatni lab, Development 2011) and the use of a Bcd tandem reporter which showed that production and degradation change during n.c. 14 (Durrieu et al., MSB 2018). We will improve the framing of these results in the revision.

7b. The diffusion coefficient estimated from FRAP measurements and reported in Ref. 3 (D = 0.4 micron^2/s) is mentioned a couple of times in the manuscript (line 66, line 395, line 411). However, this number is simply incorrect. When fast components (such as the ones clearly detected here by FCS) are present, they diffuse out of the photobleached area during the photobleaching step. If that is not corrected for during the analysis (and it wasn't in Ref. 3), then the recovery time measured is just equal to the photobleaching time, and has nothing to do with either the fast or slow fraction of the studied molecule - it has no other meaning than to give a lower bound on the value of the actual effective diffusion coefficient of the molecule. This effect (called the halo effect) is well known in the FRAP community (see e.g. Weiss 2004, Traffic 5:662-671), it has been experimental demonstrated to occur for Bcd-eGFP in the conditions used in Ref. 3 (Reference 30), and the actual diffusion coefficient that should have been extracted from the data presented in Ref. 3 has been recalculated by another group to be instead D = 0.9 micron^2/s (Castle et al., 2011, Cell. Mol. Bioeng. 4:116-121). It would therefore be better to report the corrected value from Castle et al. to help the field converge towards an accurate description of Bcd mobility.

We fully agree and will use the improved FRAP estimated value for Bcd.

*Minor comments and suggestions: *

• 8) Figure 1: From panel A, it seems that what is called "Anterior" and "Posterior" is about 150 micron away from the embryo mid-section, i.e. about 100 micron from either the anterior pole or the posterior pole (so not the tip of the embryo, but somewhere in the anterior half or posterior half). Maybe this should be made clear in the text. *

We have made changes in Figure 1A to indicate the region within which the FCS measurements are carried out. We have added the relevant details in the legend of figure 1 lines 137-138.

*9) Fig. 2A; It might be good to put this graph on a log scale, so that cytoplasmic values are seen more clearly. Also, what about reporting on nuclear to cytoplasmic ratios? *

We will rework on this graph and make necessary changes.

*10) Fig. 2: It could be interesting to plot D_effective as a function of the measured concentration of Bicoid in different locations, since the (interesting) suggestion is made several time that [Bcd] could the a determinant of the protein mobility. *

Our work provides an indication that Bcd concentration is connected to the diffusion. We did this by measuring at two locations. To extend this to a rigorous model would require substantial new measurement along the whole length of the embryo. While interesting, this represents a very large investment of time and lies beyond the current manuscript.

*11) Figure 3B&C: Is the curve for 2-component diffusion (without concentration dependence) for steady-state missing? *

We will clarify in the revision.

*12) Lines 78 and 471: What do the authors mean by "new reagents"? The word reagent evokes a chemical reaction, but there are none here. Do the authors mean new constructs? or new mutants? *

We have changed lines 78 and 479 from “new reagents” to new Bcd mutant eGFP lines”.

*13) Lines 57-59: Another good reference for FCS measurements performed to study the dynamics of a morphogen (in this case Dpp) is Zhou et al., Curr. Biol. 2012;22(8):668-75 *

We added this reference in no.70.

*14) Lines 109-111: A word must be missing. Precisely determined what? *

Precisely measure within cytoplasm, and nuclear compartments and also during interphase stages. We have changed to “precisely measure in the cytoplasmic and nuclear regions during the interphase stages of nuclear cycles (n.c.)12-14.” in line no.111-112.

*15) Line 278: The increase in the slow mode is expected. Maybe explicitly mention why. *

In line 286, we have added “due to the loss of Bcd binding to the DNA”.

*16) Line 282: "with the fast component increasing", maybe replace with "with the diffusion coefficient of the fast component increasing" or "with the fraction of the fast component increasing". *

We have changed line 289 “with the diffusion component of fast component increasing towards the posterior”.

*17) Line 517: Is there a reason why the dorsal surface is always placed in the coverslip? *

We have added these details in line 528-529 in Methods.

*18) Line 524 and on: FCS measurements: What was the duration of each individual FCS measurement? It is great that the exact number of measurements are reported in the supplementary! *

Thank you for the complement. Typically, cytoplasmic measurements are 60secs and nuclear measurements are 20-40s. We have added this in line no.528-529. We also added a column to indicate the duration of each of the measurements in the supplementary tables.

*19) An Airy unit of 120 um seems large in combination with an objective with a NA of 1.2, is there a reason for that? What was the radius of the resulting detection volume? *

Olympus microscopes have a 3x magnification stage in their confocals. This leads to the change in the Airy unit. Otherwise, it would be 40 mm.

*20) Thank you for detailing the reasons behind the choice of excitation power, an important and often omitted details. Where in the excitation path were the values of the laser power measured (before or after the objective?)? *

Thank you for the complement. The laser power is measured before the objective. We removed the objective and measured the laser power in the objective path.

*21) Line 585: "since the brightness of eGFP::Bcd..." do the authors mean the molecular brightness of a single eGFP::Bcd molecule, or the total fluorescence signal? *

It is the total fluorescence signal. We have edited line no.592.

*22) It would be good for reference to mention the approximate value of the molecular brightness recorded for these eGFP constructs at the laser power used. *

We will measure and tabulate in the revised manuscript.

*23) Reference 766: The year (and maybe other things) is missing. *

We have corrected this reference.

24) Figure 2 (Methods): The concentrations shown on the figure should be in nM not uM. * * Thanks for noticing – we have changed.

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

MAJOR POINTS

• 1) FCS measurements and fits *
• a) Please state the duration of each individual FCS measurement. *

In the cytoplasm, the measurements were carried out for 60 secs and in nuclei it is between 20-40s. We could not measure for 60s in the nuclei as the nuclear position fluctuates from its initial position. We will add another column to indicate the duration of FCS measurements in the supplementary tables.

b) The authors acknowledge potential issues with fluorophore photophysics and use different lag time ranges for the calibration dye Atto-488 (0.001 ms in Method Fig. 2) and eGFP (0.1 ms in the main figures). Given the strong influence of different parameters on data interpretation and conclusions, Method Fig. 2 should be repeated with purified eGFP. This is particularly relevant for the noisy FCS measurements in posterior regions.

Performing the experiment with purified eGFP will be a volume calibration. We routinely performed this before each imaging session, and that should be fluorophore independent. As noted by Reviewer 1, it is also important to be clear about background correction. We will provide brightness data for eGFP and background values in the revised manuscript. We can then use this to estimate the corrected concentrations.

We use 0.1 ms to start, as at that point any contribution from the photo-physics should have decayed (0.1 ms is about 3-5 times the day rate of the photophysical process, Sun et al., Analytical Chem 2015).

c) Please explain why no data is shown for "AN" around 0.1 ms lag time in Fig. 1B in contrast to all other figures.

We will add the data for AN from 0.01 in the revised figures.

d) Please state what the estimated diffusion coefficients with one-component model fits are. Please also explain why the fits in Fig. S1E do not reach a value of 1 and why they plateau higher than the experimental data at long lag times. Please constrain the fits to G=1 at 0.1 ms tau and G=0 at 1 s tau to make a fair comparison.

The experimental ACF curves reach 0 at long lag times as would be expected. The one-component fits, however, don’t describe the data well and as a result they do not reach 1 and 0 at short and long lag times, respectively. The fitting is done using a mean-squared estimation of the best approximation of the particular model function to the data. Fixing the parameters can be done, but it will further reduce fit accuracy and deviations will be larger. We will perform this analysis and tabulate the one component fits in supplementary 1 with necessary corrections.

e) Please assess the validity of all multi-component fits by comparing the relative quality of the models to the number of estimated parameters using the Akaike information criterion or similar approaches.

We will provide the values denoting the quality of the fits in the revision. We will provide the 3D 1 particle fit, the 3D 1 particle fit with triplet, the 3D 2 particle fit and the 3D 2 particle fit with triple and will provide appropriate measures of fit quality.

f) Please also present the Bcd-GFP fits with 0.001 ms that are mentioned in line 590, and present the results for the data that did not give comparable tau_D1 and tau_D2 values mentioned in line 593.

We will provide all the curves from 0.001ms in the supplementary. We did not provide these details as we have followed the methods from Abu Arish et al., 2010. As our cytoplasmic and nuclear TauD values match with Abu Arish et al., 2010 and Porcher et al., 2010, we thought the excess data would be redundant.

3) Bicoid gradient and modeling * a) Little et al. 2011 observed that the Bcd gradient decreases around n.c. 13. Can the authors of the present work observe a similar concentration decrease using FCS? This is important to i) validate the FCS concentration measurements, and ii) to resolve the controversy regarding "previous claims based on imaging the Bcd profile within nuclei, which predicted decrease in Bcd diffusion in later stages".*

This is a good point regarding conclusions from the previous literature. The Little et al. paper inferred that diffusion had to decrease from fitting to the gradient profiles. However, subsequent analysis from our lab (Durrieu et al., MSB 2018 [which uses a different method involving a tandem reporter for Bicoid] and this manuscript) strongly suggest that Bicoid remains dynamic, at least through n.c. 13 and early n.c. 14. One way to test this is to use SPIM-FCS, where longer time courses can be taken (though with slower time resolution in the FCS). We have performed preliminary experiments with SPIM-FCS and we will revisit this data to see if we can find evidence for changes in the diffusion.

We will also extend the Discussion to make the results clearer in terms of previous models and literature.

b) Please explain why the experimental Bcd-GFP gradient data does not reach a value of 1 (e.g. in Fig. 3D) despite normalization. Please also explain why the fits become flatter in Fig. 5B compared to the steep fit in Fig. 3D.

Both lines were measured under identical conditions. Therefore, we normalised to the maximum value of both experiments. We will redo, normalising to each individual experiment. Regarding Fig. 5C, the Bcd::eGFP curve is identical to Fig. 3D. The flatter curve is the line with eGFP tagged to a NLS alone.

c) For modeling, please take into account observations that the Bcd source is graded with a wide distribution (30-40% EL, see Spirov et al. 2009, Little et al. 2011, Cai et al. 2017 etc.). The extent of the source used in the present work (x_s=20 um, line 620) is at least five times too small.

Care must be taken in defining the source extent. The most careful measurements are reported in Little et al., PLoS Biology 2011 who performed single molecule FISH. They conclude “We demonstrate that all but a few mRNA particles are confined to the anterior 20% of the egg”. Further, the peak in the particle density is around 20-30um from the anterior (Figure 3, Little et al., PLoS Biology 2011), with the vast majority of counts being with 10% of the anterior pole. Further, Durrieu et al. MSB 2018, showed using a Bcd tandem reporter that there was unlikely to be an extended gradient of bcd mRNA (maximum extent of around 50um). Here, we used a simple source domain, which was arguable a little narrow, but not significantly so. We will increase the value in the revision, but the claim that there is an extended bcd mRNA gradient (Spirov et al., Development 2009) has not been substantiated by later experiments.

• d) Please discuss in the paper how well the simulations in Fig. 3B agree with the experimental data.*

We will provide these details in the revision.

• e) Please provide a precise estimate for the statement "Even with an effective diffusion coefficient of 7 μm2s-1, few molecules would be expected at the posterior given the estimated Bcd lifetime (30-50 minutes)" to turn this into a quantitative argument. How many molecules are expected to reach posterior in which model, and how does it compare to experimental observations?*

This can be estimated based on the root-mean-square distance for diffusive processes. We will provide this in the revision.

• f) The sentence "we find that a model of Bcd dynamics that explicitly incorporates fast and slow forms of Bcd (rather than a single "effective" dynamic mode) is consistent with a range of observations that are otherwise incompatible with the standard SDD model" needs to be toned down and corrected since a simple SDD appears to be sufficient to account for the observed gradients. If the authors disagree, please specifically point out in the paragraph around line 249 what observations exactly are incompatible with a standard SDD model.*

This is similar to the point raised by Reviewer 1. While the standard SDD model can explain the overall gradient shape, it is not compatible with the observed time scales and Bcd puncta tracked in the posterior pole. We will improve the Discussion around this point to make the distinctions between the models clearer.

• 5) Data presentation *
• a) In line 27 and 122 it would be better to rephrase the wording "find/found" and give credit to previous papers that first made these observations. *

We will edit in the revision.

• b) For the statement "This suggests that the dynamics of the fast fraction were not captured by previous FRAP measurements", please explain why this should not be the case even though the fast fraction is shown to be larger than the slow fraction in the current work.*

We will edit in the revision.

• c) Similarly, the sentence "The dynamics of the slower mode correspond closely to measured Bcd dynamics from FRAP" likely needs to be corrected since it neglects the contribution of the faster mode, which is fluorescent as well and should also contribute to the dynamics from FRAP.*

This is similar to the point raised by Reviewer 1 and we will edit in the revision.

d) In the absence of further evidence (see above), the sentences "We establish that such spatially varying differences in the Bcd dynamics are sufficient to explain how Bcd can have a steep exponential gradient in the anterior half of the embryo and yet still have an observable fraction of Bcd near the posterior pole" and "These results explain how a long- ranged gradient can form while retaining a steep profile through much of its range" in the abstract need to be toned down.

We are not sure here what needs to be toned down. Our results show that there are (at least) two dynamic forms of Bcd and, combined, they are capable of forming a long-ranged gradient while also ensuring the gradient remains steep in the anterior (because the diffusion coefficient itself varies across the embryo). We will go through these statements and make sure the meaning is clear.

e) The authors state that "However, we show that eGFP::Bcd in its fastest form can move quickly (~18 μm2s-1), and the fraction of eGFP::Bcd in this form increases at lower concentrations", but this has not been directly shown. Please tone down this statement or directly test the prediction that Bcd has a higher fraction of the fast form in earlier nuclear cycles when Bcd concentration is smaller.

This is a good suggestion, and we will test whether early nuclear cycles of the anterior domain show faster dynamics.

*MINOR POINTS * * 1) Introduction * * a) Please explain explicitly what exactly the contention in Bcd, Nodal and Wingless dynamics is in the cited references. *

We will add in the revision. b) In line 95, it would be better to state that this is a variation of the SDD model rather than "a new model". * We changed from “a new model” to “an improved version of SDD model” in the current version of the manuscript. 2) Methods * * a) The authors state that "The same software was also used to calculate the cross-correlation function", but I couldn't find any cross-correlation analyses. Please clarify. *

It is line 538. There is no cross correlation. We changed this to the autocorrelation function.

b) Please correct the "uM" typo to "nM" in the legend of Method Fig. 2A.

We have changed this in the current version.

• c) In the sentence "Further, since the brightness eGFP:Bcd in the anterior and posterior cytoplasm is lower compared to the nuclei", "brightness" probably needs to be changed to "concentration" since the molecular brightness is unlikely to change. *

We edited the line no.591.

• d) Please explain the background-correction method mentioned in line 612. Please also state at what temperature the experiments were performed.*

We will add a better background correction in the revision. Currently, it is the non-embryo background as background noise. The measurements are carried out at 25oC.

*3) Results * * a) Please provide labels for anterior, posterior, dorsal and ventral in Fig. 1A. * * b) Please explain the colors in Fig. 5C. * * c) Please explain the dashed lines in Fig. 3C. * We have edited Figure 1A and Figure 5C. We will edit Figure 3C in further revision.

*OPTIONAL * * 1) If possible, it would be helpful to mention whether the transgenic animals have any abnormal phenotypes or whether they can rescue the bcd mutant. * We will update in the revision.

*2) To validate the concentration measurements, it would be ideal if the authors could determine the Bcd concentration gradient using FCS along the anterior-posterior axis. This would also address whether there are further unexpected changes in diffusivity in medial regions and along the anterior-posterior axis that would have to be considered for modeling. * To measure the Bcd concentration using FCS along the whole axis would be a very challenging undertaking. To get the data for the two positions analysed already represents a significant amount of work. We have done SPIM-FCS measurements, and we will be repeating our FCS measurements in the Fritzsche lab at Oxford. Combined, we believe this provides sufficient corroboration of our results.

*3) Local photoconversion experiments, e.g. in Bcd-Dendra2 embryos if available, would provide compelling support for the relevance of the measurements in the current work. * This is a nice idea, but this would represent a substantial project in its own right and lies beyond the current work.

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

*In my estimation the experimental work is rigorous and the results fully support the conclusions of the authors. I was surprised, however, that the HD-only form localizes via very different and simpler dynamics than does full-length Bcd, but nevertheless forms at least a qualitatively similar gradient. That leads to the question as to whether the existence of the fast and slow forms and their different ratios in different parts of the embryo actually are physiologically relevant. I don't see a straightforward way to test this experimentally, because the mutations that effect Bcd gradient formation also affect essential functions of the protein that if abrogated produce severe downstream effects on embryonic development and lethality. However I would like to see this point at least addressed in the discussion. The data and the methods are presented in such a manner that they can be reproduced, and the number of replicates and statistical analysis is overall robust. * We thank the Reviewer for the positive and constructive review. They, like both previous reviewers, raise the issue of the model and how it fits with the data. As outlined above, we will improve this part of the data presentation and also the Discussion to make sure the main results are clear.

We agree that the underlying importance of the different dynamic forms of Bicoid – and why they change across the embryo – remains unknown. We believe that our careful characterisation of such behaviour is important nonetheless, as it reveals that: (1) morphogen dynamics are more complicated than typically modelled, and this may be just as relevant for ligands moving through extracellular space; and (2) dynamics can vary in space/time, providing an additional possible mechanism of control for regulating morphogen gradient profiles.

Of course, we would like to explore potential physiological relevance. Further exploration of the homeodomain and its role in regulating dynamics is a potential route, but that belongs in future work.

*Minor comments: *

• The presentation of the graphical data measuring Bcd levels along the a-p axis (Fig 1C, 1D, 4C-F and others) needs to be improved, because the grey lines that represent ACF curves are essentially invisible. This is partly because there is usually extensive overlap between the grey lines and other lines. This may be solved by using a more vivid colour than grey for the ACF curves, or perhaps the ACF lines could be made thicker but with some transparency so that overlapping data can be seen. In any event this aspect of the presentation needs to be improved. * We have made the ACF lines thicker to distinguish from the model fit.

*In Figs 2D and 2I measurements of statistical significance between the proportion of protein in fast and slow modes need to be added. * We will add in the revision.

*Relevant to line 174 and Fig 2, NLS should be defined when first used, the source of the NLS should be given (is it from Bcd?) and the rationale for looking at eGFP::NLS should be made explicit. *

We have added details on how the eGFP::NLS is generated in the methods.

*In Fig 3D the dashed lines need to be defined. I assume these are experimental error bars but this is not stated. *

We now state this in the legends.

*On lines 344-5, shouldn't this conclusion concern the HD rather than the NLS? * Yes, thanks for pointing it out it is related to only NLS not NLSHD. We removed this statement from line 351.

*On line 432, CAP is not an acronym, the correct term is 5' 'cap' or 'cap structure'. Also Cho et al. PMID 15882623 should be added to the references here. * We changed the corresponding section and added the references.

*On lines 446, 456, 469, and throughout: replace 'blastocyst' with 'blastoderm'. The former term is generally used for embryos that undergo full cellular divisions and cleavage in early embryogenesis, not for syncytial embryos such as Drosophila. * We have changed blastocyst to blastoderm throughout the manuscript.

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

Major comments: The averaged autocorrelation curves were fitted to models of diffusion with one and two components. The one-component model was insufficient to reproduce the data and the two-component model seems to fit the data. Have the authors tested models with more than two components? Could it be possible to distinguish more Bcd populations?

While it is possible to fit with further components, it rarely provides useful further insight. In particular, the error in measuring three tau_D’s is typically very large. In addition, the improvement in the fit will be marginal, and thus the extra components cannot be justified statistically. Of course, we cannot exclude a third (or more) possible dynamic modes, but within the resolution of our FCS measurements two components with triplets are in general the maximum that can be accommodated without overfitting. We will provide evidence for this claim in the supplement of the revised manuscript.

In Figure 2E, the same concentration of eGFP::NLS is estimated to exist in the cytoplasm and nucleus. Since the NLS should target eGFP to the nucleus, what is the explanation for this observation? Is it possible that the method used to estimate the concentration of molecules is underestimating the concentration in the nucleus or the opposite in the cytoplasm?

This is a good observation. There are two possible explanations. First, the regular division cycles “reset” the nuclear levels. Therefore, differences may not be so large. Second, FCS measurements of concentration can be noisy, as they depend on the very short time scales in the measurement. We will double check our measurements and clarify this in our revision.

*In the simulation of the SDD model (Figure 3B), simulations at 10 min, 25 min and 120 min are shown. Assuming that 120 min corresponds to early nc14, are simulations at earlier timepoints corresponding to nc12 and nc13 indistinguishable from the profile at 120 min? This demonstration would further support the option to merge the data from all nuclear cycles. *

This is a good point. Here, we were primarily focused on showing the time evolution of the model, rather than directly mapping onto experiment. We will clarify in the revision.

*The results obtained with the BcdN51A mutant show an increase in diffusion speed, while retaining similar proportions of fast and slow populations. In the slow fraction, a new population is found. Assuming that the BcdN51A molecules cannot bind specifically to DNA due to the mutation, what would this newly found population correspond to? Could the authors explore the possibility of nonspecific binding to DNA? The article would also win by discussing more on this aspect or other options. *

This is an interesting question. Dslow for anterior nuclei of N51A mutants increases (Dslow from ~0.2um2/s to ~1.5 um2/s), and the proportion is similar to the slow fraction of WT Bcd in the anterior nuclei (F=50%). The Dslow values of bcdWT suggest that 0.2um2/s is a result of DNA binding. For bcdN51A, Dslow of 1.5 um2/s is suggestive of nonspecific interaction of bcdN51A to the DNA. Such a nonspecific interaction is also noticed in the case of NLS::eGFP, where we see a significant amount (Dslow~ 1-1.5 um2/s , F=20%) of slow form in the anterior nuclei, likely due to non-specific interaction with the DNA.

It is worth noting that the inactive homeodomain of transcription factor sex comb reduced (scr) also interacts non-specifically with DNA at high concentration (Vukojevic et al., PNAS 2010). Non-specific interaction of eGFP fluorophore is also noted to be higher in the nuclei of AT-1 cells that suggest “obstacle-free accessible space” is low in the nuclei (Wachsmuth et al., JMB 2000). Therefore, though we do not understand the specific mechanism, our results for N51 mutants are aligned with previous observations of intra-nuclei dynamics.

The experimental rational behind the BcdMM reporter needs to be better explained as it is not clear. It was previously shown that the N51A mutation disturbs zygotic hb activation and Caudal gradient formation (see Figure 3 in Niessing et al., 2000). Since N51A already causes a strong phenotype by disturbing hb expression and Cad gradient formation, what is the reasoning being adding extra mutations to this background? Since the mutations in the PEST domain and YIRPYL motif are involved in cad translational repression, it would be more interesting to add them to the R54A mutation and further study the repression of cad? It would also shed light on the unexpected no difference or even decrease in diffusion in the cytoplasm of the R54A mutant which should increase if indeed the cad mRNA binding is being repressed.

Our rationale was to remove more elements of Bcd to see if there was some degree of redundancy – at least in terms of the dynamics.

The Bicoid homeodomain N51A mutation is physiologically known to cause de-repression of caudal and inhibit hunchback expression. Mechanistically, nuclear Bcd activates hb transcription. However, in the cytoplasm Bcd interacts with other proteins and forms a complex to de-repress caudal. Bcd binds to caudal mRNA through its HD at one end of the complex. However, in the other end, other proteins in the complex are bound to the 5’cap region caudal mRNA. Our rationale for generating the MM mutation was that the N51A mutation may not be sufficient for Bcd to be released from the protein complex. Therefore, additional mutations to N51A may release Bcd from interactions with either DNA or with other proteins through PEST domain and YIRPYL motif.

*Have the authors confirmed that their BcdR54A indeed inhibits cad translation? *

We have not tested the eGFP:bcdR54A to inhibit cad translation. We will add the data in the revision.

*How many embryos of BcdMM were analysed? The authors should also provide a table with all the values in SI as they have done for all the other reporters. *

We will add this data with the revision.

*The claims with eGFP::NLSBcdHD need to be supported by data from multiple embryos. Even if multiple ACF curves are obtained from one embryo, analysing only one embryo is not sufficient. This would clarify the fact that this reporter seems to be able to reproduce the mobility of Bcd in the nucleus. *

We agree and we are arranging to collect more data. This should be completed by the end of the summer.

*According to the methods, all reporters were expressed in a bcd null background, made with the bcd1 allele. This allele is also known as bcd085 and according to Driever and Nusslein-Volhard, 1988 (PMID: 3383244), this allele only causes an intermediate phenotype. This indicates that a truncated version of the protein probably still exists on the embryo. Do the conclusions obtained here still hold if a truncated version of the Bcd protein exists in addition to their reporters? *

We used the bcdE1 mutant, a null mutant of bcd. This was used by Gregor et al., Cell 2007 in their generation of the original Bcd::eGFP. We have also recently generated a more complete bcdKO mutation (Huang et al., eLife 2017). Our embryos do not have a clear phenotype that we can relate to the specific bcd- background used. Nonetheless, we agree it is an important point to be clear about the genetic background and we will clarify in the revised manuscript.

Minor comments: * * In line 45: "Morphogens are signalling molecules", the authors should consider removing the word "signalling" since not all morphogens are, especially the one being studied, Bicoid. * * In lines 80-81 (and also throughout the text): "We measure the Bcd dynamics at multiple locations along the embryo AP-axis", should be more accurate and changed to anterior and posterior of the embryo. Using "multiple locations along the AP axis" is ambiguous and not exact for what was done.

Yes, this is a fair comment. We have edited these sections in the current manuscript.

*Throughout the article, the authors refer multiple times to "modes for/of Bcd transport". Since they or others have not proven that Bcd is being transported, which would involve at least another factor, the authors should replace transport by movement, diffusion or a similar word with which they are comfortable. *

We have changed transport to movement wherever relevant in the text.

*Suggestion: The authors claim that the Bcd gradient is exponential up to 60% of embryo length. Would this information allow a more precise calculation of the gradient decay length in the exponential region than the 80-100µm stated on line 202? *

This is an interesting point, but our results suggest that the idea of the decay length is not so applicable in the posterior region. There, the Bcd dynamics are generally quicker, thereby increasing l. Of course, we cannot discount possible spatial variation in degradation. However, in previous work, our Bcd tandem reporter (which is sensitive to changes in degradation) did not reveal spatial variation in degradation.

In lines 258-259, the sentence "Further, Bcd binds to caudal mRNA, repressing its expression in the cytoplasm" should be improved to clarify the role of Bcd in caudal mRNA translation repression and references should be added. This should also be corrected in the following paragraph.

We will add the necessary corrections in the revision.

*In line 262, "mutations" should be singular since it corresponds to only one amino acid mutation. *

We have corrected this.

*Figure 4J needs to be corrected as the fractions of the slow and fast populations do not correspond to what is shown in Table 3. For example, Fslow fraction of AC is ~45% in the figure while it is 36% in Table 3. The problem occurs in all fractions. *

We are sorry there is a mislabelling in the corresponding figure. AN is in the place of AC. We have edited figure 4J and removed the mislabelling.

*In the discussion, in lines 379-380, "Given the changing fractions of the fast and slow populations in space, the interactions between the populations are likely non-linear". What is the reasoning for non-linearity and not interchangeability? *

If the interactions between the two populations were linear, then the fraction in each form would be constant across the embryo. Some degree of nonlinearity is required in order to have spatially varying relative populations.

*In line 432 caudal should be italicized. *

We have edited this.

*In the discussion, the authors conclude that "In the nucleus, the two populations can be largely (though not completely) explained by Bcd binding to DNA". The discussion would win by explaining all the possible options. * We will add the necessary changes in the discussion. This is also related to above reviewer comments.

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Referee #4

Evidence, reproducibility and clarity

Summary:

Athilingam et al. are interested in understanding how the Bicoid (Bcd) morphogen gradient is formed in the early stages of Drosophila embryonic development. Using fluorescence correlation spectroscopy (FCS), the authors quantify the dynamics of Bcd in nuclei and cytoplasm at anterior and posterior regions of the embryo. First, they characterize the dynamics of eGFP::Bcd in space and time. Analysing FCS autocorrelation curves at the anterior and posterior regions of the embryo during the interphases of nuclear cycles 12 to 14, they detect differences in Bcd diffusion and are able to infer and distinguish two Bcd populations with slow and fast diffusions. Moreover, these dynamics do not vary between nuclear cycles. Using the different diffusions of the slow and fast populations, they find a model capable of explaining the formation of the gradient at larger distances and the existence of the Bcd molecules in the posterior, compatible with the SDD model widely accepted in the community. Lastly, given that Bcd has multiple roles binding to DNA and also to RNA through its homeodomain, mutations affecting DNA/RNA and RNA only binding are used. Using a mutant without the ability to bind to DNA, their determine that the slower diffusion in the nuclei are due to DNA binding. They further confirm this by fusing the homeodomain of Bcd to eGFP::NLS.

Major comments:

The averaged autocorrelation curves were fitted to models of diffusion with one and two components. The one-component model was insufficient to reproduce the data and the two-component model seems to fit the data. Have the authors tested models with more than two components? Could it be possible to distinguish more Bcd populations? In Figure 2E, the same concentration of eGFP::NLS is estimated to exist in the cytoplasm and nucleus. Since the NLS should target eGFP to the nucleus, what is the explanation for this observation? Is it possible that the method used to estimate the concentration of molecules is underestimating the concentration in the nucleus or the opposite in the cytoplasm? In the simulation of the SDD model (Figure 3B), simulations at 10 min, 25 min and 120 min are shown. Assuming that 120 min corresponds to early nc14, are simulations at earlier timepoints corresponding to nc12 and nc13 indistinguishable from the profile at 120 min? This demonstration would further support the option to merge the data from all nuclear cycles. The results obtained with the BcdN51A mutant show an increase in diffusion speed, while retaining similar proportions of fast and slow populations. In the slow fraction, a new population is found. Assuming that the BcdN51A molecules cannot bind specifically to DNA due to the mutation, what would this newly found population correspond to? Could the authors explore the possibility of nonspecific binding to DNA? The article would also win by discussing more on this aspect or other options. The experimental rational behind the BcdMM reporter needs to be better explained as it is not clear. It was previously shown that the N51A mutation disturbs zygotic hb activation and Caudal gradient formation (see Figure 3 in Niessing et al., 2000). Since N51A already causes a strong phenotype by disturbing hb expression and Cad gradient formation, what is the reasoning being adding extra mutations to this background? Since the mutations in the PEST domain and YIRPYL motif are involved in cad translational repression, it would be more interesting to add them to the R54A mutation and further study the repression of cad? It would also shed light on the unexpected no difference or even decrease in diffusion in the cytoplasm of the R54A mutant which should increase if indeed the cad mRNA binding is being repressed. Have the authors confirmed that their BcdR54A indeed inhibits cad translation? How many embryos of BcdMM were analysed? The authors should also provide a table with all the values in SI as they have done for all the other reporters. The claims with eGFP::NLSBcdHD need to be supported by data from multiple embryos. Even if multiple ACF curves are obtained from one embryo, analysing only one embryo is not sufficient. This would clarify the fact that this reporter seems to be able to reproduce the mobility of Bcd in the nucleus. According to the methods, all reporters were expressed in a bcd null background, made with the bcd1 allele. This allele is also known as bcd085 and according to Driever and Nusslein-Volhard, 1988 (PMID: 3383244), this allele only causes an intermediate phenotype. This indicates that a truncated version of the protein probably still exists on the embryo. Do the conclusions obtained here still hold if a truncated version of the Bcd protein exists in addition to their reporters?

Minor comments:

In line 45: "Morphogens are signalling molecules", the authors should consider removing the word "signalling" since not all morphogens are, especially the one being studied, Bicoid. In lines 80-81 (and also throughout the text): "We measure the Bcd dynamics at multiple locations along the embryo AP-axis", should be more accurate and changed to anterior and posterior of the embryo. Using "multiple locations along the AP axis" is ambiguous and not exact for what was done. Throughout the article, the authors refer multiple times to "modes for/of Bcd transport". Since they or others have not proven that Bcd is being transported, which would involve at least another factor, the authors should replace transport by movement, diffusion or a similar word with which they are comfortable. Suggestion: The authors claim that the Bcd gradient is exponential up to 60% of embryo length. Would this information allow a more precise calculation of the gradient decay length in the exponential region than the 80-100µm stated on line 202? In lines 258-259, the sentence "Further, Bcd binds to caudal mRNA, repressing its expression in the cytoplasm" should be improved to clarify the role of Bcd in caudal mRNA translation repression and references should be added. This should also be corrected in the following paragraph. In line 262, "mutations" should be singular since it corresponds to only one amino acid mutation. Figure 4J needs to be corrected as the fractions of the slow and fast populations do not correspond to what is shown in Table 3. For example, Fslow fraction of AC is ~45% in the figure while it is 36% in Table 3. The problem occurs in all fractions. In the discussion, in lines 379-380, "Given the changing fractions of the fast and slow populations in space, the interactions between the populations are likely non-linear". What is the reasoning for non-linearity and not interchangeability? In line 432 caudal should be italicized. In the discussion, the authors conclude that "In the nucleus, the two populations can be largely (though not completely) explained by Bcd binding to DNA". The discussion would win by explaining all the possible options.

Significance

The results presented in this article advance the knowledge of the field by adding data and quantifications of Bcd mobility at four locations: anterior nucleus, posterior nucleus, anterior cytoplasm and posterior cytoplasm. Until now, FCS studies have focused mostly on measuring the dynamics of Bcd in nuclei at the anterior (Abu-Arish et al. 2010; Porcher et al. 2010) of the embryo. The results are also consistent with what was previously found for eGFP:Bcd in the anterior nucleus. Still, this is not surprising as they use the same reporter as the previous studies.

This article will be interesting to an audience comprising biologists and biophysicists interested in protein diffusion.

As a biologist, I do not have sufficient expertise to completely evaluate if the modelling is performed flawlessly. However, in my understanding, the FCS analysis is crucial for the results and their conclusions, hence the comment on the certainty of the existence of only two Bcd populations, though these populations being previously described with FCS. Comments from a physicist with experience in analysing FCS data are thus necessary.

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Referee #3

Evidence, reproducibility and clarity

The manuscript presents experiments that combine a number of techniques to produce an extremely detailed view of Bcd gradient formation in the early Drosophila embryo. The authors provide both empirical and theoretical evidence that support a conclusion that Bcd has both slow and fast moving forms, that the proportion of fast vs slow Bcd is higher at the posterior of the embryo, and that a theoretical model involving both fast and slow moving components fits empirical observations substantially better than a simple diffusion model. Next, they extend the work by investigating what functional motifs of Bcd are required for gradient formation. They demonstrate that a missense mutation N51A in the homeodomain (HD) that affects both DNA binding and cad regulation has major effects on Bcd gradient formation, while the R54A mutation that only affects cad regulation does not. They further show that a protein composed of only an NLS and the Bcd HD can form a gradient that is similar to full-length Bcd, although the two-component dynamics are not recapitulated by the HD-only form.

In my estimation the experimental work is rigorous and the results fully support the conclusions of the authors. I was surprised, however, that the HD-only form localizes via very different and simpler dynamics than does full-length Bcd, but nevertheless forms at least a qualitatively similar gradient. That leads to the question as to whether the existence of the fast and slow forms and their different ratios in different parts of the embryo actually are physiologically relevant. I don't see a straightforward way to test this experimentally, because the mutations that effect Bcd gradient formation also affect essential functions of the protein that if abrogated produce severe downstream effects on embryonic development and lethality. However I would like to see this point at least addressed in the discussion. The data and the methods are presented in such a manner that they can be reproduced, and the number of replicates and statistical analysis is overall robust.

Minor comments:

The presentation of the graphical data measuring Bcd levels along the a-p axis (Fig 1C, 1D, 4C-F and others) needs to be improved, because the grey lines that represent ACF curves are essentially invisible. This is partly because there is usually extensive overlap between the grey lines and other lines. This may be solved by using a more vivid colour than grey for the ACF curves, or perhaps the ACF lines could be made thicker but with some transparency so that overlapping data can be seen. In any event this aspect of the presentation needs to be improved.

In Figs 2D and 2I measurements of statistical significance between the proportion of protein in fast and slow modes need to be added.

Relevant to line 174 and Fig 2, NLS should be defined when first used, the source of the NLS should be given (is it from Bcd?) and the rationale for looking at eGFP::NLS should be made explicit.

In Fig 3D the dashed lines need to be defined. I assume these are experimental error bars but this is not stated.

On lines 344-5, shouldn't this conclusion concern the HD rather than the NLS?

On line 432, CAP is not an acronym, the correct term is 5' 'cap' or 'cap structure'. Also Cho et al. PMID 15882623 should be added to the references here.

On lines 446, 456, 469, and throughout: replace 'blastocyst' with 'blastoderm'. The former term is generally used for embryos that undergo full cellular divisions and cleavage in early embryogenesis, not for syncytial embryos such as Drosophila.

Significance

General assessment: The main strength of the paper is that it extends our knowledge about the dynamics of Bcd gradient formation, and by so doing it advances our understanding of the physical parameters underlying morphological gradients and patterning. The authors are admirably open about questions that were unanswered by the study, which include identifying molecular interactions that affect Bcd dynamics in the cytoplasm, whether Bcd diffusion is dependent on its concentration, and whether other morphogens form gradients in a similar manner. Answering any of these questions would involve substantial experimental work and it is appropriate to leave these questions for subsequent manuscripts.

Advance: A number of high-profile papers were published on this topic in the late 2000s and early 2010s that reached difficult conclusions, so the nature of the mechanisms underlying Bcd gradient formation have remained controversial and somewhat opaque. While this paper does not provide a definitive answer to all relevant open questions, it nevertheless represents a significant advance toward their resolution.

Audience: This paper will be of interest both to developmental biologists interested in gradients and pattern formation, and to biophysicists interested in physical parameters affect molecular movements. It is fundamental research that does not have an obvious clinical or translational component.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this study, Athilingam et al. investigated Bicoid (Bcd) protein dynamics along the anterior-posterior axis of Drosophila embryos. They performed Fluorescence Correlation Spectroscopy (FCS) experiments to analyze Bcd protein diffusion and found that Bcd had a 1.7-fold higher mobility in the posterior compared to the anterior. The authors also generated Bcd homeodomain mutations and analyzed their impact on gradient formation. They found that the BcdN51A mutation exhibited altered nuclear dynamics with faster diffusion, while cytoplasmic dynamics remained unchanged. BcdR54A embryos showed dynamics more similar to the wild-type Bcd, with a minor decrease in slow diffusion in the posterior region. Interestingly, the Bcd homeodomain alone, when fused to eGFP-NLS, was able to replicate the observed Bcd protein dynamics, particularly in the slower diffusive mode. The paper provides evidence that the Bcd homeodomain has a significant influence on protein dynamics and suggests a complex interplay of interactions between Bcd, nuclear DNA, and cytoplasmic RNA that regulate Bcd diffusion and function.

Overall, the data and methods presented in the paper are clearly described. The experiments are adequately replicated, and the statistical analysis appears sufficient. Prior studies are referenced appropriately, and the claims and conclusions are mostly supported by the data. However, the following points should be addressed to corroborate the conclusions:

Major points

1. FCS measurements and fits
   • a) Please state the duration of each individual FCS measurement.
   • b) The authors acknowledge potential issues with fluorophore photophysics and use different lag time ranges for the calibration dye Atto-488 (0.001 ms in Method Fig. 2) and eGFP (0.1 ms in the main figures). Given the strong influence of different parameters on data interpretation and conclusions, Method Fig. 2 should be repeated with purified eGFP. This is particularly relevant for the noisy FCS measurements in posterior regions.
   • c) Please explain why no data is shown for "AN" around 0.1 ms lag time in Fig. 1B in contrast to all other figures.
   • d) Please state what the estimated diffusion coefficients with one-component model fits are. Please also explain why the fits in Fig. S1E do not reach a value of 1 and why they plateau higher than the experimental data at long lag times. Please constrain the fits to G=1 at 0.1 ms tau and G=0 at 1 s tau to make a fair comparison.
   • e) Please assess the validity of all multi-component fits by comparing the relative quality of the models to the number of estimated parameters using the Akaike information criterion or similar approaches.
   • f) Please also present the Bcd-GFP fits with 0.001 ms that are mentioned in line 590, and present the results for the data that did not give comparable tau_D1 and tau_D2 values mentioned in line 593.
2. Bicoid gradient and modeling
   • a) Little et al. 2011 observed that the Bcd gradient decreases around n.c. 13. Can the authors of the present work observe a similar concentration decrease using FCS? This is important to i) validate the FCS concentration measurements, and ii) to resolve the controversy regarding "previous claims based on imaging the Bcd profile within nuclei, which predicted decrease in Bcd diffusion in later stages".
   • b) Please explain why the experimental Bcd-GFP gradient data does not reach a value of 1 (e.g. in Fig. 3D) despite normalization. Please also explain why the fits become flatter in Fig. 5B compared to the steep fit in Fig. 3D.
   • c) For modeling, please take into account observations that the Bcd source is graded with a wide distribution (30-40% EL, see Spirov et al. 2009, Little et al. 2011, Cai et al. 2017 etc.). The extent of the source used in the present work (x_s=20 um, line 620) is at least five times too small.
   • d) Please discuss in the paper how well the simulations in Fig. 3B agree with the experimental data.
   • e) Please provide a precise estimate for the statement "Even with an effective diffusion coefficient of 7 μm2s-1, few molecules would be expected at the posterior given the estimated Bcd lifetime (30-50 minutes)" to turn this into a quantitative argument. How many molecules are expected to reach posterior in which model, and how does it compare to experimental observations?
   • f) The sentence "we find that a model of Bcd dynamics that explicitly incorporates fast and slow forms of Bcd (rather than a single "effective" dynamic mode) is consistent with a range of observations that are otherwise incompatible with the standard SDD model" needs to be toned down and corrected since a simple SDD appears to be sufficient to account for the observed gradients. If the authors disagree, please specifically point out in the paragraph around line 249 what observations exactly are incompatible with a standard SDD model.
3. Data presentation
   • a) In line 27 and 122 it would be better to rephrase the wording "find/found" and give credit to previous papers that first made these observations.
   • b) For the statement "This suggests that the dynamics of the fast fraction were not captured by previous FRAP measurements", please explain why this should not be the case even though the fast fraction is shown to be larger than the slow fraction in the current work.
   • c) Similarly, the sentence "The dynamics of the slower mode correspond closely to measured Bcd dynamics from FRAP" likely needs to be corrected since it neglects the contribution of the faster mode, which is fluorescent as well and should also contribute to the dynamics from FRAP.
   • d) In the absence of further evidence (see above), the sentences "We establish that such spatially varying differences in the Bcd dynamics are sufficient to explain how Bcd can have a steep exponential gradient in the anterior half of the embryo and yet still have an observable fraction of Bcd near the posterior pole" and "These results explain how a long- ranged gradient can form while retaining a steep profile through much of its range" in the abstract need to be toned down.
   • e) The authors state that "However, we show that eGFP::Bcd in its fastest form can move quickly (~18 μm2s-1), and the fraction of eGFP::Bcd in this form increases at lower concentrations", but this has not been directly shown. Please tone down this statement or directly test the prediction that Bcd has a higher fraction of the fast form in earlier nuclear cycles when Bcd concentration is smaller.

Minor points

1. Introduction
   • a) Please explain explicitly what exactly the contention in Bcd, Nodal and Wingless dynamics is in the cited references.
   • b) In line 95, it would be better to state that this is a variation of the SDD model rather than "a new model".
2. Methods
   • a) The authors state that "The same software was also used to calculate the cross-correlation function", but I couldn't find any cross-correlation analyses. Please clarify.
   • b) Please correct the "uM" typo to "nM" in the legend of Method Fig. 2A.
   • c) In the sentence "Further, since the brightness eGFP:Bcd in the anterior and posterior cytoplasm is lower compared to the nuclei", "brightness" probably needs to be changed to "concentration" since the molecular brightness is unlikely to change.
   • d) Please explain the background-correction method mentioned in line 612. Please also state at what temperature the experiments were performed.
3. Results
   • a) Please provide labels for anterior, posterior, dorsal and ventral in Fig. 1A.
   • b) Please explain the colors in Fig. 5C.
   • c) Please explain the dashed lines in Fig. 3C.

Optional

1. If possible, it would be helpful to mention whether the transgenic animals have any abnormal phenotypes or whether they can rescue the bcd mutant.
2. To validate the concentration measurements, it would be ideal if the authors could determine the Bcd concentration gradient using FCS along the anterior-posterior axis. This would also address whether there are further unexpected changes in diffusivity in medial regions and along the anterior-posterior axis that would have to be considered for modeling.
3. Local photoconversion experiments, e.g. in Bcd-Dendra2 embryos if available, would provide compelling support for the relevance of the measurements in the current work.

Significance

The paper investigates the diffusion dynamics of Bicoid (Bcd), a transcription factor crucial for establishing the anterior-posterior axis during Drosophila embryogenesis. The authors utilize Fluorescence Correlation Spectroscopy (FCS) and various Bcd mutants fused with eGFP to understand the role of Bcd's homeodomain and other domains in its nuclear and cytoplasmic diffusion dynamics. They performed elegant experiments using insightful transgenic lines, showcasing well-designed and well-executed methodology. The paper is very nice to read, with a clear and engaging writing style and excellent presentation of the data, making it easy to follow and understand their findings.

However, there are a few limitations to consider. First, the paper does not provide evidence for the switching between slow and fast populations central for their modeling, leaving an important aspect of the dynamics unexplained. Second, there are doubts regarding the accuracy of the model used to fit the FCS data (see detailed comments in the section "Evidence, reproducibility and clarity"), underscored by the statement "While the increase in the slow mode was expected, the reason for the change in the fast mode is less clear". Third, the relevance of a potentially higher Bcd mobility for gradient formation remains unclear. For example, the fit in Fig. 3D deviates substantially from the data around 0 um embryo length, which is likely even larger than the error expected from a "simple diffusion" fit at the posterior end of the embryo. In addition, in Fig. 3C the lines for the different models appear to be indistinguishable given the noisy measurements, calling the relevance of the findings into question.

Overall, the paper extends our knowledge by providing new insights into the role of the Bcd homeodomain in determining Bcd gradient formation. The paper highlights that homeodomain interactions in the cytoplasm and nuclei are significant contributors to determining Bcd dynamics. Additionally, the paper suggests that additional components within Bcd itself or other proteins in the cytoplasm affect Bcd dynamics at different Bcd concentrations. The paper will be of interest to a broad audience in developmental biology, molecular biology, and biophysics. Researchers studying transcription factor dynamics, morphogen gradients, and Drosophila embryogenesis will find this study particularly valuable. While the study is primarily focused on basic research, the insights gained on Bcd diffusion dynamics and the role of the homeodomain may contribute to a broader understanding of transcription factor regulation and function in other systems.

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Referee #1

Evidence, reproducibility and clarity

This manuscript reports a very thorough and careful study of the mobility of Bicoid in the early embryo, explored with single-point fluorescence correlation spectroscopy. Although previous groups have looked into this question in the past, the work presented here is novel and interesting because of the different Bicoid mutants and constructs the authors have examined, in particular with the goal of understanding the role of the protein DNA-binding homeodomain. The authors convincingly show that there is a significant increase in Bicoid dynamics from the anterior to the posterior region of the embryo, and that the homeodomain plays an important role in regulating the protein's dynamics. Their experiments are very well designed and carefully analyzed. The authors also modelled gradient formation to see whether this change in dynamics might play a role in setting the shape of the gradient. I am not sure I fully agree with their conclusion that it does, as mentioned in my comment below. However, it is an interesting discussion to have, and I think this paper makes a significant advance in our understanding of Bicoid's behavior in the early embryo.

Major comments:

1. Gradient profile quantification: Some of the conclusions made by the authors rely on the comparison between their model of gradient formation (as captured in the equations in lines 232 and 233) and the Bcd intensity profile measured in the embryos. Since the differences in gradient shape predicted by the different models are very small (see Fig. 3B, which is on a log scale and therefore emphasize small differences, and Fig. 3C), it is very important to understand how reliable the experimental concentration profiles are. At the moment, I do not find the evidence that [Bcd] concentration profile is more consistent with a 2-component diffusion model than a 1-component model very strong. A few comments related to this:
   • 1a. Line 249, it is mentioned that: "observations ... incompatible with the SDD model". Which observations exactly are incompatible with the SDD model?
   • 1b. In Fig. 3D, only the prediction of the 2-component model is shown. What would the simple 1-component diffusion model look like? Is it really incompatible with the data?
   • 1c. Line 243: "The increased fraction in the fast form ... consistent with experimental observation of Bcd in the most posterior" (Mir et al.)". I am not sure how this is significant, since the simple model also predicts there will be Bcd in the posterior - the only difference is how much is there (as shown in Fig. 3C), and it's a very small difference.
   • 1d. Since the difference between models is in the posterior region where Bcd concentration is very low, when comparing the models to the data the question of background subtraction is essential. How was the subtracted background (mentioned line 612) estimated?
   • 1e. Along the same line, were the detectors on the Zeiss LSM analog or photon counting detectors, and how confident can we be that signal is exactly proportional to concentration?
   • 1f. Can the gradients created by the two Bcd mutants (FIg. 4B) be quantified as well, and are they any different from the original Bcd gradient?
   • 1e. What is the pink line in Figure 5C (I am assuming the green one is the same as in Fig. 3D)? It could be better to not use normalization here, or normalize everything respective to the eFP::Bcd data to make comparison in relative concentrations in the posterior for different constructs more evident (also maybe different colors for the three different data sets would help clarity).
   • 1f. Discussion, lines 402-403: Does the detailed shape of the Bcd in the posterior region matter at all, since the posterior is not a region where Bicoid is active, as far as we know? Could a varying Bcd dynamics have other consequences that would be more biologically relevant?
2. Model for gradient formation (lines 231-238):
   • 2a. Whether the molecules of Bcd can change from their fast to slow form is never questioned. How do we know (or why might we suspect) they do exchange?
   • 2b. The values used in the model for alpha, beta_0 and rho_0 should be mentioned. Maybe having a table with all the parameters in the method section, or even in the supplementary section, would help. The exact values of alpha and beta matter, because if they are large (fast exchange) a single exponential gradient is to be expected, if they are 0 (no exchange) a double exponential gradient is to be expected, with intermediate behavior in between. Which case are we in here?
3. Discussion about anomalous diffusion (lines 386-388): The 2-component model used by the authors to interpret their FCS data seems very well justified here (excellent fits with very small residuals). I agree with the authors' conclusion that "the dynamics of Bcd within the nucleus are more complicated than a simple model of bound versus unbound Bcd", but I don't see how that can lead to a diagnostic of anomalous diffusion instead. Maybe it is just a matter of exactly explaining what is meant by anomalous diffusion here (since this term is often used to mean different things). A more likely scenario I think, is that there are more than just two Bcd components in the system.
4. Line 440 and after: What is the evidence that the transition between the two forms might vary non-linearly with Bcd concentration? How would that help adapt to different embryo sizes? It would be good to be more explicit here instead of just referring to another paper.
5. Since an important aspect of this work is the study of different Bcd constructs in vivo, it is important that these constructs are very clearly described, so the section on the generation of the fly lines (Methods) should be expanded. In particular:
   • 5a. It seems that the eGFP:: NLS control used here was different from that first described in Ref. 64 (and used for FCS experiments in Ref. 30 and 36)? If so, what NLS sequence was used here, and precisely what type of eGFP was used (in particular, was the A206K mutation that prevents dimerization present in the eGFP used)? If it is the same construct as in Ref. 64, it should be mentioned explicitly.
   • 5b. Were the mutant N51A and R54A lines gifts as well, or have they been described before? If so, previous publications should be referenced. If not, how the plasmid was introduced in the embryo should be briefly explained.
6. Concentration calibration measurements (Methods Fig. 2, line 568 and on). It is well known that background noise is going to interfere with the measurement of N when the signal becomes equivalent to the background noise (Koppel 197, Phys Rev A 10:1938-1945, and for a recent discussion of this effect for morphogens in fly embryos: Zhang et al., 2021, Biophysical Journal 120,4230-4241). It is almost certain that in the low signal regions of the embryo (e.g. posterior cytoplasm) this is affecting the reported concentration, and should be at least acknowledged.
7. Reference 3 is mis-characterized in two different ways in the manuscript:
   • 7a. Line 50: The conclusion in Ref. 3 was not that the gradient was due to a diffusive process, on the contrary Gregor et al. argued that Bcd was too slow to form such a long-range gradient by diffusion. Studies that do present data consistent with a morphogen gradient formation mechanism driven by diffusion are reference 5, reference 30, Zhou et al., Curr. Biol. 2012;22(8):668-75 and Müller et al., Science 336 (2012) 721-724.
   • 7b. The diffusion coefficient estimated from FRAP measurements and reported in Ref. 3 (D = 0.4 micron^2/s) is mentioned a couple of times in the manuscript (line 66, line 395, line 411). However, this number is simply incorrect. When fast components (such as the ones clearly detected here by FCS) are present, they diffuse out of the photobleached area during the photobleaching step. If that is not corrected for during the analysis (and it wasn't in Ref. 3), then the recovery time measured is just equal to the photobleaching time, and has nothing to do with either the fast or slow fraction of the studied molecule - it has no other meaning than to give a lower bound on the value of the actual effective diffusion coefficient of the molecule. This effect (called the halo effect) is well known in the FRAP community (see e.g. Weiss 2004, Traffic 5:662-671), it has been experimental demonstrated to occur for Bcd-eGFP in the conditions used in Ref. 3 (Reference 30), and the actual diffusion coefficient that should have been extracted from the data presented in Ref. 3 has been recalculated by another group to be instead D = 0.9 micron^2/s (Castle et al., 2011, Cell. Mol. Bioeng. 4:116-121). It would therefore be better to report the corrected value from Castle et al. to help the field converge towards an accurate description of Bcd mobility.

Minor comments and suggestions:

1. Figure 1: From panel A, it seems that what is called "Anterior" and "Posterior" is about 150 micron away from the embryo mid-section, i.e. about 100 micron from either the anterior pole or the posterior pole (so not the tip of the embryo, but somewhere in the anterior half or posterior half). Maybe this should be made clear in the text.
2. Fig. 2A; It might be good to put this graph on a log scale, so that cytoplasmic values are seen more clearly. Also, what about reporting on nuclear to cytoplasmic ratios?
3. Fig. 2: It could be interesting to plot D_effective as a function of the measured concentration of Bicoid in different locations, since the (interesting) suggestion is made several time that [Bcd] could the a determinant of the protein mobility.
4. Figure 3B&C: Is the curve for 2-component diffusion (without concentration dependence) for steady-state missing?
5. Lines 78 and 471: What do the authors mean by "new reagents"? The word reagent evokes a chemical reaction, but there are none here. Do the authors mean new constructs? or new mutants?
6. Lines 57-59: Another good reference for FCS measurements performed to study the dynamics of a morphogen (in this case Dpp) is Zhou et al., Curr. Biol. 2012;22(8):668-75
7. Lines 109-111: A word must be missing. Precisely determined what?
8. Line 278: The increase in the slow mode is expected. Maybe explicitly mention why.
9. Line 282: "with the fast component increasing", maybe replace with "with the diffusion coefficient of the fast component increasing" or "with the fraction of the fast component increasing".
10. Line 517: Is there a reason why the dorsal surface is always placed in the coverslip?
11. Line 524 and on: FCS measurements: What was the duration of each individual FCS measurement? It is great that the exact number of measurements are reported in the supplementary!
12. An Airy unit of 120 um seems large in combination with an objective with a NA of 1.2, is there a reason for that? What was the radius of the resulting detection volume?
13. Thank you for detailing the reasons behind the choice of excitation power, an important and often omitted details. Where in the excitation path were the values of the laser power measured (before or after the objective?)?
14. Line 585: "since the brightness of eGFP::Bcd..." do the authors mean the molecular brightness of a single eGFP::Bcd molecule, or the total fluorescence signal?
15. It would be good for reference to mention the approximate value of the molecular brightness recorded for these eGFP constructs at the laser power used.
16. Reference 766: The year (and maybe other things) is missing.
17. Figure 2 (Methods): The concentrations shown on the figure should be in nM not uM.

Significance

Strengths:

Very careful and systematic study of Bcd's dynamics in the early embryo Use of several mutant and truncated forms of Bcd to pinpoint the importance of the DNA binding domain in setting this dynamics Uncovers a previously unknown change in Bcd dynamics from the anterior to the posterior of the embryo Modelling of the Bcd concentration gradient shape taking into account the measured dynamics

Limitations:

The quantitative comparison between modelled and measured gradient could be improved. The discussion of the biological implications of the work is limited

Advance:

Uncovers a previously unknown change in Bcd dynamics from the anterior to the posterior of the embryo. This raises very interesting questions about molecular mechanisms involving Bicoid. Other studies (cited in this manuscript) reported on Bcd dynamics, but the present study represents a very welcome expansion of these earlier studies, by looking at spatial dependence and by examining several Bcd constructs.

Audience:

Somewhat specialized, as this work should firstly be of interest to scientists studying morphogen gradients. However, it is also a beautiful example dynamical studies in vivo, so it will also be of interest to experimental physicists studying protein motions in vivo (a rather large community).

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Reply to the reviewers

This manuscript characterizes the ULD mouse model as a new platform for pre-clinical TB vaccine testing. Using the current tuberculosis (TB) vaccine, BCG, the manuscripts shows that three distinct parameters of protective immunity can be assessed in this model: 1) reduction of bacterial burden (which is shown to be more durable in this model than in the conventional model); 2) prevention of dissemination to the contralateral lung; and 3) prevention of detectable infection. The last parameter of protection is notable because vaccines have not been previously shown to be capable of preventing Mycobacterium tuberculosis (Mtb) infection in the mouse model, and in fact, it has been widely believed that mice lack the immune effector mechanisms necessary to prevent detectable infection. We show here that this is not true. When mice are challenged with a physiologic infectious dose of Mtb, vaccine-induced immunity can indeed prevent detectable infection. Thus, we believe this physiologic dose challenge model, provides potential for an improved platform for preclinical vaccine testing, as it allows for measurement of protective parameters that could not previously be assessed and may provide a window to assess meaningful differences between vaccine candidates. We were happy that both reviewers recognized the significance of this work, noting that the study “offers a new avenue for evaluation of TB vaccines, especially vaccines to prevent establishment of long-term infection” and “is clinically useful to test new TB vaccine candidates by improving the conventional TB vaccine model.”

We thank the reviewers for their time and excellent comments. We have addressed the Reviewer’s comments as outlined below. Most could be fully addressed by minor modifications to the manuscript’s texts or figures. Reviewer 2 requested additional studies to further assess the model’s durability at timepoints later than day 125 post-infection. In response to this comment, we have modified the manuscript to soften our conclusions about the model’s durability. However, we do not believe that performing additional experiments (which would take up to a year to perform) to further examine BCG’s durability in this model is necessary to support the manuscript’s conclusions. Changes made to the manuscript in response to the reviewers’ comments are underlined.

This report provides evidence that the ULD infection model of M. tuberculosis provides the capacity to detect 3 types of protection by BCG: 1) durable reductions in Mtb burden, 2) inhibition of Mtb dissemination, 3) prevention of detectable bacteria. In general, this is well-reasoned, well-written, transparently presented and easy to follow. A few points.

Major issues:

In Figure 2A, 2B, 3A - the authors have a highly skewed distribution with a bimodal distribution between detected bacterial counts and zero bacterial counts. This type of distribution does not lend itself to a t-test. What is the mean of two exclusive categories? For these graphs, the authors should consider plotting the median and interquartile range, then applying a non-parametric test. I suspect the p-values will be as significant, if not more, and there will be some comparisons where the median of the BCG group will be 0 CFU.

We completely agree that a t-test would not be appropriate for data with bimodal distribution, such as if the mice with detected bacterial counts and those with zero bacterial counts were both included in this analysis. We apologize that we did not sufficiently explain that only mice with detectable bacterial counts were included in our analysis of BCG’s ability to reduce bacterial burdens; those with zero bacterial counts were excluded. We recognize that doing this may underestimate the ability of a vaccine to reduce bacterial burdens as a handful of mice that might have had detectable bacterial burdens in the absence of vaccination are not included in the analysis. However, including mice all mice with undetectable bacterial burdens would be confounded by the fact that some mice in the ULD model are never infected at all, at least measurably, even in the unvaccinated group. We decided that the best way to disentangle these issues would be to analyze reduction of bacterial burdens and proportion of mice with zero detectable CFUs separately. Thus, for the former, only those with detectable CFUs are considered, and separately, we compare the proportion of mice with mice with undetectable bacterial burdens in the vaccinated mice compared to the unvaccinated controls (Figure 5). For these reasons, we believe the t-test is an appropriate test for this analysis. However, in response to this comment we have made changes to the text, figure legend, and Methods, to clearly state how and why the analysis was done this way. We thank the reviewer for these comments, as we believe the original manuscript was not sufficiently clear in this respect, and it is very important to convey how the analysis is being performed.

In 3B, instead of presenting a model of the data, can the authors present the raw data across all experiments as datapoints with violin plots, or some other form of data visualization? They could present the fixed effect model as a supplementary figure. The fixed effect model works better for plotting proportions in 4A.

We thank the reviewer for this comment. In Figure 3B of the revised manuscript, the raw data across all experiments is now shown as datapoints with violin plots.

Minor points:

Abstract, line 35. The authors state that BCG does A, B, C in a small percentage of mice. It is not clear whether this means all of A, ,B and C happen in a small percentage. Or rather whether the small percentage refers to C alone. Perhaps this can be rewritten for clarity.

We thank the reviewer for this comment. The small percentage was meant to refer to C alone, but we agree this was not clear as it was written. In the revised manuscript, this sentence in the abstract is written as follows, “We show that BCG confers a reduction in lung bacterial burdens that is more durable than that observed after conventional dose challenge, curbs Mtb dissemination to the contralateral lung, and, in a small percentage of mice, prevents detectable infection.”

Line 123. 12/20 vs 10/20. Hard to say it appeared to prevent based on these numbers. Perhaps may prevent?

In the revised manuscript we have changed “appeared to prevent” to “may prevent”, as suggested.

Line 128. The authors use the word colonized here but don't use this term elsewhere. What is the difference between colonized and infected, and why is colonized used only here?

In the revised manuscript we have changed “colonized” to “infected”, as suggested.

The power calculations could be a supplementary table if space is tight.

We are amenable to moving the power calculations to a supplementary table if this is the preference of the editor.

Reviewer #2

Manuscript "Assessing vaccine-mediated protection in an ULD mycobacterium tuberculosis murine model" is an interesting, well-documented and comprehensive study to develop new TB murine model used to assess new TB vaccine candidates. Although TB vaccine are urgently needed, many TB vaccine candidates remain in the development pipeline mainly because the conventional vaccine evaluation strategy is hindered by the lack of reliable animal models that mimic human TB pathogenic cycle. This manuscript used the ULD mouse infection model to resembles human Mtb infection to test the ability of the ULD model as a TB vaccine testing strategy by assessing the BCG vaccination in I. durability in lung bacterial burden, II. the capacity to protect Mtb dissemination to other organs, and III. levels to protect Mtb infection. Overall, this study is quite extensive and potential interest as the result can be readily used for clinical settings.

Major This study started based upon one of the biggest problems of conventional TB infection model, in which the protection efficacy can be misinterpreted as CFU burden dissipates at later time points due to relatively high burden of initial infection load. To propose that vaccine efficacy test outcomes could be better in ULD murine model compared to that of conventional TB infection model as initial infection burden in ULD model is pathogenetically similar to human infection case. This reviewer is concerned about the authors' interpretation of the results as authors monitored all experimental outcomes at maximum day 125 when lung CFU was ~ 50 fold lower than conventional TB model. Because authors didn't monitor longer period of time, it is not clear if the ULD murine model is optimal to prevent lung CFU dissipation or dissemination to other lung lobes or organs. Authors need to provide additional evidence if the ULD model results are still positive to support authors' hypothesis or the BCG vaccine efficacy in the ULD model was attributed simply to yet lower bacterial burden.

We thank the reviewer for these comments. While we agree that it would be interesting to see if the protective effects of BCG immunization were durable even beyond 125 days post-infection, we don’t believe that defining the durability further is necessary to complete the study or to support our conclusions. In many ways, we believe we have been quite comprehensive and rigorous in this study, examining over 1,000 mice at timepoints ranging from 14 to 125 days post-infection. We believe we have conclusively shown that BCG’s reduction of lung bacterial burdens is more durable in the ULD model than with a conventional dose challenge (50-100 CFU); while the difference is maintained out to days 90-125 in the former, it wanes in the latter. Similarly, BCG’s ability to prevent dissemination to the contralateral lung, a parameter that cannot be assessed in the conventional dose model, is also durable to days 90-125. Finally, because we used a large number of mice, we showed for the first time that BCG can prevent detectable infection in mice challenged with physiologic Mtb dose (pIn response to the reviewer’s comments, we have softened our statements regarding showing that BCG confers durable reductions in lung bacterial burdens in the ULD model. Now, throughout the abstract and manuscript, we say that we show that BCG confers a reduction in lung bacterial burdens that is more durable than observed with conventional dose challenge.

Minor Fig. 1 - 10 out of 20 mouse in BCG vaccinated condition didn't show bacterial burden in the lung at any time. It is not clear that this even is attributed to the failed infection or BCG vaccination mediated protection.

We agree. In this same experiment, 7 out of 20 of the unvaccinated control mice also didn’t show bacterial burden in the lung. One of the features of the ULD model is that we use such a low dose that we intentionally leave some of the mice uninfected, even in the unvaccinated controls. We believe that it is necessary to do this to achieve many of the advantages of the model (e.g., assess dissemination to the contralateral lung and prevention of detectable infection), however, an inherent challenge of the model is that in a single experiment you cannot discern whether an individual mouse with no detectable lung bacteria had infection prevented or whether it would never have been infected in the first place. In the manuscript, we do not claim the difference observed in Figure 1 (7/10 vs. 10/20 with zero CFU) is meaningful. We state in lines 123-125 that “we also observed that 7/20 of the unimmunized mice and 10/20 of the BCG-immunized mice had no detectable infection in either lung (Figure 2A), a difference that was not statistically significant in this single experiment (p=0.53).” We go on to show (Figure 5), that if results from several experiments are pooled, the difference becomes highly significant (p

As shown in Fig. 1 and 2A, at 42 day post infection, conventional TB infection model reached 5 X 106 CFU in an unimmunized condition and 5 X 105 CFU in a BCG vaccinated condition. In this ULD model, the CFU was 1 X 105 CFU in an unimmunized condition and 1 X 104 CFU in an BCG vaccinated model even at 63 day post infection. If we directly compare the CFU between ULD and conventional TB infection model, the difference was ~ 50 folds. Authors may need to show the bacterial CFU burden is still plateaued and stable bacterial dissemination even after a longer period of infection.

We have shown that differences in lung bacterial burdens and bacterial dissemination are durable as long as we’ve looked, which is to days 90-125. As discussed above, we believe this is sufficient to support our conclusions and the goals of the study.

Fig. 2 - The conventional TB model may be included as a negative control.

We show results of BCG efficacy in the conventional TB model in Figure 1. Because conventional dose and ULD infections are different doses, they cannot be in the same infection chamber at the same time and therefore they need to be shown as separate experiments. Nevertheless, the results shown in Figure 1 are highly reproducible, as shown by us and by several other groups (as referenced).

Fig. 5A - Why total challenged mouse number gets increased ?

We presume the reviewer is asking why there are more mice challenged at later timepoints than at early timepoints. Our early experiments suggested that there might be relatively more vaccinated mice with undetectable infection at late timepoints than at earlier ones. As a result, we assessed more experiments at late timepoints than at earlier ones.

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Referee #2

Evidence, reproducibility and clarity

Manuscript "Assessing vaccine-mediated protection in an ULD mycobacterium tuberculosis murine model" is an interesting, well-documented and comprehensive study to develop new TB murine model used to assess new TB vaccine candidates.

Although TB vaccine are urgently needed, many TB vaccine candidates remain in the development pipeline mainly because the conventional vaccine evaluation strategy is hindered by the lack of reliable animal models that mimic human TB pathogenic cycle. This manuscript used the ULD mouse infection model to resembles human Mtb infection to test the ability of the ULD model as a TB vaccine testing strategy by assessing the BCG vaccination in I. durability in lung bacterial burden, II. the capacity to protect Mtb dissemination to other organs, and III. levels to protect Mtb infection. Overall, this study is quite extensive and potential interest as the result can be readily used for clinical settings.

Major

This study started based upon one of the biggest problems of conventional TB infection model, in which the protection efficacy can be misinterpreted as CFU burden dissipates at later time points due to relatively high burden of initial infection load. To propose that vaccine efficacy test outcomes could be better in ULD murine model compared to that of conventional TB infection model as initial infection burden in ULD model is pathogenetically similar to human infection case. This reviewer is concerned about the authors' interpretation of the results as authors monitored all experimental outcomes at maximum day 125 when lung CFU was ~ 50 fold lower than conventional TB model. Because authors didn't monitor longer period of time, it is not clear if the ULD murine model is optimal to prevent lung CFU dissipation or dissemination to other lung lobes or organs. Authors need to provide additional evidence if the ULD model results are still positive to support authors' hypothesis or the BCG vaccine efficacy in the ULD model was attributed simply to yet lower bacterial burden.

Minor

Fig. 1 - 10 out of 20 mouse in BCG vaccinated condition didn't show bacterial burden in the lung at any time. It is not clear that this even is attributed to the failed infection or BCG vaccination mediated protection. As shown in Fig. 1 and 2A, at 42 day post infection, conventional TB infection model reached 5 X 106 CFU in an unimmunized condition and 5 X 105 CFU in a BCG vaccinated condition. In this ULD model, the CFU was 1 X 105 CFU in an unimmunized condition and 1 X 104 CFU in an BCG vaccinated model even at 63 day post infection. If we directly compare the CFU between ULD and conventional TB infection model, the difference was ~ 50 folds. Authors may need to show the bacterial CFU burden is still plateaued and stable bacterial dissemination even after a longer period of infection.

Fig. 2 - The conventional TB model may be included as a negative control.

Fig. 5A - Why total challenged mouse number gets increased ?

Significance

This study is clinically useful to test new TB vaccine candidates by improving the conventional TB vaccine model.

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Referee #1

Evidence, reproducibility and clarity

This report provides evidence that the ULD infection model of M. tuberculosis provides the capacity to detect 3 types of protection by BCG: 1) durable reductions in Mtb burden, 2) inhibition of Mtb dissemination, 3) prevention of detectable bacteria. In general, this is well-reasoned, well-written, transparently presented and easy to follow. A few points.

Major issues:

In Figure 2A, 2B, 3A - the authors have a highly skewed distribution with a bimodal distribution between detected bacterial counts and zero bacterial counts. This type of distribution does not lend itself to a t-test. What is the mean of two exclusive categories? For these graphs, the authors should consider plotting the median and interquartile range, then applying a non-parametric test. I suspect the p-values will be as significant, if not more, and there will be some comparisons where the median of the BCG group will be 0 CFU.

In 3B, instead of presenting a model of the data, can the authors present the raw data across all experiments as datapoints with violin plots, or some other form of data visualization? They could present the fixed effect model as a supplementary figure. The fixed effect model works better for plotting proportions in 4A.

Minor points:

Abstract, line 35. The authors state that BCG does A, B, C in a small percentage of mice. It is not clear whether this means all of A, ,B and C happen in a small percentage. Or rather whether the small percentage refers to C alone. Perhaps this can be rewritten for clarity.

Line 123. 12/20 vs 10/20. Hard to say it appeared to prevent based on these numbers. Perhaps may prevent?

Line 128. The authors use the word colonized here but don't use this term elsewhere. What is the difference between colonized and infected, and why is colonized used only here?

The power calculations could be a supplementary table if space is tight.

Significance

This is a strong paper and the data are compelling.<br /> The significance is that this offers a new avenue for evaluation of TB vaccines, especially vaccines to prevent establishment of long-term infection.

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Reply to the reviewers

Reviewer #1: Major comments: The key point of the manuscript is to provide resources for the plant community. The motivation for selecting these specific promoters, how they were obtained and cloned, what they are in detail and how they will be made publically available is all clearly described. The infection experiments presented in it are an added bonus and a proof of concept of the applicability of the system.

Thank you very much.

Minor comments: The promotor sequences will probably be included in the AddGene submission, however, it might be helpful to also deposit the promoter sequences at e.g. GenBank.

Indeed, we have sent all sequence files to AddGene and they will be available for download there. We will look into transferring them to GenBank as well. We have not done this before, but are generally always supportive of maintaining data in open repositories.

Line 133: "There are few exceptions to this rule...". It would probably helpful to list/mark these exceptions in Table 1

We agree. We have now marked them in the table, and included the sentence “There are a few exceptions to this rule (marked with a * in the ‘Bases’ column in table 2), where we used a defined stretch of DNA that has previously been described to complement a mutant” in lines 135-137.

Line 138: "A overhangs". In the GreenGate system, A-modules (promoters) are flanked by A- (5') and B- (3') overhangs (applies to line 144, too). Also, the B-overhang listed here (TTGT) is the reverse complement, which might be confusing for readers.

A very good point. We have modified these lines to “standard four base pair GreenGate promoter module overhangs (5´-ACCT and TTGT-3´) were added via primers during amplification of the promoter sequences (see Supplementary Table 1 for a list of primer sequences. Note that TTGT is the complementary sequence of the A-to-B-module overhang, as this is added via the reverse primer)” in lines 141-144.

Line 149 ff.: How many lines have been established per promoter tested? Did they all yield a similar expression pattern?

This is indeed a very important point which was somehow lost along the way during manuscript preparations, after being moved around between results and methods section. We have put it back in in lines 162-165 as “We recovered several independent transgenic lines for the PEP1 and 2, PEPR1 and 2, as well as BIK1 and RBOHD reporters. Out of those, a minimum of three (RBOHD) and up to seven (PEPR2) independent lines showed fluorescence, and out of those, all individual lines for each reporter showed the same expression patterns.”

Line 163: As someone not being familiar with microscoping Arabidopsis roots, I'm wondering how the authors can be sure that the tissue in question is the vasculature. Is this obvious for experts in the field?

Of course, we can’t give a totally objective answer here, but we believe that by including the transmitted light image next to the fluorescence image, it is indeed visible that the fluorescence is limited to the center of the root, not the complete circumference. At the same time, it is important to note that all images are stereomicroscopic images, not confocal images. Thus, it is indeed not possible to, e.g., conclude if pericycle cells are included or excluded in the region with expression. So, while it is, we believe, safe to assume that it is vascular cells, we can’t determine which cell types in the vascular cylinder are expressing the reporters. This would require confocal imaging, which would increase the resolution, but at the expense of a good overview, which we think is more valuable for such a proof-of-principle.

Discussion: Is there by any chance prior (cell-resolution) knowledge about the expression behaviour of any of the investigated promoters? E. g. by in-situ hybridizations? If so, do the expression patterns match?

No, the expression of these reporters in direct response to fungal infection have so far only been studied by transcriptomics.

Presentation and quality of the images need be improved. Scale bars are missing in all confocal images. In Figure 3 and 4, the name of genes examined can be labeled on the image, which will make it easier for readers. In addition, key information such as the inoculum and sampling time point after fungal inoculation should be described in the legend or the main text.

We have added the scale bars and gene names into the images. We agree that the gene names make it easier for the reader. Further, we have added the inoculum and sampling time to the legend.

More importantly, a "mock" inoculation or "before fungal inoculation" should be performed to reveal the expression changes of the marker genes after fungal inoculation.

This is information was provided in the text and via the supplemental figures, but I assume we didn’t make it clear that these results and images were indeed specific control/mock experiments, and not some ‘general’ expression analysis. We have now tried to make this clearer, specifically in lines 192-194.

Lines 172-174, the pictures are too small to see these details. The same for BIK1 (line 187).

We have split up figure 3 into two separate figures (figures 3 and 4), to allow for them to be displayed larger, so that more details can be observed. Of course, it would also be helpful to do some confocal microscopy on specific regions of interest of these stereomicroscopic images to obtain high-resolution images of these regions, but, unfortunately, we did not reach this point in this project, before our team was disbanded, and we therefore only have the overview images to get a general idea of the responsiveness of the different reporters.

Line 174-176, which results are these referring to? The same for line 200-203.

We assume that this was not clear because we previously failed to make it clear that the control supplementary figures are from experimental controls/mock. We have reworded both paragraphs to, hopefully, explain it a bit better, and included the supplementary figure number that refers to. It’s now in lines 212-215 and 237-242.

This study provides a valuable collection of vectors/constructs for investigation of transcriptional dynamics of plant immunity genes and should attract broad interest of the plant immunity field.

Thank you very much.

The current study by Calabria et al., entitled "pGG-PIP: A GreenGate (GG) entry vector collection with Plant Immune system Promoters (PIP)," reported the development of a set of GreenGate-compatible entry plasmids that contain promoter sequences of a series of immunity-related genes. This tool enables live-cell observation of immune responses at a cellular resolution. Being compatible with many other GreenGate tools, it opens up a door toward simultaneous visualization of different but overlapping immune pathways and ultimately describes the 4D dynamics of plant immunity. It is more than expected that these constructs will be used by a wide range of researchers and contribute to the ultimate understanding of plant innate immunity.

Thank you very much.

It is exciting that the authors observed the marker expression by a fluorescent stereomicroscope. This allows for non-destructive observation of response over time, keeping the system gnotobiotic. However, it was partly disappointing that the author did not take full advantage of this. It would have been much nicer if the authors observed the infection process over time, such that one could tell when and where the response starts, and whether local and systemic reactions occur simultaneously or instead require local-to-systemic signal transduction. They indeed seem to have done such time-course observation (line 378) however did not provide the results. I am curious to know what the authors could have found from those experiments. It would also be a strong appealing point of this method and is therefore highly encouraged

We absolutely agree that this temporal data would be valuable and interesting. So far, we always imaged the colonization sites in the root tips from the first day when they become visible, until the day when the entire root was colonized/dying. However, we only recorded the infection sites directly, and did not image the entire plants, and local as well as systemic responses. This is, of course, something that we would have liked to do, and planned to do in the future, but, so far, we have not gotten to that point. We also attempted to use the images of the infection sites that we have recorded over time to obtain information about disease progression, e.g., colonization speed of the fungus, but this data is not (yet) at a point, where we feel confident that we have enough information to draw solid conclusions. So, while we absolutely agree that this kind of whole-plant imaging with both, high spatial and temporal resolution, must be the aim, at this point, unfortunately, we simply are not at that place yet.

Immune responses are not always induction of expression but sometimes reduction. Some genes up-regulated in the first phase will also be down-regulated afterward in order to go back to the initial non-responding state. During such down-regulation, the expression of a fluorescence marker gene might not accurately reflect the real expression levels, because the translated proteins might stay longer even while its transcription is suppressed. To address this point, it is suggested that the authors observe the marker lines in the presence of a translation inhibitor, such as cycloheximide, and quantitatively analyze the dynamics of protein degradation when no new protein is synthesized.

This is indeed an excellent point. Unfortunately, we have to first say that due to funding issues we are currently unable to do this experiment. However, we did include two things in the revised manuscript: First, we have put in a note that this is indeed a caveat of the system that must be acknowledged (lines 334-337). Second, we have included some information from a different study, which at least addresses this point to some degree. We have imaged the transcriptional response of the WRKY11 transcription factor in response to colonization by Fo5176, and in this case, we not only see a local upregulation next to the colonization site, but we see a complete switch in expression pattern. As part of this switch, WRKY11 expression, which was expressed in all root tissues and cells in uninfected control experiments, switches expression off in all tissues and cells except the vascular cells close to the infection site. So here, we indeed have a downregulation of the reporter. In these experiments, signal from the fluorescent WRKY11 reporter disappears from the cells within a day. As we imaged once per day, we can, unfortunately not get more specific than this one-day window. The day before colonization of the tip, signal is seen in all tissues, one day later, if/when the vasculature if colonized in the tip, there is no weak/residual fluorescence left in the cells of the outer tissues. So we can at least state that we would probably also detect downregulation of expression, despite the protein lifetime. Importantly, all our imaging is done on a regular stereomicroscope, and thus, camera sensitivity is moderate. I could imagine that we may be able to detect some residual fluorescence with ultra-sensitive cameras at a spinning disc, or a sensitive detector at a laser-scanning microscope, but we have not tested this. We have added this information in lines 337-347. I apologize that we can’t add more information than this.

It is remarkable that the authors managed to clone 75 promoter sequences. However, whether all promoters work as expected was not clearly assessed in the present study. Did the authors only transform plants with PEP1, PEP2, PEPR1, and PEPR2 marker constructs? How would they know that the other promoters also work appropriately? In terms of providing these constructs to the research community, it is needed to disclose to which extent the expression has been validated in planta and which promoter has not been assessed.

This is indeed important information. We have not used the promoters in mutant complementation assays, and have added this caveat in lines 348-350.

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Referee #3

Evidence, reproducibility and clarity

The current study by Calabria et al., entitled "pGG-PIP: A GreenGate (GG) entry vector collection with Plant Immune system Promoters (PIP)," reported the development of a set of GreenGate-compatible entry plasmids that contain promoter sequences of a series of immunity-related genes. This tool enables live-cell observation of immune responses at a cellular resolution. Being compatible with many other GreenGate tools, it opens up a door toward simultaneous visualization of different but overlapping immune pathways and ultimately describes the 4D dynamics of plant immunity. It is more than expected that these constructs will be used by a wide range of researchers and contribute to the ultimate understanding of plant innate immunity.

It is exciting that the authors observed the marker expression by a fluorescent stereomicroscope. This allows for non-destructive observation of response over time, keeping the system gnotobiotic. However, it was partly disappointing that the author did not take full advantage of this. It would have been much nicer if the authors observed the infection process over time, such that one could tell when and where the response starts, and whether local and systemic reactions occur simultaneously or instead require local-to-systemic signal transduction. They indeed seem to have done such time-course observation (line 378) however did not provide the results. I am curious to know what the authors could have found from those experiments. It would also be a strong appealing point of this method and is therefore highly encouraged.

Immune responses are not always induction of expression but sometimes reduction. Some genes up-regulated in the first phase will also be down-regulated afterward in order to go back to the initial non-responding state. During such down-regulation, the expression of a fluorescence marker gene might not accurately reflect the real expression levels, because the translated proteins might stay longer even while its transcription is suppressed. To address this point, it is suggested that the authors observe the marker lines in the presence of a translation inhibitor, such as cycloheximide, and quantitatively analyze the dynamics of protein degradation when no new protein is synthesized.

It is remarkable that the authors managed to clone 75 promoter sequences. However, whether all promoters work as expected was not clearly assessed in the present study. Did the authors only transform plants with PEP1, PEP2, PEPR1, and PEPR2 marker constructs? How would they know that the other promoters also work appropriately? In terms of providing these constructs to the research community, it is needed to disclose to which extent the expression has been validated in planta and which promoter has not been assessed.

Referee cross-commenting

I agree with reviewer #1 that the authors need to disclose how many independent lines were established and assessed for each construct.

I also agree with reviewer #2 that the figure and data presentation needs to be improved.

Significance

Overall, the current study already provides a widely useful set of tools for plant researchers, and some additional work would further increase its strength and value.

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Referee #2

Evidence, reproducibility and clarity

This study provides a useful toolkit of reporter/marker constructs for investigating the gene expression of many immune-associated genes. The authors further used this toolkit to examine the expression pattern of several immune elicitor/receptor/downstream component genes after the inoculation of a fungal vascular pathogen Fusarium oxysporum. The study provides valuable tools for plant immunity study. I have some comments regarding the experiment design and data presentation as shown below.

Presentation and quality of the images need be improved. Scale bars are missing in all confocal images. In Figure 3 and 4, the name of genes examined can be labeled on the image, which will make it easier for readers. In addition, key information such as the inoculum and sampling time point after fungal inoculation should be described in the legend or the main text. More importantly, a "mock" inoculation or "before fungal inoculation" should be performed to reveal the expression changes of the marker genes after fungal inoculation.

Lines 172-174, the pictures are too small to see these details. The same for BIK1 (line 187). Line 174-176, which results are these referring to? The same for line 200-203.

Significance

This study provides a valuable collection of vectors/constructs for investigation of transcriptional dynamics of plant immunity genes and should attract broad interest of the plant immunity field.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In their manuscript, Calabria et al. primarily present a collection of 75 plant (Arabidopsis thaliana) promoters cloned by them into the GreenGate system. These promoters represent different pathways of the plant immune system. Exemplarily they used this compilation to check the involvement of several components of the PLANT ELICITOR PEPTIDE (PEP)-pathway in the response of A. thaliana roots to infection with Fusarium oxysporum strain Fo5176 via transcriptional reporters.

Major comments:

The key point of the manuscript is to provide resources for the plant community. The motivation for selecting these specific promoters, how they were obtained and cloned, what they are in detail and how they will be made publically available is all clearly described. The infection experiments presented in it are an added bonus and a proof of concept of the applicability of the system.

Minor comments:

The promotor sequences will probably be included in the AddGene submission, however, it might be helpful to also deposit the promoter sequences at e.g. GenBank.

Line 133: "There are few exceptions to this rule...". It would probably helpful to list/mark these exceptions in Table 1.

Line 138: "A overhangs". In the GreenGate system, A-modules (promoters) are flanked by A- (5') and B- (3') overhangs (applies to line 144, too). Also, the B-overhang listed here (TTGT) is the reverse complement, which might be confusing for readers.

Line 149 ff.: How many lines have been established per promoter tested? Did they all yield a similar expression pattern?

Line 163: As someone not being familiar with microscoping Arabidopsis roots, I'm wondering how the authors can be sure that the tissue in question is the vasculature. Is this obvious for experts in the field?

Discussion: Is there by any chance prior (cell-resolution) knowledge about the expression behaviour of any of the investigated promoters? E. g. by in-situ hybridizations? If so, do the expression patterns match?

Significance

As stated above, this manuscript primarily describes a technical resource useful for the plant science community.

It is GreenGate-based and therefore easily compatible with other GreenGate-based resource collections. Its primary focus is in the area of plant immune research.

The key audience is plant immunologists. However, also researchers requiring e.g. tissue-specific and/or pathogen-inducible expression might find it helpful.

My own field of expertise is plant transformation and cloning systems, thus I went over the part dealing with the proof-of-principle only as a non-expert.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Summary: Yeh et al., present novel findings that Bitesize (Btsz) a Synaptotagmin-like protein, helps organize actin during the syncytial blastoderm stages of Drosophila embryo development. Depleting Btsz leads to phenotypes in the syncytial cycles that mimic Drosophila mutants where actin or membrane trafficking is disrupted. Perhaps most interestingly, the authors show that a non-Moesin Binding Domain-containing isoform of Btsz is important for cytoskeletal regulation during syncytial cycles. The authors generated a BtszB-C terminal recombinant protein and showed using imaging and biochemistry that this conserved segment of Btsz (which is present in all isoforms) can bind to and bundle F-actin in vitro. Lastly, the authors show that Btsz localizes to the apical region of pseudocleavage furrows and cell interfaces at gastrulation, which is consistent with previous literature regarding its role in regulating adherens junctions.

Major Concern: Both the imaging data, image analysis and biochemistry, are compelling. The findings regarding expression of alternative isoforms of Btsz are interesting within this developmental context. However, the final model is very simple and may benefit from the addition of experiments or at least further attention in the discussion. For example, it is not entirely clear what the division of labor may be between isoforms that do or do not bind Moesin. Do these isoforms work to accomplish a single function; or do they perform unique functions? Are the isoforms subject to similar or different regulation? At a minimum, the authors thoughts should be included in the Discussion, and a more integrated model presented. Relatedly, the authors mention the possibility that membrane trafficking may be impacted but end abruptly there. Additional experiments would obviously increase impact. If no experiments are added, the existing text should nonetheless be edited to include a more complete Discussion of the results.

Specific Concerns:

1. While the authors claim there is an actin defect, that defect is not readily revealed by a change in actin levels. Is the change perhaps in actin stability or in mechanical properties of the actin filaments (for example, if filaments can assemble but not be bundled or appropriately tethered to the plasma membrane in the mutant)? Have the authors tried either FRAP or laser cutting of furrows in mutant embryos?
2. While prior publications mention the role for Btsz in building adherens junctions, it would still be useful to see an analysis of junction phenotypes in the hands of these authors. Also, where do junction components concentrate and what do they do, if known, in syncytial embryos? It would be helpful to include this information in the text.
3. Does imaging of golgi or endosome markers reveal any differences in membrane compartments in Btsz mutant embryos? Even negative results would be interesting.
4. In describing the Myosin network phenotype during cellularization, it is not clear what is meant by the statement that the network has "constricted" over the positions where nuclei were lost. That sounds like an active process. It seems equally possible that the Myosin is just coating the membrane that now fills the gaps where nuclei should be.
5. Some aspects of Btsz gene expression are discussed and equated with a small number of previously described genes for cellularization. Are those genes only expressed during cellularization or beyond? It appears that Btsz is expressed beyond cellularization. Do those genes also have complex splicing patterns/multiple isoforms?
6. Could the authors comment on why they chose to describe the syncytial phenotypes in Cycle 12 but not other syncytial cycles?

Significance

For strengths and limitations, see above.

Advance: The authors advance the field of regarding Synaptotagmin-like proteins (Slps) by studying alternative isoforms of the proteins which lack a Moesin-binding domain (MBD). They find a novel function for Btsz isoforms that do not contain an MBD and show that a variant of these isoforms can directly bind to and bundle F-actin to regulate actin during syncytial nuclear divisions. Since the domain(s) they tested are conserved in all isoforms, this likely means that the actin binding function of Btsz could be conserved for most Slps, including Btsz isoforms which contain MBD.

Audience: This work is of interest to cell and developmental biologists who study the regulation of actin cytoskeleton. The work as presented also has some relevance to those who study adherens junctions, membrane trafficking, and Synaptotagmin-like proteins. More broadly, this work may be of interest to those who study alternatively-spliced proteins in the context of development.

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Referee #2

Evidence, reproducibility and clarity

This manuscript by Yeh and colleagues examines the function of the Slp-family protein Bitesize in Drosophila. Btsz has been previously studied in the fly embryo and appreciated to regulate F-actin and adhesion-related properties in the formation of the epithelium, but in this manuscript Yeh et al. look at an earlier point of development (division in the early embryonic syncytium) and also examine the role of bitesize transcripts that lack the moesin-binding domain (MBD). The authors disrupt Btsz function and observe defects in actin-dependent structures, such as the apical actin cap and the pseudo-cleavage furrows, which leads to defect in the pseudo-cleavage divisions. They also perform actin-bundling assays with a btsz fragment that does not contain the MBD and see that btsz can still bundle actin, implicating the C2 domains in this function. The work appears well-done, with only one major area of concern (the imaging and analysis of actin caps, below). The manuscript is well-written, with a nice Introduction, and the Results are appropriately described and interpreted. The quantitation appears appropriate, and n number and the statistical tests used by the authors are consistently stated throughout the manuscript. A few more detailed comments are below:

1. It does not appear that the actin caps are being measured and imaged. None of the usual internal structures of the caps are apparent, and instead it appears that what is presented are the apical margins of the pseudocleavage furrows (or the very edges of the caps).
2. Along these lines, the argument that caps are smaller does not make much sense, since it appears that the "caps" are being measured late once the furrows have formed. Since these dimensions are set by the number of nuclei in the embryo, as long as the caps are growing large enough to get collisions between adjacent nuclei/caps, how can the caps be smaller unless there are fewer nuclei? These changes could also be secondary consequences of differences in nuclear distributions around the embryo periphery. For these reasons, and because of the close packing of nuclei together, usually cap growth rates are plotted in periods prior to cap collisions.
3. Sorry if this was missed, but are the cycles at which measurements are made listed in each appropriate figure? I saw "cycle 12" listed in one figure legend, but not others.
4. How do the authors know that the nuclear density defects in the CRISPR allele are due to the same mechanism? Could be through same mechanism, but could also be due to defect in nuclear anchoring, cortical portioning, etc...
5. The schematics and illustrations are nicely done.

Minor notes:

• a) Should there be actin in the top row of 1A?

Significance

This manuscript by Yeh and colleagues examines the function of the Slp-family protein Bitesize in Drosophila. Btsz has been previously studied in the fly embryo and appreciated to regulate F-actin and adhesion-related properties in the formation of the epithelium, but in this manuscript Yeh et al. look at an earlier point of development (division in the early embryonic syncytium) and also examine the role of bitesize transcripts that lack the moesin-binding domain (MBD). The authors disrupt Btsz function and observe defects in actin-dependent structures, such as the apical actin cap and the pseudo-cleavage furrows, which leads to defect in the pseudo-cleavage divisions. They also perform actin-bundling assays with a btsz fragment that does not contain the MBD and see that btsz can still bundle actin, implicating the C2 domains in this function. The work should be of interest to a developmental community and those workers interested in Slp-family function.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Yeh and colleagues report the requirement of the Drosophila Synaptotagmin-like protein, Bitesize, for the proper formation of pseudocleavage furrows of the syncytial embryo (through shRNA affecting all Bitesize isoforms). Reduced sizes of the compartments for mitotic spindles and of the sizes of mitotic spindles are also quantified. Local losses of the furrows also correlated with collisions of neighbouring nuclei, and with loss of nuclei from the syncytial embryo periphery, consistent with the known role of the furrows. Bitesize has nine predicted splice isoforms. Some include a Moesin-binding domain, which has previously been implicated in Bitesize activity at post-syncytial developmental stages, and all include a shared C-terminus, which has been implicated in actin binding in a related vertebrate protein. Results suggest expression and functional involvement of isoforms with either potential links to the actin cortex, although definitive conclusions would require further analyses (below). In vitro assays showed the ability of the purified C-terminus to bind and to bundle F-actin. An isoform encoding the C-terminus, but not the Moesin-binding domain, localized to the pseudocleavage furrows, and displayed an internal punctate distribution. The effect of Btsz shRNA on F-actin was tested at cellularization, and no effect was observed by comparing F-actin levels at the apical end of the cell to that at furrow canals. One Btsz isoform lacking the Moesin-binding domain was shown to localize apically during cellularization.

Major comments:

• The phenotypic analyses of the Btsz shRNA embryos are clear.
• The in vitro analyses of the F-actin binding and bundling of the Btsz C-terminus are clear.
• Quantifications, statistics and explanations of methods are appropriate.
• The analyses of isoform expression are a concern because it seems from Figure 1C that the primers to distinguish isoforms with and without the Moesin-binding domain could both be detecting isoform I. If this is the case, then primers to specifically detect "Non-MBD isoforms" should be used. If not, then the current primers for detecting "Non-MBD isoforms" should be clarified in relation to isoform I.
• In the Abstract, Discussion, and Results it is concluded that isoforms lacking the Moesin-binding domain function in syncytial development, but this conclusion is not clearly supported by the data. An exon 4 deletion generating a premature stop was designed to disrupt a subset of isoforms lacking the Moesin-binding domain, but it also has the potential to disrupt isoform I which contains exon 4 and the Moesin-binding domain. RT-PCR should be able to detect isoform I specifically. If it is not expressed, then the conclusion would be strengthened. If it is expressed, then is seems difficult to make a specific conclusion about the role of the of non-MBD isoforms.
• The authors say that the exon 4 deletion mutants and the Moesin-binding domain exon mutants have a weaker phenotype than Btsz shRNA embryos, but different markers were used and genetically encoded markers could contribute to the difference.
• Additional analyses to pursue a possible defect in F-actin organization in Btsz shRNA embryos could better connect the in vitro and in vivo analyses.
• That caveat that only one isoform was localized should be added to this sentence: "Unlike other actin cross-linkers involved in cellularization, BtszB did not localize to furrow canals, suggesting that the cellularization phenotypes we observed in Btsz mutants and Btsz RNAi (Figure 4D) were the result of prior syncytial division defects." The caveat also applies to this sentence in the Discussion: "Btsz is present uniquely in an apical-lateral compartment."

Minor comments:

• Within Fig 1A, the axes of the top image should be X and Z rather than X and Y.
• The Arp3 RNAi data in Figure S1B isn't mentioned in the Results. I assume it is a positive control.
• The internal punctate distribution BtszF in Fig 6A could be commented on in the Discussion paragraph about the possibility of Btsz also functioning in membrane trafficking.

Significance

• From the perspective of syncytial Drosophila development, a new factor is shown to be required for cortical reorganization.
• From the perspective of Bitesize, an earlier role in development is shown.
• From the perspective of Bitesize, an additional mechanism of action is implicated, F-actin binding and bundling, by which it could affect the cell cortex (although more work is needed to clarify this in vivo).
• From the perspective of related vertebrate proteins, an F-actin binding activity found in one of these proteins seems to be conserved in Btsz.
• The paper will be of interest to those studying Bitesize and orthologs, the cell cortex, the actin cytoskeleton, the morphogenesis of cells and tissues, and/or syncytial Drosophila development.

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Reply to the reviewers

Reply to the reviewers

Manuscript number: RC-2023-01932

Corresponding author(s): Dennis KAPPEI

We would like to thank all reviewers for their recognition of our approach and the quality of our work as well as their constructive criticism.

Reviewer #1

Reviewer #1: The manuscript by Yong et. al. describes a comparison of various chromatin immunoprecipitation-mass spectrometric (ChIP-MS) methods targeting human telomeres in a variety of systems. By comparing antibody-based methods, crosslinkers, dCas9 and sgRNA targeted methods, KO cells and various controls, they provide a useful perspective for readers interested in similar experiments to explore protein-DNA interactions in a locus-specific manner.

Response: We would like to thank the reviewer for the feedback and the appreciation of our work.

Reviewer #1: While interesting, I found it somewhat difficult to extract a clear comparison of the methods from the text. It was also difficult to compare as data and findings from each method was discussed in its own context. Perhaps it is not in their interest to single out a specific method and it is indeed true that there are caveats with each of the methods.

Response: Across our manuscript we have established one single workflow, for which we present some technical comparisons (e.g. using single or double cross-linking in Fig. 2a/b), technical recommendations such as the use of loss-of-function controls (e.g. Fig. 1c v. Fig. 2a and Extended Data Fig. 3g vs. 3i) and an application to unique loci using dCas9 (Fig. 3f). Based on the suggestions below, we believe that we will improve the clarity of communicating our approach.

Reviewer #1: I think the manuscript would be of interest but I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

Response: We thank the reviewer for highlighting this point. We do not think that the ChIP-MS comparison between U2OS WT and ZBTB48 KO clones (Fig. 2a) has experiment-specific caveats. Instead the KO controls as well as the dTAGV-1 degron system for MYB ChIP-MS (Extended Data Fig. 3) reveal antibody-specific off-targets, which are indeed false-positives. Please see below for further details.

Reviewer #1: Ln 57: What is "standard double cross-linking ChIP reactions" in this context? Is it the two different crosslinkers? The two proteins? The reciprocal IPs of one protein, and blotting for another? It's not clear here or from Extended Fig 1A. Upon further reading, it seems to pertain to the two crosslinkers - if so, the authors should briefly describe their workflow to help readers.

Response: As the reviewer correctly concludes, we indeed intended to highlight the use of two separate crosslinkers (formaldehyde/FA and DSP). This combination is important as illustrated in the side-by-side comparison of Fig. 2a and Fig. 2d. Here, we performed ZBTB48 ChIP-MS in five U2OS WT and five U2OS ZBTB48 KO clones. While in both experiments the bait protein ZBTB48 was abundantly enriched in the samples that were fixed with formaldehyde we lose about half of the telomeric proteins that are known to directly bind to telomeric DNA independent of ZBTB48 and all of their interaction partners. For instance, while the FA+DSP reaction in Fig. 2a enriched all six shelterin complex members, the FA only reaction in Fig. 2d only enriches TERF2. These data suggest that the use of a second cross-linker helps to stabilise protein complexes on chromatin fragments. This is a critical message of our manuscript as ChIP-MS only truly lives up its name if we can enrich proteins that genuinely sit on the same chromatin fragment without protein interactions to the bait protein. We will expand on this in both the text and our schematics in Fig. 1a and 3a to make this clearer for the readers.

Reviewer #1: Ln 95: It is surprising and quite unclear to me why it is that the WT ZBTB48 U2OS pulldown in Fig 1B shows 83 hits for the WT vs Ig control experiment but 27 hits for the WT vs KO condition in Fig 2A. The two WT experiments have the same design and reagents, shouldn't they be as close as technical replicates and provide very similar hits?

The authors seem to make the claim that most of the 'extra' proteins in WT vs Ig are abundant and false positives, but if this is so, shouldn't they bind non-specifically to the beads and be enriched equally in Ig control and ZBTB48 WT IPs?

Response: We again thank the reviewer for raising this point and the need to explain in more detail why we interpret the difference between 83 hits (anti-ZBTB48 antibody vs. IgG; Fig. 1c) and 27 hits (anti-ZBTB48 antibody used in both U2OS WT and ZBTB48 KO cells; Fig. 2a) primarily as false-positives. The KO controls in Fig. 2a allow to keep the ZBTB48 antibody as a constant variable while instead comparing the presence (WT) or absence (KO) of the bait protein. Hence, proteins that were enriched in the IgG comparison in Fig. 1c but that are lost in the WT vs. KO comparison in Fig. 2a are likely directly (or indirectly) recognised by the ZBTB48 antibody, akin to off-targets to this particular reagent. In a Western blot this would be equivalent to seeing multiple bands at different molecular weights with only the band belonging to the protein-of-interest disappearing in KO cells. To illustrate this we would like to refer to Extended Data Fig. 2, in which we have replotted the exact same data from Fig. 2a. However, in addition we have here highlighted proteins that were enriched in the IgG comparison in Fig. 1c. 46 proteins (in pink) are indeed quantified in the WT vs. KO comparison, but these proteins are found below the cut-offs (and most of them with very poor fold changes and p-values). In contrast to the other several hundred proteins common between both experiments that can be considered common background non-specifically bound to the protein G beads, these 46 proteins represent antibody-specific false-positives.

The above consideration is not unique to ChIP-MS as illustrated by the Western blot example. We also do not claim novelty on the experimental logic, e.g. pre-CRISPR in 2006 Selbach and Mann demonstrated the usefulness of RNAi controls in immunoprecipitations (IPs) (PMID: 17072306). However, our data suggests that ChIP-MS is particularly vulnerable to this type of false-positives given that the approach requires (double-)cross-linking to sufficiently stabilise true-positives on the same chromatin fragment.

To supplement the WT vs. ZBTB48 KO comparison, we had included a second experiment in the manuscript that illustrates the same point in even more dramatic fashion. First, KO controls are very clean in principle, but they themselves might come with caveats if e.g. the expression levels between WT and KO samples differ greatly. This might create a situation that the reviewer hinted to, i.e. differential expression of abundant proteins that would proportionally to their expression levels stick to the beads, resulting in “fold enrichments”. The resulting false positives could e.g. be controlled by matched expression proteomes. For ZBTB48 we have previously measured this (PMID: 28500257) and demonstrated that only a small number of genes are differentially expressed (~10) and hence we can interpret the WT vs. ZBTB48 KO comparison quite cleanly. However, for other classes of proteins such as transcription factors that regulate a large number of genes, E3 ligases etc. this might present a more serious concern. Therefore, we extended our loss-of-function comparison to such a transcription factor, MYB, by using the dTAGV-1 degron system. Importantly, the MYB antibody has been used in previous work for ChIP-seq applications (e.g. PMID: 25394790). Here, instead of 186 hits in the MYB vs. IgG comparison using the same MYB antibody in control-treated and dTAGV-1-treated cells (upon 30 min of treatment only) we only detect 9 hits. Again, similar to the WT vs. ZBTB48 KO comparison, 180 proteins are quantified in the DMSO vs. dTAGV-1 comparison, but these proteins fall below the cut-offs (Extended Data Fig. 3g vs. 3i). Again, we believe that this quite drastically illustrates how vulnerable ChIP-MS data is to large numbers of false-positives. This is not only a technical consideration as such datasets are frequently used in downstream pathway/gene set enrichment analyses etc. Such large false discovery rates would obviously lead to error-carry-forward and additional (unintended) misinterpretations. We will carefully expand our textual description across the manuscript to make these points much clearer. In addition, we will move the previous Extended Data Fig. 3 into the main manuscript to more clearly highlight this important point.

Reviewer #1: Volcano plots in Figs 1, 2, and Suppl. Tables etc: Are the plotted points the mean of 5 replicates? Was each run normalized between the replicates in each group, for e.g. by median normalization of the log2 MS intensities? This does not appear to be the case upon inspection of the Suppl Tables. Given the variability in pulldown efficiency, gel digest and peptide recovery, this would certainly be necessary.

Response: All volcano plots are indeed based on 4-5 biological replicates (most stringently in the WT vs. KO comparisons in Fig. 2 based on each 5 independent WT and ZBTB48 KO single cell clones). The x-axis of each volcano plot represents the ratio of mean MS1-based intensities between both experimental conditions in log2 scale. However, precisely to account for the variation that the reviewer highlighted we did not base our analysis on raw MS1 intensities but we used the MaxLFQ algorithm (PMID: 24942700) as part of the MaxQuant analysis software (PMID: 19029910) for genuine label-free quantitation across experimental conditions and replicates. In this context, we would also like to refer to a related comment by reviewer #2 based on which we will now addd concordance information for each replicate (heatmaps for Pearson correlations and PCA plots). We will improve this both in the text and methods section accordingly.

Reviewer #1: Ln 125: The authors make the claim that the ChIP-MS experiments are inherently noisy, with examples from WT cells, dTAG system and IgG controls. This is likely the case, yet their experiments with WT vs KO cells do not identify as many proteins overall. I find this inconsistency somewhat unclear and does not seem to match the claim of ChIP-MS experiments and crosslinking adding to non-specificity. Can the authors add the total number of identified proteins in each volcano plot, for easier reference?

Response: The number of identified proteins does not vary majorly between matched IgG and loss-of-function comparisons and for instance the single cross-linking (FA only) experiment in Fig. 2c has the largest number of quantified proteins among all ZBTB48 IPs. But we will of course add the requested information to all plots.

Reviewer #1: I think the manuscript is interest as it provides important benchmarks for ChIP-proteomics experiments. I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

Response: We would like to thank the reviewer for recognising our work as a source for important benchmarks for ChIP-MS experiments. We hope that with a more detailed description and discussion the highlighted aspects will be more clearly communicated. We originally conceived our manuscript as a short report and now realised that some of the information became too condensed and might therefore benefit from more extensive explanations.

Reviewer #2

Reviewer #2: Summary: In this manuscript, Yong and colleagues have introduced a optimized technique for studying actors on chromatin in specific regions with a localized approach thanks to revisited ChIP-mass spectrometry (MS) with label-free quantitative (LFQ). The authors exhibited the utility of their approach by demonstrating its effectiveness at telomeres from cell culture (human U2OS cells) to tissue samples (liver, mouse embryonic stem cells). As a proof of concept, this technique was tested by the authors with proteins from complex shelterin specific to telomeres (TERF2 and ZBTB48), transcription factors (MYB), and through dCas9-driven locus-specific enrichment. Notably, the authors created a U2OS dCas9-GFP clone and then introduced sgRNAs to target either telomeric DNA (sgTELO) or an unrelated control (sgGAL4). The cells expressing sgTELO exhibited a significant localization of telomeres and an enriched amount of telomeric DNA in ChIP with dCas9. They also found the proteins previously identified as known to be enriched at telomeres (for example, the 6 shelterin members).

Moreover, the authors illustrated the importance of double crosslinking (formaldehyde (FA) and dithiobis(succinimidyl propionate) (DSP) in ChIP-MS. Their data demonstrated also that ChIP-MS is inclined towards false-positives, possibly owing to its inherent cross-linking. However, by utilizing loss-of-function conditions specific to the bait, it can be tightly managed.

• Can you show the concordance between biological replicates for each ChIP with LFQ? (heatmap of Pearson correlation and PCA plot). This will confirm the robustness of the use of LFQ.

Response: We will add the requested concordance data for all volcano plots both in the form of heatmaps of Pearson correlation and PCA plots. Across our datasets, the replicates from the same experimental condition clearly cluster with each other and replicates have high concordance values of >0.9. As expected replicates for the target/bait samples have slightly higher concordance values compared to the negative controls (IgG or loss-of-function samples). We thank the reviewer for this suggestion as the new Extended Data panel will strengthen the illustration of our robust LFQ data.

Reviewer #2: You say that your technique is " a simple, robust ChIP-MS workflow based on comparably low input quantities » (line 139). What would be really interesting for a technical paper would be: a schematic and a table illustrating the differences between your method and the previously published methods (amount of material, timeline,...) to really highlight the novelty in your optimized techniques.

Response: We will add a comparison table with previous publications using ChIP-MS and for reference include some complementary approaches as requested by reviewer #3. On this note, we would like to stress that we are not “only” intending to use less material and to have an easy-to-adopt protocol. A cornerstone of our manuscript is to apply rigorous expectations to ChIP-MS experiments, in particular the ability to enrich proteins that independently bind to the same chromatin fragments as the bait protein (regardless of whether this is an endogenous protein or a exogenous, targeted bait such as dCas9). Otherwise, such experiments risk to be regular protein IPs under cross-linking conditions, which as illustrated by our loss-of-function comparisons are prone to yield particularly large fractions of false-positives.

Reviewer #2: It would be interesting to perform the dCas9 ChIP experiment in telomeric regions with and without LFQ. Since the novelty lies in this parameter, at no time does the paper show that LFQ really allows to have as many or more proteins identified but in a simpler way and with less material. A table allowing to compare with and without LFQ would be interesting.

Response: We do not fully understand what the suggestion “without LFQ” refers to exactly. We assume that this reviewer might suggest to use a different quantitative mass spectrometry approach other than LFQ, e.g. SILAC labelling, TMT labelling etc. Please note that we do not claim that LFQ quantification is per se superior to the various quantification methods that had been developed and widely used across the proteomics community especially before instrument setups and analysis pipelines were stable enough for label-free quantification (a name that is strongly owed to this historic order of development). However, a central goal of our workflow is to make robust and rigorous ChIP-MS accessible to the myriad of laboratories using ChIP-qPCR/-seq and that may not be extensively specialised in mass spectrometry. Both metabolic and isobaric labelling come not only at a higher cost but also present an experimental hurdle to non-specialists compared to performing biological replicates without any labelling, essentially the same way as for any ChIP-qPCR etc. experiment. We will further elaborate on these points in the manuscript to more clearly convey these notions.

In general, with the right effort different quantitative methods should and will likely yield qualitatively similar results. However, comparisons between LFQ approaches (MaxLFQ, iBAQ,…) and labelling approaches (SILAC, TMT, iTRAQ) have already been better explored and verbalised elsewhere (e.g. PMID: 31814417 & 29535314). Therefore, we believe that this will add relatively little value to our manuscript.

Reviewer #2: Put a sentence to explain "label free quantification". For a reader who is not at all familiar with this technique, it would be interesting to explain it and to quote the advantages compared to PLEX.

Response: Thanks for highlighting this. In line with the point above as well as a similar comment by reviewer #1 we will improve this both in the main text and manuscript to clearly explain the terminology, the MaxLFQ algorithm (PMID: 24942700) used and to highlight the advantages compared to labelling approaches.

Reviewer #2: what does the ranking on the right of each volcano plot represent (figure 1 b-e, figure 2a,d,e for example)? top of the most enriched proteins in the mentioned categories? Not very clear when we look on the volcano plot. it must be specified in the legend.

Response: The numbering these panels is meant to link protein names to the data points on the volcano plots. The order of hits is ranked based on strongest fold enrichment, i.e. from right to center. We will clarify this in the figure legends.

Reviewer #2: General assessment/Advance: The authors explain in their article that the ChIP exploiting the sequence specificity of nuclease-dead Cas9 (dCas9) to target specific chromatin loci by directly enriching for dCas9 was already published. Here, the novelty of this study lies in the use of LFQ mass spectrometry to optimize the technique and make it easier to handle. Some comparisons with previous papers or data generated by the lab will be interesting to really show the improvement and the advantage to use LFQ and therefore, to highlight better the novelty of the study.

Response: We thank the reviewer for this assessment and as mentioned above we will include such a comparison table. dCas9 has been used previously in a ChIP-MS approach termed CAPTURE (PMID: 28841410). While this is clearly a landmark paper that illustrated the dCas9 enrichment concept across multiple omics applications (i.e. not limited to proteomics) in their application to telomeres, the authors enriched only 3 out of the 6 shelterin proteins with quite moderate fold enrichments (POT1: 0.99, TERF2: 2.13, TERF2IP: 1.06; in log2 scale). Based on this alone, POT1 and TERF2IP would not have qualified for our cut-off criteria. In addition, while the authors had performed three replicates, detection is only reported in 1-2 out of 3 replicates. While it is difficult to reconstruct statistical values based on the publicly accessible data, it is therefore unlikely that even these 3 proteins would have robustly be considered hits in our datasets. Similarly, using recombinant dCas9 with a sgRNA targeting telomeres that was in vitro reconstituted with sonicated chromatin extracts from 500 million HeLa cells (CLASP; PMID: 29507191) the authors identified only up to 3 shelterin subunits (TERF2, TERF2IP and TPP1/ACD) based on 1 unique peptide each only. For comparison, in our dCas9 ChIP-MS dataset all 6 shelterin subunits are identified with 9-19 unique peptides, contributing to our robust quantification. Even when considering cell line-specific differences (HeLa cells have shorter telomeres and hence provide less biochemical material for enrichment per cell), these comparisons illustrate that prior attempts struggled to robustly replicate even the most abundant telomeric complex members.

Based on these findings, others had suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, we believe that our dCas9 ChIP-MS data is a proof-of-concept that the method has the genuine ability to robustly enrich key proteins at individual loci. In concordance with the comment above we will include a comparison table with previous papers and expand on these points in the discussion.

Reviewer #2: By presenting this technical paper, the authors allow laboratories across different fields to use this technique to gain insights into protein enrichment in specific chromatin regions such as the promoter of a gene of interest or a particular open region in ATACseq in a easier way and with less materials. This paper holds value in enabling researchers to answer many pertinent questions in various fields.

Response: We again thank the reviewer for this encouraging assessment and we do indeed hope that this manuscript makes a contribution to a much wider use of ChIP-MS approaches as a promising complement to existing genome-wide epigenetics analyses.

Reviewer #3

Reviewer #3: Strengths of the study:

The study is well-structured and provides a robust workflow for the application of ChIP-MS to investigate chromatin composition in various contexts.

The use of telomeres as a model locus for testing the developed ChIP-MS approach is appropriate due to its well-characterized protein composition.

The comparison of WT vs KO lines for ZBTB48 is a rigorous way to control for false-positives, providing more confidence in the results.

The direct comparison of double vs only FA-crosslinking provides valuable insights into the benefit of additional protein-protein crosslinking in ChIP-MS workflows.

Response: We thank the reviewer for this assessment and we agree that the above are several of the key features of our manuscript.

Reviewer #3: Areas for improvement: The novelty of the method is more than questionable as both ChIP-MS coupled to LFQ and dCas9 usage for locus-specific proteomics have been previously reported. The fact that the authors directly pulldown dCas9 instead of using a dCas9-fused biotin ligase and subsequent streptavidin pulldown is only a very minor change to previous methods (not even improvement). It would be more accurate for the authors to present their study as an optimization and rigorous validation of existing techniques rather than a novel approach.

Response: While we appreciate where the reviewer is coming from, it occurs to us that most of the reviewer’s comments equate ChIP approaches with other complementary methods, in particular proximity labelling. The latter is indeed a powerful experimental strategy and in fact we are ourselves avid users. As highlighted to reviewer #1 as well, our manuscript was originally conceived as a shorter report and based on the feedback we will now expand our discussion to more broadly incorporate related approaches.

However, we would like to stress that dCas9 ChIP-MS and dCas9-biotin ligase fusions are not the same thing and this is not a minor tweak to an existing protocol. While both approaches have converging aims – to identify proteins that associate with individual genomic loci – the experimental workflows differ fundamentally. Biotin ligases use a “tag and run” approach by promiscuously leaving a biotin tag on encountered proteins. Subsequently, cellular proteins are extracted and in fact proteins can even be denatured prior to enrichment with streptavidin beads. While this is an in vivo workflow that (depending on the biotin ligase used) may provide sensitivity advantages, it does not retain complex information. The latter is inherently part of ChIP workflows due to the use of cross-linkers. One obvious future application would be to maintain (= not to reverse as we have done here) the crosslink during the mass spectrometry sample preparation in order to read out cross-linked peptides to gain insights into interactions and structural features. We will now more clearly incorporate such notions into our discussion.

In addition, we would like to stress that while this reviewer focuses primarily on the dCas9 aspect of our manuscript, we believe that our general ChIP-MS workflow including the combination with label-free quantitation is useful and important already by itself as e.g. recognised by both reviewers #1 and #2.

Reviewer #3: The authors should more thoroughly discuss previous works using ChIP-MS and dCas9 for locus-specific proteomics. This would give readers a better understanding of how the current work builds on and improves these earlier methods. For a paper that aims on presenting an optimized ChIP-MS workflow it is crucial to showcase in which use cases it outperforms previously published methods.

E.g., compare locus-specific dCas9 ChIP-MS to CasID (doi.org/10.1080/19491034.2016.1239000) and C-Berst (doi.org/10.1038/s41592- 018-0006-2); how does your method perform in comparison to these?

Response: Again, while we will now incorporate more extensively comparisons with previous ChIP-MS publications (and the few prior manuscripts that included dCas9) as well as related techniques, we would like to stress that dCas9 ChIP-MS is not the same approach as CasID and C-BERST, which rely on dCas9 fusions to BirA* and APEX2, respectively. dCas9-APEX2 strategies were also published by two additional groups as CASPEX (back-to-back with the C-BERST manuscript; PMID: 29735997) and CAPLOCUS (PMID: 30805613). All of these methods target specific loci with dCas9 and promiscuously biotinylate proteins that are in proximity to the dCas9-biotin ligase fusion protein. As described above, while the application of the BioID principle (PMID: 22412018) to chromatin regions has converging aims with the dCas9 ChIP-MS part of our manuscript, they do not test the same. ChIP carries chromatin complexes through the entire workflow while the CasID approaches are independent of that. This is the same scenario if we were to compare IP-MS reactions (such as the ChIP-MS reactions presented here for endogenous proteins) and BioID-type experiments for proximity partners of the same bait proteins.

Reviewer #3: Compare likewise the described protein interactomes to previously published interactomes.

Response: We will add comparisons in form of Venn diagrams with previously published interactomes. However, we would like to stress that a key aspect of our manuscript is the smaller yet rigorous hit lists based on e.g. loss-of-function controls, higher stringencies and specificity. Simply comparing final interactomes remains reductionist relative to the importance of other variables such as experimental design, number of replicates, data analysis etc.

Reviewer #3: The authors use sgGAL4 as a control for the telomeric targeting of dCas9. The IF results (Fig3b) show that sgGAL4 barely localizes to the nucleus with very faint signals. It would be helpful to use a control with homogenous nuclear localization of dCas9 to further strengthen the author's conclusions.

Response: dCas9-EGFP in the presence of sgGAL4 localises diffusely to the nucleus as expected. We have here used a very widely used non-targeting sgRNA control that has been originally used for imaging purposes (PMID: 24360272) and has since been used in a variety of studies (e.g. PMID: 26082495, 32540968, 28427715) including a previous dCas9 ChIP-MS attempt (PMID: 28841410). In addition, to the diffuse nuclear, non-telomeric localisation we provide complementary validation of clean enrichment of telomeric DNA specifically in the sgTELO samples. Therefore, we do not see how other non-targeting sgRNAs would provide for better controls or improve our data.

Reviewer #3: The extrapolation of results from the use of telomeres as a proof-of-concept to other loci is not a given considering the highly repetitive structure of telomeric DNA. The authors should either be more cautious about generalizing the results to other loci or demonstrate that their method can also capture locus-specific interactomes at non-repetitive regions.

Response: We agree that the adoption of any locus-specific approach to single genomic loci is a steep additional hurdle and warrants rigorous data on well characterised loci with very clear positive controls. We will expand on these challenges in our discussion. However, we would like to stress that we did not make any such statement in our original manuscript apart from simply referring to our telomeric experiment as proof-of-concept evidence that locus-specific approaches are feasible by ChIP.

Reviewer #3: What are concrete biological insights from this optimized ChIP-MS workflow that previous methods failed to show?

Response: We explicitly used telomeres as an extensively studied locus with clear positive controls that at the same time allows us to evaluate likely false positives. As such the intention of the manuscript was not to yield concrete biological insights but to develop a new methodological workflow.

As also highlighted in a response to reviewer #2, based on other prior attempts to enrich telomers in ChIP-like approaches with dCas9 (PMID: 28841410 & 29507191), it had been suggested that dCas9 “might exclude some relevant proteins from telomeres in vivo” (PMID: 32152500), implying that dCas9 ChIP-MS might inherently not be feasible including at repetitive regions such as telomeres. Therefore, recapitulating the set of well-described telomeric proteins was no trivial feat and our ChIP-MS workflow (both targeted and applied to individual proteins) represents a well-validated method to in the future systematically interrogate changes in chromatin composition. As one example at telomeres, this may include chromatin changes upon the induction of telomeric fusions or general DNA damage.

Reviewer #3: For instance, the authors could compare their mouse and human TERF2 interactomes and discuss similarities and differences between both species.

Response: We thank the reviewer for this suggestion, but the comparison between mouse and human TERF2 interactomes is not suitable across the datasets that we generated. U2OS is a human osteosarcoma cell line that relies on the Alternative Lengthening of Telomeres (ALT) pathway while our mouse data is based on embryonic stem cells (mESCs) and mouse liver tissue. Even the latter, in contrast to adult human tissue, expresses telomerase. We can certainly still pinpoint (as already done in our original manuscript) individual differences among known factors, e.g. the fact that proteins such as NR2C2 are more abundantly found at ALT telomeres (PMID: 19135898, 23229897, 25723166) vs. the detection of the CST complex as telomerase terminator (PMID: 22763445) in the mouse samples. However, the TERF2 datasets contain hundreds of proteins as “hits” above our cut-offs and a key message of our manuscript is that the majority of them are likely false positives. Here, differences are likely extending to expression differences between U2OS cells, mESCs and liver samples. So while appealing in theory, this cross data set comparison would remain rather superficial and error prone at this point. As a biology focused follow-up study, this would need to be rigorously conceived based on an appropriate choice of human and murine cell line models. In addition, this would likely require the generation of FKBP12-TERF2 knock-in fusion clones to allow for rapid depletion of TERF2 for a clean loss-of-function control since sustained loss of TERF2 leads to chromosomal fusions and eventually cell death in most cell types.

Reviewer #3: The authors should also describe which interaction partners are novel and try to validate some of these using orthogonal methods.

Response: We will now highlight more explicitly two proteins, POGZ and UBTF, that are most robustly and reproducibly enriched on telomeric chromatin across datasets, including the U2OS WT vs. ZBTB48 KO comparison (Fig. 2a). However, we would like to abstain from a molecular characterization at this point. As mentioned above, the discovery of novel telomeric proteins is not the focus of this manuscript, which is primarily dedicated to method development. In addition, these type of validations in methods papers are often limited to a few assays (e.g. can 1 or 2 proteins be enriched by ChIP? Do you see some localisation by IF? etc.). However, our research group has a history of publishing in-depth mechanistic papers on the characterisation of novel telomeric proteins (e.g. PMID: 23685356, 28500257, 20639181, doi.org/10.1101/2022.11.30.518500). Therefore, a genuine validation of such factors would require functional insights and clearly warrants independent follow-up work.

Reviewer #3: Human Terf2 ChIP-MS (Fig1A) seems to be much more specific than the mouse counterpart (Fig1D) (32 TERF2 interactors out of 176 hits in human vs 12 TERF2 interactors out of 500 hits in mouse). Could the authors explain this notable difference?

Response: As eluded to above, Fig. 1A and 1D cannot be directly compared, starting with the difference in complexity in the input material – cell line vs. tissue. For comparison, the Terf2 ChIP-MS data from mouse embryonic stem cells tallies up to 19 out of 169 hits, which is much closer to the U2OS results. Again, we deem the majority of hits from the TERF2 ChIP-MS data to be false-positives and the more complex input material from mouse livers likely accounts for the difference in these numbers.

Reviewer #3: The authors used much higher cell numbers than previously published ChIP-MS experiments; while this is understandable for dCas9-based pulldowns, the cell number is expected to be down-scalable for the other IPs (TERF2, ZBTB48, MYB). Since this work primarily describes an optimized Chip-MS workflow, the authors should show that they can reasonably downscale to at least 15 Mio cells per replicate; one way of achieving this could be through digesting on the beads and not in-gel.

Response: As we will illustrate in the comparison table that was also requested by reviewer 2, our approach does not use higher cell numbers than previous ChIP-MS approaches – quite the contrary. In addition, we would like to highlight that while we state 50 million cells in Fig. 1a, we only inject 50% of our samples for MS analysis to retain a back-up sample in case of technical issues with the instruments. In other words, our workflow is already effectively based on 25 million cells and thereby pretty close to the requested 15 million cells while simultaneously requiring substantially less reagents.

Importantly, our examples are based on rather lowly expressed bait proteins such as ZBTB48 (not detected within DDA-based proteomes of ~10,000 proteins in U2OS cells). While the workflow can be applied across proteins, exact input numbers might vary depending on the bait protein, e.g. histones and its modifications would likely require less for the same absolute sample enrichment. For instance, PMID 25990348 and 25755260 performed ChIP-MS on common histone modifications but still used 300-800 million cells per replicate. Considering that we worked on substantially less abundant proteins, we here present a workflow with comparably low input samples.

Reviewer #3: It is not clear from the text or figure what the authors are trying to show in Fig2c. They should either explain this further or take the figure out.

Response: We are trying to illustrate the following: As in any IP reaction the bait protein is the most enriched protein with very high relative intensities, e.g. TERF2 in the TERF2 ChIP-MS data. Direct protein interaction partners – here the other shelterin members – follow at about 1 order of magnitude lower signal intensities. In contrast, proteins that are enriched via an interaction with the same DNA molecule (i.e. that do not physically interact with the bait protein) such as NR2C2, HMBOX1 and ZBTB48 further trail by at least 1 more order of magnitude. These are information that are not easily visualised within the volcano plots and mainly “buried” within the Supplementary Tables. However, these relative intensities displayed in Fig. 2c clearly illustrate the dynamic range challenge that ChIP-MS poses for proteins that independently bind to the same chromatin fragment. We have now modified our text to make this point more clear.

Reviewer #3: Was there any benefit in using a Q Exactive HF vs timsTOF flex?

Response: Yes, measuring the same samples (e.g. the 50% backup mentioned above) on both instruments enriches more telomeric proteins/shelterin proteins in e.g. the dCas9 ChIP-MS data set on the timsTOF fleX. However, given the difference in age of these instruments/technologies between a Q Exactive HF and a timsTOF fleX (in the context of these experiments the equivalent of a timsTOF Pro 2), this is not a fair comparison beyond concluding that a more recent instrument like the timsTOF fleX achieves better coverage and is more sensitive with otherwise comparable measurement parameters. As we did not have the opportunity to run matched samples on e.g. an Exploris 480, we would not want to make claims across vendors. As stated in the discussion we are expecting that even newer generation of mass spectrometers, such as the very recently released Orbitrap Astral or timsTOF Ultra would further improve the sensitivity and/or allow to reduce the amount of input material. Therefore, the main conclusion is that improvements in the mass spec generations improve proteomics data quality and our samples are no exception, i.e. this is not specifically pertinent to our approach.

Reviewer #3: How did the authors analyze the PTM data? This is not described in the methods section. In addition, it would be important to validate the novel PTMs described for NR2C2.

Response: We apologise for the oversight and we will add the description of PTMs as variable modifications during our MaxQuant search in the methods section. The originally deposited datasets already include this and we had simply missed this in our methods text.

While we are not 100% sure to understand the request for validation correctly, we would like to point out that the PTMs on NR2C2 have been previously reported in several high-throughput datasets and for S19 in functional work on NR2C2 (PMID: 16887930). However, the relevance in our data set is as follows: While the PTMs on TERF2 as the bait protein could occur both on telomere-bound TERF2 as well as on nucleoplasmic TERF2, NR2C2 is only enriched in the TERF2 ChIP-MS reactions due to its direct interaction with telomeric DNA. The co-detection of its modifications therefore implies that at least some of the telomere-bound NR2C2 carries these modifications. We showcase this example as an additional angle of how such ChIP-MS datasets can be analysed.

While the robust, MS2-based detection of these modified peptides in our data set and several other publicly available datasets provides strong evidence that these modifications are genuine, further functional validation would involve rather labour-intensive experiments and resource generation (e.g. phospho-site specific antibodies). We hope that the reviewer agrees with us that this would require an independent follow-up study and that this goes beyond the scope of our current manuscript.

Reviewer #3: For this kind of methods paper one would expect to see the shearing results of the ChIP-MS experiments since variations in DNA shearing can impact the detection of false-positives in the ChIP-MS experiments

Response: We will include agarose gel pictures of our sonicates, which we indeed routinely quality controlled prior to ChIP experiments as stated in our methods description.

Reviewer #3: Overall, the current state of the manuscript neither provides direct evidence that the "optimized" ChIP-MS workflow is better in certain aspects/use cases than previously published methods nor does it provide novel biological insights. At the current state it even cannot be considered as a validation of previously published methods since it does not discuss them.

Response: We politely disagree with this conclusion. Again, as mentioned above we are under the impression that this reviewer somehow equates our entire manuscript to a comparison with dCas9-biotin ligase fusions.

Instead, we here provide a workflow for ChIP-MS that incorporates label-free quantification as the experimentally easiest, most intuitive quantification method for non-mass spectrometry experts. This offers a particularly low barrier to entry aimed at making ChIP-MS more widely accessible as a complement to commonly used ChIP-seq applications. Furthermore, we showcase that as a gold standard ChIP-MS – to truly live up to its name – should have the ability to enrich proteins independently binding to the same chromatin fragment. We demonstrated that double cross-linking is critical for these assays and in return illustrate how rigorous loss-of-function controls (both KOs and degron systems) can mitigate prevalent false-positives that are exacerbated due to the cross-linking. Finally, we applied this workflow to different types of endogenous proteins (transcription factors, telomeric proteins) in cell lines and tissue and extend our work to dCas9 ChIP-MS as a targeted method.

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Referee #3

Evidence, reproducibility and clarity

Strengths of the study:

• The study is well-structured and provides a robust workflow for the application of ChIP-MS to investigate chromatin composition in various contexts.
• The use of telomeres as a model locus for testing the developed ChIP-MS approach is appropriate due to its well-characterized protein composition.
• The comparison of WT vs KO lines for ZBTB48 is a rigorous way to control for false-positives, providing more confidence in the results.
• The direct comparison of double vs only FA-crosslinking provides valuable insights into the benefit of additional protein-protein crosslinking in ChIP-MS workflows.

Areas for improvement:

• The novelty of the method is more than questionable as both ChIP-MS coupled to LFQ and dCas9 usage for locus-specific proteomics have been previously reported. The fact that the authors directly pulldown dCas9 instead of using a dCas9-fused biotin ligase and subsequent streptavidin pulldown is only a very minor change to previous methods (not even improvement). It would be more accurate for the authors to present their study as an optimization and rigorous validation of existing techniques rather than a novel approach.
• The authors should more thoroughly discuss previous works using ChIP-MS and dCas9 for locus-specific proteomics. This would give readers a better understanding of how the current work builds on and improves these earlier methods. For a paper that aims on presenting an optimized ChIP-MS workflow it is crucial to showcase in which use cases it outperforms previously published methods.
  • E.g., compare locus-specific dCas9 ChIP-MS to CasID (doi.org/10.1080/19491034.2016.1239000) and C-Berst (doi.org/10.1038/s41592-018-0006-2); how does your method perform in comparison to these?
  • Compare likewise the described protein interactomes to previously published interactomes.
• The authors use sgGAL4 as a control for the telomeric targeting of dCas9. The IF results (Fig3b) show that sgGAL4 barely localizes to the nucleus with very faint signals. It would be helpful to use a control with homogenous nuclear localization of dCas9 to further strengthen the author's conclusions.
• The extrapolation of results from the use of telomeres as a proof-of-concept to other loci is not a given considering the highly repetitive structure of telomeric DNA. The authors should either be more cautious about generalizing the results to other loci or demonstrate that their method can also capture locus-specific interactomes at non-repetitive regions.
• What are concrete biological insights from this optimized ChIP-MS workflow that previous methods failed to show?
  • For instance, the authors could compare their mouse and human TERF2 interactomes and discuss similarities and differences between both species.
  • The authors should also describe which interaction partners are novel and try to validate some of these using orthogonal methods.
• Human Terf2 ChIP-MS (Fig1A) seems to be much more specific than the mouse counterpart (Fig1D) (32 TERF2 interactors out of 176 hits in human vs 12 TERF2 interactors out of 500 hits in mouse). Could the authors explain this notable difference?
• The authors used much higher cell numbers than previously published ChIP-MS experiments; while this is understandable for dCas9-based pulldowns, the cell number is expected to be down-scalable for the other IPs (TERF2, ZBTB48, MYB). Since this work primarily describes an optimized Chip-MS workflow, the authors should show that they can reasonably downscale to at least 15 Mio cells per replicate; one way of achieving this could be through digesting on the beads and not in-gel.
• It is not clear from the text or figure what the authors are trying to show in Fig2c. They should either explain this further or take the figure out.
• Was there any benefit in using a Q Exactive HF vs timsTOF flex?
• How did the authors analyze the PTM data? This is not described in the methods section. In addition, it would be important to validate the novel PTMs described for NR2C2.
• For this kind of methods paper one would expect to see the shearing results of the ChIP-MS experiments since variations in DNA shearing can impact the detection of false-positives in the ChIP-MS experiments

Significance

Overall, the current state of the manuscript neither provides direct evidence that the "optimized" ChIP-MS workflow is better in certain aspects/use cases than previously published methods nor does it provide novel biological insights. At the current state it even cannot be considered as a validation of previously published methods since it does not discuss them.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this manuscript, Yong and colleagues have introduced a optimized technique for studying actors on chromatin in specific regions with a localized approach thanks to revisited ChIP-mass spectrometry (MS) with label-free quantitative (LFQ). The authors exhibited the utility of their approach by demonstrating its effectiveness at telomeres from cell culture (human U2OS cells) to tissue samples (liver, mouse embryonic stem cells). As a proof of concept, this technique was tested by the authors with proteins from complex shelterin specific to telomeres (TERF2 and ZBTB48), transcription factors (MYB), and through dCas9-driven locus-specific enrichment. Notably, the authors created a U2OS dCas9-GFP clone and then introduced sgRNAs to target either telomeric DNA (sgTELO) or an unrelated control (sgGAL4). The cells expressing sgTELO exhibited a significant localization of telomeres and an enriched amount of telomeric DNA in ChIP with dCas9. They also found the proteins previously identified as known to be enriched at telomeres (for example, the 6 shelterin members). Moreover, the authors illustrated the importance of double crosslinking (formaldehyde (FA) and dithiobis(succinimidyl propionate) (DSP) in ChIP-MS. Their data demonstrated also that ChIP-MS is inclined towards false-positives, possibly owing to its inherent cross-linking. However, by utilizing loss-of-function conditions specific to the bait, it can be tightly managed.

Major comments:

• Can you show the concordance between biological replicates for each ChIP with LFQ? (heatmap of Pearson correlation and PCA plot). This will confirm the robustness of the use of LFQ.
• You say that your technique is " a simple, robust ChIP-MS workflow based on comparably low input quantities » (line 139). What would be really interesting for a technical paper would be: a schematic and a table illustrating the differences between your method and the previously published methods (amount of material, timeline,...) to really highlight the novelty in your optimized techniques.
• It would be interesting to perform the dCas9 ChIP experiment in telomeric regions with and without LFQ. Since the novelty lies in this parameter, at no time does the paper show that LFQ really allows to have as many or more proteins identified but in a simpler way and with less material. A table allowing to compare with and without LFQ would be interesting.

Minor comments:

• Put a sentence to explain "label free quantification". For a reader who is not at all familiar with this technique, it would be interesting to explain it and to quote the advantages compared to PLEX.
• what does the ranking on the right of each volcano plot represent (figure 1 b-e, figure 2a,d,e for example)? top of the most enriched proteins in the mentioned categories? Not very clear when we look on the volcano plot. it must be specified in the legend.

Significance

General assessment/Advance: The authors explain in their article that the ChIP exploiting the sequence specificity of nuclease-dead Cas9 (dCas9) to target specific chromatin loci by directly enriching for dCas9 was already published. Here, the novelty of this study lies in the use of LFQ mass spectrometry to optimize the technique and make it easier to handle. Some comparisons with previous papers or data generated by the lab will be interesting to really show the improvement and the advantage to use LFQ and therefore, to highlight better the novelty of the study.

Audience: By presenting this technical paper, the authors allow laboratories across different fields to use this technique to gain insights into protein enrichment in specific chromatin regions such as the promoter of a gene of interest or a particular open region in ATACseq in a easier way and with less materials. This paper holds value in enabling researchers to answer many pertinent questions in various fields.

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Yong et. al. describes a comparison of various chromatin immunoprecipitation-mass spectrometric (ChIP-MS) methods targeting human telomeres in a variety of systems. By comparing antibody-based methods, crosslinkers, dCas9 and sgRNA targeted methods, KO cells and various controls, they provide a useful perspective for readers interested in similar experiments to explore protein-DNA interactions in a locus-specific manner.

While interesting, I found it somewhat difficult to extract a clear comparison of the methods from the text. It was also difficult to compare as data and findings from each method was discussed in its own context. Perhaps it is not in their interest to single out a specific method and it is indeed true that there are caveats with each of the methods.

I think the manuscript would be of interest but I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

Specific comments:

Ln 57: What is "standard double cross-linking ChIP reactions" in this context? Is it the two different crosslinkers? The two proteins? The reciprocal IPs of one protein, and blotting for another? It's not clear here or from Extended Fig 1A. Upon further reading, it seems to pertain to the two crosslinkers - if so, the authors should briefly describe their workflow to help readers.

Ln 95: It is surprising and quite unclear to me why it is that the WT ZBTB48 U2OS pulldown in Fig 1B shows 83 hits for the WT vs Ig control experiment but 27 hits for the WT vs KO condition in Fig 2A. The two WT experiments have the same design and reagents, shouldn't they be as close as technical replicates and provide very similar hits? The authors seem to make the claim that most of the 'extra' proteins in WT vs Ig are abundant and false positives, but if this is so, shouldn't they bind non-specifically to the beads and be enriched equally in Ig control and ZBTB48 WT IPs?

Volcano plots in Figs 1, 2, and Suppl. Tables etc: Are the plotted points the mean of 5 replicates? Was each run normalized between the replicates in each group, for e.g. by median normalization of the log2 MS intensities? This does not appear to be the case upon inspection of the Suppl Tables. Given the variability in pulldown efficiency, gel digest and peptide recovery, this would certainly be necessary.

Ln 125: The authors make the claim that the ChIP-MS experiments are inherently noisy, with examples from WT cells, dTAG system and IgG controls. This is likely the case, yet their experiments with WT vs KO cells do not identify as many proteins overall. I find this inconsistency somewhat unclear and does not seem to match the claim of ChIP-MS experiments and crosslinking adding to non-specificity. Can the authors add the total number of identified proteins in each volcano plot, for easier reference?

Significance

I think the manuscript is interest as it provides important benchmarks for ChIP-proteomics experiments. I believe that there are remaining questions that need to be addressed before publication. In particular, I found it difficult to reconcile the discrepancy in protein IDs between most experiments vs. the WT/KO experiment in Fig 2. The authors make a big deal about the importance of the KO control but I think the fewer proteins identified there may be experiment-specific and not general to the KO system. I ask that this be investigated more carefully by the authors in their revisions.

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Reply to the reviewers

1. General Statements

We thank the Reviewers for their detailed and constructive comments. As we describe below, we have now amended the manuscript to address their concerns and suggestions.

2. Point-by-point description of the revisions

Reviewer #1

__In the first paragraph the reviewer states that our study is well presented and convincing, but that it seems “an incremental advance to the previous ones, which properly accounted for PLK4 symmetry breaking and are based on similar assumptions”. __We apologise for not explaining properly why our work is an important advance on these previous studies. Although both previous models can account for some aspects of PLK4 symmetry breaking, they both have significant issues. For example, Takao et al. perform no analysis of the robustness of their model, and from the small number of simulations shown it is clear that some very odd behaviours emerge—e.g. the oscillation of the dominant PLK4 site around the 6 compartments (Figure 3C, Example 3) and the bizarre manner in which PLK4 overexpression drives the formation of multiple PLK4 peaks (Figure 4B, first two examples). The authors do not comment on, analyse, or explain these strange phenomena. This model also relies on STIL being added to the system only after PLK4 has already broken symmetry; this is not plausible in rapidly dividing systems such as the fly embryo where Ana2/STIL levels remain constant through multiple rounds of centriole duplication (Steinacker et al., JCB, 2022). The Leda et al. model predicts that inhibiting PLK4 kinase activity will deplete PLK4 from the centriole, but it is now clear that PLK4 accumulates at centrioles when its kinase activity is inhibited (e.g. Yamamoto and Kitagawa, Nat. Comms., 2019). Moreover, this model supposes no spatial relationship between PLK4-binding compartments; this has important implications for the system’s behaviour (see point 1 in our response to Reviewer #2), and is biologically highly implausible. Thus, neither of the previous models can properly account for several important aspects of PLK4 symmetry breaking.

Moreover, the two previous studies are not based on similar assumptions. It is only through our analysis that we discover that the underlying biological process driving symmetry breaking in both previous models can be described in the same terms: with short-range activation and long-range inhibition causing diffusion-driven instability. This crucial conclusion was not obvious from, nor claimed by, either of the previous publications. We believe this is an important step in model development for these systems.

__The reviewer raises a number of minor concerns, the first of which is a previous study from Chau et al. (Cell, 2012), which studies how two component systems break symmetry. Differential diffusion is not essential for symmetry breaking in some of the models considered by Chau et al., and so they wonder if it is really essential in our system. __We thank the reviewer for pointing us to this study. It can be proven mathematically that differential diffusion is essential for symmetry breaking in the Turing-type framework. In the systems studied by Chau et al., symmetry can be broken without differential diffusion if one of the two components can be depleted from the cytoplasm. Such cytoplasmic depletion does not occur in traditional Turing-type systems, and it is almost certainly not occurring during PLK4 symmetry breaking—e.g. FRAP experiments show that PLK4 continuously turns over at centrioles (Cizmecioglu et al., JCB, 2010; Yamamoto and Kitagawa, Nat. Comms., 2019). We discuss this point (p8, para.3).

__The reviewer states that it is unclear which term in equations (3-4) and (5-6) correspond to the self-activation and activation/inhibition of the other component that are indicated in the schematic summary of the models shown in Figure 1C. __As we now clarify, in general it is not always possible to pinpoint a single term in an equation that corresponds to activation/inhibition. Mathematically, a positive feedback for means that , and a negative feedback for means that . Hence, activation and inhibition can change depending on the values of these derivatives during the dynamics as these inequalities may be achieved with complex expressions that extend beyond the usual proportional relationships. We have amended the manuscript to make this clearer (p10, para.2).

The reviewer pointed out an error in the arrows in Figure 2 (we believe this is actually Figure 4). We thank the reviewer for pointing this out and have now corrected this mistake.

Reviewer #2

Major Comments:

__ 1. The reviewer points out that in all models of PLK4 symmetry breaking the overexpression of PLK4 should be able to generate multiple PLK4 peaks (as, experimentally, PLK4 overexpression can generate up to 6 procentrioles around the mother centriole). The Reviewer suggests that the two previous models can do this, but we only show examples where PLK4 overexpression generates two peaks, and the reviewer questions whether this is a general limitation that would invalidate our approach. __We are grateful to the reviewer for pointing this out, and we now expand our analysis and discussion of this important issue (p13-15). It is indeed possible to produce more peaks in our model using different parameters—e.g. decreasing diffusivity leads to thinner peaks, allowing more peaks to form (Figure 3B, Figure 5B). Importantly, however, when diffusion is decreased, the region of the parameter space in which only a single peak will form inevitably becomes smaller—as diffusion can no longer efficiently suppress the formation of additional peaks around the rest of the centriole surface. Hence, in both our original models we struggled to find a parameter regime in which PLK4 robustly formed a single peak, but also formed >3 peaks when PLK4 was overexpressed. As we now discuss in detail, we believe that this is a general problem, as any model of PLK4 symmetry breaking must involve information being communicated around the centriole surface. We now show that a possible solution to this problem is to postulate that increasing PLK4 levels leads to a decrease in PLK4 diffusivity (Figure 3C, Figure 5C)—a biologically plausible possibility (p15, para.2).

In addition, it is not correct to say that the previous formulations of these models do not have this problem (or, in the case of Leda et al., the model actually has a related problem). This problem must apply to the Takao et al. model, as it also relies on information travelling around the centriole surface. This problem is far from obvious, however, because Takao et al. do not analyse the robustness of their model. This problem does not apply to the Leda et al. model, but this is because their model supposes no spatial relationship between the individual compartments and instead assumes that communication between compartments is instantaneous. This allows their system to overcome this communication problem and so robustly form a single peak at low PLK4 concentrations, while forming multiple peaks at high concentrations (as shown in Figure 6B). However, this requires that diffusion is sufficiently fast that concentration gradients are negligible between centriolar compartments, but not so fast that the relevant species are diluted in the much larger cytoplasm. It seems implausible that both of these effects may be achieved with a single diffusion rate in the real-world physical system.

__ 2. The reviewer points out that in our modelling any multiple PLK4 peaks formed will tend to be evenly spaced around the centriole surface whereas, in their original formulations, the two previous models predict that any multiple ‘winning’ PLK4 compartments will not have any preferential spatial location with respect to each other. They ask that we address this difference and justify why we think our prediction is a better representation of PLK4 symmetry breaking. __Although it is not obvious, neither of the previous models makes clear predictions about the spacing of multiple PLK4 peaks. As described above, Leda et al. assume no spatial relationship between PLK4-binding compartments, so relative peak-spacing cannot be assessed. Moreover, from the limited analysis shown, it is not clear that Takao et al. predict random spacing. The authors show only two simulations of PLK4 overexpression (Figure 4B, first two simulations) and the behaviour of PLK4 is very odd: the initial noise in the system fades away before PLK4 levels rapidly and near-simultaneously rise at multiple, reasonably well-spaced, peaks, before fading away to low levels—even after STIL addition. At the end of the simulation the “winning” compartments contain very low levels of PLK4 (often lower than the noise initially introduced into the system), but these compartments are reasonably (simulation 1) or very (simulation 2) evenly spaced.

Nevertheless, the reviewer is correct that the even spacing of multiple peaks is a feature of our model. Unfortunately, it is not possible to compare this prediction to reality because the spacing of multiple PLK4 peaks in cells overexpressing PLK4 has not been quantified yet. Thus, one has to interpret published images, some of which support equal spacing while others do not (e.g. Kleylein-Sohn et al, Dev. Cell, 2007). Moreover, this analysis is likely to be complicated because CEP152 can form incomplete rings. This can be appreciated in Figure 2C in Hatch et al., (JCB, 2010) where the extra centrioles induced by PLK4 overexpression do not appear to be evenly spaced around the centriole, but are quite evenly spaced around the partial CEP152 ring. Therefore, equal spacing of peaks in ideal conditions is a feature predicted by our model that still needs to be fully explored experimentally. We believe that part of the power and value of our model is to suggest such hypotheses. We now discuss this important point (p26, para.2).

__ 3. The reviewer questions our attempt to discretise our continuum model (where we convert the continuous centriole surface to a series of discrete compartments on the centriole surface and show that symmetry breaking can still occur). They note that we only show one example (9 compartments), they ask for more information about how the discretisation was done, and they question the independence of the compartments as PLK4 appears to accumulate in compartments adjacent to the dominant compartment. __We apologise for the lack of clarity here. We now state that our models can break symmetry provided that there are at least two compartments, and we now include simulations showing that this happens for 2 – 10 compartments (Figure S2). The discrete model is a finite-difference discretisation of the continuum model (described in Appendix V). We also now clarify that the compartments are ‘independent’ in the sense that all chemical reactions only occur between components that are within the same compartment. The compartments are still spatially linked via a discretized diffusion (as would likely be the case at the centriole), which explains the observed relationship between neighbouring compartments.

__ 4. The reviewer asks whether all the parameter values that satisfy the mathematical constraints we calculate for our models will break symmetry. If so, they suggest we are using a circular argument when demonstrating that the models break symmetry as we use parameter values chosen specifically to satisfy these constraints. __In Turing-systems, one can mathematically calculate parameter constraints that allow symmetry breaking. As we now clarify, all parameters that satisfy these constraints can break symmetry, while any parameters outside these constraints cannot break symmetry. Thus, it was never our intention to claim something new or surprising when we illustrated the symmetry-breaking properties of our models (Figures 2 and 4, and associated parameter space analysis in Figures 3 and 5), so we apologise that our intention on this point was unclear. Rather, these Figures illustrate the detailed behaviour of each system under different conditions—something that is not possible to intuit from the equations alone.

5. The Reviewer requests more information about how we chose the particular parameter values we use to illustrate each model and asks that we convince readers that other sets of values that satisfy the derived mathematical requirements would result in the same qualitative outcomes. As described in point 4 above, and as we now state more clearly, it is a mathematical fact that parameter values that satisfy the derived mathematical requirements can break symmetry. We now discuss our reasons for choosing specific parameters in more detail (see point 6, below).

__ 6. The Reviewer asks whether the dimensionless parameters we use in our models have any biological relevance, and requests a biological interpretation of all of them. They also request that we relate the Diffusivity ratios of the Activator and Inhibitor species (____) to the experimental observations made by Yamamoto and Kitagawa. __Relating our dimensionless parameters to biologically-relevant dimensional parameters is a complex issue. For example, one can see from equations (5) and (6) that simultaneously doubling (A), (I), and (a), and decreasing (b) by a factor of 4 leaves the system unchanged. Since the concentrations of A and I are unknown at the centriole surface, this means that it is not possible to determine the dimensional values of the rate of production of I (a) and its rate of conversion to A (b). This limitation is the root of the mathematical fact that FRAP experiments can reveal “off” rates but not “on” rates. Moreover, to convert the rate of loss of A (c) and I (d) into dimensional parameters it is necessary to know the timescale of symmetry-breaking. This is unknown, but was assumed to be on the order of hours in the previous models. This corresponds to a degradation/loss rate of minutes with our current choice of parameters, which is consistent with FRAP data (e.g. Yamamoto and Kitagawa, Nat. Comms., 2019). Regarding the ratio, the effective diffusion in our model depends on both the bulk diffusion and the binding/unbinding/degradation rates – a complexity also noted by Yamamoto and Kitagawa. This makes it very difficult to relate the “effective” surface diffusivity to the bulk diffusivity. We are currently investigating the form of this dependency, but this is a complex mathematical problem that is beyond the scope of this manuscript. These issues are difficult to discuss succinctly, so we now simply state that we chose specific parameter values based, in part, on the values and ratios used in the previous modelling papers (p10, para.2; p17, para.2).

Unfortunately, we could not find any experimental measurements of diffusivity in the Yamamoto and Kitagawa paper, as the Reviewer suggests. We now clarify, however, that the ratio we use in both models (2500) is chosen to be between the effective diffusivity ratio (as the previous models used binding/unbinding rates rather than diffusivity) used by Takao et al. (10000) and Leda at al. (200). We also include a phase diagram showing how varying the diffusivity of both factors influences symmetry breaking in both models (Figure 3B, Figure 5B), and we state that we have chosen all remaining parameter values to reflect the parameter values in the original models, when adjusted to the same timescale.

__ 7. The Reviewer asks for more information about how we normalised time in our simulations and whether the time in different simulations is comparable. __We now clarify that the simulations run for a single unit of dimensionless time (so they can be compared), and that the reaction/diffusion parameters in the system are sufficiently large by comparison with unity that all simulations achieve steady state within a unit of time (p11, para.2).

8. The Reviewer asks whether concentrations of _and can be compared between simulations, and also questions our description of _ being uniformly accumulated in Figure 4D, rather than uniformly depleted. __We clarify that concentrations can be compared within a model, but not between models. This is because the dimensional values depend on the dimensional reaction rates, which differ between the models. This is not just a theoretical limitation; experimental fluorescence signals are typically compared in relative arbitrary units so the absolute values of different systems cannot be easily compared for the same reason. We agree with the reviewer that it is better to describe Figure 4D as showing uniform depletion of the activator, and we have adjusted the legend accordingly.

The reviewer makes a number of minor points that are not numbered.

__The reviewer asks for clarification of what we mean by “robustness”: does this refer to the ability to produce the same result in multiple simulations, or to the ability to produce the same result when parameter values are varied? If the latter, then the reviewer suggests our models are not very robust. __We apologise for this confusion and now more clearly define what we mean by robust (p13, para.2). As we discuss in point 1 of our response to this Reviewer, our initial models are indeed not very robust at producing a single PLK4 peak over a range of PLK4 concentrations. We now discuss why this lack of robustness is likely to be intrinsic to any PLK4 symmetry breaking system, and how robustness in all such models can be improved by allowing diffusivity to vary with PLK4 expression levels (p13-p15).

__The Reviewer points out that the original models introduce a noise term at every iteration, whereas we only introduce an initial noise term; they ask us to discuss this difference. __We have run simulations introducing a noise term at every iteration and find that this makes negligible difference (Reviewer Figure 1, attached to the end of this letter). We do not take this approach, however, as this would significantly complicate the mathematical analysis that we perform (the additional noise term turns the system of PDEs into a system of SDEs which do not fit the Turing framework as readily). We now mention this in Appendix V.

The Reviewer states that the reaction schemes are unnecessarily repeated in Figures 1, 2 and 4. We would like to keep these schematics, as in Figure 1 we show a generic scheme (illustrating the two possible Turing-type reaction diffusion systems) whereas in Figures 2 and 4 we show specific reaction regimes (specifying the relevant species) that we test in each model. We feel this information will be useful to readers in this visual format.

The Reviewer states that it is confusing that we refer to the specific reaction parameters (k11 and k12) that need to be swapped to convert the Leda et al. model to the Takao et al. model, as this information will not mean anything to readers who are not familiar with the models. We agree and have now removed this information.

The Reviewer suggests several textual amendments and/or corrections. We thank the reviewer for spotting these and have amended them all accordingly.

__Finally, the Reviewer states in their significance summary that although our key conclusions are convincing, they are not new as Takao et al. describe their model as analogous to a “reaction-diffusion system (also known as a Turing model)”. __We were aware that Takao et al. make this statement, but this does not invalidate the novelty or significance of our work. This is because although Takao et al. described their model as being analogous to a “Turing model”, it is not actually a reaction-diffusion system, and it does not exhibit the property of long-range inhibition that is central to all Turing-systems to produce a single PLK4 peak. Instead, they use lateral inhibition (in which the influence of the inhibiting species does not extend beyond the neighbouring compartments) to reduce the number of potential PLK4 binding sites from ~12 to ~6. A single winning site is subsequently selected when STIL is added to the system—with additional positive feedback (not involving reaction-diffusion) ensuring that the compartment with most PLK4 becomes the dominant site. Their analysis of the reaction-diffusion version of their system is limited to a single supplementary figure (Figure S2D), and they do not perform or refer to any of the relevant mathematical analyses of their model that makes these well-studied systems such powerful tools. We believe that the model presented here is simple enough to draw the attention of the applied mathematics community while robust and complete enough to provide a mechanistic explanation of many interesting features and suggest new possible phenomena. We now discuss these points (p22, para.1).

Reviewer #3

__The Reviewer found our manuscript well-written, and judged it of interest to centriole duplication enthusiasts. __We interpret this to mean that the Reviewer did not think it of more general interest. This seems a harsh assessment, as the precise one-for-one duplication of centrioles is generally considered to be one of the great mysteries of cell biology. It is now widely appreciated that robustly breaking PLK4 symmetry to form a single PLK4 peak is crucial to this process. Thus, our discovery that this process can be described using a well-studied mathematical framework that has already been applied to a vast range of biological processes is potentially of significance even to non-centriole enthusiasts.

The Reviewer made a number of specific comments:

Figure 1. The Reviewer felt the graphic in Figure 1A could be improved by combining it with Figure 1B, and noted that the centrioles look strange. We thank the reviewer for these suggestions and we have now rearranged this Figure. We also now clarify that the schematic depicts Drosophila centrioles, which are simpler than human centrioles.

__Figure 2. The Reviewer suggests that to make the system depicted in Figure 2A fit as a Type I Turing system we have to assume that (I) must dissociate from the centriole or be degraded at higher rates than (I) converts (A) to (I). They suggest this assumption is implicit in the model and they request further explanation. __The reviewer is correct that, in Model 1, the degradation/dissociation of () is the root of its self-inhibition. However, we do not need to make any assumption about the relationship between the rate at which converts to (b), and the dissociation/degradation rate of (d) for this system to work (as the Reviewer implies). This is because, whatever these rates are, the system will approach a steady-state where the production and degradation terms balance, and it is the stability/instability of this state that determines whether the system can break symmetry. Since the degradation rate of (the - term in equation 4) increases more rapidly than its production rate (the term in equation 4) as increases, this results in a stable (i.e. self-inhibiting) system regardless of the parameter values. We have rewritten the sections explaining these equations to try to make these points more clearly and to point readers to Appendix II where we explain the form of the equations.

__The Reviewer asks if in Model 1 it is realistic to assume no turnover or loss of PLK4 (A), and will the system still work if this is altered? __This is a good point. In Model 1, we set c=0 as this makes the analysis significantly simpler, enabling us to display the mathematical predictions alongside the numerical simulation. We have now added the (c,d) phase diagram to show the effect of varying these parameters on the symmetry breaking properties of the system (Figure 3D). We find that the value of c has a relatively weak effect on the symmetry breaking properties of the model since it does not affect the function of as an activator.

__The Reviewer asks if our 1D model would work in 2D, and notes the PLK4 peaks in our models are broad, likely limiting the number of peaks formed. They also note that in our Model 1 it is the unphosphorylated form of PLK4 that accumulates in the peak, which seems unlikely as it is widely believed that PLK4 must be active to phosphorylate STIL to promote its interactions with SAS6 and CPAP. __From a mathematical perspective, modelling our system in 2D would produce very similar results. Symmetry breaking is driven by long-range inhibition/short-range activation, and these behaviours will work analogously in 2D. As discussed in our response to Reviewer #2 (point 1), the broad peaks do indeed limit the number of centrioles that can form, but by altering the parameters we can generate more peaks that are less broad (Figures 3 and 5). The Reviewer is correct that Model 1 (based on Takao et al.) predicts that non-phosphorylated PLK4 () accumulates in the peak. This is also true of the original Takao et al. model, although this was not highlighted or commented on by the authors. We now expand our discussion of this point (p25-p26).

The Reviewer asks if our models can form multiple peaks at higher PLK4 levels. This is again related to Reviewer #2, point 1, and we now show that this is indeed possible under the appropriate parameter regime (Figure 3C and Figure 5C).

The Reviewer asks for more description of how lateral diffusion works in our system. For example, do we consider that not every molecule of (I) will diffuse laterally (as some will be lost to the cytoplasm), or that the probability of a molecule leaving the surface will increase as distance/time increases. We apologise for our lack of clarity. We now state that the proportion of molecules not rebinding to the surface is accounted for in the reaction components of all our models (p7, para.1). In reality, and as we now state, the relationship between this loss and the diffusion rates (and their relation to distance/time, for example) is complicated. We are investigating this relationship in more detail, but this is beyond the scope of the current paper.

The Reviewer asks if symmetry breaking might eventually occur if the system in which we reduce the kinase activity of PLK4 (Figure 2D) were given more time. They also ask whether reducing PLK4 levels by half would lead to a failure in site-selection. The kinase inhibited scenario we show here will not break symmetry over any period of time; this can be proven mathematically, and is verified in the numerical simulations (Figure 3A and 5A, bottom left regions of graphs), which we now state more clearly are always run for a long enough period to reach a steady-state (p11, para.2). The effect of reducing PLK4 levels in our models is analysed in the phase diagrams shown in Figure 3 and 5 (and analysed in more detail in Figure S1), where it can be seen that there are multiple PLK4 concentrations that can be halved without a failure in site selection (although, see also our response to Reviewer #2, point 1).

The Reviewer pointed out some errors in our presentation of Figure 3, (and suggested some improvements in presentation in a point further below) and also asked for more information about the parameters used to generate the data in Figures 2B-D and 4B-D. We thank the Reviewer for these suggestions and have made these changes and provided the additional information requested (e.g. marking the specific parameters used in our simulations on the phase diagrams shown in Figure 3 and Figure 5 with coloured dots).

The Reviewer points out that when PLK4 levels and activity are both high no centrioles are produced in Model 2, whereas 1 centriole is produced in Model 1—neither of which are consistent with experimental observation. We now show an expanded parameter space (new Figures 3A and 5A) where it can be seen that this is not a problem for Model 1. For Model 2, the region of high kinase levels and activity (dark blue, top right, Figure 5A) corresponds to the uniform accumulation of the activator species. Thus, while there are no peaks, this region might produce multiple centrioles, as it is equivalent to a compartmental model in which all of the compartments are occupied. We now discuss this point (p19, para.1).

__The Reviewer questions how the biology fits a Type II Turing system, pointing out that current data suggests that active PLK4 turns over more rapidly at centrioles, whereas in the Type II model we describe (based on the Leda et al. model) it is the phosphorylation state of STIL that determines which species of PLK4:STIL turns over rapidly. They also question the logic of the Model 2 Type II circuit (Figure 3A), questioning how A could drive the dephosphorylation of STIL to promote the production of I. __We agree that current data is more consistent with phosphorylated species of PLK4 turning-over more rapidly at centrioles, but this is not what Leda et al. proposed, and so this is not what we implemented in trying to reformulate their model (although this is effectively the change we make that turns the Leda et al. model into the Takao et al. model). As to the second point, the Reviewer has correctly spotted a problem with our model that arises because the direction of the arrows linking and were inadvertently flipped in Figure 4A. This mistake has been corrected, and we now explain more clearly how the biology of this system fits a Type II Turing system in the legend.

__The Reviewer points out that although we can convert the Leda et al. Model (Model 2) to the Takao et al. Model (Model 1) simply by changing the identity of the _ and _ species, the underlying assumption of the Takao et al. model (that non-phosphorylated PLK4 promotes its own accumulation) was not an inherent assumption of the Leda et al. model. __We apologise for this confusion. As we now clarify (p20, para.1) the Reviewer is correct that when we make mathematical changes to the Leda et al. model we must also assume changes in the underlying biology—so that non-phosphorylated species of PLK4 are now slow diffusing, rather than non-phosphorylated species of STIL, as originally proposed). As the Reviewer points out, current data suggests that non-phosphorylated species of PLK4 do turnover more slowly, although it is not clear why—for example, liquid-liquid phase separation driving the formation of PLK4 condensates has been postulated, but is far from proven. This remains an interesting problem that will be further probed mathematically and experimentally.

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Referee #3

Evidence, reproducibility and clarity

This manuscript attempts to address a very important question in the field of centriole biology: how does PLK4 symmetry breaking occur to produce a new procentriole in a specific single site. The work is theoretical in nature and does not offer new experimental support. Furthermore, the authors are forced to make multiple assumptions to fit PLK4 symmetry breaking into a Turing reaction-diffusion system. In some instances, these assumptions are not intuitive and don't have a strong foothold in the known behavior of the molecules involved. That notwithstanding, I found the manuscript to be well-written for a general audience and believe it will be of interest to the centriole duplication enthusiasts.

The following comments should be addressed prior to publication:

Specific comments:

Figure 1:

• The graphic in Figure 1A depicting the centriole duplication process could be more effectively presented. Perhaps combining Figure 1A and 1B with a graphic that places these events in the context of the centriole duplication process coordinated with the cell cycle would provide a better insight to the relevant biology. The centrioles also look very strange, with the procentriole width being equal to the height of the parent centriole.

Figure 2:

• I take (I) to be synonymous with kinase-active PLK4 (phosphorylated PLK4 in the authors parlance). If (I) phosphorylates (A) to make more (I), then (I) doesn't strictly inhibit the accumulation of (I). It seems to make this fit a Turing system the authors are assuming that phosphorylated (I) must dissociate from the centriole or be degraded at higher rates than it converts (A) to (I). This is an assumption implicit in the model and should be further explained.
• Is it realistic to assume no turnover or loss of unphosphorylated PLK4 (A). Will the model still work if this assumption is altered?
• The centriole surface is modeled in 1-dimensional space, when it is, of course, 2-dimensional. How does this change the model? The site selection also appears weak as the distribution of PLK4 localization is very broad. This likely limits the number of PLK4 sites that can be formed. Finally, the model allows for the accumulation of (A) at a single site. Since (A) is unphosphorylated PLK4, I am left wondering how this species could be proficient in initiating procentriole assembly. I find it unlikely that PLK4 kinase activity is only required for symmetry breaking and not procentriole assembly. Multiple PLK4 phosphorylation sites on STIL promote binding interactions with centriole proteins (SAS6 + CPAP) and are required for procentriole assembly.
• In Figure 2C, are three peaks possible at higher PLK4 levels? Figure 3A would suggest not, which is inconsistent with the known biology.
• I think it would benefit the reader to have more description of what lateral diffusion entails and what assumptions are made. When (I) is released from the centriole surface, it can rebind to the centriole at a neighboring site (a PLK4 condensate or CEP152) and thus diffuse laterally or diffuse away from the surface of the centriole. Does the model account for the fact that not all every (I) molecule produced at the centriole will diffuse laterally? Moreover, the probability of (I) leaving the surface of the centriole must increase as distance/time increases.
• In Figure 2D, would a single site of PLK4 form if a longer period of time was given? In other words, are the kinetics of site selection slowed, or will symmetry breaking never occur in this system? I presume that reducing the overall levels of PLK4 levels by half would not lead to a failure of site selection?

Figure 3:

• The figure labels do not match what is described in the text. Figure 3B should be the top right graph and the bottom two graphs for Model 2 should be labelled 3C and 3D.
• The authors should highlight on the graph which parameters were used to generate the data in the experiments in Figure 2B-D and Figure 4B-D.
• Model 2 predicts that at high levels of PLK4 protein and high levels PLK4 activity, no centrioles are produced, while Model 1 predicts one centriole would be produced. Neither is consistent with experimental observations.
• The figure organization could be adjusted to improve clarity. As presented here, the text goes from discussing Figure 3A-B and skipping Figure 3C-3D until after discussing Figure 4. Instead of having the phase diagrams in their own figure, they could be incorporated into the respective figure that they are describing (Figure 3A-B becomes Figure 2E-2F, Figure 3C-D after current Figure 4D). With this adjustment, the figures could follow the order of the text.

Figure 4:

• It is unclear to me how the biology fits with the underlying assumptions of a type II Turning reaction-diffusion system. Both (A) and (I) contain phosphorylated (and active) PLK4. Current data suggests active PLK4 turns over more rapidly at the centriole - how does this fit with these assumptions? More importantly, the (A) species contain phosphorylated STIL and represent the complex that initiates centriole assembly. (A) promotes the accumulation of more (A) through phosphorylation of STIL, but how does A also drive the dephosphorylation of STIL to promote the assembly of (I)?
• In the section 'unifying the models....', the authors propose the Leda et al model can be modified so that phosphorylated PLK4 defines the (I) species and (A) represents unphosphorylated PLK4. This modification now mirrors that of Takeda et al., and it recreates the same issue - inactive PLK4 accumulates at the site of centriole assembly. There also needs to be an assumption for how A (non-phosphorylated PLK4) would promote its own accumulation, and this is not an inherent assumption from the Leda et al. model.

Significance

Centrioles are microtubule-based structures that comprise the centrosome, the major microtubule organizing center. In mitosis, centrosomes serve to maintain the bipolar spindle to promote faithful cell division. To ensure that only two centrosomes exist in a mitotic cell, centriole copy number is tightly regulated so that centrioles duplicate once and only once per cell cycle. Centriole biogenesis is initiated by Polo-like kinase 4 (PLK4) on the wall of an existing parent centriole to produce a single new procentriole. While progress has been made in understanding how centriole copy number is regulated by PLK4, it is still unclear how procentriole formation is strictly restricted to a single site in each preexisting parent centriole. In this paper, the authors use mathematical modelling to shed some light on this critical question in the centriole field.

The prevailing model in the field is that PLK4 is recruited around the circumference of the proximal end of the parent centriole at the beginning of G1 phase, and transitions to accumulate at a single locus that marks the site of procentriole assembly at the beginning of S phase. Two mathematical models have been proposed to explain how this PLK4 symmetry breaking occurs. However, both make predictions that are inconsistent with the current experimental data. In this study, the authors reconceptualize both published mathematical models for symmetry breaking and PLK4 site selection as two-component Turing systems that rely on activator/inhibitor dynamics. The original models were thought to differ in several key assumptions. However, in this study, the authors propose that the essential features of both models can be described by Turing systems. Moreover, the authors assert that the phosphorylation status of PLK4 is the driver for symmetry breaking.

Turing systems are widely understood and have a well-characterized behavior. The central question here is can the biological observations be adequately fit into this simplified reaction scheme.

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors present a reformulation of two existing mathematical models describing PLK4 symmetry breaking around the mother centriole at the onset of centriole duplication. Rather than considering PLK4 binding to, and unbinding from, a discrete representation of the centriolar periphery as a defined number of compartments, the authors consider PLK4 to diffuse on a continuous 1D ring. Furthermore, the reaction scheme of each existing model is reinterpreted here as a two-component reaction-diffusion system. These alternative representations of the existing models are shown to reproduce the dynamics of the original descriptions of the models.

With the two existing models put in a similar framework, the authors describe how a modification of the Leda et al. model can lead to the same dynamics as the Takao et al. model. Moreover, they point out a difference in the prediction of the reformulated versions of the two models (accumulation versus depletion of I in the peak, compare Fg. 2B and 4B). Finally, the authors report that discretization of the 1D continuous line into 9 compartments also predicts the accumulation of PLK4 at a single site, and thus does not alter the predictions of the two existing models qualitatively. From this, the authors conclude that PLK4 symmetry breaking around the mother centriole can be represented as a two-component Turing reaction-diffusion system.

Major comments

1D continuous space coordinate

The key difference between the models in their new formulations and their original descriptions is the representation of the centriole periphery as a continuous 1D representation of the ring, rather than a number of discrete compartments. The authors mention that the binding and unbinding between compartments and cytoplasm effectively act as a one-dimensional diffusion process on a ring, justifying the use of a continuous space coordinate. However, this justification might not be fully warranted. As discussed in the points below, the reformulations of the centriole periphery in a continuum result in strong predictions regarding PLK4 symmetry breaking and accumulation at distinct sites that are fundamentally different from the predictions of the two existing models in their original formalism.

1. Although the authors repeatedly mention "multiple" peaks, they do not present simulations of overexpression conditions that give rise to the accumulation of PLK4 at more than two sites. Would these predictions lie outside the parameter space explored by the authors or are the reformulations of the models intrinsically not capable of recapitulating the formation of more than two foci? The latter would be in contrast to the original formulations of the models, in which a gradual increase in protein levels leads to the stepwise increase of the number of compartments PLK4 accumulates in (Figure 4B, Takao et al.; Figure 6B, Leda et al.). More importantly, PLK4 overexpression has been repeatedly observed experimentally to induce the formation of up to 6 procentrioles around the mother centriole (e.g., Vulprecht et al. 10.1242/jcs.104109). Given this, how can a model that is by design limited to the formation of a maximum of two accumulation sites be a valid representation of PLK4 dynamics around the centriole? The authors must carefully evaluate this apparently central conundrum and adapt their models if needed.
2. In the case of PLK4 accumulation at two sites (e.g., in the 2xPLK4 condition), two foci always accumulate on opposite sides of the continuous ring. This is in stark contrast to the models in their original formalism, where two 'winning' compartments do not have any preferential location with respect to each other (Leda et al.), or where a second 'winning' compartment should be at least one compartment away, but then could be located anywhere (Takao et al.). The authors should address these differences and justify why their predictions on a continuous ring are a better representation of PLK4 symmetry breaking than the previous discretization of the centriole into compartments.
3. When returning from the continuous formalism to a compartmentalized centriole surface (Figure 5), the authors report that the model remains valid if the continuum space is "divided into an arbitrary number of discrete compartments" (p. 17). However, as the authors only present one exemplary simulation of the model for 9 compartments, it is not clear if other compartment numbers indeed reproduce the formation of only one dominant focus. More fundamentally, it is not clear how the model was discretized, what sets of equations are simulated, as well as if and how diffusion between compartments is accounted for. The authors report in the legend of Figure 5 that compartments are independent, but this is unlikely given the slight accumulation of PLK4 levels in the two compartments adjacent to the dominant compartment.

Model parameters

The authors define their reaction-diffusion system of equations starting from the mathematics, leading them to a set of requirements that the parameters in their equations need to fulfill in order for the system to be able to break symmetry and resolve in a steady state with a single site of PLK4 accumulation. 4. It is not clear whether all the parameter values that satisfy the mathematical constraints wil lead to symmetry breaking. In other words, is satisfying these constraints sufficient for symmetry breaking? If yes, then it would seem that the authors use a circular argument when demonstrating that their models break symmetry using certain values for a,b,c and d, since these values were chosen in the first place to satisfy the mathematical requirement that will lead to symmetry breaking. If no, then the authors should investigate and report which parameter values that fulfill the mathematical constraints do not lead to symmetry breaking, and why. Thus, in Figure 3, the authors should clarify if regions of the parameter space where the models predict no symmetry breaking (e.g., Figure 3B, left panel, a=b=250) fulfill the mathematical constraints. If so, how can one end up with a uniform distribution -i.e., without symmetry breaking, if the mathematical constraints require this state to be unstable?

these parameters can have a steady state in the absence of diffusion, at the onset of the simulation, as well as upon diffusion, at the end of the simulation, yet without symmetry breaking.

turns into another steady state that does not involve symmetry break. that turns unstable in presence of diffusion, but not break symmetry.

This is an important point to clarify. 5. Of all the combinations of parameter values that would satisfy the requirements for symmetry breaking, the authors mention that the reason for specifically choosing the values of a,b,c and d presented in the manuscript is their simplicity (p. 11, 15). It remains however unclear why this specific set of parameter values is preferred over other combinations of values. If this set is merely an arbitrary choice, then the authors should discuss this further and convince the reader that indeed any other set of values that satisfies the derived mathematical requirements would result in the same qualitative outcomes. Alternatively, potential empirical reasons why these values are preferred should be mentioned. 6. Related to the previous point, it is unclear if the parameters presented have much biological relevance. A biological interpretation should be made even for dimensionless parameters. Moreover, this comment is not limited to the a,b,c and d parameters. Concretely, in the reformulation of the model by Takao et al.,D_I is chosen to be 200-fold higher than D_A, whereas for the reformulation of the model by Leda et al., D_I is even 1000-fold higher than D_A. As in both models I and A refer to different species of PLK4 depending on their phosphorylation state, the authors should relate the D_I/D_A ratios chosen to experimental observations of the diffusivity of PLK4 as a function of phosphorylation (Yamamoto and Kitagawa 10.1038/s41467-019-09847-x). 7. As all simulations are presented to run from t=0 to t=1, the authors must clarify what stopping criterion they used to determine the simulation time, and if they normalized the time for each simulation. At present, it is not clear if the simulation time can be compared between different simulations of parameter sets. 8. Moreover, it is not clear how the concentrations of A and I are compared between simulations. In both Figure 2D and Figure 4D, the authors report a uniform accumulation of PLK4 on the ring. However, the total level of PLK4 is 30 in Figure 2D and only 2 in Figure 4D. Here, the authors must clarify why in the case of Figure 4D the outcome should not be interpreted as a uniform depletion, rather than a uniform accumulation.

Minor comments

• It is unclear what exactly is meant when the "robustness" of the reformulated model is discussed. Robustness could be interpreted as the ability of the model to reproduce the same result in repeated simulations but with the same model parameters, or else as the ability to reproduce the same result under varying model parameters. If the latter is concluded here, then it is questionable how robust the models are given the parameter regime analyzed in Figure 3, where two-fold changes in parameter values lead the model to fail to predict symmetry breaking.
• The authors mention that an initial stochastic noise in the binding of PLK4, randomly-generated only at the onset of the simulation, will be reinforced and eventually lead to the formation of a single focus. However, in the original descriptions of the models, this noise term is randomly generated and updated every iteration. What would be the consequence of such a continuous noise in the system for symmetry breaking and maintenance of a single site of PLK4 accumulation in the reformulated model simulations presented here? This must be discussed.
• The diagrams of the reaction schemes are unnecessarily repeated in multiple figures (Figure 1, Figure 2 and Figure 4).
• It is confusing that the authors use the original notations k_11 and k_12 to refer to specific rate parameters of the Leda et al. paper (p. 17). For readers not familiar with the Leda et al. paper, this is too detailed and this information should be put in an appendix if not omitted.
• The authors write that PLK4 is recruited in a "poorly understood process" (Introduction). Although the process is indeed incompletely understood, describing the process as "poorly understood" is an overstatement given the ample literature available on this question.
• The authors refer to the existing models as being "recently" proposed (Introduction). This term may be regarded inappropriate for 5-year-old publications.
• 'Takao' is misspelled as 'Takeo' on several occasions (p. 9,10,14,16,19).
• The Takao et al. paper is referenced from the year 2018 instead of 2019 (p. 9 and in the legend of Figure 2).

Significance

Although the key conclusions of the manuscript are convincing, they are not new.

In fact, Takao et al. describe their model explicitly as a "reaction-diffusion system (also known as a Turing model)" (p. 3539, Takao et al. 10.1083/jcb.201904156) and their model already consists of two components, representing an "active" and "inactive" form of PLK4. The conclusion that a two-component Turing reaction-diffusion model can explain how mother centrioles break PLK4 symmetry to generate a single daughter is thus already evident from Takao et al.'s work.

On the other hand, the original description of the model presented by Leda et al. includes more than two components and is not explicitly labeled as a Turing-inspired reaction scheme, although this might be obvious for people familiar with Turing models. For the Leda model, the authors' reformulation in a two-component reaction-diffusion system could be of potential interest, if the reformulated models lead (the authors) to new interpretations of previous data or generate unanticipated predictions that are testable in experiments.

At present, however, the provided material fails to demonstrate the significance of the reformulation of the models, and therefore seems better suited as review or commentary piece on reaction-diffusion systems explaining PLK4 symmetry breaking.

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Referee #1

Evidence, reproducibility and clarity

The study by Wilmott and colleagues is the design and test of a novel model that accounts for the symmetry break of PLK4 around centrioles prior to duplication. Two previous models have been proposed by xx et al and xx et al and both are described in the details in the introduction. According to the authors, the two previous models are similar but differ in some key points. The model by Leda et al is discrete since PLK4 can accumulate on nine competing points, which represent the nine triplets of microtubules. It is based on numerous chemical interactions and notably the positive feedback of PLK4 on itself. But somehow it does not account very well for the effect of the inactivation of PLK4 phosphorylation on the accumulation of PLK4 all around the centriole. The model by Takao et al is more continuous since it relies on the dynamics of a condensate formed by PLK4 which consumes adjacent PLK4 and thus leads to the concentration of PLK4 in the condensate. In addition to a positive feedback of PLK4 on itself it takes into account the negative effect of PLK4 on the adjacent recruitment of PLK4. But this model is not very robust to variations in the initial conditions. Here the authors proposed a continuous model based on the equations of a Turing model. It is claimed to unify the two previous model in a more generic one that is easier to implement and to study. It accounts for all known impacts of the modulation of PLK4 phosphorylation on PLK4 symmetry break. I am not skilled enough in biochemical modeling to assess properly their description of other models, neither their own model. However, the present study is very well presented and convincingly highlights the conditions for the symmetry break to occur. It seems to be an incremental addition to the previous ones, which properly accounted for PLK4 symmetry break and it is based on similar assumptions. However, the continuous description is certainly easier in terms of computation and the Turing-like morphogenesis is an interesting novel way to think about symmetry break around the centriole.

I have few minor concerns:

• A preceding study by Chau and Lim in Cell in 2012 studied all the interactions patterns between two components that could lead to a symmetry break and the polarization of one of the components. They also studied the robustness of the polarizing patterns. It would be relevant to discuss this study and mention which of these patterns are considered here. In addition, Turing morphogenesis is not used in this study by Chau and Lim. I am not a specialist but it might means that the difference of diffusion rates between the two components might not be essential to the polarization. It would be interesting to test how critical it is in this study. It is somehow studied in the two right phase diagram in Figure 3. But it is unclear to me if the conclusion is that a robust polarization could not appear if the system is not driven by a genuine Turing-like mechanism. It is somehow obvious that if the inactivator diffuse faster than the activator, the activator will aggregates more easily, but it is unclear to me whether this is a requirement. It doesn't seem to be the case in the study by Chau and Lim.
• The study by Chau and Lim proposed a way to test the robustness of the polarizing pattern to variations of the interaction parameters and concentrations of the two species. It would be a great addition to this study.
• It is unclear which term of the equations (3-4) and (5-6) correspond to the self-activation and activation/inhibition of the other component. In model1, the positive feedback of the inactivator on itself is drawn in the scheme (Figure 1) but the corresponding term in equation 4 (a positive term depending only on the concentration of the inactivator) seems to lack. In model 2, the positive feedbacks on both the activator and the inactivator, drawn in the scheme (figure 2), are also absent from equations 5 and 6.
• The two arrows between A and I seem to be inverted in the scheme in Figure 2. I understood from the text and the equations that A must act negatively on I, and not positively, and that I must act negatively A, and not positively.

Significance

The study by Wilmott and colleagues is the design and test of a novel model that accounts for the symmetry break of PLK4 around centrioles prior to duplication. Two previous models have been proposed by xx et al and xx et al and both are described in the details in the introduction. According to the authors, the two previous models are similar but differ in some key points. The model by Leda et al is discrete since PLK4 can accumulate on nine competing points, which represent the nine triplets of microtubules. It is based on numerous chemical interactions and notably the positive feedback of PLK4 on itself. But somehow it does not account very well for the effect of the inactivation of PLK4 phosphorylation on the accumulation of PLK4 all around the centriole. The model by Takao et al is more continuous since it relies on the dynamics of a condensate formed by PLK4 which consumes adjacent PLK4 and thus leads to the concentration of PLK4 in the condensate. In addition to a positive feedback of PLK4 on itself it takes into account the negative effect of PLK4 on the adjacent recruitment of PLK4. But this model is not very robust to variations in the initial conditions. Here the authors proposed a continuous model based on the equations of a Turing model. It is claimed to unify the two previous model in a more generic one that is easier to implement and to study. It accounts for all known impacts of the modulation of PLK4 phosphorylation on PLK4 symmetry break. I am not skilled enough in biochemical modeling to assess properly their description of other models, neither their own model. However, the present study is very well presented and convincingly highlights the conditions for the symmetry break to occur. It seems to be an incremental addition to the previous ones, which properly accounted for PLK4 symmetry break and it is based on similar assumptions. However, the continuous description is certainly easier in terms of computation and the Turing-like morphogenesis is an interesting novel way to think about symmetry break around the centriole.

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Reply to the reviewers

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

Summary

Ge et al. defined the role of Gli1 in M1 macrophage activation and osteoclast differentiation in physiological conditions and inflammatory arthritis. The authors found that Gli1 expression is elevated in human RA synovial tissue relative to that in healthy donor controls. Moreover, the authors showed that the administration of GANT58, a Gli1 inhibitor, ameliorates inflammation and bone erosion in CIA mice. Gli1 expression is suppressed by LPS/IFN-____γ stimulation in Raw264.7 cells while being induced by RANKL stimulation in Raw264.7 cells. However, GANT58 suppressed LPS/IFN-____ɣ -induced expression of inflammatory cytokines and iNOS and osteoclastogenesis. The authors also identified DNMT1 and DNMT3a as downstream effectors of Gli1. Transcriptomic analysis of GANT58 treated Raw264.7 cells identified diminished protein expression of DNMT1 and DNMT3a by GANT58. Gli1 also directly interacts with DNMT1. Intriguingly, DNMT1 overexpression restores the effect of GANT58 on LPS/IFN-____ɣ-mediated activation, while DNMT3a overexpression reverses the effect of GANT58 on RANKL-induced osteoclastogenesis. Since this study defines the role of Gli1 in the function and differentiation of myeloid cells, this is interesting. In addition, GANT58 nearly completely protects mice from arthritis, suggesting a therapeutic potential of Gli1 targeting in RA. However, the details of experiments are not clearly described, and the authors present the mixed data from Raw264.7 cells and BMMs without any explanations.

Reply: Many thanks for your recognition and constructive comments on our research. In this study, used mouse macrophage-like cell line RAW264.7 and primary bone marrow-derived macrophages (BMMs). The RAW264.7 is the most commonly used mouse macrophage cell line in medical research, and it is one of the most commonly used in vitro models for osteoclasts and inflammation research. In addition, compared with cell lines, primary cells have the characteristics of unchanged genetic material and biological characteristics closer to cell physiology in vivo. Therefore, in addition to cell lines, we also extracted primary macrophages from bone marrow for experiments to improve the reliability of this study. According to your comments, we have revised the manuscript, and our point-by-point responses are shown as follows.

Major comments

Comment 1. Figs 1h and i. The author should show the histological score.

Reply: Thanks for the constructive comment. According to your suggestion, we have scored the results of H&E staining histologically and added quantitative results.

Comment 2. Pharmacological inhibitors often show non-specific effects. To complement their findings showing the effect of GANT58 on M1 macrophage activation and osteoclastogenesis, the authors should utilize Gli1-deficient cells that can be obtained by siRNAs-mediated knock down or Gli1 deletion.

Reply: Thanks for the professional and constructive comment. To make the results more reliable, we have synthesized siRNA and supplemented the related experiments to verify the role of GLI1 in M1 macrophage activation and osteoclastogenesis, which showed the same trend as GANT58 intervention. In the revised manuscript, the relevant results were shown in the Response to Reviewer File.

Comment 3. Figure 4d: The authors should measure DNMT1 and DAMT3a RNA expression in LPS/IFN-____ɣ- treated (Fig 2c and d) or RANKL treated Raw264.7 cells.

Reply: Thanks for your constructive comment. According to the suggestion, we have added the RNA expression of DNMT1 and DAMT3a to the revised Figure 4. At the same time, the corresponding contents are also described in the Results part.

__ detailed information of RNA-seq including how many genes are regulated by GANT58 and what is their cutoff (fold induction and FDR). The authors should deposit their RNA seq data in the public databases repository such as GEO.__

Reply: Thanks for the professional and constructive comment. In the revised manuscript, we have made a more detailed analysis of the sequencing results and the detail information of RNA-seq have been added in the supplementary information.

Revised in the manuscript:

2.4. GLI1 regulates the expression of DNMTs in distinct ways during the different fates of macrophages

As a nuclear transcription factor, GLI1 exerts an active effect through nuclear entry. In order to explore the potential downstream regulation mechanism of GLI1, RNA sequencing (RNA-seq) on the macrophages before and after GLI1 intervention was performed then to observe gene expression changes. The seq data showed that more genes were down-regulated (143) than up-regulated (74) in GANT58 treated cells (Fig. S7a, b). Among these differentially altered genes, we revealed through Gene Ontology (GO) analysis that GANT58's intervention in GLI1 affected multiple biological processes including macrophage chemotaxis and macrophage cytokine production (Fig. 4a). What’s more, the results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the pathway team enrichment was then performed and we showed the TOP30 enriched pathway. In these pathways, we classified them into cellular processes (red), human diseases (blue) and organismal systems (green) respectively. It showed that these down-regulated genes were involved in the development of human diseases such as rheumatoid arthritis, as well as organismal systems such as osteoclast differentiation (Fig. S8c; ____Fig. 4b). These evidences confirmed our previous results. Specifically, GANT58 reduced some of the osteoclast and inflammation-related genes in the cell resting state.

Comment 5. Figure 5c. The authors should add non-stimulating condition as a control.

__Reply: __Thanks for your constructive comment. We have re-conducted the experiment and added the control group.

Comment 6. Figure 6C: DNMT3a deficiency regulates limited number of genes such as IRF8. The authors should measure IRF8 RNA or protein expression in RANKL-treated cells.

Reply: Thanks for your constructive comment. It is reported that DNMT3a can affect the activity of IRF8 and regulate the formation of osteoclasts. Thus, according to your suggestion, we have added IRF8 gene expression detection in the revised manuscript. As shown below, the gene expression of Irf8 was decreased after being treated by RANKL. However, the expression of Irf8 was reversed by Dnmt3a knock down.

Comment 7. Although the effects of Gli1 on bone metabolism in the literature are inconclusive, Gli1 is expressed on other cell types in bone. Gli1 haplodeficiency in mice decreased bone mass with reduced bone formation and enhanced bone resorption compared to control mice (PMID:25313900). Gli1 is also used as a marker for osteogenic progenitors which are precursors of chondrocytes and osteoblasts (PMID: 29230039). Thus, the beneficial effect of GANT58 on inflammation and bone erosion in CIA mice may result from the effects of GANT58 on multiple cell types other than F4/80+ cells. The authors should include these references in the discussion on pg.9 and expand their discussion.

__Reply: __Thank you for your constructive comments. Indeed. there have been some divergent conclusions about the function of hedgehog and GLI1 in bone metabolism, which suggests that GLI1 may have multiple roles. According to your suggestion, we have expanded the relevant discussion and added related references in the Discussion part.

Discussion:

… … Although we have demonstrated that the inhibition of GLI1 by GANT58 can reduce the inflammatory response and inhibit osteoclast formation and that this mechanism is achieved through the downregulation of DNMTs, these findings also raise new questions. In the previous research report, Gli1 haplodeficiency in mice decreased bone mass with reduced bone formation compared to control mice, which was due to the osteoblasts with weakened function [44]. In this process, the osteogenic differentiation of mesenchymal stem cells also affected the function of osteoclasts. In addition, GLI1 is also used as a marker for osteogenic progenitors which are precursors of chondrocytes and osteoblasts [45]. These studies suggest that the regulation of GLI1 on bone metabolism is complex, and the therapeutic effect of GANT58 on RA may be more than just affecting the inflammatory reaction mediated by macrophages and the bone destruction mediated by osteoclasts. In addition to macrophages and osteoclasts, the functions of synovial fibroblasts and osteoblasts play essential roles in the RA microenvironment. These cells are also closely linked to each other. Synovial fibroblasts OPG and RANKL secreted by osteoblasts are important factors that regulate osteoclasts. Therefore, in a follow-up study, we will extend the study of GLI1 to its regulatory mechanism in osteoblasts.

Reference:

[44] Y. Kitaura, H. Hojo, Y. Komiyama, T. Takato, U.I. Chung, S. Ohba, Gli1 haploinsufficiency leads to decreased bone mass with an uncoupling of bone metabolism in adult mice, PLoS One 9(10) (2014) e109597.

[45] Y. Shi, G. He, W.C. Lee, J.A. McKenzie, M.J. Silva, F. Long, Gli1 identifies osteogenic progenitors for bone formation and fracture repair, Nat Commun 8(1) (2017) 2043.

Minor comments

Comment 1. CIA model: The experiment design of CIA model is not clearly described. The author should specify the time point of GANT58 injection.

__Reply: __Thank you for your comment and we are sorry for the confusion caused by vague method descriptions about animal experiments. We have added the specific design and method description of related experiments in the revised manuscript.

Revised in the manuscript:

Materials and Methods:

… … An emulsion of bovine type II collagen (Chondrex, Redmond, WA, USA) and an equal amount (1:1, v/v) of complete Freund’s adjuvant (Chondrex) was prepared to establish the CIA mouse model. First, 0.1 ml of the emulsion was injected intradermally into the base of the tail on day 0. On day 21, 0.1 mg of bovine type II collagen mixed with incomplete Freund’s adjuvant (Chondrex) was injected. From the 21st day, mice began to receive injection intervention treatment. For vehicle group, mice were injected with the same volume of placebo daily. For treatment groups, mice were injected with GANT58 or 5-AzaC solution daily. All interventions began the day after the second injection of bovine type II collagen. Arthritis score was given every three days from the second immunization. On day 49, all mice were sacrificed (in accordance with the guidelines of the Animal Welfare and Ethics Committee of the Soochow University) for the collection of specimens. … …

Comment 2. Joint inflammation of RA can be caused by many different cells. Abstract needs to be revised.

Reply: Thanks for your constructive comment. According to the suggestion, we have revised relevant descriptions in the abstract.

Abstract:

Rheumatoid arthritis (RA) is characterized by joint synovitis and bone destruction, the etiology of which remains to be explored. Many types of cells are involved in the progress of RA joint inflammation, among which the overactivation of M1 macrophages and osteoclasts has been thought an essential cause of joint inflammation and bone destruction. Glioma-associated oncogene homolog 1 (GLI1) has been revealed to be closely linked to bone metabolism. In this study, GLI1-expression in synovial tissue of RA patients showed to be positively correlated with RA-related scores and was highly expressed in collagen-induced arthritis (CIA) mouse articular macrophage-like cells. The decreased expression and inhibition of nuclear transfer of GLI1 downregulated macrophage M1 polarization and osteoclast activation, the effect of which was achieved by modulation of DNA methyltransferases (DNMTs) via transcriptional regulation and protein interaction ways. By pharmacological inhibition of GLI1, the proportion of proinflammatory macrophages and the number of osteoclasts were significantly reduced, and the joint inflammatory response and bone destruction in CIA mice were alleviated. This study clarified the mechanism of GLI1 in macrophage phenotypic changes and activation of osteoclasts, suggesting potential applications of GLI1 inhibitor in the clinical treatment of RA.

Comment 3. Figure 4g, h: are these experiments done in the resting states?

Reply: Thank you for your comment. This part of the experiments was carried out during the induction of M1 macrophage or induction of osteoclast. In this work, we found that GANT58 can inhibit GLI1 and at the same time reduce the gene expression of DNMT3a but not DNMT1 in the resting state. However, during M1 macrophage and osteoclast induction, GANT58 seemed to be able to inhibit both DNMT1 and DNMT3a protein expression. In view of the discovery that the expression of DNMT1 increased during the polarization of M1 macrophages, while the expression of DNMT3a increased during the activation of osteoclasts, we performed the binding experiment of GLI1 with DNMT1 in the process of LPS/IFN-γ induction, while the binding experiment with DNMT3a in the process of RANKL induction. We have added a detailed description to the revised manuscript.

Reviewer #1 (Significance (Required)):

Strengths: Hedgehog (hh) signaling has been implicated in the differentiation of osteogenic progenitors. Gli1+ mesenchymal progenitors are responsible for both normal bone formation and fracture repair. This study defines a new role of Gli1 in the function and differentiation of myeloid cells. In addition, GANT58 nearly completely protects mice from arthritis, suggesting a therapeutic potential of Gli1 targeting in RA.

Reply: Thank the reviewer for your recognition of our research work.

Limitations: This study mainly uses a pharmacological inhibitor to study the mechanism underlying Gli1's action. In addition, the details of experiments are not clearly described, and the authors present the mixed data from Raw264.7 cells and BMMs without any explanations. Advance: This study provides conceptual advancement for hh signaling research by demonstrating the function of Gli1 in myeloid cells.

Reply: Thank the reviewer for your constructive comments and help us to further improve the manuscript.

Audience: Basic research

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

Summary:

The paper by Ge et al seeks to identify a role for GLI1 in rheumatoid arthritis, as GLI1 is upregulated in the synovium of patients with rheumatoid arthritis. Inhibition of GLI1 by the GANT58 limited inflammation and destructive bone loss in a murine model of arthritis (Collagen Induced Arthritis). Inhibition of GLI1 increased expression of pro-inflammatory cytokines and M1 macrophage differentiation. Inhibition of GLI1 also blocked osteoclast formation. As has been shown in other settings, the function of GLI1 in M1 and osteoclast differentiation was linked to regulation by DNMTs.

Major comments:

Comment 1. There are several main problems with the text. Overall, the authors show an intriguing set of data implicating the use of GANT58 as a means to limit rheumatoid arthritis inflammation and bone destruction. The authors directly link the functions of GANT58 with loss of GLI1 activity by showing that GLI1 protein is reduced or translation to the nucleus blocked. It would be compelling if the authors would leverage a genetic model (either GLI1 knockout, or a CRISPR/siRNA approach) to see if it recapitulates key findings in vitro and in vivo. These data could further their claims that their findings are in fact directly due to GLI1.

Reply: Thanks for the professional and constructive comment. To make the results more reliable, we have synthesized siRNA and supplemented the related experiments to verify the role of GLI1 in M1 macrophage activation and osteoclastogenesis. Related experiments have been updated in the revised manuscript, which are shown in the Response to Reviewer File.

Comment 2. Overall, the paper lacks methodologic clarity that limits thorough interpretation of the data. Multiple experiments are missing from the Materials and Methods, including descriptions of the definition of trabecular bone and its analysis in micro-CT, the means by which cytoplasmic and nuclear fractions were generated, and the timing and dosing of GANT58 in vitro studies. In addition, key details regarding the reagents include the sources of primary antibodies used in the western blots and immunoprecipitation studies. Important methodologies are not well explained, which include the treatment of the Sham animals (presumably healthy) are not explained, that is, whether they receive injections of vehicle or are truly naïve. Finally, there is no statistical methodology, minimal explanation of the RNA-sequencing analyses, and no statement about how the RNA-sequencing data will be made available. This lack of detail makes a thorough assessment of the quality and interpretations of the data challenging and replication of the results impossible.

Reply: Thanks for your careful reading and constructive comments. We are sorry for the lack of some detailed methodological descriptions in the manuscript. In order to better explain how our experiment is carried out and improve the repeatability of the experiment, we have comprehensively improved the description of the experimental method in the revised manuscript.

Materials and Methods:

4.1. Experimental animals and human synovial tissue. Male DBA mice aged 6-8 weeks and weighing 15-20 g were randomly selected and fed in a specific pathogen-free (SPF) environment at a room temperature of 25℃, a relative humidity of 60%, and 12 hours of alternating light. All animal experiments were approved by the Animal Ethics Committee of the Soochow University (201910A354). The animals were divided randomly into groups (6 per group): sham group (healthy mice not received any treatment), vehicle control group (CIA model mice treated with solvent), and GANT58 (GLI1 specific inhibitor; MedChemExpress, New Jersey, USA) group (mice treated with 20 mg/kg GANT58) or 5-AzaC (DNMTs specific inhibitor; MedChemExpress) group (mice treated with 2 mg/kg 5-AzaC). An emulsion of bovine type II collagen (Chondrex, Redmond, WA, USA) and an equal amount (1:1, v/v) of complete Freund’s adjuvant (Chondrex) was prepared to establish the CIA mouse model. First, 0.1 ml of the emulsion was injected intradermally into the base of the tail on day 0. On day 21, 0.1 mg of bovine type II collagen mixed with incomplete Freund’s adjuvant (Chondrex) was injected. For vehicle group, mice were injected with the same volume of placebo daily. For treatment groups, mice were injected with GANT58 or 5-AzaC solution daily. All interventions began the day after the second injection of bovine type II collagen. Arthritis score was given every three days from the second immunization. On day 49, all mice were sacrificed (in accordance with the guidelines of the Animal Welfare and Ethics Committee of the Soochow University) for the collection of specimens. … …

4.3. Micro-CT analysis. The fixed bone samples of mice were collected. The joint samples were placed in a SkyScan 1174 Micro-CT scanning warehouse (Belgium). The parameters were set as follows: voltage 50 kV, current 800 μA, scanning range 2 cm × 2 cm, and scanning layer thickness 8 μm. The scan data were then entered into computer to conduct three-dimensional reconstruction with NRecon software (Bruker, Germany), and the bone tissue parameters were analysed with CTAn software (Bruker, Germany) after data conversion. During this procedure, we performed an analysis of bone parameters including BMD (Bone Mineral Density), BV/TV (Percentage Trabecular Area), Tb.N (Trabecular Number) and Tb.Sp (Trabecular Separation) by selecting the small joint of paws as the region of interest (ROI) in CTAn software. The three-dimensional reconstruction images were exhibited by Mimics Research software (Version 21.0; Materialise, Belgium).

4.11. Western blotting. Cells were seeded in 6-well plates at a density of 1 × 106/well with stimulation with RANKL (50 ng/ml) or LPS (100 ng/ml) + IFN-γ (20 ng/ml). First, cells were collected to extract total protein, and the BCA (Beyotime) method was used to adjust the protein concentration. Total protein was mixed with 5× loading buffer (Beyotime) and boiled at 95 °C for 10 minutes. For cytoplasmic/nucleus isolation, cells were collected and protein was extracted according to the instructions using the nuclear protein and cytoplasmic protein extraction kit (Beyotime). The proteins were separated by SDS polyacrylamide gel electrophoresis (SDS–PAGE; EpiZyme, Shanghai, China) based on their different molecular weights. Electrophoresis was performed using Bio–Rad (California, USA) equipment at 180 V for 40 minutes. Then, the proteins were transferred to a nitrocellulose membrane at 350 mA for 70 minutes using membrane transfer equipment (Bio–Rad). The membrane was removed and placed into western blot blocking buffer for 1 hour at room temperature. The diluted primary antibodies (GLI1, Abclonal, A14675; β-actin, Beyotime, AF5003; Lamin-B1, Abcam, ab16048; NFATc1, Abclonal, A1539; CTSK, Abclonal, A5871; MMP9, Abclonal, A11147; DNMT1, Abclonal, A16729; DNMT3a, Cell Signaling Technology, D23G1; GAPDH, Abclonal, A19056) were placed on the membrane and incubated at 4 ℃ for 12 hours, and then the corresponding secondary antibody was added and incubated for 1 hour at room temperature. Finally, a chemiluminescence detection system (Bio–Rad) was used to observe the results.

4.12. High-throughput sequencing (RNA-seq). To further screen for differential genes, we first subjected RAW264.7 cells to a 24-hour adaptive culture, followed by the addition of GANT58 at a final concentration of 10 μM to the GANT58 intervention group and cultured for a total of 24 h. After the cell treatment was completed, cells of the control group and GANT58 treated group were collected respectively, and RNA-seq detection and analysis were entrusted to a professional biological company (Azenta Life Sciences, Suzhou, China). Briefly, for differential expression gene analysis, the differential expression conditions were set as fold change (FC) > 1.5 and false discovery rate (FDR) 4.14. Statistical analysis. All data are presented as the mean ± standard deviation (SD). Statistical analysis was performed with an unpaired two-tailed Student’s t test for single comparisons with GraphPad Prism 8 (GraphPad Software, CA, USA). One-way analysis of variance (ANOVA) was used to compare data from more than two groups. p values less than 0.05 were considered statistically significant.

The specific statistical methods are marked in Figure legends as well.

Data Availability: The authors declare that all data supporting the findings of this study are available within this paper and its Supplementary Information and raw data are available on request from the corresponding author.

Comment 3. The authors should expand their introduction and Discussion to include a description of the history of other GLI inhibitors (such as GANT61) in rheumatoid arthritis. Further, the authors failed to cite current studies showing that GLI1 is upregulated in RA patients (DOI: 10.1007/s10753-015-0273-3 amongst others).

Reply: Many thanks to your thoughtful reading and constructive comment. According to your suggestion, we have added some revisions, including the description of GLI1 inhibitors, in the introduction and discussion sections. At the same time, we have also added descriptions and citations of GLI1 and RA-related research in corresponding positions.

Introduction:

… … To date, three mammalian GLI proteins have been identified, among which GLI1 usually acts as a transcriptional activator. On the basis of these studies, small molecular compounds such as GANT58 (selective inhibitor of GLI1) and GANT61 (inhibitor of GLI1 and GLI2) are often used as pharmacological interventions of GLI1, so as to achieve the purpose of inhibiting GLI1 activity and regulating the molecular biological process [13, 14]. Many of the physiopathological processes involved with GLIs are complex and worth discussing. Relevant studies have shown that GLI1-activated transcription promotes the development of inflammatory diseases such as gastritis, and antagonizing GLI1 transcription can alleviate the inflammatory degradation of articular cartilage [15, 16]. … …

Discussion:

… … In previous studies, GLI1 signal transduction and other pathways, including the NF-κB signaling pathway, were usually studied in tumor-associated diseases and are considered a response network that promotes cancer development [21, 22]. Qin. et al. found that the content of SHH in RA patients serum increased significantly by comparing with healthy patients [23]. At the same time, our study also showed that GLI1 was more expressed in the joint tissue of RA patients. These results suggest that HH-GLI signaling pathway may be involved in the regulation of the pathological process of RA. However, the research results of the hedgehog pathway in bone metabolism are complex. … …

Reference:

[13] X. Chen, C. Shi, H. Cao, L. Chen, J. Hou, Z. Xiang, K. Hu, X. Han, The hedgehog and Wnt/beta-catenin system machinery mediate myofibroblast differentiation of LR-MSCs in pulmonary fibrogenesis, Cell Death Dis 9(6) (2018) 639.

[14] R.K. Schneider, A. Mullally, A. Dugourd, F. Peisker, R. Hoogenboezem, P.M.H. Van Strien, E.M. Bindels, D. Heckl, G. Busche, D. Fleck, G. Muller-Newen, J. Wongboonsin, M. Ventura Ferreira, V.G. Puelles, J. Saez-Rodriguez, B.L. Ebert, B.D. Humphreys, R. Kramann, Gli1(+) Mesenchymal Stromal Cells Are a Key Driver of Bone Marrow Fibrosis and an Important Cellular Therapeutic Target, Cell Stem Cell 23(2) (2018) 308-309.

[23] S. Qin, D. Sun, H. Li, X. Li, W. Pan, C. Yan, R. Tang, X. Liu, The Effect of SHH-Gli Signaling Pathway on the Synovial Fibroblast Proliferation in Rheumatoid Arthritis, Inflammation 39(2) (2016) 503-12.

Comment 4. The antibody for GLI1 seems poor and inconsistent. Knockdown studies to show its specificity, and an example of the whole membrane stained for GLI1 would provide important validation of the reagent.

Reply: Thanks for your comment and we are sorry for showing the western blot results with poor quality. In the revised manuscript, we used the newly purchased antibody (Abclonal, Catalog: A14675) and rearranged the groupings for better comparison of protein expression and replaced the results with clearer blot images. Original images of all western blot results can be uploaded subsequently.

Comment 5. Regarding Figure S1:

The studies of RA patients are underpowered. With only three RA patients and three healthy synovial the distribution of DAS28 scores is clustered at healthy and active disease, and the correlation study is unconvincing.

Reply: Thanks for your constructive comment. We are sorry that the studies of RA patients might not be convincing enough due to the small sample size. In order to avoid controversial conclusions, we left out the results of correlation analysis between GLI1 expression and DAS28. In the follow-up study, we will collect additional clinical pathology data for statistical analysis and quantified the expression of GLI1 in healthy control patients and RA patients.

Comment 6. Regarding Figure 1 f-g and Figure 4j-k:

However, the information on inflammatory bone loss are incomplete. The methodology for the assessment of BMD and trabecular bone parameters in the hind paw is not explained. The 3D reconstructions are of the whole bone hind paw, but the anatomical region where trabecular bone is assayed not defined. It would be convincing if the authors added erosion scores in the hind paws or knees to show that the erosion in the synovium, which contributes to inflammatory arthritis, mirrors what occurs in the trabeculae.

Reply: Thanks for your constructive comment. We are sorry for incomplete description on in vivo experiments, including the micro-CT analysis and histological analysis. In the revised manuscript, we further supplemented and improved the relevant methods. The Inflammatory cell infiltration score and bone erosion score were also added according to your suggestion.

Materials and Methods:

4.3. Micro-CT analysis. The fixed bone samples of mice were collected. The joint samples were placed in a SkyScan 1174 Micro-CT scanning warehouse (Belgium). The parameters were set as follows: voltage 50 kV, current 800 μA, scanning range 2 cm × 2 cm, and scanning layer thickness 8 μm. The scan data were then entered into computer to conduct three-dimensional reconstruction with NRecon software (Bruker, Germany), and the bone tissue parameters were analysed with CTAn software (Bruker, Germany) after data conversion. During this procedure, we performed an analysis of bone parameters including BMD (Bone Mineral Density), BV/TV (Percentage Trabecular Area), Tb.N (Trabecular Number) and Tb.Sp (Trabecular Separation) by selecting the small joint of paws as the region of interest (ROI, bone tissue from ankle joint to toe) in CTAn software. The three-dimensional reconstruction images were exhibited by Mimics Research software (Version 21.0; Materialise, Belgium).

Comment 7. Regarding Figure 2:

-The methods and text do not state the dose of GANT58 used in these assays. Nor do they specify the timing of the GANT58 application in relationship to LPS and IFNg stimulation.

Reply: Thanks for your thoughtful reading and constructive comment. We apologize for not expressing the detailed dose and intervention time of GANT58 in some experiments in detail. In the revised manuscript, we have added drug dose and intervention time cutoff points in the parts of Methods, Results, and Figure Legends.

-The authors conclude that GLI1 limits the differentiation of M1 macrophages and also directly blocks the production of pro-inflammatory cytokines. The data are difficult to parse in that the directionality is not clear. If GLI1 promotes M1 macrophages, there would be less proinflammatory cytokines due to the reduction of their proliferation. To evaluate the role of GLI1 in regulating the cytokines, additional studies showing a transcriptional regulation of these cytokines is warranted.

Reply: Thank you for your professional and constructive comment. We totally agree with you that the release of inflammatory cytokines is affected not only by gene expression but also by the number of cells that proliferate. Therefore, to exclude this interference, we further examined transcriptional expression of cytokines responsible for cellular inflammation under the same conditions. The results shown in the Response to Reviewer File confirmed the inhibition of GANT58 on the expression of pro-inflammatory cytokine mRNAs, which further supported our conclusion.

-To show that the fractionation of the cytoplasm and nuclear compartments was complete, the westerns for GLI1, lamin-B1 and beta actin should be shown in the same blot.

Reply: Thank you for your professional and constructive comment. According to your suggestion, we have rearranged the groupings to show the westerns for GLI1, lamin-B1 and β-actin in the same blot for better comparison in the revised manuscript.

-In Section 2.3 ("the expression of and intranuclear transport..."), the authors state that their previous studies showed GLI was expressed in macrophages (line 80-81). It is unclear whether the authors are referring to studies in this manuscript or a previously published study and a citation is needed.

Reply: Thank you for your careful reading and helpful comment. We are sorry that the description in this part is confusing. In fact, what we want to refer to is the in vivo results described in the first section of the results part. We have changed this description in the revised manuscript.

2.3. The expression and intranuclear transport of GLI1 is involved in osteoclast activation

The over activation of osteoclast is the direct cause of bone destruction in RA. As described of the in vivo experimental results in the first part, we have found that GLI1 is highly expressed in macrophage-like cells in the subchondral bone of the joints, which raised our concerns about GLI1 and osteoclasts. … …

In response to Figure 3:

-The authors show that GANT58 has a potent impact in limiting osteoclast formation. The text states that GANT58 is a pretreatment, but the timing of this is not stated.

Reply: Thanks for your constructive comment. In order to reach the working concentration of drugs at the beginning of some experiments, we usually pretreated cells for 6-8 hours. We have added the specific time in the parts of Materials and Methods or Figure legends.

-It would be interesting to see whether there is a dose-response effect of GANT58.

Reply: Thanks for your comment. According to your comment, we set the concentration of GANT58 to 0, 1, 5 and 10 μM to intervene the induction of M1 macrophages and osteoclasts respectively. As shown in the Response to Reviewer File, with the increase of GANT58 concentration, the mean fluorescence intensity of iNOS in macrophages seems to decrease gradually, but there is no statistical significance when the concentration is below 5 μM. Similarly, when the concentration reached 10 μM, GANT58 significantly inhibited the formation of osteoclasts.

-It is not stated how long the cells are RANKL treated prior to nuclear/cytoplasmic fractionation? (3a, b, c and i).

Reply: Thanks for your constructive comment. For osteoclast induction and intervention, we treated cells for 48 h as cell transcription regulation usually occurs in the early and middle stages of osteoclast differentiation. According to your comment, we have added the description of specific intervention time information in Figure legends and other parts.

-The "Zoom" images in Figure 3j do not have a box to delineate where the higher magnification images are taken from in the top panes. The images appear to be from serial sections. This should be clarified.

Reply: Thanks for your constructive comment. In the revised Figure, we have boxed the area represented by the Zoom images. We can ensure that these images come from different groups of specimen slices. In order to better observe the number of osteoclasts, we chose a larger shooting multiple, which might make the pictures look similar. The revised images are shown in the Figure 3n, o in the Response to Reviewer File.

In Figure 3 and Figure 6e and 6f:

Although the data in BMM showed that there was no impact on cell survival was limited at low concentrations, showing that the differentiating osteoclasts are not more sensitive to apoptosis by GANT58 would be compelling. The large difference in cellularity in the presence of GANT58 provokes this question.

Reply: Thank you for your careful reading and helpful comment. As shown of the CCK8 result, GANT58 had no significant inhibitory effect neither on BMMs nor RAW264.7 cells until the concentration reached 40 μM. In the process of changing the polarization phenotype of macrophages, the cell morphology will also change to some extent. In our research results, the change of cell morphology after GANT58 intervention might be due to the inhibition of M1 macrophages. In order to observe the effect of GANT58 on BMM cell death and apoptosis, we further performed living/dead staining and apoptosis detection by fluorescence after GANT58 intervention. The results showed that GANT58 did not change the level of apoptosis nor increase the number of dead cells at the concentration of 10 μM. However, when the concentration increased to 30μM, the number of apoptotic cells increased. These results suggest that we should pay strict attention to the control of drug concentration in experimental intervention and transformation application. The supplementary results are shown in the Response to Reviewer File.

In Figure 4:

-The IP studies (4g and 4h) lack showing successful pull-down of GLI1 by western blotting as a critical control for the study.

Reply: Thanks for your constructive comment. During the performance of CO-IP experiment, we simultaneously detected the expression of GLI1 to verify the effectiveness of the antibodies used. In the revised Figure 4g and h, we have updated the corresponding results.

Revised Figure 4:

-Details about the steps involved in RNA-sequencing analyses need to be provided.

__Reply: __Thanks for your constructive comment. According to your suggestion, we have provided the steps involved in RNA-sequencing analyses in the Methods.

4.12. High-throughput sequencing (RNA-seq). To further screen for differential genes, we first subjected RAW264.7 cells to a 24-hour adaptive culture, followed by the addition of GANT58 at a final concentration of 10 μM to the GANT58 intervention group and cultured for a total of 24 h. After the cell treatment was completed, cells of the control group and GANT58 treated group were collected respectively, and RNA-seq detection and analysis were entrusted to a professional biological company (Azenta Life Sciences, Suzhou, China). Briefly, for differential expression gene analysis, the differential expression conditions were set as fold change (FC) > 1.5 and false discovery rate (FDR)

-Studies have previously shown a reduction of inflammatory arthritis by 5'-Azac and should be cited.

__Reply: __Thank you for your careful reading and helpful comment. In the discussion part of the revised manuscript, we have cited the related articles, which is shown as below.

Discussion:

… … In addition to normal physiological development, the abnormal expression of DNMTs causes the development of tumors and other diseases [35]. Through the treatment of DNMTs inhibitors, the inflammatory arthritis in mice was significantly relieved, which was consistent with the previous studies [36]. These results suggested that DNMTs might be involved in the inflammatory reaction and bone destruction of RA. Reports have suggested that the absence of DNMT3a inhibits the formation of osteoclasts, which may be due to the methylation of downstream IRF8 by DNMT3a [37]. In our study, we also verified this finding through pharmacological and genetic intervention. … …

Reference:

[36] D.M. Toth, T. Ocsko, A. Balog, A. Markovics, K. Mikecz, L. Kovacs, M. Jolly, A.A. Bukiej, A.D. Ruthberg, A. Vida, J.A. Block, T.T. Glant, T.A. Rauch, Amelioration of Autoimmune Arthritis in Mice Treated With the DNA Methyltransferase Inhibitor 5'-Azacytidine, Arthritis Rheumatol 71(8) (2019) 1265-1275.

-What is the proposed functional consequence for GLI1 binding to DNMT3a? Does GLI1 inhibition lead to hypomethylation of DNA by DNMT?

Reply: Many thanks for your constructive comment. In this study, it is interesting to find that GLI1 can affect the expression of Dnmt3a at the level of gene transcription, and affect the expression of DNMT3a and DNMT1 both in the process of protein expression. Through the CO-IP experiment, we confirmed that GLI1 protein can bind to DNMT1 instead of DNMT3a protein. These results suggested that GLI1 may regulate the expression of DNMT3a and DNMT1 at genetic level and post-translation proteinic level, respectively. Patricia Gonz á lez Rodr í Guez's latest research showed that during autophagy induction, GLI1 is upregulated, phosphorylated, translocated to the nucleus and recruited to the regions closer to the Transcription Start Site (TSS) of the Dnmt3a gene. This may be the direct mechanism of GLI1 regulating the expression of DNMT3a [1]. Theoretically, the expression of DNMTs affects the degree of methylation of related genes [2]. Thus, in the follow-up study, we will further verify the degree of genomic methylation caused by GLI1's regulation of DNMTs, and further explore more possible ways of GLI1's regulation of DNMTs and its potential role in other cell models.

Reference:

[1] P. Gonzalez-Rodriguez, M. Cheray, L. Keane, P. Engskog-Vlachos, B. Joseph, ULK3-dependent activation of GLI1 promotes DNMT3A expression upon autophagy induction, Autophagy (2022) 1-12.

[2] Dura M, Teissandier A, Armand M, Barau J, Lapoujade C, Fouchet P, Bonneville L, Schulz M, Weber M, Baudrin LG, Lameiras S, Bourc'his D. DNMT3A-dependent DNA methylation is required for spermatogonial stem cells to commit to spermatogenesis, Nat Genet 54(4) (2022) 469-480.

Figure 5:

-The groups in 5g are not well defined.

Reply: Thank you for your careful reading and comment. We're sorry that we didn’t clearly show the grouping information. In the revised Figure 5g, we have added the complete information of the groups.

-DNMT1 and DNMT3a reduction by siRNA, CRISPR or knockout would strengthen the inhibitor studies.

Reply: Thanks for your constructive comment. In the revised manuscript, we knocked down the expression of DNMT1 and DNMT3a by siRNA, and supplemented the related experimental results, which are shown in the Response to Reviewer File.

Regarding Figure 5 and 6:

-What is the impact of DNMT1 and DNMT3a overexpression on their own (not in the presence of GANT58)?

Reply: Thanks for your constructive comment. According to your comment, we observed and compared the differences in the polarization of macrophages M1 and the activation of osteoclasts between the DNMTs overexpression group and the control group. The results showed that overexpression of DNMT1 seemed to have no obvious effect on the formation of M1 macrophages. During the osteoclast activation, at day 4 of RANKL induction, the TRAP positive stained osteoclast number seemed to be no significance between WT group and Dnmt3aOE group. However, at day 3, there was more osteoclast in Dnmt3aOE group, which suggested that overexpression of Dnmt3a might accelerate the activation of osteoclasts to some extent. The results are shown in the Response to Reviewer File.

Minor comments:

Comment 1. The authors do not include a description of DNMTs in the introduction.

Reply: Thanks for your constructive comment. According to your suggestion, we have added a description of DNMTs in the Introduction.

Introduction:

DNA methylation is an important epigenetic marker playing an important role in regulating gene expression, maintaining chromatin structure, gene imprinting, X chromosome inactivation and embryo development an important epigenetic modification way to regulate gene expression, which is activated by DNA methyltransferases (DNMTs) [17]. As reported, DNMT1 and DNMT3a are involved in the progress of many physiological disorders, such as immune response and cell differentiation [18, 19]. In this study, … …

Reference:

[17] E. Li, Y. Zhang, DNA methylation in mammals, Cold Spring Harb Perspect Biol 6(5) (2014) a019133.

[18] Y. Fu, X. Zhang, X. Liu, P. Wang, W. Chu, W. Zhao, Y. Wang, G. Zhou, Y. Yu, H. Zhang, The DNMT1-PAS1-PH20 axis drives breast cancer growth and metastasis, Signal Transduct Target Ther 7(1) (2022) 81.

[19] R. Ramabadran, J.H. Wang, J.M. Reyes, A.G. Guzman, S. Gupta, C. Rosas, L. Brunetti, M.C. Gundry, A. Tovy, H. Long, T. Gu, S.M. Cullen, S. Tyagi, D. Rux, J.J. Kim, S.M. Kornblau, M. Kyba, F. Stossi, R.E. Rau, K. Takahashi, T.F. Westbrook, M.A. Goodell, DNMT3A-coordinated splicing governs the stem state switch towards differentiation in embryonic and haematopoietic stem cells, Nat Cell Biol 25(4) (2023) 528-539.

Comment 2. The descriptions of the groups are often unclear. In Figure 2, the label "GANT58" (blue bars) is presumably for a group that is treated for LPS+IFNg+GANT58 but this is not clarified.

Reply: Thanks for your careful reading and we are sorry for the ambiguous labeling. We have checked the whole manuscript and changed the related labeling information.

Comment 3. The distinction of Figure 3g as multinuclear giant cells (vs TRAP+ OCs in panel 3d) should be explained.

Reply: Thanks for your comment. Osteoclast is defined as a multinucleated giant cell with bone absorption function, which is composed of multiple monocytes/macrophages [1]. As osteoclasts mature, their cytoskeleton will undergo drastic reorganization. Filamentous actin (F-actin) firstly constitutes a podosomes with a highly dynamic structure, thereby completing the cell adhesion, migration, dissolution of bone minerals and digestion of organic matrix [2]. Therefore, in addition to observing the formation of osteoclasts by TRAP staining, we also carried out immunofluorescence staining to observe the F-actin ring formation to further evaluate the functional maturity of osteoclasts. Osteoclasts usually have 2-50 nuclei, so we mainly regarded multinucleated giant cells with complete F-actin rings as mature osteoclasts during the quantification process.

Reference:

[1] da Costa CE, Annels NE, Faaij CM, Forsyth RG, Hogendoorn PC, Egeler RM, Presence of osteoclast-like multinucleated giant cells in the bone and nonostotic lesions of Langerhans cell histiocytosis. J Exp Med 7;201(5) (2005) 687-93.

[2] Portes M, Mangeat T, Escallier N, Dufrancais O, Raynaud-Messina B, Thibault C, Maridonneau-Parini I, Vérollet C, Poincloux R, Nanoscale architecture and coordination of actin cores within the sealing zone of human osteoclasts, Elife (11) (2022) e75610.

Comment 4. The labels in 4C of "R1, R2, R3" standing for GANT58 is confusing

__Reply: __We are sorry for the confusing labeling. In the revised manuscript, we have added specific grouping information in the Figure legend, as shown below.

*Figure 4. DNA methyltransferases might be a regulatory target downstream of GLI1. a Biological process GO analysis of RNA-seq results for macrophages with or without GANT58 treatment. b KEGG rich analysis of RNA-seq results. c Heat map of parts of the relevant gene transcriptional expressions (C = control group; R = GANT58 treated group; red: increased expression; blue: decreased expression). d Relative mRNA expression of Gli1, Dnmt1 and Dnmt3a in macrophages with or without GANT58 treatment. Statistical analysis was performed using two-way ANOVA test. e RAW264.7 cells were stimulated by LPS and IFN-γ for 24 h, with or without GANT58 co-intervention. Western blot results of DNMT1 and DNMT3a protein expression and grayscale value ratio to β-actin of western blot results. n = 3. f RAW264.7 cells were stimulated by RANKL for 3 days, with or without GANT58 co-intervention. Western blot results of DNMT1 and DNMT3a protein expression and grayscale value ratio to β-actin of western blot results. n = 3. Statistical analysis was performed using two-way ANOVA test. g, h Co-IP detection of protein binding between GLI1 and DNMT1/DNMT3a. n = 3. i Protein–protein interface interaction of GLI1 and DNMT1 with PyMOL. j Micro-CT scanning and 3D reconstruction of mouse paws. k Bone parameters of BV/TV, BMD, Tb.N, Tb.Th. n = 6. Statistical analysis was performed using one-way ANOVA test. Data shown represent the mean ± SD. *p

Comment 5. In Figure S8, the numbers between the western blots are not explained.

__Reply: __Many thanks for your careful reading and comment. The numbers between the blots represent the ratio of the gray value of DNMT1 and DNMT3a immunoblot to the gray value of β-actin immunoblot, so as to reflect the relative expression of proteins. In order to avoid confusion, we made a statistical chart of the results and added it to revised Figure S8.

Comment 6. In Figure S9 there are references to asterisks which do not appear in the figure.

__Reply: __We are sorry for the mistake. We have deleted the relevant information in the revised Supplementary information. Thanks again.

Reviewer #2 (Significance (Required)):

The paper presented by Ge et al present interesting data suggesting that a GLI1 inhibitor (GANT58) has a strong impact on inflammatory arthritis in a murine model. Interesting data are presented whose novelty need better contextualization with other published studies, as previously published studies which are not cited in this manuscript include the finding that GLI1 is upregulated in patients with rheumatoid arthritis, that other GLI inhibitors have been utilized in murine models of rheumatoid arthritis, and that GLI1 has been shown to regulate DNMT expression in cancer settings. The authors connect GLI1 inhibition with DNMT activation in limiting M1 macrophage and osteoclast differentiation. However, several important controls are needed to in the in vitro studies as outlined above.

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

Summary

The manuscript by Ge et al. describes the possible roles of GLI1 in macrophage and osteoclast activation in rheumatoid arthritis via its functional interaction with DNA methyltransferases. The authors found that the GLI1 expression was elevated in RA synovial tissues and GLI1-specific inhibitor, GANT58, ameliorated arthritis in CIA mice. GLI1 expression in F4/80-positive macrophages in CIA synovial tissues led the authors to assess the roles of GLI1 in macrophages and osteoclasts. GANT58 suppressed M1 macrophage polarization by IFNg+LPS and osteoclastogenesis by RANKL. RNA-seq analysis of GANT58-treated macrophages revealed that DNA methyltransferases, DNMT1 and DNMT3a were possible targets of GLI1, and the studies with small inhibitors or overexpression of DNMTs suggest that GLI1 enhanced M1 polarization and osteoclastogenesis through DNMTs. The manuscript is well-written, the methods are accurate, and the results and data interpretation are consistent and clearly presented. This work deserves publication in Research Commons after addressing the following questions:

Major comments

Comment 1. GANT58 may inhibit GLI2 in addition to GLI1 and have off-target effects. Major findings with GANT58 in vitro, the suppressive effects on M1 polarization, osteoclastogenesis, and DNMT3a expression should be assessed with siRNA/shRNA knockdown or CRISPR/Cas9 knockout of GLI1.

Reply: Many thanks for your careful reading and constructive comment. According to your comment, we have constructed Gli1 knock-down cells and carried out related experiments. The results have been added in the revised manuscript, which are shown in the Response to Reviewer File.

Comment 2. In CIA with GANT58, the author performed only preventive treatment, not therapeutic treatment. Does GANT58 suppress adaptive immune responses via inhibiting APC function (ex. anti-CII IgG production)? Alternatively, the inhibitory effects of GANT58 on the effecter phase of RA (M1 macrophage and osteoclast activation) can be assessed using the serum-transfer arthritis models.

__Reply: __Many thanks for your constructive comments. Your question is indeed a direction worthy of attention. In our study, GANT58 was given during the stage of model establishment, showing a good effect of relieving arthritis, which was proved to come from the direct inhibition of inflammatory phenotype macrophages and osteoclasts. However, as autoimmune diseases, the enhancement of antigen presenting function and anti-Col II IgG production can enhance the immune response of the body [1]. The regulatory effect of GANT58 on macrophages suggests that it may have a potential impact on APC function. Despite this, whether GANT58 can regulate the pathological process of RA by influencing this pathway is inconclusive. Therefore, according to your suggestion, we will improve the relevant experiments in our follow-up research, and apply GANT58 to various animal models of RA to further explore the possible mechanism of GANT58 in the treatment of RA and provide more reliable theoretical support for its transformation and application.

Reference:

[1] Tsark EC, Wang W, Teng YC, Arkfeld D, Dodge GR, Kovats S, Differential MHC class II-mediated presentation of rheumatoid arthritis autoantigens by human dendritic cells and macrophages, J Immunol 1;169(11) (2002) 6625-33.

Minor comments

Comment 1. GANT58 is a water insoluble agent. Can you please include how to dissolve GANT58, administration route, and rationale of 20 mg/kg, for CIA?

__Reply: __Thank you for your professional comment. In this work, GANT58 was ordered from MedChemExpress (MCE; Cat. No.: HY-13282) Company. According to the instructions for use, we prepared 20 mg/ml ethanol solution of GANT58 into 2 mg/ml working solution for injection in vivo according to the following ratio: 10% EtOH + 90% (20% SBE-β-CD in PBS); Clear solution; Need ultrasonic. During the experiment, GANT58 was injected i.p. at a dose of 20 mg/kg daily for 28 days. With regard to the choice of drug injection concentration, according to the previous literature, most studies used a dose of 50 mg/kg for daily injection [1, 2]. Hereby, we set up concentration gradient intervention (0, 10, 20, and 50 mg/kg) in the preliminary experiment and found that 20 and 50 both had good therapeutic effects. Therefore, according to the consideration of economy and safety, we chose 20 mg/kg as our final intervention concentration.

Reference:

[1] Li G, Deng Y, Li K, Liu Y, Wang L, Wu Z, Chen C, Zhang K, Yu B, Hedgehog Signalling Contributes to Trauma-Induced Tendon Heterotopic Ossification and Regulates Osteogenesis through Antioxidant Pathway in Tendon-Derived Stem Cells, Antioxidants (Basel) 16;11(11) (2022) 2265.

[2] Lauth M, Bergström A, Shimokawa T, Toftgård R, Inhibition of GLI-mediated transcription and tumor cell growth by small-molecule antagonists. Proc Natl Acad Sci U S A. 15;104(20) (2007) 8455-60.

Comment 2. Zoom photos in Fig 1j are not clear. Is GLI1 exclusively expressed in F4/80+ macrophages in synovial tissues?

__Reply: __Many Thanks for your comment. In the revised manuscript, we have improved the resolution of the image for better observation. According to the results, although GLI1 is more expressed in F4/80 positive cells, not all GLI1 proteins are expressed in macrophages, and we can find that some GLI1 positive staining is expressed in other cells. In the follow-up study, we will continue to explore this phenomenon and study the relationship between GLI1 and cells like synovial fibroblasts in RA.

Comment 3. In Fig 2 and 3, the treatment of macrophages with IFNg+LPS and RANKL enhanced the nuclear translocation of GLI1, suggesting that these stimuli may activate hedgehog signals. Recent studies, however, suggest various non-canonical activation pathways of GLI1. Does hedgehog inhibitor (ex. SMO inhibitor) also suppress M1 polarization and osteoclastogenesis?

__Reply: __Thank you for your constructive comment. We agree with that the activation of GLI1 is regulated by many various pathways. According to your comment, we additionally used Cyclopamine, a selective inhibitor of SMO, to intervene during the polarization of M1 macrophages and the activation of osteoclasts. The results are shown in the Response to Reviewer File: Cyclopamine could also inhibit the proinflammatory polarization of macrophages to a certain extent, and a significant inhibition of the osteoclast formation could be observed as well. These results may further confirm the important role of HH/GLI1 in regulating macrophage caused inflammation and osteoclast activation.

Comment 4. In Fig 6, the overexpression of DNMT3a reversed the inhibitory effects of GANT58 in osteoclastogenesis. This supports the author's conclusion that GLI1 may enhance osteoclastogenesis via DNMT3a upregulation. However, this conclusion should be carefully evaluated by examining effects of the overexpression of DNMT3a without GANT58. Does the overexpression of DNMT3a by itself enhance osteoclastogenesis or just reverse the GANT58-mediated suppression?

Reply: Thanks for your constructive comment. According to your comment, we observed and compared the differences in the activation of osteoclasts between the DNMT3a overexpression group and the control group. The results showed that at day 4 of induction, the TRAP positive stained osteoclast number seemed to be no significance between WT group and Dnmt3aOE group. However, at day 3, there was more osteoclast in Dnmt3aOE group, which suggested that overexpression of Dnmt3a might accelerate the activation of osteoclasts to some extent. The results are shown in the Response to Reviewer File.

Comment 5. Is RNA-seq data with GANT58 compatible with known target genes of GLI1 reported in previous studies?

Reply: Thanks for your constructive comment. By consulting and comparing with other research articles, most of the data trends in RNA sequencing results are the same as those in other studies. In addition, the expression of some genes is different from other studies (MMP13 increased in our data but decreased in other study [1]), which may be caused by different cell lines and different intervention methods.

Reference:

[1] Akhtar N, Makki MS, Haqqi TM, MicroRNA-602 and microRNA-608 regulate sonic hedgehog expression via target sites in the coding region in human chondrocytes, Arthritis Rheumatol 67(2) (2015) 423-34.

Reviewer #3 (Significance (Required)):

Significance

The main limitation of this paper is the lack of siRNA knockdown study of GLI1 and DNMTs. Another limitation of this paper is that the direct in vivo data demonstrating the inhibitory effects of GANT58 on M1 macrophage and osteoclast activation in CIA is lacking. The strength is the promising activity of GLI inhibitor, GANT58 as an anti-rheumatic drug on monocyte/macrophage-associated inflammation and bone destruction. The roles of hedgehog/GLI signals in macrophage function are largely unknown, and the findings of this study may contribute to this research field. This study will be interesting to rheumatologists and immunologists.

Reply: Thanks again for your constructive comments, which helped us to improve the quality of the manuscript.

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Reply to the Reviewers

I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Referee #3

Evidence, reproducibility and clarity

Summary

The manuscript by Ge et al. describes the possible roles of GLI1 in macrophage and osteoclast activation in rheumatoid arthritis via its functional interaction with DNA methyltransferases. The authors found that the GLI1 expression was elevated in RA synovial tissues and GLI1-specific inhibitor, GANT58, ameliorated arthritis in CIA mice. GLI1 expression in F4/80-positive macrophages in CIA synovial tissues led the authors to assess the roles of GLI1 in macrophages and osteoclasts. GANT58 suppressed M1 macrophage polarization by IFNg+LPS and osteoclastogenesis by RANKL. RNA-seq analysis of GANT58-treated macrophages revealed that DNA methyltransferases, DNMT1 and DNMT3a were possible targets of GLI1, and the studies with small inhibitors or overexpression of DNMTs suggest that GLI1 enhanced M1 polarization and osteoclastogenesis through DNMTs. The manuscript is well-written, the methods are accurate, and the results and data interpretation are consistent and clearly presented. This work deserves publication in Research Commons after addressing the following questions:

Major comments

1. GANT58 may inhibit GLI2 in addition to GLI1 and have off-target effects. Major findings with GANT58 in vitro, the suppressive effects on M1 polarization, osteoclastogenesis, and DNMT3a expression should be assessed with siRNA/shRNA knockdown or CRISPR/Cas9 knockout of GLI1.
2. In CIA with GANT58, the author performed only preventive treatment, not therapeutic treatment. Does GANT58 suppress adaptive immune responses via inhibiting APC function (ex. anti-CII IgG production)? Alternatively, the inhibitory effects of GANT58 on the effecter phase of RA (M1 macrophage and osteoclast activation) can be assessed using the serum-transfer arthritis models.

Minor comments

1. GANT58 is a water insoluble agent. Can you please include how to dissolve GANT58, administration route, and rationale of 20 mg/kg, for CIA?
2. Zoom photos in Fig 1j are not clear. Is GLI1 exclusively expressed in F4/80+ macrophages in synovial tissues?
3. In Fig 2 and 3, the treatment of macrophages with IFNg+LPS and RANKL enhanced the nuclear translocation of GLI1, suggesting that these stimuli may activate hedgehog signals. Recent studies, however, suggest various non-canonical activation pathways of GLI1. Does hedgehog inhibitor (ex. SMO inhibitor) also suppress M1 polarization and osteoclastogenesis?
4. In Fig 6, the overexpression of DNMT3a reversed the inhibitory effects of GANT58 in osteoclastogenesis. This supports the author's conclusion that GLI1 may enhance osteoclastogenesis via DNMT3a upregulation. However, this conclusion should be carefully evaluated by examining effects of the overexpression of DNMT3a without GANT58. Does the overexpression of DNMT3a by itself enhance osteoclastogenesis or just reverse the GANT58-mediated suppression?
5. Is RNA-seq data with GANT58 compatible with known target genes of GLI1 reported in previous studies?

Significance

The main limitation of this paper is the lack of siRNA knockdown study of GLI1 and DNMTs. Another limitation of this paper is that the direct in vivo data demonstrating the inhibitory effects of GANT58 on M1 macrophage and osteoclast activation in CIA is lacking. The strength is the promising activity of GLI inhibitor, GANT58 as an anti-rheumatic drug on monocyte/macrophage-associated inflammation and bone destruction. The roles of hedgehog/GLI signals in macrophage function are largely unknown, and the findings of this study may contribute to this research field. This study will be interesting to rheumatologists and immunologists.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The paper by Ge et al seeks to identify a role for GLI1 in rheumatoid arthritis, as GLI1 is upregulated in the synovium of patients with rheumatoid arthritis. Inhibition of GLI1 by the GANT58 limited inflammation and destructive bone loss in a murine model of arthritis (Collagen Induced Arthritis). Inhibition of GLI1 increased expression of pro-inflammatory cytokines and M1 macrophage differentiation. Inhibition of GLI1 also blocked osteoclast formation. As has been shown in other settings, the function of GLI1 in M1 and osteoclast differentiation was linked to regulation by DNMTs.

Major comments:

There are several main problems with the text. Overall, the authors show an intriguing set of data implicating the use of GANT58 as a means to limit rheumatoid arthritis inflammation and bone destruction. The authors directly link the functions of GANT58 with loss of GLI1 activity by showing that GLI1 protein is reduced or translation to the nucleus blocked. It would be compelling if the authors would leverage a genetic model (either GLI1 knockout, or a CRISPR/siRNA approach) to see if it recapitulates key findings in vitro and in vivo. These data could further their claims that their findings are in fact directly due to GLI1.

Overall, the paper lacks methodologic clarity that limits thorough interpretation of the data. Multiple experiments are missing from the Materials and Methods, including descriptions of the definition of trabecular bone and its analysis in microCT, the means by which cytoplasmic and nuclear fractions were generated, and the timing and dosing of GANT58 in vitro studies. In addition, key details regarding the reagents include the sources of primary antibodies used in the western blots and immunoprecipitation studies. Important methodologies are not well explained, which include the treatment of the Sham animals (presumably healthy) are not explained, that is, whether they receive injections of vehicle or are truly naïve. Finally there is no statistical methodology, minimal explanation of the RNAsequencing analyses, and no statement about how the RNAsequencing data will be made available. This lack of detail makes a thorough assessment of the quality and interpretations of the data challenging and replication of the results impossible.

The authors should expand their introduction and Discussion to include a description of the history of other GLI inhibitors (such as GANT61) in rheumatoid arthritis. Further, the authors failed to cite current studies showing that GLI1 is upregulated in RA patients (DOI: 10.1007/s10753-015-0273-3 amongst others).

The antibody for GLI1 seems poor and inconsistent. Knockdown studies to show its specificity, and an example of the whole membrane stained for GLI1 would provide important validation of the reagent.

Regarding Figure S1: The studies of RA patients are underpowered. With only three RA patients and three healthy synovial the distribution of DAS28 scores is clustered at healthy and active disease, and the correlation study is unconvincing.

Regarding Figure 1 f-g and Figure 4j-k: However, the information on inflammatory bone loss are incomplete. The methodology for the assessment of BMD and trabecular bone parameters in the hindpaw is not explained. The 3D reconstructions are of the whole bone hindpaw, but the anatomical region where trabecular bone is assayed not defined. It would be convincing if the authors added erosion scores in the hind paws or knees to show that the erosion in the synovium, which contributes to inflammatory arthritis, mirrors what occurs in the trabeculae.

Regarding Figure 2:

• The methods and text do not state the dose of GANT58 used in these assays. Nor do they specify the timing of the GANT58 application in relationship to LPS and IFNg stimulation.
• The authors conclude that GLI1 limits the differentiation of M1 macrophages and also directly blocks the production of pro-inflammatory cytokines. The data are difficult to parse in that the directionality is not clear. If GLI1 promotes M1 macrophages, there would be less proinflammatory cytokines due to the reduction of their proliferation. To evaluate the role of GLI1 in regulating the cytokines, additional studies showing a transcriptional regulation of these cytokines is warranted.
• To show that the fractionation of the cytoplasm and nuclear compartments was complete, the westerns for GLI1, lamin-B1 and beta actin should be shown in the same blot.
• In Section 2.3 ("the expression of and intranuclear transport..."), the authors state that their previous studies showed GLI was expressed in macrophages (line 80-81). It is unclear whether the authors are referring to studies in this manuscript or a previously published study and a citation is needed.

In response to Figure 3:

• The authors show that GANT58 has a potent impact in limiting osteoclast formation. The text states that GANT58 is a pretreatment, but the timing of this is not stated.
• It would be interesting to see whether there is a dose-response effect of GANT58.
• It is not stated how long the cells are RANKL treated prior to nuclear/cytoplasmic fractionation? (3a, b, c and i).
• The "Zoom" images in Figure 3j do not have a box to delineate where the higher magnification images are taken from in the top panes. The images appear to be from serial sections. This should be clarified.

In Figure 3 and Figure 6e and 6f: Although the data in BMM showed that there was no impact on cell survival was limited at low concentrations, showing that the differentiating osteoclasts are not more sensitive to apoptosis by GANT58 would be compelling. The large difference in cellularity in the presence of GANT58 provokes this question.

In Figure 4:

• The IP studies (4g and 4h) lack showing successful pull-down of GLI1 by western blotting as a critical control for the study.
• Details about the steps involved in RNAsequencing analyses need to be provided.
• Studies have previously shown a reduction of inflammatory arthritis by 5'-Azac and should be cited.
• What is the proposed functional consequence for GLI1 binding to DNMT3a? Does GLI1 inhibition lead to hypomethylation of DNA by DNMT?

Figure 5:

• The groups in 5g are not well defined.
• DNMT1 and DNMT3a reduction by siRNA, CRISPR or knockout would strengthen the inhibitor studies.

Regarding Figure 5 and 6:

• What is the impact of DNMT1 and DNMT3a overexpression on their own (not in the presence of GANT58)?

Minor comments:

1. The authors do not include a description of DNMTs in the introduction.
2. The descriptions of the groups are often unclear. In Figure 2, the label "GANT58" (blue bars) is presumably for a group that is treated for LPS+IFNg+GANT58 but this is not clarified.
3. The distinction of Figure 3g as multinuclear giant cells (vs TRAP+ OCs in panel 3d) should be explained.
4. The labels in 4C of "R1, R2, R3" standing for GANT58 is confusing
5. In Figure S8, the numbers between the western blots are not explained.
6. In Figure S9 there are references to asterisks which do not appear in the figure.

Significance

The paper presented by Ge et al present interesting data suggesting that a GLI1 inhibitor (GANT58) has a strong impact on inflammatory arthritis in a murine model. Interesting data are presented whose novelty need better contextualization with other published studies, as previously published studies which are not cited in this manuscript include the finding that GLI1 is upregulated in patients with rheumatoid arthritis, that other GLI inhibitors have been utilized in murine models of rheumatoid arthritis, and that GLI1 has been shown to regulate DNMT expression in cancer settings. The authors connect GLI1 inhibition with DNMT activation in limiting M1 macrophage and osteoclast differentiation. However, several important controls are needed to in the in vitro studies as outlined above.

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Referee #1

Evidence, reproducibility and clarity

Summary

Ge et al. defined the role of Gli1 in M1 macrophage activation and osteoclast differentiation in physiological conditions and inflammatory arthritis. The authors found that Gli1 expression is elevated in human RA synovial tissue relative to that in healthy donor controls. Moreover, the authors showed that the administration of GANT58, a Gli1 inhibitor, ameliorates inflammation and bone erosion in CIA mice. Gli1 expression is suppressed by LPS/IFN-ɣ stimulation in Raw264.7 cells while being induced by RANKL stimulation in Raw264.7 cells. However, GANT58 suppressed LPS/IFN-ɣ -induced expression of inflammatory cytokines and iNOS and osteoclastogenesis. The authors also identified DNMT1 and DNMT3a as downstream effectors of Gli1. Transcriptomic analysis of GANT58 treated Raw264.7 cells identified diminished protein expression of DNMT1 and DNMT3a by GANT58. Gli1 also directly interacts with DNMT1. Intriguingly, DNMT1 overexpression restores the effect of GANT58 on LPS/IFN-ɣ-mediated activation, while DNMT3a overexpression reverses the effect of GANT58 on RANKL-induced osteoclastogenesis. Since this study defines the role of Gli1 in the function and differentiation of myeloid cells, this is interesting. In addition, GANT58 nearly completely protects mice from arthritis, suggesting a therapeutic potential of Gli1 targeting in RA. However, the details of experiments are not clearly described, and the authors present the mixed data from Raw264.7 cells and BMMs without any explanations.

Major comments

1. Figs 1h and i. The author should show the histological score.
2. Pharmacological inhibitors often show non-specific effects. To complement their findings showing the effect of GANT58 on M1 macrophage activation and osteoclastogenesis, the authors should utilize Gli1-deficient cells that can be obtained by siRNAs-mediated knock down or Gli1 deletion.
3. Figure 4d: The authors should measure DNMT1 and DAMT3a RNA expression in LPS/IFN-ɣ- treated (Fig 2c and d) or RANKL treated Raw264.7 cells.
4. The authors should provide detailed information of RNA-seq including how many genes are regulated by GANT58 and what is their cutoff (fold induction and FDR). The authors should deposit their RNA seq data in the public databases repository such as GEO.
5. Figure 5c. The authors should add non-stimulating condition as a control.
6. Figure 6C: DNMT3a deficiency regulates limited number of genes such as IRF8. The authors should measure IRF8 RNA or protein expression in RANKL-treated cells.
7. Although the effects of Gli1 on bone metabolism in the literature are inconclusive, Gli1 is expressed on other cell types in bone. Gli1 haplodeficiency in mice decreased bone mass with reduced bone formation and enhanced bone resorption compared to control mice (PMID:25313900). Gli1 is also used as a marker for osteogenic progenitors which are precursors of chondrocytes and osteoblasts (PMID: 29230039). Thus, the beneficial effect of GANT58 on inflammation and bone erosion in CIA mice may result from the effects of GANT58 on multiple cell types other than F4/80+ cells. The authors should include these references in the discussion on pg.9 and expand their discussion.

Minor comments

1. CIA model: The experiment design of CIA model is not clearly described. The author should specify the time point of GANT58 injection.
2. Joint inflammation of RA can be caused by many different cells. Abstract needs to be revised.
3. Figure 4g, h: are these experiments done in the resting states?

Significance

Strengths: Hedgehog (hh) signaling has been implicated in the differentiation of osteogenic progenitors. Gli1+ mesenchymal progenitors are responsible for both normal bone formation and fracture repair. This study defines a new role of Gli1 in the function and differentiation of myeloid cells. In addition, GANT58 nearly completely protects mice from arthritis, suggesting a therapeutic potential of Gli1 targeting in RA.

Limitations: This study mainly uses a pharmacological inhibitor to study the mechanism underlying Gli1's action. In addition, the details of experiments are not clearly described, and the authors present the mixed data from Raw264.7 cells and BMMs without any explanations. Advance: This study provides conceptual advancement for hh signaling research by demonstrating the function of Gli1 in myeloid cells.

Audience: Basic research

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Reply to the reviewers

Manuscript number: RC-2023-01939

Corresponding authors: Jiro Toshima, Junko Y. Toshima

1. __ General Statements __ We are grateful for the reviewer’s evaluation of our study. In the new manuscript, we have answered all of the points raised by the two reviewers (the altered or added text is indicated in red in the new manuscript). Reviewer #1 pointed out that definition of "Vps21 activity" is unclear throughout the manuscript. In this study we have developed a novel biochemical method capable of detecting Vps21p activity with high sensitivity (Fig. 2) and utilized this method to measure Vps21p activity, which is clearly stated in the new manuscript. The reviewer #1 also pointed out the issue that we have not clearly explained about difference of two Vps21p-residing structures, small endosome-like puncta and aberrant large structure. To clearly distinguish them, in the new manuscript we have added data showing the size distribution of Vps21p-residing structures (Fig. S2). Regarding comment #2, we think that the reviewer may have misunderstood the data (please see the response to this comment described below). Reviewer #2 did not request any additional experiments but gave us many helpful comments to improve the manuscript. In the new manuscript, we have revised all the places that the reviewer pointed out.

__ Point-by-point description of the revisions__

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

(Reviewers’ comments are in italics)

*Summary: *

In the present study Nagano et al. identify an overlapping function of clathrin adaptors in the activation of the yeast Vps21 Rab GTPase. This activation is regulated in a concerted manner by two TGN cargo adaptors, AP-1 and GGA1/2. The basis of this study is derived from the previous work Nagano et al., 2019 where authors reported that Ent3p and Ent5p are important for the formation of the Vps21p-positive endosome. By utilizing a synthetic genetic approach, the authors observed that disruption/loss of the AP-1 complex (apl4 mutant), Ent3p, Ent5p or Pik1 decreased fluorescence intensity for GFP-Vps21p and increased number of Vps21p puncta. They found that these effects for AP-1 disruption are additive, that is, each makes a distinct contribution, at least in ent3∆/ent5∆ mutant cells. They next examined the role of factors required for TGN localization of Ent3p/5p and AP-1 in Vps21p activation. The authors reported that GGA1/2, Pik1p and the Ypt31/32 Rab GTPases make modest contributions to targeting of AP-1 and Ent3/5 to the TGN. The observation that accumulation of GFP-Vps21 next to vacuolar compartments in pik1-1 ent3D mutants similar to that of ent3Dent5Dapl4D, lead authors to conclude that both PI(4)P as well as PI(4)P independent Ent3p recruitment to TGN plays a crucial role in Vps21p activation. Further they found that compared to the pik1-1 ypt31ts mutant (41%), activity of Vps21p (14%) was severely reduced in the pik1-1 ypt31ts gga1D gga2D mutant pointing towards redundancy among these factors in Vps21p activation. Finally using a class E Vps mutant authors found a fall in endosomal population of GFP-Vps9p ~29% in the ent3D ent5D mutant, which was further reduced to 0% in the ent3D ent5D apl4D* mutant. Collectively this study suggests a differential role of TGN adaptors, AP-1 and GGA in early endosome formation. Ent3p/5p and AP-1 are proposed to activate Vps21p by localizing Vps9p on endosomes and thus facilitating its transport whereas GGAs act redundantly along with Pik1p and Ypt31/32 in regulating TGN localization of Ent3p/5p and AP-1. *

Major comments:

There is a considerable amount of data that address the roles of AP-1, Ent3, Ent5, Gga1/2, and Pik1 in targeting of Vps21 and related trafficking pathway components to the TGN/endosome. The experiments are essentially genetic epistasis tests that compare the fluorescence patterns of GFP-Vps21 in a sophisticated set of strains. The genetic data are interpreted in terms of spatiotemporal dynamics of Vps21: proportion Vps21GTP on a compartment and number of GFP-Vps21 positive compartments. *Being genetic in nature, the data are open to wide interpretations in terms of molecular mechanisms that target candidate proteins Vps21p and Vps9 to the TGN/endosome. The authors presentation (Fig. 7) is based on well controlled experiments and is logical, but key questions regarding Vps9 trafficking as it relates to Vps21 endosome formation are not resolved. *

Response:

In this study, in addition to comparison of the fluorescence patterns of GFP-tagged yeast Rab5 (Vps21p), we have developed a novel biochemical method capable of detecting the amount of active Vps21p with high sensitivity. The amount of active Vps21p obtained by this method correlated well with the results obtained by imaging analysis, and we think this approach significantly increased the reliability of our results.

Using this new biochemical method and fluorescence imaging analysis, we have clarified the overall regulatory mechanisms of Vps21p by vesicle transport from the TGN. In particular, we believe that this is an important study that links the activation of Vps21p that mediates endosome formation with numerous previous studies involving vesicle transport from the TGN to the endosome.

Comment #1(a)

1. • Throughout their study the authors conflate measurements of GFP-Vps21 puncta intensity and number of Vps21p puncta as readouts of Vps21 "activity". Figure 7 exemplifies this especially: "Vps21p Activity: 100%; Vps21p Activity: 45%; Vps21p Activity: 10%". *
2. *a) Would the authors please explicitly define how they use "activity" in the manuscript? * Response:

We appreciate the reviewer’s pointing out our error. As the reviewer pointed out, since we have used the word “activity” when we explained the result obtained by the fluorescence intensity and the number of Vps21p puncta in lines 312-315 (in the new manuscript), we have revised this sentence “~ a decreased PI(4)P level reduces Vps21p activity and thus inhibits fusion of Vps21p compartments.” to “~a decreased PI(4)P level seems to inhibit fusion of Vps21p compartments.” (lines 314-315).

In other parts of the manuscript, we have used the word “activity” only when we explained the result obtained by measuring the amount of active Vps21p by the biochemical method (Fig. 2). “Vps21p Activity” depicted in Fig. 7A-C are also based on the results obtained by the biochemical assay, and thus we have added explanatory sentences in the Discussion section (lines 432-433, 447) and figure legend (lines 996-998) in the new manuscript.

Comment #1(b)

1. *b) The amounts of Vps21-GTP were measured for the ent3D ent5 and ent3D ent5 apl4D mutants (Fig. 2). Other mutant backgrounds should be analyzed in order to address the specific requirements of gga1/2, pik1 and ypt31/32 genes and to challenge the assumption that aspects of GFP-Vps21 localization correlate with the proportion of Vps21GTP. * Response:

We agree with the reviewer’s comment that it is crucial to confirm that aspects of GFP-Vps21 localization correlate with the proportion of Vps21GTP. In the previous manuscript, we have already measured the amount of active Vps21p (GTP-bound form of Vps21p) in the pik1-1, and pik1-1 ent3D mutants (Fig. 4E) and shown that it decreases to ~62% in the pik1-1 mutant, or to ~22% in the pik1-1 ent3D mutant relative to wild-type cells (Fig. 4E). The relative amount of GTP-bound form of Vps21p in these mutants correlated well with the results obtained by imaging analyses of GFP-Vps21p (Fig. 4B and C). To make it clearer, we have added sentences “and the amounts of active Vps21p in these mutants correlate well with the results obtained by imaging analyses of GFP-Vps21p (Fig. 4B, C, and H).” in lines 326-327. We have also demonstrated that the amount of active Vps21p correlated with the fluorescence intensity of GFP-Vps21p at puncta in the pik1-1 ypt31ts or the pik1-1 ypt31ts gga1D2D mutant (Figs 4F-J, S4E), and explained about this in lines 334-341.

Comment #1(c)

1. *c) Regarding the measurements of fluorescence intensity of GFP-Vps21 puncta, how were distinct puncta identified, particularly in the large clusters of puncta shown in Figs. 1D, 3A, 4F, 5A, 5C. * Response:

As the reviewer pointed out, in the previous manuscript we have not clearly explained about how we had distinguished two Vps21p-residing structures, small endosome-like puncta and aberrant large structure. To clearly distinguish them, in the new manuscript we examined the size and number of these structures and showed the data in Fig. S2. This result revealed that the ent3D5D apl4D mutant contains single large Vps21p-residing structure with a size of >100 pixels and many small Vps21p-residing puncta with a size of ~50 pixels. To explain about this, we have added sentences in lines 235-239. Regarding Fig. 5A and 5C, since these figures do not show the localization of Vps21p, we have not added explanation about them.

Comment #2

• In the representative micrographs shown in Fig. 1A (Vph1-mCH), 1B (Hse1-tdTom), 1D (Sec7-mCH) and 5A, why do only (roughly) half of the cells in each micrograph express the tagged organelle marker protein? Shouldn't all of the cells? What is especially concerning is that the appearance of GFP-Vps9 in cells that express Sec7-mCH is different than in cells that do not. Specifically, there are fewer GFP-Vps9 puncta in expressing cells and GFP-Vps9 appears to be largely cytosolic in these cells. Have the authors noted the same? *

Response:

In Fig. 1, we expressed mCherry/tdTomato-tagged protein only in wild-type cells (Fig. 1A and B) or in ent3D5D mutants (Fig. 1D) to distinguish the mutant cells from the wild-type cells, as described in the Result section (lines 156-159) and figure legends. As explained in the text (lines 156-159), by labeling only wild-type or mutant cells, we precisely evaluated the differences in the localization of GFP-Vps21p by comparing mutant cells directly alongside wild-type cells.

In Fig. 5A, we expressed Sec7-mCH only in the ent3D5D mutants to distinguish the mutants from wild-type cells (the upper panels) or the ent3D5D apl4D mutants (the lower panels), as described in figure legend. Therefore, the reviewer’s comment that “the appearance of GFP-Vps9 in cells that express Sec7-mCH is different than in cells that do not. Specifically, there are fewer GFP-Vps9 puncta in expressing cells and GFP-Vps9 appears to be largely cytosolic in these cells.” is exactly what we wanted to show in this figure. To show this more clearly, we labeled cells with “WT” or “mutant” in these micrographs (Fig. 1A, 1B, 1D, and 5A).

Comment #3

• Figure 4A: How were the proportional contributions of each factor to the TGN localization of Ent3/5, AP-1 determined? What do the percentiles indicate? *

Response:

As described in the Result section (lines 293-297), we have shown that deletion of the GGA1 and GGA2 genes significantly decreased the localization of Ent3-GFP at the TGN to ~33% of wild-type cell, without changing the localization of Ent5-GFP and Apl2-GFP (Fig. S3A, B). Based on these results, the contribution of Gga1/2p to the localization of Ent3p, Ent5p, or AP-1 was evaluated to be 37%, 0%, or 0%, respectively (Fig. 4A). To make this clearer, we have added sentence “~ and thus, we evaluated the contribution of Gga1p/2p to the localization of Ent3p, Ent5p, or AP-1 to be 37%, 0%, or 0%, respectively (Fig. 4A)” in line 296-297. Similarly, we have determined the contribution of PI(4)P by assessing the localization of Ent3p, Ent5p and Apl2p at the TGN in the pik1-1 (Fig. S3C and D), as described in lines 297-305. Regarding Rab11s (Ypt31p/32p), we have evaluated the contribution based on the data in our previous study, as described in line 305-309.

Comment #4

• In the model presented in Figure 7, the authors proposed that AP-1 is required to target Vps9 from the late TGN to the early TGN. The best characterized function of AP-1 is to concentrate integral membrane proteins to form the inner layer of a clathrin coated vesicle. Vps9 is a soluble protein that fractionates with cytosolic proteins (Burd et al., 1996). Despite measuring intensity and localizing Vps9p with different endosomal markers (Fig. 6), the basis of membrane recruitment of Vps9 by TGN clathrin adaptors is unclear. How do the authors envision AP-1 to function in targeting of Vps9, a soluble protein, between compartments? *

Response:

Like other many Rab-GEFs (e.g., Sec2p, the GEF for Sec4p or Mon1p/Ccz1p, the GEF for Rab7), we think that Vps9p transiently localizes to the donor organelle to activate Rab proteins and load them on the transport vesicle. We have previously demonstrated that Arf1p, a Golgi-resident GTPase, plays an important role in the recruitment of Vps9p to the Golgi (Nagano et al., Comm. Biol., 2019). In this study we have shown that deletion of AP-1 in the ent3D5D mutant increases the localization of Vps9p at the TGN (Fig. 5A and B). These suggest that AP-1, like Ent3p/5p (Nagano et al., Comm Bio, 2019), is dispensable for the recruitment of Vps9p to the TGN but required for the transport of Vps9p from TGN to endosomes.

In a recent study Casler et al. proposed a role of AP-1 function that maintain Golgi-resident proteins by mediating intra-Golgi recycling pathway (Casler et al., JCB, 2021). Based on this model, we have speculated that AP-1 also functions to maintain Vps9p in the TGN by recycling from the late TGN to early TGN and discussed about this in the second paragraph of the Discussion section (lines 434-454 in the new manuscript). However, as the reviewer #2 pointed out (please see comment #6 of the reviewer #2), Casler et al proposed AP-1’s role in transport from the TGN back to earlier Golgi compartment but did not discuss compartmentalization within the TGN, we have modified sentence in the Discussion from “~ the role of AP-1 that recycles Vps9p back to the early TGN might become apparent” to “~ the role of AP-1 that recycles Vps9p back to the earlier Golgi compartment might become apparent” (lines 444-445).

__Minor Comment: __

• The interchangeable terminology used to refer to Rab GTPases throughout the manuscript made it exceptionally difficult for me to focus on the presentation of the experiments. Vps21 and Rab5 are used interchangeably, but this study investigated Vps21, not Rab5. Vps21 does not even appear in the title or abstract. Similarly, Ypt31/32 is used interchangeably with Rab11, but this study investigated Ypt31/32, not Rab11. The accurate names of the yeast proteins should be used. A discussion regarding significance of the yeast proteins for understanding mammalian Rab5 and Rab11 belongs in the Discussion. *

Response:

In accordance with the reviewer’s suggestion, we have replaced Rab5 with yeast Rab5 or Ypt21p. We have also replaced Rab11 with yeast Rab11 or Ypt31p/32p.

__Reviewer #1 (Significance (Required)): __

*General assessment: In general, this is a well-executed and controlled study. The major strengths are the large quantity of data from complementary experiments that provide a rationale for the proposed mechanistic model proposed (Fig. 7). The major weaknesses lie with the genetic approach, which does not lend itself to the mechanistic interpretations that the authors propose, and the narrow scope of the work such that the study will be of interest to a small group of colleagues. The audience will likely include researchers who use yeast to investigate proteins sorting in the endo-lysosome network of organelles and colleagues who investigate signaling by Rab GTPases. *

Response:

We cannot agree with the reviewer’s comment that “the narrow scope of the work such that the study will be of interest to a small group of colleagues”, because the regulation of endosome formation by Rab5 is one of the major topics in the field of membrane traffic, and many mechanisms still remain to be elucidated. Moreover, the model we have proposed in this study is adaptable not only to yeast but to higher organisms, as discussed in the last paragraph of the Discussion section. The endolysosomal pathway is important for the regulation of a wide variety of crucial cellular processes, including mitosis, antigen presentation, cell migration, cholesterol uptake, and many intracellular signaling cascades. Our work thus also has implications for development, immunity, and oncogenesis. We believe that the studies described in our paper represent an advance in our understanding of the cellular biology of endocytic trafficking and therefore would be interesting to researchers in other fields, as well as membrane traffic filed.

__ __

__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __

(Reviewers’ comments are in italics)

*Summary: *

*The manuscript by Nagano et al. describes the results of extensive analysis on the roles of clathrin adaptors for activation of Rab5 during TGN-to-endosome traffic in budding yeast. They examined the localization and activation status of Vps21, a major Rab5 member in yeast, in a variety of mutants and showed that AP-1 had a cooperative role with Epsin-related Ent3/5 in transport of Vps9 (Rab5 GEF) to endosomes. GGAs, PI4 kinase Pik1, and Ypt31/12 (Rab11) had partially overlapping functions in recruitment of AP-1 and Ent3/5 to TGN. *

*It is an indeed extensive study but the interpretation of the results is complicated and somewhat speculative. It is most probably because the differences between mutants are partial (even though the authors tried to show statistics) and the logics to lead conclusions are not always compelling. To be honest, I had a hard time to follow rationales to justify arguments. The conclusions the authors make, that is, multiple clathrin adaptors cooperate in the TGN-to-endosome traffic, are reasonable, but I have several questions as follows, which I would like the authors to address. *

Comment #1

1. • The description about Vps21 fluorescence is often quite confusing. When the authors say fluorescence intensity, is it the total intensity of a whole cell or the average fluorescence intensity of individual puncta? For example, in Fig. 1D, it doesn't look to me at all that the GFP intensity of ent3/ent5 is lower than WT. How did the authors obtain the data of Fig. 1E? If the authors measured the fluorescence of individual puncta, how did they do it? * Response:

We agree that in the previous manuscript explanation about how we measured Vps21p fluorescence intensity was insufficient. In this study, we have measured the whole fluorescence intensity of single GFP-Vps21p punctate structure, which was subtracted the cytoplasmic fluorescence background, and shown it as the fluorescence intensity of Vps21p compartment (the aberrant large GFP-Vps21p structure (Fig. 3A) were excluded). The graphs of fluorescence intensity of GFP-Vps21p show the average of three data (each average of 50 puncta) from three independent experiments. To clarify where and how Vps21 fluorescence was measured, in the new manuscript we have revised text (lines 160-161, 163, 166, 177, 179) and added explanatory sentences in “Materials and Methods” (lines 542-546).

Regarding Fig. 1D and E, since the fluorescence intensity of GFP-Vps21p at the cytosol was increased in the ent3D5D mutant (Fig. 1D), the fluorescence intensity in the mutant may not have appeared lower than that in wild-type cell. To show the decrease of the fluorescence intensities of individual Vps21p puncta in the mutant cells more clearly, we have added the higher magnification view of GFP-Vps21p puncta in Fig. 1D in the new manuscript.

Comment #2

• Related to the previous question, how the images were taken is very important. In the legend to Fig.1, there is no description about the image analysis. Are they epifluorescence images or confocal images, and if the latter, are they ones of 2D confocal images or maximum intensity projections of Z stacks as mentioned in the legend to Fig. 3A? It matters very much. *

Response:

We appreciate the reviewer’s helpful suggestion. In Fig. 1, we have used epifluorescence images for analyzing the fluorescence intensity or number of GFP-Vps21p puncta, because Vps21p puncta have high mobility (please see also the responses to comment #9). In accordance with the reviewer’s suggestion, we have added the description about imaging method in the legend of Fig. 1 (lines 831-832, 837 and 843).

Comment #3

• It is also confusing when the authors say increase or decrease of fluorescence. Is it the intensity or the number of puncta? Please clarify which the authors intend to mention whenever relevant. There are many places that bother readers. *

Response:

We appreciate the reviewer’s helpful suggestion. In accordance with the reviewer’s suggestion, we have revised manuscript (lines 274 and 316).

Comment #4

• The method the authors developed to estimate the activation states of Vps21 is intriguing. It may provide important information without direct measurements of the GTP-binding activity. However, the results should be carefully interpreted because this kind of tricky experiments may not reflect the exact biochemical statuses in the cell. For example, I am concerned about whether release of GTP or spontaneous GTPase activity during the preparation processes is ignored. *

Response:

As the reviewer pointed out, we cannot rule out the possibility that the GTP-bound status might be changed during the preparation processes. However, this problem also occurs in the conventional pull-down assay, which assesses the amount of the GTP-bound form of Rab proteins. To confirm whether the activity of Vps21p assessed by this method reflects in vivo activation level, we have demonstrated that the level of active Vps21p correlated with the in vivo phenotypes, such as fluorescence intensity of GFP-Vps21p at the endosome and number of GFP-Vps21p puncta, that implicate defect of endosomal fusion. Thus, in the new manuscript we have added some sentences to explain about this (lines 221-222).

Comment #5

• In Discussion (p. 20, line 410), the authors describe that "Gga2p is localized predominantly at the Tlg2-residing compartment," but this is wrong. In the BioRxiv paper (2022), the authors showed that "Gga2p appears around the Sec7p-subcompartment and disappears at a similar time as Sec7p." I understand that, to explain the roles of GGAs in endosomal transport, it is reasonable to assume their presence in the Tlg2 compartment (and I agree on that), but the above description is wrong and must be corrected. *

Response:

We appreciate the reviewer’s helpful suggestion. As the reviewer described, we have recently demonstrated that Gga2p localization well overlapped with the Tlg2p-residing TGN sub-compartment that is structurally distinct from the Sec7p-residing sub-compartment (Toshima et al., BioRxiv, 2022). Thus, in accordance with reviewer's suggestion, we have changed this sentence to “Interestingly, Gga2p appears to reside at the Tlg2p sub-compartment, which is distinct from the Sec7p sub-compartment.” in the new manuscript (lines 427-428).

Comment #6

• Hypothesizing the role of AP-1 in the recycling from the late TGN to the early TGN is new. Glick's group proposed its role in transport from the TGN back to earlier compartment (Golgi) but did not discuss compartmentalization within the TGN. The authors' speculation is a fancy idea, but I am afraid there is no direct evidence for that. *

Response:

We appreciate the reviewer’s appropriate and helpful suggestion. As the reviewer pointed out, Glick's group has proposed its role in transport from the TGN back to earlier Golgi compartment, but not discussed compartmentalization within the TGN (Casler et al., 2021, JCB), and thus we modified sentence in the Discussion section from “~ the role of AP-1 that recycles Vps9p back to the early TGN might become apparent.” to “~ the role of AP-1 that recycles Vps9p back to the earlier Golgi compartment might become apparent.” (lines 444-445).

Comment #7

• The role of Ypt31/32 (Rab11) is also puzzling to me. It could be an indirect effect, which might be due to the complex network of GTPases as proposed by Chris Fromme (2014). Am I correct? *

Response:

As the reviewer pointed out, Fromme’s group has shown that Ypt31/32 forms the complex networks with several GTPases and their GEFs (McDonold and Fromme, 2014, Dev Cell; Thomas and Fromme, 2016, JCB, Thomas et al., 2019, Dev Cell), in which Ypt31/32 promotes the activation of Arf1p via its GEF Sec7p. We have previously shown that Arf1p plays an important role in the recruitment of Vps9p to the Golgi (Nagano et al., Comm. Biol., 2019). These findings suggest that disruption of Ypt31p/32p may affect the localization of Vps9p through reduced activity of Arf1p. However, arf1D and ypt31ts mutants exhibit different effects on the Vps9p localization: in arf1D mutant the recruitment of Vps9p to the TGN is impaired and in ypt31ts mutant Vps9p localization at the TGN is increased (Nagano et al., 2019, Comm Biol.). Thus, the role of Ypt31/32 in the Vps9p localization appears to be independent of Arf1p activity. In the new manuscript, we have added a brief discussion about this (lines 466-473).

Comment #8

• In the legend to Fig. 3D, the authors state that the read arrowheads indicate 50 nm vesicles and black arrowheads indicate vesicle clusters. However, the electron micrograph clearly shows that their morphologies are different. Red ones, which I estimate to be a little larger than 50 nm, often appear to have dense material inside, while those in black are even larger (probably around 200 nm) and do not look like a cluster of the same type of vesicles (I do not even think that such large structures should be called vesicles). How do the authors explain these differences? *

Response:

In the previous manuscript explanation about the electron microscopy analysis was insufficient. In the new manuscript, to clearly distinguish two Vps21p-residing structures, small endosome-like puncta and aberrant large structure, observed in ent3D5D apl4D mutant by fluorescence microscopy (Fig. 3A), we examined the size and number of these structures and showed the data in Fig. S2. This result revealed that the ent3D5D apl4D mutant contains single aberrant large aggregate with a size of >100 pixel adjacent to the vacuole and endosome-like structures with a size of Comment #9

• In Fig. 4F, the authors show different sets of images, Focal plane and Z projection. What is the purpose to do it? The results with Z projection should be more informative. Why the authors use only Focal plane data for the analysis in panel G? *

Response:

We measured the fluorescence intensity or number of individual GFP-Vps21p puncta using a single focal plane images (Figs. 1C, 1E, 3I, and 4B), because Vps21p-residing small puncta have high mobility and identical endosome often appears in multiple different planes in the Z-stack image taken by a conventional epifluorescence microscope. In contrast, we analyzed the aberrant large aggregate using Z projection image (Figs. 3B, S3G) because this structure is relatively stable and low motile, and not observed if it is not in the focal plane. In Fig. 4F, since both of small puncta and large aggregate are analyzed, we have shown both of focal plane image and Z-projection image. In new manuscript, we have added about the description about imaging method in each figure legend or text (lines 230-232, 332-334).

__Reviewer #2 (Significance (Required)): __

*It is a complicated story but I find most of the conclusions reasonable. It provides important knowledge to the understanding on the Rab5 GTPase regulation in trafficking from the TGN. *

Response:

We are very grateful for this reviewer’s favorable evaluation of our studies.

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

The manuscript by Nagano et al. describes the results of extensive analysis on the roles of clathrin adaptors for activation of Rab5 during TGN-to-endosome traffic in budding yeast. They examined the localization and activation status of Vps21, a major Rab5 member in yeast, in a variety of mutants and showed that AP-1 had a cooperative role with Epsin-related Ent3/5 in transport of Vps9 (Rab5 GEF) to endosomes. GGAs, PI4 kinase Pik1, and Ypt31/12 (Rab11) had partially overlapping functions in recruitment of AP-1 and Ent3/5 to TGN.

It is an indeed extensive study but the interpretation of the results is complicated and somewhat speculative. It is most probably because the differences between mutants are partial (even though the authors tried to show statistics) and the logics to lead conclusions are not always compelling. To be honest, I had a hard time to follow rationales to justify arguments. The conclusions the authors make, that is, multiple clathrin adaptors cooperate in the TGN-to-endosome traffic, are reasonable, but I have several questions as follows, which I would like the authors to address.

1. The description about Vps21 fluorescence is often quite confusing. When the authors say fluorescence intensity, is it the total intensity of a whole cell or the average fluorescence intensity of individual puncta? For example, in Fig. 1D, it doesn't look to me at all that the GFP intensity of ent3/ent5 is lower than WT. How did the authors obtain the data of Fig. 1E? If the authors measured the fluorescence of individual puncta, how did they do it?
2. Related to the previous question, how the images were taken is very important. In the legend to Fig.1, there is no description about the image analysis. Are they epifluorescence images or confocal images, and if the latter, are they ones of 2D confocal images or maximum intensity projections of Z stacks as mentioned in the legend to Fig. 3A? It matters very much.
3. It is also confusing when the authors say increase or decrease of fluorescence. Is it the intensity or the number of puncta? Please clarify which the authors intend to mention whenever relevant. There are many places that bother readers.
4. The method the authors developed to estimate the activation states of Vps21 is intriguing. It may provide important information without direct measurements of the GTP-binding activity. However, the results should be carefully interpreted because this kind of tricky experiments may not reflect the exact biochemical statuses in the cell. For example, I am concerned about whether release of GTP or spontaneous GTPase activity during the preparation processes is ignored.
5. In Discussion (p. 20, line 410), the authors describe that "Gga2p is localized predominantly at the Tlg2-residing compartment," but this is wrong. In the BioRxiv paper (2022), the authors showed that "Gga2p appears around the Sec7p-subcompartment and disappears at a similar time as Sec7p." I understand that, to explain the roles of GGAs in endosomal transport, it is reasonable to assume their presence in the Tlg2 compartment (and I agree on that), but the above description is wrong and must be corrected.
6. Hypothesizing the role of AP-1 in the recycling from the late TGN to the early TGN is new. Glick's group proposed its role in transport from the TGN back to earlier compartment (Golgi) but did not discuss compartmentalization within the TGN. The authors' speculation is a fancy idea, but I am afraid there is no direct evidence for that.
7. The role of Ypt31/32 (Rab11) is also puzzling to me. It could be an indirect effect, which might be due to the complex network of GTPases as proposed by Chris Fromme (2014). Am I correct?
8. In the legend to Fig. 3D, the authors state that the read arrowheads indicate 50 nm vesicles and black arrowheads indicate vesicle clusters. However, the electron micrograph clearly shows that their morphologies are different. Red ones, which I estimate to be a little larger than 50 nm, often appear to have dense material inside, while those in black are even larger (probably around 200 nm) and do not look like a cluster of the same type of vesicles (I do not even think that such large structures should be called vesicles). How do the authors explain these differences?
9. In Fig. 4F, the authors show different sets of images, Focal plane and Z projection. What is the purpose to do it? The results with Z projection should be more informative. Why the authors use only Focal plane data for the analysis in panel G?

Significance

It is a complicated story but I find most of the conclusions reasonable. It provides important knowledge to the understanding on the Rab5 GTPase regulation in trafficking from the TGN.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

In the present study Nagano et al. identify an overlapping function of clathrin adaptors in the activation of the yeast Vps21 Rab GTPase. This activation is regulated in a concerted manner by two TGN cargo adaptors, AP-1 and GGA1/2. The basis of this study is derived from the previous work Nagano et al., 2019 where authors reported that Ent3p and Ent5p are important for the formation of the Vps21p-positive endosome. By utilizing a synthetic genetic approach, the authors observed that disruption/loss of the AP-1 complex (apl4 mutant), Ent3p, Ent5p or Pik1 decreased fluorescence intensity for GFP-Vps21p and increased number of Vps21p puncta. They found that these effects for AP-1 disruption are additive, that is, each makes a distinct contribution, at least in ent3∆/ent5∆ mutant cells. They next examined the role of factors required for TGN localization of Ent3p/5p and AP-1 in Vps21p activation. The authors reported that GGA1/2, Pik1p and the Ypt31/32 Rab GTPases make modest contributions to targeting of AP-1 and Ent3/5 to the TGN. The observation that accumulation of GFP-Vps21 next to vacuolar compartments in pik1-1 ent3∆ mutants similar to that of ent3∆ent5∆apl4∆, lead authors to conclude that both PI(4)P as well as PI(4)P independent Ent3p recruitment to TGN plays a crucial role in Vps21p activation. Further they found that compared to the pik1-1 ypt31ts mutant (41%), activity of Vps21p (14%) was severely reduced in the pik1-1 ypt31ts gga1∆ gga2∆ mutant pointing towards redundancy among these factors in Vps21p activation. Finally using a class E Vps mutant authors found a fall in endosomal population of GFP-Vps9p ~29% in the ent3∆ ent5∆ mutant, which was further reduced to 0% in the ent3∆ ent5∆ apl4∆ mutant. Collectively this study suggests a differential role of TGN adaptors, AP-1 and GGA in early endosome formation. Ent3p/5p and AP-1 are proposed to activate Vps21p by localizing Vps9p on endosomes and thus facilitating its transport whereas GGAs act redundantly along with Pik1p and Ypt31/32 in regulating TGN localization of Ent3p/5p and AP-1.

Major comments:

There is a considerable amount of data that address the roles of AP-1, Ent3, Ent5, Gga1/2, and Pik1 in targeting of Vps21 and related trafficking pathway components to the TGN/endosome. The experiments are essentially genetic epistasis tests that compare the fluorescence patterns of GFP-Vps21 in a sophisticated set of strains. The genetic data are interpreted in terms of spatiotemporal dynamics of Vps21: proportion Vps21GTP on a compartment and number of GFP-Vps21 positive compartments. Being genetic in nature, the data are open to wide interpretations in terms of molecular mechanisms that target candidate proteins Vps21p and Vps9 to the TGN/endosome. The authors presentation (Fig. 7) is based on well controlled experiments and is logical, but key questions regarding Vps9 trafficking as it relates to Vps21 endosome formation are not resolved. 1. Throughout their study the authors conflate measurements of GFP-Vps21 puncta intensity and number of Vps21p puncta as readouts of Vps21 "activity". Figure 7 exemplifies this especially: "Vps21p Activity: 100%; Vps21p Activity: 45%; Vps21p Activity: 10%". - a) Would the authors please explicitly define how they use "activity" in the manuscript? - b) The amounts of Vps21-GTP were measured for the ent3D ent5 and ent3D ent5 apl4D mutants (Fig. 2). Other mutant backgrounds should be analyzed in order to address the specific requirements of gga1/2, pik1 and ypt31/32 genes and to challenge the assumption that aspects of GFP-Vps21 localization correlate with the proportion of Vps21GTP. - c) Regarding the measurements of fluorescence intensity of GFP-Vps21 puncta, how were distinct puncta identified, particularly in the large clusters of puncta shown in Figs. 1D, 3A, 4F, 5A, 5C. 2. In the representative micrographs shown in Fig. 1A (Vph1-mCH), 1B (Hse1-tdTom), 1D (Sec7-mCH) and 5A, why do only (roughly) half of the cells in each micrograph express the tagged organelle marker protein? Shouldn't all of the cells? What is especially concerning is that the appearance of GFP-Vps9 in cells that express Sec7-mCH is different than in cells that do not. Specifically, there are fewer GFP-Vps9 puncta in expressing cells and GFP-Vps9 appears to be largely cytosolic in these cells. Have the authors noted the same? 3. Figure 4A: How were the proportional contributions of each factor to the TGN localization of Ent3/5, AP-1 determined? What do the percentiles indicate? 4. In the model presented in Figure 7, the authors proposed that AP-1 is required to target Vps9 from the late TGN to the early TGN. The best characterized function of AP-1 is to concentrate integral membrane proteins to form the inner layer of a clathrin coated vesicle. Vps9 is a soluble protein that fractionates with cytosolic proteins (Burd et al., 1996). Despite measuring intensity and localizing Vps9p with different endosomal markers (Fig. 6), the basis of membrane recruitment of Vps9 by TGN clathrin adaptors is unclear. How do the authors envision AP-1 to function in targeting of Vps9, a soluble protein, between compartments?

Minor Comment:

1. The interchangeable terminology used to refer to Rab GTPases throughout the manuscript made it exceptionally difficult for me to focus on the presentation of the experiments. Vps21 and Rab5 are used interchangeably, but this study investigated Vps21, not Rab5. Vps21 does not even appear in the title or abstract. Similarly, Ypt31/32 is used interchangeably with Rab11, but this study investigated Ypt31/32, not Rab11. The accurate names of the yeast proteins should be used. A discussion regarding significance of the yeast proteins for understanding mammalian Rab5 and Rab11 belongs in the Discussion.

Significance

General assessment: In general, this is a well-executed and controlled study. The major strengths are the large quantity of data from complementary experiments that provide a rationale for the proposed mechanistic model proposed (Fig. 7).

The major weaknesses lie with the genetic approach, which does not lend itself to the mechanistic interpretations that the authors propose, and the narrow scope of the work such that the study will be of interest to a small group of colleagues. The audience will likely include researchers who use yeast to investigate proteins sorting in the endo-lysosome network of organelles and colleagues who investigate signaling by Rab GTPases.

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1. General Statements [optional]

We would like to thank all reviewers for their constructive feedback and for raising specific points that have helped to improve our manuscript. We accept that the initial submission did not include some quantitative aspects of the observed effects. These are now included together with all the suggested experiments from the reviewers with the use of additional mutants and appropriate protein markers. We believe that the manuscript offers a conceptual advance and a molecular mechanism for the effects of caffeine on cell cycle progression of eukaryotic cells and is of interest to geneticists working on cell cycle, cancer and biogerontology.

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

In the manuscript “The AMPK-TORC1 signaling axis regulates caffeine-mediated DNA damage checkpoint override and cell cycle effects in fission yeast,” the authors studied the role of genes that are potentially involved in the caffeine-mediated override of a cell cycle arrest caused by activation of the DNA damage checkpoint. The methylxanthine substance caffeine has been known to override the DNA damage checkpoint arrest and enhance sensitivity to DNA damaging agents. While caffeine was reported to target the ATM ortholog Rad3, the authors previously reported that caffeine targets TORC1 (Rallis et al, Aging Cell, 2013). Inhibition of TORC1, like caffeine, was also reported to override DNA damage checkpoint signaling. Therefore, in the present study, the authors compared the effects of caffeine and torin1 (a potent inhibitor for TORC1 and TORC2) on cell cycle arrest caused by phleomycin, a DNA damaging agent, using various gene deletion S. pombe mutants.

The authors concluded that they identified a novel role of Ssp1 (calcium/calmodulin-dependent protein kinase) and Ssp2 (catalytic subunit of AMP-activated kinase) in the cell cycle effects caused by caffeine, based on the following findings; (1) the caffeine-mediated DNA damage checkpoint override requires Ssp1 and Ssp2; (2) Ssp1 and Ssp2 are required for caffeine-induced hypersensitivity against phleomycin; (3) under normal growth conditions, caffeine leads to a sustained increase of the septation index in a Ssp2-dependent manner; (4) Caffeine activates Ssp2 and partially inhibits TORC1.

Major comments:

I do not think that many of the authors’ claims are supported by the results of the present study. The corresponding parts are detailed below.

1. The conclusion of the first paragraph in the Results (top in page 6; Our findings indicate that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively.) is not supported by the data in Figure 1. The result that caffeine, but not torin1, requires Ssp1 and Ssp2 to override the phleomycin-induced cell cycle arrest does not necessarily indicate that caffeine indirectly inhibits TORC1 via Ssp1 and Ssp2. Rather, the authors should mention that this conclusion is based on the authors’ previous reports by citing them (e.g., Rallis et al, Sci Rep, 2017). To add to Figure 1, an additional experiment using a constitutively active AMPK mutant, a temperature-sensitive TORC1 mutant, and a srk1 deletion mutant will help the authors claim their original conclusion as one possibility.

Torin1 inhibits TORC1 and 2 leading to G2 cell cycle arrest following accelerated mitosis. In contrast, caffeine has been reported to enhance the inhibitory effect of rapamycin on TORC1 signaling but does not inhibit growth. It has not been reported that TORC1 is a direct target of rapamycin. We previously demonstrated that caffeine induces Srk1 in a Sty1 dependent manner (Alao et al., 2014). Furthermore, Ssp1 plays a role in regulating Srk1/ Cdc25 activity. It is therefore possible, that Ssp1 influences the ability of caffeine to promote mitotic progression as part of the stress response while also affecting TORC1 activity via Ssp2. As ssp2∆ cells have higher intrinsic TORC1 activity, this could also attenuate the effect of caffeine on mitosis.

We have modified the first paragraph of the results section to address the reviewer’s concerns.

We have previously reported that Srk1 modulates the ability of caffeine to drive cells into mitosis (Alao et al., 2014).

1. The conclusion of the second paragraph in the Results (lower-middle in page 6; Our results indicate that caffeine induces the activation of Ssp2.) is not based on the results of Figure 2. Figure 2 simply illustrates that both caffeine and torin1 cause hypersensitivity to phleomycin dependent on Ssp1 and Ssp2.

We appreciate the reviewer’s contention and have modified the text.

1. The conclusion of the fourth paragraph in the Results (middle in page 7) is not clearly supported by the result, due to an insufficient data analysis. As the cell length and the progress through mitosis are the key assay parameters in Figure 3, the average cell length should be shown next to each micrograph of Figure 3A and 3B. In Figure 3C, a mitotic index and the average cell length should be shown next to each micrograph. A statistical analysis is necessary for the authors to compare the measurements and to claim as the headline (Caffeine exacerbates the ssp1D phenotype under environmental stress conditions), as the effect of caffeine was not evident._

We have conducted additional experiments to measure cell length and modified the figure to include this data. We believe our observation that caffeine alone induces increased cell length in ssp1 mutants, confirms a role for the Ssp1 protein in modulating the effects of caffeine. We previously showed that Caffeine activates Srk1 which in turn inhibits Cdc25 activity similar to other environmental stresses (Alao et al., 2014). Ssp1 negatively regulates Srk1 following exposure to stress. In contrast, caffeine advances mitosis in wt cells and thus does not result in increased cell length. We also demonstrate that caffeine greatly enhances cell length in ssp1 mutants exposed to heat stress in marked contrast to rapamycin and torin1. These findings indicate that Ssp1 mediates the effect of caffeine on mitosis.

1. In the middle of page 8, the statement “Accordingly, the effect of caffeine and torin1 on DNA damage sensitivity was attenuated in gsk3D mutants (Figure 5C and 5D).” is not supported by the corresponding results. Rather, Figure 5C and 5D look almost the same.

We agree with this and other reviewers that demonstrating enhanced sensitivity to caffeine is problematic. Nonetheless, our cell cycle data clearly indicate a differential role for Gsk3 in mediating the cell cycle effects of caffeine and torin1. In terms of DNA damage sensitivity, we have reproducibly observed a lower degree of DNA damage sensitivity in gsk3 mutants relative to wt cells. Hence, while caffeine is less effective at enhancing DNA damage sensitivity relative to torin1 in wt cells; we observed that caffeine and torin1 increase DNA damage sensitivity to a similar degree in gsk3 mutants.

1. The description and the conclusion of the last paragraph in the Results (bottom in page 8 – page 9) are not supported by the results of Figure 6, due to an insufficient data analysis. The extent of phosphorylation must be quantified as a ratio of the phosphorylated species (e.g., pSsp2) to all species of the protein (e.g., Ssp2).

We have carefully repeated our experiments under various conditions. Our results clearly indicate caffeine induced Ssp2 phosphorylation. These observations have not been reported previously.

From Figure 6, the authors claim that caffeine (10 mM) partially inhibits TORC1 signaling. However, the authors previously showed that the same concentration of caffeine inhibited phosphorylation of ribosome S6 kinase as strongly as rapamycin, the potent TOR inhibitor (Rallis et al, Aging Cell, 2013). The authors are advised to assess phosphorylation of S6 kinase again in the present study and compare to the results of the present results in Figure 6, because addition of that data may allow the authors to discuss that caffeine affects TORC1 downstream pathways at different intensities.

While rapamycin is a strong inhibitor of TORC1 in budding yeast, this is not the case in fission yeast. Our previous assessments of p-S6 levels and polysomal profiles as well as cell-cycle progression kinetics have shown this (Rallis et al, Aging Cell, 2013). In addition, gene expression analysis from our previous studies have shown that caffeine treatment results in a gene expression profile similar to that of cells in nitrogen starvation (TORC1 inhibition).

We have now used an Sck1-HA strain to further enhance our study and address the reviewer’s concerns. Previous studies have shown that 100 ng/mL rapamycin does not affect Sck1 phosphorylation. We demonstrate that in contrast to rapamycin (100 ng/ mL) 10 mM caffeine affects Sck1-HA expression and or phosphorylation. This effect was also observed with 5 µM torin1 albeit to a greater degree.

Also, immunoblotting of the same proteins looks somehow different from panel to panel (e.g., pSsp2 in panel A and D; Actin in panel A, C, and D). Therefore, the blotting result before clipping had better be shown as a supplementary material.

We repeated the blots were necessary and used ponceau S as a loading control. The original blots can be made available to all.

Minor comments:

1. (Figure 1) The septation index of the phleomycin-treated cells (without any further additional drugs) should be shown, as a baseline.

We have included data for untreated cultures and phleomycin-only treated cultures.

1. (Figure 1D, Optional) As a ppk18D cek1D double deletion mutant is reported, the authors are advised to add and test that mutant in this experiment.

We have added the related data for the _ppk18_Δ _cek1_Δ double mutant.

1. (Figure 2) The authors need to clarify the number of cell bodies spotted (e.g., in the Figure legend).

We have modified the figure legend accordingly.

1. (Figure 3) The different number of cells in micrographs may give an (wrong) impression on the cell proliferation rate. Therefore, it is advisable to use the micrographs in which the similar number of cells are shown for conditions with the similar cell proliferation rates.

We have included data to show the cell lengths under different conditions. We find that different conditions greatly affect proliferation rates. For instance, cells do not proliferate in the presence of torin1. We initially sought to investigate if caffeine induces a phenotype in ssp1 mutants by virtue of its interaction with the DNA damage response. The micrographs were included as representative examples and have been now complemented with cell length data.

1. (Figure 4B) ssp2D, not spp2D.

The figure legend has been edited.

1. (Figure 4) The septation index of the none-treated cells should be shown as a baseline.

We have included base line data for untreated wt cells in figure 1. We have no reason to suspect any of the mutants would provide different results over the time investigated.

1. (Figure 6B, 6E) What do the black arrows indicate? Figure Legend does not seem to explain them.

The legend has been modified to indicate what the arrows refer to.

1. (Figure 6C) Indicate which part of the Maf1-PK blot corresponds to the phosphorylated species, because Maf1-PK is probed with an anti-V5 (not a phosphorylation-specific) antibody.

These experiments have been carefully repeated under different conditions and the figure is now modified accordingly.

1. (Figure 6D) gsk3Dssp1D, not gs3Dssp1D.

We have deleted this figure and have now replaced it with data we believe is more appropriate.

Reviewer #1 (Significance):

As caffeine is implicated in protective effects against diseases including cancer and improved responses to clinical therapies, the topic of the present study is of interest and importance to the broad audience.

In the present study, the most significant finding is that caffeine- and torin1-induced hypersensitivity to phleomycin is dependent on Ssp1 and Ssp2 (Figure 2). This result may be important in chemotherapy against cancers. On the other hand, caffeine is known to activate AMPK (e.g., Jensen Am J Physiol Endocrinol, 2007). Besides, as detailed in the Major comments, many of the major conclusions are not supported by the present results. Therefore, based on my field of expertise (cell cycle, cell proliferation, and TOR signaling), I conclude that the present study hardly extends the knowledge in the field of "the cell biology of caffeine."_

We thank the reviewer for their helpful comments. We accept the constructive criticisms and have carried out extensive additional experiments to provide further roles for Ssp2 and TORC1, in mediating the cell cycle effects of caffeine. We stress that caffeine has previously been proposed its effects via inhibition of Rad3 activity. Our previous work showed that caffeine did not inhibit Rad3 mediated checkpoint signaling. As later studies suggested caffeine inhibited TORC1 activity, the major goal was to investigate if caffeine is an indirect inhibitor of TORC1 via Ssp2 which is activated by several stresses. It has never been demonstrated that caffeine signals via Ssp2. This study provides the first evidence that caffeine modulates cell cycle progression by at least partially signaling via Ssp2 and TORC1. After nearly 30 years, it is vital that its precise activity, in particular enhancing DNA damage sensitivity is properly characterized. Such work woold open the way for additional studies on how caffeine activates cell physiology. For instance, we show that caffeine at 10 mM is more effective at inhibiting Sck1 activity than Rapamycin at 100 ng/ ml. In contrast, rapamycin at this concentration is more effective at inhibiting Maf1 activity. Hence further studies on how exactly the combination of caffeine and rapamycin influences their effect on ageing and other TORC1 regulated processes.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary: In this paper, Alao and Rallis analyze the role of AMPK and TORC1 pathways, and the respective crosstalk, in regulating cell cycle progression in the presence of DNA damage in S. pombe. The authors show, almost exclusively through chemo-genetic epistasis assays, that caffeine inhibits TORC1 indirectly activating AMPK, in contrast to the specific ATP-competitive TORC1 inhibitor torin1. Specifically, it is shown that in the absence of a functional AMPK pathway caffeine is unable to revert the TORC1-inhibition-dependent override of cell-cycle arrest caused by the DNA-damaging agent phleomycin, henceforth partially suppressing the growth inhibition caused by the co-treatment.

Major comments: The overall story of the paper is convincing. However, the choice of an almost exclusively chemo-genetic approach, lack of controls in some experiments and some discrepancy in data presentation suggest that the manuscript undergoes revision before the authors claim that their conclusions are fully supported by the results. In detail:

In Figure 1, graphs of septation indexes are presented separately for each strain. This presentation prevents the reader from clearly comparing the differences of septation caused by genetic background rather than the treatment, i.e. the septation happening by treatment with torin1. I feel it would be better to group the results by drug rather than by strain/mutant. If the results are presented this way because the experiments on different strains were run separately, I further suggest that they are re-run so to always include at least the wt in every run._

We have included data for untreated and phleomycin only treated wt cells as a reference. Additionally, all experiments were repeated at least 2 times. We have used this assay for over 10 years and have found it to be reproducible and reliable. We are not able to include wt cells in every run as this would be beyond the manpower capacity and time constraints involved. It is also likely that torin1 activity is influenced by the ssp1/ 2 backgrounds due to increased basal TORC1 activity as previously reported. The main goal was to illustrate that caffeine differs from a direct inhibitor such as torin1.

Furthermore, torin1 inhibits both TORC1 and TORC2 and thus cannot be directly compared to caffeine. We do prove however, in this and other figures that in contrast to torin1 and rapamycin that caffeine signals via targets upstream of TORC1. We can therefore deduce that it functions in a manner similar to other environmental and nutrient stresses, which require with the Ssp1 and Sty1 regulated pathways to advance mitosis and other processes such as autophagy induction.

In Figure 2C-D, an inconsistency is observable between the phleo+caffeine sensitivity of ssp1Δ and ssp2Δ, the latter retaining a higher sensitivity. Provided that this is not only due to this specific replicate, how would the authors explain such a difference and fit it into their conclusion of a "cascade" signaling with Ssp1 acting upstream of Ssp2?

We agree that analyzing the different interacting pathways involved, is complex. For instance, Ssp1 is required for suppressing Srk1 following Sty1 activation independently of its effects on Ssp2 and TORC1. Furthermore, basal TORC1 activity is higher in Ssp2 mutants as previously reported. It is likely that Ssp1 exerts a more definitive role as it is required to directly reactivate Cdc25 activity following exposure to stress. In contrast Ssp2 activation eventually results in increased Cdc25 activity via inhibition of PP2A (Figure 8). These experiments are, thus, intended to compliment those in figure1 but the DNA damaging effects of caffeine must also be taken into account.

In Figure 2I, a huge discrepancy is observable compared to panel 2A in terms of phleo+caffeine (no ATP) sensitivity of wt cells. Here, cells seem to cope well with the phleomycin treatment even if co-treated with caffeine. This renders the main finding of the panel (the effect of phelo+caffeine+ATP) rather uninterpretable.

We have noted that relevant assays, at least in fission yeast, are influenced by the culture vessels (e.g., plastic type/ glass) as well as the vessel volume (probably due to different aeration, oxygen availability that affects growth and metabolism parameters). We have corrected figure 1a. In terms of ATP, these experiments are highly reproducible even if the exact mechanism remains unclear.

In Figure 3A, the simple observation of elongation is sometimes hard to assess, for example in the ATP-caused suppression of the effect of torin 1, as also acknowledge by the authors in the text. I feel it would be really necessary to quantify such results on an adequate number of cells.

We have reproducibly observed this uncharacterized effect of ATP. We have analysed the cell length in additional experiments to show that ATP influences average cell length under these conditions. It is important to note that the effects of phleomycin are pleotropic. For instance, it likely induces cell cycle arrest at various cell cycle phases as well as in early and late G2. Additionally, it may influence other cellular processes such as DNA or compete with drug targets such as TORC1 which is influenced by ATP.

In Figure 3B,C wt is missing to compare the results in the presence of the same treatments. I understand the focus on Ssp1, but the authors should show the same treatments on wt cells. Similarly, it would be better to show the drug treatments in panel C also at 30{degree sign}C. For the same reasons as in the previous point, quantifications would greatly enhance the credibility of the claims here.

Previous work by other investigators have shown that wt cells proliferate normally under these conditions. We also show in figure 1 that cell proliferation is not affected under nor cycling conditions in these assays. We have added cell length data that convincingly prove that Ssp1 is required to mediate the mitotic effects of caffeine. It appears that caffeine induces a cell cycle delay that requires Ssp1 to suppress Srk1- mediated Cdc25 inhibition. Furthermore, recent studies have demonstrated that rapamycin (which targets TORC1 downstream of Ssp1) allows cell proliferation at higher temperatures in S. pombe.

A major point is the almost complete absence of molecular data. Except for Figure 6, the data do not include a detection of the relative activation of the relevant pathways. Figure 6 could hardly fill this gap, since the samples therein analyzed are not the ones utilized in most of the other figures, but simple, single time-point treatment with a single drug. The authors usually refer in the text to previous knowledge about how a treatment influences a pathway. However, they should show it here in their experimental conditions.

We have performed extensive additional experiments including those suggested by the reviewer. These experiments conclusively show caffeine induces Ssp2 phosphorylation in an Ssp1- dependent manner. We also demonstrate that caffeine attenuates TORC1 signaling. Together with the cell cycle data, our findings strongly suggest caffeine indirectly inhibits TORC1 signaling a manner analogous to other environmental stresses. We also note that the inhibitory effect of caffeine on TORC1 has been demonstrated in several studies. What have provided further evidence for this but have for the first time demonstrated, that caffeine affects Ssp2.

Minor comments:<br /> • A different grouping of the experiments/panels would help the reader. For example, Fig. 2I would fit better together with Fig. 3A, to match the composition of the various chapters of the results.

We have performed additional experiments as suggested by the other reviewers. We believe the data is now easier to understand.

Torin 1 is sometimes referred to with a capital T or with a lowercase t, especially in the Figures. I suggest to uniform the nomenclature.

We have edited the text.

In the results, the authors state that "ATP may increase TORC1 activity or act as a competitive inhibitor towards both compounds.". It's a little bit odd to refer to ATP as a competitive inhibitor of drugs. I would rather be ATP, the physiological agonist, outcompeting two compounds which are working as ATP-competitive inhibitors.

We have modified the text accordingly.

Reviewer #2 (Significance):

The interplay between TORC1 and AMPK is of great interest in the cell signaling field, basically in every model organism.

The paper provides a conceptual advance in the field showing a genetic interaction between the two pathways using a model organism which has probably been overlooked so far, which is a pity because S. pombe is the best organism to study G2/M cell cycle/size regulation. The story would be of interest especially for an audience working in cell signaling in microorganisms, but not so much (at least at this stage) for the community working on aging, disease and chemo-/radio-sensitization, contrary to what the authors claim. Furthermore, for the above-mentioned reasons, I feel like the authors are a little bit overshooting when claiming (for example in the abstract and in the discussion), that their work provides a clear understanding of the mechanism.<br /> As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation by metabolic intermediates.

We believe that the additional requested experiments have adequately improved the manuscript and support our presented mechanistic model.

Caffeine is interest in cancer biology and the biogerontology field proven by recent reports on metabolic phenotyping, liver function testing, induction of autophagy and interplay with HIF-1, just to mention a few.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary<br /> This manuscript examines the genetic requirements for checkpoint override by caffeine in the fission yeast model organism. The main outcome is to show that checkpoint override, which has previously been linked to the downregulation of TORC1, is dependent on on the AMPK pathway (Ssp1/Ssp2). Additional analysis of downstream factors and the cross-talking Sty1 pathway implicates Greatwall kinases and Igo1 (PP2A inhibitor - endosulfine analogue) although the pleiotropic nature of these pathways and the rather blunt endpoints of septation index and phleomycin sensitivity makes robust data interpretation difficult.

Major comments<br /> For clarity the manuscript would benefit from some restructuring. In particular it would help the reader if the diagram presented in figure 7 was presented first as this would help orientate the reader with the pathways. The mammalian equivalents should be indicated.

Figure 8 (previously figure 7) summarizes our findings schematically. We believe that it works well at the end as a conclusion to the work and the discussion. Wherever appropriate we have mentioned the mammalian equivalent (e.g., for Rad3).

For scientific accuracy and clarity the manuscript requires significant attention. For example in the abstract where Rad3 is introduced it is not made clear that this is the fission yeast gene. It would be better to introduce ATR at this point? Anther example in the abstract: 'Deletion of ssp1 and ssp2 suppresses...' should read 'Deletion of ssp1 or ssp2 suppresses...' as the two genes are not deleted in the same strain. I would recommend that the authors carefully revise the manuscript paying close attention to each statement. Fore example on page 4: 'Downstream of TORC1, caffeine failed to accelerate ppk18D but not igo1D and partially overrode DNA damage checkpoint signalling'. It is unclear what the authors mean by accelerate. I assume they mean accelerate cell cycle progression, but there is no direct analysis of cell cycle kinetics in the results. Similarly on page 5: '... ppk18D mutant displayed slower cell cycle kinetics than wild type cells exposed to phleomycin and caffeine or torin1 (Figuer 1D)'. However, the figure shows no cell cycle kinetic analysis.

We have modified the wording of the abstract according to the reviewer’s suggestions.

We refer to accelerated progression into mitosis and have edited the text where appropriate. Depending on the type of DNA damage, S. pombe cells transiently or permanently arrest cell cycle progression. It is well known that caffeine overrides these cell cycle DNA damage checkpoints. We previously proved that this was not due to Rad3 inhibition. Additionally, TORC1 (which controls the timing of mitosis) inhibition overrides checkpoint signaling. Our aim was to investigate if caffeine mimics this effect at least partially, via activation of Ssp2. We have demonstrated this is the case, although the basal state of the various mutants can complicate the data analysis in terms of cell cycle progression. Following exposure to phleomycin, this septation index peaks at 60 minutes following exposure to caffeine. In ppk18 mutants this peak was delayed by 30 minutes. Thus, wt and ppk18 mutants proceed through mitosis and cytokinesis at different rates (as determined by measuring the septation index).

The authors appear to make the assumption that 'Inhibition of DNA damage signalling by caffeine and torin1 enhanced phleomycin sensitivity...' (page 6) but then clearly go on to show that the mutants used are sensitive for other unknown reasons. To make this link it would be necessary to artificially impose a G2 delay and show how much and in which circumstances this reverses the effect on sensitivity of caffeine/torin1. The authors should thus be very clear that they cannot equate sensitivity to 'checkpoint over-ride' and adjust their wording and assumptions accordingly. Assumptions on epistasis need to use the same assay and not equate between assays. As an example F1C and F2D do not equate as phleo+caffeine would be expected to be sensitised above phleo+torin1. This is not commented on in the text. Also on page 7 '... ATP also suppressed the ability of torin1 to override DNA damage checkpoint signalling albeit to a lesser degree (Figure 2I).' However, this figure only shows sensitivity, not septation index.

We accept that these results can be difficult to interpret. Firstly, caffeine appears to modulate cell cycle progression by various means. We previously demonstrated that it stabilizes Cdc25 independently of checkpoint signaling. However, it also activates Ssp2 which subsequently affects Cdc25 activity via PP2A. Its effect on mitosis can thus differ depending on the context. For instance, igo1 mutants already have high PP2A activity which would affect the subsequent effect of caffeine on Cdc25 activity. Ssp2 on the other hand appears to regulate cell fate according to the nutritional state. Its sensing of nutritional cues is not limited to ATP/ AMP levels as it also regulates the response to amino acid quality (e.g., glutamate versus torin1).

We have carried out additional experiments on the effect of ATP. While it did affect progression into mitosis, the results were complicated and have not been shown. Instead, we have provided additional data to show that it affects cell length which is an indicator of G2 cell length. In other words, longer cells spend more time in G2 prior to septation.

We also suspect that caffeine is itself a DNA damaging agent as previously reported in the early 1970s. More recent studies have also indicated a role for Rad3 and DNA repair proteins for tolerance to caffeine. In fact, TORC1 itself has been reported to be required for DNA damage repair. Thus, TORC1 inhibition could potentially enhance DNA damage sensitivity independently of mitotic progression as shown in some of our experiments.

While we have clearly identified a role for Ssp2 in mediating the cell cycle effects of caffeine, we accept that these findings will require further studies (beyond the scope of this one); to give more insights on how these caffeine- mediated effects occur. What is clear is that caffeine overrides DNA damage checkpoint signaling by at least partially inhibiting TORC1 signaling.

All the septation index graphs require an untreated (I.e no caffeine or torin1) control.

We now show in figure 1a, that the septation index does not change over the time period studied, when cells were left untreated. These assays have been routinely used for many years now and are very reproducible. The graphs clearly show the differential effects caffeine and torin1 exert on cell cycle progression in wt and mutant strains exposed to phleomycin.

Figure 3 is not quantitative and cannot support the conclusions drawn from it. If, for example, the authors wish to demonstrate ATP can suppress checkpoint override (Figure 3A) they should use the same septation assay used before. If this is not possible, then it should be explained why not and an alternative quantitative assay should be developed. It is unclear why the authors include Figure 3B,C at all.

Ssp2, on the other hand, appears to regulate cell fate according to the nutritional state. Its sensing of nutritional cues is not limited to ATP/AMP levels as it also regulates the response to amino acid quality (e.g., glutamate versus torin1). Additionally, exposure to stress may induce a transient decline in ATP levels. We thus investigated how ATP might affect caffeine or torin1. We could not detect any major changes in the septation index (not shown). Cells exposed to ATP in the presence of caffeine and phleomycin were shorter. We cannot tell how exactly suppresses the effect of caffeine and torin1 on DNA damage sensitivity.

It is unclear to this reviewer what the significance of the data with gsk3D cells is (Figure 5). The authors should introduce the protein, why there is an expectation that it would have a role in the pathway and explain its relevance. Similarly when discussing the resulting data.

Gsk3 lies downstream of TORC2 which is inhibited by torin1 but not caffeine. Gsk3 regulates Pub1 stability which is the E3 ligase for Cdc25. We showed previously that caffeine stabilizes Cdc25, suggesting it might interfere with Pub1 activity. Additionally, we are investigating caffeine as an indirect inhibitor of TORC1 with torin1 that directly inhibits both complexes. Our data provide further evidence for a differential effect of caffeine and torin1 on TORC1 signaling. We have modified the text accordingly.

Figure 5A shows a similar response of wild type cells to phleomycin regarding checkpoint override as was shown in Figure 1A. However Figure 5C is not recognisable as equivalent to Figure 2A, yet both report sensitivity to phleomycin od wild type cells under equivalent circumstances. This is a major concern as to reproducibility of these data. It is also not possible to conclude from either Figure 5C or 5D that caffeine or torin1 treatment is, or is not, sensitising cells to phleomycin treatment, yet this conclusion is made when discussing the data.

We agree with this and other reviewers that demonstrating enhanced sensitivity to caffeine is problematic. Nonetheless, our cell cycle data clearly indicate a differential role for Gsk3 in mediating the cell cycle effects of caffeine and torin1. In terms of DNA damage sensitivity, we have reproducibly observed a lower degree of DNA damage sensitivity in gsk3 mutants relative to wt cells. Hence, while caffeine is less effective at enhancing DNA damage sensitivity relative to torin1 in wt cells; we observed that caffeine and torin1 increase DNA damage sensitivity to a similar degree in gsk3 mutants.

Figure 6A shows that caffeine, but not torin1 results in Ssp2 phosphorylation. Is this experiment reproducible and does the total level of Ssp2 increase reproducibly? This should be doe ae and the results discussed. Ideally, the bands would be quantified against actin intensity and presented as a bar graph with standard deviation.

We have repeated these experiments alone and in combination with phleomycin. This data convincingly show that caffeine but not torin1 induces Ssp2 phosphorylation. In fact, torin1 suppresses Ssp2 phosphorylation, likely due to inhibition of a feedback mechanism resulting from TORC1 inhibition. In contrast, caffeine likely activates Ssp1 via the stress response, which in turn phosphorylates Ssp2.

Figure 6B, when introduced should explain the background as to why eIF2alpha phosphorylation is a readout of TORC1 activity. Importantly, the figure should be supported by an actin control and 3 repeats quantified. Figure 6C purports to establish that caffeine moderately attenuates Maf1 phosphorylation. To be able to state this, it would be essential to quantify the gel and report repeated results relative to actin and the total levels of Maf1. Similarly Figure6D and 6E require an actin control and would benefit from proper quantification.

We have repeated the Maf1 experiments to clarify the data and show that caffeine suppresses Sck1 an additional TORC1 phosphorylation target.

Minor comments<br /> p3 'cigarette smoke and other gases'?

We have edited the statement.

P4 torin1 was dissolved in DMSO (not were)

We have edited the text.

p5 phospho not phosphor Ssp2

We have edited the text.

p6 exlpain why ppk18 deletion results are surprising. Also this result could be discussed.

It had been proposed previously, that Ppk18 is the Greatwall homologue in S. pombe and thus the major regulator of PP2A and mitosis downstream of TOCR1. Later studies suggested a redundant role for Cek1 in this pathway. While deletion of cek1 in a ppk18 background modulated the effect of torin1 on cell cycle progression, it did not interfere with the effects of caffeine. At present we cannot account for this observation. We cannot rule out that caffeine activates an additional kinase that regulates Igo1 activity.

Together our data show that caffeine advances progression into mitosis in a manner that differs from direct inhibition of TORC1 by torin1.

We have now added the relevant comments on this unexpected observation within the discussion.

Explain why Cek1 is not tested

We have now tested a ppk18 cek1 double mutant.

p6 introduce what pap1 is when first mentioned

We have introduced PP2APab1 as requested.

Reviewer #3 (Significance):

The data show that fission yeast Ssp1/2 has a role in inhibiting TORC1 in response to caffeine and this influences checkpoint override. This is an incremental, but potentially interesting, observation contributing to understanding mechanism(s) of caffeine action. The lack of quantification, the pleiotropic nature of the mutants used and the rather blunt endpoints assayed make it hard to establish to what extent the direct TORC1 inhibition by Ssp2 causes the checkpoint override, which limits is potential impact. The core observation may, however, be of interest to the wider caffeine field. The referee has the perspective of a yeast cell cycle geneticist.

We thank the reviewer for identifying the significance of the study in understanding the mechanisms of caffeine effects on the cell cycle. We have added all the suggested experiments with additional mutants and protein markers as well quantitative approaches that have appropriately improved the manuscript. We believe that the mechanism provided is of more general interest and not limited to the caffeine field: manipulating the cell cycle and understanding the interplays between growth and stress are of general interest and importance.

Reviewer #4 (Evidence, reproducibility and clarity):

The authors provide a series of genetic studies identifying a role for Ssp1-Ssp2 signaling in TORC1-dependent responses to DNA damage. The main assays are cell division (i.e. septation index) and cell viability (i.e. serial dilution spot assays) following treatment with the DNA damaging agent phleomycin. The authors perform these assays in a number of genetic mutant backgrounds to determine which genes and pathways are required for the relevant cellular response. Supporting data also include microscopy images and western blots to test protein phosphorylation. In general, the results support a role for Ssp1-Ssp2 acting upstream of TORC1. However, in several cases the data do not support a straightforward relationship, and it is confusing to parse through a number of intermediate effects, which often vary between different assays. I have provided some specific comments below that might be addressed to strengthen the technical aspects of the manuscript.

Major<br /> 1. The authors conclude "that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively" based on Figure 1. This conclusion seems quite strong given the indirect nature of assays in Figure 1, which test septation in the presence of DNA damage. The conclusion would require experiments that assay TORC1 activity itself.

Both caffeine and torin1 have previously been reported to inhibit TORC1 which controls the timing of mitosis. We sought to investigate if caffeine mediates its effects via the stress response pathway. We have conducted additional experiments which clearly demonstrate that caffeine inhibits TORC1 at least partially via the activation of Ssp2. These observations make sense as we have previously shown that caffeine actives the stress response pathway to activate Srk1 which inhibits Cdc25. More recent studies my others indicate that Ssp1 is required to suppress Srk1 to allow progression into mitosis. This accounts for the failure of ssp1 mutants to advance mitosis under stress conditions. Additionally, Ssp1 activates Ssp2 which leads to the downstream inhibition of TORC1.

1. Figure 2 needs some explanation to introduce the idea that cell growth reflects an intact DNA damage response that prevented division in the presence of phleomycin. I also felt that the conclusions were very strong given the data, and the authors should discuss each case more carefully. For example, deletion of ssp1 does not really suppress the ability of torin1 to enhance phleo sensitivity (Figure 2C).

We would not expect the deletion of ssp1 to suppress the effect of torin1 under stress conditions. We have provided further evidence to show that Ssp1 is required to facilitate progression into mitosis at least in the presence of phleomycin or heat stress.

1. Microscopy imaging in Figure 3 nicely complements some of the other assays. However, it seems important to know if the cells are actively growing in each of these cases. An example is torin and rapamycin shortening ssp1 mutants at 35 degrees: are these cells actively cycling?

Our aim was to demonstrate that caffeine exacerbates the ssp1 phenotype. This would provide further evidence to show that caffeine exerts its effects at least in part by activating Ssp1. Cells do not cycle in the presence of torin1 as it inhibits both TORC complexes. We have provided additional evidence to show that caffeine does indeed interact with Ssp1. As the primary aim of the study was to determine is caffeine overrides DNA damage via Ssp1 we have not investigated if they are cycling. Their shortened size suggests that rapamycin and torin1 affect cell division in a different manner from caffeine.

1. From Figure 6A, the authors conclude that caffeine induces phosphorylation of Ssp2. However, it appears that both Ssp2 protein levels and its phosphorylation levels are both increased, which seems an important distinction.

We have repeated these experiments several times under different conditions. Some proteins become more stable when phosphorylated as has been previously demonstrated for Srk1 for instance.

1. In Figure 6D, the authors should show separate gsk3 and ssp1 mutants. It seems likely that all phosphorylation of Ssp2 is due to Ssp1, but this should be shown.

We have replaced the figure with a ssp1 single mutant.

1. I am confused about Maf1 phosphorylation in Figure 6C. It is increased upon torin1 treatment, but it is discussed as an indicator or TORC1 activity. Does that mean that loss of its phosphorylation correlates with increased TORC1 activity? As written, I thought it was a TORC1 substrate, which led to confusion about its increased phosphorylation upon torin1 treatment.

Maf1 is phosphorylated by TORC1. Inhibition of TORC1 would thus lead to a loss of phospho-Maf1 moieties and the accumulation of the unphosphorylated form. We have conducted additional experiments and under various conditions to show that caffeine weakly inhibits Maf1 phosphorylation. We note however, that different stresses result in differential outcomes following TORC1 inhibition. As such we have included new data to show that caffeine suppresses the TORC1 target Sck1. In S. pombe Sck1 and Sck2 regulate progression into mitosis.

Minor<br /> 1. An untreated control should be shown for assays in Figure 1.

We have included this data for figure 1a.

1. An untreated control should be shown for assays in Figure 4.

We have noted in the results for figure 1, that untreated cells and phleomycin only treated cells do not show any changes in septation index over the time course studied in these experiments.

Reviewer #4 (Significance):

The study has significance in connecting several conserved and central signaling pathways including TORC1, AMPK, and PP2A. Also, the study uses caffeine and torin1 that have effects in many different cell types. The connection between caffeine and torin1 effects on phleomycin-treated cells was previously established by these researchers. The significance of the current study is providing a genetic pathway for this connection. The significance is partly limited by some of the technical points raised in the previous section, such as some inconsistencies in the strength of results from different assays. Also, the role of these pathways in DNA damage response signaling is not new. While the main significance of this work might relate to a more specialized audience, it does add to a broader body of literature regarding these conserved pathways and processes.

My expertise is yeast cell biology.

While the roles of the pathways in DNA damage has been reported usinbg genetic and pharmacological combinations we dissect their relationships and provide mechanistic connections.

We thank the reviewer for identifying the significance of this study. We believe we have now addressed the technical issues raised.

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Referee #4

Evidence, reproducibility and clarity

The authors provide a series of genetic studies identifying a role for Ssp1-Ssp2 signaling in TORC1-dependent responses to DNA damage. The main assays are cell division (i.e. septation index) and cell viability (i.e. serial dilution spot assays) following treatment with the DNA damaging agent phleomycin. The authors perform these assays in a number of genetic mutant backgrounds to determine which genes and pathways are required for the relevant cellular response. Supporting data also include microscopy images and western blots to test protein phosphorylation. In general, the results support a role for Ssp1-Ssp2 acting upstream of TORC1. However, in several cases the data do not support a straightforward relationship, and it is confusing to parse through a number of intermediate effects, which often vary between different assays. I have provided some specific comments below that might be addressed to strengthen the technical aspects of the manuscript.

Major

1. The authors conclude "that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively" based on Figure 1. This conclusion seems quite strong given the indirect nature of assays in Figure 1, which test septation in the presence of DNA damage. The conclusion would require experiments that assay TORC1 activity itself.
2. Figure 2 needs some explanation to introduce the idea that cell growth reflects an intact DNA damage response that prevented division in the presence of phleomycin. I also felt that the conclusions were very strong given the data, and the authors should discuss each case more carefully. For example, deletion of ssp1 does not really suppress the ability of torin1 to enhance phleo sensitivity (Figure 2C).
3. Microscopy imaging in Figure 3 nicely complements some of the other assays. However, it seems important to know if the cells are actively growing in each of these cases. An example is torin and rapamycin shortening ssp1 mutants at 35 degrees: are these cells actively cycling?
4. From Figure 6A, the authors conclude that caffeine induces phosphorylation of Ssp2. However, it appears that both Ssp2 protein levels and its phosphorylation levels are both increased, which seems an important distinction.
5. In Figure 6D, the authors should show separate gsk3 and ssp1 mutants. It seems likely that all phosphorylation of Ssp2 is due to Ssp1, but this should be shown.
6. I am confused about Maf1 phosphorylation in Figure 6C. It is increased upon torin1 treatment, but it is discussed as an indicator or TORC1 activity. Does that mean that loss of its phosphorylation correlates with increased TORC1 activity? As written, I thought it was a TORC1 substrate, which led to confusion about its increased phosphorylation upon torin1 treatment.

Minor

1. An untreated control should be shown for assays in Figure 1.
2. An untreated control should be shown for assays in Figure 4.

Significance

The study has significance in connecting several conserved and central signaling pathways including TORC1, AMPK, and PP2A. Also, the study uses caffeine and torin1 that have effects in many different cell types. The connection between caffeine and torin1 effects on phleomycin-treated cells was previously established by these researchers. The significance of the current study is providing a genetic pathway for this connection. The significance is partly limited by some of the technical points raised in the previous section, such as some inconsistencies in the strength of results from different assays. Also, the role of these pathways in DNA damage response signaling is not new. While the main significance of this work might relate to a more specialized audience, it does add to a broader body of literature regarding these conserved pathways and processes.

My expertise is yeast cell biology.

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Referee #3

Evidence, reproducibility and clarity

Summary

This manuscript examines the genetic requirements for checkpoint override by caffeine in the fission yeast model organism. The main outcome is to show that checkpoint override, which has previously been linked to the downregulation of TORC1, is dependent on on the AMPK pathway (Ssp1/Ssp2). Additional analysis of downstream factors and the cross-talking Sty1 pathway implicates Greatwall kinases and Igo1 (PP2A inhibitor - endosulfine analogue) although the pleiotropic nature of these pathways and the rather blunt endpoints of septation index and phleomycin sensitivity makes robust data interpretation difficult.

Major comments

For clarity the manuscript would benefit from some restructuring. In particular it would help the reader if the diagram presented in figure 7 was presented first as this would help orientate the reader with the pathways. The mammalian equivalents should be indicated.

For scientific accuracy and clarity the manuscript requires significant attention. For example in the abstract where Rad3 is introduced it is not made clear that this is the fission yeast gene. It would be better to introduce ATR at this point? Anther example in the abstract: 'Deletion of ssp1 and ssp2 suppresses...' should read 'Deletion of ssp1 or ssp2 suppresses...' as the two genes are not deleted in the same strain. I would recommend that the authors carefully revise the manuscript paying close attention to each statement. Fore example on page 4: 'Downstream of TORC1, caffeine failed to accelerate ppk18D but not igo1D and partially overrode DNA damage checkpoint signalling'. It is unclear what the authors mean by accelerate. I assume they mean accelerate cell cycle progression, but there is no direct analysis of cell cycle kinetics in the results. Similarly on page 5: '... ppk18D mutant displayed slower cell cycle kinetics than wild type cells exposed to phleomycin and caffeine or torin1 (Figuer 1D)'. However, the figure shows no cell cycle kinetic analysis.

The authors appear to make the assumption that 'Inhibition of DNA damage signalling by caffeine and torin1 enhanced phleomycin sensitivity...' (page 6) but then clearly go on to show that the mutants used are sensitive for other unknown reasons. To make this link it would be necessary to artificially impose a G2 delay and show how much and in which circumstances this reverses the effect on sensitivity of caffeine/torin1. The authors should thus be very clear that they cannot equate sensitivity to 'checkpoint over-ride' and adjust their wording and assumptions accordingly. Assumptions on epistasis need to use the same assay and not equate between assays. As an example F1C and F2D do not equate as phleo+caffeine would be expected to be sensitised above phleo+torin1. This is not commented on in the text. Also on page 7 '... ATP also suppressed the ability of torin1 to override DNA damage checkpoint signalling albeit to a lesser degree (Figure 2I).' However, this figure only shows sensitivity, not septation index.

All the septation index graphs require an untreated (I.e no caffeine or torin1) control.

Figure 3 is not quantitative and cannot support the conclusions drawn from it. If, for example, the authors wish to demonstrate ATP can suppress checkpoint override (Figure 3A) they should use the same septation assay used before. If this is not possible, then it should be explained why not and an alternative quantitative assay should be developed. It is unclear why the authors include Figure 3B,C at all.

It is unclear to this reviewer what the significance of the data with gsk3D cells is (Figure 5). The authors should introduce the protein, why there is an expectation that it would have a role in the pathway and explain its relevance. Similarly when discussing the resulting data.

Figure 5A shows a similar response of wild type cells to phleomycin regarding checkpoint override as was shown in Figure 1A. However Figure 5C is not recognisable as equivalent to Figure 2A, yet both report sensitivity to phleomycin od wild type cells under equivalent circumstances. This is a major concern as to reproducibility of these data. It is also not possible to conclude from either Figure 5C or 5D that caffeine or torin1 treatment is, or is not, sensitising cells to phleomycin treatment, yet this conclusion is made when discussing the data.

Figure 6A shows that caffeine, but not torin1 results in Ssp2 phosphorylation. Is this experiment reproducible and does the total level of Ssp2 increase reproducibly? This should be doe ae and the results discussed. Ideally, the bands would be quantified against actin intensity and presented as a bar graph with standard deviation.

Figure 6B, when introduced should explain the background as to why eIF2alpha phosphorylation is a readout of TORC1 activity. Importantly, the figure should be supported by an actin control and 3 repeats quantified. Figure 6C purports to establish that caffeine moderately attenuates Maf1 phosphorylation. To be able to state this, it would be essential to quantify the gel and report repeated results relative to actin and the total levels of Maf1. Similarly Figure6D and 6E require an actin control and would benefit from proper quantification.

Minor comments

p3 'cigarette smoke and other gases'?

P4 torin1 was dissolved in DMSO (not were)

p5 phospho not phosphor Ssp2

p6 exlain why ppk18 deletion results are surprising. Also this result could be discussed.

Explain why Cek1 is not tested

p6 introduce what pap1 is when first mentioned

Significance

The data show that fission yeast Ssp1/2 has a role in inhibiting TORC1 in response to caffeine and this influences checkpoint override. This is an incremental, but potentially interesting, observation contributing to understanding mechanism(s) of caffeine action. The lack of quantification, the pleiotropic nature of the mutants used and the rather blunt endpoints assayed make it hard to establish to what extent the direct TORC1 inhibition by Ssp2 causes the checkpoint override, which limits is potential impact. The core observation may, however, be of interest to the wider caffeine field. The referee has the perspective of a yeast cell cycle geneticist.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this paper, Alao and Rallis analyze the role of AMPK and TORC1 pathways, and the respective crosstalk, in regulating cell cycle progression in the presence of DNA damage in S. pombe. The authors show, almost exclusively through chemo-genetic epistasis assays, that caffeine inhibits TORC1 indirectly activating AMPK, in contrast to the specific ATP-competitive TORC1 inhibitor torin 1. Specifically, it is shown that in the absence of a functional AMPK pathway caffeine is unable to revert the TORC1-inhibition-dependent override of cell-cycle arrest caused by the DNA-damaging agent phleomycin, henceforth partially suppressing the growth inhibition caused by the co-treatment.

Major comments: The overall story of the paper is convincing. However, the choice of an almost exclusively chemo-genetic approach, lack of controls in some experiments and some discrepancy in data presentation suggest that the manuscript undergoes revision before the authors claim that their conclusions are fully supported by the results. In detail:

• In Figure 1, graphs of septation indexes are presented separately for each strain. This presentation prevents the reader from clearly comparing the differences of septation caused by genetic background rather than the treatment, i.e. the septation happening by treatment with torin 1. I feel it would be better to group the results by drug rather than by strain/mutant. If the results are presented this way because the experiments on different strains were run separately, I further suggest that they are re-run so to always include at least the wt in every run.
• In Figure 2C-D, an inconsistency is observable between the phleo+caffeine sensitivity of ssp1Δ and ssp2Δ, the latter retaining a higher sensitivity. Provided that this is not only due to this specific replicate, how would the authors explain such a difference and fit it into their conclusion of a "cascade" signaling with Ssp1 acting upstream of Ssp2?
• In Figure 2I, a huge discrepancy is observable compared to panel 2A in terms of phleo+caffeine (no ATP) sensitivity of wt cells. Here, cells seem to cope well with the phleomycin treatment even if co-treated with caffeine. This renders the main finding of the panel (the effect of phelo+caffeine+ATP) rather uninterpretable.
• In Figure 3A, the simple observation of elongation is sometimes hard to assess, for example in the ATP-caused suppression of the effect of torin 1, as also acknowledge by the authors in the text. I feel it would be really necessary to quantify such results on an adequate number of cells.
• In Figure 3B,C wt is missing to compare the results in the presence of the same treatments. I understand the focus on Ssp1, but the authors should show the same treatments on wt cells. Similarly, it would be better to show the drug treatments in panel C also at 30{degree sign}C. For the same reasons as in the previous point, quantifications would greatly enhance the credibility of the claims here.
• A major point is the almost complete absence of molecular data. Except for Figure 6, the data do not include a detection of the relative activation of the relevant pathways. Figure 6 could hardly fill this gap, since the samples therein analyzed are not the ones utilized in most of the other figures, but simple, single time-point treatment with a single drug. The authors usually refer in the text to previous knowledge about how a treatment influences a pathway. However, they should show it here in their experimental conditions.

Minor comments:

• A different grouping of the experiments/panels would help the reader. For example, Fig. 2I would fit better together with Fig. 3A, to match the composition of the various chapters of the results.
• Torin 1 is sometimes referred to with a capital T or with a lowercase t, especially in the Figures. I suggest to uniform the nomenclature.
• In the results, the authors state that "ATP may increase TORC1 activity or act as a competitive inhibitor towards both compounds.". It's a little bit odd to refer to ATP as a competitive inhibitor of drugs. I would rather be ATP, the physiological agonist, outcompeting two compounds which are working as ATP-competitive inhibitors.

Significance

The interplay between TORC1 and AMPK is of great interest in the cell signaling field, basically in every model organism. The paper provides a conceptual advance in the field showing a genetic interaction between the two pathways using a model organism which has probably been overlooked so far, which is a pity because S. pombe is the best organism to study G2/M cell cycle/size regulation. The story would be of interest especially for an audience working in cell signaling in microorganisms, but not so much (at least at this stage) for the community working on aging, disease and chemo-/radio-sensitization, contrary to what the authors claim. Furthermore, for the above-mentioned reasons, I feel like the authors are a little bit overshooting when claiming (for example in the abstract and in the discussion), that their work provides a clear understanding of the mechanism.

As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation my metabolic intermediates.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In the manuscript "The AMPK-TORC1 signalling axis regulates caffeine-mediated DNA damage checkpoint override and cell cycle effects in fission yeast," the authors studied the role of genes that are potentially involved in the caffeine-mediated override of a cell cycle arrest caused by activation of the DNA damage checkpoint. The methylxanthine substance caffeine has been known to override the DNA damage checkpoint arrest and enhance sensitivity to DNA damaging agents. While caffeine was reported to target the ATM ortholog Rad3, the authors previously reported that caffeine targets TORC1 (Rallis et al, Aging Cell, 2013). Inhibition of TORC1, like caffeine, was also reported to override DNA damage checkpoint signaling. Therefore, in the present study, the authors compared the effects of caffeine and torin1 (a potent inhibitor for TORC1 and TORC2) on cell cycle arrest caused by phleomycin, a DNA damaging agent, using various gene deletion S. pombe mutants.

The authors concluded that they identified a novel role of Ssp1 (calcium/calmodulin-dependent protein kinase) and Ssp2 (catalytic subunit of AMP-activated kinase) in the cell cycle effects caused by caffeine, based on the following findings; (1) the caffeine-mediated DNA damage checkpoint override requires Ssp1 and Ssp2; (2) Ssp1 and Ssp2 are required for caffeine-induced hypersensitivity against phleomycin; (3) under normal growth conditions, caffeine leads to a sustained increase of the septation index in a Ssp2-dependent manner; (4) Caffeine activates Ssp2 and partially inhibits TORC1.

Major comments:

I do not think that many of the authors' claims are supported by the results of the present study. The corresponding parts are detailed below.

1. The conclusion of the first paragraph in the Results (top in page 6; Our findings indicate that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively.) is not supported by the data in Figure 1. The result that caffeine, but not torin1, requires Ssp1 and Ssp2 to override the phleomycin-induced cell cycle arrest does not necessarily indicate that caffeine indirectly inhibits TORC1 via Ssp1 and Ssp2. Rather, the authors should mention that this conclusion is based on the authors' previous reports by citing them (e.g., Rallis et al, Sci Rep, 2017).

To add to Figure 1, an additional experiment using a constitutively active AMPK mutant, a temperature-sensitive TORC1 mutant, and a srk1 deletion mutant will help the authors claim their original conclusion as one possibility.<br /> 2. The conclusion of the second paragraph in the Results (lower-middle in page 6; Our results indicate that caffeine induces the activation of Ssp2.) is not based on the results of Figure 2. Figure 2 simply illustrates that both caffeine and torin1 cause hypersensitivity to phleomycin dependent on Ssp1 and Ssp2.<br /> 3. The conclusion of the fourth paragraph in the Results (middle in page 7) is not clearly supported by the result, due to an insufficient data analysis. As the cell length and the progress through mitosis are the key assay parameters in Figure 3, the average cell length should be shown next to each micrograph of Figure 3A and 3B. In Figure 3C, a mitotic index and the average cell length should be shown next to each micrograph. A statistical analysis is necessary for the authors to compare the measurements and to claim as the headline (Caffeine exacerbates the ssp1D phenotype under environmental stress conditions), as the effect of caffeine was not evident.<br /> 4. In the middle of page 8, the statement "Accordingly, the effect of caffeine and torin1 on DNA damage sensitivity was attenuated in gsk3D mutants (Figure 5C and 5D)." is not supported by the corresponding results. Rather, Figure 5C and 5D look almost same.<br /> 5. The description and the conclusion of the last paragraph in the Results (bottom in page 8 - page 9) are not supported by the results of Figure 6, due to an insufficient data analysis. The extent of phosphorylation must be quantified as a ratio of the phosphorylated species (e.g., pSsp2) to all species of the protein (e.g., Ssp2).

From Figure 6, the authors claim that caffeine (10 mM) partially inhibits TORC1 signaling. However, the authors previously showed that the same concentration of caffeine inhibited phosphorylation of ribosome S6 kinase as strongly as rapamycin, the potent TOR inhibitor (Rallis et al, Aging Cell, 2013). The authors are advised to assess phosphorylation of S6 kinase again in the present study and compare to the results of the present results in Figure 6, because addition of that data may allow the authors to discuss that caffeine affects TORC1 downstream pathways at different intensities.

Also, immunoblotting of the same proteins looks somehow different from panel to panel (e.g., pSsp2 in panel A and D; Actin in panel A, C, and D). Therefore, the blotting result before clipping had better be shown as a supplementary material.

Minor comments:

1. (Figure 1) The septation index of the phleomycin-treated cells (without any further additional drugs) should be shown, as a baseline.
2. (Figure 1D, Optional) As a ppk18D cek1D double deletion mutant is reported, the authors are advised to add and test that mutant in this experiment.
3. (Figure 2) The authors need to clarify the number of cell bodies spotted (e.g., in the Figure legend).
4. (Figure 3) The different number of cells in micrographs may give an (wrong) impression on the cell proliferation rate. Therefore, it is advisable to use the micrographs in which the similar number of cells are shown for conditions with the similar cell proliferation rates.
5. (Figure 4B) ssp2D, not spp2D.
6. (Figure 4) The septation index of the none-treated cells should be shown as a baseline.
7. (Figure 6B, 6E) What do the black arrows indicate? Figure Legend does not seem to explain them.
8. (Figure 6C) Indicate which part of the Maf1-PK blot corresponds to the phosphorylated species, because Maf1-PK is probed with an anti-V5 (not a phosphorylation-specific) antibody.
9. (Figure 6D) gsk3Dssp1D, not gs3Dssp1D.

Significance

As caffeine is implicated in protective effects against diseases including cancer and improved responses to clinical therapies, the topic of the present study is of interest and importance to the broad audience.

In the present study, the most significant finding is that caffeine- and torin1-induced hypersensitivity to phleomycin is dependent on Ssp1 and Ssp2 (Figure 2). This result may be important in chemotherapy against cancers. On the other hand, caffeine is known to activate AMPK (e.g., Jensen Am J Physiol Endocrinol, 2007). Besides, as detailed in the Major comments, many of the major conclusions are not supported by the present results. Therefore, based on my field of expertise (cell cycle, cell proliferation, and TOR signaling), I conclude that the present study hardly extends the knowledge in the field of "the cell biology of caffeine."

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Referee #3

Evidence, reproducibility and clarity

Summary

Cheng et al. use single-cell sequencing to determine how Nodal signaling influences endodermal and prechordal plate fate specification in zebrafish. Much data and data analyses are presented, but the conclusions that can be drawn remain vague and do not go far beyond what previous studies have already established. While the datasets are a potentially useful resource, the conceptual impact is limited.

Major comments

1. The major weakness of the paper is that previous studies have already shown that differential Nodal signaling (and additional mechanisms) can induce anterior endoderm versus prechordal plate. In particular, the studies of Barone et al. (2017) and Sako et al. (2016) have provided much more convincing insights, because they combine genetic manipulations with in vivo imaging. In contrast, the current study mostly infers fate specification from scRNA-seq data. This approach is fraught with artifacts, because pseudotime trajectories are only a proxy for developmental processes, and UMAPs can misrepresent relationships between different cell states and types. The potentially more novel findings (roles for ripply; role of chromatin accessibility) are quite preliminary. Therefore, the conceptual advances provided by the study are minor.
2. The study attempts to distinguish between anterior endoderm and prechordal plate, but there is little evidence that anterior endoderm versus most/all endoderm is studied. Clear markers for anterior endoderm would be needed (or live imaging as in Barone et al.).
3. The claim that prechordal plate gives rise to prechordal plate and endoderm is confusing. The initial prechordal plate is different from the later prechordal plate. Please use a more precise nomenclature.
4. Gsc is described to be expressed highly in anterior endoderm progenitors but Figures 1C and 1J do not support this.
5. I am not sure what to make of the Nodal and Lefty manipulations. There is plenty of data but previous studies by the Heisenberg lab have provided much more definitive insihgts into the role Nodal signaling in this fate decision. Please put your results into the context of these studies.
6. The chromatin accessibility results and conclusions seem trivial in light on previous observations that Nodal signaling (and many other signaling pathways) activate gene expression via enhancers, a hallmark of which is increased accessibility upon activation.
7. The ripply1 overexpression result is potentially interesting, but needs to be complemented with a loss of function analysis.

Referee cross-commenting

It is gratifying to see that all three reviewers appreciate the potential of the data, but they find the results not as conclusive as one might wish, and they question the conceptual novelty of the claims when compared to previous studies. I share their suggestions and concerns.

Significance

The study provides new single-cell data and analyses but does not provide major conceptual advances when compared to previous studies (e.g. Barone et al. (2017); Sako et al. (2016)). In its current form a small group of researchers in the zebrafish Nodal field might be interested in further exploring the data in this paper and combine it with in situ gene expression analyses and fate mapping.

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Referee #2

Evidence, reproducibility and clarity

In this study, Chen and colleagues examine the molecular basis for the segregation of mesodermal and endodermal fates during zebrafish gastrulation. For this, they focused on cell fate separation within the anterior mesendoderm, which gives rise to prechordal plate (pp) and anterior endoderm (endo) progenitors. Using a combination of "-omics" approaches and live imaging in both embryos and explants, the authors find that endo progenitors derive from the pp and are specified by comparatively lower levels of Nodal signaling. Mechanistically, higher Nodal signaling levels in pp cells correlates with chromatin openness. Furthermore, the authors provided evidence that gsc and ripply1 activity can repress endo specification. Overall, the authors suggest a model whereby different Nodal signaling levels promote cell fate diversification through modulation of epigenetic states.

The paper is well written, and the data presented in this study nicely supports the author's interpretation that anterior endo cells derive from the pp and that it requires lower Nodal signaling levels. However, it is unclear what are the core differences between this model and previous work (Sako, et al. 2016; Barone, et al. 2017) and how to interpret these findings in light of recent work in the field, implicating FGF signaling as a critical regulator of the segregation between mesoderm and endoderm in zebrafish (Economou et al. 2023). To reinforce their findings, it would thus be important for the authors to use their datasets to investigate further between these distinct models and explore the role of FGF signaling in this process. For instance, can they observe differential activation of FGF targets in early progenitors cells with higher vs. lower Nodal signaling?

Similarly, the link between Nodal signaling and chromatin openness is interesting, however, it is still unclear how causative these differences are for the cell fate segregation investigated in this study (this is in line with the way the authors describe these findings). However, given my previous point, I think it would be important to dissect this link further to strengthen the novelty of the study.

Finally, to prove that gsc and ripply1 cooperate in wild-type embryos for the segregation between pp and endo progenitors, it would be important to include further functional data. For instance, describe the respective loss-of-function phenotypes, as well as whether, at endogenous levels, they can partially compensate for each other's loss-of-function. A similar analysis should be included for osr1 to validate the need for cooperation between these distinct transcriptional repressors in anterior endo specification (a hypothesis nicely raised by the authors in the discussion of this study). Finally, is the expression of these transcriptional repressors restricted to the ppl? If so, why? Would they require peak levels of Nodal signaling, only present in the pp, for their induction? What are the expression levels of these regulators in the morphants for ndr1 and lefty1 (which show differences in Nodal signaling levels)?

Significance

This study tackles an important question in vertebrate gastrulation, which has been under intense investigation over the last years. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that anterior endo progenitors derive from the pp, which is nicely confirmed using live imaging. The findings that anterior endo progenitors are specified by comparatively lower levels of Nodal signaling than pp constitutes a major part of this manuscript. However, it is not clear i) what is necessarily new compared to previous studies implicating Nodal signalling in this process and, ii) how to interpret these findings in the light of recent work in the field disputing this more Nodal-based model. Accordingly, it was previously shown that the duration of Nodal signaling, partially through the action of gsc, played a key role in the differentiation between pp and endo progenitors (Sako, et al. 2016). Furthermore, previous work showed that endo cells leaving the ppl showed shorter-lived cell-cell contacts and, thus, on average lower Nodal signaling (Barone, et al. 2017). Since a recent model challenged the idea that differences in Nodal signaling are sufficient to account for the segregation between mesoderm and endoderm progenitors and instead suggested that an interplay between Nodal and FGF is necessary for the stochastic switch between these two cell fates (Economou et al. 2023), it would be important for the authors to use their datasets to investigate further these distinct models. This would synthetize both previous and current findings into a conceptual framework explaining how endoderm progenitors are specified.

Audience: This study would be relevant for a broad audience of cell and developmental biologists, interested in morphogen signaling, cell fate specification and pattern formation.

Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In zebrafish embryos, progenitor cells for both the prechordal plate and anterior endoderm reside at the dorsal margin in early gastrulation. Both cell populations are induced via signaling through the Nodal signaling pathway, however the mechanisms that send Nodal-exposed cells to one fate versus the other remain a matter of debate. Cheng et al use single-cell RNA sequencing to investigate the mechanistic origins of this developmental decision. They argue that both populations emerge from a common progenitor pool marked by the prechordal-plate marker gene goosecoid (gsc). By adding single-cell ATACseq analysis, they go on to argue that Nodal signaling encourages open chromatin states at target genes, and that this may underly the distinction between prechordal plate and endodermal fates. Finally, they suggest two potential regulators (gsc and ripply1) that may repress commitment to the endodermal fate.

Major Comments:

1. In lines 128-136, the authors describe a live imaging experiment to support the argument that anterior endodermal cells emerge from a gsc+ progenitor pool. The claim is that sox17+ cells (marked by RFP fluorescence) arise in gsc+ cells (marked by GFP fluorescence). From the presented data, I find it very hard to evaluate this claim. The GFP signal appears quite close to background in the highlighted cell. Additionally, the argument- as presented-turns on the behavior of a single highlighted cell. I think that this analysis should be clarified and extended to support the claim.

I suggest that the authors (1) plot average cell fluorescence over time rather than a 'line scan' across the cell, (2) draw cell borders from the mask used in each frame for clarity of presentation, and (3) plot the trajectories of gsc+/sox17+, gsc-/sox17- and gsc+/sox17-cells for comparison.

Alternatively, it could be helpful to extract fluorescence intensities for each cell in the field of view and scatter the RFP vs. GFP intensity for each cell. If the claim is true, three distinct subpopulations should be visible (i.e. gsc+/sox17-, gsc-/sox17- and gsc+/sox17+). Statistical analysis supporting the significance of these differences (e.g. comparing the means of each reporter within the populations) would be clarifying.

OPTIONAL: The live imaging experiment the authors present is quite ambitious, but perhaps overly difficult for the task at hand. I think this point could be more easily and clearly demonstrated by using two-color fluorescent in situs or HCR staining for gsc and sox17. Using an endpoint measurement would allow for deeper sampling across multiple embryos, and would likely yield clearer signals for cell type quantifications.<br /> 2. In the same section, I suggest that the authors address the possibility that the sox17+ cells observed don't go on to become part of the anterior endoderm. I commend the authors experimental work to support their scRNA-Seq data, however observation of the expression of a reporter gene (injected on a plasmid) is not equivalent to demonstrating that those cells adopt a given fate in the end. Is it not possible that the sox17 expression is transient, and these cells revert to prechordal plate fate? This point would be sealed by a formal fate mapping study (e.g. photoconversion of sox17::kaede cells), but I don't think this is a necessary bar for publication.<br /> 3. In Figure 1 M, the explant data does not seem to clearly support the claim that higher Nodal signaling intensities favor prechordal plate over endoderm. It appears that, for the endodermal panel, 2/3 replicates for 6 pg and 10 pg injections resulted in no endodermal cells observed. Could the authors clarify how this reflects the certainty of the conclusion? No statistical analysis is indicated on this panel or the one below.<br /> 4. OPTIONAL: The analysis presented in Fig. 1M strikes me as rather indirect (i.e. deconvolution of bulk RNA-Seq data to infer cell population proportions), and not strongly compelling. I think a stronger support of this point would be to inject Nodal into embryos and measure positive cell counts for gsc and an endodermal marker (e.g. sox32 or sox17) via HCR or in situ hybridization. This would yield a direct measurement of the cell counts in question. I think this would greatly support the claim, but I don't think should be considered a requirement for publication.<br /> 5. In Fig. 2H, the authors analyze responses to ectopic Nodal gradients in order to corroborate the results of their LIANA analysis. This experiment is a welcome addition to the argument, but has weak points that should be addressed.<br /> - a. The description of image analysis procedures used to construct the quantification plots are inadequate. It seems likely that the nuclei were segmented from the DAPI images, but this was not clear from the methods section. The authors should completely describe the segmentation pipeline and include sample code in the supplementary material.<br /> - b. The methods section seems to suggest that the analysis was performed exclusively on maximum intensity projections. I think this procedure may make the data hard to interpret and should be modified/support with additional analysis. For example, there is no reason that, at any given position in the image, the brightest DAPI and pSmad2 channel pixels occur in the same plane. Segmentation boundaries may therefore not reliably match between channels in the maximum intensity projection. The segmentation should be performed using the full Z-stack images. This can be done using widely-available software packages (e.g. CellProfiler).<br /> - c. The fluorescence images in 2H (specifically for the pSmad2 channel) look like they may contain some artifacts that carry through into the quantification. Specifically, there appears to be substantial non-specific background (both hazy and punctate) in the lft1 mutant that may artificially elevate the quantified intensity. This is evident in the quantification as a larger 'offset' to which the gradient decays than in the other presented images. This may be another explanation for the observation that pSmad2 staining is stronger in this background. I suggest that the authors (a) present all fluorescence images from the dataset in the supplement to allow for visual inspection, and (b) estimate the effect of fluorescence background on their quantifications to ensure that this artifact is not the source of the claimed difference.<br /> 6. In lines 267-284 and Fig. 4 L, the authors make the argument that ripply1 acts as a cell-autonomous repressor of endodermal fate. I find the argument for the cell autonomous character of its function hard to follow. Specifically, the authors lean on the experiment in which a plasmid with a sox17 promoter-ripply1 construct is injected, resulting in a decrease in endodermal cell count. Could the authors elaborate on how this proves a cell autonomous effect? Is it not possible that ripply1 expressed from this construct induces a signal that influences neighboring cells?<br /> 7. The suggestion that prechordal plate fate is favored (over endodermal fate) by higher Nodal signaling levels is interesting. This claim is supported by the derivation of a 'Nodal score' from RNAseq data. However, I don't see where the score is defined in the Methods section or in the supplementary materials. If this was accidentally omitted (my apologies if I am just missing it), it should be added. Additionally, I found the description in the main text to be opaque, and the paper would benefit from a more intuitive/friendly explanation of this metric.

Additionally, could the authors comment on what they believe-in terms of Nodal signaling history for a given cell- this score represents? Does it correlate with integrated Nodal exposure? Nodal exposure duration? Peak Nodal exposure? Given the results of Sako et al-that Nodal exposure duration is a critical determinant of prechordal plate fate- it would be useful to know if the authors believe their Nodal score findings point toward a different mechanism.

Minor Comments:

1. Line 84: The authors refer to the prechordal plate cells being 'more mature' than endoderm. It is unclear what the claim is here; some elaboration would be helpful.
2. The fluorescence images in Fig. S2 are virtually invisible in the PDF. The images should be rescaled to make them visible.
3. Fig. 2H would be easier to make sense of if the image panels were labeled. Please indicate which color corresponds to which stain.

Significance

I believe that this study fills in some details on the process of anterior endoderm specification that will be of interest to specialists in zebrafish Nodal signaling. I believe that the strongest and most novel section is the combined scRNA-Seq/ATAC-Seq analysis. This dataset is likely to be of interest to researchers who want to dig into potential mechanisms for the separation anterior endoderm and prechordal plate. Further, the singling out of ripply1 as a potential regulator of endodermal specification is interesting, and I hope that the authors follow this promising lead in future work.

While this study does provide a useful single-cell view of the specification of anterior endoderm, I didn't feel that it came to a concrete conclusion about the mechanism of separation of the anterior endoderm and prechordal plate. A few interesting processes/players are suggested by the findings- for example, Nodal/Lefty signaling between the populations or ripply1 expression could tip the balance- but I don't believe these hypotheses were tested clearly. The authors correctly point out that models for Nodal-driven endoderm/mesoderm separation have recently emerged in the literature, however the findings presented here don't rule out either of these models or compellingly support an alternative. I don't believe that this should preclude publication, however I do think it will limit the reach of the paper. Experiments that more concretely test the possible mechanisms hinted at here- for example, studying the separation of the two lineages in ripply1 mutants- would strengthen the paper's reach.

My enthusiasm for the paper is also somewhat reduced by the fact that some key findings of the paper can be found in earlier work. Acknowledgement of this prior work in the relevant sections could be improved. Specifically:

1. The finding that anterior endoderm cells emerge from a gsc-expressing population in the dorsal margin was strongly suggested in the classic Warga et al paper on the origin of zebrafish endoderm. There, fate mapping experiments demonstrate that dorsal marginal cells (in the first two cell tiers) in the late blastula can go on to form both endoderm and mesoderm. This strongly implies that anterior endoderm cells emerge from a gsc+ population, given that these cells are firmly within the gsc expression domain. I also note that the scRNAseq data from Fig. 2 in Farrell et al directly demonstrates that some sox17+ endoderm cells express gsc in their developmental trajectory. The findings in this paper are a welcome confirmation of these earlier observations, however this context should be discussed.
2. The observation that squint and lefty single mutants (either lefty1 or lefty2) can alter the propensity to adopt endodermal or mesodermal fates has also been observed previously. See for example Fig.1 in Norris et al, Figs 3 and 4 in Rogers et al, or Fig.1 in Chen et al. Acknowledging some of these earlier findings would benefit the paper.

As a reviewer, I feel most qualified to comment on the embryological aspects of the presented work. While I am generally familiar with the single-cell genomics toolkit, I am not in a position to rigorously assess the technical merit of that side of this work. Accordingly, I have tried to restrict my comments to the embryology side.

References:

1. Warga, R.M. and Nüsslein-Volhard, C., 1999. Origin and development of the zebrafish endoderm. Development, 126(4), pp.827-838.
2. Farrell, J.A., Wang, Y., Riesenfeld, S.J., Shekhar, K., Regev, A. and Schier, A.F., 2018. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science, 360(6392), p.eaar3131.
3. Norris, M.L., Pauli, A., Gagnon, J.A., Lord, N.D., Rogers, K.W., Mosimann, C., Zon, L.I. and Schier, A.F., 2017. Toddler signaling regulates mesodermal cell migration downstream of Nodal signaling. Elife, 6, p.e22626.
4. Rogers, K.W., Lord, N.D., Gagnon, J.A., Pauli, A., Zimmerman, S., Aksel, D.C., Reyon, D., Tsai, S.Q., Joung, J.K. and Schier, A.F., 2017. Nodal patterning without Lefty inhibitory feedback is functional but fragile. Elife, 6, p.e28785.
5. Chen, Y. and Schier, A.F., 2002. Lefty proteins are long-range inhibitors of squint-mediated nodal signaling. Current Biology, 12(24), pp.2124-2128.
6. Sako, K., Pradhan, S.J., Barone, V., Ingles-Prieto, A., Müller, P., Ruprecht, V., Čapek, D., Galande, S., Janovjak, H. and Heisenberg, C.P., 2016. Optogenetic control of nodal signaling reveals a temporal pattern of nodal signaling regulating cell fate specification during gastrulation. Cell reports, 16(3), pp.866-877.

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Reply to the reviewers

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

Summary: In this manuscript by Berg et al the authors demonstrate that RNA polymerase activity is important for the formation of nuclear blebs. This is an interesting and significant finding because prior work has suggested nuclear bleb formation is a result of changes in nuclear rigidity (lamins) or chromatin (via histone modifications). Overall I thought the manuscript was quite interesting and the data well presented. I think the inclusion of multiple mechanisms of blebbing (VPA treatment, as well as lamin B KO) helps to further support the importance of RNA polymerase/transcription activity in the blebbing process. However, I do have some concerns regarding the conclusions of the data that I think should be addressed as a revision.__

We appreciate that Reviewer states that “the manuscript was quite interesting and the data well presented”, it is a “significant advancement”, and “the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.”

In the points below, the Reviewer specifically suggests that we: 1) clarify possible contributions from RNA pol III, 2) address how global vs. local chromatin motion might contribute to our findings, and 3) discuss the force production capabilities of RNA pol II. We also appreciate the feedback regarding the conclusions and have made the specific changes requested in the revision.

Major Comments:____ 1. One concern I have is that the alpha-amanitin inhibitor has been shown to also inhibit RNA polymerase III. In an old study (1974 Weinmann PNAS) it appears that the inhibitor starting at 1 to 10 ug/ml. In this study the authors are using 10 uM alpha-amanitin, which is ~ 9 ug/ml and within the range of inhibiting some RNA polymerase III. Additionally, the other drug (actinomycin D) is even less specific for RNA polymerase II. I would suggest that the authors consider one of the following approaches 1) acknowledge in the manuscript the potential for RNA polymerase III to be important in the blebbing process 2) try a 10-fold lower dose of alpha-amanitin and see if that also inhibits blebbing, 3) try to find a way to demonstrate that RNA polymerase III activity is not inhibited at the 10 uM alpha-amanitin dosage, or 4) consider an alternate method to perturb RNA polymerase II activity (see Zhang Science Advances 2021 for an auxin-based approach to downregulate RNA polymerase II).

The Reviewer raises the point that alpha-amanitin inhibits both RNA pol II and III. In the revised manuscript, we provide new data to further support that the observed effects arise from RNA pol II. We now include new data from cells treated with the transcription inhibitors flavopiridol (which inhibits RNA pol II elongation) and triptolide (which inhibits RNA pol I and II initiation). These transcription inhibitors also suppress nuclear blebbing in VPA-treated nuclei (Figure 2C) as well as three other nuclear blebbing perturbations in chromatin and lamins (Supplemental Figure 1A). These new experiments directly show that nuclear bleb suppression by transcription inhibitors can be observed without possible inhibition of RNA pol III by alpha-amanitin.

__ A second concern I have is that the inhibition of RNA polymerase is global. Thus it is difficult to know for sure the biophysical function of the polymerase occurs immediately at the bleb, or instead is somehow affecting the overall chromatin state throughout the entire nucleus. I agree that figure 3 does provide some evidence that major mechanical and biophysical properties of the nuclei are not changed in response to the inhibition of the polymerase. However, micromanipulation experiments are done with isolated nuclei, which may be somehow mechanically altered already by isolation from cells. I feel that there still must be given some consideration in the discussion of the possibility that RNA polymerase activity outside of the bleb may be having some role in the stabilization of the chromatin and blebbing propensity.__

We appreciate the Reviewer’s insightful comments and we have revised the manuscript to clarify that we do not attribute blebbing purely to local effects. Instead, we argue that global changes in chromatin motion driven by transcription could contribute to nuclear blebs.

We did not intend to communicate that alterations to chromatin or its dynamics were necessarily only local. Indeed, we found that relative levels in RNAP Ser2 and Ser5 phosphorylation were different inside the blebs (Figure 6). Nonetheless, transcription was perturbed globally in our experiments, so we realized that blebbing could be driven by global changes (Figure 1). We hypothesize that global regulation of transcription can stimulate nuclear blebbing since transcription and its inhibition can, respectively, drive and suppress correlated chromatin motion throughout the entire nucleus (as previously observed by Zidovska et al. (PNAS 2013) and Shaban et al. (NAR 2018, Genome Biol. 2020), among others). We have revised the manuscript to clarify this point (Discussion section, page 15). We have also added new simulation snapshots showing global chromatin motions and how these motions are coupled to nuclear morphology (Figure 7C).

In response to the concern that isolated nuclei exhibit different mechanical properties than nuclei inside of cells, we refer to our previously published micromanipulation measurements (Stephens et al. MBoC 2017). There, we found that nuclei within the cell and outside of the cell have quantitatively similar spring constants and qualitatively similar force-extension curves. Therefore, we are confident that the lack of change in nuclear stiffness measured by micromanipulation accurately reflects the mechanics of nuclei inside of cells across different perturbations.

__ While I lack expertise to evaluate the basis of the model, I appreciate the model can show that motor activity can influence bulge. But it is not clear in the manuscript that RNA polymerase can generate these kinds of forces. The Liu citation is a model, and does not provide direct evidence that the RNA polymerase can generate force, or forces large enough to be meaningful. To me the model in this paper (Figure 7) felt as if it was only a possible hypothesis of why the RNA polymerase has an effect on blebbing, but I imagine there could be other hypotheses that would cause the same effect. The authors state (in the abstract) that RNA pol II can generate active forces, but I am concerned this is not sufficiently established. Since this motor/force activity of RNA polymerase is not experimentally demonstrated in this paper the authors should either do a better job of including evidence of this from the literature or consider removing this part of the manuscript.__

RNA polymerase is capable of exerting forces in excess of 10 pN (e.g., see Wang et al. Science 1998; Herbert et al., Annu Rev Biochem 2008). The collective activity of many motors (10’s of thousands, e.g., see Zhao et al. Proc. Natl. Acad. Sci. 2014) may generate even larger forces. As discussed in our earlier modeling paper, this force scale is consistent with the motor strengths studied in our simulations (Liu et al. Phys. Rev. Lett. 2021); in the present work, we present simulation results for motors that generate 0.14 pN forces. Thus, transcription, in principle, could generate forces even larger than the ones we considered in the model.

Additional experiments indicate that at larger length scales, RNA polymerase activity appears to drive coherent motions of chromatin throughout the cell nucleus (Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol 2020). It is these motions, driven by motors, that appear to drive the formation of nuclear bulges in our model (please see new panel Figure 7C).

Therefore, the aim of the model is to build on established and new results to better understand how transcription could alter nuclear morphology. Our model is adapted from earlier models, which could reproduce observations of chromatin-based nuclear rigidity, (Stephens et al. MBoC 2017, Banigan et al. Biophys J 2017, Strom et al. eLife 2021), some aspects of nuclear morphology (Banigan et al. Biophys J 2017, Lionetti et al. Biophys J 2020), and possibly explain how nonequilibrium motor activity (such as RNA pol II) can drive coherent chromatin dynamics (Liu et al. PRL 2021), which have been observed in live-cell imaging experiments (e.g., Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol. 2020, among others). The precise form of the motor activity is not the focus of our model (or the previous motor model in Liu et al. PRL 2021). Instead, our simulation result indicates that the relatively small motor forces that generate coherent chromatin dynamics could explain the surprising observation that transcription is a critical component of nuclear blebbing.

To address the Reviewer’s comment, we have added additional text to the Introduction and the Results sections to support the inclusion of motors to model the possible effects of transcription on chromatin dynamics and nuclear shape.

In the Introduction (page 4), we now write:

Simulations suggest that chromatin connectivity combined with the forces generated by polymerase motor activity (~10 pN per polymerase (Herbert et al. 2008)) could generate these dynamics (Liu et al., 2021).

In the Results section (page 10), we write:

We consider motors that generate sub-pN forces, well below the 10 pN forces that may be generated by individual RNA polymerases (Herbert et al. 2008).

Additionally, we have updated Table 1 to include the simulated motor strength.__ __

__ Minor Comments: 1. Did the authors do any analysis to see if the increased RNA transcription with VPA treatment (Figure 1B) has any spatial relationship to where the bleb occurs? Could an analysis of this be done similar to Figure 6 (with a bleb/body ratio)?__

The Reviewer raises an interesting point about measuring RNA localization relative to the bleb. We measured RNA intensity in the bleb and the nuclear body for wild type cells only. We find that RNA levels are significantly decreased in the bleb (80% of body signal, p

__ Is there anything known about lamin B1 KO cells as to whether or not they have increased transcription? Or could the authors do an analysis like they did with VPA treatment to check this?____ If they were to have increased transcription this would further support the authors' proposed mechanism of transcription itself (or RNA polymerase activity) driving blebbing).__

In the revised manuscript, we show that several nuclear perturbations that are known to decrease nuclear stiffness and cause increased nuclear blebbing also rely on active transcription. Lamin B1 knockout or knockdown cells have been shown to result in changes in transcription. However, it was difficult to find data that shows whether the overall level of transcription changes. Collaborators of ours have unpublished data that indicates that twice as many genes are upregulated as downregulated upon lamin B1 knockdown, but this still does not assess the total level of transcription within the nucleus. Alternatively, increasing transcription via other means is fraught with off-target effects, which would require many additional complementary experiments. We thank the Reviewer for this interesting suggestion, but we believe this is beyond the scope of this manuscript, in which we have focused on showing that transcription inhibition suppresses bleb formation.

__ Figure 1D, the VPA ser2 image appears much brighter than the untreated image. Yet the graph shows they are similar. Perhaps a more representative image should be used?__

The image used reflects the data that Ser2 signal is brighter (by ~10%) in VPA-treated cells but is not significantly altered compared to wild type (unt), and thus it is an accurate reflection of the data.

__ Can the authors comment if there is less DNA at the bleb site? In Figure 6 A this appears to be the case (based on the VPA image). If true, is the alpha-amanitin treatment rescuing this such that there is more DNA at the bleb (maybe causing the bleb to be smaller?).__

We find that there is less DNA signal intensity per unit area in the nuclear bleb as compared to the nuclear body (bleb has ~60% the signal of the body; see teal dots/data in Figure 6B). This agrees with previously published work from our lab (Stephens et al. 2018 MBoC).

Alpha-amanitin treatment does not rescue this effect. Decreased DNA enrichment in the bleb remains with alpha-amanitin treatment (p > 0.05, comparing across all 4 conditions in Figure 6B).

__ What is the significance of bleb vs non-bleb nuclear rupture? Is there anything known in the literature as to how these ruptures may be different in terms of biophysics, impact to DNA, repair? It would be helpful to have some context, as well as to understand if non-bleb rupture is something that may have been previously missed in other contexts.__

The Reviewer asks a valid and interesting question that this manuscript only begins to address. In general, we believe that ruptures occurring with blebs vs. without blebs may reflect aspects of the underlying mechanism(s) of blebbing and rupture, in the presence or absence of transcription. We offer a few further thoughts below.

1) Non-bleb nuclear ruptures have been reported in a few papers by our group (Stephens et al., 2019 MBoC) and others (Chen et al., 2018 PNAS), but much is still unknown.

2) Non-bleb nuclear rupture is part of normal nuclear behavior, as it accounts for ~20% of nuclear ruptures in wild type and perturbed cells (VPA and LMNB1-/-).

3) Overall, we think that bleb-based and non-bleb-based ruptures may occur through different mechanisms. The simplest difference is that bleb-based nuclear ruptures follow the nucleus’ ability to form blebs, whereas non-bleb-based nuclear rupture occurs in cases where there is less bleb formation, suggesting that factors other than the ability to form blebs may also be important for rupture. In the current study, we observed that bleb-based nuclear ruptures (and bleb formation) require transcription. In another manuscript from our lab under review, bleb-based nuclear ruptures (and nuclear blebbing) can be suppressed by actin contraction inhibition and increased by increased actin contraction (Pho et al., biorxiv 2022).

Additionally, we note it was reported that non-bleb-based nuclear ruptures, at least some of which are driven by microtubule prodding, result in increased levels of DNA damage (Earle et al. Nat Mater 2020), as has been observed for bleb-based ruptures (Stephens et al., 2019 MBoC; Xia et al. J Cell Bio 2018). Thus, nuclear rupture in general is thought to lead to DNA damage. However, total levels of DNA damage due to rupture may be controlled by different cellular processes.

In the revision, we have clarified our motivation for quantifying ruptures with and without blebs. We have also added a few remarks, drawn from the above comments, to the Discussion section (pages 11-14).

Reviewer #1 (Significance (Required)):____ General assessment: This study is a careful analysis of how RNA polymerase inhibition reduces nuclear blebbing. The study demonstrates this very well, using a variety of approaches. However, some limitations are the overstatement of some conclusions (specifically that it is RNA polymerase II when the inhibitor may also affect RNA polymerase III; that the RNA polymerase activity is important at the bleb and involves motor activity). Advance: This paper is a significant advancement because it shows the role of transcription in the biophysics of the nuclear shape. To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field. Audience: I think the findings are of broad interest, including beyond the nuclear mechanics field. I think the audience would be the entire cell biology community. Expertise: My expertise is in cell mechanics, including forces at the the nuclear LINC complex. While I do not work in the field of nuclear blebbing and rupture, I follow this field quite closely.

We greatly appreciate the Reviewer’s statement that “To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.__” __We thank the Reviewer for their thoughtful comments and suggestions, which have helped to improve the manuscript. __

__

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

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells

Reviewer #2’s greatest concern throughout the review was that we focused on the use of VPA as a model for generating increased nuclear blebbing and 24-hour treatment with alpha-amanitin as a transcription inhibitor. In the revised manuscript, we provide new data to show that nuclear blebbing generated by a variety of different nuclear perturbations (VPA, DZNep, LMNB1-/-, and LA KD Figure 2D __and __Supplemental Figure 1A) is reliant on active transcription in two different cell lines (MEF and HT1080, Figure 2 A and B). This is supported by use of four different transcription inhibition drugs, which work over varying time periods (24 hrs in alpha-amanitin, triptolide, or flavopiridol; actinomycin D for 1.5 hrs Figure 2C). We also timelapse imaged during drug treatment to show that transcription inhibitors for which we used 24-hour incubation times, can suppress nuclear blebs within 8 hours (Supplemental Figure 1B). __We also show that nuclear bleb formation and stability in wild type is transcription dependent (__Figure 5). We believe the new data added in our revised manuscript addresses the concerns of the Reviewer that the findings were specific to VPA and alpha-amanitin together only.

__Reviewer #2 (Significance (Required)):____

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells. __

While somewhat interesting, this is a rather specific condition that is further restricted by the limited use of experimental approaches. For example, the only deacetylase inhibitor used is VPA. Is this because VPA is the only one to trigger the effect? The authors should expand their approach to include additional inhibitors or, preferably, a directed knockdown tactic that targets the specific HDACs driving their phenomena.

The Reviewer is concerned that we have used limited experimental approaches by focusing on VPA treatment to induce nuclear blebs and alpha-amanitin overnight treatment to suppress nuclear blebbing. VPA treatment is a well-established perturbation to induce nuclear blebbing via HDAC inhibition, and it is similar to a variety of other nuclear perturbations that also induce blebs (Stephens et al. MBoC 2018, 2019; Kalinin et al. MBoC 2021; Pho et al. biorxiv 2022).

Nonetheless, to clearly address the Reviewer’s concerns we have provided new data which shows that four different nuclear perturbations are suppressed by transcription inhibition and that four different transcription inhibitors suppress nuclear blebbing. In addition to these perturbations, we also note that transcription inhibition affects bleb formation and stability in wild type cells. Below we outline the diverse experimental approaches that support the major conclusion of our manuscript.

Our data shows that transcription inhibition suppresses nuclear blebbing through data for:

1. Multiple cell lines (MEF and HT1080, Figure 2, A and B) – original data
2. Multiple transcription inhibitors (Figure 2C __and Supplemental Figure 1__):
3. Alpha-amanitin (RNA pol II and III degradation) – original data
4. Triptolide (RNA pol I and II initiation inhibition) – new data
5. Flavopiridol (RNA pol II elongation inhibition) – new data

6. Actinomycin D (DNA intercalation) – original data

7. Multiple perturbations that cause nuclear blebbing (Figure 2D ____and Supplemental Figure 1):

8. VPA histone deacetylase inhibitor, which increases euchromatin and chromatin decompaction; used because it is the most highly studied treatment by our lab (Stephens et al., 2017, 2018, 2019 MBoC; Pho et al., 2022 biorxiv) – original data
9. DZNep histone methyltransferase inhibitor, which decreases heterochromatin and chromatin decompaction (Stephens et al., 2018, 2019 MBoC) – new data
10. Lamin B1 null cells (LMNB1-/- or LB1-/-) (many previous works, including Stephens et al. MBoC 2018) – original data

11. Lamin A constitutive knockdown cells (LA KD) (Vahabikashi et al., 2022 PNAS) – new data

12. Nuclear bleb formation and stabilization in wild type cells is dependent on transcription in addition to VPA (Figure 5). – original data

13. Time dependence of suppression of nuclear blebbing requested by Reviewers 2 & 3:
14. Actinomycin D treatment of 1.5 hrs is sufficient to suppress nuclear blebs (Figure 2C) – original data
15. Transcription inhibition with alpha-amanitin, triptolide, and flavopiridol all show an increased rate of nuclear bleb reabsorption in the first 8 hrs of treatment for both VPA and LMNB1-/- perturbations (Supplemental Figure 1B) – new data.
16. This new data indicates that even formed blebs require active transcription to remain blebbed for long times
17. This new data also shows that the effect of transcription inhibition on nuclear blebbing does not require 24 hours of treatment.

__Moreover, the authors imply that VPA works through histone deacetylation yet do not provide direct evidence. It is equally likely that the application of VPA alters the acetylation pattern of a non-histone protein that eventually alters nuclear blebbing. __

The Reviewer questions whether histone deacetylation due to VPA treatment is responsible for nuclear blebbing. As the Reviewer notes in their next point below, histone deacetylation (e.g., by VPA or TSA treatment) as a mechanism for nuclear blebbing was previously established by work from our lab (Stephens et al., 2018 and 2019 MBoC) and others (Kalinin et al. MBoC 2021). This was described and referenced in the original manuscript’s introduction.

To summarize previous work, inhibition of histone deacetylation by VPA induces chromatin decompaction (Stypula-Cyrus et al. PLoS One 2013, Lleres et al. J Cell Bio 2009), increasing histone acetylation/euchromatin (Göttlicher et al. EMBO J 2001; Krämer et al. EMBO J 2003). In turn, this softens the nucleus (Stephens et al. MBoC 2017; Shimamoto et al. MBoC 2017), which succumbs to nuclear blebbing (Stephens et al., MBoC 2018). Softening and blebbing effect can also be induced by histone hyperacetylation via TSA or histone demethylation via DZNep (Stephens et al., MBoC 2018). This effect can be reversed by chromatin compaction via increased histone methylation/heterochromatin formation (Stephens et al. MBoC 2019).

In the present work, we measured histone acetylation (H3K9ac) in both VPA and VPA+alpha-amanitin perturbations to ensure that alpha-amanitin does not simply reverse the increase in VPA-based histone acetylation and thereby decrease nuclear blebbing, which it does not (Figure 3, A and B).

Altogether, inhibition of histone deacetylation by VPA as a mechanism for nuclear blebbing is established by the previous literature. The present work builds on those results to uncover a surprising new driver of nuclear blebbing which is transcirption. Therefore, we consider it to be unnecessary to provide further confirmatory measurements of VPA-treated cells beyond what is already provided in the manuscript. Finally, we point to the inclusion of new data from three other nuclear perturbations that cause nuclear blebbing that can be suppressed by transcription inhibition (Figure 2).

__Regardless, the reported findings with VPA were previously reported (Stephens et al. 2018) and the influence of alpha amanitin only represents an incremental advancement in our understanding of nuclear blebs. __

The finding that alpha-amanitin inhibits nuclear blebbing implies that a previously unknown mechanism/pathway, involving an essential genomic process, is critical to nuclear shape regulation. We therefore strongly disagree with the Reviewer that bleb inhibition upon alpha-amanitin treatment represents an incremental advance.

Moreover, the existing literature generally argues that nuclear blebbing is caused by actin-based compression and confinement. It is widely believed that the cytoskeleton deforms the nucleus, which can herniate a nuclear bleb in softer nuclei. Here, we show that with transcription inhibition there are no overt changes to actin contraction (Supplemental Figure 2), actin confinement (Figure 3E), and nuclear mechanics (Figure 3G). However, levels of blebbing change anyway! This will be a new and surprising result to those who believe the current prevailing narrative from the literature. We have now shown for the first time that transcription is also needed to form and stabilize nuclear blebs; to our knowledge, this was almost entirely unknown until now.

Further supporting our belief in the significance of our findings, Reviewer #1 and Reviewer #3 clearly state that our work is novel and important:

Reviewer #1 “To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.”

Reviewer #3 “This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid.”

To address the Reviewer’s concern, we have revised the manuscript to clarify that active transcription is required to form nuclear blebs across all of the perturbations now presented in this manuscript. Furthermore, we have clarified that transcription inhibition appears to suppress blebbing without altering other cellular components and properties (actin, nuclear stiffness) that are widely believed to control blebbing (see Results page 7, Results page 10, Discussion page 14).

Adding to the concern is that actinomycin D does not have the same level of influence as alpha amanitin (Figure 2), which suggests the alpha amanitin is having a pleotropic impact on blebbing. To validate that the changes in blebbing in the presence of VPA are dependent upon active transcription, the authors should use the anchor-away technique to remove RNAP from the nucleus thereby avoiding any indirect effects of the drugs (i.e., alpha amanitin) in use. Further adding concern that it is an indirect outcome is the prolonged incubation period (16-24 hours) that is apparently needed to observe the changes (page 5 paragraph 4). If it is active transcription that is causing the change in blebbing, then this should be apparent in a much shorter time frame (The Reviewer is worried about possible differences between transcription inhibitors actinomycin D and alpha amanitin. To further address these concerns in the revised manuscript, we now present new data for VPA without transcription inhibitor and VPA with transcription inhibition vy four different transcription inhibitors (__Figure 2C). Inhibitors include alpha-amanitin (RNA pol II degradation), triptolide (transcription initiation inhibition), flavopiridol (transcription elongation inhibition), and actinomycin D (DNA intercalation). All VPA plus transcription inhibitor treatments result in a significant decrease in nuclear blebbing relative to VPA treatment alone (p (p > 0.05, Figure 2C). Thus, there is no significant difference in the degree of nuclear blebbing suppression between the four different transcription inhibitors used.

Furthermore, the Reviewer raises concerns about the time interval from the start of transcription inhibitor treatment to suppression of nuclear blebbing. We agree that considering this time interval is valuable. However, we need to consider that the time interval for each of the different transcription inhibitors to take effect is different (Bensaude 2011 Transcription). Alpha-amanitin inhibits transcription in 4-8 hours (10 µM, Nguyen et al., 1996 NAR), triptolide (1 µM, Chen et al. 2014 Genes Dev) and flavopiridol (0.5 µM, Chen et al., 2005 Blood) work in 2-4 hours, and actinomycin D works in about 1 hour (10 mg/mL, Lai et al. 2019 Methods). These times are now mentioned in the manuscript (Figure 2 legend and Methods section).

It was not, however, known in advance how long it would take for transcription inhibition to have an effect on nuclear morphology. Therefore, the time to observe bleb suppression could have been longer than these treatment durations. As mentioned above, treatment with actinomycin D for 1.5 hours results in a similar decrease in nuclear blebbing as compared to the other inhibitors with 24-hour treatment (Figure 2C). To further address these concerns, we provide new data in the revised manuscript showing tracking of nuclear bleb reabsorption during the first 8 hours of treatment with alpha amanitin, triptolide, and flavopiridol via live cell imaging. Nuclear bleb reabsorption for both VPA and LMNB1-/- perturbations goes from ~5 % to 30% or greater during the first 8 hours of treatment with each of the transcription inhibitors (Supplemental Figure 1B), consistent with the time required to fully inhibit transcription. This supports our conclusion that transcription is essential to stabilizing nuclear blebs.

__In addition to these issues, the authors rely on immunofluorescence signals to measure the levels of various factors including the Ser5 and Ser2 phosphorylation, which is capturing the total levels of these factors and not the DNA bound forms. If the changes in blebbing actually involve transcription initiation, then the authors should include measurements on the DNA-bound factors. __

We are measuring Ser5 and Ser2 phosphorylation of RNA polymerase to track the actively DNA transcribing population. These markers appear on DNA-bound RNAP. Ser5 and Ser7 of RNAP are phosphorylated during initiation, and subsequently dephosphorylated during transcription elongation, while Ser2 is added at that time (Hsin and Manley 2012 Genes Dev). Ser2 is removed at transcription termination. Therefore, we expect immunofluorescence to measure DNA-bound RNAP.

__As reported the authors conclude that there is no changes in Ser2 and Ser5 phosphorylation yet they report that total RNA levels rise (Figure 1). How is the disconnect between RNA levels and Ser2 and Ser5 phosphorylation occurring? __

The Reviewer raises a question about how VPA treatment increases RNA levels but not levels of active RNA pol Ser2 and Ser5. While this is an interesting question, without a dedicated investigation, we can only speculate, at best; this question is beyond the scope of the paper focused on how transcription inhibition suppresses nuclear blebbing. The point of this data is to show that treatment with alpha-amanitin alone and along with VPA causes decreases in both RNA and RNA pol II Ser2 and 5 confirming transcription inhibition.

__Comparably, they use H3K9ac immunofluorescence as a measure of euchromatin. While the authors might be gaining a view on the total levels of H3K9ac under these experimental conditions, it is not clear whether this is DNA associated or not. Minimally, the authors should perform ATAC-Seq to judge the changes in euchromatin. __

The Reviewer questions the use of H3K9ac immunofluorescence as measurement of euchromatin levels, particularly in VPA-treated cells. The relationship between VPA and chromatin decompaction / euchromatin levels has been previously established (e.g., Stypula-Cyrus et al. PLoS One 2013, Felisbino et al. J Cell Biochem 2014, Lleres et al. J Cell Bio 2009). New data in Figure 3B shows that heterochromatin marker H3K9me2,3 also is not altered by alpha-amanitin treatment. In the case VPA + alpha-amanitin treatment, micromanipulation and nuclear height measurements provide further evidence that chromatin decompaction remains, since chromatin-based force response is unchanged from VPA treatment alone (Figure 3, E and G).

Again, we note that our manuscript focuses on the effects of transcription on nuclear blebbing and rupture, which were not previously reported and differ from the current understanding in the literature. Furthermore, ATAC-seq is a major undertaking that is simply not appropriate for further proving an auxiliary point about a previously established effect.

In summary, the original manuscript addresses this point. The specific experiment requested by the Reviewer is not necessary and is far beyond the scope of this study.

A final major concern is the lack of a correlation between the blebbing and nuclear ruptures (page 7 paragraph 3; Figure 4). If ruptures are not correlating with the blebbing, what is the relevance of the blebbing?

The Reviewer is asking for a clarification of the importance of nuclear blebbing in relation to nuclear ruptures. We have revised the manuscript to add new text to the Figure 4 legend clarifying the measurements and to the Discussion section describing the importance of this data (Discussion pages 12-13 and page 14). We discuss this in more detail below.

We would like to clarify that blebbing and nuclear rupture are not uncorrelated, as suggested by the Reviewer. We and others have shown that nuclear blebs are sites of high curvature that result in nuclear ruptures. In the present manuscript, timelapse imaging of nuclear bleb formation has been observed to result in nuclear rupture within minutes in all imaged cases (Figure 5). This data in the manuscript agrees with previous published data from our lab of bleb formation to rupture in >95% of the time (Stephens et al., 2019 MBoC). Furthermore, stabilized nuclear blebs persist for hours (Supplemental Figure 1B) and undergo more rupture, as shown in Figure 4D. Therefore, ruptures remain correlated with nuclear blebs in our study.

What we have shown, however, is that the percentage of cells that undergo at least one nuclear rupture during the time lapse is not statistically significantly decreased from VPA-treated levels by the addition of alpha-amanitin (Figure 4B). This appears to be due to two factors: 1) a basal level of nuclear rupture (see wild type data in Figure 4) and 2) an increase in the level of non-bleb-based nuclear rupture. However, importantly, non-bleb-based ruptures appear to occur less frequently for cells that undergo nuclear ruptures. Of the cells that exhibit nuclear rupture, those with non-bleb-based ruptures on average undergo only a single rupture over a 3-hour timelapse whereas those undergoing bleb-based rupture undergo an average of > 2 ruptures over the same time (Figure 4D).

Altogether, these data point to a correlation between blebbing and rupture, where blebbing can promote nuclear rupture, but is not essential for rupture. Therefore, observations of blebs are important in that they correspond to increases in nuclear rupture and corresponding nuclear dysfunction, such as DNA damage. The observation of non-bleb-based rupture, while not entirely a new (Chen et al. PNAS 2018, Stephens et al. MBoC 2019, Pho et al. bioRxiv 2022), is interesting because it may be driven by a different mechanism; transcription is not essential for nuclear ruptures in the absence of nuclear blebs but promotes rupture in the presence of blebs. These results add to our knowledge of the factors regulating nuclear integrity and shape, and we anticipate that they will be further investigated in future studies.

Finally, beyond these findings, we speculate that blebbing itself may be harmful to cell nuclear function. Previous studies have observed that nuclear deformations can cause DNA damage (Shah et al. Curr Biol 2021), chromatin reorganization (Jacobson et al. BMC Biol 2018, Golloshi et al. EMBO J 2022), and alterations to mechanotransduction (reviewed in Kalukula et al. Nat Rev Mol Cell Biol 2022). The extent to which the changes associated with these “nuclear deformations” require blebbing, rupture, or both is under investigation by various labs. Furthermore, previous studies (Shimi et al. Genes Dev 2008; Pfleghaar et al. Nucleus 2015) along with the present study (RNA Pol Ser2 and Ser5; Figure 6) have shown that chromatin content and, possibly, functionality is different within the nuclear bleb. Data in another manuscript in preparation from our lab, further suggests that there is limited exchange of biomolecular content between the nuclear body and bleb. Therefore, while we cannot conclusively claim that blebs are themselves deleterious to function, there is a growing body of suggestive evidence that this is the case.

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

This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid. The data that supports this is in Figure 2, collected in two different cell types (MEFs and HT1080 cells). The effect appears robust. New data is also provided that a marker of initiation of transcription but not transcriptional elongation is enriched in valproic acid-induced blebs.

We thank Reviewer #3 for positive comments that our study is “interesting”, “reproducible”, and data that shows the effect of transcription on nuclear blebbing “for the first time”.

This Reviewer asks for clarifications on 1) how transcription is a new mechanism for nuclear bleb formation and not part of the traditional view, 2) the generality of our conclusions (similar to Reviewer #2) since we report “on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin”, and 3) the insight the modeling provides. We have provided new data and made changes to the manuscript to address all the Reviewer’s comments.

__ Major comments

1. The paper makes general claims about transcription and nuclear shape, when in reality, it is only reporting on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin. This scenario under which the experiments were performed, for which there is no obvious physiological counterpart, ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces. Cytoskeletal forces are well-known to establish nuclear shape; nuclear shape in this context, is generally taken to refer to the gross shape of the nucleus (e.g. elliptical, circular, etc.), and not small local blebs that may form due to F-actin based confinement or other mechanisms. Thus, this interpretation is overstated:

"Surprisingly, we find that while nuclear stiffness largely controls nuclear rupture, it is not the sole determinant of nuclear shape. This contrasts with previous studies, which suggested that the nucleus maintains its shape by resisting cytoskeletal and/or other external antagonistic forces (Khatau et al., 2009; Le Berre et al., 2012; Hatch and Hetzer, 2016; Stephens et al., 2018; Earle 12 et al., 2020)."

__

The Reviewer appears to be concerned with two issues in this comment. First, the Reviewer is concerned about our use of the word shape, which could be interpreted too generally, rather than as categorizing the blebbing and rupture phenomena that we observe in this study. We appreciate the Reviewer’s feedback and have made changes to this sentence as well as the paper in general to clarify that we are focused on nuclear blebs. Second, there is the issue of to what degree our results modify our understanding of the role of nuclear stiffness in nuclear blebbing and rupture. We discuss this below.

To address the Reviewer’s comment that the results are limited to “the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin” we provide new data and context for our results. The revised manuscript includes 1) new data using four transcription inhibitors and four nuclear blebbing perturbations and 2) original data showing that nuclear blebs are persistent rather than small and transient, and they alter gross nuclear shape. Our results are relevant to a wider range of blebbing/rupture and bleb/rupture suppression scenarios, as exemplified by the different nuclear perturbations, transcription inhibitors, cell types tested in our experiments, and long lifetimes for nuclear blebs. More specifically:

1) The Reviewer notes that our original studies were done with VPA and alpha-amanitin, similar to Reviewer #2 concerns. We provide new data to now show that 4 different transcription inhibitors can suppress nuclear blebbing across 2 chromatin and 2 lamin perturbations (Figure 2 and Supplemental Figure 1). Thus, our new data supports the idea that transcription is broadly required for nuclear blebbing.

2) The Reviewer states that blebs are small and transient, and that “shape” is meant to reflect the gross shape (e.g., circular). In fact blebs are long-lived as we show with new data that most (>95%) of VPA and LMNB1-/- blebs, remain at the end of an 8-hour timelapse (Supplemental Figure 1B). Furthermore, on average, nuclear blebs account for 15% of the nuclear size in VPA-treated cells (Figure 6E). While not measured in this paper, many studies have shown that nuclear blebs cause gross circularity to decrease significantly and that changes in circularity are associated with nuclear rupture (e.g., Stephens et al. MBoC 2018, Xia et al. JCB 2018). Most recently, we show nuclear blebs decreased nuclear circularity significantly in another manuscript under review (Pho et al., 2022 biorxiv).

The Reviewer also argues that our data showing the importance of transcription in nuclear blebbing “ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces.” We acknowledge that our results are not sufficient to rule out the broad assertion made by the Reviewer. However, our data shows for the first time that nuclear blebbing relies on transcriptional activity, while we measure no change in actin contraction or confinement or nuclear stiffness (respectively, Supplemental Figure 2 and Figure 3, C-E). Consequently, these results are a challenge to the current understanding, which must be updated by our results and future experiments. At the same time, we note that this manuscript’s Discussion section acknowledges that we have data in another preprint in which inhibition of actin contraction decreases nuclear blebbing to near 0% in wild type and perturbations (Pho et al., 2022 biorxiv). Together, these observations suggest a complicated picture in which multiple factors are jointly responsible for regulating nuclear blebbing and rupture.

__ As an aside, the data in the paper does not appear to support the interpretation that "nuclear stiffness largely controls nuclear rupture". It is unclear what the authors mean by this statement.__

We originally intended that comment to state the previous understanding in the literature, but we realize it was unclear. We appreciate the Reviewer’s feedback and have revised the text.__ __

__ 2. Further to point 2, treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells. Thus, the data are specifically applicable to valproic acid-treated cells. As such, the broad interpretations related to nuclear shape and mechanics should be tempered.__

The Reviewer is concerned that we cannot support the claim that this effect is broad and general; these concerns are also raised by Reviewer #2. We have provided new data and highlight original data to support that this effect is in fact broad and general, and moreover, that the data supports a role for transcription in nuclear blebbing.

We specifically address the Reviewer’s statement: “treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells”. In the original manuscript, we provided data that showed that wild type nuclear bleb formation and stability are suppressed upon transcription inhibition (Figure 5) even though the percentage of wild type nuclei exhibiting a bleb is not changed by alpha-amanitin treatment (Figure 2). We also provided data showing that the predominant type of nuclear rupture changes with alpha-amanitin treatment, including in wild type cells (blebbed vs. not, Figure 4C). Thus, while the effects of transcription inhibition are most easily visible in VPA-treated cells, they are also present in wild type cells in how blebs are formed and stabilized (Figure 5). We have revised the manuscript to better highlight this important point.

In addition, we again emphasize that our results extend beyond VPA-induced blebs. Our revised manuscript now includes new data of 4 different perturbations (to chromatin histone modifications and lamins A and B) that induce nuclear blebs, which can be suppressed by 4 different transcription inhibitors (Figure 2 and Supplemental Figure 1). As previously noted by both Reviewers 1 and 3, this effect is reproducible in different cell lines. This new data directly addresses the concern that the effect is only applicable to VPA and alpha amanitin.

Nonetheless, we agree with the Reviewer that we cannot support broader claims that nuclear mechanical properties are unaltered by transcription inhibitors across all scenarios, as we only measured this change in VPA-treated cells. Micromanipulation force experiments are detailed and time consuming, making it difficult to include data for multiple perturbations. We chose VPA because we have the most measurements of this perturbation which have remained consistent over the life of micromanipulation force measurements. Therefore, we have revised our statements on nuclear mechanics in the revised manuscript (page 14).

__ T____he motor model for RNA pol II activity assumes that the motor 'repels' nearby chromatin units. It is not clear how this is related to the mechanism of motor action of RNA pol II on chromatin during transcription.__

The point of the model is not to precisely reproduce the manner in which transcribing RNA pol II exerts forces on the chromatin fiber. Instead, we have developed a coarse-grained model to study how the collective activity of molecular motors might drive chromatin dynamics and consequently, changes in nuclear shape, either global or local.

The model itself is based on our earlier models, which were used to recapitulate and understand how changes to chromatin mechanical properties governed nuclear rigidity (Stephens et al. MBoC 2017, Banigan et al. Biophys J 2017, Strom et al. eLife 2021; also see a similar model by Lionetti et al. Biophys J 2020) and how nonequilibrium activity due to molecular motors, such as RNA pol II, can drive coherent chromatin dynamics (Liu et al. PRL 2021), which have been observed in live-cell imaging experiments (e.g., Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol. 2020, among others). The current model therefore explores how the newly observed connection between transcription and nuclear blebbing could be explained by known phenomena.

We note that the "repelling” motors used to model RNA pol II activity in the present work are in many ways qualitatively similar to the dipolar “extensile” motors used by other researchers to model motor-driven chromatin dynamics (e.g., see Saintillan et al. PNAS 2018). More generally, study of “active matter” over the last 20-30 years (and statistical physics over the last century) has shown that precise details of active molecular agents are often unimportant to the larger-scale behavior of the system (e.g., see Marchetti et al. Rev Mod Phys 2013). Thus, we view the repulsive motors as modeling the effective behavior of many RNA pol II within a sub-micron region of chromatin. Better establishing the differences between different choices of motor activities is the subject of a modeling paper in preparation.

To address the Reviewer’s concern, we have more clearly stated the scientific foundations of the model, and we have revised our description of the model to clarify that we do not intend to model the behavior of individual RNA pol II by individual repulsive motors (see Results section, page 10).

__The motor model also does not seem to add conclusive insight to the manuscript, as the nuclear shapes predicted are not directly comparable to the experimental shapes which are flat and smooth with only an occasional, single, local bleb. __

The Reviewer raises two related points with this comment: that bulges and blebs are not directly comparable, and therefore, that the model “does not seem to add conclusive insight to the manuscript.”

We agree with the Reviewer that bulges in the simulations are not blebs as they are observed in the experiments. However, it seems likely to us that bulges are necessary precursors to bleb formation; it is difficult to envision how a large local nuclear protrusion could form without first bulging outward from the nuclear body. Furthermore, we disagree with the assertion that nuclei are generally flat and smooth, as qualitative and quantitative analysis of imaging data reveals that nuclei exhibit shape fluctuations and irregularities across multiple scales (see, for example, Chu et al. PNAS 2017, Patteson et al. JCB 2019, Stephens et al. MBoC 2019, Liu et al. PRL 2021).

Nonetheless, the observation of bulges but not blebs is a shortcoming of the simulation model. We believe this shortcoming reflects a tradeoff made in developing this model; we chose to develop and study a model with relative simplicity compared to a real cell nucleus. A more complicated model might better capture some aspects of nuclear blebbing at the expense of additional complexity. For example, the current model does not allow lamin-lamin or chromatin-lamin bonds to rupture, either stochastically or due to high forces. This effect, which is likely present in vivo, might be necessary for generating more bleb-like structures in simulations. Developing and refining such a model is an active pursuit within our collaboration, but for the moment, it is beyond the present purpose of the model.

Instead, the purpose of the model is to determine whether the observed effect of transcription inhibition on nuclear blebbing / localized shape deformations can be understood through known biophysical phenomena. Established models – to the extent that they exist – were insufficient because they typically relied on nuclear mechanics, which our experiments provide data that transcription is not changing nuclear mechanical rigidity. The current model demonstrates how motor activity within chromatin can alter the structure and dynamics of the lamina. The simulations are certainly not proof that transcription affects nuclear blebbing through the proposed mechanism. However, they are a first-of-their kind demonstration of how nonequilibrium biophysical activity (such as that generated by transcription) within a biopolymer system (chromatin) can emergently alter the geometry of the confining boundary (the lamina). This new result provides a plausible interpretation for the experiments in the manuscript.

In the revised manuscript, we have clarified our modeling approach and objectives in the Results and Discussion sections, and we have more clearly identified and discussed the limitations of the model (Results pages 10-11, Discussion page 15).

The model offers 'proof of principle', but is not capable of ruling out alternative mechanisms (such as nuclear pressurization by confinement, chromatin decompaction, or changes to osmotic pressure). It may be more appropriate to include the model in the discussion as opposed to presenting it as a new result that can be reliably interpreted through comparisons with experiment.

We respectfully disagree with the suggestion to include the model in the Discussion section instead of the Results. As discussed above, the model is new biophysics research and the simulations produced new scientific results, even if the overall interpretation remains open.

However, we have some thoughts about the alternatives suggested by the Reviewer. This is discussed in detail below, but briefly: experimental data, rather than the model itself, suggests that the alternative mechanisms mentioned by the Reviewer do not explain the effects of transcription. After treatment with alpha-amanitin, we do not observe changes to actin-based confinement or contraction (Figure 3E, Supplemental Figure 2), and there are no changes to chromatin histone modifications or nuclear rigidity (Figure 3). We also are skeptical of osmotic pressure arguments since 1) fluid, ions, and small biomolecules should freely flow through nuclear pores to maintain osmotic pressure balance between the nucleus and the cytoplasm, especially on hours-long time scales, and 2) increasing the osmotic pressure by fragmenting chromatin has previously been observed to have either no effect or a suppressive effect on nuclear stiffness (Stephens et al. MBoC 2017, Belaghzal et al. Nat Genet 2021), which would potentially increase blebbing (the opposite of the effect suggested by the Reviewer). We have addressed this further in the revised Results section (page 10) and below.__ __

__ 4. The data in the paper is not strong enough to rule out the more conventional mechanism of nuclear pressurization, which could be caused by F-actin based confinement or chromatin decompaction, or changes to osmotic pressure. Immunostaining of myosin is not a reliable way to compare myosin activity across conditions. It is possible that the long treatment with alpha-amanitin (unto 24 h, Fig. 2) relieves the pressure in the nucleus without measurable changes in the already established cell shape and hence the nuclear shape (height changes in spread cells are small at best -- valproic acid appears to reduce height by ~0.5 microns in Figure 3E which is smaller than the optical resolution along the z-axis of a typical confocal microscope).__

The Reviewer has proposed several alternative mechanisms and questioned the use of immunostaining and nuclear height measurements in the manuscript. We address each of these below.

Specifically, the Reviewer is concerned that we cannot rule out the more conventionally believed mechanisms of 1) actin confinement, 2) actin contraction 3) chromatin decompaction and/or 4) osmotic pressure. We have revised the text to clarify that our data and data from others strongly supports that these four “conventional” mechanisms are not responsible for transcription inhibition-based nuclear blebbing suppression (revisions on pages 7, 10, 14).

1) Actin confinement, as measured by nuclear height does not change upon transcription inhibition (Figure 3, C-E). Thus, our data supports the idea that transcription inhibition suppresses nuclear blebbing through a different mechanism. The Reviewer objects to this measurement on the basis that even the 0.5 µm change observed for VPA-treated cells is below optical resolution. However, optical resolution is not relevant to this measurement because we are not resolving two objects; rather, we are measuring the size of one object, the nucleus.

When two dots/objects are separated in the same frame or in different z slices, one needs to clearly distinguish two gaussians point spreads from the two objects a distance X apart. That is resolution and that is not the relevant limitation here. We measure the size of one object (the nucleus) using full-width half-maximum, which can quantify changes in nuclear height at scales finer than the optical resolution. For example, the FWHM of a fluorescence bead can be observed to change by just 10’s of nm depending on the light emitted; with small wavelengths, one has smaller FWHM (from the Rayleigh criterion, θ = 1.22λ/D, where λ is the wavelength of the light). Our measurements are through a z-stack at 200 nm steps, thus the change in distance from wild type to VPA-treated of 0.5 µm is 2.5 z steps (not smaller than one z step). Finally, we have additional data showing our ability to measure these differences many times over (Pho et al. 2022 biorxiv).

Image left is from: https://en.wikipedia.org/wiki/Full_width_at_half_maximum

Image right is a crop of Figure 3D from the manuscript.

2) Actin contraction, as measured by γMLC2, does not change either (Supplemental Figure 2). However, we know that actin contraction is a major determinant of nuclear blebbing (Mistriotis et al., 2019 JCB and Pho et al., 2022 biorxiv). Therefore, our data support that transcription affects blebbing in some other way than actin contraction.

The Reviewer disputes this finding by stating that “immunostaining of myosin is not a reliable way to compare myosin activity across conditions.” Published reports show that γMLC2 immunostaining is a reliable way to measure actin contractility changes (Wan et al. MBoC 2012; Ramachandran et al. Mol Vision 2011; Duan et al. Cell Cycle 2016; Nishimura et al. PLOS One 2020). We have another preprint showing that alterations to actin contraction as measured by immunostaining of phosphorylated myosin light chain 2 (γMLC2) determine nuclear blebbing, independent of changes in actin confinement (Pho et al., 2022 biorxiv). There, we clearly show that changes in γMLC2 immunostaining can measure changes in actin contraction due to well-established modulators. Similarly, the ROCK inhibitor Y27632 in Supplemental Figure 2 can be viewed as a positive control in that γMLC2 immunostaining is clearly decreased after treatment with the inhibitor.

3) Chromatin decompaction via H3K9ac and chromatin-based nuclear rigidity are not rescued by transcription inhibition. New data also shows that levels of heterochromatin H3K9me2,3 does not change upon transcription inhibition (Figure 3B). The new data presented in this manuscript shows that transcription inhibition also suppresses blebbing in DZNep-treated cells (Figure 2D), where chromatin compaction by heterochromatin formation is inhibited (Stephens et al. MBoC 2019). Together, these experiments suggest that transcription inhibition is not suppressing nuclear blebs through increases in heterochromatin-based chromatin compaction.

Furthermore, the lack of change in the measurement of nuclear stiffness via micromanipulation (Figure 3G) provides a complementary metric suggesting that chromatin compaction is unchanged, at least in the case of VPA + alpha-amanitin.

Altogether, these results are inconsistent with transcription inhibition suppressing blebs through alterations to chromatin compaction.

4) Osmotic pressure is the least or not at all established of the four “traditional” mechanisms. The Reviewer proposes that transcription inhibitors, such as alpha-amanitin, could relieve osmotic pressure within the nucleus. We disagree with this explanation in that it is implausible for the nucleus to maintain an osmotic pressure imbalance in VPA-treated cells over long periods of time. Fluid, ions, and small biomolecules likely can flow through nuclear pores to maintain osmotic balance between the nucleoplasm and cytoplasm, especially over the hours long duration of VPA treatment. Furthermore, we are skeptical that VPA treatment, even with its chromatin-decompacting effects, significantly increases osmotic pressure because nuclear stiffness actually decreases after VPA treatment (Stephens et al. MBoC 2017, 2018, 2019; Krause et al. Phys Bio 2013; Shimamoto et al. MBoC 2017; Hobson et al. MBoC 2020) . Increased osmotic pressure should cause the nucleus to be stiffer. Moreover, nuclei in VPA-treated cells consistently undergo blebbing and rupture, which would naturally relieve any pressure imbalance. Thus, the notion that the measurements after hours VPA or VPA+aam treatment (Figures 2-5) are the result of a steady-state change in osmotic pressure is simply inconsistent with the experimental data.

We note that in cases of acute osmotic shock, where the osmotic pressure balance of the nucleus may be altered, the nucleus changes in size (e.g., see Finan et al., 2009 Ann Biomed Eng), which we do not observe in our experiments. Our measurements of nuclear area (Figure 6C) and height (Figure 3E) show no change nuclear size upon transcription inhibition (for more on the issue of height measurement, see the previous point).

To further address concerns about overnight treatment causing off-target effects, we have provided new data from a shorter treatment duration in the manuscript. The new data shows that within 8 hours, blebs exhibit more reabsorption after alpha-amanitin, triptolide, and flavopiridol treatment in both VPA-treated and LMNB1-/- cells (Supplemental Figure 1B). Additionally, we note that actinomycin D decreased nuclear blebbing in 1.5 hours, and thus did not require overnight treatment.

In summary, our original and new data clearly show that transcription contributes to nuclear blebbing. Transcription inhibition does not change other factors (such as actin-based confinement or contraction, changes in chromatin compaction, or osmotic pressure), that have been shown or may be thought to contribute to nuclear blebbing. The revised manuscript addresses this issue through the inclusion of new data, as discussed above.

__

Further to point 4, the data in Figure 4B and 4D both show a decrease in the mean of the % of ruptured nuclei and rupture frequency (please provide units for this frequency on the Y-axis). With more experiments, perhaps the data would have reached statistical significance?__

The Reviewer is asking for clarification on the data included in Figure 4 B and D reporting the percentage of cells that display a nuclear rupture.

We have revised the manuscript to clarify that Figure 4B is the percentage of all nuclei that show at least one nuclear rupture. The measurement unit, percent (listed as “[%]”), is shown on the y-axis. The revised manuscript also clarifies that Figure 4D reports, for the nuclei that rupture, the average number of times a nucleus ruptures during the 3-hour time-lapse.

The Reviewer stats that “with more experiments, perhaps the data would reach statistical significance?” To address this comment, we have altered the text to explain that % of all nuclei that rupture at least once does not significantly decrease by t-test but does show a non-statistically significant decrease. The data in Figure 4B shows that VPA causes 18.5 +/- 2.7 % rupture and VPA+alpha-amanitin causes 12.4 +/- 1.5 % rupture. Student’s t-test is p = 0.08 which is not statistically significant (p > 0.05) for six biological replicates each consists of n = 100-300 cells. We feel the data speaks for itself without us doing more experiments with the sole purpose of getting a lower p value. The stronger data is in Figure 4D, which clearly shows less nuclear ruptures per nucleus. We appreciate the Reviewer’s perspective and have modified the text in the Results and Discussion sections to reflect these important points (pages 8 and 14). __ __

__ Minor comments.

1. Confirmatory data, which has already been published in the same cell line in the past, could be moved if possible to supplemental information. Figure 1 seems to be a characterization of the efficacy of alpha-amanitin which is well-known, and therefore does not represent an original finding. It should perhaps be in supplemental information.__

We understand the Reviewer’s point but would like to leave Figure 1 as a main text figure to provide a clearer story for all readers of our manuscript.__ __

__ 2. Did the counting method used to collect data in Figure 4B exclude nuclei that rupture multiple times? This should be specified in the manuscript.__

No, Figure 4B is the percentage of nuclei that rupture, which includes nuclei that rupture any number of times as a single nucleus that ruptures. We have revised the Figure 4 legend to clarify this point. __ __

__ 3. This statement should be rephrased: "Since transcription is needed to form and stabilize nuclear blebs, at least some aspect of nuclear shape deformations appears to be non-mechanical" - deformation in the model in Figure 7 is clearly 'mechanical' - driven by motor force.__

We appreciate the Reviewer’s feedback and have rewritten the text changes this to “independent of the bulk mechanical strength of the nucleus”. __ __

__ 4. It is important to specify the times for which cells were treated with the various drugs in each figure (and not just in figure 2).__

We appreciate the Reviewer’s feedback and have added this information to each figure legend.__

__

__

Reviewer #3 (Significance (Required)):

This paper reports new data that nuclear blebbing induced by treatment with valproic acid can be inhibited by co-treatment with alpha-amanitin. The data provided are reproducible across different cell lines. The data suggest that inhibition of transcription inhibits blebs which are induced by valproic acid treatment, but it does not inhibit blebs in cells untreated with valproic acid. Immunostaining reveals some enrichment of RNA pol II phosphorylated at Ser5 in valproic acid-induced blebs, suggesting an enhancement of transcription-initiation (but not transcriptional elongation) in the bleb. Alpha-amanitin treatment reduces bleb formation and bleb lifetime.

While the data are clearly presented, and interesting in terms of relating transcription to blebbing, the proposed interpretation in terms of a new mechanism of blebbing is not strongly supported by the data or by the computational model. More definitive evidence is required to rule out that blebbing in valproic acid treated cells is not caused by a pressurization of the nucleus due to valproic acid treatment, which could be released by treatment with alpha-amanitin treatment for upto 24 h. The manuscript generalizes the findings to 'nuclear shape', and interprets them as suggestive of an alternative mechanism of establishment of nuclear shape; this generalization seems unsupported by the data.__

Overall, the data provided is novel and interesting to cell biologists, provided more definitive evidence can be provided to rule out other models and to establish the new proposed model for nuclear blebbing. Else, the claims of an alternative mechanism for blebbing could be toned down, and the data on the relation between transcription and blebbing, which is the novel and interesting finding in this paper, could be presented in a more focused way.

We appreciate that the Reviewer points out that “the data are clearly presented and interesting” and “reproducible across different cell lines.” The Reviewer’s main concerns appear to be with: 1) the effect of transcription inhibition on blebbing that is not induced by VPA, 2) alternatives or limitations to our proposed interpretation of the results, and 3) describing our results as applicable to “nuclear shape” in general.

We have addressed each of these concerns in detail in the above response and the revised manuscript. To summarize:

• We have included new data to show that four different transcription inhibitors combined with four different nuclear perturbations exhibit the same effects (Figure 2 and Supplemental Figure 1). Furthermore, we have clarified in the revised manuscript that even wild type (“untreated”) nuclei exhibit changes to blebbing dynamics (decreased stability, increased reabsorption) after transcription inhibition (Figure 5). Furthermore, concerns about time intervals was addressed by time lapse imaging showing that bleb reabsorption (return to normal shape) increases six-fold in the first 8 hours of transcription inhibitor treatment (Supplemental Figure 1B).
• The original manuscript, new data, and previous data from the literature provides evidence that alternative mechanisms involving “pressurization” (discussed above), the actin cytoskeleton (Figure 3E and Supplemental Figure 2), and chromatin and nuclear rigidity (Figure 3) do not explain the observed effects of transcription inhibition. We discuss this in detail in the revised manuscript and the above response. Furthermore, we have revised our presentation and discussion of the simulation model to describe its relevance more clearly to the results, support its inclusion in the manuscript, and provide appropriate caveats on our computational findings.
• We have revised the manuscript to clarify that our results primarily concern nuclear blebbing and rupture. The Reviewer is correct that the current investigation does not particularly focus on larger-scale shape such as circularity/ellipticity. In summary, our data clearly indicate that transcription contributes to nuclear blebbing and rupture. Previously suggested mechanisms of blebbing are generally inconsistent with the observed effect in combination with our other measurements. The model investigates a plausible new, complementary mechanism, which in itself represents an advance in biophysical modeling and ties the manuscript together.

We thank the Reviewer for their thorough critique, which we have now addressed. We believe that the new experimental data and analysis and computational modeling in our manuscript significantly advances our overall understanding of nuclear blebbing, even as it raises new questions to be addressed by future work.

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Referee #3

Evidence, reproducibility and clarity

This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid. The data that supports this is in Figure 2, collected in two different cell types (MEFs and HT1080 cells). The effect appears robust. New data is also provided that a marker of initiation of transcription but not transcriptional elongation is enriched in valproic acid-induced blebs.

Major comments

1. The paper makes general claims about transcription and nuclear shape, when in reality, it is only reporting on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin. This scenario under which the experiments were performed, for which there is no obvious physiological counterpart, ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces. Cytoskeletal forces are well-known to establish nuclear shape; nuclear shape in this context, is generally taken to refer to the gross shape of the nucleus (e.g. elliptical, circular, etc.), and not small local blebs that may form due to F-actin based confinement or other mechanisms. Thus, this interpretation is overstated:

"Surprisingly, we find that while nuclear stiffness largely controls nuclear rupture, it is not the sole determinant of nuclear shape. This contrasts with previous studies, which suggested that the nucleus maintains its shape by resisting cytoskeletal and/or other external antagonistic forces (Khatau et al., 2009; Le Berre et al., 2012; Hatch and Hetzer, 2016; Stephens et al., 2018; Earle 12 et al., 2020)."

As an aside, the data in the paper does not appear to support the interpretation that "nuclear stiffness largely controls nuclear rupture". It is unclear what the authors mean by this statement. 2. Further to point 2, treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells. Thus, the data are specifically applicable to valproic acid-treated cells. As such, the broad interpretations related to nuclear shape and mechanics should be tempered. 3. The motor model for RNA pol II activity assumes that the motor 'repels' nearby chromatin units. It is not clear how this is related to the mechanism of motor action of RNA pol II on chromatin during transcription. The motor model also does not seem to add conclusive insight to the manuscript, as the nuclear shapes predicted are not directly comparable to the experimental shapes which are flat and smooth with only an occasional, single, local bleb. The model offers 'proof of principle', but is not capable of ruling out alternative mechanisms (such as nuclear pressurization by confinement, chromatin decompaction, or changes to osmotic pressure). It may be more appropriate to include the model in the discussion as opposed to presenting it as a new result that can be reliably interpreted through comparisons with experiment. 4. The data in the paper is not strong enough to rule out the more conventional mechanism of nuclear pressurization, which could be caused by F-actin based confinement or chromatin decompaction, or changes to osmotic pressure. Immunostaining of myosin is not a reliable way to compare myosin activity across conditions. It is possible that the long treatment with alpha-amanitin (unto 24 h, Fig. 2) relieves the pressure in the nucleus without measurable changes in the already established cell shape and hence the nuclear shape (height changes in spread cells are small at best -- valproic acid appears to reduce height by ~0.5 microns in Figure 3E which is smaller than the optical resolution along the z-axis of a typical confocal microscope). 5. Further to point 4, the data in Figure 4B and 4D both show a decrease in the mean of the % of ruptured nuclei and rupture frequency (please provide units for this frequency on the Y-axis). With more experiments, perhaps the data would have reached statistical significance?

Minor comments.

1. Confirmatory data, which has already been published in the same cell line in the past, could be moved if possible to supplemental information. Figure 1 seems to be a characterization of the efficacy of alpha-amanitin which is well-known, and therefore does not represent an original finding. It should perhaps be in supplemental information.
2. Did the counting method used to collect data in Figure 4B exclude nuclei that rupture multiple times? This should be specified in the manuscript.
3. This statement should be rephrased: "Since transcription is needed to form and stabilize nuclear blebs, at least some aspect of nuclear shape deformations appears to be non-mechanical" - deformation in the model in Figure 7 is clearly 'mechanical' - driven by motor force.
4. It is important to specify the times for which cells were treated with the various drugs in each figure (and not just in figure 2).

Significance

This paper reports new data that nuclear blebbing induced by treatment with valproic acid can be inhibited by co-treatment with alpha-amanitin. The data provided are reproducible across different cell lines. The data suggest that inhibition of transcription inhibits blebs which are induced by valproic acid treatment, but it does not inhibit blebs in cells untreated with valproic acid. Immunostaining reveals some enrichment of RNA pol II phosphorylated at Ser5 in valproic acid-induced blebs, suggesting an enhancement of transcription-initiation (but not transcriptional elongation) in the bleb. Alpha-amanitin treatment reduces bleb formation and bleb lifetime.

While the data are clearly presented, and interesting in terms of relating transcription to blebbing, the proposed interpretation in terms of a new mechanism of blebbing is not strongly supported by the data or by the computational model. More definitive evidence is required to rule out that blebbing in valproic acid treated cells is not caused by a pressurization of the nucleus due to valproic acid treatment, which could be released by treatment with alpha-amanitin treatment for upto 24 h. The manuscript generalizes the findings to 'nuclear shape', and interprets them as suggestive of an alternative mechanism of establishment of nuclear shape; this generalization seems unsupported by the data.

Overall, the data provided is novel and interesting to cell biologists, provided more definitive evidence can be provided to rule out other models and to establish the new proposed model for nuclear blebbing. Else, the claims of an alternative mechanism for blebbing could be toned down, and the data on the relation between transcription and blebbing, which is the novel and interesting finding in this paper, could be presented in a more focused way.

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Referee #2

Evidence, reproducibility and clarity

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells

Significance

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells. While somewhat interesting, this is a rather specific condition that is further restricted by the limited use of experimental approaches. For example, the only deacetylase inhibitor used is VPA. Is this because VPA is the only one to trigger the effect? The authors should expand their approach to include additional inhibitors or, preferably, a directed knockdown tactic that targets the specific HDACs driving their phenomena. Moreover, the authors imply that VPA works through histone deacetylation yet do not provide direct evidence. It is equally likely that the application of VPA alters the acetylation pattern of a non-histone protein that eventually alters nuclear blebbing. Regardless, the reported findings with VPA were previously reported (Stephens et al. 2018) and the influence of alpha amanitin only represents an incremental advancement in our understanding of nuclear blebs. Adding to the concern is that actinomycin D does not have the same level of influence as alpha amanitin (Figure 2), which suggests the alpha amanitin is having a pleotropic impact on blebbing. To validate that the changes in blebbing in the presence of VPA are dependent upon active transcription, the authors should use the anchor-away technique to remove RNAP from the nucleus thereby avoiding any indirect effects of the drugs (i.e., alpha amanitin) in use. Further adding concern that it is an indirect outcome is the prolonged incubation period (16-24 hours) that is apparently needed to observe the changes (page 5 paragraph 4). If it is active transcription that is causing the change in blebbing, then this should be apparent in a much shorter time frame (<1 hour). In addition to these issues, the authors rely on immunofluorescence signals to measure the levels of various factors including the Ser5 and Ser2 phosphorylation, which is capturing the total levels of these factors and not the DNA bound forms. If the changes in blebbing actually involve transcription initiation, then the authors should include measurements on the DNA-bound factors. As reported the authors conclude that there is no changes in Ser2 and Ser5 phosphorylation yet they report that total RNA levels rise (Figure 1). How is the disconnect between RNA levels and Ser2 and Ser5 phosphorylation occurring? Comparably, they use H3K9ac immunofluorescence as a measure of euchromatin. While the authors might be gaining a view on the total levels of H3K9ac under these experimental conditions, it is not clear whether this is DNA associated or not. Minimally, the authors should perform ATAC-Seq to judge the changes in euchromatin. A final major concern is the lack of a correlation between the blebbing and nuclear ruptures (page 7 paragraph 3; Figure 4). If ruptures are not correlating with the blebbing, what is the relevance of the blebbing?

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Referee #1

Evidence, reproducibility and clarity

Summary: In this manuscript by Berg et al the authors demonstrate that RNA polymerase activity is important for the formation of nuclear blebs. This is an interesting and significant finding because prior work has suggested nuclear bleb formation is a result of changes in nuclear rigidity (lamins) or chromatin (via histone modifications). Overall I thought the manuscript was quite interesting and the data well presented. I think the inclusion of multiple mechanisms of blebbing (VPA treatment, as well as lamin B KO) helps to further support the importance of RNA polymerase/transcription activity in the blebbing process. However, I do have some concerns regarding the conclusions of the data that I think should be addressed as a revision.

Major Comments:

1. One concern I have is that the alpha-amanitin inhibitor has been shown to also inhibit RNA polymerase III. In an old study (1974 Weinmann PNAS) it appears that the inhibitor starting at 1 to 10 ug/ml. In this study the authors are using 10 uM alpha-amanitin, which is ~ 9 ug/ml and within the range of inhibiting some RNA polymerase III. Additionally, the other drug (actinomycin D) is even less specific for RNA polymerase II. I would suggest that the authors consider one of the following approaches 1) acknowledge in the manuscript the potential for RNA polymerase III to be important in the blebbing process 2) try a 10-fold lower dose of alpha-amanitin and see if that also inhibits blebbing, 3) try to find a way to demonstrate that RNA polymerase III activity is not inhibited at the 10 uM alpha-amanitin dosage, or 4) consider an alternate method to perturb RNA polymerase II activity (see Zhang Science Advances 2021 for an auxin-based approach to downregulate RNA polymerase II).
2. A second concern I have is that the inhibition of RNA polymerase is global. Thus it is difficult to know for sure the biophysical function of the polymerase occurs immediately at the bleb, or instead is somehow affecting the overall chromatin state throughout the entire nucleus. I agree that figure 3 does provide some evidence that major mechanical and biophysical properties of the nuclei are not changed in response to the inhibition of the polymerase. However, micromanipulation experiments are done with isolated nuclei, which may be somehow mechanically altered already by isolation from cells. I feel that there still must be given some consideration in the discussion of the possibility that RNA polymerase activity outside of the bleb may be having some role in the stabilization of the chromatin and blebbing propensity.
3. While I lack expertise to evaluate the basis of the model, I appreciate the model can show that motor activity can influence bulge. But it is not clear in the manuscript that RNA polymerase can generate these kinds of forces. The Liu citation is a model, and does not provide direct evidence that the RNA polymerase can generate force, or forces large enough to be meaningful. To me the model in this paper (Figure 7) felt as if it was only a possible hypothesis of why the RNA polymerase has an effect on blebbing, but I imagine there could be other hypotheses that would cause the same effect. The authors state (in the abstract) that RNA pol II can generate active forces, but I am concerned this is not sufficiently established. Since this motor/force activity of RNA polymerase is not not experimentally demonstrated in this paper the authors should either do a better job of including evidence of this from the literature or consider removing this part of the manuscript.

Minor Comments:

1. Did the authors do any analysis to see if the increased RNA transcription with VPA treatment (Figure 1B) has any spatial relationship to where the bleb occurs? Could an analysis of this be done similar to Figure 6 (with a bleb/body ratio)?
2. Is there anything known about lamin B1 KO cells as to whether or not they have increased transcription? Or could the authors do an analysis like they did with VPA treatment to check this? If they were to have increased transcription this would further support the authors' proposed mechanism of transcription itself (or RNA polymerase activity) driving blebbing).
3. Figure 1D, the VPA ser2 image appears much brighter than the untreated image. Yet the graph shows they are similar. Perhaps a more representative image should be used?
4. Can the authors comment if there is less DNA at the bleb site? In Figure 6 A this appears to be the case (based on the VPA image). If true, is the alpha-amanitin treatment rescuing this such that there is more DNA at the bleb (maybe causing the bleb to be smaller?).
5. What is the significance of bleb vs non-bleb nuclear rupture? Is there anything known in the literature as to how these ruptures may be different in terms of biophysics, impact to DNA, repair? It would be helpful to have some context, as well as to understand if non-bleb rupture is something that may have been previously missed in other contexts.

Significance

General assessment:

This study is a careful analysis of how RNA polymerase inhibition reduces nuclear blebbing. The study demonstrates this very well, using a variety of approaches. However, some limitations are the overstatement of some conclusions (specifically that it is RNA polymerase II when the inhibitor may also affect RNA polymerase III; that the RNA polymerase activity is important at the bleb and involves motor activity).<br /> Advance: This paper is a significant advancement because it shows the role of transcription in the biophysics of the nuclear shape. To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field. Audience: I think the findings are of broad interest, including beyond the nuclear mechanics field. I think the audience would be the entire cell biology community. Expertise: My expertise is in cell mechanics, including forces at the the nuclear LINC complex. While I do not work in the field of nuclear blebbing and rupture, I follow this field quite closely.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their positive statement and the significance of our work.

2. Point-by-point description of the revisions

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

This paper contains a set of highly valuable information on the physicochemical parameteters of betain lipids - which are synthesized in microalgae and some other lower eukaryotic organisms.

The authors, using advanced biophysical techniques - neutron diffraction and small-angle scattering (SANS) as well as molecular dynamics (MD) simulations - established key physicochemical parameters of synthetic betaine lipid DP-DGTS, and compared it with those of the DPPC phospholipid. They "show that DP-DGTS bilayers are thicker, more rigid, and mutually more repulsive than DPPC bilayers". These are important findings.

The authors also analyzed the phylogenetic tree of the appearance and disappearance of DGTS biosynthesis enzymes, which - together with the observed "different properties and hydration response of PC and DGTS" led them to explain "the diversity of betaine lipids observed in marine organisms and for their disappearance in seed plants". The authors tentatively suggest "A physicochemical cause of betaine lipid evolutionary loss in seed plants" (Title with "?")

We put a question mark because our work suggests that the difference of sensitivity to hydration between DGTS and PC bilayers could be an explanation for the betaine lipid disappearance in seed plants due to the dry stage of the seed. In our hands, we never managed to obtain 35S-BTA1 overexpressing plant that produce seed. However, we do not have a formal evidence for this fact. We propose to change the title into: “The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants.”

May major concerns with this suggestion are:

• In thylakoid membranes (TMs) the only phospholipid, PG, plays key roles in PSII and PSI functions (Wada and Murata 2007 Photosynth Res, Hagio et al. Plant Physiol 2000, Domonkos et al. 2004 Plant Physiol; it is difficult to explain how these roles would be overtaken by betaine lipids. In fact, data of Huang et al. (https://www.sciencedirect.com/science/article/pii/S2211926418309366) indicate betaine lipids constitute the major compounds of non-plastidial membranes" and compensation mechanism operate according to which "by the increase of PG in thylakoid membranes, suggesting a transfer of P from non-plastidial membranes to chloroplasts that would maintain a stable lipid composition of thylakoid membranes".
• Although neutron diffraction and SANS data, as well as MD simulationa might indicate important differences, the behavior of membranes (e.g. stacking interactions, overall structure and structural dynamics of TMs, protein embedding conditions / membrane thickness etc), TMs are more dominantly determined by protein-protein interactions, mainly because these membranes, contain only small areas occupied by the bilayer phase. Similar arguments hold true for the inner mitochondrial membranes (IMMs). I suggest to take into account these severe limitations when extrapolating the data and trying to reach general conclusions. In general, I suggest a more cautious interpretation of data.

We fully agree with the reviewer’s comments. We indeed wrote in the introduction: “In algae, under phosphate starvation, a situation commonly met in the environment, betaine lipids replace phospholipids in extraplastidic membranes. Because betaine lipids are localized in these membranes [11, 12] and share a common structural fragment with the main extraplastidic phospholipid phosphatidylcholine (PC) (Figure 1A and B), it can be speculated that these two lipid classes are interchangeable, but this was never demonstrated.”

Plastidial membranes are mainly composed of the non-phosphorus glycerolipids MGDG, DGDG and SQDG. It is well known that in phosphate starvation, in plants and algae, the main phospholipid present in thylakoid membranes, PG, is replaced by SQDG because they are both anionic and bilayer forming lipids (Hölzl G, Dörmann P. Chloroplast Lipids and Their Biosynthesis. Annu Rev Plant Biol. 2019 Apr 29;70:51-81. doi: 10.1146/annurev-arplant-050718-100202; Endo K, Kobayashi K, Wada H. Sulfoquinovosyldiacylglycerol has an Essential Role in Thermosynechococcus elongatus BP-1 Under Phosphate-Deficient Conditions. Plant Cell Physiol. 2016 Dec;57(12):2461-2471; Van Mooy BA, Rocap G, Fredricks HF, Evans CT, Devol AH. Sulfolipids dramatically decrease phosphorus demand by picocyanobacteria in oligotrophic marine environments. Proc Natl Acad Sci U S A. 2006 Jun 6;103(23):8607-12.; Kobayashi K, Fujii S, Sato M, Toyooka K, Wada H. Specific role of phosphatidylglycerol and functional overlaps with other thylakoid lipids in Arabidopsis chloroplast biogenesis. Plant Cell Rep. 2015 Apr;34(4):631-42.). We recently showed by the same kind of neutron diffraction approaches that PG and SQDG share similar physicochemical properties that can explain their conserved replacement by each other in plastidial membranes (Bolik S, Albrieux C, Schneck E, Demé B, Jouhet J. Sulfoquinovosyldiacylglycerol and phosphatidylglycerol bilayers share biophysical properties and are good mutual substitutes in photosynthetic membranes. Biochim Biophys Acta Biomembr. 2022 Dec 1;1864(12):184037. ). However, nothing is known about mitochondrial membranes and DGTS localization. Because PC is a major lipid component of mitochondria in plants and fungi and PC is absent in Chlamydomonas reinhardtii, mitochondria membranes could contain DGTS at least in Chlamydomonas.

To clarify this statement, we added in the introduction the sentences: “Betaine lipid synthesis is located in the ER [13,14] and betaine lipids are expected to be absent in photosynthetic membranes [12]. Therefore, this PC-betaine lipid replacement is not expected to occur in photosynthetic membranes. However, it might occur at the surface of the chloroplast envelope where PC might be present [15–17]. Nothing is known about the composition of mitochondrial membranes in algae but because PC is a major lipid component in plant and fungal mitochondria, this replacement might also occur in mitochondria.” In the discussion, we replaced “cellular membrane” with “extraplastidial membrane”.

A minor point - just to avoid possible misunderstanding: betaine can be present in large quantities in many photosynthetic organisms. A short statement on betaine would help.

To avoid any confusion with betaine as a soluble molecule and betaine lipid, we added this sentence in the introduction: “The presence of betaine lipids is not linked to the synthesis of betaine, a soluble compound present in almost every organism including most animals, plants, and microorganisms, acting as protectant against osmotic stress [22].”

**Referee cross-commenting**

I agree with the evaluation of Reviewer #2 - while keeping mine

Reviewer #1 (Significance (Required)):

The physico-chemical properties of betaine lipids have not been established. These lipids - under P starvation of microalgae - accummulate in large quentites. Thus, their detailed characterization and comparison to (otherwise similar) phospolipids are of high importance and advance our knowledge about the roles of these lipids and the organization and structural / functional plasticity of biological membranes.

As outlined above, I suggest a more cautious interpretation of the data and conclusions regarding e.g. the energy-converting membranes.

I think the audience is relatively broad: (i) basic research of lipid models and (ii) methodology as well as calling the attention of membrane biologists to the scarcely studied betaine lipids.

My field is the biophysics photosynthesis - the stability and plasticity of the oxygenic photosynthetic machinery at different levels of complexity; the and closest to this topic is the polymorphic lipid phase behavior of plant TMs.

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

This manuscript nicely presents the effect of phosphate depletion on how betaine lipids function as effective replacements in a water-rich environment. The mix of computational and wet lab experiments provides details on membrane structure and general effects when phospholipids are changed to betaine lipids. I found this manuscript easy to read and understand and is worthy of publishing. However, I do have a few minor comments below to improve the manuscript.

Minor Comments:

1. Phases in PC lipids with saturated tails: The authors present a gel to liquid crystalline phase change for DPPC at 40oC. However, this is at the ripple-liquid crystalline phase transition and the gel doesn't occur until about 34-35oC. This should be noted in the manuscript.

We indeed completed the sentence in the first result section by : “The DSC data show a sharp phase transition at 40.2 ± 0.1°C for DPPC corresponding to the transition between the ripple phase and the fluid phase, which is consistent with earlier reports on DPPC large unilamellar vesicles [25].”

Page 4: I am confused with the following phase: "indicating either weak cooperativity between lipid bilayers or that phase co-existence is not a thermodynamic disadvantage, while this phenomenon is not observed for DPPC bilayers." What is meant by phase co-existence is not a thermodynamic disadvantage? Could this also be due to some frustration in phase coexistence and the presence of a ripple phase that kinetically is inhibited and thus a sharp transition is not observed?

We did not observe a ripple phase in DP-DGTS as it is defined in DPPC bilayer either by DSC, neutron diffraction or SANS experiments. We don’t know if it exists in DP-DGTS bilayers. What we observe in neutron diffraction is a coexistence of gel phase and fluid phase domains in oriented multilayer films of DP-DGTS over a wide range of humidity whereas for DPPC we observe only a gel phase or a fluid phase. Because the thicknesses of the DP-DGTS bilayers are not so different between the gel phase and the fluid phase, we suppose that the free energy difference between the two phases is very small over a wide osmotic pressure range and that could explain the broad phase transition.

To further clarify our point, we have reworded the sentence in the following way: “As seen in Figure 2A , by increasing the humidity, DPPC molecules transit from the gel to the fluid phase via a ripple phase through a narrow window of osmotic pressures as previously reported [30,31]. In contrast, DP-DGTS bilayers show a phase coexistence that can be observed over a wide P-range and without the appearance of a third phase that could be attributed to a distinct ripple phase (Figure 2B) before forming a single fluid phase at high humidity (i.e., at low P). Based on DSC and neutron diffraction as two independent techniques, we can safely conclude that the phase transition for DP-DGTS is broad. This observation indicates that the free energy difference between the two phases is very small over a wide osmotic pressure range and may be connected to the shapes of the pressure-distance relations in the two phases, which are discussed further below.” We also added in the legend of figure 4 (SANS experiment): “No ripple phase Pb was detected for DP-DGTS bilayers.”

DOI for computational methods: The DOI listed computational files (https://doi.org/10.18419/darus-2360) does not work.

Unfortunately, we did not ask for publication of the URL upon submission of the manuscript and thank the reviewer for carefully checking this. Since DaRUS is a peer-reviewed repository ensuring high quality data sets according to the FAIR principle, peer review is still ongoing. The provided link will work definitely only when the manuscript will be published. In the meantime, we provide a temporary link for reviewing :

https://darus.uni-stuttgart.de/privateurl.xhtml?token=cbfac341-0e4a-4403-8f73-87bce31ca805

Reviewer #2 (Significance (Required)):

This work has broad significance and would be of general interest to those in membrane biophysics to plant biology and evolution. The work nicely touches on all these topics, and I find this fills a gap in details of these betaine lipids structure and relation to evolution in terrestrial vs. marine plants.

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Referee #2

Evidence, reproducibility and clarity

This manuscript nicely presents the effect of phosphate depletion on how betaine lipids function as effective replacements in a water-rich environment. The mix of computational and wet lab experiments provides details on membrane structure and general effects when phospholipids are changed to betaine lipids. I found this manuscript easy to read and understand and is worthy of publishing. However, I do have a few minor comments below to improve the manuscript.

Minor Comments:

1. Phases in PC lipids with saturated tails: The authors present a gel to liquid crystalline phase change for DPPC at 40oC. However, this is at the ripple-liquid crystalline phase transition and the gel doesn't occur until about 34-35oC. This should be noted in the manuscript.
2. Page 4: I am confused with the following phase: "indicating either weak cooperativity between lipid bilayers or that phase co-existence is not a thermodynamic disadvantage, while this phenomenon is not observed for DPPC bilayers." What is meant by phase co-existence is not a thermodynamic disadvantage? Could this also be due to some frustration in phase coexistence and the presence of a ripple phase that kinetically is inhibited and thus a sharp transition is not observed?
3. DOI for computational methods: The DOI listed computational files (https://doi.org/10.18419/darus-2360) does not work.

Significance

This work has broad significance and would be of general interest to those in membrane biophysics to plant biology and evolution. The work nicely touches on all these topics, and I find this fills a gap in details of these betaine lipids structure and relation to evolution in terrestrial vs. marine plants.

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

This paper contains a set of highly valuable information on the physicochemical parameteters of betain lipids - which are synthesized in microalgae and some other lower eukaryotic organisms.

The authors, using advanced biophysical techniques - neutron diffraction and small-angle scattering (SANS) as well as molecular dynamics (MD) simulations - established key physicochemical parameters of synthetic betaine lipid DP-DGTS, and compared it with those of the DPPC phospholipid. They "show that DP-DGTS bilayers are thicker, more rigid, and mutually more repulsive than DPPC bilayers". These are important findings.

The authors also analyzed the phylogenetic tree of the appearance and disappearance of DGTS biosynthesis enzymes, which - together with the observed "different properties and hydration response of PC and DGTS" led them to explain "the diversity of betaine lipids observed in marine organisms and for their disappearance in seed plants". The authors tentatively suggest "A physicochemical cause of betaine lipid evolutionary loss in seed plants" (Title with "?")

May major concerns with this suggestion are:

• (i) In thylakoid membranes (TMs) the only phospholipid, PG, plays key roles in PSII and PSI functions (Wada and Murata 2007 Photosynth Res, Hagio et al. Plant Physiol 2000, Domonkos et al. 2004 Plant Physiol; it is difficult to explain how these roles would be overtaken by betaine lipids. In fact, data of Huang et al. (https://www.sciencedirect.com/science/article/pii/S2211926418309366) indicate betaine lipids constitute the major compounds of non-plastidial membranes" and compensation mechanism operate according to which "by the increase of PG in thylakoid membranes, suggesting a transfer of P from non-plastidial membranes to chloroplasts that would maintain a stable lipid composition of thylakoid membranes"
• (ii) Although neutron diffraction and SANS data, as well as MD simulationa might indicate important differences, the behavior of membranes (e.g. stacking interactions, overall structure and structural dynamics of TMs, protein embedding conditions / membrane thickness etc), TMs are more dominantly determined by protein-protein interactions, mainly because these membranes, contain only small areas occupied by the bilayer phase. Similar arguments hold true for the inner mitochondrial membranes (IMMs).

I suggest to take into account these severe limitations when extrapolating the data and trying to reach general conclusions. In general, I suggest a more cautious interpretation of data.

A minor point - just to avoid possible misunderstanding: betaine can be present in large quantities in many photosynthetic organisms. A short statement on betaine would help.

Referee cross-commenting

I agree with the evaluation of Reviewer #2 - while keeping mine

Significance

The physico-chemical properties of betaine lipids have not been established. These lipids - under P starvation of microalgae - accummulate in large quentites. Thus, their detailed characterization and comparison to (otherwise similar) phospolipids are of high importance and advance our knowledge about the roles of these lipids and the organization and structural / functional plasticity of biological membranes.

As outlined above, I suggest a more cautious interpretation of the data and conclusions regarding e.g. the energy-converting membranes.

I think the audience is relatively broad: (i) basic research of lipid models and (ii) methodology as well as calling the attention of membrane biologists to the scarcely studied betaine lipids.

My field is the biophysics photosynthesis - the stability and plasticity of the oxygenic photosynthetic machinery at different levels of complexity; the and closest to this topic is the polymorphic lipid phase behavior of plant TMs.

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Our reply to reviewers contain figures that has been uploaded as a word file with the rest of the files. Here, we cannot past figures into the reply box, and because we don't wish to submit an incomplete response, we cannot provide a response in this format.

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Referee #2

Evidence, reproducibility and clarity

Lateral root production is a process regulated by auxin, among others. The expression of auxin-dependent genes requires the activity of transcription factors of the ARF family. In this study by Ebstrup et al., the authors suggest that selective autophagy would be involved in the degradation of the ARF7 factor involved in lateral root initiation and production in Arabidopsis thaliana, even though the accumulation of ARF7 in autophagy-deficiency mutant may not affect lateral root initiation.

Major remarks and comments:

1. In general, some experimental data do not facilitate appropriate comparisons due to lack of statistical analysis. This is particularly the case for Figures 1-a,b,c and 4-a,b,c,d.
2. Confocal microscopy images are not always convincing, due to a lack of necessary controls and also qualitatively. It would be useful, for example, to clearly indicate the objects of interest that the reader can use for comparisons. It is for example difficult to understand that chlorophyll fluorescence and GFP fluorescence (from the BiFC signal) colocalize almost in the same organelles (fig. 2c). The parent lines expressing the Venus and mCherry fusions should also serve as controls for figure EV3. Another point concerning fig. 2 a, b (IP): how do the authors explain the "GFP" signal, especially the apparent size and the doublet present only in one of the "YFP" controls after IP?
3. It would be important for the authors to clarify whether the different fluorescent fusions used are indeed functional or not. This is particularly important in the context of the proteins being studied and the possible regulatory process(es).
4. Apparently ARF7 would be degraded by the UPS system and the selective autophagy pathway. Would autophagy-deficient mutants, including atg2-1 and atg5-1 be more or less sensitive to MG132 (relative levels of ARF7 accumulation)? This is not clear from the data and its discussion.
5. The authors seem to insist that NBR1-mediated degradation of ARF7 by selective autophagy would be observable only preferentially in mature root tissues (probably to prevent them from forming lateral roots?). If this is the case, the title of their paper should reflect this conclusion. The authors have the tools (described in their manuscript) to unambiguously clarify this important point. Just as it would be important to demonstrate that the ARF7 proteins that accumulate would indeed be ubiquitylated.

Minor comments:

1. Some of the figures would benefit from qualitative improvement, especially the photographs and micrographs.
2. The authors' attention is drawn to the existence of several typos in the text and the absence of certain references cited in the bibliography.

Significance

Although the biological question is of unquestionable interest and importance, the data presented in this manuscript unfortunately do not allow us to rightly assess the contribution of this work to the state of our knowledge.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This manuscript shows the involvement of both the proteasome and autophagy pathways in the turnover and therefore regulation of ARF7, an auxin-responsive factor involved in lateral root formation. The authors bring crucial information for the understanding of how autophagy is involved in auxin-signaling.

Major comments:

The key conclusions appear overall convincing yet this reviewer would strongly advise to take into account the following remarks for a clearer and more convincing line of inquiry. This reviewer also believes that the additional experiments could be performed relatively fast apart for the point 9) where the establishment of a homozygous line could take 6 months or more.

1. Figure 1 & Figure EV1: The nature of the loading control should be stated as it appears to be a specific protein detected by immunoblotting. Furthermore, if the authors wish to make a stronger point as to whether ARF7 is degraded by the proteasome (considering the reserves mentioned in the Discussion section), I would recommend to perform the same assays as in Figure 1 but using an alternative proteasome inhibitor such as Bortezomib and to include a proteasome subunit KO mutant such as rpt2a-2.
2. The statement "The experiment revealed that both NBR1 (Fig 2A) and ATG8a (Fig 2B), but not free YFP, co-immunoprecipitated with ARF7-Venus." Is false as the authors did not try to co-immunoprecipitate free YFP with ARF7-Venus, they used a free YFP expressing line as a negative control for their GFP-immunoprecipitation (IP). It should further be noted that although NBR1 is detected in their free YFP IP, ATG8 is at very low levels so it should be stated that they see an enrichment of ATG8 in their ARF7-Venus IP.
3. Authors state "we were unable to detect ARF7-Venus in the input of both Co-IPs which can likely be explained by the fact that ARF7-Venus is under the control of its native promoter and thus lowly expressed.", yet putative degradation products (i.e. a smear) can be observed in the input of Figure 2A, similarly to the bands observed in both IP blots. It would be interesting to repeat these co-IPs with proteolysis inhibitors such as MG132 or Pepstatin & E64-d to pinpoint the proteolytic machinery at the origin of ARF7-Venus degradation in the IPs.
4. Figure 2: The use of multicolor BiFC "mcBiFC" should be stated as such for an easier understanding of the reader. It would be helpful for the reader if the "GFP" signal resulting from the complementation would be highlights thanks to some arrows. Moreover, a western blot to verify the expression levels should be performed since every construct has an epitope tag as stated in Gehl et al. 2009.
5. General remark: all drug/chemical treatments performed in this study use a "non-treated" negative control, yet it should be pointed out that the correct corresponding negative controls should have the solvent used to dissolve the respective drug/chemical in order to exclude any effect of the solvent or vehicle.
6. Figure 4, Figure EV4: Considering the variability in size and staining of the Rubisco large-subunit in the 4 immunoblot panels, I would suggest blotting with another antibody such as anti-tubulin or anti-histone 3 as a loading control for a more convincing quantification. Moreover, the nature of the staining used to stain the Rubisco large-subunit should be stated. The authors also state "differences in ARF7 accumulation in atg5 compared to Col-0" yet no immunoblot is shown where both genotypes are present on the same membrane, in order to verify this statement.
7. Figure 5: In regards to LR density measurements, I recommend reading "Quantitative Analysis of Lateral Root Development: Pitfalls and How to Avoid Them" by Dubrovsky & Forde (Plant Cell, 2012) for a more robust method of evaluating lateral root density.
8. Discussion: The authors state that "autophagy blockage leads to increased ARF7 cytoplasmic condensates". To support this statement, I recommend crossing pARF7::gARF7-Venus into atg mutants and analysing the localization and the fluorescence intensity of ARF7-Venus in specific parts of the root, as well as performing immunoblotting in order to assess overall ARF7 accumulation in autophagy deficient genetic backgrounds.

Minor comments

1. The following statement: « In contrast, plants are able to tolerate disruption of autophagy activity without major penalties" holds true to A. thaliana of some other plants but it must be noted that in O. sativa, autophagy-deficiency may lead to male sterility, which should be considered a major penalty for evolutionary fitness. For review see Norizuki et al. 2020 (Front. Plant Sci.).
2. Figure 2: The molecular weights appear to be potentially misannotated as free YFP aligns with the 35 kDa marks although it should appear around 27 kDa.
3. Figure EV3: There are 2 merged image columns, the furthest one to the right appears to include a DIC or Trans image on top of both fluorescence channels. It would be more helpful for the reader if the DIC or Trans image was shown with the overlay of fluorescent channels in order to assess the effect of 10% 1,6-hexandiol on the plant tissue. Moreover, demonstrating the absence of tissue damage or cell-death after 1,6-hexandiol treatment would be a plus.
4. There is a typo throughout the manuscript: ZT should be "Zeitgeber" not "zeitberg".

Significance

This manuscript has the quality of describing the proteolytic balance of ARF7 and thereby, the involvement of the autophagy pathway in regulating auxin-signaling components. This research adds on to the growing interest in how autophagy participates in developmental cues, and how hormonal signaling is regulated throughout the plant.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors performed an arrayed CRISPR loss-of-function screen targeting 18,253 genes with the goal of uncovering gene products that regulate cytoplasmic dynein-1 motor function. In order to assess the impact of gene knockout, the authors optimized a protocol for transfecting pools of cells with mRNA encoding Cas9 and scalably delivering arrayed pools of synthetic guides targeting a single gene to knock-out. In order to link gene knockouts to dynein-1 function the authors employed (1) a previously developed cell model U-2 OS PEX and (2) anti-EEA1 and anti-a-Tubulin antibodies and (3) hoechst as high-content fluorescent readouts for their genome-wide screen.

The authors then picked a subset of genes to move forwards with that were deemed as hits. A secondary round of screening was performed on these hit genes and unsupervised phenotypic clustering was performed on the feature vectors derived from the high-content images. These analyses revealed several distinct phenotypic clusters that can be categorized by the dynein cargoes or other functional categories including proteostasis related functions. The authors identified the gene SUGP1, which has never previously been linked to dynein-dynactin functionality.

The authors then show that targeting SUGP1 reduces the mRNA of both LIS1 and DYNC1l2 and the subsequent protein abundance of only LIS1.

In summary, the authors provide an optimized method for performing what they have termed 'one-shot' genome wide arrayed screening with pools of synthetic guides. They additionally have generated a data resource for others interested in understanding early endosome pathways and dynein-dynactin functionality.

The technical feat of generating such a large dataset and optimizing the protocol for arrayed synthetic guide pools will undoubtedly be useful for the community. However, this work has several limitations including (1) lack of adequate documentation for reproducing the analyses and (2) minimal mechanistic insight into the function of SUGP1.

Major Comments:

• The authors do not provide code or even pseudocode for the algorithms used to generate the features from the high-content images. If the authors are claiming that this would be a resource for the community to use then the authors need to provide an easy way for others to recreate their analysis.
• The authors mention that they will make the images from their screen publicly available, which is an essential part of making their work a useful resource for the community. However, more details need to be provided about how they will share the results. While a "data dump" of images will be useful to a narrow group of computationally savvy scientists, the broader community will require an interactive interface to enable browsing of the data. The authors should establish such a platform and make it available to reviewers of the revised manuscript to evaluate its usefulness.
• The authors highlight SUGP1 as an example for "novel mechanistic insights" - but the insights they provide are really minimal. If they authors want to claim mechanistic insights, they should experimentally address questions such as: Does SUGP1 physically interact with LIS1 mRNA? Which region of LIS1 mRNA confers regulation by SUGP1? Can the authors generate a version of LIS1 resistant to SUGP1 regulation to show that the effect of SUGP1 loss is mediated by LIS1 (and not additional factors?).

Minor Comments:

• Primary and Secondary antibody pairs are described nowhere in this paper. This would be impossible for anyone to recreate with just the list of primary and secondary antibodies used here.
• The authors provide no description of how the segmentation was performed or any reference to the code that they used for segmentation regarding the definition of perinuclear region. Considering so many of the results are based on these values it is important that others are able to recreate these values.
• Line 132: The authors do not explain what a min-max analysis is anywhere in the paper. This should be explained.
• There is no discussion of how the authors quantify micronuclei formation. If they state that they are the first to do this and that this is a novel technique they at the minimum need to explain the methods for quantifying micronuclei.
• Supplemental Fig 4C if a per cell intensity quantification is done I would like to see a metric for the segmentation accuracy on these cells overlaid with a cytoskeletal stain.
• It would be nice to have examples of nuclei or morphology that were excluded from downstream analysis, perhaps in a supplemental figure.
• Nowhere in the manuscript is it explained how the SUGP1 intensity measurement in Figure 6D is calculated, is this one a per well basis or a per cell basis?

Significance

The generation of the dataset described in this manuscript is impressive. However, to reach its full significance and usefulness for the scientific community, the authors should provide relevant technical details, in particular of their analysis pipeline, and share the screen results in an accessible, interactive interface. If they want to claim mechanistic insights into SUGP1, more mechanistic work is required.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript the authors have conducted a genome-wide CRISPR loss-of-function screen in human cells to find regulators of cytoplasmic dynein, a microtubule-based motor that plays a major role in the transport of cargo towards microtubule minus ends. The screen was carried out to address how dynein is synthesised and assembled, and how its activity is controlled to enable the motor to selectively transport a wide variety of cargoes. Cells were fixed cells 72 hours after transfection and fluorescently stained for intracellular markers. Several read-outs were used in the screen, of which the major ones were the distribution of dynein-tethered peroxisomes and early endosomes. The authors used a 384 well format (61 unique 384-well plates) and a fluorescence microscopy-based imaging readout to gauge dynein activity. From a guide RNA library targeting 18,253 genes, the authors recovered 195 validated hits. For one gene (SUGP1) follow-up studies demonstrate that the protein encoded by this gene controls the levels of the dynein activator LIS1 and thereby promotes cargo trafficking by dynein. The dataset reported here represents a source for investigating proteins that might be involved in minus-end microtubule-based transport, as well as in other aspects of cellular organisation that were captured in the high-content imaging approach.

I find this an interesting and well written resource manuscript, both from the perspective of how to conduct and analyse a high-content imaging screen, as well as from the dynein biology view. Results presented in this manuscript deserve follow-up studies. I do have a number of comments.

Major comments

1. On page 6 the authors state they used 61 x 384 wells. This equals 23,424 wells, but the authors state they analysed (8,150,065 cells from) 24,576 wells. What causes this difference in number? More importantly, the authors target 18,253 genes with four guides per gene. If I understand correctly these four guides per gene are present in a single well and the high-content imaging experiment was only done once. Although many cells were analysed per well (four fields of view per well; median of 345 cells analysed per well) and results are interesting and appear solid, I do think a replicate experiment is necessary.
2. The screen was developed based on the U-2 OS PEX line, in which tethering of dynein to peroxisomes is achieved by addition of rapamycin acting via a split BICD2 protein. Thus, the assay depends on the BICD2 adapter. Is this limiting when one is looking for dynein regulators?
3. Related to the question above, the authors do not recover BICD1 in their screen. Is this because BICD1 is not expressed in the cell systems used or is there another reaosn?
4. It has very recently been shown (doi.org/10.1038/s41467-023-38116-1) that BICD2 phosphorylation by CDK1 in the G2 phase of the cell cycle promotes its interaction with PLK1. This is followed by PLK1 phosphorylation in the N-terminus of BICD2, which in turn facilitates interaction with dynein and dynactin, allowing the formation of active motor complexes. Thus, adaptor activation through phosphorylation regulates dynein activity. In the present manuscript the authors use PLK1 as a read-out of cell viability. However, PLK1 also appears to regulate dynein via BICD2 phosphorylation. Given the latest results would the authors interpret their PLK1 data differently? Would it be preferable to screen for regulators of dynein activity in non-dividing cells?
5. Using the 377 genes listed in Supplementary Table 4 I performed a Metascape analysis. The results suggest that many of the hits are proteins involved in RNA metabolism or the cell cycle and that many of the encoded proteins form complexes. Based on this I wonder whether the screen yielded many proteins that are involved in controlling the steady state levels of dynein, microtubules, or of the dynein regulators. SUGP1 is an example of this. I suggest that the authors include an extensive Metascape analysis in a new version of the manuscript.
6. On page 11 a UMAP plot is described, which is shown in Figure 4B. How were the "members of the same protein complexes, such as histones, ribosomal proteins, RNA polymerase II, the RUVBL and TRiC/CCT chaperonins, FAM160A2- AKTIP-HOOK3 and dynein-dynactin" identified?
7. How do the complexes identified in Figure 4B relate to the MCODE-based complexes identified in Metascape?

Significance

I think the present manuscript is an interesting resource paper for the dynein community. The advance is technical rather than conceptual.

I am a cell biologist with an interest in microtubules and how this cytoskeletal network controls cell shape and function. I analyse this using fluorescence microscopy and -omics approaches. I am not an expert in high-content imaging screens and analyses but the data presented here seem solid and novel to me.

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Referee #1

Evidence, reproducibility and clarity

Cytoplasmic dynein-1 (dynein) is the predominant minus-end directed microtubule-based motor involved in the transport of numerous cellular cargoes in addition to mitotic functions. Although in vitro analysis of dynein assembly and interactions are becoming more common, cell biological studies that aim at identifying different dynein cargoes and functions are lacking. To shed light on dynein's function, Wong et al., performed a genome-wide CRISPR loss-of-function screen using peroxisome-tethered and endosome localization assays as readouts. Detailed data analysis and supervised and unsupervised phenotypic clustering of targets from an extensive RNA gene library (~20,000 genes) revealed ~200 genes disrupted in cargo trafficking processes. The authors also showed that a novel gene SUGP1, identified in their screen, promotes the expression of a critical dynein activator, LIS1. The generated datasets provide a rich library of genes that can be further mined by other researchers in the field.

Major comments:

This manuscript reads well, and the conclusions are mostly supported by experimental data. The authors went to great lengths to optimize their high throughput assay setup by testing different cell lines and transfection conditions and included different positive and negative controls, which is a strength of the study. The use of two functional readouts (early endosome and BICD2-driven peroxisome distribution) in the initial screen followed up by a validation screen of a smaller subset of genes using readouts for dynein disruption phenotypes (Golgi fragmentation, lysosome clustering) is another strength. In addition, the follow-up identification of an RNA-binding protein SUGP1 as a regulator of LIS1 mRNA levels provides an interesting new way of regulating dynein function.

I have a few concerns about the experimental design and conclusions.

1. The images in Fig. 1E and 1G for the dynein control (crDYNHC1) show some clustering around the cell nucleus while crLIS1 knockout shows no perinuclear clustering. Is this expected? Shouldn't dynein knockout prevent perinuclear clustering? Is it possible that crDYNHC1 does not lead to a complete knockout? Given that this is a proof-of-principle control in the assay, a more detailed validation of DYNHC1 knockout using western blotting and RT-qPCR, in addition to the validation shown in Fig. 1C would strengthen the claim that this control works as expected. These experiments should be fast and easy to do.
2. The use of multiple crRNAs together to target a single gene can increase off-target effects, however, the authors never test for off-target phenotypes or address the possibility of off-targeting. Can the authors show using a few examples that their approach does not lead to significant off-targeting? This should also be addressed in the text.
3. It is unclear to me how the authors established the limits for the quantification of localization ratios. As described in methods, the perinuclear region was defined as having an outer limit of 7 μm from the nuclear envelope. However, the cells are not the same size (also seen in representative images), which could skew the calculation of ratios solely based on fixed distance limits. Have the authors considered taking into account cell size? Perhaps a more accurate calculation would be to measure the distance from the nucleus to the cell periphery for each cell and normalize this value to the cell size to account for cell size differences. The perinuclear region could then be defined as the percentage of the distance from the nuclear envelope of the normalized cell radius. It is also unclear how the size and intensity of each "spot" are accounted for in the analysis as this is an important aspect of the quantification given that the "spots" are not the same size/intensity. Redoing this analysis would not require the authors to collect any new data but could help in gene identification, especially given that the authors only identified ~50% of the known dynein-dynactin complex components to be disrupted in their assay. These genes might have more subtle phenotypes that could be amplified by doing more precise image analysis and quantification.

Minor comments:

1. It would be helpful if the authors could change gene names to a bold or brighter font in scatter plots in all figures. It is hard to read the names the way they are right now.
2. Line 185 the authors say: "We also analysed the induction of micronuclei (Supplementary figure 6B), which to our knowledge has not been assessed in earlier screens." What screens are the authors referring to? Could you add references here?
3. Line 256: "Each cargo was assayed in two independent screens, in which there was good agreement in general between the effects of the crRNA pools (Supplementary figure 8)." The authors also indicate in the legend for Supplementary figure 8 that "The only metric with a poor R2 score (proportion of cells with two γ- Tubulin puncta) was not used for hit calling." However, the EEA1 localization ratio also shows poor R2 score, shouldn't this screen also be excluded? In general, what was the cutoff for R2 score? This information should be included.

Significance

This work is the first genome-wide loss-of-function CRISPR study (to my knowledge) aimed at identifying dynein-driven trafficking disruption phenotypes. In general, the data generated in this study will enrich the field's understanding of how dynein is regulated and how it achieves its broad cargo and functional specificity.This manuscript will also provide a resource and experimental setup for the design of other genome-wide loss-of-function CRISPR studies.

I have broad expertise in the cytoskeleton field with a detailed understanding of dynein's function from a mechanistic and functional perspective. I have minimal experience with high throughput screening, but I am experienced with CRISPR-based assays and cell imaging techniques.

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Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity____:

Summary: the paper suggested a new approach to study in vivo possible interaction between glioblastoma cells and glioblastoma associated macrophages. By using single cells transcriptome profiling and in vitro and in vivo functional experiments the authors also suggested LGALS1 as possible key factor in the suppression of the immune system and a new target for immune modulation in glioma patients. The experimental plan is well described, and the results are beautifully presented using images, clear drawings, and videos.

Major comments: none

Minor comments:

• The number of zebrafish embryos analyzed after the xenograft is highly variable (e.g. 3-18; 4-22 in Figure 6). These numbers can be reported in the results section (not only in the legends) and the authors may comment on them in the discussion. The reproducibility of thexenotransplant experiments is always challenging as it is quite difficult to inject the same number of cells in every embryo and to have the same survival rate of injected cells and of transplanted embryos. For these reasons the volume of each xenograft can vary significantly in different embryos and in different experimental session. Accordingly, the number of macrophages associated to the tumor can vary and the statistical analysis can be deeply influenced by the number of replicates for each experimental group (a group with 3 embryos is very different in term of quality and quantity of information in respect to a group of 18 embryos). It could be useful for the reader, who has no experience in this technique, be aware of the advantages and disadvantages of the procedure including the possible influence of the temperature (34°C instead of 37°C) on the embryo survival and the replication rate of glioma cells or macrophages behavior. Comment on these aspects does not weaken the power and the relevance of the model but unveil the critical aspects that every scientist has to evaluate before planning these kinds of experiments.

__Response: __We agree with Reviewer #1 that the zebrafish avatar model is challenging, and it is difficult to obtain reproducible tumor sizes and survival rates. To be even more transparent about this, we have added a few sentences about the variable n number in the Results section and a critical comment about it in the Discussion section.

• An aspect that could be interesting to address, to further validate the avatar model, is to monitor the level of pro-inflammatory cytokines (Tumor Necrosis Factor and Interleukin 1, 6, and 8) that are expressed at basal level in the early developing zebrafish embryos. Do their expression level increase after the xenotransplantation? Can the zebrafish cytokines affect the behavior of glioma associated macrophages (i.e. macrophages polarization)?

__Response: __This is an interesting point, indeed. We have injected murine melanoma (B16) cells into Tg(mpeg1:mCherry-F); Tg(TNFa:eGFP-F) embryos, a TNFa reporter line. Some (but not all) macrophages expressed TNFa and their expression decreased over time, which is consistent with previous reports (Póvoa et al, 2021). We further observed that TNFa-expressing macrophages mostly had a round, “tumor-attacking” phenotype. This is in line with our hypothesis that the tumor induces a phenotype switch in GAMs. Of note, we did not see TNFa expression in the rest of the brain tissue. We would be happy to add this data if deemed useful.

We did not investigate other cytokines in the developing zebrafish, but we believe this is not essential for the following reasons: We are mainly interested in the differences between the patient-derived GBM stem cell cultures (GSSCs), and since they are all used in the same avatar model, we expect that if zebrafish cytokines would have an effect on GAMs and their polarization, this effect would be consistent in all avatars, and can thus be ignored when comparing different GSCCs. More importantly, our findings in the zebrafish avatar model were consistent with those in the in vitro model. We observed the same phenotype switch in the co-culture model, indicating that the key interaction is between tumor cells and macrophages.

Significance____:

Strengths and limitation. The manuscript is the result of a well-orchestrated effort to dissect a biological problem by complementary approaches and provide new data with high impact translational value. The image processing pipeline developed by the authors is a step forward in the in vivo analysis of cells interaction in living embryos. The identification of LGALS1 as a potential target for immune modulation can support the development of new therapeutical strategy implementing chemo- or immunotherapy protocols. The described zebrafish avatar can represent a new tool for personalized drug testing recapitulating in a in vivo model the heterogeneity of GBM found in patients.

Audience: All the scientist interested in cell biology, cancer cell biology, imaging techniques, translational medicine, in vivo models for cancer research, precision medicine.

Reviewer expertise: applied developmental biology

Reviewer #2

Evidence, reproducibility and clarity____:

Finotto et al aim to address the polarisation of macrophages within GBM in their study. To do this, they have developed two different models. The first model is an in-vitro co-culture model of patient derived GSC lines and human monocyte derived macrophages. This model was used for single cell sequencing to understand the transcriptomic changes of macrophages upon contact to GBM cells. The second model is a zebrafish xenograft model. Here GFP labeled GBM cells were transplanted into the larval zebrafish ventricle. These experiments were done in the transgenic mpeg zebrafish which allowed to monitor responses of macrophages in vivo.

In my opinion both models are not sophisticated enough to draw solid conclusions on macrophage polarisation in GBM. The in vitro model is highly artificial and is far from the complex situation in GBM. Within GBM the GAM population represents a heterogenous mix of resident microglia and infiltrating macrophages. These are influenced by the heterogeneous environment (which consists of tumour cells but also other host cells) and show diverse transcriptomic adaptations as shown in rodent models as well as sequencing studies of patient derived tumour samples. Studying monocyte derived macrophages in vitro does not provide any reliable insight.

Response: We understand the reviewer’s concern about the complexity of our in vitro model. However, these simple models are needed to gain more insight into the complex in vivo situation. Others have demonstrated their usefulness in the past (C. Jayakrishnan et al, 2019; Zhou et al, 2022; Hubert et al, 2016; Chen et al, 2020; Coniglio et al, 2016; Li et al, 2022). Moreover, it may be advantageous to look at only two different cell types and unravel their reciprocal interaction, without the influence of other cell types, making it too complex to draw conclusions. We acknowledge that GAMs are a heterogeneous mix of both microglia and bone marrow-derived macrophages. Considering that bone marrow-derived macrophages have been shown to play an important role in tumor progression and are by far the most abundant immune cell population in GBM tumors (which even increases in recurrent GBM) (Pombo Antunes et al, 2021; Abdelfattah et al, 2022), we chose to focus initially on bone marrow-derived macrophages. Notably, it has already been reported that microglia were associated with significantly better survival, suggesting that they are anti-tumorigenic, whereas macrophages were associated with worse survival, suggesting that they are pro-tumorigenic (Pombo Antunes et al, 2021; Abdelfattah et al, 2022). This justifies our approach to focus on this cell type. Furthermore, although this model may be rather simplistic, it allowed us to screen different GSCCs side by side in a standardized way, through which we found an apparent phenotype switch within the macrophages, even without the complex interplay with other cell types. Because the results obtained using the in vitro model were also confirmed in GBM patient material and KO experiments in the zebrafish avatar model, our work shows that reliable and important insights can be derived. This, combined with its simplicity, makes our co-culture model an exceptionally relevant model that is scalable, screenable and allows us to study the effect of perturbations. Finally, the immunosuppressive role of the target we identified using this model, LGALS1, has been previously demonstrated by others (Verschuere et al, 2014; Van Woensel et al, 2017; Chen et al, 2019), which proves our approach is valid.

Although the zebrafish can be a great model to understand the progression of tumours and the role of immune cells, I don't think that the model developed by the authors is suitable to address their questions. Transplantation of GBM cells into the the ventricle of larval zebrafish doesn't seem to be the right approach here. The poor survival of the transplanted cells is a clear indication of that. Many other groups have reported growth and proliferation of human cancer cells in the larval zebrafish. Direct transplantation into the brain parenchyma would be the better approach here. The brain parenchyma would provide the right environment for the GBM cells including a resident microglial population. This would also allow to study the complex mix of microglia and infiltrating macrophages in the context of GBM.

Response: The reviewer does not specify which articles have reported growth and proliferation of human cancer cells in zebrafish larvae. Most research groups reporting this, did not follow tumor growth/proliferation over time or used immortalized cell lines (Vargas-Patron et al, 2019; Pan et al, 2020; Pudelko et al, 2018; Breznik et al, 2017; Vittori et al, 2017; Hamilton et al, 2016), which obviously have a much higher proliferation rate than the patient-derived cell lines used in this work. Second, although the number of patient-derived tumor cells decreases over time, we observed a clear invasive and migratory behavior, indicating that the human tumor cells reside well in the zebrafish microenvironment. Furthermore, it is important to note that the zebrafish avatars are grown at 34°C, a temperature that is suboptimal for tumor cell growth. The tumor cells still proliferate, albeit at a lower rate than at 37°C.

To our knowledge, there is only one publication that reports the growth of patient-derived GBM tumors over time (Almstedt et al, 2022). However, here, zebrafish embryos were grown at 33°C. Also, prior to injection, patient-derived GBM cells were resuspended in medium containing polyvinylpyrrolidone, a polymer that enhances extracellular matrix deposition and cell proliferation. Furthermore, the authors observed substantial differences in proliferative capacity, ranging from growth to decline of signal, and represented only two patient-derived cell lines with growing tumors. Similar to our findings, another article has demonstrated that injected patient-derived GBM tumor cells progressively underwent mitotic arrest, while maintaining an invasive and aggressive growth pattern (Rampazzo et al, 2013).

Although the tumor cells are injected into the hindbrain ventricle, they end up in the brain parenchyma, as evidenced by the presence of the typical brain vasculature of the zebrafish embryo. Notably, in Tg(mpeg1:mCherryF)ump2 zebrafish embryos, both macrophages and microglia are labeled with mCherry, meaning that we have studied both cell types in our zebrafish avatar model. Therefore, we consider the reviewer’s comment to be unfounded.

Reviewer #3

__ Evidence, reproducibility and clarity: __

In this study, Finotto and colleagues developed patient-derived Glioblastoma (GBM) stem cell cultures from 7 patients. These GBM stem cell cultures were either co-cultured in vitro with human macrophages combined with single-cell RNA sequencing or injected into the orthotopic zebrafish xenograft to study live GBM-macrophage/microglia interactions. Authors aimed at studying tumor heterogeneity and GBM-associated macrophages (GAMs) which often exhibit immunosuppressive features that promote tumor progression. Their analyses revealed substantial heterogeneity across GBM patients in GBM-induced macrophages polarization and the ability to attract and activate GAMs - features that correlated with patient survival. Also authors show 3 distinct macrophage subclusters (MC1-3), highlighting that the simple M1/M2 polarization phenotypes is too reductive and there are no clear "markers". Authors associate these profiles with morphology and macrophage behaviour. Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified / confirmed that LGALS1 as a primary regulator of immunosuppression.

Cheng et ( DOI: 10.1002/ijc.32102) had previously shown the immunosuppression effect of LGALS1 - but this work shows as a proof of concept that the authors approach is a valuable and interesting approach to find immune regulators.

Response: We fully agree with Reviewer #3. In fact, the immunosuppressive role of LGALS1 has already been described by several research groups (Van Woensel et al, 2017; Verschuere et al, 2014), which indeed proves that our approach is valid. The reference cited by the reviewer was already included in the manuscript, along with other references.

Major comments:

In general claims are supported by date - very carefully presented and well characterized data with numbers, stats. It is an interesting descriptive study that illustrates the complexity and diversity of glioblastoma and the induced TME. I just have a few comments or clarifications that I would like to have elucidated:

• I did not understand why not single cell sequence the original tumor - without in vitro passaging and have the original patient population of MACs/microglia and monocytes sequenced? In other words why sequence the in vitro system-with its inherent caveats of in vitro culturing and not the original tumor? Can you please clarify.

Response: We agree with Reviewer #3 that our in vitro model does indeed have caveats inherent to patient-derived cell culture models. However, we chose this model to specifically focus on the reciprocal interaction between GBM tumor cells and macrophages in a way that also allows us to investigate how perturbations affect these interactions. This is not possible when using original tumors (e.g. we cannot make KO cells, as we did for LGALS1, and study the effects of genes of interest). (See also the response to the comment of Reviewer #2)

We do have scRNAseq data from one original tumor sample (LBT123) that is currently being analyzed. Unfortunately, scRNAseq is not available for the other tumor samples. Also, for some of the patients, there is no original material left to use for sequencing. For LBT123, we will compare the scRNAseq data from the original tumor with the in vitro data from the co-culture model.

• Mac signatures - out of curiosity- authors could not find TNFa and IFN signatures in any population?

Response: Our analyses did not reveal TNF or IFN as cluster signature genes. However, we did find that TNF expression was slightly higher in MC2, the pro-inflammatory macrophages, although still at low levels. We did not find IFN expression in the macrophage subclusters, but we did find low expression of some IFN receptors. We found a gradient for IFNGR1 with the highest expression in MC3, followed by MC1 and the lowest expression in MC2. IFNGR2 was expressed at slightly higher levels in MC1 compared to the other subclusters. IFNAR1 and IFNAR2 were expressed at comparable low levels in all subclusters. Finally, IFNLR1 expression was higher in MC3 compared to the other two macrophage subclusters. Considering the overall low expression of IFN receptors, we believe that the differences in expression are rather negligible. Furthermore, it has been previously shown that IFN exerts its anti-tumor effect primarily through the responsiveness of endothelial cells and not of myeloid cells, such as macrophages (Kammertoens et al, 2017). Since vascular cells were not present in the co-culture model, low IFN receptor expression is not surprising. We are happy to investigate this in more detail and include it if deemed useful.

• 8 please show controls side by side with the KO

Response: We thank Reviewer #3 for this comment. We are not quite sure which panel the reviewer is referring to. If it is panel F, we agree with Reviewer #3 and have changed the order of the bars in the revised version. If it is panel E, the corresponding control images are shown in Figure 5I. Since we believe that these images should not be repeated, we have added a figure reference to Figure 5I in the figure legend of Figure 8, in addition to the figure reference already provided in the text. Furthermore, images of all embryos are presented side by side in Figure S8D-E.

• Figure 5: if each pair of images are separated and have the legend on top would be easier to *read and follow. *

Response: We appreciate the comment that the figure should be intuitively easy to read and follow. However, we have chosen a compromise between overview and visibility of details (e.g. morphological features of GAMs). Since this figure already has the maximum width, the images would become smaller if they needed to be separated. Reducing the size would compromise the visibility of important details.

Significance:

It is a very interesting study, carefully designed and performed that highlights the heterogeneity of glioblastoma and how GBM can modulate the macrophage population into 3 different subsets. This study constitutes a proof of concept of the combination of and in vitro approach and an in vivo approach to find new players and treatments in glioblastoma. I believe that it would be important and interesting to have a the original tumor sequenced to compare to the in vitro platform and understand how the in vitro selection impacts on the tumor biology and even if it changes the heterogeneity and differential composition of the tumor and macrophage profiles.

References:

Abdelfattah N, Kumar P, Wang C, Leu JS, Flynn WF, Gao R, Baskin DS, Pichumani K, Ijare OB, Wood SL, et al (2022) Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat Commun13

Almstedt E, Rosen E, Gloger M, Stockgard R, Hekmati N, Koltowska K, Krona C & Nelander S (2022) Real-time evaluation of glioblastoma growth in patient-specific zebrafish xenografts. Neuro Oncol 24: 726–738

Breznik B, Motaln H, Vittori M, Rotter A & Turnšek TL (2017) Mesenchymal stem cells differentially affect the invasion of distinct glioblastoma cell lines. Oncotarget 8: 25482–25499

Jayakrishnan P, H. Venkat E, M. Ramachandran G, K. Kesavapisharady K, N. Nair S, Bharathan B, Radhakrishnan N & Gopala S (2019) In vitro neurosphere formation correlates with poor survival in glioma. IUBMB Life 71: 244–253

Chen JWE, Lumibao J, Leary S, Sarkaria JN, Steelman AJ, Gaskins HR & Harley BAC (2020) Crosstalk between microglia and patient-derived glioblastoma cells inhibit invasion in a three-dimensional gelatin hydrogel model. J Neuroinflammation 17

Chen Q, Han B, Meng X, Duan C, Yang C, Wu Z, Magafurov D, Zhao S, Safin S, Jiang C, et al (2019) Immunogenomic analysis reveals LGALS1 contributes to the immune heterogeneity and immunosuppression in glioma. Int J Cancer145: 517–530

Coniglio S, Miller I, Symons M & Segall JE (2016) Coculture assays to study macrophage and microglia stimulation of glioblastoma invasion. Journal of Visualized Experiments 2016

Hamilton L, Astell KR, Velikova G & Sieger D (2016) A zebrafish live imaging model reveals differential responses of microglia toward glioblastoma cells in vivo. Zebrafish 13: 523–534

Hubert CG, Rivera M, Spangler LC, Wu Q, Mack SC, Prager BC, Couce M, McLendon RE, Sloan AE & Rich JN (2016) A three-dimensional organoid culture system derived from human glioblastomas recapitulates the hypoxic gradients and cancer stem cell heterogeneity of tumors found in vivo. Cancer Res 76: 2465–2477

Kammertoens T, Friese C, Arina A, Idel C, Briesemeister D, Rothe M, Ivanov A, Szymborska A, Patone G, Kunz S, et al(2017) Tumour ischaemia by interferon-γ resembles physiological blood vessel regression. Nature 545: 98–102

Li H, Yan X & Ou S (2022) Correlation of the prognostic value of FNDC4 in glioblastoma with macrophage polarization. Cancer Cell Int 22

Pan H, Xue W, Zhao W & Schachner M (2020) Expression and function of chondroitin 4-sulfate and chondroitin 6-sulfate in human glioma. FASEB Journal 34: 2853–2868

Pombo Antunes AR, Scheyltjens I, Lodi F, Messiaen J, Antoranz A, Duerinck J, Kancheva D, Martens L, De Vlaminck K, Van Hove H, et al (2021) Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat Neurosci 24: 595–610

Póvoa V, Rebelo de Almeida C, Maia-Gil M, Sobral D, Domingues M, Martinez-Lopez M, de Almeida Fuzeta M, Silva C, Grosso AR & Fior R (2021) Innate immune evasion revealed in a colorectal zebrafish xenograft model. Nat Commun12

Pudelko L, Edwards S, Balan M, Nyqvist D, Al-Saadi J, Dittmer J, Almlöf I, Helleday T & Bräutigam L (2018) An orthotopic glioblastoma animal model suitable for high-throughput screenings. Neuro Oncol 127: 415

Rampazzo E, Persano L, Pistollato F, Moro E, Frasson C, Porazzi P, Della Puppa A, Bresolin S, Battilana G, Indraccolo S, et al (2013) Wnt activation promotes neuronal differentiation of glioblastoma. Cell Death Dis 4

Van Woensel M, Mathivet T, Wauthoz N, Rosière R, Garg AD, Agostinis P, Mathieu V, Kiss R, Lefranc F, Boon L, et al(2017) Sensitization of glioblastoma tumor micro-environment to chemo- and immunotherapy by Galectin-1 intranasal knock-down strategy. Sci Rep 7: 1–14

Vargas-Patron LA, Agudelo-Dueñãs N, Madrid-Wolff J, Venegas JA, González JM, Forero-Shelton M & Akle V (2019) Xenotransplantation of human glioblastoma in zebrafish larvae: in vivo imaging and proliferation assessment. Biol Open 8

Verschuere T, Toelen J, Maes W, Poirier F, Boon L, Tousseyn T, Mathivet T, Gerhardt H, Mathieu V, Kiss R, et al (2014) Glioma-derived galectin-1 regulates innate and adaptive antitumor immunity. Int J Cancer 134: 873–884

Vittori M, Breznik B, Hrovat K, Kenig S & Lah TT (2017) RECQ1 helicase silencing decreases the tumour growth rate of U87 glioblastoma cell xenografts in zebrafish embryos. Genes (Basel) 8

Zhou F, Shi Q, Fan X, Yu R, Wu Z, Wang B, Tian W, Yu T, Pan M, You Y, et al (2022) Diverse macrophages constituted the glioma microenvironment and influenced by PTEN status. Front Immunol 13

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Referee #3

Evidence, reproducibility and clarity

In this study, Finoto and colleagues developed patient-derived Glioblastoma (GBM) stem cell cultures from 7 patients. These GBM stem cell cultures were either co-cultured in vitro with human macrophages combined with single-cell RNA sequencing or injected into the orthotopic zebrafish xenograft to study live GBM-macrophage/microglia interactions. Authors aimed at studying tumor heterogeneity and GBM-associated macrophages (GAMs) which often exhibit immunosuppressive features that promote tumor progression. Their analyses revealed substantial heterogeneity across GBM patients in GBM-induced macrophages polarization and the ability to attract and activate GAMs - features that correlated with patient survival. Also authors show 3 distinct macrophage subclusters (MC1-3), highlighting that the simple M1/M2 polarization phenotypes is too reductive and there are no clear "markers". Authors associate these profiles with morphology and macrophage behaviour.

Differential gene expression analysis, immunohistochemistry on original tumor samples, and knock-out experiments in zebrafish subsequently identified / confirmed that LGALS1 as a primary regulator of immunosuppression. Cheng et ( DOI: 10.1002/ijc.32102) had previously shown the immunosuppression effect of LGALS1 - but this work shows as a proof of concept that the authors approach is a valuable and interesting approach to find immune regulators.

Major comments

In general claims are supported by date - very carefully presented and well characterized data with numbers, stats. It is an interesting descriptive study that illustrates the complexity and diversity of glioblastoma and the induced TME. I just have a few comments or clarifications that I would like to have elucidated: 1. I did not understand why not single cell sequence the original tumor - without in vitro passaging and have the original patient population of MACs/microglia and monocytes sequenced? In other words why sequence the in vitro system-with its inherent caveats of in vitro culturing and not the original tumor? Can you please clarify 2. Mac signatures - out of curiosity- authors could not find TNFa and IFN signatures in any population? 3. Fig. 8 please show controls side by side with the KO 4. Figure 5 if each pair of images are separated and have the legend on top would be easier to read and follow.

Significance

It is a very interesting study, carefully designed and performed that highlights the heterogeneity of glioblastoma and how GBM can modulate the macrophage population into 3 different subsets. This study constitutes a proof of concept of the combination of and in vitro approach and an in vivo approach to find new players and treatments in glioblastoma.

I believe that it would be important and interesting to have a the original tumor sequenced to compare to the in vitro platform and understand how the in vitro selection impacts on the tumor biology and even if it changes the heterogeneity and differential composition of the tumor and macrophage profiles.

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Referee #2

Evidence, reproducibility and clarity

Finotto et al aim to address the polarisation of macrophages within GBM in their study. To do this, they have developed two different models. The first model is an in-vitro co-culture model of patient derived GSC lines and human monocyte derived macrophages. This model was used for single cell sequencing to understand the transcriptomic changes of macrophages upon contact to GBM cells. The second model is a zebrafish xenograft model. Here GFP labeled GBM cells were transplanted into the larval zebrafish ventricle. These experiments were done in the transgenic mpeg zebrafish which allowed to monitor responses of macrophages in vivo.

In my opinion both models are not sophisticated enough to draw solid conclusions on macrophage polarisation in GBM. The in vitro model is highly artificial and is far from the complex situation in GBM. Within GBM the GAM population represents a heterogenous mix of resident microglia and infiltrating macrophages. These are influenced by the heterogeneous environment (which consists of tumour cells but also other host cells) and show diverse transcriptomic adaptations as shown in rodent models as well as sequencing studies of patient derived tumour samples. Studying monocyte derived macrophages in vitro does not provide any reliable insight.

Although the zebrafish can be a great model to understand the progression of tumours and the role of immune cells, I don't think that the model developed by the authors is suitable to address their questions. Transplantation of GBM cells into the the ventricle of larval zebrafish doesn't seem to be the right approach here. The poor survival of the transplanted cells is a clear indication of that. Many other groups have reported growth and proliferation of human cancer cells in the larval zebrafish. Direct transplantation into the brain parenchyma would be the better approach here. The brain parenchyma would provide the right environment for the GBM cells including a resident microglial population. This would also allow to study the complex mix of microglia and infiltrating macrophages in the context of GBM.

Significance

In my opinion both models are not sophisticated enough to draw solid conclusions on macrophage polarisation in GBM.

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Referee #1

Evidence, reproducibility and clarity

Summary: the paper suggested a new approach to study in vivo possible interaction between glioblastoma cells and glioblastoma associated macrophages. By using single cells transcriptome profiling and in vitro and in vivo functional experiments the authors also suggested LGALS1 as possible key factor in the suppression of the immune system and a new target for immune modulation in glioma patients. The experimental plan is well described, and the results are beautifully presented using images, clear drawings, and videos.

Major comments: none

Minor comments: The number of zebrafish embryos analyzed after the xenograft is highly variable (e.g. 3-18; 4-22 in Figure 6). These numbers can be reported in the results section (not only in the legends) and the authors may comment on them in the discussion. The reproducibility of the xenotransplant experiments is always challenging as it is quite difficult to inject the same number of cells in every embryo and to have the same survival rate of injected cells and of transplanted embryos. For these reasons the volume of each xenograft can vary significantly in different embryos and in different experimental session. Accordingly, the number of macrophages associated to the tumor can vary and the statistical analysis can be deeply influenced by the number of replicates for each experimental group (a group with 3 embryos is very different in term of quality and quantity of information in respect to a group of 18 embryos). It could be useful for the reader, who has no experience in this technique, be aware of the advantages and disadvantages of the procedure including the possible influence of the temperature (34{degree sign}C instead of 37{degree sign}C) on the embryo survival and the replication rate of glioma cells or macrophages behavior. Comment on these aspects does not weaken the power and the relevance of the model but unveil the critical aspects that every scientist has to evaluate before planning these kinds of experiments. An aspect that could be interesting to address, to further validate the avatar model, is to monitor the level of pro-inflammatory cytokines (Tumor Necrosis Factor and Interleukin 1, 6, and 8) that are expressed at basal level in the early developing zebrafish embryos. Do their expression level increase after the xenotransplantation? Can the zebrafish cytokines affect the behavior of glioma associated macrophages (i.e. macrophages polarization)?

Significance

Strengths and limitation. The manuscript is the result of a well-orchestrated effort to dissect a biological problem by complementary approaches and provide new data with high impact translational value. The image processing pipeline developed by the authors is a step forward in the in vivo analysis of cells interaction in living embryos. The identification of LGALS1 as a potential target for immune modulation can support the development of new therapeutical strategy implementing chemo- or immunotherapy protocols. The described zebrafish avatar can represent a new tool for personalized drug testing recapitulating in a in vivo model the heterogeneity of GBM found in patients.

Audience: All the scientist interested in cell biology, cancer cell biology, imaging techniques, translational medicine, in vivo models for cancer research, precision medicine.

Reviewer expertise: applied developmental biology

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

Extracellular vesicles (EV) from three different phylotypes (A1, H1, H2) of Cutibacterium acnes were analyzed for size distrubution, protein content and inflammatory effects on cellular systems in vitro. Main findings were that EV composition differs between the the phylotypes that by other have been suggested to have pathogenic or more beneficial properties. Furthermore, A1 EVs induce more proinflammatory signals than H1 EVs. The conclusion are that EVs are key modulators of during skin alterations, and H1 EVs are suggested as a treatment of acne vulgaris symptomatology.

Major comments:

1. The purpose is stated as "to study the role of EVs secreted by three different phylotypes of C. acnes (A1 as 25 pathogenic, H1 as beneficial and H2 as commensal)". This is in my mind a quite vague purpose that needs to be sharpened. What role are you talking about and where is it relevant? During infection, in experimental systems or as potential treatment?
2. The result section in the abstract is very short. At present it is not possible to understand what you have done by reading this. I suggest reducing the introduction part of the abstract and focus on your detailed results instead.
3. The conclusions in the abstract is a bit difficult to understand a potentially very far reaching. You have not in my mind shown that EVs are "key modulators during skin alterations". You have shown that EVs can modulate cellular responses in vitro which is far from skin alterations, but important in its own right.

Furthermore, "EVs as an alternative based-natural treatment to fight acne vulgaris symptomatology" is not really a supported conclusion but rather a potential future implication. The wording is also very odd; what does "alternative based-natural treatment" mean? You also come back to this in the main conclusion in the end. This needs to be clarified! 4. Line 465. You assume that your strains have lost virulence factors during evolution. This is just a wild guess without genetic analysis. It could be anything from real gene loss, via gene regulation, to post-translational regulation. 5. Line 510. You should tone done your claims about a closer picture of real skin in acne vulgaris. You are using models! 6. Since you are claiming an essential role for EVs during skin alterations, you are missing essential controls in your system. What happens if you add just washed bacteria to your systems? If you get the same signals, EVs are not essential but can have the same effects as the parent bacteria.

Minor comments:

Reference list All species should be in italics, No Upper Case Within Article Titles, and journals should be abbreviated consistently.

Significance

General assessment:

The most important aspect of this study is that EVs from different phylotypes of C. acnes have cellular effects that correlates with the pathogenic or beneficial profile of the parent bacteria. The major limitations are that the EVs are not compared with the parental bacteria in the systems and unsupported far reaching conclusions about using EVs as treament.

Advance:

The study is the first one looking at detailed protein patterns in different phylotypes of C. acnes and trying to link this to biological activity.

Audience:

Basic researchers in microbiology will read this with interest. At the moment, the translational/aspects are just suggested and not tested.

Expertise:

Infection medicine, experimental treatment, development of biological drugs, inflammatory diseases, anaerobic bacteria, antimicrobials, bacteriophages, commensals

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Referee #2

Evidence, reproducibility and clarity

The work by Pol et al. describes the proteome and the in vitro effect, in four different cell culture models, of extracellular vesicles (EVs) isolated from Cutibacterium acnes of three different phylotypes found in acneic (A1) and normal skin (H1 and H2). They found that the EVs from H1 and H2 strains seem to evoke less pro-inflammatory effector mechanisms and even anti-inflammatory cytokines. A1 EVs apparently carry a higher number of proteins that include virulence factors and possible pro-inflammatory molecules that have not been specified. Although the number of proteins tends to be quite high in microorganism EVs, the presence of other groups of molecules can not be ignored when drawing conclusions, however. Their results seem to support the conclusions that should anyway be softened, however some points related to the EV concentrations used in the analysis should be cleared, besides other details, in order to make the results stronger to the eyes of expert readers. 1. The abstract needs improvement. You can decrease the background information to a minimum necessary and give more result information.2. In introduction and throughout the text be careful to cite highly updated works when starting with "recent data..." - for e.g., in lines 39-43 the reference cited dates to 2017.3. The Brucella culture medium was chosen to grow C. acnes for isolation of EVs. This is a complex rich medium whose components could eventually associate to the bacteria and EV surfaces, resulting in proteome artifacts. How was that controlled? On the other hand, culture time was long (7 days) for that species. By 7 days in that medium, in which phase of the growth curve are the bacteria, considering that at stationary phase membranes from dead cells could co-precipitate with EVs? 4. EV preps were kept at -20oC. For how long? Are the properties of these EVs maintained fairly intact at these conditions?5. l. 317: I suppose NTA is a more quantitative imaging technique, but not more precise.6. l. 321: Can you present the EV yield/bacteria for each sample? Do the phylotypes grow at similar speed rates? 7. l. 328-331: There seems to be some artifactual effect in the Qubit protein estimation because it's clear from the SDS-PAGE gel that the 3 samples do not have the same 7 micrograms that they should according to the dosage methodology. Sample A1 possibly does considering that many of the protein bands are quite fat. Therefore, you can not compare the samples in terms of diversity or amount if there is not an internal quantity control in the proteome analysis. All the experiments that compared different concentrations of EVs among phylotypes could be compromised by an artifactual protein estimation. Please comment and justify.8. Gene ontology analysis refers to total EV proteins analyzed in each phylotype, is that correct? Supporting information Tables 1, 2, 3: The table headlines should specify that those identified proteins were found EXCLUSIVELY in each of the haplotypes. 9. l. 356: The extension of red staining within the cells seems to reflect that many EVs were internalized. Could you estimate the number of internalized EVs/cell? Please change "positive control" in the figure for EV or anything you choose, considering that this is your experimental result and not a positive control.10. l. 406 and others: the EVs induced or stimulated (not displayed) secretion of mediators.11. In Discussion, please exploit even further the individual proteins found in the proteome to suggest their presumed function in the  various effects observed in the work. In l. 555-556, please rephrase the sentence to make it clear, also softening the conclusion by using "suggested" instead of "proved". Although English is readable throughout the text, edition is needed to improve grammar specially in Discussion where there are numerous inadequacies.

Significance

The characterization of the microorganism EVs and their role in pathogenesis and eventual protection of the diseases they cause has incresed significantly and any new investigation brings new light to the subject. With the present work, the authors claim that EVs isolated H1 strains from normal skin could be useful in the treatment of acne based on their set of results.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This manuscript describes differences between three different C. acnes strains in their proteomics, internalization, and induction of host cell gene expression. These differences are assessed through what the manuscript reports to be extracellular vesicles, but the relevance of these particles is not clear. The strong differences in immunomodulatory phenotypes across strains is of potential interest, but more work needs to be done to demonstrate rigor, relevance, and clear interpretation.

Major concerns:

My primary concern is the relevance of these purported extracellular vesicles, which could have been produced by the filtering and centrifugation process. Can these particles be observed without this processing? Alternatively, can the authors demonstrate that these particles have distinct proteomic properties from the cells from which they were isolated?

A second major concern is the framing of these 3 strains. The evidence of association of type I strains and acne is weak. Most papers making this claim have not compared the same sampling sites on subjects of similar ages, or at least have not reported subject information well enough for this evidence to be clear. Moreover, it is not clear if the difference across 3 strains here are generalizable to these large clades or specific to these chosen strains. Notably, H1 strains are much more closely related to H2 strains than either is to A1 strains, challenging the meaning of the distinction between H1 and H2 strains and more generally the classification as probiotic, commensal, and pathogen. Type IB strains (which includes H1 and H2) have even been suggested to be more harmful in prosthetic infections [PMID: 34361935]. Regardless of the naming, a comparison between A strains and H strains is of interest - but a second A strain is required to make this evolutionary comparison.

• Line 321: A claim is made about EV production rate across strains, but no statistics are performed and a number is only provided for one of the three strains.
• Line 332: H1 and H2 are much more similar in their genomic content. Do the authors have a proposed reason for A1 and H1 sharing more proteomic similarity than H1 and H2?
• Which genome was chosen for analysis of the proteomic data? Might classification be biased towards strains for which the reference genome more closely matches the amino acid content of the analyzed strains?
• Line 341: For the biological function analysis, it is not clear if enrichment presented is relative to other strains or to the reference genome.
• Figure 6 needs to show individual dots of experimental replicates to enable assessment of variation.
• How were the probes for qPCR chosen? This is important for understanding multiple hypothesis correction. Were any others tested?

Minor concerns:

• Line 39: What about the vaginal microbiome?
• Line 51: Context for this depth is needed. How deep is a pilosebaceous unit?
• Line 56: It would be more correct to say that microbiome alterations are associated with the development of acne.
• Line 62: C acnes is known to break down sebum, so why is it assumed that sebum is a barrier for C acnes contact with host cells? What about secreted products that aren't in EVs?
• Line 64: 500 nm is getting close to the size of a bacterial cell
• Line 66: Citation needed.
• Line 92: Is this also done inside of a bag system as in the above section or in an anaerobic chamber?
• Line 96: The point of a 75 mm filter is not understood by this reviewer.
• Line 371: I could not locate a p-value in this entire section around gene expression induction in host cells. I see that there are statistics in the figures, but this needs to be indicated in the text as well.

Significance

This manuscript describes differences between three different C. acnes strains in their proteomics, internalization, and induction of host cell gene expression. These differences are assessed through what the manuscript reports to be extracellular vesicles, but the relevance of these particles is not clear. The strong differences in immunomodulatory phenotypes across strains is of potential interest to those studying C acnes biology, but more strains would need be tested to understand the relevance of these differences evolutionarily and more work would have to be done to understand the relevance of these EV particles. Claims about relevance to acne overstate the knowledge and consensus in the filed.

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Reply to the reviewers

Manuscript number: RC-2023-01899

Corresponding author(s): Laia Querol Cano

1. General Statements [optional]

We thank the reviewers for their positive evaluation and constructive feedback and appreciate the reviewers’ assessment that our findings are highly interesting and novel. We have already addressed some of their questions (see section 3 below and highlighted changes in the manuscript text) and initiated experiments to address the reviewers’ suggestions. We anticipate these experiments can be completed within three months.

2. Description of the planned revisions

Reviewer #1

Summary This paper is focused on the role of galectin-9 in dendritic cells using monocyte-derived DCs. To study the functional characteristics of galectin-9, they depleted galectin-9 with the use of gal9 siRNA. They found galectin-9 is required for TNF-alpha, IL-6, IL-10 and IL-12 secretion. Galectin-9 was found to be involved in TNF trafficking and interacted with Vamp-3 to regulate the release of TNF. They concluded that galectin-9 controls cytokine intracellular trafficking and secretion through functional interaction with the SNARE protein Vamp-3 in DCs through an endosomal compartment.

The findings seem significant in understanding the role of galectin-9 in dendritic cells which has not been previously explored, and they also expand our understanding of the roles of galectins and their potential function in intracellular cytokine trafficking. However, there are some concerns about the findings and conclusions of the study.

Major comments

Supplementary Figure 2A. No obvious decrease in TNF, IL-6, or IL-12 was observed in gal9 siRNA-treated cells that were stimulated with LPS and zymosan, and results in this panel are inconsistent with those of Suppl. Fig. 2B. An explanation for this discrepancy should be provided.

*We thank the reviewer for pointing this out. The discrepancy can be explained by the different time points used in Supplementary Fig. 2A versus Supplementary Figure 2B and main Figure 1. Supplementary Figure 2A depicts the optimisation carried out to screen the best stimuli for each of the cytokines analysed after 36h, which is considerably longer than the timepoints used throughout the manuscript (4-16 h). To make this consistent, we will repeat these experiments using 16 h stimulation. *

The data is sufficient to support the notion that galectin-9 is required for cytokine secretion, but intracellular staining of an additional cytokine (IL-6, IL-10, and IL-12) would be a good addition with controls included (non-transfected cells vs WT vs galectin-9-depleted).

This is a good suggestion and we performed intracellular stainings to detect IL-6, IL-10 and IL-12 on WT and galectin-9 depleted moDCs. Whereas intracellular IL-6 or IL-12 could not be detected with any of the commercial antibodies (IL-6 clone MQ2-13A5, Biolegend #501103; IL-12 clone 20C2, BD Biosciences #557020), we set-up intracelullar IL-10 stainings in primary DCs. IL-10 intracellular accumulation was observed in gal-9 KD DCs compared to WT cells upon LPS treatment although to a lesser extent than TNFα (see figure 1 for reviewer). We are now repeating this assay on multiple donors and this data will be incorporated to the revised manuscript.

Figure 1 for reviewer. Intracellular flow cytometry showing IL-10 levels in NT siRNA (black) and gal9 siRNA (light grey) moDCs treated with LPS for 6 h. Isotype control is depicted with unfilled dashed line. Numbers represent geometric mean intensity.

Addition of an immunofluorescent experiment using Vamp-3 and TNF co-localization in gal9 siRNA-treated cells would strengthen observations regarding galectin-9 association with Vamp-3 in immunoprecipitation.

• We agree and will address this by:*

• Immunofluorescence studies with moDCs (three independent donors with galectin-9 depletion (and Non Targeting siRNA counterparts) stimulated with LPS for 2, 4 and 6 h. Staining of Vamp-3, TNF-α, galectin-9 and DAPI is already established as have used these antibodies throughout the manuscript and thus do not anticipate any issues when performing these assays.

• *Co-localisation of galectin-9 and Vamp-3 will be determined by quantifying both the Manders’ and Pearson’s correlation coefficients (See Major point #4 from reviewer #1 in section 3). *

Figure 7A. Vamp-3 does not appear to redistribute towards the cell membrane following LPS stimulation in this figure. Either a different set of images needs to be selected or the text needs to be revised.

*To address this issue, we will include an enlarged zoomed in image of a representative cell in the revised version of the manuscript. Furthermore, we will include a Golgi marker (GM130) in our staining panel to quantify Vamp-3- Golgi co-localisation in WT and Galectin-9 depleted moDCs treated with LPS. *

All findings in this study regarding galectin-9 immunoreactivity are dependent on a single goat anti-galectin-9 antibody (AF2045). Findings would be strengthened by the use of a second galectin-9-specific antibody in at least one additional experiment (either immunofluorescence or immunoprecipitation).

*We have purchased another anti galectin-9 antibody (clone 9M1-3, BioLegend #348902) that will be used for immunofluorescence experiments to confirm our findings. *

Minor comments

Figure 2D needs at least one more experiment before results can be depicted. An n=2 is not sufficient to merit publication.

• We agree and will conduct the same experiment as described in Figure 2D with one additional donor to obtain n=3. Data from panel 2E will be also updated to incorporate the new data set.*

Supplementary Figure 2 has additional small square symbols in panels A and B that should be removed.

• We apologise for this. Supplementary Figure 2A will be re-made with new donors for the revised version of the manuscript without the square symbols. Supplementary Figure 2B has been remade to include four donors for each time point and stimulation and the square symbols have been removed.*

Supplementary Figure 2 legend. This legend has repetitive text regarding representative data from one donor. How many donors were tested for these experiments?

• Please see minor comment #5 above. Only a representative donor was included for panel A but experiments are being conducted to replace this figure with a more complete one including data from at least three independent experiments. Panel B has been remade and now includes data from four independent donors.*

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

This paper shows that knock down of galectin-9 suppresses secretion of certain cytokines in activated dendritic cells. Then they show that this correlates with failure of the peripheral localization of the SNARE- protein VAMP3 and suggest that this is due to a direct interaction with galectin-9.

Most of the data consist of comparing wt and galectin-9 KD cells regarding secretion and trafficking of selected cytokines (with focus on TNFa), and of cellular localization of the vSNARE VAMP3. The cells are mainly LPS activated human dendritic cells derived from differentiation on blood monocytes from donors, but differentiated THP-1 cells are also used. These data show convincingly that cytokine secretion is inhibited in galectin-3 KD cells, and for TNFa studied on detail, this is due to failure of post-Golgi trafficking of the, shown in different ways, including a RUSH assay. The trafficking of the vSNARE VAMP3 to the periphery of the cells is also inhibited by absence of galecytin-9, leading to its retention in the Golgi nearer the nucleus. Thus this couples in a surprising the unsolved question of how SNAREs themselves are trafficked to their correct destinations, to the function of a cytosolic galectin.

The weak part of the paper is the molecular interaction between galectin-9 and VAMP3. This is based on co-immunoprecipitations followed by proteomic characterization and Western blots as summarized in Fig. 6. These data show that galectin-9 and VAMP3 occur in the same precipitated complex, but not that they interact directly. Many other proteins are also in these complexes, and the Western blot data are not very strong. Thus additional experiments would be needed to claim the direct interaction as depicted in Fig.8 for example using purified recombinant proteins, or sharpened focus using mutants of the two interactors.

• We thank the reviewer for these positive comments and agree that protein co-immunoprecipitation does not warrant direct interaction. To discern whether galectin-9 and Vamp-3 directly interact or are part of a bigger protein complex, we will use purified recombinant proteins in GST-pull down assays. Briefly, we will generate and express Vamp-3-GST constructs that will be incubated with recombinant Galectin-9 protein, which has been performed in a similar manner in Miller et al., Cell. 2011. Protein complexes will be resolved and analysed by SDS-Page. GST-only beads will be used as negative control and a known SNARE complex (Syntaxin 4 together with Snap23) will be used as positive control in these experiments. We have experience in producing recombinant proteins in HEK293 cells And GST-pull down experiments within our department. *

3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1:

Major Comments

1. Page 4, Results. The assertion of 90% downregulation of galectin-9 protein is not substantiated by the data shown in Supplementary Figure 1 (it is not indicated which data, the flow or Western blot, provides the source for this statement). It must be assumed it is from Western blots, as the data from flow cytometry does not show a reduction of 90%. This assertion would be strengthened by density analysis of Western blots shown as a graph beside the blots in Supplementary Figure 1C. *We thank the reviewer for addressing this point. This refers to the Western blot data. We have now quantified four independent donors and have added this graph as a new panel in Supplementary Figure 1 (new panel D). *

Mander's correlation coefficient is typically not advised for use in co-localization of immunofluorescence since it has been found to downplay associations in low intensity fluorescent staining and favors high intensity and co-occurrence. It also ignores blank pixels. What is your reasoning for using Mander's instead of Pearson's correlation coefficient in your study?

• Mander’s correlation coefficient describes co-occurrence and is used to determine the fraction of the protein of interest that co-localises with another protein (Arruda et al., 2014. Nature Medicine; Horner et al., 2011. PNAS). Our question was to quantify to what extend do cytokines (using TNFα as proof of principle) localise within a particular organelle and therefore we believe using Mander’s correlation coefficient is appropriate. The reviewer is right in saying that Mander’s calculations ignore blank pixels although Pearson coefficients are also highly dependent on pixels coming from unlabelled regions, which can be an issue when quantifying the presence of cytokines in vesicles and requires the use of a threshold to discern background from relevant signal. We have calculated the Pearson’s correlation coefficient for all the relevant figure panels, which shows similar co-localisation as the Mander’s quantification. This data can be found in new Supplementary Figure 5.*

Page 8, Discussion. It would be interesting to suggest a mechanism that explains galectin-9-mediated depletion of Vamp-3 protein levels (which is suggestive of transcriptional repression), particularly since experiments suggest that gal9 siRNA treatment did not affect transcription of cytokines.

We have modified the discussion in page 10 to further speculate on the mechanisms by which galectin-9 may reduce Vamp-3 protein levels. Based on our RNAseq data of WT and galectin-9 depleted moDCs that show no differences in Vamp-3 gene expression, we believe galectin-9 is important for Vamp-3 stabilisation rather than participating in Vamp-3 transcriptional regulation.

Page 8, Discussion. The statement that the findings from this study is in line with the report that extracellular recombinant galectin-9 enhances IL-6 and IL-8 secretion in mast cells and IL-12 secretion in moDCs is somewhat confusing. Does extracellular galectin-9 cross the cell membrane into the cytoplasm? What is the evidence that it is capable of acting intracellularly when exogenously applied?

• We regret that this explanation did not come across clearly in the manuscript. We agree with the reviewer that there is no evidence that exogenously added galectins can re-enter the cytoplasm and be functionally active. To avoid any confusion, we have rephrased the text to “in addition, other studies report…”. *

Minor Comments

1. Figure 2A needs to have better colors chosen to indicate gal9 KD_LPS and gal9 KD_LPS/BFA+Mon samples, as they show similar colors. *We agree and have modified the figure accordingly. *

Figure 6D. Vamp-3 immunoreactivity shown in the blot does not appear to diminish on gal9 siRNA treatment as suggested in Fig. 6E. A better representative blot should be shown.

*For clarification purposes, we have added the quantification of the Vamp-3 representative blot to Figure 6D. To emphasise the differences in Vamp-3 levels upon galectin-9 depletion we have also added the quantification of Snap23 to Figure 6E. *

Supplementary Figure 1 A and B need a legend to show NT and gal9 siRNA-treated samples.

• We have now added this legend to Supplementary Figure 1A and B.*

Supplementary Figure 4, is it possible to have a merged image without galectin 9 (nuclei + TNF) for better clarity of TNF localization relative to nuclei?

We addressed this point and replaced the previous merged image with one only containing the fluorescent signals corresponding to nuclei (DAPI) and TNF*α. *

Please explain more explicitly why TNF+ cell % increased in gal9 siRNA-treated cells while secretion trended downward in these cells. Presumably TNF is retained in the GM130+ Golgi apparatus following knockdown of galectin-9 but this is not clearly explained in the text on page 6.

• We have rephrased the text on page 6 to address the reviewer’s concern. The text has been modified to “Overall, these results demonstrate TNFα is retained in the GM130 positive Golgi complex following galectin-9 depletion thus establishing galectin-9 as essential for cytokine trafficking to the plasma membrane via the endosomal machinery”.*

Lack of literature/rationale support for the use of CD80, CD86, CD83, HLA-DR as markers for plasma membrane protein trafficking being unaffected by galectin 9 depletion. These require further support to explain their use as good markers for general cellular trafficking.

Dendritic cells are well-known to upregulate CD80, CD86, CD83 and HLA-DR membrane expression upon maturation (Immunobiology of Dendritic Cells., Banchereau et al., 2000. Annual Review of Immunology; Reis e Sousa., 2006. Nat Rev Immunology). In resting dendritic cells, these proteins are stored in the endosomal compartment but traffic to the membrane upon activation and are therefore well-established markers for intracellular transport and membrane re-organisation upon dendritic cell activation (Klein et al., 2005. International Immunology; Baravalle et al., 2011. Journal of Immunology). We have added a sentence to the results section (page 4) to explain the rationale for choosing these markers.

Figure 8. Please show cortical actin cytoskeleton in this figure and correct spelling for vesicle in left panel.

• We have added the actin cytoskeleton to the figure and corrected the spelling mistake. *

Reviewer #2:

A brief summary/discussion of the time aspect would also be of interest as the experimental setups are quite complex. The KD after siRNA obviously takes some hours, but after that stimulation with LPS appears to take many more hours to affect VAMP3 distribution in wt cells (Fig. 7). What is it that takes so much time? The time from ER - plasma membrane is more like 30 minutes for a constitutively secreted protein. The RUSH experiment (Fig. 5) also show a relatively fast passage of TNFa out of ER-Golgi in wt cells.

• We thank the reviewer for raising this point. It takes 36-48 h for the gal9 siRNA to be effective after which galectin-9 levels stay depleted for up to 72 h. We chose 6 h to analyse Vamp-3 redistribution based on our ELISA experiments (Supplementary Figure 2B) that show cytokine secretion peaks 16 h after LPS stimulation. We agree with the reviewer that cytokine gene transcription, translation and protein trafficking also occurs earlier but endogenous intracellular cytokine levels are not high enough to detect them using confocal microscopy. Similarly, the RUSH experiment (Figure 5) was done using an over-expression system in which much higher levels of TNFα are being produced. *

• To further clarify this we have included a schematic depicting the experimental setup and times (see figure 2 for reviewer). *

Figure 2 for reviewer. Experimental setup. Monocyte-derived dendritic cells (moDC) are obtained from blood samples and differentiated for 6 days to generate immature dendritic cells (DCs). At day 3 of the differentiation moDCs are transfected with either non-targeting (NT) or gal9 siRNA to deplete galectin-9 protein levels and obtain wild type or galectin-9 knockdown (gal-9 KD) DCs. Six days after isolation, cells were treated with LPS for 6 h (to allow sufficient endogenous cytokine to accumulate intracellularly) prior to being fixed and immunofluorescent experiments (IF) conducted.

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

*This section is not applicable. *

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Referee #2

Evidence, reproducibility and clarity

This paper shows that knock down of galectin-9 suppresses secretion of certain cytokines in activated dendritic cells. Then they show that this correlates with failure of the peripheral localization of the SNARE- protein VAMP3 and suggest that this is due to a direct interaction with galectin-9.

Most of the data consist of comparing wt and galectin-9 KD cells regarding secretion and trafficking of selected cytokines (with focus on TNFa), and of cellular localization of the vSNARE VAMP3. The cells are mainly LPS activated human dendritic cells derived from differentiation on blood monocytes from donors, but differentiated THP-1 cells are also used. These data show convincingly that cytokine secretion is inhibited in galectin-3 KD cells, and for TNFa studied on detail, this is due to failure of post-Golgi trafficking of the, shown in different ways, including a RUSH assay. The trafficking of the vSNARE VAMP3 to the periphery of the cells is also inhibited by absence of galecytin-9, leading to its retention in the Golgi nearer the nucleus. Thus this couples in a surprising the unsolved question of how SNAREs themselves are trafficked to their correct destinations, to the function of a cytosolic galectin.

The weak part of the paper is the molecular interaction between galectin-9 and VAMP3. This is based on co-immunoprecipitations followed by proteomic characterization and Western blots as summarized in Fig. 6. These data show that galectin-9 and VAMP3 occur in the same precipitated complex, but not that they interact directly. Many other proteins are also in these complexes, and the Western blot data are not very strong. Thus additional experiments would be needed to claim the direct interaction as depicted in Fig.8 for example using purified recombinant proteins, or sharpened focus using mutants of the two interactors.

A brief summary/discussion of the time aspect would also be of interest as the experimental setups are quite complex. The KD after siRNA obviously takes some hours, but after that stimulation with LPS appears to take many more hours to affect VAMP3 distribution in wt cells (Fig. 7). What is it that takes so much time? The time from ER - plasma membrane is more like 30 minutes for a constitutively secreted protein. The RUSH experiment (Fig. 5) also show a relatively fast passage of TNFa out of ER-Golgi in wt cells.

Referees cross-commenting

I agree with all the comments of REviewer 1. Especially major comment 1 and 2 about the confusion of the figures. Major comment 8 is also highlöy relevant, as I also raiused but phrased ion a different way. There is so far no evidence that externally added galectin can reenter the cytosolic compoartment; it is only taken up inside vesicles,

Significance

This is highly interesting and novel. Very few papers have coupled SNARE function to galectins before, with key exception from group of Deretic et al regarding secretory autophagy suggesting association of galectin-3 to TRIM16 (not a SNARE, but can be SNARE associated).

Many follow up questions come up which could have been addressed in this paper or in a future paper. For example, could the Galectin-9 KD phenotype be rescued by added galectin-9 from the outside, as seen for many cases with other galectins? Were any other SNAREs affected?

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Referee #1

Evidence, reproducibility and clarity

Summary

This paper is focused on the role of galectin-9 in dendritic cells using monocyte-derived DCs . To study the functional characteristics of galectin-9, they depleted galectin-9 with the use of gal9 siRNA. They found galectin-9 is required for TNF-alpha, IL-6, IL-10 and IL-12 secretion. Galectin-9 was found to be involved in TNF trafficking and interacted with Vamp-3 to regulate the release of TNF. They concluded that galectin-9 controls cytokine intracellular trafficking and secretion through functional interaction with the SNARE protein Vamp-3 in DCs through an endosomal compartment.

The findings seem significant in understanding the role of galectin-9 in dendritic cells which has not been previously explored, and they also expand our understanding of the roles of galectins and their potential function in intracellular cytokine trafficking. However, there are some concerns about the findings and conclusions of the study.

Major Comments

1. Page 4, Results. The assertion of 90% downregulation of galectin-9 protein is not substantiated by the data shown in Supplementary Figure 1 (it is not indicated which data, the flow or Western blot, provides the source for this statement). It must be assumed it is from Western blots, as the data from flow cytometry does not show a reduction of 90%. This assertion would be strengthened by density analysis of Western blots shown as a graph beside the blots in Supplementary Figure 1C.
2. Supplementary Figure 2A. No obvious decrease in TNF, IL-6, or IL-12 was observed in gal9 siRNA-treated cells that were stimulated with LPS and zymosan, and results in this panel are inconsistent with those of Suppl. Fig. 2B. An explanation for this discrepancy should be provided.
3. The data is sufficient to support the notion that galectin-9 is required for cytokine secretion, but intracellular staining of an additional cytokine (IL-6, IL-10, and IL-12) would be a good addition with controls included (non-transfected cells vs WT vs galectin-9-depleted)
4. Mander's correlation coefficient is typically not advised for use in co-localization of immunofluorescence since it has been found to downplay associations in low intensity fluorescent staining and favors high intensity and co-occurrence. It also ignores blank pixels. What is your reasoning for using Mander's instead of Pearson's correlation coefficient in your study?
5. Addition of an immunofluorescent experiment using Vamp-3 and TNF co-localization in gal9 siRNA-treated cells would strengthen observations regarding galectin-9 association with Vamp-3 in immunoprecipitation.
6. Figure 7A. Vamp-3 does not appear to redistribute towards the cell membrane following LPS stimulation in this figure. Either a different set of images needs to be selected or the text needs to be revised.
7. Page 8, Discussion. It would be interesting to suggest a mechanism that explains galectin-9-mediated depletion of Vamp-3 protein levels (which is suggestive of transcriptional repression), particularly since experiments suggest that gal9 siRNA treatment did not affect transcription of cytokines.
8. Page 8, Discussion. The statement that the findings from this study is in line with the report that extracellular recombinant galectin-9 enhances IL-6 and IL-8 secretion in mast cells and IL-12 secretion in moDCs is somewhat confusing. Does extracellular galectin-9 cross the cell membrane into the cytoplasm? What is the evidence that it is capable of acting intracellularly when exogenously applied?
9. All findings in this study regarding galectin-9 immunoreactivity are dependent on a single goat anti-galectin-9 antibody (AF2045). Findings would be strengthened by the use of a second galectin-9-specific antibody in at least one additional experiment (either immunofluorescence or immunoprecipitation).

Minor Comments

1. Figure 2A needs to have better colors chosen to indicate gal9 KD_LPS and gal9 KD_LPS/BFA+Mon samples, as they show similar colors.
2. Figure 2D needs at least one more experiment before results can be depicted. An n=2 is not sufficient to merit publication.
3. Figure 6D. Vamp-3 immunoreactivity shown in the blot does not appear to diminish on gal9 siRNA treatment as suggested in Fig. 6E. A better representative blot should be shown.
4. Supplementary Figure 1 A and B need a legend to show NT and gal9 siRNA-treated samples.
5. Supplementary Figure 2 has additional small square symbols in panels A and B that should be removed.
6. Supplementary Figure 2 legend. This legend has repetitive text regarding representative data from one donor. How many donors were tested for these experiments?
7. Supplementary Figure 4, is it possible to have a merged image without galectin 9 (nuclei + TNF) for better clarity of TNF localization relative to nuclei?
8. Please explain more explicitly why TNF+ cell % increased in gal9 siRNA-treated cells while secretion trended downward in these cells. Presumably TNF is retained in the GM130+ Golgi apparatus following knockdown of galectin-9 but this is not clearly explained in the text on page 6.
9. Lack of literature/rationale support for the use of CD80, CD86, CD83, HLA-DR as markers for plasma membrane protein trafficking being unaffected by galectin 9 depletion. These require further support to explain their use as good markers for general cellular trafficking.
10. Figure 8. Please show cortical actin cytoskeleton in this figure and correct spelling for vesicle in left panel.

Referees cross-commenting

I agree with the comments of Reviewer #2 and especially that galectin-9 and VAMP-3 co-IP does not necessarily indicate that they are bound together. I also concur that a galectin-9 rescue experiment would be valuable.

Significance

General Assessment.

Strengths of study: Novel findings showing a role for galectin-9 in regulating cytokine trafficking and release from dendritic cells. This is a new observation which has not be reported before for galectin-9.

Weaknesses: Some results need verification and additional experiments are required to confirm the findings.

Advance: There is no existing knowledge for a role for galectin-9 in cytokine trafficking and this study fills this gap in existing published knowledge. The kind of advance that the study makes is fundamental and conceptual.

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Reply to the reviewers

1. General Statements

__Response: __Thank you to all the reviewers for their helpful efforts on behalf of our manuscript. At current, we have addressed most of the reviewers’ major comments, including providing additional replicates for many experiments and clarifying ambiguous points in the text. Related data, figures and text have been adjusted accordingly. We believe that these changes have improved our manuscript, both strengthening our main conclusions and clarifying ambiguous text.

Several still-ongoing experiments are elaborated below. These experiments are well within the abilities of our lab and can be completed in short order.

Specific responses to the individual concerns addressed by the reviewers are outlined below.

Please feel free to contact me if I can be of any help in the decision process.

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]

Comment: Across the manuscript, NIX levels appear to be unresponsive to most treatments in the MDA-MB-231 line, including hypoxia treatment. This is an unusual result and raises questions about the role of NIX in MDA-MB-231 line, mainly that BNIP3 is the primary driver of mitophagy in this system. Indeed, Figure 7D indicates that there is very little mitophagy contribution by NIX since knockout of BNIP3 is sufficient to abolish mitophagy almost completely. Therefore, the effects seen on mitophagy following EMC3 knockout in Figure 7 might be smaller in a line that is responsive to NIX mitophagy. It would be beneficial to analyse basal mitophagy flux in an additional cell line, for example U2OS (FigS1E) in which NIX is responsive to hypoxia.

Response: Thank you for bringing this intriguing insight to our attention. We have seen that EMC3 knockout prevents lysosomal delivery of BNIP3 in U2OS cells (Fig S2D). However, we don’t know what the effects on mitophagy are in U2OS, or the extent to which mitophagy is dependent on BNIP3 and/or NIX. To test this, we will perform the suggested experiment, taking mt-Keima expressing U2OS cells testing the role of NIX and/or BNIP3 in mitophagy.

Comment: Following on from comment 1 above, Figure 7 would benefit with an analysis of hypoxia (or DFP, or cobalt chloride) stimulation of mitophagy to assess whether mitophagy levels are higher in EMC3 KOs. The authors argue that BNIP3 is trafficked to the ER during mitophagy and is not turned over by mitophagy itself, it would therefore be interesting to test if BNIP3 is prevented from being removed from mitochondria whether this would affect the rate or levels of mitophagy under stimulating conditions.

• *

__Response: __To address this question, we will perform mitoflux analysis on EMC3 KO cells +/- hypoxia.

Comment: Figure 4B: The localisation of tf-BNIP3 is reminiscent of ER in BTZ treated samples. How much of the protein is on mitochondria in the presence of BTZ? Does MLN4924 cause a similar issue?

__Response: __To address this question, we will perform fluorescence microscopy of tf-BNIP3 cells co-expressing mito-BFP under these treatments and utilize our Coloc2 plugin pipeline to monitor correlation.

• *

Comment: Can the authors assess whether BNIP3 that is on mitochondria is transferred to the ER (perhaps through photoswitchable GFP-BNIP, activated on mitos and then observe its transfer to ER)? This seems important in order to address the possibility that BNIP3 that is being turned over by the endolysosome is being delivered directly to the ER.

• *

__Response: __This is an interesting question and a curiosity also shared by Reviewer #2. To test this hypothesis, we will utilize a photo-switchable Dendra2 fluorophore to track BNIP3 in the cell via microscopy.

• *

[Reviewer #2]

Comment: How is BNIP3 inserted into the outer membrane? A previous study from the Weissman lab proposed that MTCH2 serves as insertase. The authors did not mention MTCH1 and MTCH2 in context of Fig. 2B. Were these proteins not found? Did the authors test the relevance of MTCH2 in their assay? This aspect should be addressed and mentioned.

__Response: __Thank you for the insight and suggestion. We were intrigued when the Weissman/Voorhees paper characterizing MTCH1/2 was published. Consistent with their findings, MTCH2 was found in the “suppressor” population of our tf-BNIP3 CRISPR screen, but given our 0.5-fold change threshold, the gene was not validated (fold change value = 0.46, Table S1). We suspect the lack of significance stems from the redundancy with MTCH1. Consequently, we would hypothesize that MTCH1/2 are the responsible insertases. To formally address this suggestion, we plan to genetically perturb MTCH1/2 and look at BNIP3 localization and mitophagy.

• *

Comment: The authors generated an interesting BNIP3 mutant with a C-terminal Fis1 anchor. This variant is constantly located in the outer membrane (which is shown here). The physiological consequence of the constitutive distribution on mitochondria is however only superficially studied. The authors should characterize this interesting mutant in some more depth.

• *

__Response: __In the original manuscript, we characterized BNIP3(Fis1TMD) for lysosomal delivery and mitophagy. Going forward, we will perform Seahorse oxygen consumption experiments and mitochondrial network analysis to view the physiological consequences of constitutive expression of BNIP3(Fis1TMD) on the outer membrane.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

• *

[Reviewer #1]

Comment: Continuing from comment 2, given that the authors conclude that BNIP3 is not turned over by mitophagy, can they examine whether BNIP3 is excluded from sealed mitophagosomes?

__Response: __We have softened the wording of our conclusions to reflect that the vast majority of BNIP3 lysosomal degradation is by this alternative pathway and not mitophagy. However, we do not wish to completely dismiss that BNIP3 is present on mitophagosomes. Rather, if mitophagosomes contain BNIP3, they seemingly account for only a very small portion of BNIP3 degradation in the cell, to the extent that it is not easily detectable by our assays (Lines 414-419). Definitively identifying whether BNIP3 is in sealed mitophagosomes will be part of future studies using CLEM or FIB-SEM techniques.

Comment: Is the BNIP3(FisTMD) expressed to equivalent levels to WT BFP-BNIP3? Given that theFis1 form of BNIP3 cannot traffic to endolysosomes, its levels might be higher. In addition, overexpression of the BNIP3-Fis construct was used to make the argument that dimerization is not important for mitophagy. But the authors should also take into account the possibility that with overexpression, the potential efficiency afforded to mitophagy via dimerization of endogenous proteins may be negated, and therefore hidden. Given this, I don’t think that the authors can confidently conclude that dimerization does not contribute to mitophagy, and that instead its main role is ER-endolysosomal turnover of BNIP3.

__Response: __We thank the reviewer for pointing out the possible over-interpretation of our data. Overexpression is an important caveat to consider. We would expect the Fis1 form of BNIP3 to be higher in protein levels given its deficiency in endolysosomal trafficking. Still, as the reviewer points out, over-expression could be mitigating the effect of our dimerization mutants. This caveat is now discussed in the manuscript and our interpretations regarding this fact have been greatly softened (Lines 373-376, Lines 449-462).

• *

Comment: Please include molecular weight markers for all western blots.

• *

__Response: __All western blots have now been labeled with molecular weight markers.

Comment: Figure 5A-G: These data do not make a convincing case for the role of dimerization and are very difficult to follow. Only the mislocalized S172A mutant was responsive to Baf treatment, while the LG swap mutant which is mitochondrial and cannot dimerize is unaffected by Baf treatment. Figure 5H-I utilize a construct of BNIP3 that is missing most of the protein and which has very low turnover (Figure 5B). Unfortunately these results don’t make a highly convincing case about the biology of native, full length, mitochondrial BNIP3. The authors are advised to either strengthen the dimerization argument, or perhaps lighten the language around the main conclusions from these data.

Response: __Thank you for bringing the lack of clarity to our attention. Both dimer mutants of BNIP3 (S172A and LG swap) are insensitive to Baf-A1 treatment. These results hold for full-length BNIP3 using either the tf (__Fig 5D) or IRES (Fig 5I) reporter. To demonstrate that defects in lysosomal transport were due to dimerization defects (and not other, unanticipated effects of the mutations), we looked at whether chemically induced dimerization could reverse the trafficking defects. Indeed, forced dimerization of the ER-restricted variant rescued ER-to-lysosome trafficking. From this, we conclude that that dimerization is a critical facet of BNIP3 trafficking to the lysosome.

We have re-worked the relevant text (both in results and discussion) to clarify major points and lighten the language around the conclusions from these data (described below).

First, as mentioned above, we have added a significant discussion about the limitations of our assay and of possible interpretations. (Lines 300-303, Lines 323-326, Lines 483-489).

Second, with regards to the specific construct used in this experiment, we have expanded the results section to better describe our rationale and approach (Lines 304-308). In short, because dimerization of native BNIP3 occurs within the membrane, we aimed to place the DmrB domain as close to the TM segment as possible. Due to the topology of TA proteins, a C-terminal tag isn’t possible. Therefore, we used the shortest truncation version of BNIP3 (117-end) that undergoes measurable lysosomal delivery. This was an important experimental consideration, and one we did not sufficiently rationalize in the original manuscript. We now include this point in the text.

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[Reviewer #2]

Comment: The authors show that BNIP3 on the ER is not stable but degraded by the proteasome. Does this require ERAD factors? Is the mitochondrial BNIP3 protein likewise degraded by proteasomal degradation? It is not clear whether both BNIP3 pools are constantly turned over or whether degradation exclusively/predominantly occurs on the ER surface.

Response: __These are fascinating mechanistic questions. We hope to thoroughly address these questions in a subsequent study. However, as a teaser, we have included the basic answer to these questions in __Fig 5I.

To preliminarily characterize the proteasomal degradation of ER- and mitochondrial-BNIP3, we utilized our IRES reporter system - adapted from Steve Elledge’s system for degron monitoring (Fig 5I). Strikingly, our ER-restricted BNIP3 mutation (S172A) is sensitive to inhibition of both the proteasome and the AAA-ATPase p97/VCP, a key extractase for ERAD substrates. These data tentatively suggest an ERAD-dependent degradation mechanism (although many follow-up studies will be needed to confirm the mechanistic details). In sharp contrast, our mitochondrial-restricted mutant (LG Swap) is sensitive to proteasome inhibition by Bortezomib, but it is insensitive to VCP inhibition. The differential requirement for VCP suggests that proteasomal degradation occurs on both cellular pools of BNIP3 albeit through different mechanisms.

Comment: The results of the screen shown in Fig. 2B are particularly interesting for readers. The glutathione peroxidase GPX4 was found as a top hit among the EMC components. GPX4 protects membranes (including those of mitochondria) against oxidative damage, is a major component of ferroptosis and linked to mitochondrial dysfunction and mitophagy. The authors should mention this interesting hit in the context of their discussion of the lipid-sensing properties of the dimerizing TM domains of BNIP3.

__Response: __Thank you to Reviewer #2 for bringing this to our attention. The relationship between GPX4 and BNIP3 flux is very interesting. We have incorporated GPX4 into the discussion section (Lines 457-459).

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[Reviewer #3]

Comment: For all of the tf-BNIP3 FACS data (all violin plots), it is unclear how many biological replicates were performed. The author only stated that at least 10,000 cells were analyzed per sample, but I believe this is for each biological replicate. To better demonstrate the biological replicates, the authors should consider using bar graphs of the medians(triplicates) with error bars.

Response: We have included biological replicates of FACS data in all primary figures (except for Fig.1C). Biological replicates, represented as medians (in triplicate), are indicated in figure legends.

Comment: In Fig 3D, it is unclear as to why there is no basal state accumulation of BNIP3 protein levels compared to Baf1A treated condition especially with USO1 and SAR1A KO samples. Is this because BNIP3 are targeted for proteasomal degradation? I think Fig 3D should include a BTZ treatment next to Baf1A to account for the lack of basal state accumulation of BNIP3.

Response: We apologize for the lack of clarity on this point. Yes, the reviewer’s interpretation of the data is correct. This point is more clearly elaborated in the text of our revised manuscript (Lines 219-223). Our results indicate that when lysosomal degradation is diminished, the expected increase in total BNIP3 protein levels is attenuated by proteasomal degradation (as evidenced by the hyperstability of BNIP3 upon Bortezomib treatment in mutant backgrounds). As requested, we have included the same knockout panel, now treated with BTZ (Fig S2E). These genetic data are further supported by Fig 3E, where a small molecule inhibitor of vesicle trafficking, Brefeldin-A, ameliorates the effect of lysosomal inhibition (BafA1) but exacerbates the effect of proteasome inhibition.

Comment: Truncation of proteins could affect their protein stability even during their synthesis. For Fig 5B and 6B, the authors should show the blots for the expression of the different truncated mutants to prove that the change in BNIP3 stability and their effect of mitoflux (or lack thereof), is not due to poor expression of these mutants.

Response: These were important potential caveats to document, and we thank the reviewer for their comment.

We note that, due to differences in transduction efficiency, western blot data is an incomplete measure for relative expression levels – it cannot distinguish between fraction of cells transduced and expression level per cell. However, RFP fluorescence (Fig 5B) and BFP fluorescence (Fig 6B) are fluorescent internal controls allowing us to assess expression levels with single cell resolution. We have provided histograms of RFP and/or BFP intensity (new Fig S4A, Fig S5B), which provides support that overall expression levels of these constructs are similar. Critically, any variation we observe does not correlate with any of the effects we report.

In addition, we have clarified the figure axis in Fig 5B to indicate that the value we are reporting is the “fold-stabilization upon BafA1 treatment”. The original figure legend wasn’t clear. Our metric (fold-stabilization) is internally normalized to compensate for differences in expression level. This is an important clarification.

Comment: For the data in Fig 7, the authors demonstrated that treating cells with proteasomal inhibitor increases mitoflux. Since the proteasome targets monomeric BNIP3 for degradation, the logical assumption is that BTZ drives dimerization of BNIP3. Can the authors demonstrate this in an approach similar to Fig 5C? This simple experiment will add significant insight into the study.

Response: __Thank you for the suggestion. As Fig 5C relied on BNIP3 over-expression, we thought it even more informative to assess the effects of BTZ on dimerization of endogenous BNIP3. Indeed, we see accumulation of an SDS-resistant BNIP3 dimer in cells treated with BTZ (__new Fig S2E, line 221). We hypothesize that BTZ indirectly drives dimerization of BNIP3 by accumulating the total levels of the protein, potentiating monomers to form additional stable dimers.

Comment: In line 168-169, "In addition, multiple suppressor genes identified from our screen had previously been reported including TMEM11..." -- Unclear what biology they are reported to be involved in

__Response: __We have clarified this line to read: "In addition, we recovered multiple known suppressors of BNIP3 flux, including outer membrane protein spatial restrictor TMEM11, mitochondrial protein import factors DNAJA3 and DNAJA11, and mitochondrial chaperone HSPA9"

Comment: Along the line with Major comment 2, the explanation for Fig 3D needs to be better elaborated, perhaps to include the role of proteasome already at this point (if the authors think this is the reason why basal BNIP3 levels remains low with USO1 and SAR1A KO).

__Response: __We have included a discussion about compensation by the proteasome in these genetic backgrounds (lines 219-226) and have referred to the newly incorporated western blot (new Fig S2E).

Comment: Line 302-304, I believe that statement only refers to Fig S4C and the statement for Fig5G is in the next sentence. Please remove Fig5G from line 304. It was confusing to read.

Response: __The reference of __Fig 5G has been removed.

Comment: Line 367, there is a reference for Fig S5C but that figure is missing.

__Response: __The spurious reference has been removed.

Comment: Line 410-411, are there any reported clinical cases of EMC mutations with phenotypes that could be explained by elevated mitophagy?

__Response: __Thank you for the suggestion. There are clinical presentations of EMC mutations and splice variants in diseases and conditions related to the central nervous system (PMID: 23105016, PMID: 26942288, PMID: 29271071). However, all characterization has been done in the clinical setting looking at clinical presentations/symptoms and not molecular or cellular characterization. We have added a line to the discussion about this speculative correlation between EMC deficiency and mitophagy (lines 516-519).

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

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

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[Reviewer #1]

Comment: Figure 3B: Are the red puncta observed in USO1 and SAR1A cells a product of higher levels of ER-phagy owing to BNIP3's high presence on the ER membrane?

__Response: __This is an intriguing hypothesis. We will test whether this is true using a USO1/ATG9A dual KO. However, we don’t think this result is critical to the overall arc of the manuscript and we will not include these data if they indicate otherwise.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, the authors provided a comprehensive study on the regulation of BNIP3 protein levels by both the non-autophagic lysosomal and proteasomal degradation pathways. Using various nifty tools, the authors demonstrated that ER-localised BNIP3 dimers are rerouted via the ER-lysosomal pathway for non-autophagic lysosomal degradation whereas BNIP3 monomers on both the ER and mitochondria are targeted for proteasomal degradation. Together, these pathways help to repress hyperactivation of basal mitophagy.

Overall, I find this project very well executed and the manuscript is also very clear and concise. The key conclusions of this work are supported by orthogonal approaches, thus making the data highly convincing. I only have a few major and several minor comments for this manuscript:

Major comments:

1. For all of the tf-BNIP3 FACS data (all violin plots), it is unclear how many biological replicates were performed. The author only stated that at least 10,000 cells were analysed per sample but I believe this is for each biological replicate. To better demonstrate the biological replicates, the authors should consider using bar graphs of the medians (triplicates) with error bars.
2. In Fig 3D, it is unclear as to why there is no basal state accumulation of BNIP3 protein levels compared to Baf1A treated condition especially with USO1 and SAR1A KO samples. Is this because BNIP3 are targeted for proteasomal degradation? I think Fig 3D should include a BTZ treatment next to Baf1A to account for the lack of basal state accumulation of BNIP3.
3. Truncation of proteins could affect their protein stability even during their synthesis. For Fig 5B and 6B, the authors should show the blots for the expression of the different truncated mutants to prove that the change in BNIP3 stability and their effect of mitoflux (or lack thereof), is not due to poor expression of these mutants.
4. For the data in Fig 7, the authors demonstrated that treating cells with proteasomal inhibitor increases mitoflux. Since the proteasome targets monomeric BNIP3 for degradation, the logical assumption is that BTZ drives dimerization of BNIP3. Can the authors demonstrate this in an approach similar to Fig 5C? This simple experiment will add significant insight into the study.

Minor comments:

1. In line 168-169, "In addition, multiple suppressor genes identified from our screen had previously been reported including TMEM11..." -- Unclear what biology they are reported to be involved in
2. Along the line with Major comment 2, the explanation for Fig 3D needs to be better elaborated, perhaps to include the role of proteasome already at this point (if the authors think this is the reason why basal BNIP3 levels remains lowl with USO1 and SAR1A KO).
3. Line 302-304, I believe that statement only refers to Fig S4C and the statement for Fig 5G is in the next sentence. Please remove Fig5G from line 304. It was confusing to read.
4. Line 367, there is a reference for Fig S5C but that figure is missing.
5. Line 410-411, are there any reported clinical cases of EMC mutations with phenotypes that could be explained by elevated mitophagy?

Significance

My expertise lies in organelle-selective autophagy and protein homeostasis. Overall, I think this is a very strong manuscript and the data are very solid. The work adds to our current understanding of the basal regulation of BNIP3 which was not previously explored. The novelty of this work lies in the unexpected regulation of BNIP3 via an autophagy-independent, lysosomal pathway and the observation has the potential to be extended to the regulation of the stability of other tail-anchored proteins. This is a very specialised study and will be of interest to the mitophagy and transmembrane protein regulation community.

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Referee #2

Evidence, reproducibility and clarity

Mitochondria are degraded by autophagy in a reaction that depends on mitophagy receptors, such as BNIP3 and BNIP3L/NIX. These receptors are regulated on the level of gene expression, but also on a post-translational level. This study elucidates the processes which control the levels of BNIP3 in a process that relies on the alternative distribution of the protein to different cellular compartments. BNIP3 is a tail-anchored protein that is inserted into the mitochondrial outer membrane and, alternatively, into the ER membrane. The authors show that insertion into the ER is dependent on the EMC complex and, to a lesser extent, on the GET complex. After insertion into the ER membrane, BNIP3 can be trafficked to lysosomes for degradation or, alternatively, be degraded by proteasomal proteolysis. This study provided evidence that the conditional distribution of BNIP3 to these different intracellular locations is used to control mitophagy even though the specific conditions which determine the alternative destinations remain largely unexplored. The study is of high technical quality, all important controls are shown, and the text is well written.

Specific points

1. The mechanism of the alternative distribution is not addressed here. Is the location of BNIP3 dependent on where the newly synthesized protein is initially targeted to (such as in the case of Pink1) or is there a constant redistribution and flux of the protein between the two membranes? This is an important aspect which should be experimentally addressed and some data to this should be already published as part of this study since this aspect is important for the final model proposed.
2. How is BNIP3 inserted into the outer membrane? A previous study from the Weissman lab proposed that MTCH2 serves as insertase. The authors did not mention MTCH1 and MTCH2 in context of Fig. 2B. Were these proteins not found? Did the authors test the relevance of MTCH2 in their assay? This aspect should be addressed and mentioned.
3. The authors show that BNIP3 on the ER is not stable but degraded by the proteasome. Does this require ERAD factors? Is the mitochondrial BNIP3 protein likewise degraded by proteasomal degradation? It is not clear whether both BNIP3 pools are constantly turned over or whether degradation exclusively/predominantly occurs on the ER surface.
4. The authors generated an interesting BNIP3 mutant with a C-terminal Fis1 anchor. This variant is constantly located in the outer membrane (which is shown here). The physiological consequence of the constitutive distribution on mitochondria is however only superficially studied. The authors should characterize this interesting mutant in some more depth.
5. The results of the screen shown in Fig . 2B are particularly interesting for readers. The glutathione peroxidase GPX4 was found as a top hit among the EMC components.GPX4 protects membranes (including those of mitochondria) against oxidative damage, is a major component of ferroptosis and linked to mitochondrial dysfunction and mitophagy. The authors should mention this interesting hit in the context of their discussion of the lipid-sensing properties of the dimerizing TM domains of BNIP3.

Significance

Many studies in the last years focused on the roles of Pink and Parkin in the context of mitophagy, a system that also relies on alternative protein targeting (in that case between the inner and outer membrane of mitochondria). The study here shows that BNIP3, another highly important mitophagy receptor, uses in principle a similar strategy, however, here the alternative targeting occurs between the mitochondrial outer membrane and the ER membrane. Mechanistic insights are provided, for example also into the different domains of BNIP3 and their relevance for targeting and mitophagy. The study therefore addresses an important aspect, is of excellent quality and will be of interest for a broad readership.

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Referee #1

Evidence, reproducibility and clarity

Recent work by several groups has revealed that NIX and BNIP3 levels can be regulated through ubiquitination, mediated by FBXL4, to restrict mitophagy. In this study, the authors identify an additional avenue for the regulation of BNIP3 levels involving the transfer BNIP3 from mitochondria to the ER and eventually into the endolysosomal system for degradation. In addition, the authors argue that most of BNIP3 turnover during mitophagy is through the newly identified ER pathway and not through mitophagy. There was little to no endolysosomal turnover observed for NIX, and therefore ethe authors predominantly focused on BNIP3. Key ER transfer factors required for BNIP3 endolysosomal turnover were identified through whole genome CRISPR/Cas screening, and include EMC3. Knockout of EMC3 results in slightly higher levels of mitophagy under basal conditions, and higher levels of mitophagy following proteasome inhibition with BTZ, supporting the overall conclusion that BNIP3 levels are regulated by lysosomal turnover.

Major Comments:

1. Across the manuscript, NIX levels appear to be unresponsive to most treatments in the MDA-MB-231 line, including hypoxia treatment. This is an unusual result and raises questions about the role of NIX in MDA-MB-231 line, mainly that BNIP3 is the primary driver of mitophagy in this system. Indeed, Figure 7D indicates that there is very little mitophagy contribution by NIX since knockout of BNIP3 is sufficient to abolish mitophagy almost completely. Therefore, the effects seen on mitophagy following EMC3 knockout in Figure 7 might be smaller in a line that is responsive to NIX mitophagy. It would be beneficial to analyse basal mitophagy flux in an additional cell line, for example U2OS (Fig S1E) in which NIX is responsive to hypoxia.
2. Following on from comment 1 above, Figure 7 would benefit with an analysis of hypoxia (or DFP, or cobalt chloride) stimulation of mitophagy to assess whether mitophagy levels are higher in EMC3 KOs. The authors argue that BNIP3 is trafficked to the ER during mitophagy and is not turned over by mitophagy itself, it would therefore be interesting to test if BNIP3 is prevented from being removed from mitochondria whether this would affect the rate or levels of mitophagy under stimulating conditions.
3. Continuing from comment 2, given that the authors conclude that BNIP3 is not turned over by mitophagy, can they examine whether BNIP3 is excluded from sealed mitophagosomes?
4. Figure 4B: The localisation of tf-BNIP3 is reminiscent of ER in BTZ treated samples. How much of the protein is on mitochondria in the presence of BTZ? Does MLN4924 cause a similar issue?
5. Is the BNIP3(FisTMD) expressed to equivalent levels to WT BFP-BNIP3? Given that the Fis1 form of BNIP3 cannot traffic to endolysosomes, its levels might be higher. In addition, overexpression of the BNIP3-Fis construct was used to make the argument that dimerization is not important for mitophagy. But the authors should also take into account the possibility that with overexpression, the potential efficiency afforded to mitophagy via dimerization of endogenous proteins may be negated, and therefore hidden. Given this, I don't think that the authors can confidently conclude that dimerization does not contribute to mitophagy, and that instead its main role is ER-endolysosomal turnover of BNIP3.
6. Can the authors assess whether BNIP3 that is on mitochondria is transferred to the ER (perhaps through photoswitchable GFP-BNIP, activated on mitos and then observe its transfer to ER)? This seems important in order to address the possibility that BNIP3 that is being turned over by the endolysosome is being delivered directly to the ER.

Minor comments:

1. Figure 3B: Are the red puncta observed in USO1 and SAR1A cells a product of higher levels of ER-phagy owing to BNIP3's high presence on the ER membrane?
2. Please include molecular weight markers for all western blots.
3. Figure 5A-G: These data do not make a convincing case for the role of dimerization and are very difficult to follow. Only the mislocalized S172A mutant was responsive to Baf treatment, while the LG swap mutant which is mitochondrial and cannot dimerize is unaffected by Baf treatment. Figure 5H-I utilise a construct of BNIP3 that is missing most of the protein and which has very low turnover (Figure 5B). Unfortunately these results don't make a highly convincing case about the biology of native, full length, mitochondrial BNIP3. The authors are advised to either strengthen the dimerization argument, or perhaps lighten the language around the main conclusions from these data.

Significance

Overall, this is a valuable and important study that provides an important new advance into how mitophagy is regulated by mitophagy receptors. It adds another layer of regulation in addition to the ubiquitin-proteasome mediated restriction of mitophagy reported by others. The data are predominantly convincing and make a strong argument for endolysomal turnover of BNIP3 to regulate its levels. This study will be of high interest to the field of mitophagy. There is also general interest to the field of mitochondrial biology that a TA mitochondrial (and peroxisomal) protein can be extracted from mitochondria, transferred to the ER, and eventually to the endolysosomal system.

Reviewer expertise: mitophagy mechanisms, autophagosome formation

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Reply to the reviewers

Reply to the reviewers

Dear Editor and reviewers,

We would like to thank the three reviewers for their thorough review of our manuscript and their detailed comments and very helpful suggestions to improve the manuscript. Overall, we thought the reviews were very positive with the reviewers commenting that our discovery of a novel genetic code variant is a “cause for celebration” and that our study is “technically solid” and “rigorous”. All three reviewers agree that our manuscript would “stimulate new discussions in the field of genetic code evolution” and also be of broad interest to evolutionary cell biologists, protistologists and the translation/protein synthesis community at large. The reviewers highlight the particular novelty of the genetic code variant described here due to it being an exception to the wobble hypothesis which adds a new level of complexity to stop-codon reassignment. The reviewers share our frustration about the lack of proteomics data due to being unable to establish a stable culture but acknowledge that we address this limitation frankly in our discussion and agree that it is “frustrating but it's not a limitation”.

We present an updated and improved version of the manuscript after taking on board the reviewers’ suggestions. Our point-by-point responses to their comments and our modifications are detailed below in bold.

Point-by-point description of the revisions

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

Summary

This study by J. McGowan and colleagues reports the discovery of a ciliate species that uses a variant genetic code where the codons UAA and UAG, which are stop codons in the canonical code, instead code for lysine and glutamate respectively. The primary data are genomic and transcriptomic sequence libraries from single cells. The genetic code was predicted by aligning coding sequences to references from other species and examining the most frequent amino acids in positions homologous to putative coding-UAA/UAGs. They also identified suppressor tRNAs for UAA and UAG, and tandem in-frame stop UGAs (but not UAA/UAG) in the 3'-UTR, which further support the recoding of UAA and UAG.

A limitation of this study (and several other recent studies on variant genetic codes) is that the predictions are based on nucleic acid sequencing, without confirmation from proteomics. The authors acknowledge and briefly but frankly discuss the limitations in their manuscript (lines 258-261).

Major comments

Controls against contamination and sequence chimeras

The ciliate species studied here was an environmental isolate, and sequence libraries were prepared by amplification from small pools of cells sorted by FACS. The genome assembly was produced by co-assembly of multiple amplified libraries. Given the potential for contamination and amplification artefacts (such as sequence chimeras) associated with these methods, I think it is important to demonstrate that the data truly originate from one species, so as to rule out the possibility that the co-assembly may be chimeric, i.e. representing two or more organisms with different genetic codes (one with UAA recoded and the other with UAG recoded, for instance). Even if the cell sorting was accurate, contamination could still enter down the line during library preparation so it would be important to show internal evidence from the sequence data too.

We understand the reviewer's concerns about the possibility of contamination as it can be a major issue in environmental single cell sequencing experiments. We have addressed the individual points below in detail to demonstrate that we have generated a clean genome assembly of a single ciliate species but also summarise here:

• The cells we sequenced originated from the same clonally isolated cell propagated in culture
• We have manually curated the assembly
• The assembly has a unimodal GC content peak with a low BUSCO duplication score
• Most genes (95.9 %) contain both in-frame UAA and UAG codons
• We recovered a single identical ciliate 18S rRNA gene across all 10 samples
• De novo assemblies of the 10 individual gDNA libraries are virtually identical in terms of average nucleotide identity
• We also predicted the genetic code for each of the genome and transcriptome samples individually

• 85% of the final assembly is taxonomically classified as Ciliophora. The remainder is either unclassified (i.e. no hits) or has spurious/inconsistent hits

  Specifically:

(a) From the description in Methods under "Sampling, Ciliate isolation, culturing, and cell-sorting", it is not clear whether all the cells that were ultimately sequenced originated from the same clone (i.e. the same well in the 96-well plate described in line 389). Could the authors confirm whether this was the case?

Yes. All the sorted cells originated from the same ciliate clone. A single-cell was isolated and cleaned (without removing all the environmental bacteria). The ciliate single-cell divided and we established a mono-clonal ciliate culture that we used for the cell sorting and sequencing. This culture grew but only for a relatively short period. We could not establish a long term culture.

(b) What % of genes have in-frame coding UAA, UAG, or both? How per gene on average? Counts are given for the conserved genes/domains identified by PhyloFisher or Codetta (lines 192-207), and overall frequencies per codon are addressed later in lines 263 onward, but how often do they occur together in the same genes?

My reasoning behind this is that if genes with both in-frame coding UAA and UAGs are common then it is very unlikely to be the result of chimeric sequence artefacts from whole-genome amplification.

We have updated the text to include this information. From the PhyloFisher analysis, we had reported that 58 genes contained in-frame UAA codons and 46 genes contained in-frame UAG codons. We have now added the text “Amongst the genes identified by PhyloFisher, 27 contained both an in-frame UAA codon and an in-frame UAG codon.”

Additionally, from our annotated gene set, we had reported that 98.6% of genes contain at least one UAA codon and 96.4% of genes contain at least one UAG codon. We have now added text to report how many genes contain both codons “The reassigned codons are widely used across genes with 95.9% of genes containing both a UAA codon and a UAG codon”.

The example gene (tubulin gamma chain protein) shown in Figure 1 contains both in-frame UAA codons and in-frame UAG codons, with the UAA codons aligning to lysine and the UAG codons to glutamic acid.

(c) What is the sequence identity of conserved marker sequences between the individual amplified replicate libraries?

I would naively expect that individual replicates may not have the full set of markers because of uneven amplification, but if the sequences originate from the same clone they should have overlapping coverage of the conserved markers, and these should be +/- identical between replicates (save for allele variants). If so this would support the claim that contaminant sequences were mostly removed during sequence QC and that the cells were clonal.

We generated an individual assembly for each of the 10 gDNA libraries and calculated average nucleotide identity at the whole assembly level. On average, the 10 assemblies are 99.43% identical to each other, with the least similar pair being 99.37% identical to each other. This level of variation includes not only allelic variants but also sequencing/assembly errors as the individual libraries are relatively low coverage. In terms of assembly alignment coverage (i.e. the fraction of each assembly that is aligned to another assembly), the average value is 76.5% and the value for the lowest pair is 59.1%. We have now also made the individual 10 assemblies available in the Zenodo repository (10.5281/zenodo.7944379) and updated the methods section.

Furthermore, as an additional quality control step, we predicted the genetic code for each of the 10 individual genome assemblies and obtained the same predictions that UAA encodes lysine and UAG encodes glutamic acid for all 10 individual assemblies. We also predicted the genetic code for each individual RNA-Seq sample based on individual transcriptome assemblies which yielded consistent predictions.

(d) Line 392: "Non-axenic" presumably refers to environmental prokaryotes. This also appears to contradict the statement that the cells were "free of any other contaminant" (line 387). Could authors confirm whether they mean "non-axenic but monoeukaryotic"?

In line 387, when we say "free of any other contaminant” we mean that we isolated a ciliate single-cell from the environmental sample, and the picked ciliate cell was washed 3 times until it was free of any other eukaryotes, but still containing environmental bacteria. In line 392, when we say non-axenic, we mean that the mono-clonal ciliate culture contained environmental bacteria and was monoeukaryotic.

We have modified the text in the methods section to say “free from any other eukaryote” and “non-axenic but monoeukaryotic”.

(e) Lines 448-451: More details should be given on the criteria used to identify and bin out contaminants. MetaBAT typically bins prokaryotic genomes quite well, but not eukaryotic ones. What did the bins look like and how were the eukaryotic ones chosen?

We routinely use MetaBAT2 to assist with separating bacterial contigs from protist genomes. From our experience we find that it generally performs well but requires careful manual curation. We only use tetranucleotide frequencies when binning single-cell assemblies and not coverage variance as this is heavily skewed due to amplification bias from single-cell amplification. We integrated the binning results from MetaBAT2 with taxonomic classification from tools such as CAT, Blobtools and Tiara, and manually curated the assembly.

We have modified both the results and methods section to clarify that the assembly was manually curated to remove contaminant contigs.

For example, using CAT, which taxonomically classifies contigs based on blast/diamond hits to open reading frames:

The final curated assembly is 69.7 Mb in length.

59.5 Mb (85.4%) is classified as Ciliophora.

9.7 Mb (13.9%) is unclassified.

The remaining 0.5 Mb (0.7%) have inconsistent, low-identity hits to 22 different Eukaryotic and Bacterial phyla (due to lack of closely related species in public databases).

Furthermore, we recovered only a single ciliate 18S rRNA gene and the final curated assembly has a unimodal GC content peak with a low BUSCO duplication score and high cDNA mapping rate.

__Minor comments __

Line 52: Not strictly true, some germline-limited segments contain mobile elements with coding sequences, e.g. TBE elements in Oxytricha (doi:10.1371/journal.pgen.1003659)

Thank you for pointing this out. We have rephrased “excision of non-coding sequences” to “excision of micronucleus-limited sequences” to describe the process of macronuclear development more generally.

Lines 229-231, Supplementary Table 1: Presenting the identity matrix as a distance tree may make it easier to see the pattern of similarity between the tRNAs

We have added a phylogenetic network of tRNA genes as a supplementary figure to better visualise the relationships between tRNA genes.

Lines 274-275: Suggest stating the criterion for classifying genes as "highly expressed" on the first mention of this in the Results, although it's explained later on in the Methods.

We have clarified this in the results section by adding the text: ‘We defined a subset of genes as “highly expressed” based on the 10% of genes with the highest transcripts per million (TPM) values for comparison below.’

Lines 298-299: What is the frequency of tandem UGA stops in the 3'-UTR in genes with coding-UAA/UAG vs. genes without, and is there a significant difference? The argument in this paragraph is that UAA+UAG reassignment increases selective pressure to minimize translational readthrough. Therefore I think that it would make sense to compare the frequency in genes with and without these codons.

Following the reviewer’s suggestion, we have looked at tandem UGA stop codons in the 3’-UTR of genes that don’t use UAA and genes that don’t use UAG. We found similar enrichment for in-frame UGA codons at the beginning of the 3’-UTR in these small subsets of genes.

To clarify, the hypothesis from the literature is that there may be stronger selective pressure to maintain tandem stop codons in ciliates with reassigned genetic codes, particularly those that use only UGA as a stop codon. Within a genome, we wouldn’t expect a difference if a gene contains UAA/UAG codons.

Lines 353-354, Figure 5: Suggest marking the internal nodes where genetic code changes likely occurred. At the moment only the leaves of the tree are annotated with the genetic codes of the respective species. This would make it clearer how one counts the numbers of independent origins as reported in the text (e.g. "... a fourth independent origin of UGA being translated as tryptophan").

We have decided not to label the internal nodes on the phylogeny. We think that deeper sampling will reveal that some of these genetic code changes occurred independently, so we don’t want the figure to be misleading. Also, for the species with the genetic code UAA=Q, UAG=Q and UGA=W, we can’t determine the order of events.

Lines 371-372: Question out of curiosity (not necessary to address for the manuscript at hand): Do the authors think the recoding of UAA and UAG happened simultaneously in both codons or stepwise, or is there insufficient information to speculate?

An initial guess would be that it happened as a stepwise process but without deeper sampling of this lineage it is not possible to determine the order of events.

This highlights the need for deeper sampling and sequencing across undersampled lineages of ciliates and demonstrates the utility of single-cell OMICs approaches for species that are not yet amenable to culturing.

Line 395: "10uL" should use the actual symbol for "micro" prefix. Also, the choice of spacing or no spacing between numerical figure and units should be made consistent in manuscript.

Fixed

Line 403: "Biotynilated" should be "Biotinylated"

Fixed

Line 414 and elsewhere: "2" in MgCl2 should be subscripted

Fixed

Lines 419-420: Clarify whether the "r" and "+" symbols are to be read as prefixes or suffixes, i.e. is the modified base the preceding or succeeding one.

We have clarified in the text that these symbols are to be read as prefixes.

Table 1: What is the difference between the two sets of BUSCO completeness scores reported? One is given under "Genome assembly" and the other under "Genome annotation", but the annotation is based on the same assembly, right? I'm assuming this has to do with different modes in which BUSCO can be run, but this should be explained in the Methods (lines 452-453, 496-497) and briefly explained in the Table caption.

Yes this is because we ran BUSCO in two different modes. BUSCO is run in genome mode on the genome assembly and in protein mode on the genome annotation. In genome mode gene prediction is performed by Augustus guided by amino acid BUSCO group block-profiles while in protein mode the gene set described in our methods is the input to BUSCO classification. The superior BUSCO results for the protein mode reflect the superiority of our final annotation over that generated by BUSCO Augustus. We have added text to the methods section and to the table caption to clarify which mode was used.

**Referee Cross-commenting** I generally agree with the other reviewers' comments. Specifically I like reviewer #3's suggestion #3 to have a more detailed summary of the codon frequencies, perhaps as a graphic, and to compare the tandem stop frequencies with other ciliate species, especially those with all three canonical stops.

Reviewer #1 (Significance (Required)):

Any new genetic code variant discovered is a cause for celebration! This is a basic biological fact with inherent significance and should be generally interesting to biologists because the rarity of variant codes stands in contrast to the diversity of most biological systems.

This variant code would also stimulate new discussions in the field of genetic code evolution specifically because, as the authors point out, when both UAA and UAG are recoded they both usually encode same amino acid, but here they are recoded to different ones. This is an apparent exception to the "wobble" hypothesis for why these codons often evolve in concert, which was well explained with relevant citations in the Introduction.

For context: My expertise is in genomics and environmental microbiology.

END reviewer 1

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

This study reports the reassignment of the UAA and UAG stop codons to lysine and glutamic acid, respectively, in the ciliate Oligohymenophorea sp PL0344. The paper is nicely written, easy to read and the experimental approach, ideas and questions are easy to follow. The work is technically solid both at the NGS - in house library preparation, sequencing and data interpretation - as well as phylogeny levels. The conclusions are consistent with the comparative genomic and transcriptomic data obtained by the study.

__Reviewer #2 (Significance (Required)): __

The work extends current knowledge on codon reassignment in ciliates, confirming previous discoveries of existence of very high stop codon assignment flexibility in these organisms. The assignment of UAA and UAG to two different amino acids by two different tRNAs is very interesting and reinforces the idea that stop codon reassignment in ciliates is rather common. It also raises important questions about the parallel evolution of the release factor-1 (eRF1), Lysine and Glutamine tRNAs, as the reassignment requires loss of recognition of both UAA and UAG by eRF1 with parallel appearance of the new Lysine and Glutamic Acid suppressor tRNAs.

The main issue of this work is the inability to cultivate the ciliate Oligohymenophorea sp PL0344 in the laboratory to prepare protein extracts for direct analysis of the amino acids inserted at UAA and UAG sites by Mass Spectrometry. The comparative genomic and transcriptomic data, as well as the identification of cognate tRNA anticodons for UAA and UAG, are likely correct, but provide indirect evidence for the assignment of UAA to Lysine and UAG to Glutamic Acid. This issue is relevant because one cannot exclude the possibility of insertion of other amino acids at UAA and UAG sites beyond Lysine and Glutamic acid, respectively; nor can one exclude the possibility that such amino acids are inserted at high level. The authors do acknowledge the limitations of the unavailability of protein extracts for direct MS analysis of the reassignment, but should consider, in particular in the discussion, the possibility of multiple amino acid insertions in a context where Lysine and Glutamine Acid are the major but not the only amino acid species being inserted at those sites.

Based on my expertise of studying codon reassignments in fungi of the CTG clade, I believe this work is very interesting and appealing to the genetic code community, and is of relevance to the evolution and protein synthesis research communities at large.

We thank the reviewer for their positive review. They raise an important point about the possibility of amino acids other than lysine and glutamic acid being inserted for UAA/UAG codons which we hadn’t considered. We have added text and relevant references to our discussion to highlight this possibility:

“Additionally, while the genomic and transcriptomic data provide strong evidence that lysine and glutamic acid are the major translation products of UAA and UAG codons, respectively, we cannot rule out the possibility that other amino acids are (mis)incorporated at these sites which could be detected using mass-spectrometry [38, 39].”

Krassowski T, Coughlan AY, Shen X-X, Zhou X, Kominek J, Opulente DA, et al. Evolutionary instability of CUG-Leu in the genetic code of budding yeasts. Nat Commun. 2018;9:1887. Mordret E, Dahan O, Asraf O, Rak R, Yehonadav A, Barnabas GD, et al. Systematic Detection of Amino Acid Substitutions in Proteomes Reveals Mechanistic Basis of Ribosome Errors and Selection for Translation Fidelity. Molecular Cell. 2019;75:427-441.e5.

END reviewer 2

__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __

Summary: from genome and transcriptome sequencing of what appears to be a novel ciliate from the class Oligohymenophorea, McGowan et al provide convincing evidence of a protist in which the stop codons UAA and UAG have almost certainly been recoded to specify incorporation of different amino acids (UAA = K; UAG = E) during translation. Several ciliates from different classes use a non-standard genetic code (as do a narrow variety of other protists), but this is an unusual observation in that stop codons which differ only in the wobble position code for different amino acids in the ciliate identified here.

I say 'almost certainly' the stop codons have been recoded in Oligohymenophorea sp. PL0344 because in the absence of being able to retain the ciliate in culture the authors have not been able to complete the proteomics which would unequivocally (a) show stop codons now code for amino acids and (b) confirm the identity of the amino acids now encoded (the authors discuss this issue on p12).

Comments: overall this manuscript is straightforward to read and the analyses realistically taken as far as is realistic in the absence of a continuous culture method. My suggested revisions should be straightforward for the authors to address.

1) The manuscript appears to report the identification and genome/transcriptome sequencing of a novel ciliate species - clarity should be provided by the authors. However, it disappointed me that this manuscript was crafted entirely from nucleotide sequencing. I would have welcomed seeing the morphology of the ciliate identified here and would have anticipated that there was sufficient material to perform microscopy at the light level (for DIC images) and by scanning or transmission electron microscopy.

Yes, based on the 18S rRNA sequence and phylogenies of protein-coding genes, this is a novel species that hasn’t been described before. The most similar hits to the 18S rRNA gene are to other unnamed/environmental sequences. We haven’t attempted to name or describe this species as we weren’t able to establish a culture, so have referred to it as Oligohymenophorea sp. PL0344. We have clarified in the text that this is a novel, unnamed ciliate species.

The genomic and transcriptomic data was generated from a single cell isolate propagated into micro-cultures of 10’s of cells. These were done in the strictest conditions in an attempt to minimise contamination. Consistent with this approach it was not possible to obtain useful SEM/TEM as it would be very hard to recover EM imaging from 10’s of cells (a process that would have drastically reduced our ability to do replete genome sampling). Similarly, our approach to culturing limited our ability to acquire useful DIC images. After discovering that this ciliate uses a novel genetic code, we attempted on a number of occasions to re-isolate the same species from the same and surrounding water bodies but failed.

2) It is unfortunate that the ciliate could not be maintained in culture (or cryopreserved). Coordinates for the University Parks pond are provided, but I got the impression that this ciliate could be repeatedly isolated. Thus, in the absence of culture methods could the authors indicate the points in the year when the ciliate could be isolated (i.e. is there a season element to when PL0344 could be isolated) and how frequently when sampling was performed could PL0344 be seen? From the environmental sequence data that is publicly available is there any evidence for the presence of PL0344 anywhere else in the world? I'd be surprised if this was a UK-specific ciliate.

The water sample from which this ciliate was isolated was collected in April 2021. After having sequenced its genome and identifying the genetic code change, we made several attempts to reisolate it from the same pond but were unsuccessful. Regarding the geographic distribution of this ciliate, in the text we mention that the most similar 18S rRNA sequence in GenBank is to an unnamed species recovered in a metabarcoding study in France with 99.81% identity. We assume that this is the same species. We also examined other publicly available environmental datasets such as the PR2/metaPR2 database. The most similar match in the metaPR2 database was to a sequence “OLIGO4_XX_sp”. In the metaPR2 database this sequence is unique to Lake Garda in Italy (sample name: “Lake_Garda-LTER-euphotic-water”). However, this hit was only 98% identical with a partial alignment so we did not discuss it in the text. We agree that it is very unlikely that this is a UK-specific ciliate but cannot determine its geographic range based on the publicly available environmental sequence data, other than the single hit to a sequence from France. We think it is important to stress that it was not the aim of our paper to describe the taxonomy and biogeographical range of this ciliate but rather to report the exciting shift in codon usage.

3) I felt the statistics presented on pages 13-14 (lines 277-301) for codon usage were a little superficial. It would be helpful to see how frequently other E and K codons are used in PL0344 and ideally to see how similar codon usage differs in the more model ciliates Paramecium, Tetrahymena or Stentor. To complete an analysis and justify/confirm conclusions drawn, I would also like to see how frequently in-frame, downstream stop codons are seen in ciliates where stop codons have NOT been reassigned - although the data in Fig 5 indicates genome/transcriptome sequences are not necessarily complete for many ciliate species (where stop codons are not reassigned), there is certainly more varied data to look at than when Fleming and Cavalcanti published their PLoS One work (which is cited in the manuscript).

We have shortened this section about UAA and UAG usage, with supplementary table 3 showing usage of all codons in all genes compared to our subset of highly expressed genes.

We have also added a sentence stating how many genes contain both in-frame UAA and UAG codons based on the point from Reviewer 1: “The reassigned codons are widely used across genes with 95.9% of genes containing both a UAA codon and a UAG codon.“

According to our knowledge, there are no new genome assemblies available for ciliates that use the canonical genetic code since the Fleming and Cavalcanti publication from 2019, certainly not any with annotated gene sets available for comparison. The species in Fig 5 which use the canonical genetic code are all from transcriptome data (other than Stentor) that have generally low completeness. We do not think comparison with low-quality transcriptome assemblies would make a fair comparison as they would be biased towards transcripts with higher expression. Furthermore, they likely include many fragmented transcripts which are not suitable for detailed comparisons of the stop codon/3-UTR region.

4) Given the presence of just one stop codon in PL0344 have the authors looked genome-wide at nucleotide composition 5' and 3' to UGA. The nucleotide sequences 5' and 3' to a stop can influence whether read through is and thus potentially limits the frequency of or tendency for unwanted readthrough?

We thank the reviewer for this suggestion which is something we did not investigate initially but have now added a short section in the manuscript to address. Many studies in model organisms have demonstrated that UGA is the least robust stop codon and the most prone to read through. As the reviewer alludes to, this is particularly interesting for ciliates with reassigned genetic codes that use only UGA as a stop codon. Experimental data from model organisms have shown that the sequence composition surrounding a stop codon can influence the frequency of read through, with the nucleotide immediately downstream of the stop codon (“+4 position”) being particularly important.

We have now looked at the sequence composition around stop codons for Oligohymenophorea sp. PL0344 and our results show that cytosine tends to be avoided following the UGA stop codon. From the literature, presence of a cytosine following UGA (i.e., UGAC) leads to a substantial increase in translational read through. Furthermore, when examining the subset of highly expressed genes, there are significantly fewer cases of UGAC when compared to all genes. This trend has previously been reported in Paramecium and Tetrahymena based on EST data (Salim, Ring and Cavalcanti; 2008).

We have added a short section to the text reporting this and a supplementary figure showing a sequence frequency logo around the stop codon for all genes and for the subset of highly expressed genes. We are very cautious, however, that there is a paucity of experimental studies investigating stop codon robustness in ciliates. While several publications hypothesise that read through may happen at higher rates in ciliates due to a combination of factors (e.g., ERF-1 mutations, presence of tandem stop codons, competition from suppressor/near-cognate tRNA genes, etc..) we are careful not to speculate without experimental evidence.

__Reviewer #3 (Significance (Required)): __

Strengths - I found this a straightforward manuscript to read - aside from the interesting and unexpected observation about genetic code use in PL0344, Fig 5 draws together a lot of earlier published information into an easily accessible form - I felt this a particularly useful part of the manuscript.

I don't feel the absence of proteomics to back up the genome/transcriptome analysis is a notable limitation - it's perhaps frustrating but it's not a limitation. However, the work does perhaps inevitably feel a little bit observational - there's not really a lot of insight or new insight into why the genetic code can be revised in some microbial eukaryotes - in contrast, for instance, to a recently published study of the aptly named Blastocrithidia nonstop. McGowan et al's manuscript, however, will be of interest and should be formally published.

Descriptions of organisms that have tweaked the standard genetic code are not new; coupled to the limited insight into why the genetic code can be rewritten so readily in ciliates, this limits the general appeal of the work. However, the study executed is rigorous and it should be of interest to a wide variety of protistologists, evolutionary cell biologists, and researchers in the translation field.

END reviewer 3

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Referee #3

Evidence, reproducibility and clarity

Summary: from genome and transcriptome sequencing of what appears to be a novel ciliate from the class Oligohymenophorea, McGowan et al provide convincing evidence of a protist in which the stop codons UAA and UAG have almost certainly been recoded to specify incorporation of different amino acids (UAA = K; UAG = E) during translation. Several ciliates from different classes use a non-standard genetic code (as do a narrow variety of other protists), but this is an unusual observation in that stop codons which differ only in the wobble position code for different amino acids in the ciliate identified here.

I say 'almost certainly' the stop codons have been recoded in Oligohymenophorea sp. PL0344 because in the absence of being able to retain the ciliate in culture the authors have not been able to complete the proteomics which would unequivocally (a) show stop codons now code for amino acids and (b) confirm the identity of the amino acids now encoded (the authors discuss this issue on p12).

Comments: overall this manuscript is straightforward to read and the analyses realistically taken as far as is realistic in the absence of a continuous culture method. My suggested revisions should be straightforward for the authors to address.

1. The manuscript appears to report the identification and genome/transcriptome sequencing of a novel ciliate species - clarity should be provided by the authors. However, it disappointed me that this manuscript was crafted entirely from nucleotide sequencing. I would have welcomed seeing the morphology of the ciliate identified here and would have anticipated that there was sufficient material to perform microscopy at the light level (for DIC images) and by scanning or transmission electron microscopy.
2. It is unfortunate that the ciliate could not be maintained in culture (or cryopreserved). Coordinates for the University Parks pond are provided, but I got the impression that this ciliate could be repeatedly isolated. Thus, in the absence of culture methods could the authors indicate the points in the year when the ciliate could be isolated (i.e. is there a season element to when PL0344 could be isolated) and how frequently when sampling was performed could PL0344 be seen? From the environmental sequence data that is publicly available is there any evidence for the presence of PL0344 anywhere else in the world? I'd be surprised if this was a UK-specific ciliate.
3. I felt the statistics presented on pages 13-14 (lines 277-301) for codon usage were a little superficial. It would be helpful to see how frequently other E and K codons are used in PL0344 and ideally to see how similar codon usage differs in the more model ciliates Paramecium, Tetrahymena or Stentor. To complete an analysis and justify/confirm conclusions drawn, I would also like to see how frequently in-frame, downstream stop codons are seen in ciliates where stop codons have NOT been reassigned - although the data in Fig 5 indicates genome/transcriptome sequences are not necessarily complete for many ciliate species (where stop codons are not reassigned), there is certainly more varied data to look at than when Fleming and Cavalcanti published their PLoS One work (which is cited in the manuscript).
4. Given the presence of just one stop codon in PL0344 have the authors looked genome-wide at nucleotide composition 5' and 3' to UGA. The nucleotide sequences 5' and 3' to a stop can influence whether read through is and thus potentially limits the frequency of or tendency for unwanted readthrough?

Significance

Strengths - I found this a straightforward manuscript to read - aside from the interesting and unexpected observation about genetic code use in PL0344, Fig 5 draws together a lot of earlier published information into an easily accessible form - I felt this a particularly useful part of the manuscript.

I don't feel the absence of proteomics to back up the genome/transcriptome analysis is a notable limitation - it's perhaps frustrating but it's not a limitation. However, the work does perhaps inevitably feel a little bit observational - there's not really a lot of insight or new insight into why the genetic code can be revised in some microbial eukaryotes - in contrast, for instance, to a recently published study of the aptly named Blastocrithidia nonstop. McGowan et al's manuscript, however, will be of interest and should be formally published.

Descriptions of organisms that have tweaked the standard genetic code are not new; coupled to the limited insight into why the genetic code can be rewritten so readily in cliates, this limits the general appeal of the work. However, the study executed is rigorous and it should be of interest to a wide variety of protistologists, evolutionary cell biologists, and researchers in the translation field.

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Referee #2

Evidence, reproducibility and clarity

This study reports the reassignment of the UAA and UAG stop codons to lysine and glutamic acid, respectively, in the ciliate Oligohymenophorea sp PL0344. The paper is nicely written, easy to read and the experimental approach, ideas and questions are easy to follow. The work is technically solid both at the NGS - in house library preparation, sequencing and data interpretation - as well as phylogeny levels. The conclusions are consistent with the comparative genomic and transcriptomic data obtained by the study.

Significance

The work extends current knowledge on codon reassignment in ciliates, confirming previous discoveries of existence of very high stop codon assignment flexibility in these organisms. The assignment of UAA and UAG to two different amino acids by two different tRNAs is very interesting and reinforces the idea that stop codon reassignment in ciliates is rather common. It also raises important questions about the parallel evolution of the release factor-1 (eRF1), Lysine and Glutamine tRNAs, as the reassignment requires loss of recognition of both UAA and UAG by eRF1 with parallel appearance of the new Lysine and Glutamic Acid suppressor tRNAs.

The main issue of this work is the inability to cultivate the ciliate Oligohymenophorea sp PL0344 in the laboratory to prepare protein extracts for direct analysis of the amino acids inserted at UAA and UAG sites by Mass Spectrometry. The comparative genomic and transcriptomic data, as well as the identification of cognate tRNA anticodons for UAA and UAG, are likely correct, but provide indirect evidence for the assignment of UAA to Lysine and UAG to Glutamic Acid. This issue is relevant because one cannot exclude the possibility of insertion of other amino acids at UAA and UAG sites beyond Lysine and Glutamic acid, respectively; nor can one exclude the possibility that such amino acids are inserted at high level. The authors do acknowledge the limitations of the unavailability of protein extracts for direct MS analysis of the reassignment, but should consider, in particular in the discussion, the possibility of multiple amino acid insertions in a context where Lysine and Glutamine Acid are the major but not the only amino acid species being inserted at those sites.

Based on my expertise of studying codon reassignments in fungi of the CTG clade, I believe this work is very interesting and appealing to the genetic code community, and is of relevance to the evolution and protein synthesis research communities at large.

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Referee #1

Evidence, reproducibility and clarity

Summary

This study by J. McGowan and colleagues reports the discovery of a ciliate species that uses a variant genetic code where the codons UAA and UAG, which are stop codons in the canonical code, instead code for lysine and glutamate respectively. The primary data are genomic and transcriptomic sequence libraries from single cells. The genetic code was predicted by aligning coding sequences to references from other species and examining the most frequent amino acids in positions homologous to putative coding-UAA/UAGs. They also identified suppressor tRNAs for UAA and UAG, and tandem in-frame stop UGAs (but not UAA/UAG) in the 3'-UTR, which further support the recoding of UAA and UAG.

A limitation of this study (and several other recent studies on variant genetic codes) is that the predictions are based on nucleic acid sequencing, without confirmation from proteomics. The authors acknowledge and briefly but frankly discuss the limitations in their manuscript (lines 258-261).

Major comments

Controls against contamination and sequence chimeras

The ciliate species studied here was an environmental isolate, and sequence libraries were prepared by amplification from small pools of cells sorted by FACS. The genome assembly was produced by co-assembly of multiple amplified libraries. Given the potential for contamination and amplification artefacts (such as sequence chimeras) associated with these methods, I think it is important to demonstrate that the data truly originate from one species, so as to rule out the possibility that the co-assembly may be chimeric, i.e. representing two or more organisms with different genetic codes (one with UAA recoded and the other with UAG recoded, for instance). Even if the cell sorting was accurate, contamination could still enter down the line during library preparation so it would be important to show internal evidence from the sequence data too.

Specifically:

• (a) From the description in Methods under "Sampling, Ciliate isolation, culturing, and cell-sorting", it is not clear whether all the cells that were ultimately sequenced originated from the same clone (i.e. the same well in the 96-well plate described in line 389). Could the authors confirm whether this was the case?
• (b) What % of genes have in-frame coding UAA, UAG, or both? How per gene on average? Counts are given for the conserved genes/domains identified by PhyloFisher or Codetta (lines 192-207), and overall frequencies per codon are addressed later in lines 263 onward, but how often do they occur together in the same genes?

My reasoning behind this is that if genes with both in-frame coding UAA and UAGs are common then it is very unlikely to be the result of chimeric sequence artefacts from whole-genome amplification. - (c) What is the sequence identity of conserved marker sequences between the individual amplified replicate libraries?

I would naively expect that individual replicates may not have the full set of markers because of uneven amplification, but if the sequences originate from the same clone they should have overlapping coverage of the conserved markers, and these should be +/- identical between replicates (save for allele variants). If so this would support the claim that contaminant sequences were mostly removed during sequence QC and that the cells were clonal. - (d) Line 392: "Non-axenic" presumably refers to environmental prokaryotes. This also appears to contradict the statement that the cells were "free of any other contaminant" (line 387). Could authors confirm whether they mean "non-axenic but monoeukaryotic"? - (e) Lines 448-451: More details should be given on the criteria used to identify and bin out contaminants. MetaBAT typically bins prokaryotic genomes quite well, but not eukaryotic ones. What did the bins look like and how were the eukaryotic ones chosen?

Minor comments

Line 52: Not strictly true, some germline-limited segments contain mobile elements with coding sequences, e.g. TBE elements in Oxytricha (doi:10.1371/journal.pgen.1003659)

Lines 229-231, Supplementary Table 1: Presenting the identity matrix as a distance tree may make it easier to see the pattern of similarity between the tRNAs

Lines 274-275: Suggest stating the criterion for classifying genes as "highly expressed" on the first mention of this in the Results, although it's explained later on in the Methods.

Lines 298-299: What is the frequency of tandem UGA stops in the 3'-UTR in genes with coding-UAA/UAG vs. genes without, and is there a significant difference? The argument in this paragraph is that UAA+UAG reassignment increases selective pressure to minimize translational readthrough. Therefore I think that it would make sense to compare the frequency in genes with and without these codons.

Lines 353-354, Figure 5: Suggest marking the internal nodes where genetic code changes likely occurred. At the moment only the leaves of the tree are annotated with the genetic codes of the respective species. This would make it clearer how one counts the numbers of independent origins as reported in the text (e.g. "... a fourth independent origin of UGA being translated as tryptophan").

Lines 371-372: Question out of curiosity (not necessary to address for the manuscript at hand): Do the authors think the recoding of UAA and UAG happened simultaneously in both codons or stepwise, or is there insufficient information to speculate?

Line 395: "10uL" should use the actual symbol for "micro" prefix. Also, the choice of spacing or no spacing between numerical figure and units should be made consistent in manuscript.

Line 403: "Biotynilated" should be "Biotinylated"

Line 414 and elsewhere: "2" in MgCl2 should be subscripted

Lines 419-420: Clarify whether the "r" and "+" symbols are to be read as prefixes or suffixes, i.e. is the modified base the preceding or succeeding one.

Table 1: What is the difference between the two sets of BUSCO completeness scores reported? One is given under "Genome assembly" and the other under "Genome annotation", but the annotation is based on the same assembly, right? I'm assuming this has to do with different modes in which BUSCO can be run, but this should be explained in the Methods (lines 452-453, 496-497) and briefly explained in the Table caption.

Referee Cross-commenting

I generally agree with the other reviewers' comments. Specifically I like reviewer #3's suggestion #3 to have a more detailed summary of the codon frequencies, perhaps as a graphic, and to compare the tandem stop frequencies with other ciliate species, especially those with all three canonical stops.

Significance

Any new genetic code variant discovered is a cause for celebration! This is a basic biological fact with inherent significance and should be generally interesting to biologists because the rarity of variant codes stands in contrast to the diversity of most biological systems.

This variant code would also stimulate new discussions in the field of genetic code evolution specifically because, as the authors point out, when both UAA and UAG are recoded they both usually encode same amino acid, but here they are recoded to different ones. This is an apparent exception to the "wobble" hypothesis for why these codons often evolve in concert, which was well explained with relevant citations in the Introduction.

For context: My expertise is in genomics and environmental microbiology.

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Reply to the reviewers

1. General Statements [optional]

Thank you for your letter dated on May 5, 2023 concerning our manuscript (MS# RC-2023-01906) entitled “Activation of Nedd4L Ubiquitin Ligase by FCHO2-generated Membrane Curvature.”

We thank the reviewers for their constructive comments and suggestions. We have considered all reviewers’ comments and plan to revise our manuscript accordingly.

We believe that our revision plan will greatly improve the quality of our manuscript.

1. Description of the planned revisions

__Reviewer #1 __

I enjoyed reading the paper by Sakamoto and colleagues, where they show that Nedd4L ubiquitin ligase activity is stimulated by membranes and in particular positive membrane curvature. This paper is a conceptual advance that hopefully will be extended by many other groups where membranes topology participates in the activation of associated enzymes, giving rise to added complexity but also specificity and further compartmentalization. It is an important paper for all cell biologists to understand.

1. My comments are all relatively minor and I hope can improve the readability of the paper, but will not alter the overall conclusion as this is well backed up. In general I would like to see more/better statistics/quantitation and better figure legends. I found that often one had to read the paper to understand a figure where reading the figure legend should suffice.

__Reply: __According to the reviewer’s comment, we will quantify the experiments (Fig. 1C, Fig. 2, Fig. 9B, and Fig. 10B) and add descriptions of statistics (Fig. 5, Fig. 6, B and D, and Fig. 7C). We will also write better figure legends to enable the readers to easily understand experiments.

1. This paper reminds me of a paper from Gilbert Di Paolo's lab on the activation of synaptojanin PIP2 hydrolysis by high membrane curvature. One would expect that there may be many such proteins whose activities will be dependent on their membrane environment. I find it conceptually rather likely that a protein which interacts with membranes via a C2 domain (which has membrane insertions and will thus likely be curvature sensitive) will likely show some positive curvature sensitivity. Can I suggest this paper is referenced and discussed in the light of the discussion statement "Thus, our findings provide a new concept of signal transduction in which a specific degree of membrane curvature serves as a signal for activation of an enzyme that regulates a number of substrates."

Reply: __According to the reviewer’s comment, we will cite the paper entitled “synaptojanin-1-mediated PI(4,5)P2 hydrolysis is modulated by membrane curvature and facilitates membrane fission” by Chang-Ileto et al. (Dev. Cell __20, 206–18 , 2011). We will also discuss this paper in the light of the discussion statement.

1. Where the paper could be improved (or I have not understood fully). In figure 1 there is a robust endocytosis of ENaC that is FCHo2 and Nedd4L sensitive. There is a rescue for FCHo2 in a fluorescence image (unquantified), so it would be good to have the more quantitative approach of rescue with both FCHo2 and Nedd4L in the biochemical assay.

__Reply: __Although the reviewer suggests a rescue experiment in the biochemical assay, the experiment is difficult because the transfection efficiency is low (about 50%). On the other hand, we agree with the reviewer that a quantitative approach is required in the rescue experiment (Fig. 1C). Therefore, we plan to quantify the rescue experiment for FCHO2 in the immunofluorescence assay. The reviewer also suggests a rescue experiment for Nedd4L as well as FCHO2. However, since the involvement of Nedd4L in ENaC endocytosis is well established, we do not think that the rescue experiment for Nedd4L is further required.

1. In figure 2 there is nice co-localisation between clathrin/FCHo2 and ENaC but not with Nedd4L. It would be good to have some quantitation of the co-localisation. But also one should use a Nedd4L mutant or a mutant of ENaC and so be able to visualise co-localisation between receptor and ub-ligase. I find it strange that there is no (or much less) Nedd4L-GFP visible in the cells overexpressing ENaC... Is there an explanation? Does overexpression of ENaC lead to more auto-ubiquitination of Nedd4L. Also the Nedd4L-GFP signal in other cells is punctate, while in the next figure Myc-Nedd4L is not.

__Reply: __According to the reviewer’s comment, we will perform quantitative colocalization analysis in Fig. 2.

We have found that a catalytically inactive Nedd4L mutant, C922A, co-localizes with cell-surface αENaC and FCHO2 in αβγENaC-HeLa cells. According to the reviewer’s comment, these data will be added in the revised manuscript.

In Fig. 2C, Nedd4L was transiently transfected in cells stably expressing ENaC. In Nedd4L-transfected cells, overexpression of Nedd4L stimulated ENaC internalization, resulting in the disappearance of ENaC at the cell surface. On the other hand, in non-transfected cells, cell-surface ENaC was detected. Thus, Nedd4L-negative cells are non-transfected cells (cell-surface ENaC positive cells). This explanation will be added in the revised manuscript.

The staining pattern of Nedd4L depends on what section of the cell a confocal microscope was focused on. Nedd4L-GFP signals were punctate at the bottom section of the cell in Fig. 2, whereas Myc-Nedd4L was diffusely distributed at the upper section (cytoplasm) of the cell (Fig. 3). Thus, Nedd4L shows distribution throughout the cytoplasm and punctate staining at the bottom (cell surface). The staining pattern of Nedd4L is also affected by the expression amount of Nedd4L in cells. When Nedd4L was highly expressed in COS7 and HEK293 cells in Fig. 3, the punctate staining was hardly detected. This localization pattern of Nedd4L will be clearly described in the revised manuscript.

1. In figure 3 it appears to me that there is co-localization between ENaC and amphiphysin. Is this not a positive piece of information? I am not sure that FBP17 is a good F-BAR domain to use given its oligomerization may well prevent membrane association of Nedd4L. Minor comment: I don't see tubules for amphiphysin in panel B.

__Reply: __The reviewer states that there is co-localization between Nedd4L and amphiphysin1 (Fig. 3A). However, Nedd4L was not recruited to membrane tubules generated by amphiphysin1. We will clearly show that there is no colocalization between Nedd4L and amphiphysin1.

The reviewer states that FBP17 may not be a good F-BAR domain to use because its oligomerization may well prevent membrane association of Nedd4L. However, we have shown that FCHO2 as well as FBP17 forms oligomer (Uezu et al. Genes Cells, 16, 868-878, 2011). Furthermore, we have found that FCHO2 inhibits the membrane binding and catalytic activity of Nedd4L when the PS percentage in liposomes is elevated (unpublished data and Fig. 9C). Thus, since FBP17 and FCHO2 probably have similar properties, we presume that FBP17 is a good F-BAR domain to use.

As the reviewer pointed out, membrane tubules generated by amphiphysin1 were hardly detected in HEK293 cells (Fig. 3B). It showed punctate staining, but did not co-localized with Nedd4L. This description will be added in the revised manuscript.

1. Figure 5: The affinity of Nedd4 C2 domain for calcium is quite high given we normally assume a cytosolic concentration of 100nM (approximate). The authors have rightly buffered the calcium with EGTA. Normally we would check that the buffering is sufficient by varying the protein concentration and making sure the affinity is still the same, so can I suggest the authors use 3 or 4 times the amount of C2 domain and make sure the curve does not change (provided liposomes are not limiting). Minor comment: How many experiments and what are error bars (SD?).

__Reply: __According to the reviewer’s comment, we will check that the buffering is sufficient by varying the protein concentration (Fig. 5). We will also add a description of statistics to the legend to Fig. 5.

1. Figure 6: Controls have been performed to ensure that liposomes are pelleted, according to methods. In Figure 6B can the authors show that there is the same amount of liposomes in each sample by showing more of the coomassie gel so that the reader can see the Neutravidin band is the same in each sample. Also I believe a student t-test should not be used in this experiment (but perhaps an Anova test), and in panel D there does not appear to be a description of statistics.

__Reply: __To ensure that the same amounts of liposomes were pelleted, the reviewer suggests that we show more of the Coomassie gel to present the neutravidin bands in Fig. 6B. However, as the molecular weight of neutravidin is about 15 kDa, neutravidin run out of the gel (7% SDS-PAGE gel) where Nedd4L (As the reviewer pointed out, we will use an Anova test in Fig. 6B. We will also add a description of statistics in Fig. 6D.

1. Figure 11: In panel B I note that the FCHo2 BAR domain on small liposomes appears to inhibit Ubiquitination. Is this consistent with the BAR domain not preventing Nedd4L binding?

__Reply: __The FCHO2 BAR domain enhances the liposome binding and catalytic activity of Nedd4L when the strength of interaction of Nedd4L with liposomes (20% PS) is weak. In contrast, we have also found that the FCHO2 BAR domain inhibits the membrane binding and catalytic activity of Nedd4L when the interaction of Nedd4L with liposomes is increased by elevating the PS percentage in liposomes (unpublished data and Fig. 9C). The reason for the different effects of FCHO2 on Nedd4L is considered as follows: When liposomes (20% PS) are used (the interaction of Nedd4L with PS in liposomes is weak), Nedd4L binds to liposomes mainly through ENaC (Fig. 8F). The liposome binding is hardly mediated by PS. Addition of the FCHO2 BAR domain increases the strength of interaction Nedd4L with PS by generating membrane curvature. Consequently, the FCHO2 BAR domain newly induces the PS-mediated liposome binding of Nedd4L, resulting in the enhancement of liposome binding and catalytic activity of Nedd4L. On the other hand, when the interaction of Nedd4L with PS in liposomes is increased by elevating the PS percentage in liposomes (50% PS), the liposome binding of Nedd4L is mainly mediated by PS. Addition of the FCHO2 BAR domain inhibits the PS-mediated liposome binding of Nedd4L. Since both FCHO2 and Nedd4L are PS-binding proteins, they compete with each other to bind to PS in liposomes. Therefore, the results in Fig. 11B are consistent, because the interaction of Nedd4L with PS is increased by 0.05 µm pore-size liposomes. This explanation will be added in the revised manuscript.

__Reviewer #2 __

The authors have reported the involvement of the BAR domain-containing protein FCHO2 in the Nedd4L-mediated endocytosis of ENaC. They propose a model in which the membrane curvature induced by the BAR domain-FCHO2 relieves the auto-inhibition of E3 ligase causing its activation and recruitment. The paper describes a series of in vitro reconstituted experiments that are interesting but not fully connected with the mechanism of ENaC endocytosis. Additional experiments are needed to fully support the authors' conclusions.

Major comments:

1. Although the data reported by the authors regarding FCHO2 and Nedd4L involvement in ENaC endocytosis are convincing, it is suggested that the authors perform the same ENaC endocytosis assay presented in Fig.1B under conditions of FBP17 and amphiphysin1 siRNA to formally prove the selective involvement of FCHO2 in the process among other BAR-containing proteins.

__Reply: __The reviewer suggests the same ENaC endocytosis assay presented in Fig. 1B under conditions of FBP17 and amphiphysin1 siRNA to prove the selective involvement of FCHO2 in ENaC endocytosis. There seems to be a misunderstanding. Similar to FCHO2, FBP17 and amphiphysin are well known to be involved in clathrin-mediated endocytosis. As ENaC is internalized through clathrin-mediated endocytosis, FBP17 and amphiphysin siRNA presumably inhibit ENaC endocytosis. We cannot understand the significance of FBP17 and amphiphysin1 siRNA in the ENaC endocytosis assay.

1. According to the previous point, it will be interesting to see not only a snapshot image of the internalisation assay performed by immunofluorescence (Fig.1C) but a more quantitative analysis of the different time points (as in Fig.1B) in condition of FCHO2 siRNA and eventually FBP17 and amphiphysin1 siRNA.

__Reply: __According to the reviewer’s comment, we will perform a quantitative analysis in Fig. 1C. The reviewer also suggests the immunofluorescence assay at the different time point in Fig. 1C. However, we show the time course of ENaC internalization in Fig. 1B. We do not think that the time course in the immunofluorescence assay is further required. As for FBP17 and amphiphysin siRNA, our response is the same as that to the comment 1 of this reviewer.

1. In Fig.2B, overexpression of the catalytically inactive version of Nedd4L (Nedd4L C922A) would help to see Nedd4L-ENaC co-localization.

__Reply: __This comment is the same as the comment 4 of the reviewer#1.

1. In Fig.4D, the authors need to analyse ENaC ubiquitination in the same experimental setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L in the presence or absence of FCHO2 BAR. It is also recommended to include Nedd4L C922A as an additional control.

__Reply: __The reviewer requests us to analyse ENaC ubiquitination in the same setting as Fig. 4A. However, an in vivo autoubiquitination assay is widely used to determine the catalytic activity of E3 Ub ligase, because the E3 activity is typically reflected in their autoubiquitination. Therefore, the autoubiquitination assay is sufficient to show that Nedd4L is specifically activated by membrane tubules generated by FCHO2 in cells. Furthermore, we have found it very difficult to compare ENaC ubiquitination among many GFP-BAR proteins (GFP alone, GFP-FCHO2, GFP-FBP17, amphiphysin1-GFP, GFP-FCHO2 mutant) in the same experimental setting as Fig. 4A. In Fig. 4A, three types of cDNAs (HA-Ub, Myc-Nedd4L, and GFP-BAR protein) were transfected in cells. The expression amounts of Myc-Nedd4L were similar among the GFP-BAR proteins. On the other hand, in Fig. 4D, four types of cDNA (HA-Ub, Myc-Nedd4L, GFP-BAR protein, and FLAG-αENaC) were transfected in cells. Under these conditions, it is very difficult to adjust the expression amounts of Nedd4L and αENaC among many GFP-BAR proteins. Even when comparing two GFP-BAR proteins (GFP alone and GFP-FCHO2), it was necessary to assess the expression amounts of Nedd4L by transfection with various cDNA amounts of Nedd4L (Fig. 4D). Moreover, as shown in Fig. 4D, enhancement of ENaC ubiquitination by FCHO2 is decreased at higher expression of Nedd4L (1.0 and 1.5 μg DNA), although the reason is unknown. Therefore, we are not sure that we will able to accurately analyse ENaC ubiquitination in the same setting as Fig. 4A instead of transfecting cells with increasing amounts of Nedd4L.

According to the reviewer’s comment, we will examine the effect of Nedd4L C922A on ENaC ubiquitination.

1. While discussing the role of hydrophobic residues in Nedd4L C2 domain,the authors never mentioned the publication by Escobedo et al., Structure 2014 (DOI:10.1016/j.str.2014.08.016), which highlighted how I37 and L38 are directly involved in Ca2+ binding. This aspect should be discussed since the authors show the importance of Ca2+ for PS binding in the sedimentation assay.

__Reply: __According to the reviewer’s comment, we will cite the reference (Escobedo et al.) and discuss the aspect (I37 and L38 are directly involved in Ca2+ binding).

1. As stated by the authors those two residues I37 and L38 are also involved in E3 enzyme activation by relieving C2-HECT interaction. It is important to further demonstrate the effect of these mutations on ENaC substrate.

__Reply: __To prove that the I37 and F38 residues are involved in E3 enzyme activation by relieving C2-HECT interaction, the reviewer requests us to further demonstrate the effect of Nedd4L I37A+F38A on ENaC ubiquitination. However, these two residues are critical noy only for Nedd4L activation but also for membrane binding and curvature sensing of Nedd4L. We also show that membrane binding of Nedd4L is critical for ENaC ubiquitination. Actually, we have found that Nedd4L I37A+F38A mutant, which loses membrane binding, shows little ENaC ubiquitination (unpublished data), whereas it enhances autoubiquitination (Fig. 4C). Thus, the effect of the I37A+F38A mutant on ENaC ubiquitination is not appropriate to prove that the two residues are involved in E3 enzyme activation.

1. There are some concerns regarding the in vitro ubiquitination assay performed in Fig.8 and following figures. The Nedd4L proteins used during the assay has been produced as His tagged at the C-terminus, it was reported (Maspero et al, Nat Struct Mol Biol 2013 DOI: 10.1038/nsmb.2566), at least for the isolated HECT domain, that modification of the C-terminal residue of the protein affects its activity. It would be important to judge the activity of the purified proteins used in the assay. Moreover, as additional control it is suggested the introduction of a mSA-ENaC PY mutant protein. The authors claimed the importance of membrane localized PY motif for recruitment and activation of Nedd4L, it would be informative to perform the experiment in presence of PY mutated ENaC.

__Reply: __The reviewer states that there are some concerns regarding His-tagged Nedd4L proteins. We have prepared Nedd4L that has no tag at its N- or C-terminus. N-terminal GST-tagged, C-terminal untagged Nedd4L was expressed in E. coli and purified by Glutathione-Sepharose column chromatography. The GST tag was cleaved off and Nedd4L was further purified by Mono Q anion-exchange column chromatography. Using this purified sample, we have examined the catalytic activity of untagged Nedd4L. We have found that concerning Ca2+-dependency, PS-dependency, and curvature-sensing, the properties of untagged Nedd4L are similar to those of C-terminal His-tagged Nedd4L (unpublished data).

According to the reviewer’s comment, we will perform the experiment in the presence of PY-mutated ENaC.

1. It is not clear why increasing the concentration of PS (from 20% to 50%) the presence of BAR domain doesn't allow ENaC ubiquitination (Fig.9C), is Nedd4L not recruited to the pellet? It would be interesting to see the sedimentation experiment of Fig.9A done in presence of 50% PS.

__Reply: __This comment is essentially the same as the comment 8 of the reviewer#1. We have found that FCHO2 BAR domain inhibits the membrane binding of Nedd4L when the PS percentage in liposomes is elevated (~50%) (unpublished data). According to the reviewer’s comment, these data will be added in the revised manuscript.

1. This reviewer is not an expert of lipids biology, thus the explanations related to the effect of FCHO2 BAR in presence of PI(4,5)P2 (Fig. 10) or 0.05 pore-size liposomes (Fig.11) were not clear. Does FCHO2 BAR have a different effect in inducing membrane tubulation in these two conditions? Is this parameter measurable by tubulation assay?

__Reply: __According to the reviewer’s comment, we will write more clearly the explanation related to the effect of FCHO2 BAR domain in the presence of PI(4,5)P2 or 0.05 μm pore-size liposomes.

Minor Comments

1. It would be appreciated if a nuclei staining panel is included in all immunofluorescence images, as it would help to identify the number of cells in the field of view (e.g., Fig. 1C, Fig. 2B).

__Reply: __According to the reviewer’s comment, we will show immunofluorescence images to identify the number of cells in Fig. 1C and Fig. 2B.

1. It would be recommended to include colocalization analysis, such as Pearson's correlation coefficient or Manders coefficient in immunofluorescence images.

__Reply: __According to the reviewer comment, we plan to perform quantitative colocalization analysis in Fig. 2.

1. It is not clear how the quantitation of mSA-ENaC ubiquitination in Fig.8D, 8C, and 9B was performed. Did the authors normalise the detected Ub signal over the amount of unmodified mSA-ENaC?

__Reply: __We did not normalize the detected Ub signals over the amount of unmodified mSA-ENaC, because the same amount of mSA-ENaC was added in each assay. The chemiluminescence intensity of Ub signals was quantified by scanning using ImageJ. According to the reviewer’ comment, we will clearly describe how the quantification of mSA-ENaC ubiquitination was performed.

__Reviewer #3 __

--- Summary ---

The manuscript by Sakamoto et al. describes how the ubiquitin ligase Nedd4L is activated by membrane curvature generated by the endocytic protein FCHO2. For their experiments, the authors use the epithelial sodium channel (ENaC) as a model Nedd4L target and CME cargo. The authors start their manuscript by showing in cells the importance of FCHo2 and Nedd4L in ENaC internalization. Using a combination of experiments in cells and biochemistry, the authors show that Nedd4L binds preferentially to membranes with the same curvature generated by FCHO2. Next, the authors show that a combination of membrane composition (PS), calcium concentration, PY domain presence and membrane curvature all act in concert to recruit Nedd4L to membranes and fully release its ubiquitination activity. Crucially, the authors show that role of FCHO2 in Nedd4L recruitment is not direct, with FCHO2 simply generating an optimal membrane curvature for Nedd4L binding. Taken together, the authors suggest a mechanism by which the curvature of early clathrin coated pits, generated by FCHO1/2 define an optimal environment for the recruitment and activation of the ubiquitin ligase Nedd4L.

The manuscript convincingly shows the membrane curvature-dependent mechanism of Nedd4L activation. The biochemistry experiments in the manuscript are well designed and the results are of clear. The quality of these experiments is very high. The experiments in cells are, however, not of the same level of quality.

--- Major comments ---

1) The results do not show convincingly that Nedd4L is recruited to CCPs. There is plenty of indirect evidence, but to support the model shown in the last figure, authors need to show more than the staining in figure 2C. Live-cell imaging showing the post-FCHo2 recruitment of Nedd4L would be required. I understand that the recruitment would possibly occur in a fraction of events and may be difficult to catch. The cmeAnalysis script from the danuser lab(https://doi.org/10.1016/j.devcel.2013.06.019 can facilitate the identification of these events.

__Reply: __According to the reviewer comment, we plan to examine by live-cell TIRF microscopy that Nedd4L is recruited to CCPs.

2) What happens to ENaC in Nedd4L and FCHO2 knockdown cells? One would expect accumulation of the receptor on the surface.

__Reply: __We have found that upon Nedd4L or FCHO2 knockdown, αENaC accumulates at the cell surface in αβγENaC-HeLa cells. According to the reviewer’s comment, we will show these data in the revised manuscript.

*3) In the experiments in figure 1, it would be important to use a standard CME cargo as an internal control (transferrin). This will serve as a functional confirmation of FCHO2 knockdown and help the reader to put the Need4L knockdown experiments into the context of CME. *

__Reply: __According to the reviewer’s comment, we will use a standard CME cargo as an internal control (transferrin).

*4) Quantification for the rescue experiment is required (figure 1C). if not possible, at least a picture where the reader can see transfected and non-transfected cells side-by-side is necessary. *

Reply: This comment is the same as those of the reviewer#1 (comment 3) and reviewer#2 (comment 2). According to the reviewer’s comment, we plan to quantify the rescue experiment (Fig. 1C).

*--- Minor comments --- *

*1) The experiments in figure 3 must be presented in order as they are in the text. For example, figure 3E is cited in the text into the context of figure 7. It is very confusing. *

__Reply: __According to the reviewer’ s comment, we will present the experiments in Fig. 3 in order they are in the text.

*2) A better explanation of the assay in 1C would facilitate its understanding for the non-specialist reader. The reader needs to read the methods section to understand how it was done. *

__Reply: __According to the reviewer’ comment, we will write a better explanation of the assay in the Fig. 1C legend to enable the readers to understand how it was done.

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Referee #3

Evidence, reproducibility and clarity

Summary

The manuscript by Sakamoto et al. describes how the ubiquitin ligase Nedd4L is activated by membrane curvature generated by the endocytic protein FCHO2. For their experiments, the authors use the epithelial sodium channel (ENaC) as a model Nedd4L target and CME cargo. The authors start their manuscript by showing in cells the importance of FCHo2 and Nedd4L in ENaC internalization. Using a combination of experiments in cells and biochemistry, the authors show that Nedd4L binds preferentially to membranes with the same curvature generated by FCHO2. Next, the authors show that a combination of membrane composition (PS), calcium concentration, PY domain presence and membrane curvature all act in concert to recruit Nedd4L to membranes and fully release its ubiquitination activity. Crucially, the authors show that role of FCHO2 in Nedd4L recruitment is not direct, with FCHO2 simply generating an optimal membrane curvature for Nedd4L binding. Taken together, the authors suggest a mechanism by which the curvature of early clathrin coated pits, generated by FCHO1/2 define an optimal environment for the recruitment and activation of the ubiquitin ligase Nedd4L.

The manuscript convincingly shows the membrane curvature-dependent mechanism of Nedd4L activation. The biochemistry experiments in the manuscript are well designed and the results are of clear. The quality of these experiments is very high. The experiments in cells are, however, not of the same level of quality.

Major comments

1. The results do not show convincingly that Nedd4L is recruited to CCPs. There is plenty of indirect evidence, but to support the model shown in the last figure, authors need to show more than the staining in figure 2C. Live-cell imaging showing the post-FCHo2 recruitment of Nedd4L would be required. I understand that the recruitment would possibly occur in a fraction of events and may be difficult to catch. The cmeAnalysis script from the danuser lab(https://doi.org/10.1016/j.devcel.2013.06.019 can facilitate the identification of these events.
2. What happens to ENaC in Nedd4L and FCHO2 knockdown cells? One would expect accumulation of the receptor on the surface.
3. In the experiments in figure 1, it would be important to use a standard CME cargo as an internal control (transferrin). This will serve as a functional confirmation of FCHO2 knockdown and help the reader to put the Need4L knockdown experiments into the context of CME.
4. Quantification for the rescue experiment is required (figure 1C). if not possible, at least a picture where the reader can see transfected and non-transfected cells side-by-side is necessary.

Minor comments

1. The experiments in figure 3 must be presented in order as they are in the text. For example, figure 3E is cited in the text into the context of figure 7. It is very confusing.
2. A better explanation of the assay in 1C would facilitate its understanding for the non-specialist reader. The reader needs to read the methods section to understand how it was done.

To end on a positive note - I applaud the authors for experiment 6A. It is critical to show that liposome extrusion beyond 0.2um does not guarantee liposomes at that size.

Referee cross-commenting

I also agree with the other comments. Nothing to add.

Significance

The manuscript convincingly describes a novel mechanism for the activation of the ubiquitin ligase Nedd4L. From a biochemical point of view, the manuscript is solid. However, to be able to put this mechanism in the context of a CME event, the authors need stronger evidence in cells. To be clear, I think that the results presented do suggest a CME link. However, one could argue, for example, that the results could also be explained by ubiquitination of ENaC post CME, in an endosomal compartment with similar curvature.

Expertise of the reviewer: F-BAR proteins, endocytosis, cell biology and biochemistry.