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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:
- Are there any data showing no change in DNA methylation level of WT DLD-1 and DNMT NA DLD-1 cells?
- 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.
- In Figure 2D, it is better to present the results in a more quantitative manner with appropriate statistical analysis.
- 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:
- Regarding IAA-induced DNMT1 degradation, the authors should provide complete DNMT1 blots to show that no additional isoforms are present.
- In Fig. S1C. Molecular weight was not labeled in the immunoblot.
- In Fig. S2B. NeonGreen-IAA 10days images appear to be exposed for different lengths of time.
- In Fig. 4B-C, S1E, S2E. It is better to present the results in a more quantitative manner with appropriate statistical analysis.
- 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:
- 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?
- 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.
- 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
The authors do not wish to provide a response at this time
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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
- 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.
- 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.
- 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?
- 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.
- 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.
- 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.
- Fig. 3B,C: The interaction between FAM111A and FAM111B needs to be validated by coimmunoprecipitation-WB of endogenous proteins.
- 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.
- Fig. 4E: The increase in ssDNA intensities is mild and might not be biologically significant.
- 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.
- 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).
- 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.
- 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?
- 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
- 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.
- 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:
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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?
- 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.
- 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?
- 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?
- 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.
- 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).
- 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
- 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.
- 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).
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
- 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.
- 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.
- 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
- 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".
- 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.
- 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
- If possible, it would be helpful to mention whether the transgenic animals have any abnormal phenotypes or whether they can rescue the bcd mutant.
- 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.
- 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.
- FCS measurements and fits
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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:
- 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?
- 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?
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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?
- 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.
- Figure 3B&C: Is the curve for 2-component diffusion (without concentration dependence) for steady-state missing?
- 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?
- 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
- Lines 109-111: A word must be missing. Precisely determined what?
- Line 278: The increase in the slow mode is expected. Maybe explicitly mention why.
- 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".
- Line 517: Is there a reason why the dorsal surface is always placed in the coverslip?
- 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!
- 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?
- 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?)?
- 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 would be good for reference to mention the approximate value of the molecular brightness recorded for these eGFP constructs at the laser power used.
- Reference 766: The year (and maybe other things) is missing.
- 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).
- 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:
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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:
- 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?
- 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.
- Does imaging of golgi or endosome markers reveal any differences in membrane compartments in Btsz mutant embryos? Even negative results would be interesting.
- 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.
- 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?
- 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:
- 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).
- 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.
- 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.
- 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...
- 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.
- 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.
- 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.
- 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
- 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:
- 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
- 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.
- 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
- GANT58 is a water insoluble agent. Can you please include how to dissolve GANT58, administration route, and rationale of 20 mg/kg, for CIA?
- Zoom photos in Fig 1j are not clear. Is GLI1 exclusively expressed in F4/80+ macrophages in synovial tissues?
- 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?
- 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?
- 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:
- The authors do not include a description of DNMTs in the introduction.
- 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.
- The distinction of Figure 3g as multinuclear giant cells (vs TRAP+ OCs in panel 3d) should be explained.
- The labels in 4C of "R1, R2, R3" standing for GANT58 is confusing
- In Figure S8, the numbers between the western blots are not explained.
- 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
- Figs 1h and i. The author should show the histological score.
- 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.
- 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.
- 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.
- Figure 5c. The authors should add non-stimulating condition as a control.
- 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.
- 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
- CIA model: The experiment design of CIA model is not clearly described. The author should specify the time point of GANT58 injection.
- Joint inflammation of RA can be caused by many different cells. Abstract needs to be revised.
- 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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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Manuscript number: RC-2023-01939
Corresponding authors: Jiro Toshima, Junko Y. Toshima
- __ 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)
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- 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? * 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)
- *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)
- *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
-
- 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.
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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.
- 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?
- 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.
- 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.
- 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.
- 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.
- 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.
- 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?
- 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?
- 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.
-
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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:
- 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.
- 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).
- 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.
- 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.
- 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.
- 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:
- (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.
- (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.
- (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.
- (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.
- (Figure 4B) ssp2D, not spp2D.
The figure legend has been edited.
- (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.
- (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.
- (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.
- (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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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).
- 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?
- 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.
- 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.
- 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
- An untreated control should be shown for assays in Figure 1.
- 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.
- 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:
- (Figure 1) The septation index of the phleomycin-treated cells (without any further additional drugs) should be shown, as a baseline.
- (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.
- (Figure 2) The authors need to clarify the number of cell bodies spotted (e.g., in the Figure legend).
- (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.
- (Figure 4B) ssp2D, not spp2D.
- (Figure 4) The septation index of the none-treated cells should be shown as a baseline.
- (Figure 6B, 6E) What do the black arrows indicate? Figure Legend does not seem to explain them.
- (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.
- (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
- 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.
- 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.).
- 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.
- Gsc is described to be expressed highly in anterior endoderm progenitors but Figures 1C and 1J do not support this.
- 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.
- 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.
- 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:
- 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:
- 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.
- The fluorescence images in Fig. S2 are virtually invisible in the PDF. The images should be rescaled to make them visible.
- 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:
- 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.
- 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:
- Warga, R.M. and Nüsslein-Volhard, C., 1999. Origin and development of the zebrafish endoderm. Development, 126(4), pp.827-838.
- 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.
- 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.
- 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.
- 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.
- 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:
- Multiple cell lines (MEF and HT1080, Figure 2, A and B) – original data
- Multiple transcription inhibitors (Figure 2C __and Supplemental Figure 1__):
- Alpha-amanitin (RNA pol II and III degradation) – original data
- Triptolide (RNA pol I and II initiation inhibition) – new data
- Flavopiridol (RNA pol II elongation inhibition) – new data
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Actinomycin D (DNA intercalation) – original data
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Multiple perturbations that cause nuclear blebbing (Figure 2D ____and Supplemental Figure 1):
- 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
- DZNep histone methyltransferase inhibitor, which decreases heterochromatin and chromatin decompaction (Stephens et al., 2018, 2019 MBoC) – new data
- Lamin B1 null cells (LMNB1-/- or LB1-/-) (many previous works, including Stephens et al. MBoC 2018) – original data
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Lamin A constitutive knockdown cells (LA KD) (Vahabikashi et al., 2022 PNAS) – new data
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Nuclear bleb formation and stabilization in wild type cells is dependent on transcription in addition to VPA (Figure 5). – original data
- Time dependence of suppression of nuclear blebbing requested by Reviewers 2 & 3:
- Actinomycin D treatment of 1.5 hrs is sufficient to suppress nuclear blebs (Figure 2C) – original data
- 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.
- This new data indicates that even formed blebs require active transcription to remain blebbed for long times
- 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
- 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.
- 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.__
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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
- 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.
- 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.
- Did the counting method used to collect data in Figure 4B exclude nuclei that rupture multiple times? This should be specified in the manuscript.
- 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.
- 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:
- 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).
- 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.
- 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:
- 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)?
- 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).
- 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?
- 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?).
- 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
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:
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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:
- 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.
- 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?
- 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.
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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:
- 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.
- 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?
- 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).
- 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.
- 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:
- Some of the figures would benefit from qualitative improvement, especially the photographs and micrographs.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.).
- 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.
- 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.
- 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
- 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.
- 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?
- 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?
- 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?
- 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.
- 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?
- 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.
- 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.
- 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.
- 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:
- 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.
- 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?
- 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:
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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.
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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:
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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.
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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.
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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.
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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:
- 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?
- 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.
- 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.
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We agree and will address this by:*
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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
- 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
- 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.
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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. *
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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
- 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.
- 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.
- 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)
- 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?
- 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.
- 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.
- 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.
- 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?
- 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
- 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.
- Figure 2D needs at least one more experiment before results can be depicted. An n=2 is not sufficient to merit publication.
- 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.
- Supplementary Figure 1 A and B need a legend to show NT and gal9 siRNA-treated samples.
- Supplementary Figure 2 has additional small square symbols in panels A and B that should be removed.
- Supplementary Figure 2 legend. This legend has repetitive text regarding representative data from one donor. How many donors were tested for these experiments?
- 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?
- 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.
- 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.
- 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.
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[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.
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__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.
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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.
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__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.
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[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.
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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.
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__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.
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[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).
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Comment: Please include molecular weight markers for all western blots.
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__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:
- 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.
- 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.
- 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.
- 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:
- 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
- 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).
- 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.
- Line 367, there is a reference for Fig S5C but that figure is missing.
- 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
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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?
- 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?
- 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.
- 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:
- 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?
- Please include molecular weight markers for all western blots.
- 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
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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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- What happens to ENaC in Nedd4L and FCHO2 knockdown cells? One would expect accumulation of the receptor on the surface.
- 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.
- 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
- 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.
- 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.
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Referee #2
Evidence, reproducibility and clarity
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:
- 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.
- 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.
- In Fig.2B, overexpression of the catalytically inactive version of Nedd4L (Nedd4L C922A) would help to see Nedd4L-ENaC co-localization.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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?
Minor Comments
- 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).
- It would be recommended to include colocalization analysis, such as Pearson's correlation coefficient or Manders coefficient in immunofluorescence images.
- 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?
Referee cross-commenting
I agree with the comments of other two reviewers.
Significance
Unfortunately do to limited knowledge of the reviewer on the lipids biology field it is difficult to judge strengths and limitations of the last part of the manuscript.
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Referee #1
Evidence, reproducibility and clarity
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 compartmentmentalization. It is an importnat paper for all cell biologists to understand.
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.
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."
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.
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.
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.
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?).
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.
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?
Significance
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 compartmentmentalization. It is an importnat paper for all cell biologists to understand.
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Reply to the reviewers
Reviewer #1:
Major comments:
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- The relevance of these findings to human biology remains unclear. In Figures 1-4, the authors present data showing that AATBC is enriched in thermogenic fat, and they argue that it regulates thermogenesis and mitochondrial biology. However, in Figures 6-7, where the authors look at AATBC in different human cohorts, they actually find that it is enriched in visceral fat, which is thought of as being the least thermogenic fat depot. The authors do not explain this seeming paradox, and thus, the role of AATBC in fat remains uncertain. *
RESPONSE: We thank the reviewer for this comment and have clarified the discussion to address this point. It has been recently shown (PMID: 28529941) that the pattern of browning genes in human white adipose tissue depots is actually inverted to mice, making visceral adipose tissue in humans actually more thermogenic than subcutaneous. This aligns well with our findings of AATBC is predominantly expressed in thermogenic adipose tissue.
- In many of the experiments, insufficient controls are provided, or the data are not at all convincing. For example:*
(a) The first four figures rely on in vitro adipocyte models, but the authors do not present data to show these cells differentiate properly and equally. This is especially relevant for the gain and loss of function studies.
RESPONSE: We agree with the reviewer that equal differentiation is necessary for in vitro adipocyte models. Therefore, we added Oil-red-O stainings and the corresponding quantifications to Supp. Fig. 4 (see below) for the differentiation of hMADS in the absence of AATBC. We also want to emphasize, that the expression levels of PLIN1, a surrogate marker for differentiation was unchanged in our experiments, as already shown in the initial draft of the manuscript. On top of that, in all experiments presented in the original draft of the manuscript, AATBC gene expression was only altered in mature adipocytes.
(b) Some of the experiments in Figure 1 (K-L) seem to only show an N of 1.
RESPONSE: Figure 1 highlights a screening process to find new lncRNA regulated during thermogenesis. The forskolin sample was included to achieve an additional dimension in the filtering process. The displayed values in K&L demonstrate the validity of the sample. The validation of AATBC as a target was performed with statistical power in the work displayed in the following figures.
(c) The RNAscope data in Figure 2 is not at all convincing for nuclear localization
RESPONSE: We respectfully disagree. In our opinion, the RNAScope is convincing for nuclear localization of the lncRNA. However, we have repeated the experiments with different probes that strengthen our data (see figure for the reviewer)
(d) The ASO mediated knockdown of AATBC in Figure 3 only reduced expression slightly. A more complete knockdown or deletion may elicit a stronger phenotype.
RESPONSE: We thank the reviewer for the feedback. We have repeated the knockdown experiments but were not able to reduce the expression further, even after designing additional ASOs. However, already with current approach, the reduction in AATBC expression elicited a phenotype, highlighting the importance of AATBC in a dose-dependent manner.
(e) In Figure 4, OPA1 is shown as a single band in panel E and a doublet in panel N. Based on this, are the authors certain they are detecting OPA,1 or could this be a nonspecific band?
RESPONSE: We thank the reviewer for this comment. Protein extraction has been performed at different research institutes with slightly different buffers. Multiple bands (cleaved/uncleaved) have been described for OPA1 in the past, therefore we are certain that the correct protein has been detected.
*(f) The correlations in Figure 6 I-L and Figure 7 do not include any statistical analysis. *
REPONSE: For better readability, the statistical analysis is being mentioned in the figure legend. The reviewer might have overlooked this information.
- The gain of function studies in mice are problematic. The authors have performed a large amount of invasive studies in a short period of time. The animals will undoubtedly lose weight after each study and with insufficient time to recover, this could influence the subsequent studies.*
RESPONSE: These general concerns are valid, but all controls are in place and the animals gained weight during the experiments, as one would have been expected with animals of that age (see below).
*In addition, since the authors present data in Figures 1-4 arguing that AATBC overexpression is associated with increased thermogenesis, it is surprising that the authors never looked at this in Figure 5 (aside from measuring Ucp1 mRNA). It would be interesting to measure energy expenditure by indirect calorimetry and cold tolerance. *
RESPONSE: We agree with the reviewer on this point but are due to animal protocol limitations in conjunction with the viral approach are unable to perform these experiments.
- The authors do not provide any mechanistic insights into how AATBC may be acting.*
RESPONSE: Certainly, more mechanistic insight into the direct mode of action of AATBC would be interesting. To address this point, over the past year we performed multiple attempts to perform pulldown of AATBC using the ChIRP technology. However, we were unable to achieve a sufficient enrichment, which would have allowed us to give further information about direct interaction partners of AATBC. However, we believe that our data regarding mitochondrial dynamics, which we now also have confirmed in in vivo experiments, explain the connection of AATBC and thermogenicity. In future, we aim to work on this point further but for multiple reasons have decided to close this chapter here.
Minor comments:
- The introduction is rather long and would benefit from being condensed.*
RESPONSE: We have edited the text for better readability.
Reviewer #2:
Major Comments:
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- The key conclusion that AATBC is a novel obesity-linked regulator of adipocyte plasticity is made relatively clear with the comparison between various stages of adipocytes and the loss and gain of function with AATBC. - Figure 1 H and J do not seem to be consistent with the data in Figure 1F in LINC00473 level-There is no difference in Control vs NE in the heatmap but in Figure1J, the difference seems to be quite obvious; Figure 1K does not seem to be consistent with AATBC level-The measurement in Control VS Fsk group showed no difference in AATBC in heatmap, but in Figure K, there seem to be a dramatic increase. Therefore, the claims that there is a difference in these two lncRNA expression in these cell groups needs further clarification. *
RESPONSE: To combine the different approaches to identify novel lncRNA into one heatmap the data need to be normalized over experiments. As the fold change of the expression of AATBC in BAT compared to WAT (on average ~100x) is higher than with forskolin (~4x), this will stand out in the heatmap and will to some extent overshadow the smaller fold changes. The same holds true for LINC00473, which is drastically induced with forskolin, which to some extent masks the higher expression in the other approaches. Therefore, we decided to show both the heatmap to represent the general approach and the “zoomed in” versions to show the consistent increases. We are confident this clarifies the issue.
- Figure 4H and I, the difference in the representative immunoblot seem to be minimal and inconsistent with the decrease shown in the bar graph. *
RESPONSE: We agree with the reviewer and have removed the claim from our manuscript.
- In Figure 5, after overexpressing human AATBC in murine adipose tissue , is it possible to look at the mitochondria changes that were seen before in cell lines? If there are similar changes in murine adipose tissue, then it would prove the changes in vitro hold up with the in vivo model. But if the mitochondria changes were not seen, then it would indicate the changes in leptin, triglyceride levels may due to other mechanisms. The length of the suggested experiment to look into the mitochondrial differences in mice may vary depending on whether there are preserved samples from previous experiments. If there are, then the time period would be couple of weeks for immunblot and analysis. If there are no samples preserved, then the estimated period for the suggested experiments may be around 1.5 to 2 months at least .*
RESPONSE: We thank the reviewer for the suggestion. We performed Western Blot analysis on the tissues from the in vivo study and have included them in Fig. 5, further strengthening the link between AATBC and mitochondrial dynamics (please see figure on the right).
- The data are convincing overall in that the replicates are clearly marked with dots in many figures. Some immune blot and expression level are inconsistent with other data showing the same results however. *
RESPONSE: We thank the reviewer and have removed the necessary quantifications.
- Figure 6 and 7 are provocative and significant, reporting strong associations of AATBC with well-known markers of metabolism in adipocytes. The sex difference for adiponectin and AATBC expression is particularly intriguing. Further discussion of this point would be interesting. However, there is no information provided about the medication status of the obese subjects that were consented for samples used in the analysis. Specifically, many of the obese subjects (mean BMI 45 or more with a range going up to 97.3) would be expected also to have metabolic diagnoses and to be treated with numerous medications, including Metformin, GLP1 agonists, Orlistat, Liraglutide, Bupropion/Naltrexone and combinations. It is unreasonable to ignore possible effects of major medications on AATBC expression. Please comment on the strengths and weaknesses of the analysis that ignores medications, or if some annotations of clinical data are available, perhaps to explain outliers in the plots, please discuss. *
RESPONSE: We thank the reviewer for this suggestion. Unfortunately, we are unable to exclude additional diagnoses and medication of our patients due to the points the reviewer stated. However, given the large size of the cohorts we are confident that such effects are being compensated for. We have added a part on weaknesses of the study in the discussion.
Minor Comments:
- The labeling of figure 2 A-K is not clear because the use of the same color of bars is easily misunderstood as the same source of cells, but it is in fact not. For example, the grey color that appeared in 2B and 2C are not the same source but can be misunderstood. *
RESPONSE: The coloring of Fig.2A&G has been changed.
- Figure 3 ASO-AATBC has two repeats #1 and #2, and over-expression of AATBC has one, even though there are enough repeats. It would be less confusing to present all of the repeats in ASO_AATBC together in one bar.*
RESPONSE: The two different ASO target different areas of AATBC. In line with general guidelines for ASO use, those are not pooled but used separately, which is why the results are also split up. As the overexpression is additional genomic information of AATBC, it is impossible to use different variants in this case, therefore only one bar for overexpression is shown.
- The experimental outline can be a bit more detailed and explain some of the words like Thermo versus Browning.*
RESPONSE: The manuscript has been revised regarding this point.
- Some of the panels in Figure 7 could be put into supplementary if space is at a premium, and present the representative graph would be enough*
RESPONSE: We think that all our data of Fig. 7 warrants enough attention to be considered in a main figure, but if space is sparse, we are very happy to oblige. We would kindly ask the editors for input on this matter.
Reviewer #3:
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- Throughout the study, the data provided are mainly correlative and in some cases not robust. In Fig. 2, AATBC expression is described to be elevated in the so-called "thermogenic condition", which contained prolonged PPARg agonist treatment (rosiglitazone) known to promote adipogenesis. Consistent with this notion, adipogenic markers, such as PLIN1 and FABP4, are higher in "thermogenic adipocytes" (Suppl Fig. 2). As such, the result may only suggest that AATBC has higher expression in mature adipocytes vs pre-adipocytes. *
RESPONSE: We thank the reviewer for the suggestion. We have added Oil-Red-O-Stainings to Suppl. Fig. 2 to show unchanged lipid content upon modulation of AATBC gene expression, which can be seen as a surrogate for differentiation. Concerning the use of rosiglitazone as a browning agent, we want to emphasize that rosiglitazone was used during the entirety of differentiation until day 9, where it was removed in the “non-thermogenic” group. At this point we already observe fully differentiated adipocytes. This is an established protocol. Furthermore, the data is in line with using norepinephrine or forskolin as a short-term inducer of browning, making it very likely that the effect seen is due to the “more thermogenic” character of the adipocytes.
- Along the same vein, whether and how AATBC affects adipogenesis is unclear. Suppl Fig. 3H and 3L (misplaced as Suppl Fig. 4) show the adipocyte differentiation marker FABP4 is down-regulated by both ASO- and AV-AATBC. Since mitochondrial respiration (and other parameters including UCP1 expression) is tightly linked to adipogenic efficiency, the authors need to address whether these manipulations affect adipocyte differentiation. *
RESPONSE: We agree with the reviewer that differences in differentiation capacity would falsify our data on mitochondrial dynamics. We have added Oil-Red-O-Staining to Suppl. Fig. 2 to show that no significant difference in lipid content exists during modulation of AATBC gene expression, which can be seen as a surrogate for differentiation. Furthermore, in all experiments presented in the manuscript, the modulation of AATBC occurs in already fully differentiated adipocytes. Accordingly, we are confident that AATBC does not influence differentiation but mainly acts through the modulation of mitochondrial dynamics.
- The data in Fig. 4 supporting a role for AATBC in regulating mitochondrial dynamics are superficial and not robust. Fig. 4A/4J do not have high enough resolution to provide accurate assessment of the mitochondrial network.*
RESPONSE: We respectfully disagree with the reviewer on this point. State of the art methods and algorithms were used to image and analyze the mitochondrial network. Furthermore, we have used multiple established markers of mitochondrial dynamics in western blot analysis to further strengthen our assessments of the immunofluorescence. In summary, we feel like have given enough evidence for an accurate assessment of the mitochondrial network.
- The level of loading control TUBB is clearly lower in siAATBC in Fig. 4H. In addition, OPA1 should have multiple isoforms and Fig. 4E/4N show inconsistent patterns. As such, mitochondrial dynamics is not likely an underlying mechanism. *
RESPONSE: We agree with the reviewer on the assessment of the expression of complex 5 and have removed this claim from the manuscript. Regarding the expression of OPA1, protein extraction has been performed at different research institutes with slightly different buffers. Multiple bands (cleaved/uncleaved) have been described for OPA1 in the past, therefore we are certain that the correct protein has been detected.
- Notably, RNAseq data in Suppl Fig. 4 (misplaced as Suppl Fig. 3) seem to indicate that AATBC over-expression promotes TG synthesis, while AATBC knockdown modulates cell death. The authors should consider exploring the leads from RNAseq analysis?*
RESPONSE: We thank the reviewer for the feedback. The small number of altered genes in the RNASeq make us believe in a rather post-transcriptional role of AATBC. We investigated cell death and oxidative stress response as GO terms were highlighted in the analysis, but we were unable to detect any differences in the absence of AATBC, pointing to a minimal effect on transcriptional level (See figure below for the reviewers).
- In Fig. 5, the AV-AATBC transduction in WAT/BAT is localized, transient and not homogeneous. Not surprisingly, this manipulation does not produce any robust effects. The difference in circulating leptin/leptin expression appears to be driven by 4-5 mice in the control group (Fig. 5H/5N). The correlation data in Fig. 6 and Fig. 7, although relevant, do not provide additional mechanistic insights. Unfortunately, the efforts in Fig. 5-7 fail to lead to information related to the biological function of adipose AATBC.*
RESPONSE: We agree with the reviewer on the limitations of the AV model, but we have performed these experiments with the highest technical standard. As the reviewer states, the overexpression, especially in WAT, has different magnitudes depending on the individual mouse, but the overexpression is present and consistently high in every animal. We would expect even bigger alterations in a genetic model, which, however, is beyond the scope of this first manuscript on AATBC in adipocytes. We are disappointed that the reviewer does not value the human data presented, as it very strongly hints to a relevant function of our human lncRNA in vivo by robust correlations with established biomarkers mirroring the effects seen in vitro and in the mouse model. A limitation of human studies is in virtually every case that it is based on correlations, as manipulation of gene expression, which would be necessary to delineate a biological process as requested by the reviewer, is not possible in humans. We do not concur on dismissing our human data on that behalf.
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Referee #3
Evidence, reproducibility and clarity
In the current work, the authors have characterized a human IncRNA AATBC whose expression is up-regulated in conditions favoring a "thermogenic phenotype" of fat cells. Results derived from transient knockdown/over-expression approaches indicate that AATBC may play a role in modulating mitochondrial functions. In addition, gene expression analyses have demonstrated that AATBC is positively correlated with thermogenic genes, such as UCP-1 and PGC-1a and negatively correlated with adipogenic genes, including PPARg, FABP4 and Leptin in humans.
The association between AATBC and BMI is of potential interest. However, restricted by the limitations of the employed approaches, the current study falls short of robust evidence supporting a role for AATBC in adipocyte plasticity and mitochondrial dynamics/respiration (and certainly a link between the two events). A substantial amount of work would be needed to tie up loose ends for a cohesive study with mechanistic insights.
Specific comments:
- Throughout the study, the data provided are mainly correlative and in some cases not robust. In Fig. 2, AATBC expression is described to be elevated in the so-called "thermogenic condition", which contained prolonged PPARg agonist treatment (rosiglitazone) known to promote adipogenesis. Consistent with this notion, adipogenic markers, such as PLIN1 and FABP4, are higher in "thermogenic adipocytes" (Suppl Fig. 2). As such, the result may only suggest that AATBC has higher expression in mature adipocytes vs pre-adipocytes.
- Along the same vein, whether and how AATBC affects adipogenesis is unclear. Suppl Fig. 3H and 3L (misplaced as Suppl Fig. 4) show the adipocyte differentiation marker FABP4 is down-regulated by both ASO- and AV-AATBC. Since mitochondrial respiration (and other parameters including UCP1 expression) is tightly linked to adipogenic efficiency, the authors need to address whether these manipulations affect adipocyte differentiation.
- The data in Fig. 4 supporting a role for AATBC in regulating mitochondrial dynamics are superficial and not robust. Fig. 4A/4J do not have high enough resolution to provide accurate assessment of the mitochondrial network. The level of loading control TUBB is clearly lower in siAATBC in Fig. 4H. In addition, OPA1 should have multiple isoforms and Fig. 4E/4N show inconsistent patterns. As such, mitochondrial dynamics is not likely an underlying mechanism. Notably, RNAseq data in Suppl Fig. 4 (misplaced as Suppl Fig. 3) seem to indicate that AATBC over-expression promotes TG synthesis, while AATBC knockdown modulates cell death. The authors should consider exploring the leads from RNAseq analysis.
- In Fig. 5, the AV-AATBC transduction in WAT/BAT is localized, transient and not homogeneous. Not surprisingly, this manipulation does not produce any robust effects. The difference in circulating leptin/leptin expression appears to be driven by 4-5 mice in the control group (Fig. 5H/5N). The correlation data in Fig. 6 and Fig. 7, although relevant, do not provide additional mechanistic insights. Unfortunately, the efforts in Fig. 5-7 fail to lead to information related to the biological function of adipose AATBC.
Significance
Thermogenic adipocytes are thought to be a druggable target to combat obesity and related metabolic diseases. The current study aims to identify genes associated with thermogenic capacity in human adipocytes. To this end, the authors have characterized a human IncRNA AATBC whose expression is up-regulated by thermogenic stimulations in cultured adipocytes. Of potential interest is the association between adipose AATBC expression and BMI in human samples.
Reviewer's field of expertise: metabolic regulation, obesity and related metabolic diseases, molecular physiology.
Referee Cross-commenting
I think there is a good consensus about the strengths and weaknesses of the study. My comments were very similar to those of reviewer 1. My view is the authors did not provide sufficient evidence to support their claims. Some of the data are also not robust enough to reach meaningful conclusions.
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Referee #2
Evidence, reproducibility and clarity
This investigation deals with a significant problem: how to discover and understand the signals that regulate white and brown or beige adipogenesis, which is central to energy balance. New approaches to promote brown, thermogenic adipogenesis have been considered as potential metabolic therapies to combat the current epidemic of obesity and obesity-related complications. Interestingly, the authors have identified a long non-coding RNA that associates with the thermogenic phenotype, and then through functional studies offer strong evidence that this lncRNA (AATBC) has potential as a regulator of adipocyte plasticity. The investigation is rigorously conducted, novel and important. The report may be suitable for publication upon completion of the improvements suggested here.
Major Comments:
- The key conclusion that AATBC is a novel obesity-linked regulator of adipocyte plasticity is made relatively clear with the comparison between various stages of adipocytes and the loss and gain of function with AATBC.
- Figure 1 H and J do not seem to be consistent with the data in Figure 1F in LINC00473 level-There is no difference in Control vs NE in the heatmap but in Figure1J, the difference seems to be quite obvious; Figure 1K does not seem to be consistent with AATBC level-The measurement in Control VS Fsk group showed no difference in AATBC in heatmap, but in Figure K, there seem to be a dramatic increase. Therefore, the claims that there is a difference in these two lncRNA expression in these cell groups needs further clarification.
- Figure 4H and I, the difference in the representative immunoblot seem to be minimal and inconsistent with the decrease shown in the bar graph.
- In Figure 5, after overexpressing human AATBC in murine adipose tissue, is it possible to look at the mitochondria changes that were seen before in cell lines? If there are similar changes in murine adipose tissue, then it would prove the changes in vitro hold up with the in vivo model. But if the mitochondria changes were not seen, then it would indicate the changes in leptin, triglyceride levels may due to other mechanisms.
- The length of the suggested experiment to look into the mitochondrial differences in mice may vary depending on whether there are preserved samples from previous experiments. If there are, then the time period would be couple of weeks for immunblot and analysis. If there are no samples preserved, then the estimated period for the suggested experiments may be around 1.5 to 2 months at least.
- The data are convincing overall in that the replicates are clearly marked with dots in many figures. Some immune blot and expression level are inconsistent with other data showing the same results however.
- Figure 6 and 7 are provocative and significant, reporting strong associations of AATBC with well-known markers of metabolism in adipocytes. The sex difference for adiponectin and AATBC expression is particularly intriguing. Further discussion of this point would be interesting.
However, there is no information provided about the medication status of the obese subjects that were consented for samples used in the analysis. Specifically, many of the obese subjects (mean BMI 45 or more with a range going up to 97.3) would be expected also to have metabolic diagnoses and to be treated with numerous medications, including Metformin, GLP1 agonists, Orlistat, Liraglutide, Bupropion/Naltrexone and combinations. It is unreasonable to ignore possible effects of major medications on AATBC expression. Please comment on the strengths and weaknesses of the analysis that ignores medications, or if some annotations of clinical data are available, perhaps to explain outliers in the plots, please discuss.
Minor Comments:
- The labeling of figure 2 A-K is not clear because the use of the same color of bars is easily misunderstood as the same source of cells but it is in fact not. For example, the grey color that appeared in 2B and 2C are not the same source but can be misunderstood.
- Figure 3 ASO-AATBC has two repeats #1 and #2, and over-expression of AATBC has one, even though there are enough repeats.
- It would be less confusing to present all of the repeats in ASO_AATBC together in one bar.
- The experimental outline can be a bit more detailed and explain some of the words like Thermo versus Browning.
- Some of the panels in Figure 7 could be put into supplementary if space is at a premium, and present the representative graph would be enough.
Significance
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Adipocyte plasticity and physiology has been linked to a lot of diseases such as diabetes, cardiovascular diseases and cancer. Therefore the conclusion coming from this paper reveals other aspects when looking at adipocytes in healthy or disease conditions-long noncoding RNA. lncRNA can be seen as a biomarker of an indicator of the physiology of adipose tissue, and may be able to account for changes that cannot be explained by cell genome analysis. There has been a surge of interest in RNA containing in exosomes, which serves as vesicles that travel between cells. Some studies have also shown that the content of exosomes arrives at the nucleolus of the recipient cell. The overlap of the location of exosomal RNA and lncRNA is a representative of a whole set of regulation of genes and expressions that was not noticed before.
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The audience of the paper may be interested in the outcome of the changes of mitochondria change in the context of disease such as obesity, diabetes, etc. For example, if there is a casual relationship between AATBC level and the status of obesity.
Referee Cross-commenting
Thank you, I concur.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Giroud et al. describes a role for the human-specific lncRNA AATBC in adipocyte plasticity. By overlaying datasets from tissues (white vs. brown fat) and cell lines (treated with norepinephrine or forskolin), the authors identified a limited number of lncRNAs demonstrating coordinate regulation. One of these lncRNAs is AATBC, which has not previously been studied in adipocytes. The authors show that AATBC is enriched in thermogenic adipose tissues/cells. They then perform gain and loss of function studies in cellular models and argue that AATBC is involved in thermogenesis and appears to be associated with the state of the mitochondrial network. The authors then explain that modulating AATBC has minimal effects on global transcription, and so they argue it mainly works via post-transcriptional mechanisms, though these are not defined. The authors then expressed AATBC in adipose tissue of mice and observed a decrease in plasma leptin levels and an increase in triglyceride levels, while other metabolic phenotypes were unchanged. Finally, the authors analyzed associations between adipose tissue AATBC and a variety of metabolic parameters in a few human cohorts. While the identification of a novel lncRNA involved in adipocyte biology and systemic metabolism would be of great interest, the data presented here does not convincingly support the conclusions made. Substantial additional experiments are needed to support the claims in this paper.
Major comments:
- The relevance of these findings to human biology remains unclear. In Figures 1-4, the authors present data showing that AATBC is enriched in thermogenic fat, and they argue that it regulates thermogenesis and mitochondrial biology. However, in Figures 6-7, where the authors look at AATBC in different human cohorts, they actually find that it is enriched in visceral fat, which is thought of as being the least thermogenic fat depot. The authors do not explain this seeming paradox, and thus, the role of AATBC in fat remains uncertain.
- In many of the experiments, insufficient controls are provided or the data are not at all convincing. For example: (a) The first four figures rely on in vitro adipocyte models, but the authors do not present data to show these cells differentiate properly and equally. This is especially relevant for the gain and loss of function studies. (b) Some of the experiments in Figure 1 (K-L) seem to only show an N of 1. (c) The RNAscope data in Figure 2 is not at all convincing for nuclear localization. (d) The ASO mediated knockdown of AATBC in Figure 3 only reduced expression slightly. A more complete knockdown or deletion may elicit a stronger phenotype. (e) In Figure 4, OPA1 is shown as a single band in panel E and a doublet in panel N. Based on this, are the authors certain they are detecting OPA1 or could this be a nonspecific band?( f) The correlations in Figure 6 I-L and Figure 7 do not include any statistical analysis.
- The gain of function studies in mice are problematic. The authors have performed a large amount of invasive studies in a short period of time. The animals will undoubtedly lose weight after each study, and with insufficient time to recover, this could influence the subsequent studies. In addition, since the authors present data in Figures 1-4 arguing that AATBC overexpression is associated with increased thermogenesis, it is surprising that the authors never looked at this in Figure 5 (aside from measuring Ucp1 mRNA). It would be interesting to measure energy expenditure by indirect calorimetry and cold tolerance.
- The authors do not provide any mechanistic insights into how AATBC may be acting. The manuscript contains some potentially interesting observations, but without some mechanistic insight, it is hard to understand how AATBC might regulate adipocyte plasticity.
Minor comments:
- The introduction is rather long and would benefit from being condensed.
Significance
This manuscript may represent an interesting advance in terms of highlighting a new lncRNA with a role in adipocyte biology. These findings would be of broad interest to researchers interested in obesity and metabolism. I myself am in this field of research, so feel quite qualified to evaluate this manuscript. However, as noted above, major concerns would need to be addressed in order to justify the conclusions made here.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
*The current manuscript by Shiryaev et al describes their observation of the new function of zika NS2B-NS3 proteases. They have shown that NS2B-NS3 protease lacking the helicase domain binds to RNA and the interaction can be affected by protease inhibitors. Main two new findings are presented in the manuscript: super open conformation of the protease; RNA binding activity of the protease region. Although the manuscript is interesting, the design of the experiments is not convincing. *
Major issues:
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- the claim of a super open confirmation is problematic. Using an artificial construct lacking the C-terminal portion of NS2B will of course generate the open conformation. This is a wrong definition unless you observe such a conformation in living cells.*
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We understand the skepticism towards a less known super-open confutation of flavivirus NS2B-NS3pro complex. In addition to our own structure of ZIKV NS2B-NS3pro (PDB ID 7M1V), the crystal structure of another orthologous flavivirus Japanese encephalitis virus (JEV) NS2B-NS3pro (PDB ID 4R8T) was discovered in 2015 1. However, no functional analysis was provided for this crystal structure resulting in the lack of attention paid by the research community. We computed the overlay of the ZIKV NS2B-NS3 protease structures in the super-open conformation (PDB ID 7M1V, deposited by us in 2021) with the crystal structure of JEV protease (PDB ID 7M1V ) (Rebuttal Figure 1). We observed an almost identical organization of the critical NS3pro C-terminal loop between these two structures (RMSD 0.6A). Polypeptides with over 35% identity are very likely to have a similar fold2. Given over 50% identity(!) between flaviviral proteases across the family3,4, we posit that the super-open conformation demonstrated for JEV and ZIKV NS2B-NS3pro is a common feature of the Flaviviridae family. Further, NS2B peptide is always tightly associated with NS3pro via a three-strand beta-barrel (aa 49-58 of NS2B), which remains intact in all NS3Pro conformations. The C-terminal portion of NS2B progressively loses association with NS3pro, being mostly associated in the closed conformation, less so in the open, and even less in the super-open conformation. The G4SG4 linker between NS2B and NS3pro remains unstructured in all conformations. The native C-terminal portion of NS2B (TGKR) is equally unstructured when competed out of the protease active site by another substrate. It is unclear to us why “lacking the C-terminal portion of NS2B will of course generate the open conformation”.
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It is odd that authors made homology model to generate open conformation structures. the authors did not cite the two papers of eZiPro (Phoo et al 2016 NC) and bZiPro (Zhang et al 2016, Science). these two structures show the closed conformation of protease in the absence and presence of a natural substrate.*
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We agree with the reviewer that in both constructs eZiPro5 and bZiPro6 of ZIKV NS2B-NS3pro are likely to exist in the closed conformation as documented by the crystal structures. However, in both cases, the active center of ZIKV NS2B-NS3pro is occupied with a short peptide fragment, which is sufficient to induce the closed conformation of NS2B-NS3 protease. We superimposed eZiPro (PDB ID 5GJ4) with bZiPro (PDB ID 5GPI) to better demonstrate that the active center in both structures is occupied either by tetrapeptide TGKR (T127-G128-K129-R130 ) originating from the NS2B C-terminus (eZiPro) or by a tetrapeptide KKGE (K14-K15-G16-E17) originating from a neighboring NS3 molecule (bZiPro) (Rebuttal Figure 2). Indeed, Zheng et al., 2016 6 stated that: “the structure (bZiPro) does capture the protease in complex with a reverse peptide. The tetrapeptide K14K15G16E17 folds into a small hairpin loop to occupy the active site.” Further, Phoo et al., 2016 5 stated that “binding of the ‘TGKR’ peptide to the catalytic site stabilizes the protease (eZiPro)”. To the best of our knowledge, so far there are no crystal structures of flaviviral NS2B-NS3 proteases in the closed conformation without peptide/inhibitor in the active center. We take it as a hint that the closed conformation is always induced by a substrate present in the active center.
Finally, we would like to draw the attention of this reviewer to the fact that the 15N R2 NMR signal from NS2B residues 65-85 is missing in bZiPro alone but re-appears when AcKR is added. This is consistent with the idea that without AcKR, bZiPro exists in the open conformation where much of the C-terminal part of NS2B is dissociated from NS3Pro and remains unstructured, thus resulting in the lack of NMR signal.
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RNA binding is novel, but is it observed in cells? only one method was used for testing the interactions, not other biophysical methods are used.*
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Given a complex network of protein-RNA interactions and the fact that NS3pro and NS3hel are connected by a single polypeptide, separating dynamically bound 11kB RNA to NS3pro from that to NS3hel in a native cell is a major technical challenge beyond the scope of this work. We employed a fluorescent polarization assay to demonstrate ssDNA and ssDNA binding to ZIKV NS2B-NS3pro. Subsequently, we employed a proteolytic activity assay with labeled peptide mimicking natural substrate for protease to demonstrate that the presence of ssRNA and ssDNA can efficiently inhibit proteolytic activity. To the best of our knowledge, this is the first indication that ssRNA or ssDNA could block proteolytic activity for any serine proteases, let alone a viral protease. Therefore, we consider the proteolytic activity assay used in the current work an orthogonal biochemical method supporting ssRNA binding to ZIKV NS2B-NS3pro.
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binding studies with RNA used artificial construct, how about the one with KTGR present like eZiPro. Keep in mind that the P1-P4 residues are present under native conditions.*
__- __As mentioned by the reviewer, TGKR peptide was found in the active center in the eZiPro crystal. Indeed, the junction region between NS2B and NS3 protease contains native cleavage sites for the NS2B-NS3Pro and is naturally cleaved by protease during the viral polyprotein processing. However, the TGKR peptide representing P1-P4 positions will have to leave the active center after the cleavage to ensure enzyme processivity/cleaving additional targets (otherwise, the protease would get stacked after the first cleavage). Proteolytic activity assay utilizes the fluorogenic peptide labeled with FAM (such as TGKR-FAM; where FAM is a group representing P1’ position in this case). TGKR-FAM peptide will compete and easily replace cleaved TGKR peptide from the active center in proteolytic activity assay. In sum, the C-terminal end of NS2B will be competed out of the protease active center by the next substrate, and there is no evidence that it will be naturally placed back in the active center after each round of protease proteolytic activity. Indeed, several crystal structures of flaviviral NS2B-NS3Pro in open conformation lack the C-terminal part of NS2B in the active center. Our unpublished NMR studies demonstrated that the C-terminal part of NS2B is unstructured in solution if the substrate peptide or small molecule inhibitor are not present in the active center of the protease.
- authors built up nice models, it is great to consider the full length NS2B, but authors haven't taken into account the effect of NS2B on the open or closed conformation of the protease. *
- __ All crystal structures of flavivirus NS2B-NS3pro in the closed, open, or super-open conformations have NS2B associated withNS3pro via a beta-barrel (__Rebuttal Figure 3), which is located at the opposite side from the RNA binding site. The transition from the closed to the open and to the super-open conformation is associated with the progressive dissociation of NS2B from NS3pro. Therefore, the effect of NS2B on NS3Pro is progressively diminished. In the closed conformation of NS3Pro, the negatively charged C-terminal part of NS2B is associated with the same positively charged grove as the RNA in the open conformation of NS3Pro. The C-terminal part of NS2B is dissociated from NS3Pro in the open conformation.
Minor issues:
*This manuscript shows the novel function of zika protease and conclude that protease binds to RNA. This is a novel finding, but the conclusion needs to be further confirmed, to avoid misinterpretations by future readers *
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closed, and super open conformations. But the definition was not carefully compared with current literatures. I am surprised that the two important papers are not cited. It is well known the G4SG4 linker affect the conformation of the protease.*
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The crystal structures and the proteolytic activities of gZiPro, eZiPro, and bZiPro are rather similar. In fact, Km (μM) are 2.86 ± 0.90 for gZiPro, 6.332 ± 2.41 for bZiPro, and the IC 50s of BPTI inhibition for gZiPro, eZiPro and bZiPro are 350, 76 and 12 nM respectively. NS2B and NS3pro have a large binding area in the closed conformation. Upon changing the conformation to the open conformation (and even more so to the super-open conformation), the C-terminal part of NS2B is progressively dissociated from NS3Pro. Therefore, possible minor effects introduced by the G4SG4 linker is unlikely to affect any of the conclusions in our work.
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Authors need to show super open conformation is present in nature e.g. the model in which full length NS2B and NS3pro.*
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A full-length NS2B has 2 transmembrane domains, which tether the NS2B-NS3pro complex to the cell membrane (we have modeled the presence of such transmembrane domains to account for the orientation of NS2B-NS3pro with respect to the cell membrane). The full-length complex has never been crystallized or tested in any assay due to the major technical challenges associated with the modeling of complex transmembrane proteins.
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RNA is a charged molecule under some conditions, NS3 also have charged residues, it is important to show whether the binding between RNA-protease is relevant to the function{Luo, 2010 #9270;Chernov, 2008 #9275;Xu, 2019 #10006}, or is this due to the application of the artificial constructs used in this study. Why so many mutants are used? *
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The requirement of NS3pro for the helicase function was shown by several investigators 7–9. Given the structural independence of NS3pro and NS3hel, which mostly rules out the allosteric effect, RNA binding by NS3pro is a newly proposed function of NS3pro for the helicase activity. We demonstrated biochemically that RNA-bound to NS3pro inhibits its protease function. A variety of mutants were used to constrain the conformations of NS2B-NS3pro (e.g. enforce the super-open confirmation) for crystallization studies.
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Using a construct close to the native protease, at least the P1-P4 residues should be present. Using a peptide in the assay is also useful.*
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We were unable to interpret this critique.
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Test binding of RNA with protease using another method such as biophysical methods, or even gel shift assay*
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We thank the reviewer for this suggestion. Although the gel-shift assay seems to be a reasonable method to test the binding, given the ease of spontaneous conformational change (i.e. into the super-open conformation), this assay could result in a progressive loss of bound RNA during migration in the gel.
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I don't know the correlation between Figure 7 and Figure 6. The authors describe ploy A binding to protease, while Figure 7 is talking about Helicase binds to dsRNAs. *
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There is no correlation. Figure 6 describes the models for NS2B-NS3pro binding to ssRNA. Figure 7 describes a separate point, the direction of dsRNA processing by NS3hel.
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I am glad to see the consideration of full length NS2B, NS3 in the models Figure 8, 9 and 11, but there is no data to support any of the model proposed. *
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There is no experimental data. We have modeled the N-terminal and C-terminal parts of full NS2B, which are predicted to be inserted into the cell membrane due to their characteristic amphipathic helical structure.
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Is the linker a ploy G not G4SG4? *
The linker is GGGGSGGGG (G4SG4) as stated in Materials and Methods of the manuscript.
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Do the mutant sustain their protease activity? *
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All mutants with intact catalytic centers have protease activity, except the mutants with a disulfide bridge that fixes the polypeptides in the super-open conformation.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
*The manuscript by Shiryaev et al., submitted to BioRXiv is an exploration of the ability of NS2B-NS3protease to bind RNA and its subsequent role in NS3 helicase processivity. The authors first utilize fluorescence polarization assays to demonstrate that NS2B-NS3protease can bind ssRNA with a strong affinity (and also ssDNA with lower affinity). They subsequently utilize mutational and small molecule inhibitor strategies in these assays to force the NS2B-NS3protease into different conformations, with the associated results inferring that the "open" conformation is responsible for ssRNA binding affinity. Furthermore, they demonstrate that ssRNA binding impairs protease activity, suggesting these roles may be exclusive in the viral life cycle. They also identified a number of small molecule ligands that target the putative ssRNA binding channel, and demonstrate that these ligands inhibit ssRNA binding by NS2B-NS3protease, providing potential inhibitor candidates for ZIKV. Finally, the authors utilized their crystal structures and others for the various conformations of NS2B-NS3protease to model ssRNA binding by the domain and the full NS3 protein, and used these models to propose a reverse inchworm model for NS3 travelling along ssRNA as it unwinds the dsRNA duplex. Overall, the authors utilize a comprehensive approach to demonstrate a number of novel findings (ssRNA binding by NS2B-NS3protease, small molecule ligands that inhibit this interaction) that would be of interest to both virologists and structural biologists. However, there are some important experimental design limitations and viral life cycle considerations that the authors should address before acceptance of the manuscript. Major and minor comments intended to improve the manuscript are outlined in more detail below. *
Major Comments:
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- While the quantity of indirect data (ruled out closed and super-open, inhibitors of ssRNA binding pocket) suggest that the open conformation of NS2B-NS3protease is associated with ssRNA binding, the argument would be greatly strengthened by direct experimental data. Is there a mutational or small molecule approach to locking the NS2B-NS3 protease in the open conformation? If so, the authors should perform such experiments to strengthen the foundation of their argument.*
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Unfortunately, despite significant efforts, mutations or small molecules locking the NS2B-NS3 protease in the open conformation have not been identified for the ZIKV protease. However, several structures for NS2B-NS3 proteases have been documented in other flaviviruses (i.e., DENV PDB IDs 2FOM and 5T1V; WNV PDB ID 2GGV). Polypeptides with over 35% identity are very likely to have a similar fold2. Given over 50% identity(!) between flaviviral proteases across the family3,4, there is little doubt that ZIKV NS2-NS3 protease adopts an open conformation similar to all flaviviral proteases. Our modeling demonstrated that there are no sterically/structural problems in folding NS2B-NS3 protease into the open conformation.
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A negative control should be used in Figure 4A to strengthen the claim that ssRNA binding in the open conformation impairs protease activity (ie. include a curve for dsRNA). Such an experiment would lend support to ssRNA inhibition being due to specific binding instead of some other non-specific effect of increasing local nucleic acid concentration.*
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To address this critique, we have conducted the modeling of dsRNA binding to the open conformation of NS2B-NS3Pro. The model revealed that dsRNA could not be accommodated by the open conformation of the NS2B-NS3Pro complex (Rebuttal Figure 4). Indeed, dsRNA has a very different rigid structure compared to the extended form of the ssRNA chain. The dsRNA is unable to provide continuous interactions between negatively RNA backbone and positively charged side chain amino acids in NS3pro. The continuous interface on NS2B-NS3 protease interacting with ssRNA is an extension of the exit groove for one of the ssRNA strands exiting the NS3 Helicase after unwinding. Therefore, the ssRNA, but not dsRNA is naturally always present in close proximity of the NS2B-NS3Pro complex.
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Due to the highly coupled roles of NS5 and NS3 in replication, the authors should include some more consideration of the role of NS5 in their complex. They very briefly address this interplay in the fifth paragraph of the discussion, but then neglect to discuss the implications any further. In particular (perhaps in a brief comparison to an NS3/NS5 modeling approach such as Brands et al., 2017; WIRES), the authors should consider some of the following questions: could the channel on protease domain lead to ssRNA entry site on RdRp?*
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Indeed, our model suggests that the negative strand (-)ssRNA exits from NS2B-NS3protease facing the ER membrane in the area where the protease is anchored to the ER membrane via the NS2B transmembrane domains. It is possible that NS3pro interacts with NS5 polymerase and “handles” (-)ssRNA to the NS5 polymerase. This scenario would modify Brands et al., 2017 model to add NS2B-NS3Pro complex between NS3Hel and NS5. However, at present, the NS3-NS5 (or NS2B-NS3-NS5) complex together has not been crystallized. It would be logical for NS5 polymerase to access the (-)ssRNA strand after it is released from NS2B-NS3Pro since the (-)ssRNA strands are used as a template for the (+)ssRNA which is used for polyprotein synthesis and packaging into viral particles.
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would NS5 interaction constrain or augment inchworm model of NS2B/NS3 translocation? *
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Yes, integrating NS5 interaction with the NS2B-NS3pro handling (-)ssRNA will augment the utility of the suggested reverse inchworm model.
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how does increased activity of NS3 when complexed with NS5 (**Xu et al. 2019) align with proposed inchworm model? *
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We appreciate the reviewer's question. We think that NS2, NS3, NS4, and NS5 work in concert as one coordinated complex where various subunits of NS2 and NS4 may provide anchoring of the entire complex to the ER membrane. Indeed, such a complex has recently been proposed6. Also, see our response to the previous reviewer’s point (#4). We have incorporated this discussion into the revised manuscript.
Minor Comments: 1. Introduction, 4th paragraph, NS3-NS4 should read NS3-NS4A.
- We corrected this sentence.
* ** Throughout the manuscript, the authors should denote some key amino acid residues in each figure to help orient the reader better to the observed structural changes and rotations. Inclusion, at least in the supplement, of the crystal structures of mutants solved herein should **also be included. *
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We annotated the key residues in all figures (e.g. catalytic residues, loop interacting with the membrane, position of NS2B, and other elements) and kept the same orientation of complexes in all figures.
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Section: RNA binding inhibits the proteolytic activity of ZIKV NS2B-NS3pro, last sentence, NS2N-NS3pro should be NS2B-NS3pro*.
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We corrected this sentence.
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Section: Allosteric inhibitors of NS2B-NS3 protease interfere with RNA binding- first sentence: "The open conformation of NS2B-NS3pro is achieved by the rearrangement of NS2B cofactor (its dissociation from the C-terminal half of NS3pro) leading to a loss of proteolytic activity [32]. - the reference is not correct. I could not find the reference the authors refer to here and had not heard before that NS2B cofactor was able to disassociate from the C-terminal half of NS3pro; hence, this really needs to be appropriately referenced. *
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We have revised this sentence and added additional references. “The open conformation of NS2B-NS3pro is achieved by the rearrangement of NS2B cofactor (partial dissociation from NS3pro), leading to a loss of proteolytic activity4,11.”
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Section: Modeling RNA binding to ZIKV NS2B-NS3, first sentence - unwinds should be unwind*.
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We corrected this sentence.
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With respect to the results of Figure 3A, the authors should address that adding the linker alone to the NS3 protease may not be an accurate examination of its role/importance. The linker in this scenario is only constrained at its N-terminus, while it is always constrained at both termini during infection (and even more so by the interactions of those two linked domains [protease and helicase] with each other). As such, the authors statement that "observations suggests that the 12-aa linker region modulates RNA binding to NS2B-NS3pro" should be more strongly qualified to this effect. In addition, it would be interesting to see the effects of the linker mutations on ssRNA binding in the context of the full NS3 protein, albeit admittedly more complex due to the confounding ssRNA binding by the helicase domain.*
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We agree with this reviewer that the protease-helicase linker is also restrained at both termini. We have rephrased the statement in the revised manuscript. The goal of the experiment shown in Figure 3A was to examine whether a negatively charged linker is able to compete with ssRNA binding as we expected from the structural model. The mutational analysis of the protease helicase linker is, indeed, a very interesting subject that is, however, beyond the scope of this work.
7. The NS#hel should be changed to NS3hel in part (C) of figure legend for Figure 11. - We corrected this mishap.
- The authors data in Figure 4A (and even more so the nature of the viral life cycle where 1000s of viral polyproteins are created from the first genome during infection) disputes the depiction in the inchworm model of how NS3 protease cleaves the polyprotein while the helicase binds ssRNA. At minimum, the authors need to discuss this discrepancy, and it is recommended that they modify the cartoon in their model to not include the ssRNA binding on the protease side of the equation (or show as alternative on that side to the existing cartoon).*
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Indeed, as proposed by our reverse inchworm model, ssRNA is not bound to NS3Pro in the closed conformation, while NS2B-NS3pro has a protein substrate in the active center (Figure 11A). We agree that NS2B-NS3Pro in the closed conformation cannot bind ssRNA as we demonstrated in competitive cleavage assay. Only large amounts of ssRNA can shift the balance towards the open conformation which binds ssRNA. We think that most of the time NS2B-NS3Pro cycles between the open and the super conformations handling ssRNA (Figure 11(B-C_D), but as soon as protein substrate becomes available (typically a loop from a transmembrane viral polypeptide), NS2B-NS3Pro quickly switches to the closed proteolytically active conformation to act as protease.
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In the third paragraph of the discussion, the authors state "An alternative model of coupled transcription and translation where viral RNA is associated with ribosomes right after the release from NS2B-NS3 is also possible". Considering there is abundant evidence that translation and replication are exclusive and that translation does not take place in ROs, it would be prudent to remove such statements from the discussion. Without any supporting evidence, these statements will be misleading to readers by providing a false equivalency. The preceding discussion of RFs would be sufficient to contextualize your inchworm model in the broader viral life cycle (which was done quite well). *
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We have adjusted the discussion in the revised manuscript to avoid a false equivalency.
10. There were a number of aspects I appreciated about the manuscript and will briefly list a few here: ** i) the focus on how different non-structural proteins effect the structure and function of ** each other during the viral life cycle, which forms a more comprehensive and informative model ** ii) the use of structural and functional assays as complementary approaches to studying the intra- and inter-protein relationships of NS3 ** iii) the depiction of the forks in Figure 10, which effectively demonstrated the channels and oriented the reader to the conservation data ** *iv) the use of small molecule inhibitors to modify structure and function of NS3, which greatly deepened the richness of the story from both a basic and applied science view point *
- We are very grateful to the Reviewer for these kind remarks.
Reviewer #2 (Significance (Required)): ** Strengths and limitations: ** - provides some experimental and modeling data to provide a new model for RNA interactions with the NS3pro-hel; may help inform models for enzyme function, mostly consistent with previous literature ** - leaves out the NS5 RdRp, known to contribute to NS3 activity. ** - some suggestions are made which might strengthen the conclusions and inclusions of additional controls would improve the data. ** Advance ** - conceptual, perhaps may provide some insight into mechanism; although limited by the lack of NS5 RdRp which is crucial to helicase activity. It is unclear if the ssRNA would be oriented this way given interactions with NS5 RdRp and MT domains (is the ssRNA routed to NS5 or along NS3, or potentially are both happening?) ** Audience: ** - quite specialist, but may include structural biologists and virologist alike. ** Expertise of the reviewer(s): ** *- molecular virologists, RNA viruses - including flaviviruses; replication complex biogenesis, protein-RNA and RNA-RNA interactions. While comfortable with the concepts regarding complex formation, the appropriateness of computational modeling and RNA docking tools as well as structural biology is out of our area of expertise. *
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
*This paper investigates the nucleic acid binding properties of zika virus protease. In particular the data suggest that single stranded RNAs and DNAs are capable of binding to and inhibiting ZIKV protease at micromolar concentrations. With the use of active site inhibitors and mutants that lock the protease in closed and super-open conformation, the authors concluded that RNA binds to the open conformation. Through extensive modeling of the protease and helicase domains, this manuscript provides a model of how ssRNAs can bind to all conformations of the proteas, but the open conformation provides two positively charged forks that should be available to bind RNA. *
* SECTION A - Evidence, reproducibility, and clarity ** Major comments: **
*·The main conclusions of this paper rely on the existence of the super-open conformation, however this conformation has not been reported in the scientific literature previously. Structures deposited in the pdb are referenced in this manuscript, however no citation for an accompanying publication is provided. This calls into question the biological relevance of this super open conformation. This is of particular concern because in other highly-homologous flaviviral proteases, structures that have been observed crystallographically (e.g. the open conformation of dengue virus protease) appear to be only very sparsely populated in solution. What is the evidence that the super-open conformation exists in solution.
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Please, see our reply to question #1 from Reviewer 1.
- The activity of each of the constructs used was not reported making it impossible to directly compare the impact of these changes on intrinsic activity. In particular, the NS2B-NS3 long construct is predicted to exist in the super-open conformation. If this is correct, it should show no activity against a peptide substrate. *
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We appreciate these concerns. The NS2B-NS3pro-long construct is proteolytically active (only NS2B-NS3pro-short construct is proteolytically inactive because its NS3pro C-terminal part is too short to fold into the closed conformation). It is unconstrained and likely capable of adopting all possible conformations (closed, open, super open). As we suspected, the negatively charged linker interferes with RNA binding, potentially via direct competition. Investigating the role of the protease-helicase linker is an exciting subject of a separate manuscript in preparation.
- This paper reports that the IC50 is much weaker than the Kd for binding of ssRNA to ZIKV NS2B-NS3pro. Are orthogonal assays, such as thermal shift assay, available which could distinguish between the reported IC50 and the Kd. *
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Binding of ssRNA occurs in an area distinct from the protease active center. We think that there is a constant competition between C-terminal NS2B binding/release versus ssRNA binding/release from NS3pro. We think that ssRNA “catches” the moment when protease has the open conformation and freezes that conformation by blocking the C-terminal of NS2B from binding to NS3Pro. In terms of thermal shift assay, the structure of NS3Pro is changed, only the C-terminal of NS2B is affected. Note that the 15N R2 NMR signal from NS2B residues 65-85 is missing in bZiPro alone but re-appears when AcKR is added6. This is consistent with the idea that without AcKR, bZiPro exists in the open conformation where much of the C-terminal part of NS2B is dissociated from NS3Pro and remains unstructured, thus resulting in the lack of NMR signal. Taken together, these observations suggest that thermal shift assay is unlikely to be of much help.
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*This paper suggests that ssRNA binds to the open conformation of ZIKV NS2B-NS3pro, however no experimental evidence, only modeling has been used to suggest binding to the open conformation. In Dengue virus protease, the M84P variant has been reported to lock the protease into the open conformation. How does the F84P variant of ZIKV NS2B-NS3pro impact ssRNA binding? *
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We appreciate this question. Indeed, M84P mutation shifts Dengue NS3Pro to the open conformation, which is proteolytically inactive12, consistent with our reverse inchworm model. We have not investigated the effect of this mutation on ZIKV NS3pro. We expect this mutation has a similar effect in ZIKV NS3pro in Dengue NS3Pro.
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The relevance of the discussion on the co-crystallization of NSC86314 with the Mut7was not clear. What point was being made?
- We provide a proof-of-principle for a novel class of allosteric inhibitors that specifically target newly identified druggable pockets present in the open and super-open conformations of ZIKV NS2B-NS3pro. Our results suggest that such allosteric inhibitors can interfere with the RNA-binding activities of NS2B-NS3pro in addition to blocking the protease activity. The co-crystallization of NSC86314 with the Mut7 confirms a novel pocked bound by NSC86314.
*- These data show that both active site and allosteric inhibitors block binding of ssRNA to the protease. The paper also suggests that ssRNA only binds to the open conformation. What is the evidence that the allosteric inhibitors do not enable or promote formation of the open conformation? *
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We thank this reviewer for an interesting question. Indeed, we have no evidence of whether allosteric inhibitors enable or promote the formation of the open conformation. This is formally possible and will need to be investigated.
- This paper makes two claims about the function of the protease. The title should specify what those dual functions are (proteolytic activity and ssRNA-recruitment).*
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We appreciate this reviewer's suggestions for the title.
- The discussion of Figures 6 and 9 are highly similar. The main takeaway points for both figures seem to be nearly identical: the presence of two positively charged pitchfork on the open conformation. The distinction between these two figures should be more significantly and explicitly stated. *
- Figure 6 presents several models that provide evidence for the open conformation of ZIKV NS2B-NS3pro being uniquely suitable to bind RNA. Figure 9 presents several models of the entire RNA-NS2B-NS3pro-NS3hel complex anchored into the ER membrane. Figure 9 illustrates that the open conformation of NS2B-NS3pro provides two positively charged/polar forks, contiguous with the positively charged groove on NS3hel. Figure 6 does not illustrate that point.
*- Mention explicitly in the materials and methods if the 12-amino acid linker is present in all the mutants used. *
- This is mentioned explicitly and shown in Supplementary Figure 2A.
Minor comments: ** · Figure 1. The rotation that promotes the transitions from orientation in panel A to that in panel B should be drawn. ** · FAM should be defined in the legend of Figure 2. ** · The term Cold should be changed to unlabeled. ** · Please check labels for the supplementary Figure 2. For example one label states 1-1 but it ** should be 1-170. ** · Figure 1C does not exist and it is referenced in the results section under "NS2B-NS3pro substrate-mimicking inhibitors compete with RNA binding." ** · As discussed above, if the super open conformation is going to be addressed in this paper, then either a reference for the manuscript describing those structures should be included, or this manuscript should include in the materials and methods the procedure on crystallization, data collection, structure determination, refinement, and analysis as well as a table for crystallographic data and refinement statistics. ** · Adjust figure arrangement (ABCED to ABCDE) in Figure 11.
- We thank this reviewer for all minor comments. We corrected the above-mentioned errors in the manuscript.
Reviewer #3 (Significance (Required)): ** It is well established that the flaviviral proteases exist in different conformations but most of the structures published are concentrated on the closed conformation which is the one required for effective substrate processing. The open conformation has recently been the subject of increased interest, especially with the discovery of allosteric inhibitors for which modeling suggests that these compounds result in the dissociation of the C-terminal region of NS2B from the NS3. This paper adds important insights into the function of the open conformation and in general implicitly shows the importance of the dynamic nature of ZIKV NS2B-NS3pro. In addition to these insights, this paper aptly demonstrates that ssRNA can bind and inhibit these proteases as has not been shown previously. ** I am a senior graduate student working on characterizing and understanding the mechanism of action of allosteric compounds against viral proteases, specifically proteases from Zika and dengue viruses.
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Referee #3
Evidence, reproducibility and clarity
This paper investigates the nucleic acid binding properties of zika virus protease. In particular the data suggest that single stranded RNAs and DNAs are capable of binding to and inhibiting ZIKV protease at micromolar concentrations. With the use of active site inhibitors and mutants that lock the protease in closed and super-open conformation, the authors concluded that RNA binds to the open conformation. Through extensive modeling of the protease and helicase domains, this manuscript provides a model of how ssRNAs can bind to all conformations of the proteas, but the open conformation provides two positively charged forks that should be available to bind RNA.
SECTION A - Evidence, reproducibility, and clarity
Major comments:
- The main conclusions of this paper rely on the existence of the super-open conformation, however this conformation has not been reported in the scientific literature previously. Structures deposited in the pdb are referenced in this manuscript, however no citation for an accompanying publication is provided. This calls into question the biological relevance of this super open conformation. This is of particular concern because in other highly-homologous flaviviral proteases, structures that have been observed crystallographically (e.g. the open conformation of dengue virus protease) appear to be only very sparsely populated in solution. What is the evidence that the super-open conformation exists in solution.
- The activity of each of the constructs used was not reported making it impossible to directly compare the impact of these changes on intrinsic activity. In particular, the NS2B-NS3 long construct is predicted to exist in the super-open conformation. If this is correct, it should show no activity against a peptide substrate.
- This paper reports that the IC50 is much weaker than the Kd for binding of ssRNA to ZIKV NS2B-NS3pro. Are orthogonal assays, such as thermal shift assay, available which could distinguish between the reported IC50 and the Kd.
- This paper suggests that ssRNA binds to the open conformation of ZIKV NS2B-NS3pro, however no experimental evidence, only modeling has been used to suggest binding to the open conformation. In Dengue virus protease, the M84P variant has been reported to lock the protease into the open conformation. How does the F84P varian of ZIKV NS2B-NS3pro impact ssRNA binding?
- The relevance of the discussion on the co-crystallization of NSC86314 with the Mut7was not clear. What point was being made?
- These data show that both active site and allosteric inhibitors block binding of ssRNA to the protease. The paper also suggests that ssRNA only binds to the open conformation. What is the evidence that the allosteric inhibitors do not enable or promote formation of the open conformation?
- This paper makes two claims about the function of the protease. The title should specify what those dual functions are (proteolytic activity and ssRNA-recruitment).
- The discussion of Figures 6 and 9 are highly similar. The main takeaway points for both figures seem to be nearly identical: the presence of two positively charged pitchfork on the open conformation. The distinction between these two figures should be more significantly and explicitly stated.
- Mention explicitly in the materials and methods if the 12-amino acid linker is present in all the mutants used.
Minor comments:
- Figure 1. The rotation that promotes the transitions from orientation in panel A to that in panel B should be drawn.
- FAM should be defined in the legend of Figure 2.
- The term Cold should be changed to unlabeled.
- Please check labels for the supplementary Figure 2. For example one label states 1-1 but it should be 1-170.
- Figure 1C does not exist and it is referenced in the results section under "NS2B-NS3pro substrate-mimicking inhibitors compete with RNA binding."
- As discussed above, if the super open conformation is going to be addressed in this paper, then either a reference for the manuscript describing those structures should be included, or this manuscript should include in the materials and methods the procedure on crystallization, data collection, structure determination, refinement, and analysis as well as a table for crystallographic data and refinement statistics.
- Adjust figure arrangement (ABCED to ABCDE) in Figure 11.
Significance
It is well established that the flaviviral proteases exist in different conformations but most of the structures published are concentrated on the closed conformation which is the one required for effective substrate processing. The open conformation has recently been the subject of increased interest, especially with the discovery of allosteric inhibitors for which modeling suggests that these compounds result in the dissociation of the C-terminal region of NS2B from the NS3. This paper adds important insights into the function of the open conformation and in general implicitly shows the importance of the dynamic nature of ZIKV NS2B-NS3pro. In addition to these insights, this paper aptly demonstrates that ssRNA can bind and inhibit these proteases as has not been shown previously.
I am a senior graduate student working on characterizing and understanding the mechanism of action of allosteric compounds against viral proteases, specifically proteases from Zika and dengue viruses.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Shiryaev et al., submitted to BioRXiv is an exploration of the ability of NS2B-NS3protease to bind RNA and its subsequent role in NS3 helicase processivity. The authors first utilize fluorescence polarization assays to demonstrate that NS2B-NS3protease can bind ssRNA with a strong affinity (and also ssDNA with lower affinity). They subsequently utilize mutational and small molecule inhibitor strategies in these assays to force the NS2B-NS3protease into different conformations, with the associated results inferring that the "open" conformation is responsible for ssRNA binding affinity. Furthermore, they demonstrate that ssRNA binding impairs protease activity, suggesting these roles may be exclusive in the viral life cycle. They also identified a number of small molecule ligands that target the putative ssRNA binding channel, and demonstrate that these ligands inhibit ssRNA binding by NS2B-NS3protease, providing potential inhibitor candidates for ZIKV. Finally, the authors utilized their crystal structures and others for the various conformations of NS2B-NS3protease to model ssRNA binding by the domain and the full NS3 protein, and used these models to propose a reverse inchworm model for NS3 travelling along ssRNA as it unwinds the dsRNA duplex. Overall, the authors utilize a comprehensive approach to demonstrate a number of novel findings (ssRNA binding by NS2B-NS3protease, small molecule ligands that inhibit this interaction) that would be of interest to both virologists and structural biologists. However, there are some important experimental design limitations and viral life cycle considerations that the authors should address before acceptance of the manuscript. Major and minor comments intended to improve the manuscript are outlined in more detail below.
Major Comments:
- While the quantity of indirect data (ruled out closed and super-open, inhibitors of ssRNA binding pocket) suggest that the open conformation of NS2B-NS3protease is associated with ssRNA binding, the argument would be greatly strengthened by direct experimental data. Is there a mutational or small molecule approach to locking the NS2B-NS3 protease in the open conformation? If so, the authors should perform such experiments to strengthen the foundation of their argument.
- A negative control should be used in Figure 4A to strengthen the claim that ssRNA binding in the open conformation impairs protease activity (ie. include a curve for dsRNA). Such an experiment would lend support to ssRNA inhibition being due to specific binding instead of some other non-specific effect of increasing local nucleic acid concentration.
- Due to the highly coupled roles of NS5 and NS3 in replication, the authors should include some more consideration of the role of NS5 in their complex. They very briefly address this interplay in the fifth paragraph of the discussion, but then neglect to discuss the implications any further.
In particular (perhaps in a brief comparison to an NS3/NS5 modeling approach such as Brands et al., 2017; WIRES), the authors should consider some of the following questions: - could the channel on protease domain lead to ssRNA entry site on RdRp? - would NS5 interaction constrain or augment inchworm model of NS2B/NS3 translocation? - how does increased activity of NS3 when complexed with NS5 (Xu et al. 2019) align with proposed inchworm model?
Minor Comments:
- Introduction, 4th paragraph, NS3-NS4 should read NS3-NS4A.
- Throughout the manuscript, the authors should denote some key amino acid residues in each figure to help orient the reader better to the observed structural changes and rotations. Inclusion, at least in the supplement, of the crystal structures of mutants solved herein should also be included.
- Section: RNA binding inhibits the proteolytic activity of ZIKV NS2B-NS3pro, last sentence, NS2N-NS3pro should be NS2B-NS3pro
- Section: Allosteric inhibitors of NS2B-NS3 protease interfere with RNA binding- first sentence: "The open conformation of NS2B-NS3pro is achieved by the rearrangement of NS2B cofactor (its dissociation from the C-terminal half of NS3pro) leading to a loss of proteolytic activity [32]. - the reference is not correct. I could not find the reference the authors refer to here and had not heard before that NS2B cofactor was able to disassociate from the C-terminal half of NS3pro; hence, this really needs to be appropriately referenced.
- Section: Modeling RNA binding to ZIKV NS2B-NS3, first sentence - unwinds should be unwind
- With respect to the results of Figure 3A, the authors should address that adding the linker alone to the NS3 protease may not be an accurate examination of its role/importance. The linker in this scenario is only constrained at its N-terminus, while it is always constrained at both termini during infection (and even more so by the interactions of those two linked domains [protease and helicase] with each other). As such, the authors statement that "observations suggests that the 12-aa linker region modulates RNA binding to NS2B-NS3pro" should be more strongly qualified to this effect.
In addition, it would be interesting to see the effects of the linker mutations on ssRNA binding in the context of the full NS3 protein, albeit admittedly more complex due to the confounding ssRNA binding by the helicase domain. 7. The NS#hel should be changed to NS3hel in part (C) of figure legend for Figure 11. 8. The authors data in Figure 4A (and even more so the nature of the viral life cycle where 1000s of viral polyproteins are created from the first genome during infection) disputes the depiction in the inchworm model of how NS3 protease cleaves the polyprotein while the helicase binds ssRNA. At minimum, the authors need to discuss this discrepancy, and it is recommended that they modify the cartoon in their model to not include the ssRNA binding on the protease side of the equation (or show as alternative on that side to the existing cartoon). 9. In the third paragraph of the discussion, the authors state "An alternative model of coupled transcription and translation where viral RNA is associated with ribosomes right after the release from NS2B-NS3 is also possible". Considering there is abundant evidence that translation and replication are exclusive and that translation does not take place in ROs, it would be prudent to remove such statements from the discussion. Without any supporting evidence, these statements will be misleading to readers by providing a false equivalency. The preceding discussion of RFs would be sufficient to contextualize your inchworm model in the broader viral life cycle (which was done quite well). 10. There were a number of aspects I appreciated about the manuscript and will briefly list a few here:
- i) the focus on how different non-structural proteins effect the structure and function of
each other during the viral life cycle, which forms a more comprehensive and informative model - ii) the use of structural and functional assays as complementary approaches to studying the intra- and inter-protein relationships of NS3 - iii) the depiction of the forks in Figure 10, which effectively demonstrated the channels and oriented the reader to the conservation data - iv) the use of small molecule inhibitors to modify structure and function of NS3, which greatly deepened the richness of the story from both a basic and applied science view point
Significance
Strengths and limitations:
- provides some experimental and modeling data to provide a new model for RNA interactions with the NS3pro-hel; may help inform models for enzyme function, mostly consistent with previous literature
- leaves out the NS5 RdRp, known to contribute to NS3 activity.
- some suggestions are made which might strengthen the conclusions and inclusions of additional controls would improve the data.
Advance
- conceptual, perhaps may provide some insight into mechanism; although limited by the lack of NS5 RdRp which is crucial to helicase activity. It is unclear if the ssRNA would be oriented this way given interactions with NS5 RdRp and MT domains (is the ssRNA routed to NS5 or along NS3, or potentially are both happening?)
Audience:
- quite specialist, but may include structural biologists and virologist alike.
Expertise of the reviewer(s):
- molecular virologists, RNA viruses - including flaviviruses; replication complex biogenesis, protein-RNA and RNA-RNA interactions. While comfortable with the concepts regarding complex formation, the appropriateness of computational modeling and RNA docking tools as well as structural biology is out of our area of expertise.
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Referee #1
Evidence, reproducibility and clarity
The current manuscript by Shiryaev et al describes their observation of the new function of zika NS2B-NS3 proteases. They have shown that NS2B-NS3 protease lacking the helicase domain binds to RNA and the interaction can be affected by protease inhibitors. Main two new findings are presented in the manuscript: super open conformation of the protease; RNA binding activity of the protease region. although the manuscript is interesting, the design of the experiments is not convincing.
Major issues
- the claim of a super open conformation is problematic. Using an artificial construct lacking the C-terminal portion of NS2B will of course generate the open conformation. This is a wrong definition unless you observe such a conformation in living cells.
- It is odd that authors made homology model to generate open conformation structures. the authors did not cite the two papers of eZiPro (phoo et al 2016 NC) and bZiPro (Zhang et al 2016, Science). these two structures show the closed conformation of protease in the absence and presence of natural substrate.
- RNA binding is novel, but is it observed in cells? only one method was used for testing the interactions, not other biophysical methods are used.
- binding studies with RNA used artificial construct, how about the one with KTGR present like eZiPro. Keep in mind that the P1-P4 residues are present under native conditions.
- authors built up nice models, it is great to consider the full length NS2B, but authors haven't taken into account the effect of NS2B on the open or closed conformation of the protease.
Significance
This manuscript shows the novel function of zika protease and conclude that protease binds to RNA. This is a novel finding, but the conclusion needs to be further confirmed, to avoid misinterpretations by future readers
It is great to introduce the conformational changes of a protease through defining open, closed, and super open conformations. But the definition was not carefully compared with current literatures. I am surprised that the two important papers are not cited. It is well known the G4SG4 linker affect the conformation of the protease, it is problematic to introduce the super-open conformation here. Authors need to show super open conformation is present in nature e.g. the model in which full length NS2B and NS3pro. RNA is a charged molecule under some conditions, NS3 also have charged residues, it is important to show whether the binding between RNA-protease is relevant to the function, or is this due to the application of the artificial constructs used in this study. Why so many mutants are used?
Other minors
- Using a construct close to the native protease, at least the P1-P4 residues should be present. Using a peptide in the assay is also useful.
- Test binding of RNA with protease using another method such as biophysical methods, or even gel shift assay
- I don't know the correlation between Figure 7 and Figure 6. The authors describe ploy A binding to protease, while Figure 7 is talking about Helicase binds to dsRNAs.
- I am glad to see the consideration of full length NS2B, NS3 in the models Figure 8, 9 and 11, but there is no data to support any of the model proposed.
- Is the linker a ploy G not G4SG4?
- Do the mutant sustain their protease activity?
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Referee #3
Evidence, reproducibility and clarity
The authors ask the important question whether post-embryonic organ formation follows the same mechanisms as during embryonic development. They focus on neuromasts of the lateral line system in the caudal fin of medaka. They used live imaging to find that the post-embryonic caudal-neuromast-cluster develops from organ-founder neural stem cells as a bud (and not as individual migrating stem cells as in zebrafish) that detaches and migrates from the founding neuromast (P0). They show that the formation of post-embryonic neuromasts does not require the lateral line nerve, which establishes another difference to the process in zebrafish. Artificial reduction in Cxcr4b chemokine signalling slows down stem cell delamination, which invariably occurs at the anterior aspect of the P0-neuromast. They then show (via changes in cadherin-type gene expression and cellular imaging) that stem cell delamination from the P0 neuromast involves an epithelial-to-mesenchymal transition. Forcing this process in the entire neuromast accelerates new organ formation, but directionality is maintained. Finally they ask whether the stem cells required for this process are pre-specified or are generated from the neuromast by ablating the pre-bud stem cells. They find that other stem cells within the same organ rearrange and re-establish organ founder cells. The authors liken this new mechanism of organ formation to pathological metastases in humans and name it metastatic-like organogenesis.
Major comments
- In Fig. 2D both P0- and PE1-neuromasts appear with fewer hair cells, the reasons for this should be explained. Did the ablation also damage the primordium or is the pLL nerve required for complete neuromasts to form?
- p. 6: The reasoning behind generating a cxcr4b l-o-f mutant does not become clear, since a mutant already existed with a non-migratory primordium. Why did the authors expect their mutant would have a different phenotype?
- p. 7: The authors state that K15::cxcr7 larvae lack secondary embryonic neuromasts, but it seems from Figs. 3B,D that they might simply be delayed (note that the last one is missing in 3B). This delay may have been taken over from the delay in embryonic primordium formation of P0 and this result (as shown in Fig. 3) would not contradict the assumption that PE1 can form in the absence of cxcr4b signalling. I suggest that the authors actually show and quantify that K15::cxcr7 adults have fewer CNC neuromast numbers, because this seems to be the definitive proof that overexpressing the "sink" may be enough to reduce cxcr4b signaling to a level where its requirement for the formation of the PE1-neuromast can be assayed.
- Fig. 6A': The scheme is at least ambiguous and interpretation of it requires more supporting information: In A the stippled lines represent position within the neuromast, but what do they represent in A', numbers of BrdU+ cells? So what does e.g. the grey peak at the anterior mean - position or number of cells? Is the area inside the coloured lines important or the edge-points? Stage III is the only distribution with a clear left-bias, the others are centered (with a left-extreme for stage II), so what in the figure is the anterior proliferation peak? These are just some questions this reviewer had. Maybe the problem lies in the octagonal lines, meaning different things in both images? It is further unclear how the means given in the text can be derived from the figure. Maybe it would be best to try to represent the data with a heat map-overlay of the image in A, one for each stage?
- The authors propose two competing models regarding the origin of founder stem cells (p. 9, first sentence: early determination vs. in situ generation). In the third sentence again two scenarios are presented as to why experimentally prompting EMT does not trigger organ founder cell migration. This paragraph would benefit from stating more precisely which of these questions is addressed by the BrdU- and ablation experiments, together with a clearer statement at the end of that section as to which hypothesis in each case (origin and migration) is preferred.
Minor comments
- K15+ cells are described as neuromast stem cells and Fig. 1 suggests that these are the mantle cells: Please comment on the question whether all mantle cells are stem cells.
- p. 5: The reference (Seleit, Krämer et al., 2017) is ambiguous, as there are 2 references listed that fit the abbreviation.
- Fig.1B-F': Even though, as the authors state, there is variation in the timing of the budding process, it would be helpful to add an exemplay time frame to stages I-V.
- Fig. 4A'-D': The cell bodies of the support cells should have a distinct colour, otherwise they are easily confused with the nuclei of the other cell types. This would make it easier to understand the schematic at first sight.
- p. 9: T2A and H2A should be explained.
- Nuclei are shown protruding posteriorly in wildtype neuromasts (Fig. 5A-A'), while P0 neuromasts stem cells protrude anteriorly. Please explain the significance of the difference.
- Fig. 5 legend: Quantification is "E"; "and increased" should probably read "an increased"?
- p. 10: Unconventionally, Fig 7 is mentioned prior to Fig. 6B-C, I suggest combining both figures into one.
Significance
The manuscript describes a new mechanism of post-embryonic organ formation. Investigating how accesory neuromasts are formed during growth of juvenile medaka, the authors find that stem cells from a founder neuromast undergo epithelial-mesenchymal transition and migrate away directionally to form a complete new organ. This new mechanism is likened to that of cancer metastases.
Importantly, and different from zebrafish, this process is not dependent on innervation of the neuromast and is not a budding process, but relies on neural stem cells leaving the organ.
The interesting question posed by the authors, and answered here positively for accessory neuromasts in juvenile fish, is whether the mechanisms of organ formation differ between embryonic and post-embryonic development. The reported findings should be of interest to the stem cell communities and to researchers interested in post-embryonic development.
This review was written by a developmental biologist.
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Referee #2
Evidence, reproducibility and clarity
Authors study how new sensory organs of the lateral line form post-embryonically in medaka fish.
Evidence, reproducibility, and clarity
Overall, I find the study very preliminary, since hardly any conclusions are sufficiently backed by quantifications and statistical analysis as detailed below.
Major comments:
Figure 1: Authors describe events as "detached cells migrate" in the results text and in the legend as "detached stem cells...move". They should clarify how these images were exactly generated, since this has implication on whether they can conclude that cells migrate. Are these frames from movies in which the entire process was seen to occur in individual larvae? Are they derived from repeated imaging of the same larvae? Or are they images from different larvae and authors assume that the sequence of events occurs as presented? While it's likely that cell migration occurs, a wave of de novo transgene expression could also travel through the tissue. Thus, authors can only conclude that it's migration if they have seen cells move relative to their surroundings using live imaging or via lineage-tracing. e.g. using a photoconvertible fluorophore.
Page 5: "The process of post-embryonic neuromast formation in the CNC displays variability in timing". Authors should add information on the actual timing. This relates to issue 1, maybe they don't know, but if they did live imaging or repeated imaging of the same larvae they should have information on how long it takes from delamination of cells to new neuromast formation.
Figure 2A: where is the data showing that the laser ablation actually worked and supporting the statement "All ablated fish showed escaping cells from the P0-neuromast (N=7/7 fish)"?
Figure 2B-D: I find it difficult to see how these data support the conclusions the authors draw. First of all, it would be helpful to show the structures labeled by the individual transgenes for the non-initiated readers, so readers can judge themselves which of the signals are derived from the kremen and which from the eya1 transgene. Second, in B' readers are supposed to "Notice the uninjured primordium that continued migration and embryonic neuromast formation", yet where is the uninjured primordium? There are a lot of signals in the image. And how do we know that it "continued migration?" Relative to what? Finally in D, the label "PE1 neuromast" is printed in between a structure that is weakly and one that is strongly labeled. Do the authors suggest that there are 2 PE1 neuromasts here? Both the P0 and the P1 neuromast(s?) on the experimental side seem to be much smaller that on the control side, but authors do not mention this. Does this show that neuron proliferation or differentiation are nerve dependent?
Cxcr4 mutant: it's unclear why the authors mention the new mutant, albeit they don't use it. The only reason to justify this would be if the community were to benefit from this new allele. However, this requires that authors clarify how it differs from the previously existing one (e.g by showing the predicted protein sequence) and whether it has any advantages, e.g. is it more likely to be null? Further, where are the data supporting this statement? "cxcr4bD625 larvae display the same phenotype as the previously published cxcr4b mutants".
Figure 3: authors should validate the k15:cxcr7 line by showing cxcr7 (over) expression in k15+ cells using in situ hybridization.
Figure 3B: how do the authors distinguish single and double transgenics? If they cannot and only assume that those that have a phenotype are the double transgenic they need to confirm this by genotyping embryos post imaging.
Figure 3D: sample size is missing. Significance should be tested.
"Adult Tg(K15::Cxcr7) fish display significantly lower CNC neuromast numbers compared to wild type fish (N= 5 WT fish, 3.8 organs per CNC; N= 4 Tg(K15:Cxcr7), 2.2 organs per CNC". Which statistical test has been used to support the use of the term "significantly" in this statement? Images should be shown to support this conclusion.
Fig. 4C: authors should show YFP and CFP channels to allow readers to see why authors have false-colored 2 cells. What do they mean by the term "repolarization" that they put in the figure label?
Fig. 4D: "Subsequently, the organ-founder stem cells start elongating lamellipodia-like processes in the anterior direction". How do the authors know that this is "subsequently"? These are obviously not frames from a movie, so how do they know that the cells the point their arrow at delaminated from the neuromast considering that there are also quite a few other YFP+ cells in this frame?
Fig. 4C, D: These observations must be supported by a least rudimentary information on reproducibility. How many neuromasts have the authors analyzed? How often did they see this?
Fig. 4F, G: These conclusions must be supported by quantifications and statistics (e.g. E- and N-cadherin staining intensities).
Fig. 5: is the number of neuromasts with protruding cells significantly different between the control and experimental group? % should be rounded to significant figures.
Fig. 5D: these data are not convincing since it is unclear how authors identify the neuromast, considering that almost all cells seem to contain H2B-GFP.
Fig. 5E: is there a significant difference?
Fig. 6A: Exemplatory images of BrdU+ cells should be shown.
Fig. 6A: "This approach revealed a proliferation peak at stage I-II (mean: 6 and 5.4 BrdU+ cells, respectively". This statement does not support that this is a peak in the absence of information on how much proliferation there is at other stages. Why don't the authors show a graph plotting this with variation and statistical analyses to support that the numbers differ at different stages.
Fig.6A: also the conclusion that more proliferation happens in anterior positions must be backed by statistics.
Fig. 6: ablation data. Text mentions that PE1 formed in 7/8 ablated larvae, but images show differences between the control side, where PE1 has not yet formed and the ablated side where it has... Is this a non-representative image? Authors should clarify.
Fig. 7: Supplementary Movie 2 does not work, so I could not review it.
Fig. 7: Conclusions need to be supported by quantification and statistics, it's not sufficient to show only frames from one movie.
Fig. 7: Where is the data supporting this statement "(mean: 0.05 BrdU+ stem cells; N=8 P0-neuromasts)"? How was the BrdU incorporation experiment performed?
This statement needs to be supported by quantifications and statistical analysis: "We noticed that in most cases, the ablated P0-founder neuromast was considerably smaller than the non-ablated founder organ in the contralateral side (Fig.6 B',C)(N=4/7 larvae)"
This statement needs to be supported by quantifications and statistical analysis: The PE1-neuromast, however, reached the regular size in all cases, regardless of the status of the P0-founder neuromast.
Minor:
I think the author pitch the fact that they are examining "organ-founder stem cells" a bit too aggressively. It would be more appropriate to stick to the term "mantle" cells in all figures that describe data and reserve the "organ-founder stem cells" term to text where they interpret their results. It's particularly strange that Fig. 4C is labelled with such an interpretation.
Significance
There are many interesting open questions about post-embryonic development and in particular about how novel structures/organs form in those species where this happens. Thus, the overall research topic of this study is very interesting. The model is also well suited to derive novel mechanistic insight into these questions. The problem with the study in its current form is that I find it quite anecdotal, since hardly any conclusions are sufficiently backed by thorough data, in particular there is a rather shocking lack of quantifications and statistical analyses throughout. However, if the authors back their conclusions with such data, it'll certainly make a very interesting paper.
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Referee #1
Evidence, reproducibility and clarity
Groß and colleagues explore the mechanisms by which post-embryonic organogenesis occurs. To do so, they use medaka caudal fin neuromasts as a model, in which they follow the genesis of a new neuromast, PE1, from an existing neuromast, P0. Previously the group has established that ablation of the P0 neuromast results in the absence of PE1 neuromast formation, and lineage tracing confirmed that the P0 neuromast gives rise to all the neuromasts in the caudal-neuromast-cluster.
To dynamically assess PE1 formation, the authors use a broad lateral line transgene reporter, labeling keratin 15-expressing cells (K15+). Through this method, they observe that anterior K15+ cells of the P0 neuromast detach from the organ, migrate anteriorly, and give rise to the PE1 neuromast. By ablating the posterior lateral line (pLL) nerve prior to PE1 formation, which innervates the P0 neuromast initially, the authors report that the pLL is not necessary for proper PE1 formation, as it still develops in the absence of this nerve. Further, the authors explore chemokine signals which may underlie this process. The authors discuss a previous finding that the Cxcr4b/Cxcr7/Cxcl12a signaling pathway has been shown to regulate primordium migration, which is an organ required for neuromast formation. Therefore, the authors modulated the signaling pathway by first generating a missense cxcr4b mutant, only to find that this resulted in the absence of P0 formation. To circumvent this issue, the authors instead overexpressed Cxcr7, a non-signaling receptor that competes with Cxcr4b for the Cxcl12a ligand, in K15+ cells. Results from these experiments displayed a role for the Cxcr4b/Cxcr7/Cxcl12a signaling pathway in the temporal regulation of post embryonic neuromast formation. At the developmental time that wild type fish develop post embryonic neuromasts, Tg(K15:Cxcr7) animals have reduced numbers of post embryonic neuromasts. However, the authors find that post embryonic neuromasts do form in these animals at a later stage of development. Finally, the authors show throughout the report that the organ founder stem cells, which go on to form the PE1 neuromast, arise exclusively from the anterior side of the P0 neuromast. They questioned whether some cells were pre-specified to become organ-founder stem cells or if all stem cells have the same capacity to generate a new organ. To understand this, the authors used laser ablation on the anterior side of the P0 neuromast and found that the neuromast cells were capable of rearranging and initiating migration from the anterior side.
In all, this report highlights a potentially novel cellular mechanism by which a post embryonic neuromast is generated from a pre-established neuromast. By using longitudinal imaging, the authors were able to observe this process in developing animals and begin to probe its molecular mechanism. Several aspects of the manuscript should be strengthened prior to publication to more rigorously support the authors' model.
Major Comments:
A key aspect of the current manuscript is whether the morphogenetic process that they report is fundamentally different from the "budding" mechanism previously observed in zebrafish during neuromast stitch formation (Wada et al., 2010; Wada et al., 2013) or simply represents a different description of an analogous process in medaka. Inspection of the previous work in zebrafish reveals several similarities at the cellular level that leave this reviewer unconvinced that this is really a different process, rather than differences due to reporters used (membrane and nuclear here vs cytoplasmic previously). Specifically, Figures 2 and 3 in Wada et al., (2010) and Figure 2 in Wada et al., (2013) look quite similar to the morphogenetic process herein. Wada et al., (2013) describe stitch formation as: "a budding process that begins when a few cells elongate away from the founder neuromast". Here, on pg 4 Groß and colleagues describe a process that "is executed by a few organ-founder neural stem cells that detach from their original organ and migrate to generate a new organ remotely." How exactly are these two processes different? One key mechanistic difference is potentially the nerve dependence, but there are concerns with the interpretation of this experiment (see comment below). Unless the authors can definitively demonstrate that what they describe is a distinct process, they might be better served by reframing their results in light of previous findings by Wada and colleagues.
A second important thrust of the paper is that migrating K15+ cells act as "organ founder stem cells." However, the authors do not show conclusive evidence of K15+ cells acting as stem cells during PE1 formation. To support their claim, the authors should more rigorously define the K15+ organ-founder cells as stem cells. This could be done by several approaches (e.g., via lineage tracing, molecular analysis, or live-cell imaging). Importantly, it has previously been shown that zebrafish hair cell regeneration is predominantly driven by surrounding support cell proliferation (reviewed by Lush and Piotrowski, 2014 doi: 10.1002/dvdy.24167). Therefore, it is important for the authors to determine whether their organ-founder cells are in fact stem cells or, alternatively, migrate along with K15- support cells from the P0 neuromast.
The rates of post-embryonic development in teleost fish, including medaka (Iwamatsu et al., 2003 doi: 10.2108/zsj.20.607), depend on several housing conditions (e.g., rearing density and feeding). It was surprising to this reviewer, therefore, that the authors performed all of their staging by days post-fertilization rather than standard length. Results from most experiments, especially those in Figures 1 and 3, would be more easily interpretable (and reproducible across different labs) if authors were to report standard length of the animals being used. For example, are Tg(K15:Cxcr7) animals smaller than their wildtype counterparts? If so, could this explain why PE1 neuromast formation is delayed? This goes for the interpretation of Figure 5E as well.
The authors should include statistical analyses of all their quantification. For example, it would be appropriate to use a statistical test to compare groups in Figures 3D and 5E.
The authors use the transgene Eya1:mCFP to visualize and laser ablate the pLL nerve, stating that Eya1 labels the nerve. However, a few sentences after introducing this transgene, the authors now use the transgene Kremen:mYFP to label the pLL nerve, and in double transgenic Kremen:mYFP; Eya1:mCFP fish, the Eya1 transgene is used to "assess the differentiation state of the putative newly formed PE1 neuromast". In the figure legend, the authors explain that Tg(Eya1:mCFP) labels the pLL in Figure 2A, but in Figure 2B it is used to visualize the primordium. The primordium is not the pLL nerve, and if the Eya1 transgene is labeling the primordium, and not the pLL (the text suggests it fulfills both roles in different scenarios) then the authors should repeat this experiment using the Kremen transgene to appropriately label the pLL nerve for accurate ablation. If, however, the Eya1 transgene labels both the primordium and the pLL (it is hard to tell from the double labeled images, as they are in grayscale), then this should be made explicitly clear.
Consider tempering the interpretation of the pLL nerve ablation experiment. Since the neurons and associated Schwann cells are still present following the severing of the nerve, albeit at a distance, could the nerve not still signal via a diffusible paracrine molecule? Alternatively, the authors could ablate the pLL ganglion. Since pLL ablation is performed 18 days prior to imaging, it may be possible that there is nerve regeneration occurring. To make the authors' findings more convincing, use of a secondary reporter, or antibody staining with a pan-neuronal antibody to confirm the absence of neuromast innervation at 21 dpf should be considered. Additionally, in Wada et al., (2013), neuromasts still extend cellular processes after nerve ablation. Since Wada et al. uses a membrane reporter, and here we see the use of a nuclear reporter, is it possible that we are seeing the same results as Wada et al.? To enhance the strength of this figure, authors should also consider using a membrane reporter or performing IHC similar to that in Figure 4D, so that readers can visualize there is no cellular process connecting the P0 and PE1 neuromasts in this condition.
Supplemental Figure 1 is a colorimetric in-situ hybridization image which depicts the localization of cxcl12 transcripts. The authors state that there is transcript expression in the vicinity of the P0 neuromast, however, the image is of poor quality and the expression does not appear spatially restricted to the anterior portion of the neuromast. Consider either discussing how a uniformly localized chemokine cue fits with their model and/or providing more detailed evidence of the expression pattern, e.g., by co-staining with a neuromast-specific marker and performing high-resolution imaging. Ideally the authors could quantify cxcl12 expression relative to the A/P axes of the P0 neuromast.
In Figure 4, the authors provide evidence that: 1) K15+ cells in the anterior of the P0 neuromast change shape and extend invasive, lamellipodia-like protrusions, and 2) that a cluster of cells to the anterior of the P0 neuromast show decreased E-cadherin and increased N-cadherin staining. These results are consistent with a subset of K15+ cells undergoing a MET. However, the authors could strengthen this portion of the manuscript in several ways. First, is it possible to live-image delamination from the neuromast? This would provide unambiguous evidence in support of their model. Second, is it possible to combine E- and N-cadherin staining with visualization of the K15+ population? The authors state that "N-cadherin is clearly upregulated in migrating organ-founder stem cells", but at the moment the evidence is circumstantial that K15+ cells switch cadherin expression. Co-visualization of the K15+ population would strengthen this point. Ideally, the authors could quantify E- and N-cadherin levels in K15+ cells relative to neighboring cells to support their claim. Minor point: have the E- and N-cadherin antibodies previously been validated in medaka? If so, the authors should cite the relevant work. If not, the authors should provide evidence of antibody specificity.
In Figure 5, the authors use Tg(K15:snail1b-T2A-H2A-mCherry) to address whether snail1b expression is sufficient to drive ectopic exit of cells from the P0 neuromast. The argument that snail1b is functional is an increase in neuromasts with protruding cells, but it is not demonstrated that any cells actually undergo EMT. A simple explanation for the observed lack of ectopic delamination is that snail1b is not expressed at sufficient levels from the transgene. With the current data, this reviewer would suggest tempering the claims related to interpretation of this experiment. On a related point, is snail1b normally expressed in delaminating cells? If so, this would provide further evidence to support the authors' EMT model in Figure 4. Minor point: the authors state that "not a single case of ectopic cell migration was observed when we analyzed the P0-neuromast by live imaging" - please clarify: what was the imaging time window and at what stage was the imaging performed?
Minor Comments:
Abstract, first sentence. This sentence is confusing since mammalian organs certainly grow at post-embryonic stages.
On page 6 of the manuscript, in the "pLL nerve is dispensable for organ-founder stem cell migration and PE1-neuromast formation" section, the authors describe the transgenic animals used in their pLL ablation studies as, "double Tg(Eya1:mCFP) - to label the pLL nerve - (K15:H2B-RFP) - to visualize migrating neuromast stem cells". The phrasing of the transgenic description is cluttered and confusing to read. Authors should consider rewriting this description as something such as, "double Tg(Eya1:mCFP); (K15:H2B-RFP) animals were used to visualize the pLL nerve as well as migrating neuromast stem cells, respectively".
On page 6 of the manuscript, in the "pLL nerve is dispensable for organ-founder stem cell migration and PE1-neuromast formation" section, the authors use the term escaping point. This is the first and only time the term is used, and it is not well defined. Presumably this refers to the anterior side of the P0 neuromast. This should be rewritten to more clearly articulate the meaning of this term.
Could the authors refer the reader to Figure 1A in the corresponding section of the introduction? It might also be helpful to the reader to label the primary vs secondary neuromasts on the schematic diagram.
In figure 2, the authors conclude that PE1 neuromast formation is not hindered by pLL ablation, however, in Figure 2C and D, it is apparent that the resulting PE1 neuromast post-pLL ablation is significantly smaller in size. The authors should address this, especially since they refer to the new PE1 neuromast in this condition as "mature". Is an organ mature if it is substantially smaller than in control conditions? Are the other resident cell types present in the proper proportions? Does the size of this organ grow to become wild-type further along in development?
On page 6, in the results section for Figure 2C and D, Eya1:GFP is being used to visualize "post-mitotic neurons" in the P0 and PE1 neuromasts, however, Figures 2C and 2D look more representative of the K15 reporter. Also this reviewer is not aware of post-mitotic neurons within neuromasts. The corresponding figure legend states that Tg(Eya1:EGFP) labels neuromast hair cells. Could the authors please clarify what is being labeled?
"Although specific for neural stem cells in the mature neuromast, the K15 promoter drives expression at earlier stages, after primary neuromasts were deposited by the primordium". This statement leads to confusion about the specificity of the K15 promoter, indicating it may be more broadly expressed than the authors state. The K15 promoter should be more rigorously described in the text, and evidence for its specificity should be clearly cited/provided.
The last sentence of the first paragraph of the Discussion is unnecessary and possibly overstating the findings within the report, as no evidence for "hijacking" of this post embryonic neuromast formation process was assayed.
The authors might consider discussing similarities and differences between their work and anchor cell invasion in C. elegans, which also involves post-embryonic organ remodeling by an invasive cellular behavior.
Methods transgene construction - please provide concentrations of nucleotides and proteins injected.
Methods Live-imaging section - "tranquilized" should probably read "anesthetized". More details on the imaging are needed. e.g., at what temperature was the imaging performed? What objective(s) was used?
Methods BrdU section - how were animals fixed? Please also describe the antigen retrieval step in detail.
It would be helpful for the supplemental movies to have labels for the transgenes, axes, and timestamps (as appropriate).
Supplemental Movie 2 shows significant xy movement between timepoints. Perhaps registration of the timepoints would help eliminate this and make the movie easier to interpret?
Significance
Groß and colleagues present an intriguing new model for post-embryonic morphogenesis of neuromasts in medaka. However, in its current state it is unclear whether these findings truly represent a new model for organ morphogenesis, rather than an alternative description of a previously described process. If it is the latter, the manuscript still has new cellular and molecular insights, but should be reframed. This work is likely to appeal to basic scientists, e.g., developmental biologists interested in organogenesis and neurobiologists interested in cell-cell interactions. This reviewer has expertise in teleost development and organogenesis.
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- May 2023
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Reply to the reviewers
Dear Editor and reviewers,
Thank you very much for the thorough assessment of our manuscript. We have carefully considered the comments and reflected most of them in the new version. We recognized the need to shorten and clarify the manuscript. Therefore, we have omitted particularly the less important passages concerning metabolism and the loss of genes encoding mitochondrial proteins, which cut the text by six pages in the current layout. We have also removed the text relating this model to eukaryogenesis. Finally, we have slightly changed the structure and linked the different sections to improve the flow of the story and to emphasize the key messages, which are the absence of mitochondria in a large proportion of oxymonads and the impact of this loss, loss of Golgi stacking and transformation to endobiotic lifestyle on selected gene inventories. We hope the manuscript is now clear and more concise and will be of interest to a broad readership interested in the evolution of eukaryotes, mitochondria and protists.
1. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity):
This is a very interesting paper that investigates through detailed comparative genomics the tempo and mode of the evolution of microbial eukaryotes/protists members of the Metamonada with a focus on Preaxostyla, currently the only known lineage among eukaryotes to have species that have lost, by all accounts, the mitochondria organelle all together. Notably, it includes a free-living representative of the lineage allowing potential interesting comparison between lifestyles among the Preaxostyla. This is a generally nicely crafted manuscript that presents well supported conclusions based on good quality genome sequence assemblies and careful annotations. The manuscript presents in particular (i) additional evidence for the common role of LGT from various bacterial sources into eukaryotic lineages and (ii) more details on the transition from a free-living lifestyle to an endobiotic one and (iii) the related evolution of MROs and associated metabolism.
Thank you very much for the positive assessment.
I have some comments to improve a few details:
In the introduction, lines 42-43, the last sentence should be more conservative by replacing "whole Oxymonadida" with "...all known/investigated Oxymonadida".
The sentence has been changed to: "Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species (M. exilis, B. nauphoetae, and Streblomastix strix) extending the amitochondriate status to all investigated Oxymonadida."
Similarly on line 62, the sentence could state "... contain 140 described...".
The sentence has been changed to: "Oxymonadida contain approximately 140 described species of morphologically divergent and diverse flagellates exclusively inhabiting digestive tracts of metazoans, of which none has been shown to possess a mitochondrion by cytological investigations (Hampl 2017)."
When discussing the estimated completeness of the genome are discussed (lines 117-120) and contrasted with the values for Trypanosoma brucei and other genomes, the author should explicitly state that these genomes are considered complete, which seems is what they imply, is that the case? If so, please provide more details to support this idea.
We have elaborated on this part also in reaction to comments of other reviewers. The text now reads: "It should be noted that, despite their wide usage, BUSCO values are not expected to reach 100% in lineages distant from model eukaryotes simply due to the true absence (or high sequence divergence) of some of the assessed marker genes. For example, various Euglenozoa representatives with highly complete genome sequences, including Trypanosoma brucei, have BUSCO completeness estimates in the range of 71-88% (Butenko et al. 2020), and representatives of Metamonada fall within the range of 60-91% (Salas-Leiva et al. 2021). Specifically in the case of oxymonad M. exilis, the improvement of the genome assembly using long-read resequencing from 2092 scaffolds to 101 contigs led to only a marginal increase of BUSCO value from 75.3 to 77.5 (Treitli et al. 2021). "
Also please see the detailed table prepared in response to reviewers 2 and 3 summarizing the presence/absence of genes from BUSCO set in the selected representatives of Metamonada and Trypanosoma brucei. The table is commented in the answer to Reviewer 3 comment (page 18)
The supplementary file named "132671_0_supp_2540708_rmsn23" is listed as a Table SX? (note: I found it rather difficult to establish exactly what file corresponds to what document referred in the main text)
We apologize for this mistake. We have checked and corrected references to tables, figures and supplementary material throughout the manuscript and hope it now does not contain any errors.
Lines 243-245, where 46 LGTs are discussed, it is relevant that the authors investigate their functional annotations. Indeed, it is suggested that these could have adaptive values, hence investigating their functional annotation will allow the authors to comment on this possibility in more details and precision. When discussing LGTs it would also be very useful to cite relevant reviews on the topic - covering their origins, functional relevance when known, distribution among eukaryotes. This is done when discussing the evolution and characteristics of MROs but not when discussing LGTs, with several reviews cited and integrated in the discussion of the data and their interpretation.
Available annotations of all putative LGT genes are provided in Supplementary_file_3 and also in the Supplementary_file_6 if the gene belongs to a manually annotated cellular system. Although we agree with the reviewer that the discussion of 46 species-specific LGTs might be interesting, for the sake of conciseness and brevity of the manuscript, we have decided not to expand the discussion further. However, note that we discuss selected cases of P. pyriformis-specific LGTs in the part “P. pyriformis possesses unexpected metabolic capacities” which follows right after the lines reviewer is referring to.
The sentence, lines 263-265, where the distribution of some LGTs are discussed, needs to be made more precise. When using the work "close" the authors presumably refer to shared/similar habitat,s or else? Entamoeba is not a close relative to the other listed taxa.
The “close relatives” mentioned in the text were meant as close relatives of all p-cresol-synthesizing taxa discussed in the paragraph, including Mastigamoeba, i.e. a specific relative of Entamoeba. We have modified the text such as to make the intended meaning easier to follow.
Lines 346-348, that sentence needs to end with a citation (e.g. Carlton et al. 2007).
The citation proposed by the reviewer has been added. The sentence was changed to: " The most gene-rich group of membrane transporters identified in Preaxostyla is the ATP-binding cassette (ABC) superfamily represented by MRP and pATPase families, just like in T. vaginalis (Carlton et al. 2007). "
In the paragraph (line 580-585) discussing ATP transporters, note that Major et al. (2017) did not describes NTTs but distantly related members of MSF transporter, shared across a broader range of organisms then the NTTs. Did the authors check if the genome of interest encoded homologues of these transporters too?
The citation has been removed; we admit that it was not the most appropriate one in the given
context. Concerning the NTT-like transporters, encouraged by the reviewer we searched for them in the Preaxostyla genome and transcriptome assemblies and found no candidates. This is not explicitly stated in the revised manuscript. The paragraph now reads: “MROs export or import ATP and other metabolites typically using transporters from the mitochondrial carrier family (MCF) or sporadically by the bacterial-type (NTT-like) nucleotide transporters (Tsaousis et al. 2008). We did not identify any homolog of genes encoding proteins from these two families in any of the three oxymonads investigated. In contrast, MCF carriers, but not NTT-like nucleotide transporters, were recovered in the number of four for each P. pyriformis and T. marina (Supplementary file 6).“
Line 920-921, I don't understand how the number 30 relates to "guarantee" inferring the directionality of LGTs events. This will be very much dataset dependent, 100 sequences might still not allow to infer directionality of LGT events. The authors probably meant to "increase the possibility to infer directionality".
We agree the original wording has not been particularly fortunate, so the sentence has changed to: "Files with 30 sequences or fewer were discarded, as the chance directionality of the transfer can be determined with any confidence is low when the gene family is represented by a small number of representatives."
Reviewer #2 (Evidence, reproducibility and clarity):
Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest.
Having seen reflections on the manuscript by three reviewers we carefully reconsidered its content and attempted to make it shorter and more compact by removing some of the less substantial material. Namely, we have dispensed completely with the original last section of Results and Discussion (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”) and made various cuts throughout other sections. We hope that the revised version makes a substantially better job of delivering the key messages of our study to the readers compared to the original submission.
This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion.
This is a very crude and superficial assessment of our data. We have actually good reasons to believe that the genome assemblies are close to complete. Please see the discussion on this topic below and an answer to a particular comment from reviewer 3 (page 18).
This is noteworthy in light of other protist genome reports published in the last few years that differ in this respect, including previous work by this group. And for sequencing-only data, this paper - https://doi.org/10.1016/j.dib.2023.108990 - might offer an example of where we are at in 2023.
Frankly, we do not think it is fair or relevant to compare our study to the paper pointed to by the reviewer, as that paper reports on a metagenomic study that delivers a set of metagenomically assembled genomes (MAGs) of varying quality retrieved from environmental DNA samples without providing any in-depth analysis of the gene content. Our study is very different in its scope and aims, and we are not certain what lesson we should take from this reviewer’s point. We have good reasons to believe that the datasets are close to complete. Please see the discussion on this topic below and answer to comment of reviewer 3 (page 18).
With respect to previous work of the group (Karnkowska et al. 2016 and 2019), this submission is very similar (analysis pattern, even some figures and more or less the conclusion), i.e. to say, the overall progress for the broader audience is rather incremental. Then there are also some incidents, where the data presented conflicts with the author‘s own interpretation.
It was our intention to use the previous analytical experiences and approaches, which at the same time makes the new results comparable with those published before. Although the format is intentionally similar, this work is a substantial step forward because only with our present study the amitochondrial status of the large part of Oxymonadida group can be considered solidly established. This in turn allows us to estimate the timing of the loss of mitochondrion (more than 100 MYA) demonstrating that the absence of mitochondrion in this group is not an episodic transient state but a long-established status. We do not understand what exactly the reviewer had in mind when pointing to “incidents, where the data presented conflicts with the author‘s own interpretation” – we are not aware of such cases.
The text (including spelling and grammar) needs some attention and the choice of words is sometimes awkward. The overuse of quotation marks ("classical", "simple", "fused", "hits", "candidate") is confusing (e.g. was the BLAST result a hit or a "hit").
The whole text has been carefully checked and the language corrected whenever necessary by a one of the co-authors, who is a native English speaker. The use of quotation marks has been restricted as per the reviewer’s recommendation.
In its current formn the manuscript is, unfortunately, very difficult to review. This reviewer had to make considerable efforts to go through this very large manuscript, mainly because of issues affecting to the presentation and the lack of clarity and conciseness of the text. It would be greatly appreciated if the authors would make more efforts upfront, before submission, to make their work more easily accessible both to readers and facilitate the task of the reviewers.
We admit that the story we are trying to tell is a complex one, consisting of multiple pieces whose integration into a coherent whole is a challenging task. As stated above, the reports provided by the reviewers provided us with an important stimulus, leading us to substantially modify the manuscript to make it more concise, less ambiguous when it comes to particular claims, and easier to read. We hope this intention has been fulfilled to a larger degree.
About a fifth of the two genome is missing according the authors prediction (table 1). Early on they explain the (estimated) incompleteness of the genomes to be a result from core genes being highly divergent. In light of this already suspected high divergence, using (the simplest NCBI) sequence similarity approach to call out the absence of proteins (for any given lineage) may need lineage-specific optimization. The use of more structural motif-guided approaches such as hidden Markov models could help, but it is not clear whether it was used throughout or only for the search for (missing) mitochondrial import and maturation machinery. The authors state that the low completeness numbers are common among protists, which, if true, raises several questions: how useful are then such tools/estimates to begin with and does this then not render some core conclusions problematic? The reader is just left with this speculation in the absence of any plausible explanation except for some references on other species for which, again, no context is provided. Do they have similar issues such as GC-content, same core genes missing, phylogenetic relevance?, etc.. No info is provided, the reader is expected to simply accept this as a fact and then also accept the fact that despite this flaw, all conclusions of the paper that rests on the presence/absence of genes are fine. This is all odd and further skews the interpretations and the comparative nature of the paper.
The question of the completeness of the data sets was raised also by reviewer 3 and we would like to provide an explanation at this point. First, it should be stated that there is no ideal and objective way how to measure the completeness of the eukaryotic genomic assembly. In the manuscript, we have used the best established method, adopted by the community at large, which is based on the search for a set of „core eukaryotic genes“ using a standardized pipeline BUSCO or previously popular CEGMA. The pipeline uses its own tools to identify the homologues of genes/proteins which ensures standardization of the procedure. This answers the question of reviewer 2, why we have not used more sensitive tools for these searches. We did not use them, because we followed the procedure that is the gold standard for such assessments, for comparability with other genomes and to make this as clear to the reader as possible. Although the result of the pipeline is usually interpreted as the completeness of the assembly, this is a simplification. Strictly speaking, the result is a percentage of the genes from the set of 303 core eukaryotic genes (in our case) which were detected in the assembly by the pipeline. Even in complete assemblies, the value is usually below 100% because some of the genes are not present in the organism and some diverged beyond recognition. We do not see any other way how to deal with this drawback than to compare with related complete genome assemblies acting as standards. This we have done in Supplementary file 11, where we list the presence/absence of each gene for Preaxostyla species and three highly complete assemblies of Trypanosoma brucei, Giardia intestinalis and Trichomonas vaginalis. T. brucei and G. intestinalis are assembled into chromosomes. As you can see, in these three „standards“ 63, 148 and 77 genes from the core were not detected resulting in BUSCO completeness values of 79%, 51% and 75%, respectively. 18 of the non-detected genes function in mitochondria (shown in red), which are highly reduced in some of these species, so the absence of the respective genes is therefore expected. Simply not considering these genes would increase the “completeness measure” for oxymonads by 6%. The values for our standards are not higher than the values for Preaxostyla (69-82%). In summary, the BUSCO incompleteness measure is far from ideal, particularly in these obscure groups of eukaryotes. The values received for Preaxostyla give no reason for concern about their incompleteness. See also our answer to reviewer 3 (page 18).
At the same time, we admit that the BUSCO values do not confirm the high completeness of our assemblies. So, why do we think they are highly complete? One reason is that we do not see suspicious gaps in any of the many pathways which we annotated but the main reason is the high contiguity of the assemblies. Thanks to Nanopore long read sequencing, the assembly of P. pyriformis and B. nauphoetae compose of 633 and 879 scaffolds, suggesting that there are “only” hundreds of gaps. Although this may still sound too much, it is a relatively good achievement for genomes of this size and the experience shows that a further decrease in the number of scaffolds would allow the detection of additional genes but not in huge numbers. As we have shown for M. exilis (Treitli et al. 2021, doi:10.1099/mgen.0.000745) the decrease from 2 092 scaffolds to 101 contigs, i.e., filling almost 2 000 gaps, allowed the prediction of additional 1 829 complete gene models, of which 1 714 were already present in the previous assembly but only partially and just 115 were completely new. None of these newly predicted genes was functionally related to the mitochondrion. Thus, we infer the chance that all mitochondrion-related genes are hidden in the gaps of assemblies is very low.
We have provided these arguments in a condensed form in the text following the description of genome assemblies: “It should be noted that, despite their wide usage, BUSCO values are not expected to reach 100% in lineages distant from model eukaryotes simply due to the true absence (or high sequence divergence) of some of the assessed marker genes. For example, various Euglenozoa representatives with highly complete genome sequences, including Trypanosoma brucei, have BUSCO completeness estimates in the range of 71-88% (Butenko et al. 2020), and representatives of Metamonada fall within the range of 60-91% (Salas-Leiva et al. 2021). Specifically in the case of oxymonad M. exilis, the improvement of the genome assembly using long-read resequencing from 2092 scaffolds to 101 contigs led to only a marginal increase of BUSCO value from 75.3 to 77.5 (Treitli et al. 2021).”
As a side note, this will also influence the number of proteins absent in other lineages and as such has consequences on LGT calls versus de novo invention. For the cases with LGT as an explanation, it would help to briefly discuss the candidate donors and some details of the proteins in the eco-physiological context (e.g. lines 263-268 suggest that HPAD may have been acquired by EGT which was facilitated by a shared anaerobic habitat and also comment on adaptive values for acquiring this gene). Exchanging metabolic genes via LGT (Line 163) blurs the differences between roles and extent of LGT in prokaryote vs eukaryote, and therefore is exciting and could use support/arguments other than phylogenies. I guess the number of reported LGTs among protists (whatever the source) over the last decade has by now deflated the novelty of the issue in more general; a report of the numbers is expected but they alone won't get you far anymore in the absence of a good story (such as e.g. work on plant cell wall degrading enzymes in beetles).
We agree with the reviewer that the cases of LGT involving Preaxostyla would deserve more discussion in the manuscript. On the other hand, we also agree that none of them provides such a “cool” story that would deserve a special chapter or even a separate paper. Therefore, we have decided, also with regard to keeping the text in a reasonable dimension, not to expand the discussion of LGTs with the exception of HgcAB, where some new information has been included and the phylogeny of the genes updated. Please note that we had discussed in the original manuscript the donor lineages and ecological/biochemical context in the cases of GCS-L2, HPAD, UbiE, and NAD+ synthesis and this material has been kept also in the revised version.
It would help to clarify which parts of the mitochondrial ancestor were reduced during the process of reductive evolution at what time in their hypothesized trajectory. For instance, loosing enzymes of anaerobic metabolism conflicts with the argued case of an aerobic (as opposed to facultative anaerobic) mitochondrial ancestor followed by gains of anaerobic metabolism in the rest of the eukaryotes via LGT, and some papers the authors themselves cite (e.g. the series by Stairs et al.). There is no coherent picture on LGT and anaerobic metabolism, although a reader is right to expect one.
These are very interesting questions, that would fill a separate article. In the manuscript, we focus on the Preaxostyla lineage only and there the trajectory seems relatively simple: replacement of the mitochondrial ISC by cytosolic SUF in the common ancestor of Preaxostyla, loss of methionine cycle and in in consequence mitochondrial GCS and the mitochondrion itself. We have modified the first conclusion paragraph in this sense and it now reads the following:
“The switch to the SUF pathway in these species has apparently not affected the number of Fe-S-containing proteins but led to a decrease in the usage of 2Fe-2S clusters. The loss of MRO impacted particularly the pathways of amino acid metabolism and might relate also to the loss of large hydrogenases in oxymonads. “
It is not clear to us how to understand the reviewer’s remark concerning the conflict between loss of enzymes of anaerobic metabolism and the (presumed) aerobic nature of the mitochondrial ancestor. Provided that we read the reviewer’s rationale correctly, is it really so implausible that the anaerobic metabolism gained laterally by a particular lineage was then secondarily lost in specific descendant lineages? As a clear example demonstrating the feasibility of such an evolutionary pattern consider the evolution of plastids. There is no doubt these organelles move across eukaryotes by secondary or higher-order endosymbiosis or kletoplastidy, establishing themselves in lineages where there was no plastid before. Secondary simplification of such plastids, e.g. by the loss of photosynthesis, in its extreme form culminating in the complete loss of the organelle, has been robustly documented from several lineages, such as Myzozoa (e.g., https://pubmed.ncbi.nlm.nih.gov/36610734/). Hence, we see absolutely no reason to rule out the possibility that the ancestral mitochondrion was obligately aerobic and enzymes of anaerobic metabolism spread secondarily by eukaryote-to-eukaryote LGT, with their secondary loss in particular lineages. We really do not see any conflict here and we do not agree with the interpretation provided by the reviewer. That said, we admit that the discussion on the earliest stages of mitochondrial evolution is not an essential ingredient of the story we try to tell in our manuscript, so to avoid any unnecessary misunderstanding we have removed the original last sentence of Conclusions (“Thorough searches revealed …”) from the revised manuscript.
In light of their data the authors also discuss the importance of the mitochondrion with respect to the origin of eukaryotes:
First, the mitochondrion brought thousands of genes into the marriage with an archaeon, surely hundreds of which provided the material to invent novel gene families through fusions and exon shuffling and some of which likely went back and forth over the >billion years of evolution with respect to localizations. The authors look at a minor subset of proteins (pretty much only those of protein import, Fig. 6) to conclude, in the abstract no less: „most strikingly the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species." I do not question the lack of a mitochondrion here, but this abstract sentence is theatrical in nature, nothing that data on an extant species could ever proof in the absence of a time machine, and is evolutionary pretty much impossible. A puzzling sentence to read in an abstract and endosymbiont-associated evolution.
We feel that the reviewer is putting too much emphasis on an aspect of our original manuscript that is rather peripheral to its major message. Indeed, the manuscript is not, and has never been thought to be, primarily about eukaryogenesis and the exact role the mitochondrion played in it. We are, therefore, somewhat reluctant to react in full to the very long and complex argument the reviewer has raised in his/her report, so we keep our reaction at the necessary minimum. Concerning the criticized sentence in the original version of the abstract, it alluded to a section of the manuscript (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”) that we have removed from the revised version, and hence we have modified also the abstract accordingly by removing the sentence. We still think our original arguments were valid, but apparently, much more space and more detailed analyses are required to deliver a truly convincing case, for which there is no space in the manuscript.
Second, using oxymonads as an example that a lineage can present eukaryotic complexity in the absence of mitochondria and conflating it with eukaryogenesis is a logical fallacy. This issue already affected the 2019 study by Hampl et al.. We have known that a eukaryote can survive without an ATP-synthesizing electron transport chain ever since Giardia and other similar examples and the loss of Fe-S biosynthesis and the last bit of mitosome (secondary loss) doesn't make a difference how to think about eukaryogenesis. It confuses the need and cost to invent XYZ with the need and cost of maintenance. How can the authors write "... and undergo pronounced morphological evolution", when they evidently observe the opposite and show so in their Fig. 1? The authors only present evidence for reductive evolution of cellular complexity with the loss of a stacked Golgi. What morphological complexity did oxymonads evolve that is absent in other protists? A cytosolic metabolic pathway doesn't count in this respect, because it is neither morphological, nor was it invented but likely gained through LGT according to the authors. This is quite confusing to say the least. A recent paper (https://doi.org/10.7554/eLife.81033) that refers to Hampl et al. 2019 has picked this up already, and I quote: "Such parasites or commensals have engaged an evolutionary path characterized by energetic dependency. Their complexity might diminish over evolutionary timescale, should they not go extinct with their hosts first." Here the authors raise a red flag with respect to using only parasites and commensals that rely on other eukaryotes with canonical mitochondria as examples. If we now look at Fig. 1 of this submission, Novak et al. underpin this point perfectly, as the origin of oxymonads is apparently connected to the strict dependency on another eukaryote (or am I wrong?), and they support the prediction with respect to complexity reducing after the loss of mitochondria - mitosome gone, Golgi almost gone. What's next? This is a good time to remember that extant oxymonads are only a single picture frame in the movie that is evolution, and their evolution might be a dead-end or result in a prokaryote-like state should they survive 100.000s to millions of years to come.
It seems that in this point the reviewer is particularly concerned with the following sentence that is part of the Introduction and which relates to the existence of amitochondrial eukaryotes we are studying: “The existence of such an organism implies that mitochondria are not necessary for the thriving of complex eukaryotic organisms, which also has important bearings to our thinking about the origin of eukaryotes (Hampl et al. 2018).“ Even after re-reading the sentence we confess we stay with it and find it perfectly logical. Nevertheless, we decided to omit it from the text so as not to distract from the main topic of the study.
Next, when mentioning “… pronounced morphological evolution” we mean the evolution of four oxymonad families (Streblomastigidae, Oxymonadidae, Pyrsonymphidae and Saccinobaculidae) comprising almost a hundred described species with often giant and morphologically elaborated cells that evolved from a simple Trimastix-like ancestor (Hampl 2017, Handbook of Protists, 0.1007/978-3-319-32669-6_8-1). This is a fact that can hardly be dismissed. Also, given the current oxymonad phylogenies (Treitli et al. 2018, doi.org/10.1016/j.protis.2018.06.005) and the reported absence of a mitochondrion in M. exilis, B. nauphoetae, and S. strix we can infer that the mitochondrion was lost in the common ancestor of the three species at latest. This organism must have lived more than 100 MYA, as at that time oxymonads were clearly diversified into the families (Poinar 2009, 10.1186/1756-3305-2-12). So, these organisms indeed have lived without mitochondria for at least 100 MY. We think that these facts and our inferences based on them are solid enough to keep in the conclusion the following statement: “This fact moves this unique loss to at least 100 MYA deep past, when oxymonads had been already diversified (Poinar 2009), and shows that a eukaryotic lineage without mitochondria can thrive for eons and undergo pronounced morphological evolution, as is apparent from the range of shapes and specialized cellular structures exhibited by extant oxymonads (Hampl 2017).” Furthermore, as documented in Karnkowska et al. 2019 (https://pubmed.ncbi.nlm.nih.gov/31387118/), apart the loss of the mitochondrion oxymonads are surprisingly “normal” and complex eukaryotes, in fact much less reduced than, e.g., Giardia, Microsporidia, or even S. cerevisiae (in terms of the number of genes, introns, etc.). We strongly disagree with the claim that “Golgi is almost gone” in oxymonads, and our manuscript shows exactly the opposite. Viewing oxymonads as a lineage heading towards a prokaryote-like simplicity is dogmatic and ignores the known biology of these organisms.
Some more thoughts: Line 47-52: Hydrogenosome or mitosome is a biological and established label as (m)any other and I find the use of the word "artificial" in this context strange. While the authors are correct to note that there is a (evolutionary) continuum in the reduction - obviously it is step by step - they exaggerate by referring to the existing labels as "artificial". You make Fe-S clusters but produce no ATP? Well, then you're a mitosome. It's a nomenclature that was defined decades ago and has proven correct and works. If the authors think they have a better scheme and definition, then please present one. Using the authors logic, terms such as amyloplast or the TxSS nomenclature for bacterial secretions systems are just as artificial. As is, this comes across as grumble for no good reason.
We agree that the original wording sounded like unwarranted grumbling and we have changed the sentence in the following way: "However, exploration of a broader diversity of MRO-containing lineages makes it clear that MROs of various organisms form a functional continuum (Stairs et al. 2015; Klinger et al. 2016; Leger et al. 2017; Brännström et al. 2022)."
Line 158: A duplication-divergence may also explain this since sequence similarity-based searches will miss the ancestral homologues.
We do not disagree about this, in fact, the gene the reviewer’s point is concerned with for sure is a result of duplication and divergence, as it belongs to a broader gene family (major facilitator superfamily, as stated in the manuscript) together with other distant homologs. Nevertheless, this is not in conflict with our conclusion that it “may represent an innovation arising in the common ancestor of Metamonada”.
Lines 201-202: Presence of GCS-L in amitochondriate should be explained in light of this group once having a mitochondrion, which then makes ancestral derivation and differential loss (as invoked for Rsg1) also a likely explanation along with eukaryote-to-eukaryote LGT.
Yes, this most likely holds for the standard paralogue GCS-L1 (in P. pyriformis PAPYR_5544), which has the expected distribution and phylogenetic relationships and is absent in oxymonads. The discussion is, however, mainly about the rare, divergent and until now overlooked paralogue GCS-L2 (in P. pyriformis PAPYR_1328), which we found only in three distantly related eukaryote groups, Preaxostyla, Breviatea, and Archamoebae, which strongly suggests inter-eukaryotic LGT.
Lines 356-392: Describes plenty of genomic signal for Golgi bodies but simultaneously cites literature suggesting the absence of a morphologically an identifiable Golgi in oxymonads. An explicit prediction regarding what to observe in TEM for the mentioned species might be nice to stimulate further work.
We thank the reviewer for their suggestion and are glad that they are enthusiastic about this aspect of the manuscript. Unfortunately, the morphology of unstacked Golgi ranges from single cisternae (yeast, Entamoeba), vesicles (Mastigamoeba), and a “tubular membranous structure” in Naegleria. Therefore, no strong prediction is possible of what the oxymonad Golgi might look like under light or TEM. However, the data that we have provided should lead to molecular cell biological analyses aimed at identifying the organelle, giving target proteins to tag or against which to create antibodies as Golgi markers. An additional sentence to this effect has been added to the manuscript, “They also set the stage for molecular cell biological investigations of Golgi morphological variation, once robust tools for tagging in this lineage are developed.”
Lines 414: The preceding paragraphs in this result section describes only the distribution, without mentioning origins - a sweeping one-line summary that proclaims different origin needs some context and support. Furthermore, the distribution of glycolytic enzymes might indeed be patchy, but to suggest it represents an 'evolutionary mosaic composed of enzymes of different origins' without discussing the alternative of a singular origin and different evolutionary paths (including a stringer divergence in one vs. another species) discredits existing literature and the authors own claim with respect to why BUSCO might fail in protists.
The part of the text about glycolysis the reviewer alluded to has been removed while shortening the manuscript.
Line 486: How uncommon are ADI and OTC in lineages sister to metamonada?
This is an interesting but difficult question. Firstly, we are uncertain what is the sister lineage to Metamonada. Discoba, maybe, but a recent unpublished rooting of the eukaryotic tree does not support it (https://pubmed.ncbi.nlm.nih.gov/37115919/). Generally, the individual genes of the pathway (ADI, OTC and CK) are quite common in eukaryotes, but the combination of all three is rare (Metamonada, the heterolobosean Harpagon, the green algae Coccomyxa and Chlorella, the amoebozoan Mastigamoeba, and the breviate Pygsuia), see figure 1 in Novak et al 2016, doi: 10.1186/s12862-016-0771-4.
Line 504: It might help an outside reader to include a few lines on consequences and importance of having 2Fe-S vs 4Fe-S clusters and set an expectation (if any) in Oxymonads.
We apologize for omitting this explanation. The 2Fe-2S proteins are more common in mitochondria where 2Fe-2S clusters are synthesized in the early pathway of FeS cluster assembly, while the cytosolic CIA pathways produce 4Fe-4S clusters (https://pubmed.ncbi.nlm.nih.gov/33007329/). The original expectation therefore is that species without mitochondria should not have 2Fe-2S cluster proteins. Obviously, the switch to the SUF pathway affects this expectation as we do not know, what type of cluster this pathway produces in oxymonads (https://www.biorxiv.org/content/10.1101/2023.03.30.534840v1). For the sake of brevity, we have included a short statement as the beginning of the sentence in question, which now reads as follows: “As 2Fe-2S clusters are more frequent in mitochondrial proteins, the higher number of 2Fe-2S proteins in P. pyriformis compared to the oxymonads may reflect the presence of the MRO in this organism.”
Any explanations on what unique selection pressures and gene acquisition mechanisms may be operating in P. pyriformis which might allow for the unique metabolic potential?
Every species exhibits a unique combination of traits that results from changing selection pressures imposed on historical contingency (including neutral evolutionary processes such as genetic drift). We lack real understanding of these factors for a majority of taxa including the familiar ones, so we should not expect to have a good answer to the reviewer’s question. In fact, we do not know how unique is the particular combination of P. pyriformis traits discussed in our manuscript, as there has been no comprehensive comparative analysis that would include ecologically and evolutionarily comparable taxa. We note that Paratrimastix represents only a third free-living metamonad with a sequenced genome (together with Kipferlia and Carpediemonas), so more data and additional analyses are needed to be in a position when we may start hoping answers to questions like the one posed by the reviewer are in reach.
** Referees cross-commenting** To R3: Hampl et al. 2019, to which Novak et al. refer, is about eukaryogensis and that is exactly the context in which this is discussed again and what Raval et al. 2022 had decided to touch upon. If the authors do not bring this up in light of the ability to evolve (novel) eukaryote complexity, then what else? Maybe they can elaborate, especially with respect to energetics to which they explicitly refer to in 2019 (and here). And with respect to text-book eukaryotic traits (and the evolution of new morphological ones), I do not see any new ones evolving in any oxymonad, but reduction as Novak et al. themselves picture it in this submission. Is a change in the number of flagella pronounced morphological evolution? Maybe for some, but I believe this needs to be seen in light of the context of how they discuss it. I see a reduction of eukaryotic complexity and not a gain. They have an elaborate section on the loss of Golgi characteristics (and a figure), but I fail to read something along the same lines with respect to the gain of new morphological traits. Again, novel LGT-based biochemistry does not equal the invention of a new morphology such as a new compartment. Oxymonads depend on mitochondria-bearing eukaryotes for their survival or don't they? This is the main point, and if evidence show that I am wrong, then I will be the first to adapt my view to the data presented.
While we do see the logic of the reviewer’s point, a good reply would have to be too elaborate and certainly beyond the scope of the current manuscript. As the reviewers’ reports led us to reconsider the structure of the manuscript and to make it more focused and concise, we decided to simplify the matter by removing the allusions to eukaryogenesis, realizing that it is perhaps more suitable for a different type of paper (opinion, review). The comment on the evolution of complex morphology has been answered previously (see above).
I have concerns with the presentation of a narrative that in my opinion is too one-sided and that has been has been publicly questioned in the community (in press, at meetings, personally). For the benefit of science and of the young authors on this study, this reviewer feels strongly that these issues should be taken very seriously and discussed openly in a more balanced way. . We only truly move forward on such complex topics, if we allow an open and transparent discussion.
We agree that opinions on specific details of eukaryogenesis are divided in the community and that the topic requires a nuanced discussion for which there is perhaps no place in the current manuscript. As stated in the reply to the previous point, we have removed the discussion of the implications of our current study to eukaryogenesis from the revised manuscript.
Having said that, I am happy that R3 has picked up exactly the same major concerns as I did with respect to e.g. the phrasing on mito (gene) loss and the BUSCO controversy.
We appreciate these comments and hopefully have resolved the concern in the previous answers.
Reviewer #2 (Significance):
Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest. This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion.
We have addressed the concern about the possible incompleteness of our genome data above, demonstrating it is not substantiated ad stems from an inadequate interpretation of quality measures we provide in the manuscript. We hope that the revised manuscript, which is streamlined and more concise compared to the initial submission, conveys the key messages in a substantially more persuasive way and will be appreciated by a broad community of readers.
Reviewer #3 (Evidence, reproducibility and clarity):
Summary: The genome sequences of two members of the protist group Preaxostyla are presented in this manuscript: Paratrimastix pyriformis and Blattamonas nauphoetae. The authors use a comparative genomics and phylogenetic approaches and compare the new genome datasets with three previously available genomes and transcriptomes from the group. The availability of genome-scale data from five Preaxostyla species is powerful to address interesting basic evolutionary questions. A substantial part of the manuscript is spent on testing the hypothesis of mitochondrial loss in the oxymonad lineage, which turns out to be supported. The datasets are also explored regarding the role of lateral gene transfer in the group, metabolic diversification and the evolution of Golgi.
Major comments: I find the manuscript very interesting with many different fascinating results presented. However, the manuscript is very long. Two genome sequences are presented and it is not clear to me what the main question was when this project was initiated and why these two species was selected to answer this question. I do not see an obvious reason for sequencing the P. pyriformis genome if the mitochondrial loss was the main question (given that a transcriptome was already available). Why not spend the time and resources on a member of Preoxystyla, which lacked previous data? The authors should more clearly state why these organisms were chosen to answer the main question or questions of the study.
We are sorry for having done a poor job when explaining the choice of the taxa for the comparison. The idea was to sample an outgroup of oxymonads (P. pyriformis) and a representative of other clades of oxymonads than M. exilis (B. nauphoetae and S. strix) for which it was feasible to obtain the data, or the data were already available. Obviously, more representatives of morphologically a probably also genetically diverse oxymonads should be investigated (e.g. Pyrsonympha, Oxymonas, Saccinobacullus) and we have such a plan but these organisms are difficult to work with. We considered it necessary to sequence the genome of P. pyriformis, and not rely on the transcriptome only, to avoid the issue of data set incompleteness (raised also by R2). Transcriptomes by nature provide an incomplete coverage of the full gene complement of the species, while our genome assemblies are close to complete, as we explain elsewhere.
The evolution of MROs have received substantial attention from the protist research community since the 1990's. During this period the mitochondrial organelle have been considered essential for eukaryotes. Therefore, the result presented in the manuscript has a high significance. However, I am not convinced that it is appropriate to use the term "evolutionary transition" for the mitochondrial loss. The loss of MRO is the endpoint of a gradual change of the internal organisation of the cell that probably started when the ancestor of these organism adapted to an anaerobic lifestyle. The last step described in the manuscript probably had little impact on how these organisms interacted with their environment. The presence or absence of biosynthesis of p-cresol by some, but not all, Preaxystyla probably is much more significant from an ecological point of view. My point is that the authors need to consider how they use the term evolutionary transition and be explicit about that.
We appreciate the comment concerning the use of the term “evolutionary transition”. Nevertheless, we believe there is no real consensus in the literature on what is and what is not an “evolutionary transition”, and the application of the term to specific cases is more or less arbitrary. For a lack of a standardized or better terminology, we have kept the term to refer to three evolutionary changes in the evolution of the Preaxostyla lineage that are particularly important from the cytological or ecological perspective, i.e. dispensing with the mitochondrion, reorganizing the Golgi apparatus by losing the stacked arrangement of the cisternae, and gaining the endobiotic life style.
In the abstract the main finding is describes as "the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species (M. exilis, B. nauphoetae, and Streblomastix strix) extending the amitochondriate status to the whole Oxymonadida.". I find this a really interesting observation, but I do find the wording a bit too bold for several reasons: • Not every protein that has participated in the mitochondrial function is known. • Mitochondrial proteins could be present in oxymonads, but divergent beyond the detection limit for existing methods. • Genes for one or several mitochondrial proteins could be present in one or more oxymonad genomes, but remain undetected due to the incomplete nature of the datasets.
Although I do think that the authors' claim very well could be true, I don't think their data fully support it. Therefore, it needs to be rephrased.
As a result of our decision to streamline the manuscript by removing the final part of Results and Discussion (“No evidence for subcellular retargeting of ancestral mitochondrial proteins in oxymonads”, the revised manuscript no longer support the statement “the data confirm the complete loss of … every protein that has ever participated in the mitochondrion function for all three oxymonad species” that is criticized by the reviewer, and hence the statement has been removed from the abstract. This addresses bullet point 1. As for bullet points 2 and 3, the proof of absence is in principle impossible to deliver, and we have been fighting with this already in the Karnkowska et al. 2016 paper. Although our certainty will never reach 100% (this is in fact impossible for a scientific, i.e., falsifiable, hypothesis), the mounting of evidence through studies gives the hypothesis on the amitochodriate status of oxymonads more and more credit. The genes for mitochondrial marker proteins have not been detected by the most sensitive methods available neither in the first genome assembly of M. exilis (Karnkowska et al. 2016), nor in the improved M. exilis genome assembly composed of only 101 contigs (Treitli et al. 2021), nor in either of the other two oxymonad species investigated here. On the other hand, they were readily detected in the data sets of P. pyriformis and T. marina. What is the probability that these genes always hide in the assembly gaps, or that they have all escaped recognition? Obviously, this probability is not zero, but we believe it is approaching so low values that it is reasonably safe to make the conclusion on the amitochondriate status of these species.
The sentence was changed to: "Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria for all three oxymonad species investigated (M. exilis, B. nauphoetae, and Streblomastix strix), suggesting the amitochondriate status may be common to Oxymonadida."
The third point maybe could be analysed further. BUSCO scores are reported, but also argued not being reliable for this group of organisms (which is true). Would it, for example, be useful to analyse how large fraction of the BUSCO proteins found in all non-Preoxystyla metamonada genomes that are present in the various Preoxystyla datasets?
We provide a comprehensive answer to a similar comment of reviewer 2 above (page 6-8). We performed the requested analysis and provide the result in Supplementary file 11. In this table, we record presence/absence of each gene from the BUSCO set for our data sets and the highly complete “standard” datasets of Trypanosoma brucei, Giardia intestinalis and Trichomonas vaginalis. Of the 303 genes, 117 were present in all data sets and 17 in none (see column I). 20 were present only in Trypanosoma and not in metamonads. 6 were present in all Preaxostyla and absent in other metamonads (Trichomonas and Giardia), 44 were present in all Preaxostyla and Trichomonas and absent in Giardia, suggesting high divergence of this species. Only 23 (marked by *) were present in the three “standard” genomes and absent in one or more Preaxostyla species. Of those 8 and 8 were absent specifically in S. strix and P. pyriformis, respectively, but only 1 was absent specifically in M. exilis and no such case was observed in B. nauphoetae. We conclude that this non-random pattern argues for lineage-specific divergence rather than incomplete data sets, particularly in the case of M. exilis and B. nauphoetae.
Line 160-161: 15 LGT events specific for the Preaxostyla+Fornicata clade is reported. This is an exciting finding because it supports a phylogenetic relationship between these two groups. But such an argument is only valid if the observed pattern is more common than the alternative hypotheses (Preaxostyla+Parabasalids and Fornicata+Parabasalids). How many LGT events support each of these groupings? How are these observation affected by the current taxon sampling with the highest number of datasets from Fornicata? How were putative metamonada-to-metamonada LGTs treated in this context?
19 LGT are uniquely shared between Preaxostyla+Parabasalids, which is more than the number of shared LGTs between Preaxostyla and Fornicata. No common LGT was unique to Fornicata+Parabasalids. However, the latter is a direct consequence of our investigation method, which involved reconstruction phylogenies of genes present in Preaxostyla, and not across all metamonads. So, we do not have a way to investigate LGT gene families uniquely shared between Fornicata and parabasalids.
When it comes to the effect of taxon sampling, we agree that it is possible that the number of genes of horizontal origin shared between parabasalids and Preaxostyla is underestimated because of the lower taxon sampling in parabasalids. However, it is still larger (19) than the number of LGTs shared uniquely between fornicate and Preaxostyla (15). In addition, while the taxon sampling is larger in fornicates, it also contains some representatives of closely related lineages (e.g., Chilomastix caulleryi and Chilomastix cuspidate) which, while they increase the number of fornicate representatives, does not increase the detection of shared genes between fornicates and Preaxostyla. Altogether, it's difficult to estimate how the current taxon sampling is biasing the detection of LGTs one way or another.
Regarding metamonad-to-metamonad putative LGTs: we did not consider this possibility for the sake of not overestimating the number of gene transfers for two main reasons. First of all, our LGT detection relies on the incongruence between species tree and gene tree. The closer the lineages are in the species tree, the more difficult it is to interpret any incongruence in the gene tree as single protein phylogenies are notoriously poorly resolved because they rely on the little phylogenetic signal contained in few amino-acid positions. Because of this, small incongruences with the species tree could either reflect recent LGT events between metamonads, or simply blurry phylogenetic signal. Second, we can certainly use the argument that a limited taxonomic distribution among metamonads favors an LGT event between them. However, here again, the closer the lineages involved are, the more difficult it is to distinguish a scenario where one lineage was the recipient of an LGT from prokaryote before donating it to another metamonad, from a scenario involving a single ancestral LGT from prokaryotes to metamonads, followed by differential loss, leading to a patchy taxonomic distribution. Finally, we are working with both limited taxon sampling and incomplete genomic/transcriptomic data, which makes it more difficult to identify true absences. For all these reasons, we chose to be conservative and invoke the smallest number of LGT events.
The authors have used a large-scale approach to make single-gene trees for inferences of LGT. In other parts of the manuscript inferences of evolutionary origins of single genes are made without support of phylogenetic trees. I find this inconsistent and argue that the hypothesis of the origin of a specific protein should be tested with the same rigor whether it is a putative LGT, gene duplication, gene loss or an ancestral member of LECA. Specific cases where I think a phylogenetic analysis is needed includes: • Line 222-223: It is concluded that Rsg1 is a component of LECA. • Line 307: HgcAB are argued to be acquired by LGT of a whole opeon. • Lines 350-355: It is unclear how the different numbers of transporters are interpreted (loss or expansion by duplication). This could be address with phylogenetics. • Lines 407-408: A tree should support the claim of LGT origin. (PFP) • Lines 414-415: The different origins of glycolytic enzymes should be supported by data or references. • Line 486: Trees or a reference (if available) should support the claim for LGT.
As requested, trees were constructed for HgcA, HgcB, PFP and the transporters AAAP, CTL, ENT, pATPase, and SP. Citations were added for the glycolytic enzymes and the ADI pathway. No tree for Rsg1 is needed, as this is a eukaryote-specific protein lacking any close prokaryotic relatives. The inference on its presence in the LECA is based on the phylogenetically wide, however patchy, distribution across the eukaryote phylogeny. Testing possible eukaryote-eukaryote LGTs is hampered by a limited phylogenetic signal in the short and rapidly evolving Rsg1 sequences, resulting in very poorly resolved relationships among Rgs1 sequence in a tree we attempted to make (data not shown). For this reason, we opt for not presenting any phylogenetic analysis for Rsg1.
Lines 530-531 and 773-774: "The switch to the SUF pathway in these species has apparently not affected the number of Fe-S-containing proteins but led to a decrease in the usage of 2Fe-2S clusters." I find it difficult to evaluate if the data support this because no exact numbers or identities are given for 2Fe-2S and 4Fe-4S proteins in the various genomes in Suppl. Fig. S4 or Supplementary file 4.
The functional annotation of all detected FeS clusters containing proteins is provided in Supplementary Table S8 including the types of predicted clusters (columns G or F). Basically, the only putative 2Fe2S cluster containing proteins in species of oxymonad is xanthine dehydrogenase, while Paratrimastix and Trimastix contain also 2Fe2S cluster-containing ferredoxins and hydrogenases.
The method used in the paper varies between the different parts of the paper. One example is single gene phylogenies, which are described three times in the method section [Lines 959-973, lines 1011-1034, lines 1093-1101], in addition to the automated approach within the LGT detection pipeline lines 923-926]. The approaches are slightly different with, for example, different procedures for trimming. This makes it difficult to know how the different presented analyses were done in detail. No rationale for using different approaches is given. At the least, it should be clear in the method section which approach was used for which analysis.
The reviewer is correct, and we apologize for the inconsistency. The reason is only historical –the analyses were performed by different laboratories in different periods of time. We believe this fact does not make our results less robust, although it does not “look” nice and makes the description of the methods employed longer. We have double-checked the description and introduced slight changes as to make it maximally clear which method has been used for particular analyses presented in the Results and Discussion.
Specific comments on single gene phylogenies:
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Line 966-967: Why max 10 target sequences?
The limit of 10 was applied in order to keep the datasets in manageable dimensions. The sentence has been changed to: " In order to detect potential LGT from prokaryotes while keeping the number of included sequences manageable, prokaryotic homologues were gathered by a BLASTp search with each eukaryotic sequence against the NCBI nr database with an e-value cutoff of 10-10 and max. 10 target sequences.”
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Lines 996-998: Is it a problem that these are rather old datasets?
Although the publications are slightly older the set of queries is absolutely sufficient for the purpose.
Minor comments: I appreciate that many data is included as supplementary material. However, the organisation of the data could be improved. The numbering of the files is not included in their names or within the files, as far as I could find. Descriptions of the files are often missing and information on the annotation such as colour coding is not always included. These aspects of the supplementary material needs to be strengthened in order to make it more useful. Specific comments: • Supplementary file 1, Table 1: accession numbers are missing. Kipferlia bialta appears to have a much smaller number of sequences than reported in the publication. The file consists of three tables and it would be very helpful if the reference in the main manuscript indicate the table number. • Supplementary file 4: The trees lack proper species names and a documented colour coding. There are multiple trees in the file, which make it difficult to find the correct tree. I would appreciate if the different trees were labelled A, B, C, etc., and if these were used in the main text.
Supplementary file 1: Accession numbers were added.
Supplementary file 4: Species names and alphabetical labelling were added. Colour coding was explained in the text at the first mention of the file: "(Supplementary file 4 H; Preaxostyla sequences in red)."
o There is no HPAD-AE tree (as indicated on line 258), but a HPAD tree. Which part of the tree contain the described fusion protein?
Thank you for spotting the mistake. There should have been “HPAD” instead of “HPAD-AE” indicated in the text. The sentence has been changed to:" The P. pyriformis HPAD sequence is closely related to its homolog in the free-living archamoebid M. balamuthi (Supplementary file 4 K), the only eukaryote reported so far to be able to produce p-cresol (Nývltová et al. 2017)."
o Line 280-281: "UbiE homologs occur also in some additional metamonads, including the oxymonad B. nauphoetae and certain fornicates." These sequences should be clearly highlighted in the tree.
We discovered these additional UbiE homologs only after the tree presented in the supplement had been constructed, so these sequences are missing from it. To ensure consistency we have decided to remove the remark on the presence of UbiE homologs metamonads other than P. pyriformis, so it is no longer part of the revised manuscript.
o Lines 538-544: A three-gene system is mentioned, but only two AmmoMemoRadiSam trees are found.
This part has been removed while streamlining the manuscript.
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Supplementary file 6: I find it difficult to find the proteins discussed in the text, for example "the biosynthesis of p-cresol from tyrosine (line 254-255)".
Abbreviations identifying the different enzymes have now been added to all mentions in the text, facilitating their localization in the supplementary file: "P. pyriformis encodes a complete pathway required for the biosynthesis of p-cresol from tyrosine (Supplementary file 6), only the second reported eukaryote with such capability. This pathway consists of three steps of the Ehrlich pathway (Hazelwood et al. 2008) converting tyrosine to 4-hydroxyphenyl-acetate (AAT, HPPD, ALDH) and the final step catalyzed by a fusion protein comprised of 4-hydroxyphenylacetate decarboxylase (HPAD) and its activating enzyme (HPAD-AE)."
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Supplementary file 11: Which group of species are highlighted in red? How do I know from which species these sequences are (I can make educated guesses, but prefer full species names). I do not find any reference to this file in the main manuscript.
We apologise for this inconvenience. The taxon labels in the treed in this supplementary file have been corrected to contain full species names.
Line 227-228: "630 OGs seem to be oxymonad-specific or divergent, without close BLAST hits". It is unclear if BLAST searches includes only a representative of each 630 OGs, or every single protein in these OGs.
The BLAST searches include every single protein in the investigated OGs. We clarified it in the text: “Of these, 630 OGs seem to be oxymonad novelties or divergent ancestral genes, without close BLAST hits (e-value -15) to any of these sequences.”
Line 243: I think it is five LGT mapped to internal nodes of Preoxystyla in Figure 1 (1+3+1).
You are correct, we apologize for the mistake. The sentence has been changed to: "Also, 46 LGT events were mapped to the terminal branches and 5 to internal nodes of Preaxostyla, suggesting that the acquisition of genes is an ongoing phenomenon, and it might be adaptive to particular lifestyles of the species."
Lines 325-331: The argument would be stronger with a figure showing the fusion and the alignment indicating the conserved amino acids mentioned in the text.
We agree with the reviewer but for the sake of space, we finally decided not to include a new figure.
Lines 425: "none of the species encoded" should be replaced by something like "none of the enzyme could be detected in any of the species" (the datasets are incomplete).
The sentence has been changed to: "None of the alternative enzymes mediating the conversion of pyruvate to acetyl-CoA, pyruvate:NADP+ oxidoreductase (PNO) and pyruvate formate lyase (PFL), could be detected in any of the studied species."
Line 455: "suggesting a cytosolic localization of these enzymes in Preaxostyla." The absence of a phylogenetic affiliation with the S. salmonicida homolog does not preclude a MRO localisation.
The sentence was changed to: "Phylogenetic analysis of Preaxostyla ACSs (Supplementary file 4 B) shows four unrelated clades, none in close relationship to the S. salmonicida MRO homolog, consistent with our assumption that these enzymes are cytosolic in Preaxostyla."
Lines 570-571: "Manual verification indicated that all the candidates recovered in oxymonad data sets are false positives" Using which criteria?
The manual verification was based on the annotation of predicted proteins by BLAST and InterProScan. If the annotations did not correspond to the suggested function, they were considered false positives. For example, the protein BLNAU_15573 of Blattamonas nauphoetae was detected by Sam50 HMM profile and thus was considered a candidate for Sam50 proteins. Its functional annotation from BLAST was, however, unrelated to Sam50 (“putative phospholipase B”). Therefore, this candidate was concluded as a false positive hit of the HMM search resulting from the very high sensitivity of this method.
We clarified this in the Results
“Reciprocal BLASTs indicated that all the candidates recovered in oxymonad data sets are very likely to be false positives based on the annotations of their top BLAST hits (mainly vaguely annotated kinases, peptidases and chaperones) (Fig. 6, Supplementary file 9).”.
And Material and Methods
“Any hits received by the methods described above were considered candidates and were furter inspected as follows. All candidates were BLAST-searched against NCBI-nr and the best hits with the descriptions not including the terms 'low quality protein', 'hypothetical', 'unknown', etc. were kept. For each hit, the Gene Ontology categories were assigned using InterProScan-5.36-75.0. If the annotations received from BLAST or InterProScan corresponded to the originally suggested function, the candidates were considered as verified. Otherwise, they were considered as false positives.”
Lines 743-755: "Similar observations were made in other protists with highly reduced mitochondria, such as G. intestinalis or E. histolytica,..." References are needed.
This part of the manuscript has been removed while streamlining the text.
Line 849: How was the manually curation done for the gene models in the training set?
The sentence has been changed to: "For de novo prediction of genes, Augustus was first re-trained using a set of gene models manually curated with regard to mapped transcriptomic sequences and homology with known protein-coding genes."
Lines 853-856: It is a bit unclear which dataset was used for BUSCO and downstream analysis. Was it the Augustus-predicted proteins, or the EVM polished?
The sentence has been changed to: "The genome completeness for each genome was estimated using BUSCO v3 with the Eukaryota odb9 dataset and the genome completeness was estimated on the sets of EVM-polished protein sequences as the input."
Lines 858: What is it meant that KEGG and similarity searches was used in parallel (what if both gave a functional annotation?)?
A sentence has been added for clarity: "KEGG annotations were given priority in cases of conflict."
Lines 861-862 and 1007-1008: Which genes or sub-projects does this apply to? How many genes were detected in this procedure?
The sentence has been changed to make this clear: "Targeted analyses of genes and gene families of specific interest were performed by manual searches of the predicted proteomes using BLASTp and HMMER (Eddy 2011), and complemented by tBLASTn searches of the genome and transcriptome assemblies to check for the presence of individual genes of interest that were potentially missed in the predicted protein sets (single digits of cases per set). Gene models were manually refined for genes of interest when necessary and possible."
Lines 878-879: It is not clear to me why the sum of the two described numbers should be as high as possible and would appreciate an argument or a reference.
When optimizing the inflation parameter of OrthoMCL, we reasoned that the optimal level of grouping/splitting for our purpose should result in the highest number of orthogroups containing all representatives of the groups of interest (i.e. Preaxostyla) but no other species – pan-Preaxostyla orthogroups. When going down with the values, you observe more and more groupings of pan-Preaxostyla OGs with others (indication of overgrouping) in the opposite direction you observe splitting of pan Preaxostyla OGs which indicates oversplitting. Because we were optimizing the inflation parameter for Preaxostyla and Oxymonadida at the same time, we maximized the sum of pan-Preaxostyla and pan-Oxymonadida groups.
Lines 879-881: "Proteins belonging to the thus defined OGs were automatically annotated using BLASTp searches against the NCBI nr protein database (Supplementary file 1)." Why were these annotated in a different way (compare lines 857-859).
This little inconsistency resulted from the fact that these parts of the analyses were performed by different researchers who did not cross-standardize the procedures. This inconsistency has no effect on the downstream analyses and conclusions as the annotations from Supplementary file 1 were not used in any further analyses.
Lines 894-957: "Detection of lateral gene transfer candidates": • It is not clear which sequences were tested in the procedure. All Preaxostyla, or all metamonada? I think I am confused because in the result sections you only report numbers for Preaxostyla, but in the method section metamonada is mentioned repeatedly.
Thank you for noticing. There was indeed some inconsistency in our writing.
We did an all-against-all search using all metamonads. However, we filtered out all homologous families in which Preaxostyla were not present or that had no hit against GTDB. So in the end, the LGT search was restrained to protein families containing Preaxostyla homologues. We corrected the wording in our method section.
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It would be easier to follow the procedure if numbers are provided for the different steps.
We are not sure what numbers the reviewer refers to here.
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Why was only small oxymonad proteins discarded (line 900)?
This is indeed a mistake. We meant “Preaxostyla proteins”. This is because we only considered Preaxostyla sequences with significant hits against GTDB as a starting point, so we aimed to first remove those that might be too short to yield reliable phylogenies.
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Line 911: How many sequences were collected?
Up to 10,000 hits were retained. We have added that information to the text.
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Lines 916-919: What is the difference between the protein superfamilies (line 916) and the OGs (line 919)? Are the OGs the same orthogroups that is described earlier in the method section? How are the redundancy of NCBI nr entries retrieved in different searches dealt with?
We understand the confusion here. It primarily stemmed from two different ways to establish homologous families across the manuscript because of different researchers being responsible for different parts. Protein superfamilies that were used for reconstructing the single protein trees used for the LGT analyses were assembled based on the procedure describe line 916-919 (“Protein superfamilies were assembled by first running DIAMOND searches of all metamonad sequences against all (-e 1e-20 --id 25 --query-cover 50 --subject-cover 50). Reciprocal hits were gathered into a single FASTA file, as well as their NCBI nr homologues.”). However, this was a somewhat stricter procedure than the one used to establish the OGs that are discussed in the rest of the manuscript (because of the e-value and identity cut-off used), so we eventually enriched the datasets with the putatively missing metamonad sequences that were present in the OGs but not in the initial superfamily assembly. However, since these were often more divergent sequences, we did not use these as queries for our BLAST searches against prokaryotes.
Line 987-989: "...was facilitated by Rsg1 being rather divergent from other Ras superfamily members" This statement is vague. What does it mean in practise?
The sentence has been changed to: " The discrimination was facilitated by Rsg1 having low sequence similarity to other Ras superfamily members (such as Rab GTPases)."
Lines 1037-1038: Why were these proteins re-annotated?
They were not. We are sorry for this mistake, which has been fixed in the revised manuscript.
Figures: The figures would be easier to follow if the colour coding for the five different species were consistent between the figures.
This is a good point, the colour coding has been unified across all figures.
Figure 1: It appears that the Venn diagram in C only shows the Preaxostyla-specific protein in B, not all OGs for which contain Preaxostyla proteins. This is not clear from legend or from the figure itself. The same comment applies to D.
The interpretation of the figure by the reviewer is correct; we have modified the legend to make the meaning of the figure easier to understand.
Figures 2 and 6: It would be clearer with panel labels A, B, etc, instead of "upper" and "lower" panel, as in the other figures.
This is a fair point, we have added the alphabetical labels proposed by the reviewer to the figures.
Figure 6: What is the colour code in the figure? The numbers within the boxes are not aligned.
We have added an explanation of the color code to the legend and edited the figure to make it aesthetically more pleasing.
Supplementary figures 1-3: What do green and magenta indicate in the figure?
As with the previous figure, the color code is now explained in the revised legend.
** Referees cross-commenting** I agree with the other reviewers that the discussion of the functional and ecological implications of the LGTs could be developed.
We understand the reviewers but as already explained in response to Reviewer 1, we have decided not to extend the already rather long manuscript further. We believe that the several exemplar LGT cases that we do discuss in detail provide a good impression of the significance of LGT in the evolution of Preaxostyla.
In contrast to reviewer 2, I do not see that the authors discuss their result in the context of eukaryogenesis in this manuscript. Maybe the reference reviewer 2 mention could be cited in the introduction together with Hampl et al. 2018 to acknowledge that there are different views about the importance of secondarily amitochondrial eukaryotes on our thinking about the origin of eukaryotes. I disagree with reviewer 2's objection against the wording "... and undergo pronounced morphological evolution" because I think Fig. 4 in Hampl 2017 shows a large morphological diversity among oxymonads.
We are glad to see that our perspective is not shared by other colleagues in the field. Nevertheless, having carefully considered the case we have decided to remove any mentions of eukaryogenesis from the revised manuscript, as we admit this topic is peripheral to the key message of our present study. On the other hand, we appreciate very much the note by the reviewer on the large morphological diversity among oxymonads – we have now added a similar remark to the revised manuscript (the last sentence of Conclusions).
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Referee #3
Evidence, reproducibility and clarity
Summary:
The genome sequences of two members of the protist group Preaxostyla are presented in this manuscript: Paratrimastix pyriformis and Blattamonas nauphoetae. The authors use a comparative genomics and phylogenetic approaches and compare the new genome datasets with three previously available genomes and transcriptomes from the group. The availability of genome-scale data from five Preaxostyla species is powerful to address interesting basic evolutionary questions. A substantial part of the manuscript is spent on testing the hypothesis of mitochondrial loss in the oxymonad lineage, which turns out to be supported. The datasets are also explored regarding the role of lateral gene transfer in the group, metabolic diversification and the evolution of Golgi.
Major comments:
I find the manuscript very interesting with many different fascinating results presented. However, the manuscript is very long. Two genome sequences are presented and it is not clear to me what the main question was when this project was initiated and why these two species was selected to answer this question. I do not see an obvious reason for sequencing the P. pyriformis genome if the mitochondrial loss was the main question (given that a transcriptome was already available). Why not spend the time and resources on a member of Preoxystyla, which lacked previous data? The authors should more clearly state why these organisms were chosen to answer the main question or questions of the study.
The evolution of MROs have received substantial attention from the protist research community since the 1990's. During this period the mitochondrial organelle have been considered essential for eukaryotes. Therefore, the result presented in the manuscript has a high significance. However, I am not convinced that it is appropriate to use the term "evolutionary transition" for the mitochondrial loss. The loss of MRO is the endpoint of a gradual change of the internal organisation of the cell that probably started when the ancestor of these organism adapted to an anaerobic lifestyle. The last step described in the manuscript probably had little impact on how these organisms interacted with their environment. The presence or absence of biosynthesis of p-cresol by some, but not all, Preaxystyla probably is much more significant from an ecological point of view. My point is that the authors need to consider how they use the term evolutionary transition and be explicit about that.
In the abstract the main finding is describes as "the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species (M. exilis, B. nauphoetae, and Streblomastix strix) extending the amitochondriate status to the whole Oxymonadida.". I find this a really interesting observation, but I do find the wording a bit too bold for several reasons: - Not every protein that has participated in the mitochondrial function is known. - Mitochondrial proteins could be present in oxymonads, but divergent beyond the detection limit for existing methods. - Genes for one or several mitochondrial proteins could be present in one or more oxymonad genomes, but remain undetected due to the incomplete nature of the datasets.
Although I do think that the authors' claim very well could be true, I don't think their data fully support it. Therefore, it needs to be rephrased.
The third point maybe could be analysed further. BUSCO scores are reported, but also argued not being reliable for this group of organisms (which is true). Would it, for example, be useful to analyse how large fraction of the BUSCO proteins found in all non-Preoxystyla metamonada genomes that are present in the various Preoxystyla datasets?
Line 160-161: 15 LGT events specific for the Preaxostyla+Fornicata clade is reported. This is an exciting finding because it supports a phylogenetic relationship between these two groups. But such an argument is only valid if the observed pattern is more common than the alternative hypotheses (Preaxostyla+Parabasalids and Fornicata+Parabasalids). How many LGT events support each of these groupings? How are these observation affected by the current taxon sampling with the highest number of datasets from Fornicata? How were putative metamonada-to-metamonada LGTs treated in this context?
The authors have used a large-scale approach to make single-gene trees for inferences of LGT. In other parts of the manuscript inferences of evolutionary origins of single genes are made without support of phylogenetic trees. I find this inconsistent and argue that the hypothesis of the origin of a specific protein should be tested with the same rigor whether it is a putative LGT, gene duplication, gene loss or an ancestral member of LECA. Specific cases where I think a phylogenetic analysis is needed includes: - Line 222-223: It is concluded that Rsg1 is a component of LECA. - Line 307: HgcAB are argued to be acquired by LGT of a whole opeon. - Lines 350-355: It is unclear how the different numbers of transporters are interpreted (loss or expansion by duplication). This could be address with phylogenetics. - Lines 407-408: A tree should support the claim of LGT origin. - Lines 414-415: The different origins of glycolytic enzymes should be supported by data or references. - Line 486: Trees or a reference (if available) should support the claim for LGT.
Lines 530-531 and 773-774: "The switch to the SUF pathway in these species has apparently not affected the number of Fe-S-containing proteins but led to a decrease in the usage of 2Fe-2S clusters." I find it difficult to evaluate if the data support this because no exact numbers or identities are given for 2Fe-2S and 4Fe-4S proteins in the various genomes in Suppl. Fig. S4 or Supplementary file 4.
The method used in the paper varies between the different parts of the paper. One example is single gene phylogenies, which are described three times in the method section [Lines 959-973, lines 1011-1034, lines 1093-1101], in addition to the automated approach within the LGT detection pipeline lines 923-926]. The approaches are slightly different with, for example, different procedures for trimming. This makes it difficult to know how the different presented analyses were done in detail. No rationale for using different approaches is given. At the least, it should be clear in the method section which approach was used for which analysis. Specific comments on single gene phylogenies: - Line 966-967: Why max 10 target sequences? - Lines 996-998: Is it a problem that these are rather old datasets?
Minor comments:
I appreciate that many data is included as supplementary material. However, the organisation of the data could be improved. The numbering of the files is not included in their names or within the files, as far as I could find. Descriptions of the files are often missing and information on the annotation such as colour coding is not always included. These aspects of the supplementary material needs to be strengthened in order to make it more useful. Specific comments: - Supplementary file 1, Table 1: accession numbers are missing. Kipferlia bialta appears to have a much smaller number of sequences than reported in the publication. The file consists of three tables and it would be very helpful if the reference in the main manuscript indicate the table number. - Supplementary file 4: The trees lack proper species names and a documented colour coding. There are multiple trees in the file, which make it difficult to find the correct tree. I would appreciate if the different trees were labelled A, B, C, etc., and if these were used in the main text. - There is no HPAD-AE tree (as indicated on line 258), but a HPAD tree. Which part of the tree contain the described fusion protein? - Line 280-281: "UbiE homologs occur also in some additional metamonads, including the oxymonad B. nauphoetae and certain fornicates." These sequences should be clearly highlighted in the tree. - Lines 538-544: A three-gene system is mentioned, but only two AmmoMemoRadiSam trees are found. - Supplementary file 6: I find it difficult to find the proteins discussed in the text, for example "the biosynthesis of p-cresol from tyrosine (line 254-255)". - Supplementary file 11: Which group of species are highlighted in red? How do I know from which species these sequences are (I can make educated guesses, but prefer full species names). I do not find any reference to this file in the main manuscript.
Line 227-228: "630 OGs seem to be oxymonad-specific or divergent, without close BLAST hits". It is unclear if BLAST searches includes only a representative of each 630 OGs, or every single protein in these OGs.
Line 243: I think it is five LGT mapped to internal nodes of Preoxystyla in Figure 1 (1+3+1).
Lines 325-331: The argument would be stronger with a figure showing the fusion and the alignment indicating the conserved amino acids mentioned in the text.
Lines 425: "none of the species encoded" should be replaced by something like "none of the enzyme could be detected in any of the species" (the datasets are incomplete).
Line 455: "suggesting a cytosolic localization of these enzymes in Preaxostyla." The absence of a phylogenetic affiliation with the S. salmonicida homolog does not preclude a MRO localisation.
Lines 570-571: "Manual verification indicated that all the candidates recovered in oxymonad data sets are false positives" Using which criteria?
Lines 743-755: "Similar observations were made in other protists with highly reduced mitochondria, such as G. intestinalis or E. histolytica,..." References are needed.
Line 849: How was the manually curation done for the gene models in the training set?
Lines 853-856: It is a bit unclear which dataset was used for BUSCO and downstream analysis. Was it the Augustus-predicted proteins, or the EVM polished?
Lines 858: What is it meant that KEGG and similarity searches was used in parallel (what if both gave a functional annotation?)?
Lines 861-862 and 1007-1008: Which genes or sub-projects does this apply to? How many genes were detected in this procedure?
Lines 878-879: It is not clear to me why the sum of the two described numbers should be as high as possible and would appreciate an argument or a reference.
Lines 879-881: "Proteins belonging to the thus defined OGs were automatically annotated using BLASTp searches against the NCBI nr protein database (Supplementary file 1)." Why were these annotated in a different way (compare lines 857-859).
Lines 894-957: "Detection of lateral gene transfer candidates": - It is not clear which sequences were tested in the procedure. All Preaxostyla, or all metamonada? I think I am confused because in the result sections you only report numbers for Preaxostyla, but in the method section metamonada is mentioned repeatedly. - It would be easier to follow the procedure if numbers are provided for the different steps. - Why was only small oxymonad proteins discarded (line 900)? - Line 911: How many sequences were collected? - Lines 916-919: What is the difference between the protein superfamilies (line 916) and the OGs (line 919)? Are the OGs the same orthogroups that is described earlier in the method section? How are the redundancy of NCBI nr entries retrieved in different searches dealt with?
Line 987-989: "...was facilitated by Rsg1 being rather divergent from other Ras superfamily members" This statement is vague. What does it mean in practise?
Lines 1037-1038: Why were these proteins re-annotated?
Figures: The figures would be easier to follow if the colour coding for the five different species were consistent between the figures.
Figure 1: It appears that the Venn diagram in C only shows the Preaxostyla-specific protein in B, not all OGs for which contain Preaxostyla proteins. This is not clear from legend or from the figure itself. The same comment applies to D.
Figures 2 and 6: It would be clearer with panel labels A, B, etc, instead of "upper" and "lower" panel, as in the other figures.
Figure 6: What is the colour code in the figure? The numbers within the boxes are not aligned.
Supplementary figures 1-3: What do green and magenta indicate in the figure?
** Referees cross-commenting**
I agree with the other reviewers that the discussion of the functional and ecological implications of the LGTs could be developed.
In contrast to reviewer 2, I do not see that the authors discuss their result in the context of eukaryogenesis in this manuscript. Maybe the reference reviewer 2 mention could be cited in the introduction together with Hampl et al. 2018 to acknowledge that there are different views about the importance of secondarily amitochondrial eukaryotes on our thinking about the origin of eukaryotes. I disagree with reviewer 2's objection against the wording "... and undergo pronounced morphological evolution" because I think Fig. 4 in Hampl 2017 shows a large morphological diversity among oxymonads.
Significance
The findings presented in this manuscript can be divided into two parts: the mitochondrial loss and the metabolic and Golgi analyses. The latter is a substantial contribution to the knowledge of metabolic adaptation in unicellular eukaryotes where it builds on previous similar works in other organismal groups. These findings should be of general interest for the protist field.
The loss of mitochondria in M. exilis has been reported by the authors in several previous publications (Karnkowska, et al. (2016, 2019), Treitli, et al. (2021)). Here they show that a distantly related oxymonad (B. nauphoetae) also lack all signs of the mitochondria, suggesting that all oxymonads might have lost the mitochondrion completely. This shows that M. elixis is not a weird lineage, which recently lost the organelle and therefore is on a fast evolutionary dead end. Rather, a whole group of microbial eukaryotes have lived for long evolutionary times without any organelle with mitochondrial ancestry.
This shows that eukaryotes can be successful without any kind of mitochondrial organelle. Such a conclusion should be of interest to a wide audience.
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Referee #2
Evidence, reproducibility and clarity
Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest. This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion. This is noteworthy in light of other protist genome reports published in the last few years that differ in this respect, including previous work by this group. And for sequencing-only data, this paper - https://doi.org/10.1016/j.dib.2023.108990 - might offer an example of where we are at in 2023. With respect to previous work of the group (Karnkowska et al. 2016 and 2019), this submission is very similar (analysis pattern, even some figures and more or less the conclusion), i.e. to say, the overall progress for the broader audience is rather incremental. Then there are also some incidents, where the data presented conflicts with the authors own interpretation. The text (including spelling and grammar) needs some attention and the choice of words is sometimes awkward. The overuse of quotation marks ("classical", "simple", "fused", "hits", "candidate") is confusing (e.g. was the BLAST result a hit or a "hit").
In its current form the manuscript is, unfortunately, very difficult to review. This reviewer had to make considerable efforts to go through this very large manuscript, mainly because of issues affecting to the presentation and the lack of clarity and conciseness of the text. It would be greatly appreciated if the authors would make more efforts upfront, before submission, to make their work more easily accessible both to readers and facilitate the task of the reviewers.
About a fifth of the two genome is missing according the authors prediction (table 1). Early on they explain the (estimated) incompleteness of the genomes to be a result from core genes being highly divergent. In light of this already suspected high divergence, using (the simplest NCBI) sequence similarity approach to call out the absence of proteins (for any given lineage) may need lineage-specific optimization. The use of more structural motif-guided approaches such as hidden Markov models could help, but it is not clear whether it was used throughout or only for the search for (missing) mitochondrial import and maturation machinery. The authors state that the low completeness numbers are common among protists, which, if true, raises several questions: how useful are then such tools/estimates to begin with and does this then not render some core conclusions problematic? The reader is just left with this speculation in the absence of any plausible explanation except for some references on other species for which, again, no context is provided. Do they have similar issues such as GC-content, same core genes missing, phylogenetic relevance?, etc.. No info is provided, the reader is expected to simply accept this as a fact and then also accept the fact that despite this flaw, all conclusions of the paper that rests on the presence/absence of genes are fine. This is all odd and further skews the interpretations and the comparative nature of the paper.
As a side note, this will also influence the number of proteins absent in other lineages and as such has consequences on LGT calls versus de novo invention. For the cases with LGT as an explanation, it would help to briefly discuss the candidate donors and some details of the proteins in the eco-physiological context (e.g. lines 263-268 suggest that HPAD may have been acquired by EGT which was facilitated by a shared anaerobic habitat and also comment on adaptive values for acquiring this gene). Exchanging metabolic genes via LGT (Line 163) blurs the differences between roles and extent of LGT in prokaryote vs eukaryote, and therefore is exciting and could use support/arguments other than phylogenies. I guess the number of reported LGTs among protists (whatever the source) over the last decade has by now deflated the novelty of the issue in more general; a report of the numbers is expected but they alone won't get you far anymore in the absence of a good story (such as e.g. work on plant cell wall degrading enzymes in beetles). It would help to clarify which parts of the mitochondrial ancestor were reduced during the process of reductive evolution at what time in their hypothesized trajectory. For instance, loosing enzymes of anaerobic metabolism conflicts with the argued case of an aerobic (as opposed to facultative anaerobic) mitochondrial ancestor followed by gains of anaerobic metabolism in the rest of the eukaryotes via LGT, and some papers the authors themselves cite (e.g. the series by Stairs et al.). There is no coherent picture on LGT and anaerobic metabolism, although a reader is right to expect one.
In light of their data the authors also discuss the importance of the mitochondrion with respect to the origin of eukaryotes:
First, the mitochondrion brought thousands of genes into the marriage with an archaeon, surely hundreds of which provided the material to invent novel gene families through fusions and exon shuffling and some of which likely went back and forth over the >billion years of evolution with respect to localizations. The authors look at a minor subset of proteins (pretty much only those of protein import, Fig. 6) to conclude, in the abstract no less: „most strikingly the data confirm the complete loss of mitochondria and every protein that has ever participated in the mitochondrion function for all three oxymonad species." I do not question the lack of a mitochondrion here, but this abstract sentence is theatrical in nature, nothing that data on an extant species could ever proof in the absence of a time machine, and is evolutionary pretty much impossible. A puzzling sentence to read in an abstract and endosymbiont-associated evolution.
Second, using oxymonads as an example that a lineage can present eukaryotic complexity in the absence of mitochondria and conflating it with eukaryogenesis is a logical fallacy. This issue already affected the 2019 study by Hampl et al.. We have known that a eukaryote can survive without an ATP-synthesizing electron transport chain ever since Giardia and other similar examples and the loss of Fe-S biosynthesis and the last bit of mitosome (secondary loss) doesn't make a difference how to think about eukaryogenesis. It confuses the need and cost to invent XYZ with the need and cost of maintenance. How can the authors write "... and undergo pronounced morphological evolution", when they evidently observe the opposite and show so in their Fig. 1? The authors only present evidence for reductive evolution of cellular complexity with the loss of a stacked Golgi. What morphological complexity did oxymonads evolve that is absent in other protists? A cytosolic metabolic pathway doesn't count in this respect, because it is neither morphological, nor was it invented but likely gained through LGT according to the authors. This is quite confusing to say the least. A recent paper (https://doi.org/10.7554/eLife.81033) that refers to Hampl et al. 2019 has picked this up already, and I quote: "Such parasites or commensals have engaged an evolutionary path characterized by energetic dependency. Their complexity might diminish over evolutionary timescale, should they not go extinct with their hosts first." Here the authors raise a red flag with respect to using only parasites and commensals that rely on other eukaryotes with canonical mitochondria as examples. If we now look at Fig. 1 of this submission, Novak et al. underpin this point perfectly, as the origin of oxymonads is apparently connected to the strict dependency on another eukaryote (or am I wrong?), and they support the prediction with respect to complexity reducing after the loss of mitochondria - mitosome gone, Golgi almost gone. What's next? This is a good time to remember that extant oxymonads are only a single picture frame in the movie that is evolution, and their evolution might be a dead-end or result in a prokaryote-like state should they survive 100.000s to millions of years to come.
Some more thoughts:
Line 47-52: Hydrogenosome or mitosome is a biological and established label as (m)any other and I find the use of the word "artificial" in this context strange. While the authors are correct to note that there is a (evolutionary) continuum in the reduction - obviously it is step by step - they exaggerate by referring to the existing labels as "artificial". You make Fe-S clusters but produce no ATP? Well, then you're a mitosome. It's a nomenclature that was defined decades ago and has proven correct and works. If the authors think they have a better scheme and definition, then please present one. Using the authors logic, terms such as amyloplast or the TxSS nomenclature for bacterial secretions systems are just as artificial. As is, this comes across as grumble for no good reason.
Line 158: A duplication-divergence may also explain this since sequence similarity-based searches will miss the ancestral homologues.
Lines 201-202: Presence of GCS-L in amitochondriate should be explained in light of this group once having a mitochondrion, which then makes ancestral derivation and differential loss (as invoked for Rsg1) also a likely explanation along with eukaryote-to-eukaryote LGT.
Lines 356-392: Describes plenty of genomic signal for Golgi bodies but simultaneously cites literature suggesting the absence of a morphologically an identifiable Golgi in oxymonads. An explicit prediction regarding what to observe in TEM for the mentioned species might be nice to stimulate further work.
Lines 414: The preceding paragraphs in this result section describes only the distribution, without mentioning origins - a sweeping one-line summary that proclaims different origin needs some context and support. Furthermore, the distribution of glycolytic enzymes might indeed be patchy, but to suggest it represents an 'evolutionary mosaic composed of enzymes of different origins' without discussing the alternative of a singular origin and different evolutionary paths (including a stringer divergence in one vs. another species) discredits existing literature and the authors own claim with respect to why BUSCO might fail in protists.
Line 486: How uncommon are ADI and OTC in lineages sister to metamonada?
Line 504: It might help an outside reader to include a few lines on consequences and importance of having 2Fe-S vs 4Fe-S clusters and set an expectation (if any) in Oxymonads
Any explanations on what unique selection pressures and gene acquisition mechanisms may be operating in P. pyriformis which might allow for the unique metabolic potential?
** Referees cross-commenting**
To R3: Hampl et al. 2019, to which Novak et al. refer, is about eukaryogensis and that is exactly the context in which this is discussed again and what Raval et al. 2022 had decided to touch upon. If the authors do not bring this up in light of the ability to evolve (novel) eukaryote complexity, then what else? Maybe they can elaborate, especially with respect to energetics to which they explicitly refer to in 2019 (and here). And with respect to text-book eukaryotic traits (and the evolution of new morphological ones), I do not see any new ones evolving in any oxymonad, but reduction as Novak et al. themselves picture it in this submission. Is a change in the number of flagella pronounced morphological evolution? Maybe for some, but I believe this needs to be seen in light of the context of how they discuss it. I see a reduction of eukaryotic complexity and not a gain. They have an elaborate section on the loss of Golgi characteristics (and a figure), but I fail to read something along the same lines with respect to the gain of new morphological traits. Again, novel LGT-based biochemistry does not equal the invention of a new morphology such as a new compartment. Oxymonads depend on mitochondria-bearing eukaryotes for their survival or don't they? This is the main point, and if evidence show that I am wrong, then I will be the first to adapt my view to the data presented.
I have concerns with the presentation of a narrative that in my opinion is too one-sided and that has been has been publicly questioned in the community (in press, at meetings, personally). For the benefit of science and of the young authors on this study, this reviewer feels strongly that these issues should be taken very seriously and discussed openly in a more balanced way. . We only truly move forward on such complex topics, if we allow an open and transparent discussion.
Having said that, I am happy that R3 has picked up exactly the same major concerns as I did with respect to e.g. the phrasing on mito (gene) loss and the BUSCO controversy.
Significance
Using draft genome sequencing of the free-living Paratrimastix pyriformis and the sister lineage oxymonad Blattamonas nauphoetae, Novack et al. infer the metabolic potential of the two protists using comparative genomics. The authors conclude that the common oxymonad ancestor lost the mitochondrion/mitosome and discuss general strategies for adapting to commensal/symbiotic life-style employed by this taxon. Some elaborations on pathways go on for several paragraphs and feel unnecessarily stretched, which made those sections of the paper rather difficult to digest. This might be also be because the work, and all conclusions drawn, depend entirely on incomplete (ca. 70-80%) genome data and simple similarity searches, and e.g. no kind of biochemistry or imaging is presented to underpin the manuscripts discussion.
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Referee #1
Evidence, reproducibility and clarity
This is a very interesting paper that investigates through detailed comparative genomics the tempo and mode of the evolution of microbial eukaryotes/protists members of the Metamonada with a focus on Preaxostyla, currently the only known lineage among eukaryotes to have species that have lost, by all accounts, the mitochondria organelle all together. Notably, it includes a free-living representative of the lineage allowing potential interesting comparison between lifestyles among the Preaxostyla. This is a generally nicely crafted manuscript that presents well supported conclusions based on good quality genome sequence assemblies and careful annotations. The manuscript presents in particular (i) additional evidence for the common role of LGT from various bacterial sources into eukaryotic lineages and (ii) more details on the transition from a free-living lifestyle to an endobiotic one and (iii) the related evolution of MROs and associated metabolism. I have some comments to improve a few details: In the introduction, lines 42-43, the last sentence should be more conservative by replacing "whole Oxymonadida" with "...all known/investigated Oxymonadida". Similarly on line 62, the sentence could state "... contain 140 described...". When discussing the estimated completeness of the genome are discussed (lines 117-120) and contrasted with the values for Trypanosoma brucei and other genomes, the author should explicitly state that these genomes are considered complete, which seems is what they imply, is that the case? If so, please provide more details to support this idea. The supplementary file named "132671_0_supp_2540708_rmsn23" is listed as a Table SX? (note: I found it rather difficult to establish exactly what file corresponds to what document referred in the main text) Lines 243-245, where 46 LGTs are discussed, it is relevant that the authors investigate their functional annotations. Indeed, it is suggested that these could have adaptive values, hence investigating their functional annotation will allow the authors to comment on this possibility in more details and precision. When discussing LGTs it would also be very useful to cite relevant reviews on the topic - covering their origins, functional relevance when known, distribution among eukaryotes. This is done when discussing the evolution and characteristics of MROs but not when discussing LGTs, with several reviews cited and integrated in the discussion of the data and their interpretation. The sentence, lines 263-265, where the distribution of some LGTs are discussed, needs to be made more precise. When using the work "close" the authors presumably refer to shared/similar habitat,s or else? Entamoeba is not a close relative to the other listed taxa. Lines 346-348, that sentence needs to end with a citation (e.g. Carlton et al. 2007). In the paragraph (line 580-585) discussing ATP transporters, note that Major et al. (2017) did not describes NTTs but distantly related members of MSF transporter, shared across a broader range of organisms then the NTTs. Did the authors checked if the genome of interest encoded homologues of these transporters too? Line 920-921, I don't understand how the number 30 relates to "guarantee" inferring the directionality of LGTs events. This will be very much dataset dependent, 100 sequences might still not allow to infer directionality of LGT events. The authors probably meant to "increase the possibility to infer directionality".
Significance
This is a very interesting paper that investigates through detailed comparative genomics the tempo and mode of the evolution of microbial eukaryotes/protists members of the Metamonada with a focus on Preaxostyla, currently the only known lineage among eukaryotes to have species that have lost, by all accounts, the mitochondria organelle all together. Notably, it includes a free-living representative of the lineage allowing potential interesting comparison between lifestyles among the Preaxostyla. This is a generally nicely crafted manuscript that presents well supported conclusions based on good quality genome sequence assemblies and careful annotations.
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Reply to the reviewers
*Reviewer #1 (Evidence, reproducibility and clarity (Required)): **
This study evaluates the effect of fungal toxin candidalysin on neutrophils. The authors show that candidalysin induces NETosis when secreted by hyphae, but when candidalysin is added on its own, NLS are formed instead which are distinct from NETs. The authors have done lots of carefully controlled experiments, and delineated key components of the pathway inducing NLS, including the role of ROS and histone modifications. The data provided is high quality and well presented in figures.
Reviewer #1 (Significance (Required)):
Strengths are the depth of analysis - many different aspects of NETosis is assessed and robustly tested.
Comments: 1. I was a bit confused by what should be the main message of the paper - is it that candidalysin on its own doesn't induce NETosis but only NLS? The answer to this question wasn't well addressed in my opinion, but the paper switches between using live fungi and purified candidalysin so it became confusing at times.
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Responses:
Thank you for this important comment. We have clarified our narrative on candidalysin throughout the manuscript to provide a red thread for the readership. Our message is that candidalysin alone has not the capacity to induce a full cycle of signalling events which result in canonical NET formation. Our data show that candidalysin alone falls short and can only produce NLS. On the other hand, our data show that in the context of growing C. albicans cells candidalysin is able to promote the release of NETs. This is important, since previously the hyphal form of C. albicans has been reported to be a formidable inducer of NETs, whereas the yeast form was not. Our data put candidalysin in the centre of this observation, showing that it indeed is a major contributor to NET formation when present with growing C. albicans cells. Since candidalysin expression and release is strictly connected to hyphal growth our new data agrees with previous assessment and provides new insight in how this hyphae-specific inductive effect is accomplished.
In the revised manuscript, at first, we describe the difference of neutrophil stimulation when using strains expressing and lacking candidalysin as compared to candidalysin stimulation alone. We added or modified the following phrases:
- Line (163) “As candidalysin-expressing C. albicans strains induced more NETs than candidalysin-deficient strains, we investigated the role of the toxin alone in stimulating neutrophil extracellular trap release”.
- Line (173) “In order to ensure consistency in NET/NLS quantification, NLS were quantified with the same criteria as previous described for NETs.”
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Line (182) “In summary, candidalysin alone triggers morphologically distinct NLS in a time- and dose-dependent manner, whereas candidalysin-producing C. albicans hyphae induce canonical NETs (Fig. 1a).” Next, we describe the different morphology of NLS triggered by candidalysin alone in comparison to canonical NETs triggered by C. albicans strains expressing candidalysin. We added or modified the following phrases:
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Line (198) “To investigate candidalysin-triggered NLS in further detail, we used scanning electron microscopy (SEM) that allows a more detailed view of the neutrophil-derived structures (Fig 3a).” Furthermore, to prevent switching between experiments using candidalysin alone and experiments with different Candida strains, we have moved the next paragraph “Candidalysin-expressing strains induce more NETs and higher citrullination levels than candidalysin-deficient strains” to the end of the result section (old Fig. 4 is now new Fig. 8). In doing so, we focus on the direct morphological, signalling and functional effects of candidalysin alone on neutrophils and towards the end, we analyse how the strains are affected by different neutrophil killing mechanisms (phagocytosis and NETs). Subsequently, we synthetize our findings by showing that candidalysin is the main driver of histone citrullination by quantifying this histone modification in the context with NET induction comparing candidalysin-expressing and -deficient strains. We conclude that citrullination-induced chromatin decondensation in combination with candidalysin-induced ROS production are most probably the main contributors of increased NET formation stimulated by C. albicans hyphae expressing candidalysin. This is the also a good conclusion of the manuscript showing that candidalysin alone is not enough but together with growing C. albicans cells it contributes to NET induction and increased NETs in turn inhibit growth and limit spreading of C. albicans.
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We modified and added the following sentences to the discussion section: (line 457) “The data suggests that candidalysin is the key driver of histone citrullination in neutrophils infected with C. albicans and that addition of evenly distributed, external candidalysin in high concentration (15 µM) drives neutrophils towards NLS despite the presence of C. albicans cells. We conclude that, during infection, candidalysin-triggered Ca2+ influx and histone hypercitrullination amplify processes in neutrophils which are induced by C. albicans hyphae. These amplified processes culminate in a strongly increased release of NETs that in turn are formidable weapons to control hyphal filaments.”
*2. If candidalysin on its own only induces NLS - what is the relevance of this for disease? A lot of work has been provided on the pathway driving NLS formation, but it wasn't clear to me why this is important. More in discussion needed or evidence of disease relevance. *
Responses:
Thank you for giving us the opportunity to clarify this issue. Candidalysin expression strongly increases with and is restricted to hyphal growth, which is the adhesive growth form of C. albicans. Given that epithelial cells expunge candidalysin for their own protection while hyphae remain attached, it could be possible that neutrophils get exposed to candidalysin before they encounter C. albicans cells. Therefore, it is relevant to understand how candidalysin per se shapes neutrophil responses. We have added the following sentences to the discussion section: (line 527) ”As epithelial cells are able to expunge candidalysin for protection while C. albicans hyphae remain adeherent 46 recruited neutrophils may encounter candidalysin before direct contact with hyphae.”
With regard to relevance for candidiasis the observation that candidalysin-deficient strains are poor inducers of NETs is most important. Since candidalysin expression is entirely restricted to hyphal growth. this finding gives crucial, new insight into the previous observation that hyphae are better NET inducers than yeast from C. albicans. In this context, we wanted to make it very clear to the reader that this effect only works when C. albicans cells and candidalysin are combined and that candidalysin alone does not lead to full-blown NET formation. Therefore, we have included a thorough investigation of the effects of candidalysin on neutrophils to be able to better contextualize our findings comparing candidalysin-expressing and candidalysin-deficient strains.
To make this point clearer. we added the following sentence to the summary at the end of the discussion section: (line 565) “Neutrophils encountering candidalysin-expressing hyphae are able to adequately respond by releasing increased amounts of NETs whereas secretion of candidalysin does not allow hyphae to evade from neutrophil attack.”
In addition, we are convinced that the move of former Fig.4 to the end of the result section (now Fig. 8) additionally helps the reader to better understand the importance to first delineate the effect of candidalysin on neutrophils alone and then to conclude the manuscript with experiments using different C. albicans strains to put the findings into context.
To add more substance to our conclusions we wrap up the new version of the manuscript with data comparing wild-type and candidalysin-deficient strains in neutrophil antimicrobial assays and quantification of histone citrullination. With the newly added antimicrobial assays we demonstrate that candidalysin expression does not affect phagocytic killing (Fig. 7d and 7e) as assessed by plating assays and that candidalysin does not affect inhibition by PMA-induced NETs (Fig. 7f and 7g). Thus, as stated above, during the interaction of hyphae and neutrophils candidalysin promotes the release of more NETs, but does otherwise not affect anti-Candida activity by neutrophils. Increased NETs in turn, however, inhibit growth and limit spreading of C. albicans. The manuscript now ends with the data on differences in histone citrullination when using wild-type and candidalysin-deficient strains indicating that citrullination-induced chromatin decondensation in combination with C. albicans cells ultimately leads to increased NET release.
We added the following text to the manuscript: (Line 416) “To corroborate, whether candidalysin deficiency affects C. albicans’ susceptibility to neutrophil attack we performed two antimicrobial assays. In the first assay we determined NET-mediated anti-Candida activity by preformed NETs comparing wild-type and candidalysin-deficient strains. We used the same imaged-based analysis with calcofluor white staining. To be able to better observe differences in susceptibility of the different strains we used a slightly higher MOI than for the previous NET inhibition assays which explains higher survival percentage (Fig. 7c, black bars on the right side). As expected, candidalysin did not affect the inhibitory effect on C. albicans imposed by NETs (Fig. 7c). In the second assay, we determined short-term anti-Candida activity of intact neutrophils, which is predominantly phagocytic elimination, by serial dilution and plating for colony counts. Candidalysin-deficient and wild-type strains are killed similarly over the time of 1 to 4 h, both at MOI 1 and 3 (Fig. 7d and 7e). This indicates that candidalysin expression does not enable evasion from neutrophil phagocytic attack and this result agrees well with our previous finding that wild-type C. albicans engulfed by human neutrophils are unable to escape by hyphal outgrowth 16. In conclusion, while candidalysin strongly increases the NET-inductive capacity of C. albicans hyphae, the toxin does neither affect the anti-Candida effect of intact neutrophils nor of NETs.”
Notably, it is not informative to use C. albicans as inducer of NETs and as target of anti-Candida activity by NETs in the same assay, since both induction and anti-Candida activity are dependent on the amount of C. albicans cells. We therefore chose to show two separate assays where we (i) quantify short-term killing by plating (mainly phagocytosis) and (ii) quantify growth inhibition of C. albicans by pre-stimulated NETs.
*3. In Figure 2, it would be helpful to include images of ionomycin-stim neutrophils for comparison of the NLS structures across different stim conditions. *
Response:
This is a very good point. We supply a structural comparison between NLS and NETs induced by PMA, ionomycin and candidalysin in Figure 3. Additionally, the time-dependent changes for ionomycin are now included in the supplementary Figure S1.
4. Few places where reference manager has failed (see bottom on page 10, line 190 for example)
Response:
We have fixed this issue, thank you for pointing it out.
*5. Lines 191-198 - I was confused here by the text. I thought the point was that candidalysin induced NLS similar to ionomycin, but here the point is being made that the two are different? This led me to being confused as to the point of all the comparisons made between ionomycin NLS and candidalysin NLS... this could be made clearer. *
Responses:
Thank you for highlighting this. According to previous literature ionomycin, a bacterial peptide toxin, was the most prominent example for induction of leukotoxic hypercitrullination. Therefore, we used ionomycin to put our findings with candidalysin, a fungal peptide toxin, into context. We find that candidalysin share similarities but also some striking differences to ionomycin. While we could not investigate the nature of these differences in more detail, this could be the basis of a follow-up study, we think it is important to give the reader the comparison in order to better understand how candidalysin shapes neutrophil responses. One clear difference which we show in the manuscript is that candidalysin induces some ROS whereas ionomycin does not at all (Fig. 4).
We changed the text in the result section accordingly to make our point clearer: (line 203) “PMA exposure generated widespread chromatin fibers in the extracellular space (Fig. 3a, left panels) whereas ionomycin exposure resulted in more compact, patchy areas occasionally dispersed with long, thin chromatin fibres (Fig. 3b, middle panels). With regard to morphological changes, candidalysin treatment resulted in compact, fibrous structures resembling those stemming from ionomycin treatment, however long, thread-like structures were absent in candidalysin-treated neutrophil samples (Fig. 3a right panels, for 7 h treatment see Fig. S1c).”
And did so as well in the discussion section: (Line 513) ”While ionomycin- and candidalysin-induced NLS shared similar key features, such as increased histone citrullination, our study revealed striking differences between the two toxins. In contrast to ionomycin, candidalysin stimulation led to ROS production in neutrophils.”
*6. Could the authors include some unstim neutrophil control images in Fig 3 for the SEM? Can the SEM sample processing affect neutrophil structure in anyway? Feels like an important control although I don't have much experience with SEM personally *
Response:
This is of course a relevant control image. We have included an image showing unstimulated neutrophils from similar time points, but without exposure to candidalysin (Fig. 3). The unstimulated neutrophils are spherical and morphologically distinctly different from candidalysin-treated neutrophils.
*7. I was very intrigued by the experiments where the authors added candidalysin in to neutrophils infected with ece1-null strain. Those experiments showed that candidalysin addition still drove NLS instead of NETosis. Can the authors investigate why this is? Is membrane intercalation different when candidalysin is delivered by hyphae vs added on its own? Could that explain some of the differences they have seen? *
Responses:
Thank you for this comment. Yes, there is a clear difference, since we add candidalysin to the medium such that the peptide is evenly distributed and reaches membranes rather evenly from the extracellular space. When released from growing C. albicans hyphae candidalysin is then predominantly released on hyphal tips as demonstrated in the referenced article (doi.org/10.1111/cmi.13378). Hyphal tips in turn are readily attacked by human neutrophils (doi.org/10.1189/jlb.0213063). Hence, we can safely assume according to these previous publications that there will be a more uneven distribution of candidalysin concentrations over neutrophil membranes, when the sole source of the toxin stems from growing hyphae interacting with neutrophils. It would of course be very interesting to know how the toxin exactly intercalates into membranes and which morphologies potential pores may have. These questions are currently under investigation in the laboratories of Profs Hube and Naglik. To include these findings here would certainly be far beyond the scope of this study.
We include and modify the following sentences to the discussion of this manuscript to clarify the issue: (Line 541). ”One of the main goals of the study was to delineate contribution of candidalysin to neutrophil responses either as factor released by C. albicans hyphae or as singular peptide toxin. Our data demonstrates that candidalysin is the main driver of histone citrullination in neutrophils infected with C. albicans (Fig. 8). Lack of candidalysin production in C. albicans results in significantly reduced histone citrullination, accompanied with decreased NET formation. However, citrullination is not required for NET release, but rather governs the formation of NLS, which is dominant when candidalysin is added exogenously with even distribution throughout the cell suspension. With regard to C. albicans hyphae secreting candidalysin, local concentrations of the toxin are likely to vary to a large degree, particularly when the candidalysin-secreting hypha is engulfed by a neutrophil. Therefore, it may be difficult to discriminate NLS form NETs during the interaction of neutrophils and C. albicans, as both structures may be induced concurrently 10. It seems logical that the pore-forming activity of candidalysin augments the release of NET fibres during C. albicans infection, where PRRs will additionally be triggered on neutrophils, resulting in combinatorial activation of downstream pathways. In line with this notion, candidalysin drives histone citrullination, which contributes to chromatin decondensation.”
*8. Is phagocytosis needed for NETosis induction by candidalysin? What happens if you add beads or beta-glucan particles with candidalysin stimulation? Do you get NLS or NETs? *
Responses:
This is an interesting question. Physical contact is required for the induction of NET formation (10.1111/j.1462-5822.2005.00659.x, 10.1371/journal.ppat.1000639) and physical contact leads to pattern recognition unequivocally followed by phagocytic events in neutrophils. Hence, at the least indirectly, phagocytosis and NET formation are connected, but may not be so causally.
While glucan-covered particles have been shown to induce NETs (10.1159/000365249), we show that C. albicans cells devoid of candidalysin induce NETs, but to a much lesser extent than wild-type C. albicans. In addition, the experiment shown in Fig. 8 shows exactly that. Instead of glucan-covered beats we used C. albicans cells (Fig. 8f) which by virtue are glucan covered.
*9. Please confirm what the n numbers refer to in the figure legends - are these biological or technical replicates? How many experiments are the representative images representing? *
Response:
Thank you very much for pointing this out. We adapted our figure legends accordingly and added the number of biological and technical replicates (n=x(y), x=biological replicates, y=technical replicates). Each experiment has been performed with at least three biological replicates which includes the use of different neutrophil donors.
*Reviewer #2 (Significance (Required)):
*
*The advantage of this work is the presentation of the mechanism associated with NLS formation in contact with candidalysin, where activation of NADPH oxidase and calcium influx have been documented to be important. This toxin can trigger ROS production and activate downstream signaling that is important for morphological changes and NLS formation. The important finding is also that NLS are resistant to nuclease treatment and increase the ability of neutrophils to control C. albicans hyphae formation and fungal cell growth. These findings provide a better understanding of the role of neutrophils in the treatment of infections caused by these microorganisms. Below I present are minor suggestions that, in my opinion, will improve the text and correct the presentation of the results, making this set of results a valuable source for explaining such a complex problem.
*
Response:
Thank you for this assessment. In cases which we have identified as crucial for our message we have decided to include additional experiments to better convey our message (Fig. 6e-f and Fig. 7d-g). We also included a time course for ionomycin stimulation of neutrophils in Fig. S1. We appreciate that the overall assessment was that no additional experiments were required.
1/ The authors should decide what thesis about NLS they want to prove: 100 NLS are less fibrous and ....... than canonical NETs and are triggered in an NADPH oxidase-independent fashion.
* 121 NLS were dependent on NADPH oxidase-mediated reactive oxygen species (ROS) production
*
Response:
This was indeed imprecisely formulated from our side. NLS were previously described as NADPH-independent processes stimulated by toxins (see ionomycin). Candidalysin seems to trigger NADPH-dependent and NADPH-independent pathways. However, the main differentiation criteria were described through the hypercitrullination which we could observe for candidalysin. To clarify, we have modified the following sentence: (line 121) ”In contrast to previously described stimuli of NLS, candidalysin induced NLS in partial dependence on NADPH oxidase-mediated reactive oxygen species (ROS) production, wheras PAD4-mediated histone citrullination could be observed as well. Notably, candidalysin alone failed to induce NETs as indicated by a lack of cell cycle activation determined via lamin A/C phosphorylation assays.”
*2/ for the experiment described in the line below, MOI 2 was chosen; did the authors conduct an analysis of the response/eventual change in it, depending on the MOI?
*
Response:
Yes, from our experience in in vitro experiments with human neutrophils MOI3 C. albicans overgrows too quickly. This is why an MOI 1-3 is the best option to analyse NET induction capacities.
131 we infected neutrophils with wild-type C. albicans, ECE1-deficient (ece1ΔΔ), and corresponding revertant (ece1ΔΔ*+ECE1) strains,
3/ Has the effect of deletion of ECE1 on other aspects of virulence, such as adhesion, virulence factor production, or biofilm formation, been analyzed? *
Response:
Yes indeed, the effect of candidalysin on other aspects has been studied. Candidalysin has no effect on adhesion and is expressed during biofilm formation. It has a broad effect on virulence in general and promotes neutrophil recruitment indirectly by a robust induction of damages responses. To clarify the amount of studies investigating these other aspects and to pinpoint the knowledge gap for direct interaction of neutrophils and candidalysin we include the following sentence: (line 132) “C. albicans hyphae release candidalysin and while the effects of the toxin for instance on virulence in general and on adhesion to host cells have been widely studied 17,18,23,28,30, the direct impact of candidalysin on the neutrophil immune response towards C. albicans, remains poorly understood. To investigate the role of candidalysin, we infected neutrophils with wild-type C. albicans,…”
*137 the ECE1- and candidalysin-deficient strains triggered reduced levels
4/ Fig.1 - How were C. albicans cells stained? Does 100%NET mean the number of cells netting after PMA treatment? This information should be given.
*
Response:
Thank you for pointing this out. We were a bit unclear here. We added details in the respective figure legend and method section. C. albicans cells were visualised with anti-Candida antibody (1 µg/mL, ProSci, Cat#35-645). Furthermore, C. albicans nuclei are stained by DAPI, too. 100% NETs would mean that every single neutrophil (an image event which stains for neutrophil markers) in the analysed microscopic picture shows NET or NET-like morphology. We did not normalize to PMA treated cells.
5/ 168 dependent effect with increased NLS formation from 3 μM to 15 μ*M. However, the reduced NLS
How was determined the limiting concentration value of the toxin, for which an increase in NLS was observed? Was a wide range of concentrations used in the analysis or was the determination made only for these three selected values? A complete concentration analysis should be performed. *
Response:
This is of course a valid point. We showed data on these concentrations as established from previous studies of our collaborators (10.1111/cmi.13378; 10.1038/nature17625; 10.1038/s41467-019-09915-2). Under 3 µM we did not observe much measurable results and therefore omitted these. Concentrations above 70 µM did not change the outcome anymore than at 70 µM, so higher concentrations were omitted. We, thus, show 3µM at which we see mild effects, show 15 µM (a 5-fold increase compared to 3µM) at which we see profound effects and show 70 µM (again approximately a 5-fold increase compared to 15 µM) at which we see an overwhelming effect. Additional concentrations in between the applied concentration values would not add much new information.
6/ 169 formation was observed at 70 μ*M (Fig. 2b), which can be explained by neutrophil cell death induced by the toxin as determined by a DNA Sytox Green assay (Fig. S1a).
Was another viability test conducted? AnnexinV? Caspase 3/7? Sytox is not a specific staining in this regard. Furthermore, in Fig. S1a you state the kinetics of cell death, also after PMA treatment. On the one hand, you say that the production of candidalysin of NLS above 70 uM is reduced due to cell death, but at the same time you define as cell death the changes under PMA, which induce netosis. Please explain this reasoning better. *
Responses:
Thank you for pointing this out. We have no indication that candidalysin stimulates apoptosis in neutrophils. Therefore, no AnnexinV/Caspase 3/7 stain was performed. What we wanted to emphasize is that at 70 µM candidalysin the cytotoxic character of candidalysin is overwhelming leading to rather quick cell death, as assessed by the Sytox assay. Sytox is specific in the regard that it determines whether the plasma membrane is permeable and gives the stain access to the nuclear DNA to result in a positive signal. We use this assay to quantify NET formation, since it is a quantitative assay and less laborious than microscopy. However, we always back up NET assays with microscopic, image-based analyses and do not use the Sytox assay as standalone experiment for NET quantification, since the Sytox assay is not specifically staining netting cells, but it also stains other types of cell death.
We clarify this in the text as follows: (line 659) “Neutrophil cell death or the presence of extracellular DNA was quantified using a Sytox Green-based (Invitrogen) fluorescence assay similar to previous descriptions 2,35. To ultimately quantify NETs or NLS we always used image-based assys, the cell death assay was only used as complementation.”
*7/ 175 mixing of granular and nuclear components at ~120 min after stimulation (Fig. 2d and Fig. S2).
Figure S2 does not show mixing with the content of the granules. You are not labeling any granule component, only histones. You cannot draw that conclusion from these results. *
Response:
We respectfully disagree. As indicated in the figure legend for Figure 2d we were labelling for neutrophil elastase (red) which is located in azurophilic granules and thereby presents a marker for granular content. Since we wrongfully referred to Figure S2 here, we removed this from the text. The latter reference probably remained erroneously from a previous version.
*8/ Fig. 2. What concentration of PMA was used? What does 100% NLS mean? How is it different from 100% NET, since you are using PMA in both cases. Please explain. *
Responses:
We have now defined PMA concentration in the respective figure legend (100nM). The criteria for image-based assessment of NLS and NET quantification are the same for reason of comparison. PMA is included in each of the experiments as a positive control to show that the used neutrophils react upon stimulation. To clarify, we now specify at the y-axis %NETs or NLS. As stated above, 100% NLS means that each cell event in the image has increased in diameter such that it is considered as a NET or NLS. Hence, we use a common coordinate system to quantify extracellular events (NETs and NLS) based on size.
We have adjusted the figure legend as follows: (line 186) “Fig 2. Candidalysin induces ____NLS ____in human neutrophils. Candidalysin, but not scrambled candidalysin or pep2, another Ece1p-derived peptide (all 15 µM), induce (a) DNA decondensation in human neutrophils after 4 h (n = 4(10-14)) in a (b) dose-dependent manner (n = 3(10-14)). To allow comparability, NLS were quantified with the same criteria as previously described for NETs. Data shown as mean ± SEM. Confocal images (c) of immunostained cells display morphological changes involving nuclear and granular proteins after 4 h compared to unstimulated cells or 100 nM PMA, or cells exposed to scrambled candidalysin and pep2. The morphological changes evoked by PMA considerably deviate from morphological changes evoked by candidalysin and, hence, are defined as NETs (for PMA) and NLS (for candidalysin). Time-dependent progression of morphological changes (d) in neutrophils induced by candidalysin over the course of 5 h (all images are with 60X magnification).”
*9/ 181 NLS were quantified with the same criteria as previous described for NETs.
The criterion for NETs was an area above 100um2, so what is the criterion for NLS? If we assume that this is the same as for NETs, then what is the difference between NLS and NETs? The criteria adopted do not differentiate between the two forms and appear to be subjective. *
Responses:
As stated above, for us it was very important to find a common coordinate system to quantify NETs and NLS, since we wanted to deliver comparable and solid quantitative data. Hence, the quantification method does not discriminate between NETs and NLS. The notable morphological differences of NETs and NLS are thoroughly described with Figure 2 and Figure 3 and defined by differences in their structure. In addition, we present differences and similarities of induced pathways leading to canonical NETs or candidalysin-induced NLS in Figure 6 and Figure 7. We are convinced that, since NETs and NLS vary in size (DNA area covered), it will not be accurate for quantification purposes to include an additional size cut-off in the attempt to discriminate NLS and NETs. Instead we have established that candidalysin alone induces morphologically distinct NLS, whereas Candida albicans hyphae induce morphologically distinct NETs. By combination of quantitative data and image-based assessment, both structures can be discriminated from each other. In addition, we have established that during neutrophil and C. albicans interaction, citrullination of histone mainly stems from candidalysin. We show here and others have shown previously (10.3389/fimmu.2018.01573) that citrullination of histone occurs during but is not required for NET formation. But histone citrullination is promoted mainly by candidalysin and is also required for formation of NLS. Thus, histone citrullination constitutes another important discriminatory factor between NETs and NLS.
We added modified and added text to the respective figure legend: (line 188) ”To allow comparability, NLS were quantified with the same criteria as previously described for NETs. Data shown as mean ± SEM. Confocal images (c) of immunostained cells display morphological changes involving nuclear and granular proteins after 4 h compared to unstimulated cells or 100 nM PMA, or cells exposed to scrambled candidalysin and pep2. The morphological changes evoked by PMA considerably deviate from morphological changes evoked by candidalysin and, hence, are defined as NETs (for PMA) and NLS (for candidalysin).”
*10/ 190 allows a more detailed view of the neutrophil-derived structures (Error! Reference source not Please, eliminate this error. *
Response:
Thank you for pointing this out to us. We have fixed this error.
*11/ 193 Ionomycin has been previously reported to induce NLS, also... 194 Both, PMA and ionomycin generated widespread chromatin fibers in the extracellular space 197 In addition, C. albicans hyphae induced NETs with observable fibers and 198 threads similar to PMA- and ionomycin-stimulated neutrophils (Fig. 3b). 199 Image-based quantification of NLS events (candidalysin and ionomycin)
In a sentence earlier (193) you mentioned that the action of PMA leads to classical netosis and ionomycin leads to NLS. You pointed out earlier that NLS are poorly developed NETs (line 100), and here you write that PMA and ionomycin generate the same developed structures. You again differentiate between these structures depending on the stimulating factors. Pointing out the differences between the two forms, you should be more precise and consistent in your descriptions. This comment applies to the entire manuscript. *
Responses:
Thank you, we agree that consistency and clarity is required to describe the observed phenomena. We therefore modified or included the following sentences to the manuscript:
- (line 203) ”PMA exposure generated widespread chromatin fibres in the extracellular space (Fig. 3a, left panels) whereas ionomycin exposure resulted in more compact, patchy areas occasionally dispersed with long, thin chromatin fibres (Fig. 3b, middle panels). With regard to morphological changes, candidalysin treatment resulted in compact, fibrous structures resembling those stemming from ionomycin treatment, however long, thread-like structures were absent in candidalysin-treated neutrophil samples (Fig. 3a right panels, for 7 h treatment see Fig. S1c)”
- (Line 513) ”While ionomycin- and candidalysin-induced NLS shared similar key features, such as increased histone citrullination, our study revealed striking differences between the two toxins. In contrast to ionomycin, candidalysin stimulation led to ROS production in neutrophils.”
12/ 203 NLS after 3 h and 5 h, respectively, and led to overall fewer NLS events. This was confirmed by observation. 204 area-based analysis of the events (Fig. 3d). The average area per event that exceeded 100 μ*m2 was 205 determined using the images from the DNA stain. What is the accepted criterion for distinguishing between NLS and NETs? *
Response:
The main criteria distinguishing canonical NETs from NLS is a higher compactness for NLS and an increased citrullination of histones, the latter being absent in canonical NETs (10.3389/fimmu.2016.00461; 10.1016/j.mib.2020.09.011). Please see our comment above (regarding reviewer comment 9). Comparing candidalysin and ionomycin as stimuli for NLS they share key similarities, such as increased citrullination of histone (Fig. 3) and more compact structures than NETs (Fig. 3) with an average size of 151 µm2 for candidalysin-induced and 149 µm2 for ionomycin-induced NLS compared to 262 µm2 for PMA-induced and 231 µm2 for C. albicans-induced NETs (for clarification these average sizes are stated in the text). However, the NLS triggered by candidalysin and ionomycin also show differences. Ionomycin occasionally results in extended chromatin threads, whereas candidalysin does not. Ionomycin induces no ROS at all, whereas candidalysin does to some extent. By consistent usage of the definitions for NETs and NLS and by pinpointing the differences between ionomycin and candidalysin in terms of NLS induction (which are previously unknown) we hope we have sufficiently addressed this comment.
*13/ line 218, 243 - reference error *
Response:
Thank you, we have fixed this error
14/ What form are we actually talking about? Are we focusing on the effect of a natural agent or a synthetic one in relation to NLS/NET? Perhaps it is more important to focus on the citrullination process.
- 247 synthetic candidalysin only induces NLS, we concluded that candidalysin augments NET release when the toxin is secreted by C. albicans hyphae. 256 This confirmed that candidalysin promotes C. albicans-triggered NET release. 262 Interestingly, the addition of synthetic candidalysin resulted in a shift to NLS, 274 External addition of synthetic candidalysin resulted in a shift to NLS structures rather than NETs as visualized by microscopy after 5 h incubation (20X).*
Response:
We used the adjective “synthetic” here to make clear that this is a synthetized peptide and not candidalysin isolated from growing C. albicans. Having said that, we fully agree that the synthetized peptide and the one released by C. albicans cells are essentially identical on the molecular level and thus it is irrelevant and confusing to state in this context here. Therefore, we removed the adjective “synthetic” throughout the study and refer the reader to the method section for information on the origin of candidalysin used in the study. At times, we state “candidalysin alone” when we want to emphasize that candidalysin was the sole trigger used for the respective assay.
15/ Has there been any method to track candidalysin production during contact of C. albicans with neutrophils?
Responses:
Thank you for this comment. Yes, there is a QVQ nanobody that can be used which is currently not to our disposal (doi.org/10.1111/cmi.13378). However, we already know from this publication that candidalysin concentrations vary when released naturally. The concentrations are particularly high in invasion pockets or dense biofilms. We also know that if we add candidalysin to the medium we have even distribution throughout and this is by definition different from concertation spikes at host cell-fungal interaction sites. As we have stated above, hyphal tips in turn are readily attacked by human neutrophils (doi.org/10.1189/jlb.0213063). Hence, we can safely assume, according to these previous publications, that there will be a more uneven distribution of candidalysin concentrations over neutrophil membranes, when the sole source of the toxin stems from growing hyphae interacting with neutrophils. It would of course be very interesting to know how the toxin exactly intercalates into membranes and which morphologies potential pores may have. These questions are currently under investigation in the laboratories of B. Hube and J. Naglik. To incorporate these findings here would certainly be far beyond the scope of this study.
We include and modify the following sentences to the discussion of this manuscript to clarify the issue: (Line 544). ” Lack of candidalysin production in C. albicans results in significantly reduced histone citrullination, accompanied with decreased NET formation. However, citrullination is not required for NET release, but rather governs the formation of NLS, which is dominant when candidalysin is added exogenously with even distribution throughout the cell suspension. With regard to C. albicans hyphae secreting candidalysin, local concentrations of the toxin are likely to vary to a large degree, particularly when the candidalysin-secreting hypha is engulfed by a neutrophil. Therefore, it may be difficult to discriminate NLS form NETs during the interaction of neutrophils and C. albicans, as both structures may be induced concurrently 10. It seems logical that the pore-forming activity of candidalysin augments the release of NET fibres during C. albicans infection, where PRRs will additionally be triggered on neutrophils, resulting in combinatorial activation of downstream pathways. In line with this notion, candidalysin drives histone citrullination, which contributes to chromatin decondensation.”
*16/ In Figure 4f-the given information indicates 1,2 hour incubation, in the caption of the figure there is information about 5 hour incubation - please clarify. The description of the stains used is lacking. *
Response:
Microscopic analysis performed after 5h incubation time, whereas candidalysin has been added to different time points indicated in the Figure (in the new version this is now Figure 8f). We clarified in the legend as follows: (line 472) “(f) Neutrophils were infected with C. albicans and 15 µM candidalysin was added 0 h, 1 h or 2 h after the infection. Addition of candidalysin at the different time points after C. albicans infection resulted in a shift to NLS structures rather than NETs as visualized by microscopy after 5 h total incubation (20X).” The description of the strains is depicted directly in the Figure, next to the microscopic images.
*17/ Fig. 5 - result for 15 uM MitoTEMPO - adds nothing to the results and introduces image information noise - should be removed. No information on the concentration of the peptide used. *
Responses:
We would like to keep the 15 µM MitoTEMPO concentration, since it is the more reasonable concentration at which we do not observe an effect. This argues that ROS is more-likely derived from NADPH oxidase and not mitochondrial ROS. We show TEMPOL effects at 15 µM and at 100 µM to document the dose dependency and for the sake of comparability, we would like to keep both concentrations also for MitoTEMPO.
The indicated peptide concentration was added to the figure legend. Thank you for pointing this out.
*18 / Fig. 5, line 309: and cell-permeable Sytox Green DNA dye (250310 nM) to determine the total number of cells".
Please correct the information on the use of both dyes, according to the manufacturer's description: "SYTOX® Green nucleic acid stain is an excellent green-fluorescent nuclear and chromosome counterstain that is impermeant to live cells, making it a useful indicator of dead cells within a population." *
Response:
Thank you for highlighting this error. Indeed, we used Syto Green for this particular staining, a dye which stains both live and dead cells since the dye is cell-permeable. We corrected the error at this section of the text.
*19/ 324 At later time points, BAPTA-AM led to an increase in NLS, probably due to toxic effects as indicated by higher background levels of NLS formation in non-stimulated, BAPTA-AM-treated neutrophils (Fig. 6d).
If such an assumption is made, the toxic effect should also be observed for the control. *
Response:
The toxic effect was observed while conducting the experiments, but cannot be seen in the size-base quantification which is the read out for this particular experiment. We have performed a cytotoxicity assay using flow cytometry and PI staining to confirm the effect. The results are added as supplemental Figure (Fig. S3b).
*20/Fig. 6C PAD inhibitor should affect PMA-induced netosis, but the figure presents NLS existence - how was this change found? *
Responses:
We are grateful for the opportunity to explain this more thoroughly. PMA does not trigger histone citrullination (10.3389/fimmu.2016.00461) and thereby there is no effect of the PAD inhibitor on PMA-induced NETs. Notably, some level of histone citrullination can also be observed in unstimulated neutrophils (see Fig. 3, 5 and 8), since histone modification is not exclusively dependent on stimulation. However, upon PMA stimulation we observe a decrease (Fig. S1b), not an increase, of histone citrullination consistent with previous reports.
We adjusted the text as follows: (line 235) “. Expectedly, citH3 levels upon PMA stimulation did not increase, but rather decreased which is consistent with previous reports 10 (Fig. 3d and Suppl. Fig. S1b). While citrullination levels in unstimulated neutrophils decreased over time, ionomycin stimulation sustained high levels over 5 h.
*21/ line320 "This indicates that candidalysin most probably causes Ca2+ influx via pore formation and not via direct receptor stimulation" And: line 358. As C.albicans hyphae bind to pathogen recognition receptors (PRRs), activate neutrophils and ultimately promote the release of NETs, we aimed to elucidate whether candidalysin alone leads to the activation of similar pathways in neutrophils. Hence, we stimulated neutrophils with candidalysin in the presence or absence of specific inhibitors for SYK, PI3K, and Akt.
Lack of consistency in conclusion. *
Response:
Thank you for pointing this out. We adjusted the paragraph (line 331) as follows: “As C. albicans hyphae bind to pathogen recognition receptors (PRRs), activate neutrophils and ultimately promote the release of NETs, we aimed to elucidate whether candidalysin alone can trigger similar pathways in neutrophils via signalling cross talk induced by Ca2+ influx. Hence, we stimulated neutrophils with candidalysin in the presence or absence of specific inhibitors for SYK, PI3K, and Akt (Fig. 6b).”
*22/ Fig. 7 It would be good to verify these results with experiments using mutants. Figures 7b, 7c, and 7d can be combined to make the whole drawing clearer. *
Response:
We thought this is very relevant and included additional experiments showing that the mutant strains also induce phosphorylation of lamin A/C independent of the expression of candidalysin (new Fig. 6e and 6f).
*23/ line 603 'The percentage of dead cells was calculated using TritonX-100 lysed neutrophils as 100% control' - maybe use " treated or permeabilized" *
Response:
Thank you, we changed the phrasing accordingly.
-
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Referee #2
Evidence, reproducibility and clarity
The study presented the role of cytolic fungal toxin - candidalysin, secreted by the hyphal form of Candida albicans, in the formation of neutrophil extracellular traps during C. albicans contact with neutrophils, which serve as the first line of the responses. The key conclusions are convincing. The authors considered the whole mechanism of NLT formation, which explained previous observations made by others. Some of the additional proposed experiments are not necessary to perform. They would only complement the results already presented by the authors. A few missing citations in the text need to be filled in. The others have been used appropriately and present earlier work on the subject. The reviewer indicated minor corrections to the drawings in the detailed comments. This paper is very interesting and is crucial to understanding some unusual observations made earlier about NET production in fungal infections. On the other hand, the text requires minor corrections to understand better the occurrence of both forms of extracellular neutrophil traps. The authors of the paper have experience in the subject matter presented, and the lead authors are among the leading researchers who analyze this problem. The presented research is a valuable addition to the previous work.
Significance
The advantage of this work is the presentation of the mechanism associated with NLS formation in contact with candidalysin, where activation of NADPH oxidase and calcium influx have been documented to be important. This toxin can trigger ROS production and activate downstream signaling that is important for morphological changes and NLS formation. The important finding is also that NLS are resistant to nuclease treatment and increase the ability of neutrophils to control C. albicans hyphae formation and fungal cell growth. These findings provide a better understanding of the role of neutrophils in the treatment of infections caused by these microorganisms.
Below I present are minor suggestions that, in my opinion, will improve the text and correct the presentation of the results, making this set of results a valuable source for explaining such a complex problem.
- The authors should decide what thesis about NLS they want to prove: 100 NLS are less fibrous and ....... than canonical NETs and are triggered in an NADPH oxidase-independent fashion. 121 NLS were dependent on NADPH oxidase-mediated reactive oxygen species (ROS) production
- for the experiment described in the line below, MOI 2 was chosen; did the authors conduct an analysis of the response/eventual change in it, depending on the MOI?
131 we infected neutrophils with wild-type C. albicans, ECE1-deficient (ece1ΔΔ), and corresponding revertant (ece1ΔΔ+ECE1) strains, 3. Has the effect of deletion of ECE1 on other aspects of virulence, such as adhesion, virulence factor production, or biofilm formation, been analyzed?
137 the ECE1- and candidalysin-deficient strains triggered reduced levels 4. Fig.1 - How were C. albicans cells stained? Does 100%NET mean the number of cells netting after PMA treatment? This information should be given. 5. 168 dependent effect with increased NLS formation from 3 μM to 15 μM. However, the reduced NLS
How was determined the limiting concentration value of the toxin, for which an increase in NLS was observed? Was a wide range of concentrations used in the analysis or was the determination made only for these three selected values? A complete concentration analysis should be performed. 6. 169 formation was observed at 70 μM (Fig. 2b), which can be explained by neutrophil cell death induced by the toxin as determined by a DNA Sytox Green assay (Fig. S1a).
Was another viability test conducted? AnnexinV? Caspase 3/7? Sytox is not a specific staining in this regard. Furthermore, in Fig. S1a you state the kinetics of cell death, also after PMA treatment. On the one hand, you say that the production of candidalysin of NLS above 70 uM is reduced due to cell death, but at the same time you define as cell death the changes under PMA, which induce netosis. Please explain this reasoning better. 7. 175 mixing of granular and nuclear components at ~120 min after stimulation (Fig. 2d and Fig. S2).
Figure S2 does not show mixing with the content of the granules. You are not labeling any granule component, only histones. You cannot draw that conclusion from these results. 8. Fig. 2. What concentration of PMA was used? What does 100% NLS mean? How is it different from 100% NET, since you are using PMA in both cases. Please explain. 9. 181 NLS were quantified with the same criteria as previous described for NETs.
The criterion for NETs was an area above 100um2, so what is the criterion for NLS? If we assume that this is the same as for NETs, then what is the difference between NLS and NETs? The criteria adopted do not differentiate between the two forms and appear to be subjective. 10. 190 allows a more detailed view of the neutrophil-derived structures (Error! Reference source not Please, eliminate this error. 11. 193 Ionomycin has been previously reported to induce NLS, also... 194 Both, PMA and ionomycin generated widespread chromatin fibers in the extracellular space 197 In addition, C. albicans hyphae induced NETs with observable fibers and 198 threads similar to PMA- and ionomycin-stimulated neutrophils (Fig. 3b). 199 Image-based quantification of NLS events (candidalysin and ionomycin)
In a sentence earlier (193) you mentioned that the action of PMA leads to classical netosis and ionomycin leads to NLS. You pointed out earlier that NLS are poorly developed NETs (line 100), and here you write that PMA and ionomycin generate the same developed structures. You again differentiate between these structures depending on the stimulating factors. Pointing out the differences between the two forms, you should be more precise and consistent in your descriptions. This comment applies to the entire manuscript. 12. 203 NLS after 3 h and 5 h, respectively, and led to overall fewer NLS events. This was confirmed by observation. 204 area-based analysis of the events (Fig. 3d). The average area per event that exceeded 100 μm2 was 205 determined using the images from the DNA stain. What is the accepted criterion for distinguishing between NLS and NETs? 13. line 218, 243 - reference error 14. What form are we actually talking about? Are we focusing on the effect of a natural agent or a synthetic one in relation to NLS/NET? Perhaps it is more important to focus on the citrullination process:
247 synthetic candidalysin only induces NLS, we concluded that candidalysin augments NET release when the toxin is secreted by C. albicans hyphae. 256 This confirmed that candidalysin promotes C. albicans-triggered NET release. 262 Interestingly, the addition of synthetic candidalysin resulted in a shift to NLS, 274 External addition of synthetic candidalysin resulted in a shift to NLS structures rather than NETs as visualized by microscopy after 5 h incubation (20X). 15. Has there been any method to track candidalysin production during contact of C. albicans with neutrophils? 16. In Figure 4f-the given information indicates 1,2 hour incubation, in the caption of the figure there is information about 5 hour incubation - please clarify. The description of the stains used is lacking. 17. Fig. 5 - result for 15 uM MitoTEMPO - adds nothing to the results and introduces image information noise - should be removed. No information on the concentration of the peptide used. 18. Fig. 5, line 309: and cell-permeable Sytox Green DNA dye (250310 nM) to determine the total number of cells".
Please correct the information on the use of both dyes, according to the manufacturer's description: "SYTOX® Green nucleic acid stain is an excellent green-fluorescent nuclear and chromosome counterstain that is impermeant to live cells, making it a useful indicator of dead cells within a population." 19. 324 At later time points, BAPTA-AM led to an increase in NLS, probably due to toxic effects as indicated by higher background levels of NLS formation in non-stimulated, BAPTA-AM-treated neutrophils (Fig. 6d).
If such an assumption is made, the toxic effect should also be observed for the control. 20. Fig. 6C PAD inhibitor should affect PMA-induced netosis, but the figure presents NLS existence - how was this change found? 21. line320 "This indicates that candidalysin most probably causes Ca2+ influx via pore formation and not via direct receptor stimulation" And: line 358. As C.albicans hyphae bind to pathogen recognition receptors (PRRs), activate neutrophils and ultimately promote the release of NETs, we aimed to elucidate whether candidalysin alone leads to the activation of similar pathways in neutrophils. Hence, we stimulated neutrophils with candidalysin in the presence or absence of specific inhibitors for SYK, PI3K, and Akt.
Lack of consistency in conclusion. 22. Fig. 7 It would be good to verify these results with experiments using mutants. Figures 7b, 7c, and 7d can be combined to make the whole drawing clearer. 23. line 603 'The percentage of dead cells was calculated using TritonX-100 lysed neutrophils as 100% control' - maybe use " treated or permeabilized"
-
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Referee #1
Evidence, reproducibility and clarity
This study evaluates the effect of fungal toxin candidalysin on neutrophils. The authors show that candidalysin induces NETosis when secreted by hyphae, but when candidalysin is added on its own, NLS are formed instead which are distinct from NETs. The authors have done lots of carefully controlled experiments, and delineated key components of the pathway inducing NLS, including the role of ROS and histone modifications. The data provided is high quality and well presented in figures.
Significance
Strengths are the depth of analysis - many different aspects of NETosis is assessed and robustly tested.
Comments:
- I was a bit confused by what should be the main message of the paper - is it that candidalysin on its own doesn't induce NETosis but only NLS? The answer to this question wasn't well addressed in my opinion, but the paper switches between using live fungi and purified candidalysin so it became confusing at times.
- If candidalysin on its own only induces NLS - what is the relevance of this for disease? A lot of work has been provided on the pathway driving NLS formation, but it wasn't clear to me why this is important. More in discussion needed or evidence of disease relevance.
- In Figure 2, it would be helpful to include images of ionomycin-stim neutrophils for comparison of the NLS structures across different stim conditions.
- Few places where reference manager has failed (see bottom on page 10, line 190 for example)
- Lines 191-198 - I was confused here by the text. I thought the point was that candidalysin induced NLS similar to ionomycin, but here the point is being made that the two are different? This led me to being confused as to the point of all the comparisons made between ionomycin NLS and candidalysin NLS... this could be made clearer.
- Could the authors include some unstim neutrophil control images in Fig 3 for the SEM? Can the SEM sample processing affect neutrophil structure in anyway? Feels like an important control although I don't have much experience with SEM personally.
- I was very intrigued by the experiments where the authors added candidalysin in to neutrophils infected with ece1-null strain. Those experiments showed that candidalysin addition still drove NLS instead of NETosis. Can the authors investigate why this is? Is membrane intercalation different when candidalysin is delivered by hyphae vs added on its own? Could that explain some of the differences they have seen?
- Is phagocytosis needed for NETosis induction by candidalysin? What happens if you add beads or beta-glucan particles with candidalysin stimulation? Do you get NLS or NETs?
- Please confirm what the n numbers refer to in the figure legends - are these biological or technical replicates? How many experiments are the representative images representing?
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Reply to the reviewers
1. General Statements
We would like to thank the reviewers for their professional comments and constructive suggestions. Our current plan is to revise the manuscript and supplemental materials in response to the reviewers’ requests and suggestions. Toward this goal we began experiments to obtain new data requested by the reviewers and anticipate the outlined experiments can be completed within the next three months.
2. Description of the planned revisions:
__Reviewer #1: __
The results of experiments where Arp2/3 is blocked (Fig.2) should be confirmed by Arp2/3 knock-down and with an independent Arp2/3 inhibitor. Several are available (CK-869, Benproperine, Pimozide). For Fig.3 and 4, that would not be necessary, but to establish the specificity of the effect in fig.2 this is absolutely required.
__Response: __As requested, we will include new data with CK-869, the indicated Arp2/3 complex inhibitor. We purchased the inhibitor and are currently confirming its efficacy before testing whether it inhibits transition to the hESC naïve state. However, we respectfully disagree with generating a Arp2/3 knock-down hESC line. Arp2/3 complex genes are known to be essential genes in both mouse and human embryonic stem cells (PMID: 29662178 and PMID: 31649057). Furthermore, reports on successful knockout of complex subunits indicate that additional genetic manipulations are needed to maintain cell survival, including knockout of INK4A/ARF to bypass apoptosis associated with Arp2 shRNA knockdown (PMID: 22385962) and genetic manipulations in mouse models (PMID: 22492726. Thus, knock-down of Arp2/3 complex members in our cells is beyond the scope of this manuscript.
Yilmaz A, Peretz M, Aharony A, Sagi I, Benvenisty N. Defining essential genes for human pluripotent stem cells by CRISPR-Cas9 screening in haploid cells. Nat Cell Biol. 2018 May;20(5):610-619. doi: 10.1038/s41556-018-0088-1. Epub 2018 Apr 16. PMID: 29662178.
Shohat S, Shifman S. Genes essential for embryonic stem cells are associated with neurodevelopmental disorders. Genome Res. 2019 Nov;29(11):1910-1918. doi: 10.1101/gr.250019.119. Epub 2019 Oct 24. PMID: 31649057; PMCID: PMC6836742.
Wu C, Asokan SB, Berginski ME, Haynes EM, Sharpless NE, Griffith JD, Gomez SM, Bear JE. Arp2/3 is critical for lamellipodia and response to extracellular matrix cues but is dispensable for chemotaxis. Cell. 2012 Mar 2;148(5):973-87. doi: 10.1016/j.cell.2011.12.034. PMID: 22385962; PMCID: PMC3707508.
I believe that the status of the actin cytoskeleton in both states is not well enough characterized. This is especially obvious for branched actin networks themselves that depend on the Arp2/3. To this end, the authors may localize Arp2/3 or cortactin, a useful surrogate that often gives a better staining. This point is particularly important since contractile fibers are not made of branched actin. Myosin cannot walk or pull along branched actin networks because of steric hindrance. It might well be that branched actin networks are debranched after Arp2/3 polymerization. I suggest staining tropomyosins that would indicate where the transition between branched and unbranched actin would be. Along this line, phosphoERMs should be localized and revealed by Western blots (we expect an increase from primed to naive state) because they cannot perform the proposed function of linker between the membrane and actin filaments if they are not phosphorylated.
Response: As requested we will include new data with cortactin immunolabeling, which we already completed. These new data, shown below, confirm that cortactin, which binds to branched actin filaments, co-localizes with the F-actin fence around hESC naïve colonies, suggesting that the fence includes branched F-actin. Also as requested, we are currently immunoblotting for phosphorylated ERMs to more thoroughly assess if they may serve as a linker between the membrane and actin filaments.
Branched actin is required for cell cycle progression and cell proliferation in normal cells. This requirement is lost in most cancer cells (Wu et al., Cell 2012; Molinie et al., Cell Res 2019). This would be really important to know whether ESCs stop proliferating upon CK-666 treatment. In other words, do they behave like normal cells or transformed cells. Proliferation is a major function that depends on the YAP pathway. Cell counts and EdU incorporation can easily provide answers to this important question.
__Response: __As requested, we will include new data on proliferation. We anticipate that these new data will complement data we already have showing that CK-666 does not impair proliferation compared with hESC controls. We also note that the role of the actin cytoskeleton in proliferation is well established and an increase in proliferation is a hallmark of acquisition of the naïve state of pluripotency (PMID: 35005567).
Chen C, Zhang X, Wang Y, Chen X, Chen W, Dan S, She S, Hu W, Dai J, Hu J, Cao Q, Liu Q, Huang Y, Qin B, Kang B, Wang YJ. Translational and post-translational control of human naïve versus primed pluripotency. iScience. 2021 Dec 17;25(1):103645. doi: 10.1016/j.isci.2021.103645. PMID: 35005567; PMCID: PMC8718978.
Minor Comments:
What about the rescue of cell morphology? Does active YAP restore the intercellular contractile bundle?
__Response: __As requested, we obtained these data, as shown below. Expression of the YAP-S127A mutant does rescue the formation of the actin ring architecture in the presence of CK666. We are currently performing additional dedifferentiation assays to immunolabel for pMLC and address the question of if expression of YAP-S127A restores the contractile bundle.
__Reviewer #2: __
The authors found that a ring of actin filaments at the colony periphery was characteristic of the naive hESCs. However, because all the data are presented as an image of a single confocal section, the 3D organization of the actin filaments is not clear. Although the authors drew a scheme for this actin ring being located in the apical domain of polarized cells, such data have not been provided in the manuscript. Since naive hESCs form dome-like colonies, it is important to show the 3D organization of actin filaments in the colony. 3D reconstruction of confocal microscopy images of the naive hESC colonies is required to show the relationship between actin filaments, adherens junctions, and the nuclei (as a reference for the Z axis). If 3D reconstruction is not technically possible, confocal images at different Z levels and maximum projection images should be obtained and provided.
__Response: __As requested, we are currently generating 3D images of the actin fence by using Imaris software, which we previously used to show 3D images of mitochondrial morphology (PMID: 34038242)
Manoli SS, Kisor K, Webb BA, Barber DL. Ethyl isopropyl amiloride decreases oxidative phosphorylation and increases mitochondrial fusion in clonal untransformed and cancer cells. Am J Physiol Cell Physiol. 2021 Jul 1;321(1):C147-C157. doi: 10.1152/ajpcell.00001.2021. Epub 2021 May 26. PMID: 34038242; PMCID: PMC8321791.
Some of the statistical analyses were inappropriate. The authors have used Student's t-test for all analyses; however, one-way ANOVA and post-hoc analysis must be used to compare three or more groups (Figs. 2B, D, E, 3G, 4B, D, E).
__Response: __As requested, we will re-evaluate our statistical analysis. We note that our submission reports comparisons between two groups, and hence, Student’s t-test is appropriate. For example, we compared primed and naïve to demonstrate successful acquisition of naïve pluripotency, and then we compared the naïve condition to the CK666-treated conditions to demonstrate the impact of CK666-treatment. As Reviewer 2 suggests we will reanalyze all quantifications using one-way ANOVA with post-hoc analysis in the full revision and we will also discuss with Stuart Gansky, a statistician at UCSF whom we previously consulted for most appropriate statistical analysis of our studies.
Minor Comments:
Page 9, second paragraph. In the discussion section, authors have written that "Cells within the ICM of mouse blastocysts exclude YAP from the nucleus whereas cells within the ICM of human blastocysts maintain nuclear YAP." However, a recent study has reported that the ICM/epiblast of mouse late blastocysts also express nuclear YAP. Epiblast Formation by TEAD-YAP-Dependent Expression of Pluripotency Factors and Competitive Elimination of Unspecified Cells. Hashimoto M, Sasaki H. Dev Cell. 2019, 50:139-154.e5. doi: 10.1016/j.devcel.2019.05.024.
__Response: __As requested, we will revise our Discussion section to include findings from the indicated new publication.
Reviewer #3:
Many of their conclusions seem to be based on the qualitative analysis of a single image (e.g. Figures 1D-G, Fig 2G, Supplementary Figure 2). The authors should provide quantitative information regarding these analyses and indicate the number of cells/replicas collected for each experiment.
__Response: __As requested, our revision will have added quantitative data when feasible. We note that in the field, traction force microscopy isn’t commonly quantified beyond including scale bars, which our original manuscript shows. Moreover, pluripotency is standardly not quantified because it is a binary switch - cells are either double positive or they are not. We show 100% double positive, and rtPCR data with known stage-specific markers.
The actin ring surrounding hESCs colonies was previously described by Närvä et al. Although the authors cited this previous work, they do not discuss in deep the differences and similarities with their observations.
__Response: __As requested, our revised manuscript with include additional detail comparing our results with those from Närvä et al. In brief, we observe the formation of this actin ring only in the naïve state of pluripotency, whereas Närvä et al. observe an actin architecture in the primed state. One possible source of difference between their study and ours are the cells used for analysis. Närvä et al. utilize induced pluripotent stem cells, long since proposed to be closer to naïve pluripotency than primed stem cells as conventionally isolated and maintained (see PMID: 27424783 and PMID: 19497275). Additionally, we observe that the contractile actin ring in naïve pluripotent stem cells is in a higher z-plane than reported by Närvä et al., although a direct comparison is difficult to make.
Theunissen TW, Friedli M, He Y, Planet E, O'Neil RC, Markoulaki S, Pontis J, Wang H, Iouranova A, Imbeault M, Duc J, Cohen MA, Wert KJ, Castanon R, Zhang Z, Huang Y, Nery JR, Drotar J, Lungjangwa T, Trono D, Ecker JR, Jaenisch R. Molecular Criteria for Defining the Naive Human Pluripotent State. Cell Stem Cell. 2016 Oct 6;19(4):502-515. doi: 10.1016/j.stem.2016.06.011. Epub 2016 Jul 14. PMID: 27424783; PMCID: PMC5065525.
Nichols J, Smith A. Naive and primed pluripotent states. Cell Stem Cell. 2009 Jun 5;4(6):487-92. doi: 10.1016/j.stem.2009.05.015. PMID: 19497275.
The qualitative observation of Figure 3F suggests a lower overall YAP levels in primed and +CK666 cells in comparison to naive cells. Could the authors check if this is correct and, if this is the case, explain the observation?
__Response: __As requested, our revision will include new data on YAP protein expression by immunoblotting.
The authors should discuss deeper the rationale of the pan-ERM immunostaining experiments (since they used the individual antibodies afterwards) and provide a brief discussion of their results and, in particular, the colocalization with moesin but not with ezrin or radixin.
__Response: __As requested, our revised manuscript will include a more detailed discussion of our results with ERM immunolabeling.
2. Description of the revisions that have already been incorporated in the transferred manuscript:
__Reviewer #1: __
Minor Comments:
Fig2F: non-representative pictures or wrong quantification of the CK666 condition.
__Response: __We thank the review for alerting us to this error. The CK666 Primed and Naïve condition images were swapped. We have edited the figure to correct this.
Fig3A: Y-axis? What is it? How is it adjusted? -Log P?
__Response: __Please see the methods section. Differential expression analysis was performed using DESeq2 R package. The resulting P values were adjusted (padj) using the Benjamini and Hochberg’s approach for controlling the False Discovery Rate (FDR). Genes with a padj
Colors of dots not really visible (in reference to Figure 3A).
__Response: __We thank the reviewer for this comment and have updated the figure to use more standard, colorblind-friendly color choices (see the above figure). Additionally, we fixed a drawing error in the figures when creating the volcano plots.
Typos: Apr2/3 in the abstract, Hoeschst in Fig.S1B.
__Response: __We thank the review for alerting us to these errors. We have edited the manuscript to correct them.
__Reviewer #3: __
There are many experimental details missing that are extremely relevant to fully understand the experiments and evaluate the robustness of the analyses (e.g., microscopy setup, fluorescent probes used for immunostaining, incubation conditions with the inhibitors SMIFH2 and CK666).
__Response: __As requested we have updated the Materials and Methods section with more detailed information on procedures and reagents.
Minor Comments:
The Introduction makes the reader think that actin is the only cytoskeletal network involved in embryo development and stem cell properties. They should also include a brief discussion on the relevance of the other cytoskeletal networks in mechanotransduction and cell fate decisions.
__Response: __As requested, we will revise our Introduction. We note, however, that in the field additional cytoskeleton components, including intermediate filaments and microtubules have mostly been shown for interacting with the nucleus with limited evidence for roles in differentiation.
Many of the images seem to require a flat-field correction. Could the authors check that the illumination is homogeneous? This artifact could affect the data analysis.
__Response: __As we indicate in the Methods section, the spinning disc confocal microscopes used in our study are equipped with a Borealis to mitigate uneven illumination across the field of view. Additionally, quantification in Figures 2C-E, Figures 3F-G, and Figures 4A-D are comparing measurements to a local background (i.e. cytoplasm nearby) in order to normalize for any uneven illumination effects.
There are many abbreviations that are not defined in the text and are extremely specific to the field.
__Response: __As requested, we have expanded the definition of many abbreviations in the text and any additional abbreviations changes will be clearly defined in our revised manuscript.
Could the authors explain the selection of the pluripotency markers studied by qPCR? Specifically, why they studied DNMT3L, DPPA3, KLF2, and KLF4 (Fig. 1B) and the different set PECAM1, ESRRB, KLF4, and DNMT3L in Fig. 2B.
__Response: __Defining the exact molecular and cell behavioral characteristics of naïve pluripotency remains an evolving point of development within the field. The pluripotency markers used in both original panels are known and established markers of naïve pluripotency. The original panel of DNMT3L, DPPA3, KLF2, and KLF4 was established based upon RNAseq datasets publicly available, whereas the secondary panel of PECAM1, ESRRB, KLF4, and DNMT3L was a more targeted analysis of genes found in the literature which have been interrogated in more detail for potential roles in naïve pluripotency. To facilitate clarity within the manuscript, we have updated Fig. 1B to match Fig. 2B for the purposes of defining a transcriptional hallmark of naïve pluripotency for the purposes of this manuscript.
Figures 1G and 2G, please include the images of the colonies.
__Response: __As requested, our revised manuscript will include phase contrast images, which we already have, as shown below. These images will be included Supplemental Figure 1 and Supplemental Figure 2 for the colonies used to show representative tractions in Figure 1G and 2G, respectively.
3. Description of analyses that authors prefer not to carry out
Reviewer #1:
The results of experiments where Arp2/3 is blocked (Fig.2) should be confirmed by Arp2/3 knock-down and with an independent Arp2/3 inhibitor. Several are available (CK-869, Benproperine, Pimozide). For Fig.3 and 4, that would not be necessary, but to establish the specificity of the effect in fig.2 this is absolutely required.
__Response: __As requested, we will include new data with CK-869, the indicated Arp2/3 complex inhibitor. We purchased the inhibitor and are currently confirming its efficacy before testing whether it inhibits transition to the hESC naïve state. However, we respectfully disagree with generating a Arp2/3 knock-down hESC line. Arp2/3 complex genes are known to be essential genes in both mouse and human embryonic stem cells (PMID: 29662178 and PMID: 31649057). Furthermore, reports on successful knockout of complex subunits indicate that additional genetic manipulations are needed to maintain cell survival, including knockout of INK4A/ARF to bypass apoptosis associated with Arp2 shRNA knockdown (PMID: 22385962) and genetic manipulations in mouse models (PMID: 22492726. Thus, knock-down of Arp2/3 complex members in our cells is beyond the scope of this manuscript.
Yilmaz A, Peretz M, Aharony A, Sagi I, Benvenisty N. Defining essential genes for human pluripotent stem cells by CRISPR-Cas9 screening in haploid cells. Nat Cell Biol. 2018 May;20(5):610-619. doi: 10.1038/s41556-018-0088-1. Epub 2018 Apr 16. PMID: 29662178.
Shohat S, Shifman S. Genes essential for embryonic stem cells are associated with neurodevelopmental disorders. Genome Res. 2019 Nov;29(11):1910-1918. doi: 10.1101/gr.250019.119. Epub 2019 Oct 24. PMID: 31649057; PMCID: PMC6836742.
Wu C, Asokan SB, Berginski ME, Haynes EM, Sharpless NE, Griffith JD, Gomez SM, Bear JE. Arp2/3 is critical for lamellipodia and response to extracellular matrix cues but is dispensable for chemotaxis. Cell. 2012 Mar 2;148(5):973-87. doi: 10.1016/j.cell.2011.12.034. PMID: 22385962; PMCID: PMC3707508.
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Referee #3
Evidence, reproducibility and clarity
The manuscript entitled "Arp2/3 Complex Activity Enables Nuclear YAP for Naïve Pluripotency of Human Embryonic Stem Cells" by Nathaniel Meyer, Tania Singh, Matthew Kutys, Todd Nystul, and Diane Barber analyzes the formation of the actin ring that surrounds naive but not primed colonies of human embryonic stem cells (hESCs). The authors claim that the formation of this actin ring requires Arp2/3 which also modulates YAP localization. Despite the overall topic is relevant to understand key aspect of stem cells and embryo development, I have found several flaws in the manuscript (stated below) that, in my opinion, prevent its publication in its current form:
- Many of their conclusions seem to be based on the qualitative analysis of a single image (e.g. Figures 1D-G, Fig 2G, Supplementary Figure 2). The authors should provide quantitative information regarding these analyses and indicate the number of cells/replicas collected for each experiment.
- Many of the images seem to require a flat-field correction. Could the authors check that the illumination is homogeneous? This artifact could affect the data analysis.
- The actin ring surrounding hESCs colonies was previously described by Närvä et al. Although the authors cited this previous work, they do not discuss in deep the differences and similarities with their observations.
- There are many experimental details missing that are extremely relevant to fully understand the experiments and evaluate the robustness of the analyses (e.g., microscopy setup, fluorescent probes used for immunostaining, incubation conditions with the inhibitors SMIFH2 and CK666).
- The qualitative observation of Figure 3F suggests a lower overall YAP levels in primed and +CK666 cells in comparison to naive cells. Could the authors check if this is correct and, if this is the case, explain the observation?
- The authors should discuss deeper the rationale of the pan-ERM immunostaining experiments (since they used the individual antibodies afterwards) and provide a brief discussion of their results and, in particular, the colocalization with moesin but not with ezrin or radixin.
Minor observations:
- The Introduction makes the reader think that actin is the only cytoskeletal network involved in embryo development and stem cell properties. They should also include a brief discussion on the relevance of the other cytoskeletal networks in mechanotransduction and cell fate decisions.
- There are many abbreviations that are not defined in the text and are extremely specific to the field.
- Could the authors explain the selection of the pluripotency markers studied by qPCR? Specifically, why they studied DNMT3L, DPPA3, KLF2, and KLF4 (Fig. 1B) and the different set PECAM1, ESRRB, KLF4, and DNMT3L in Fig. 2B.
- Figures 1G and 2G, please include the images of the colonies.
Significance
The manuscript analyzes the formation of the actin ring that surrounds naive but not primed colonies of human embryonic stem cells (hESCs). The authors claim that the formation of this actin ring requires Arp2/3 which also modulates YAP localization. Despite the overall topic is relevant to understand key aspect of stem cells and embryo development, I have found several flaws in the manuscript (stated below) that, in my opinion, prevent its publication in its current form (see Evidence, reproducibility and clarity)
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Referee #2
Evidence, reproducibility and clarity
Summary:
This paper describes the involvement of the actin regulator Arp2/3 in the dedifferentiation of primed human embryonic stem cells (hESCs) into naive pluripotency. The authors initially demonstrated a reorganization of the actin cytoskeleton during the transition to naive pluripotency, which included the formation of a contractile actin ring at the colony periphery. Actin reorganization was also associated with a reduction in cell-substrate traction forces and an increase in cell-cell junction traction forces. The authors showed that the activity of the Arp2/3 complex was required for actin reorganization and acquisition of a naive pluripotent state. RNA-seq analysis revealed that the Arp2/3 complex regulates Hippo signaling. Furthermore, inhibition of Arp2/3 suppressed the nuclear localization of YAP, and expression of nuclear-localized YAP restored naive dedifferentiation of the Arp2/3 inhibited hESCs. Based on these results, the authors have proposed a model in which naive pluripotency is characterized by Arp2/3 complex-dependent remodeling of the actin cytoskeleton and colony mechanics. Additionally, it has been suggested that Arp2/3 activity facilitates naive dedifferentiation by promoting the nuclear translocation of YAP.
Major comments:
The experiments were of high quality, and the paper was clearly written with these major conclusions being supported by the experiments. However, the three-dimensional (3D) organization of F-actin, including the actin ring surrounding naive colonies, is unclear.
- The authors found that a ring of actin filaments at the colony periphery was characteristic of the naive hESCs. However, because all the data are presented as an image of a single confocal section, the 3D organization of the actin filaments is not clear. Although the authors drew a scheme for this actin ring being located in the apical domain of polarized cells, such data have not been provided in the manuscript. Since naive hESCs form dome-like colonies, it is important to show the 3D organization of actin filaments in the colony. 3D reconstruction of confocal microscopy images of the naive hESC colonies is required to show the relationship between actin filaments, adherens junctions, and the nuclei (as a reference for the Z axis). If 3D reconstruction is not technically possible, confocal images at different Z levels and maximum projection images should be obtained and provided.
- Some of the statistical analyses were inappropriate. The authors have used Student's t-test for all analyses,; however, one-way ANOVA and post-hoc analysis must be used to compare three or more groups (Figs. 2B, D, E, 3G, 4B, D, E).
Minor comments:
- Page 9, second paragraph. In the discussion section, authors have written that "Cells within the ICM of mouse blastocysts exclude YAP from the nucleus whereas cells within the ICM of human blastocysts maintain nuclear YAP." However, a recent study has reported that the ICM/epiblast of mouse late blastocysts also express nuclear YAP.
Epiblast Formation by TEAD-YAP-Dependent Expression of Pluripotency Factors and Competitive Elimination of Unspecified Cells. Hashimoto M, Sasaki H. Dev Cell. 2019, 50:139-154.e5. doi: 10.1016/j.devcel.2019.05.024.
Significance
The importance of actin dynamics and cell mechanics as regulators of cell fate transition has been demonstrated in several systems, including the differentiation of hESCs (ref 54). The importance of YAP in the generation of naive hESCs has been reported previously (24). This study further extends this knowledge by showing the importance of actin dynamics and cell mechanics during the naive dedifferentiation process of hESCs. Although the advancement is not significant, the identification of the Arp2/3 complex as an essential upstream regulator of actin dynamics, cell mechanics, and YAP provides novel and important information for the field of stem cell biology, specifically for researchers working on hESC reprogramming and regenerative medicine.
The field of expertise of this reviewer is mouse preimplantation development and Hippo signaling.
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Referee #1
Evidence, reproducibility and clarity
This manuscript by Meyer et al., shows that the transition from primed to naïve human Embryonic Stem cells is associated with changes with the organization of the actin cytoskeleton, mechanics exerted on the substratum and YAP activity. These changes require Arp2/3 activity and if these changes are blocked with an Arp2/3 inhibitor, the phenotype can be rescued by the expression of a constitutively active YAP form.
This brief manuscript is overall well written and presented. The results are quite original, since branched actin polymerized by Arp2/3 is generally associated with membrane protrusions, and not with contractile actin fibers, as described here. Similarly, YAP activation has been shown to be regulated by RhoA-mediated contractility and here seems to depend on branched actin networks. I have nothing against these provocative conclusions, but I believe that to make their point stronger, more than just the use of an Arp2/3 inhibitor is required.
Major Comments
- The results of experiments where Arp2/3 is blocked (Fig.2) should be confirmed by Arp2/3 knock-down and with an independent Arp2/3 inhibitor. Several are available (CK-869, Benproperine, Pimozide). For Fig.3 and 4, that would not be necessary, but to establish the specificity of the effect in fig.2 this is absolutely required.
- I believe that the status of the actin cytoskeleton in both states is not well enough characterized. This is especially obvious for branched actin networks themselves that depend on the Arp2/3. To this end, the authors may localize Arp2/3 or cortactin, a useful surrogate that often gives a better staining. This point is particularly important since contractile fibers are not made of branched actin. Myosin cannot walk or pull along branched actin networks because of steric hindrance. It might well be that branched actin networks are debranched after Arp2/3 polymerization. I suggest staining tropomyosins that would indicate where the transition between branched and unbranched actin would be. Along this line, phosphoERMs should be localized and revealed by Western blots (we expect an increase from primed to naive state) because they cannot perform the proposed function of linker between the membrane and actin filaments if they are not phosphorylated.
- Branched actin is required for cell cycle progression and cell proliferation in normal cells. This requirement is lost in most cancer cells (Wu et al., Cell 2012; Molinie et al., Cell Res 2019). This would be really important to know whether ESCs stop proliferating upon CK-666 treatment. In other words, do they behave like normal cells or transformed cells. Proliferation is a major function that depends on the YAP pathway. Cell counts and EdU incorporation can easily provide answers to this important question.
Minor comments
- Fig2F: non-representative pictures or wrong quantification of the CK666 condition.
- Fig3A: Y-axis ? What is it ? How is it adjusted ? -Log P ?
- Colors of dots not really visible.
- What about the rescue of cell morphology ? Does active YAP restore the intercellular contractile bundle ?
- Typos: Apr2/3 in the abstract, Hoeschst in Fig.S1B.
Significance
This brief report would be a strong report if the major points are addressed. The conclusions are original, with a role of branched actin in inducing an intercellular contractile bundle and activating YAP, and important for a cell system of importance, human ESC. It would interest a wide variety of readers with either an interest in the actin cytoskeleton or in stem cells.
I believe that the time required to address these 3 points is reasonable in the order of 3 months only.
My expertise is the actin cytoskeleton.
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Reply to the reviewers
1. General Statements
We thank the reviewers for their constructive feedback, which has helped us improve the manuscript considerably (no comment on whether the improvements are “significant”). Below are our point-by-point responses. We have also highlighted all changes in the manuscript.
2. Point-by-point description of the revisions
Reviewer 1
Summary
In this study, Obodo et al. present a new iteration of their popular rhythm analysis tool LimoRhyde. The conceptual advancement in this new iteration is the focus on effect sizes (in the form of point estimates of amplitude and their prediction intervals) rather than the p-values, which has been the predominant form of statistical testing for rhythm analysis. Therefore, compared to a well-established non-parametric method for rhythm testing, LimoRhyde2 selects genomic features with larger amplitudes (effect-sizes) as it is designed to do.
Major Comments
- (LimoRhyde2 algorithm, Page 2-) It is unclear what exactly the contributions/advancements of the authors are? Is it a novel statistical method, the combination of well-established tools in a novel workflow, or is it a novel application to a new field (rhythms)? I am afraid the sentence "LimoRhyde2 builds on previous work by our group and others to rigorously analyze data from genomic experiments [9,16,17], capture non-sinusoidal rhythms [18], and accurately estimate effect sizes [14,19]." is rather ambiguous.
We have revised this sentence in the last paragraph of the Introduction to clarify LimoRhyde2’s contributions.
- (Moderate model coefficients, Page 3-) The authors implement empirical Bayes shrinkage on the coefficients. But the state-of-the-art methods used in LimoRhyde2 for linear model fitting, such as DESeq2/limma-voom/limma-trend, already implement shrinkage for the coefficients. Does algorithm implement a second round of Bayes shrinkage on the rhythm effect-sizes? How or why is this a statistically valid procedure? If not, how does Limorhyde2 add to shrinkage already implemented in DESeq2/limma-voom/limma-trend? Please elaborate.
To our understanding, the two shrinkage procedures work at different levels and serve different purposes. Limma applies shrinkage on residual variances to account for any technical variation and to give a higher power to detect effects for data with smaller sample sizes within each condition; it does not shrink coefficients. In practice, limma’s shrinkage has little effect given the relatively large sample sizes of most circadian experiments. LimoRhyde2, on the other hand, uses mashr to apply shrinkage to the coefficients themselves to account for shared patterns of effects and variation across both features and conditions. We see no reason this approach is invalid, and in our conversations with Matthew Stephens, the author of ashr and mashr, he felt the same. We elaborate on each method’s contributions in the Discussion (paragraph 2).
- I think the goal to move to effect-sizes which lead to more reproducible results and better biological significance is sound and highly appreciated. However, to make the community switch to a completely different way of viewing their genomic analysis requires more convincing examples(s)/use-cases on why they should abandon the old method that they are used to. Now, results section merely shows that this algorithm performs as designed (to find large amplitude rhythms).
We appreciate the comment and acknowledge that some readers may be particularly attached to p-values and our current analysis may not wholly convince them of the value of effect sizes. We believe the manuscript stands on its own, however, and are using LimoRhyde2 to guide experiments whose conclusions we hope to describe in future work. Nonetheless, we have revised the Discussion (paragraph 4) to clarify that some known relevant genes highly ranked by LimoRhyde2 were underappreciated by BooteJTK.
- Related to point 3, others have previously proposed using amplitude (effect-size) thresholds in addition to the p-value cutoffs (Lück & Westermark, 2016, Pelikan et al, 2022), how would the results of Limorhyde2 compare in a fairer contrast where both p-value and amplitude thresholds are implemented? Does the proposed sound method outperform the two-step approach. The authors may perform this analysis on their chosen datasets as well.
Thank you for raising this point. Indeed, one way to view LimoRhyde2 is as a data-driven balancing of raw effect size and p-value. However, the approach of considering both raw amplitude and p-value is uncommon and requires yet another arbitrary cutoff, which complicates any genewise ranking and side-by-side comparison with other methods. Thus, we have decided to not perform this analysis, and instead mention what we see as the advantage of LimoRhyde2 in the Discussion (paragraph 2).
- I am also not completely convinced of the author's approach to compare their tool against BooteJTK. P-values only show ordering when the alternative hypothesis is true. P-values under the null hypothesis are uniformly distributed in [0,1] so would be meaningless for the purpose of ordering. Without knowing the ground-truth, ordering by p-values is rather risky. I understand the authors' difficulty. But maybe point 4 above yields a better evaluation strategy for LimoRhyde2.
If one accepts that these datasets have a non-zero number of “true” rhythmic genes, which to us seems more than reasonable, then we don’t see this is a large issue. Ranking by (adjusted) p-value is also the standard in differential expression analyses.
- (OPTIONAL) LimoRhyde2 orders results by the point estimates of the effect-sizes (amplitudes). Is this biologically the most meaningful? Should the effect-size CIs be ordered at all? Maybe we only care about whether the lower limit of the CI is greater than a chosen threshold without any ordering. A discussion of this would be valuable to a user.
We discussed this issue amongst ourselves as well, and ultimately elected for simplicity in ranking by only the point estimate and not the credible interval. We have now mentioned this issue in the penultimate paragraph of the Discussion.
- (OPTIONAL) If indeed the authors want to move away from p-values, one could argue that most of the insights from p-value analysis are or could be biased. So why compare against ordering by p-values at all in the results?
We are not arguing that results from p-value-based analyses are biased. We seek to show the differences on real data between an analysis based on p-values, the dominant approach in the field, and one based on estimated effect sizes. We believe this has greater potential to promote thoughtful progress than does outright rejection of p-values based on a purely theoretical argument.
Minor Comments
1. In page 3, it is unclear why averaging the three fits is the best thing to do? How bad would the performance be if m = 1 was chosen compared to m=3.
We have elaborated the relevant section of the Methods. For most genes in most datasets, the difference between m=1 and m=3 wasn’t much. However, m=1 tended to go noticeably sideways for some of the most rhythmic genes, depending on the relative locations of timepoints and spline knots, whereas m=3 did not.
- In page 4, "To account for this uncertainty, LimoRhyde2 constructs..." was difficult to understand and sounded arbitrary. Please explain further.
We have revised this sentence.
- Lachmann et al. (2021) also use bootstrap confidence intervals rather than p-values to quantify rhythmicity that ought to be mentioned.
We have now cited this paper in the Introduction.
Significance Comments
1. General assessment: The authors present an exciting new way of viewing results of high-throughput data analysis in the context of biological rhythms using a Bayesian-like approach. Previously work has revealed the flaws in focusing on p-values and how focusing of effect-sizes (in this context amplitudes) can yield more robust, reproducible results. Although this promises to also yield more biological meaningful results, it is unclear from this study how this might be.
See reply to Major Comment 3 above.
- Advance: This study presents the first tool in the context of the rhythm analysis to provide prediction intervals for different rhythm parameters to facilitate a move away from the hypothesis testing framework of p-values. This is a technical advance in the field of rhythm analysis, but it is unclear what insights this could yield.
See reply to Major Comment 6 above.
Reviewer 2
Major Comments
1. The manuscript introduces a new tool to select rhythmic genes and to quantify amplitudes and phases. The authors combine splines, linear regression, Bayes sampling, and Mash. They focus on amplitudes instead p-values as in other packages. The performance and independence of JTK methods are illustrated using selected circadian expression profiles from different mammalian tissues. The paper is clearly written and provides a valuable extension of existing tools. I miss, however, an intuitive explanation of Mash.
Thank you.
- I agree with their claim that amplitudes are quite important for physiological regulations. However, p-values are also helpful to explore, e.g., transcription factor binding sites. Moreover, amplitudes are taken into account in many studies (see e.g. papers of Naef, Korencic, Westermark, Ananthasubramaniam...). Since JTK or RAIN are non-parametric methods amplitudes are not in focus. The authors should discuss the biological relevance of amplitudes more clearly.
Thanks for raising this point. We are careful to limit our claims to bulk transcriptome data, and have tried to cite the relevant prior work. We have revised the Discussion to clarify what we see as the potential value of amplitudes, as illustrated by our analysis.
- The selection of the 3 data sets and of specific genes seems reasonable since a range of technologies (microarrays versus RNS-seq), of durations (1 day versus 2 days), and of gene amplitudes are represented. Still the authors should comments their selections of data sets and genes.
We have added justification for our choices.
- I find also the tissue-dependent phase distributions of clock-controlled genes of interest. However, a comparison with other studies (Zhang, GTEx from Talamanca et al.) and a discussion how amplitude thresholds such as 10%, 25%, 50% affect the phase distributions would be valuable.
Thank you for the suggestion. We initially explored several values of the amplitude threshold for those histograms (Figure S4C) before selecting the top 25%, all led to the same conclusion. We consider this a minor issue and tangential to the main point of the paper, so we have left the figure as is. We invite any interested reader to explore the publicly available results.
Reviewer 3
Summary
The authors developed LimoRhyde2, a method for quantifying rhythmicity in genomic data, and applied it to mouse transcriptome data from liver, lung, and suprachiasmatic nucleus (SCN) tissues. The method uses periodic spline-based linear models and an Empirical Bayes procedure (Mash) to produce posterior fits and rhythm statistics. LimoRhyde2 prioritizes high-amplitude rhythms of various shapes rather than monotonic rhythms with high signal-to-noise ratios, which contrasts with previous methods like BooteJTK. The authors demonstrated the value of LimoRhyde2 in quantifying rhythmicity and highlighted some of its advantages over traditional methods. However, they also acknowledged limitations, such as the inability to compare rhythmicity between conditions and the assumption of fixed rhythms.
Major Comments
1. The key conclusions are convincing, as the authors demonstrated LimoRhyde2's ability to fit non-sinusoidal rhythms and prioritize high-amplitude rhythms over monotonic rhythms with high signal-to-noise ratios. This is shown by the comparison with BooteJTK, a popular method in the field, and by the analysis of real circadian transcriptome data from mouse tissues. However, the authors acknowledged some limitations that could impact the method's broader applicability.
Thank you.
- Data and methods are presented in a reproducible manner, with detailed descriptions of the periodic spline-based linear models, the use of Mash for moderating raw fits, and the calculation of rhythm statistics. This information is sufficient for other researchers to replicate the study and apply the LimoRhyde2 method to their own datasets. The code is available already.
Thank you.
- Adequate replication and statistical analysis are provided, with the authors analyzing the same datasets using both LimoRhyde2 and BooteJTK to compare their performance. The use of Spearman correlation to assess the relationship between the adjusted p-values from BooteJTK and the amplitudes from LimoRhyde2 further supports the statistical rigor of the study.
Thank you.
Minor Comments
1. Addressing LimoRhyde2's limitations would help improve the study.
We have extensively addressed the method’s limitations to the best of our knowledge in Discussion paragraphs 6 and 7.
- Authors could provide more details on how LimoRhyde2 could be applied to single-cell RNA-seq data to improve the presentation. Single-cell quantification over time would be a challenging task, so some insight into this would be appreciated, rather than a brief comment at the end of the paper.
Thank you for your interest in this topic. To do it justice, however, requires its own project and paper, so scRNA-seq is beyond the scope of the current paper.
Significance Comments
1. This study represents a technical advance in the field of genomic analysis of biological rhythms by introducing LimoRhyde2, a method that prioritizes high-amplitude rhythms and directly estimates biological rhythms and their uncertainty. The method's ability to capture non-monotonic rhythms and account for uncertainty makes it a valuable tool for researchers interested in understanding circadian systems and their physiological impact.
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The work is placed in the context of existing literature, as the authors compare LimoRhyde2 with BooteJTK, a refinement of the popular JTK_CYCLE method. The comparison highlights the differences in output, prioritization, and runtime, demonstrating LimoRhyde2's potential advantages over traditional methods in the field.
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However, BooteJTK is relatively underused compared to many other methods, partly because of the difficulty and time required to run the analysis. The paper would be improved by comparing LimoRhyde2 to JTK_Cycle itself, as well as RAIN and ARSER. The latter are the most commonly used methods for rhythm detection, and thus the value of the paper's findings would be far greater by comparing to these methods. Like LimoRhyde2, they are also not resource-intensive to run.
Thanks for your feedback on this point, which is one we discussed at length amongst ourselves. In the end, we decided on BooteJTK because it seems to be the best performing version of the most common method. ARSER and RAIN are simply not the standard, and based on our interpretation of the evidence, not generally superior to JTK. If we had selected the vanilla JTK_Cycle, we felt a reviewer could discard our results by saying "well, they're comparing their method to a version of a method known to be flawed". Given our objective to highlight the differences between prioritization based on estimated effect size and prioritization based on p-value, we do not see the value of including additional methods in the analysis.
- LimoRhyde2's ability to efficiently prioritize large effects with functional significance in the circadian system can provide valuable insights for these researchers and advance the understanding of biological rhythms. The LimoRhyde2 approach is different to conventional reliance on arbitrary p- or q-values, which are taken as almost sacrosanct in the field as a measure of a dataset's worth. LimoRhyde2 could thus help to change this false perception of how to rate a circadian rhythm, which has particularly been ushered in by a reliance on JTK_Cycle p- and q-values as the method of choice for assigning meaningfulness to rhythms. Unfortunately, JTK_Cycle is very conservative and is limited to detecting sinusoidal-type rhythms. LimoRhyde2 could overcome these limitations (as RAIN does too) if widely adopted. However, to do this, it must be compared to things like JTK_Cycle directly.
See reply to Significance Comment 3 above.
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Referee #3
Evidence, reproducibility and clarity
Summary
The authors developed LimoRhyde2, a method for quantifying rhythmicity in genomic data, and applied it to mouse transcriptome data from liver, lung, and suprachiasmatic nucleus (SCN) tissues. The method uses periodic spline-based linear models and an Empirical Bayes procedure (Mash) to produce posterior fits and rhythm statistics. LimoRhyde2 prioritizes high-amplitude rhythms of various shapes rather than monotonic rhythms with high signal-to-noise ratios, which contrasts with previous methods like BooteJTK. The authors demonstrated the value of LimoRhyde2 in quantifying rhythmicity and highlighted some of its advantages over traditional methods. However, they also acknowledged limitations, such as the inability to compare rhythmicity between conditions and the assumption of fixed rhythms.
Major comments:
- The key conclusions are convincing, as the authors demonstrated LimoRhyde2's ability to fit non-sinusoidal rhythms and prioritize high-amplitude rhythms over monotonic rhythms with high signal-to-noise ratios. This is shown by the comparison with BooteJTK, a popular method in the field, and by the analysis of real circadian transcriptome data from mouse tissues. However, the authors acknowledged some limitations that could impact the method's broader applicability.
- Data and methods are presented in a reproducible manner, with detailed descriptions of the periodic spline-based linear models, the use of Mash for moderating raw fits, and the calculation of rhythm statistics. This information is sufficient for other researchers to replicate the study and apply the LimoRhyde2 method to their own datasets. The code is available already.
- Adequate replication and statistical analysis are provided, with the authors analyzing the same datasets using both LimoRhyde2 and BooteJTK to compare their performance. The use of Spearman correlation to assess the relationship between the adjusted p-values from BooteJTK and the amplitudes from LimoRhyde2 further supports the statistical rigor of the study.
Minor comments:
- Addressing LimoRhyde2's limitations would help improve the study.
- Authors could provide more details on how LimoRhyde2 could be applied to single-cell RNA-seq data to improve the presentation. Single-cell quantification over time would be a challenging task, so some insight into this would be appreciated, rather than a brief comment at the end of the paper.
Significance
- This study represents a technical advance in the field of genomic analysis of biological rhythms by introducing LimoRhyde2, a method that prioritizes high-amplitude rhythms and directly estimates biological rhythms and their uncertainty. The method's ability to capture non-monotonic rhythms and account for uncertainty makes it a valuable tool for researchers interested in understanding circadian systems and their physiological impact.
- The work is placed in the context of existing literature, as the authors compare LimoRhyde2 with BooteJTK, a refinement of the popular JTK_CYCLE method. The comparison highlights the differences in output, prioritization, and runtime, demonstrating LimoRhyde2's potential advantages over traditional methods in the field.
- However, BooteJTK is relatively underused compared to many other methods, partly because of the difficulty and time required to run the analysis. The paper would be improved by comparing LimoRhyde2 to JTK_Cycle itself, as well as RAIN and ARSER. The latter are the most commonly used methods for rhythm detection, and thus the value of the paper's findings would be far greater by comparing to these methods. Like LimoRhyde2, they are also not resource-intensive to run.
- LimoRhyde2's ability to efficiently prioritize large effects with functional significance in the circadian system can provide valuable insights for these researchers and advance the understanding of biological rhythms. The LimoRhyde2 approach is different to conventional reliance on arbitrary p- or q-values, which are taken as almost sacrosanct in the field as a measure of a dataset's worth. LimoRhyde2 could thus help to change this false perception of how to rate a circadian rhythm, which has particularly been ushered in by a reliance on JTK_Cycle p- and q-values as the method of choice for assigning meaningfulness to rhythms. Unfortunately, JTK_Cycle is very conservative and is limited to detecting sinusoidal-type rhythms. LimoRhyde2 could overcome these limitations (as RAIN does too) if widely adopted. However, to do this, it must be compared to things like JTK_Cycle directly.
-
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Referee #2
Evidence, reproducibility and clarity
The manuscript introduces a new tool to select rhythmic genes and to quantify amplitudes and phases. The authors combine splines, linear regression, Bayes sampling, and Mash. They focus on amplitudes instead p-values as in other packages. The performance and independence of JTK methods are illustrated using selected circadian expression profiles from different mammalian tissues. The paper is clearly written and provides a valuable extension of existing tools. I miss, however, an intuitive explanation of Mash.
Significance
I agree with their claim that amplitudes are quite important for physiological regulations. However, p-values are also helpful to explore, e.g., transcription factor binding sites. Moreover, amplitudes are taken into account in many studies (see e.g. papers of Naef, Korencic, Westermark, Ananthasubramaniam...). Since JTK or RAIN are non-parametric methods amplitudes are not in focus. The authors should discuss the biological relevance of amplitudes more clearly.
The selection of the 3 data sets and of specific genes seems reasonable since a range of technologies (microarrays versus RNS-seq), of durations (1 day versus 2 days), and of gene amplitudes are represented. Still the authors should comments their selections of data sets and genes.
I find also the tissue-dependent phase distributions of clock-controlled genes of interest. However, a comparison with other studies (Zhang, GTEx from Talamanca et al.) and a discussion how amplitude thresholds such as 10%, 25%, 50% affect the phase distributions would be valuable.
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this study, Obodo et al. present a new iteration of their popular rhythm analysis tool LimoRhyde. The conceptual advancement in this new iteration is the focus on effect sizes (in the form of point estimates of amplitude and their prediction intervals) rather than the p-values, which has been the predominant form of statistical testing for rhythm analysis. Therefore, compared to a well-established non-parametric method for rhythm testing, LimoRhyde2 selects genomic features with larger amplitudes (effect-sizes) as it is designed to do.
Major Comments:
- (LimoRhyde2 algorithm, Page 2-) It is unclear what exactly the contributions/advancements of the authors are? Is it a novel statistical method, the combination of well-established tools in a novel workflow, or is it a novel application to a new field (rhythms)? I am afraid the sentence "LimoRhyde2 builds on previous work by our group and others to rigorously analyze data from genomic experiments [9,16,17], capture non-sinusoidal rhythms [18], and accurately estimate effect sizes [14,19]." is rather ambiguous.
- (Moderate model coefficients, Page 3-) The authors implement empirical Bayes shrinkage on the coefficients. But the state-of-the-art methods used in LimoRhyde2 for linear model fitting, such as DESeq2/limma-voom/limma-trend, already implement shrinkage for the coefficients. Does algorithm implement a second round of Bayes shrinkage on the rhythm effect-sizes? How or why is this a statistically valid procedure? If not, how does Limorhyde2 add to shrinkage already implemented in DESeq2/limma-voom/limma-trend? Please elaborate.
- I think the goal to move to effect-sizes which lead to more reproducible results and better biological significance is sound and highly appreciated. However, to make the community switch to a completely different way of viewing their genomic analysis requires more convincing examples(s)/use-cases on why they should abandon the old method that they are used to. Now, results section merely shows that this algorithm performs as designed (to find large amplitude rhythms).
- Related to point 3, others have previously proposed using amplitude (effect-size) thresholds in addition to the p-value cutoffs (Lück & Westermark, 2016, Pelikan et al, 2022), how would the results of Limorhyde2 compare in a fairer contrast where both p-value and amplitude thresholds are implemented? Does the proposed sound method outperform the two-step approach. The authors may perform this analysis on their chosen datasets as well.
- I am also not completely convinced of the author's approach to compare their tool against BooteJTK. P-values only show ordering when the alternative hypothesis is true. P-values under the null hypothesis are uniformly distributed in [0,1] so would be meaningless for the purpose of ordering. Without knowing the ground-truth, ordering by p-values is rather risky. I understand the authors' difficulty. But maybe point 4 above yields a better evaluation strategy for LimoRhyde2.
- (OPTIONAL) LimoRhyde2 orders results by the point estimates of the effect-sizes (amplitudes). Is this biologically the most meaningful? Should the effect-size CIs be ordered at all? Maybe we only care about what whether the lower limit of the CI is greater than a chosen threshold without any ordering. A discussion of this would be valuable to a user.
- (OPTIONAL) If indeed the authors want to move away from p-values, one could argue that most of the insights from p-value analysis are or could be biased. So why compare against ordering by p-values at all in the results?
Minor Comments:
- In page 3, it is unclear why averaging the three fits is the best thing to do? How bad would the performance be if m = 1 was chosen compared to m=3.
- In page 4, "To account for this uncertainty, LimoRhyde2 constructs..." was difficult to understand and sounded arbitrary. Please explain further.
- Lachmann et al. (2021) also use bootstrap confidence intervals rather than p-values to quantify rhythmicity that ought to be mentioned.
Significance
General assessment:
The authors present an exciting new way of viewing results of high-throughput data analysis in the context of biological rhythms using a Bayesian-like approach. Previously work has revealed the flaws in focusing on p-values and how focusing of effect-sizes (in this context amplitudes) can yield more robust, reproducible results. Although this promises to also yield more biological meaningful results, it is unclear from this study how this might be.
Advance:
This study presents the first tool in the context of the rhythm analysis to provide prediction intervals for different rhythm parameters to facilitate a move away from the hypothesis testing framework of p-values. This is a technical advance in the field of rhythm analysis, but it is unclear what insights this could yield.
Audience:
This will be useful to all chronobiologists (clinical and basic research) who use high-throughput genomic assays. Since this is an open R-package, I suspect most of those who want to will be able to easily use it. My expertise is in chronobiology, data science and systems biology.
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Referee #2
Evidence, reproducibility and clarity
In this report, Bilgic and colleagues study the diversity of progenitor cell types in the developing ferret cerebral cortex, a valuable in vivo model to understand cortex expansion and folding, as in primates including human. Using a single-cell transcriptomics approach, they describe a diversity of progenitor cell types and their interrelation by transcriptomic trajectories, which are conserved but biased as development progresses. Most interestingly, they identify in ferret a type of cell only identified in human before, tRG, which they then characterize throughoutly by transcriptomics. They also identify these cells in histological sections, and via time-lapse videomicroscopy they characterize their cell type of origin. They also provide indirect evidence that tRG may be the source of ependymal cells in the ventricle of the mature cerebral cortex, as well as astroglial progenitor cells. Finally, they extend their analyses to identify oRG in ferret based on previous human single cell data, concluding that they have in ferret a quite different transcriptomic profile than in human.
Major issues:
The authors must provide evidence that the cortical area they are examining will give rise to Somatosensory cortex. Their sampling area appears more like Cingulate cortex, while somatosensory may be a bit more lateral. The cingulate cortex is a very unique region, with some unique characteristics including lamination and connectivity. It would be important to provide some justification as to why they chose this particular part of the cerebral cortex, and keep this into consideration when discussing the general value of their findings.
It seems that the single cell datasets were collected from only 1 replica at each developmental stage. Current best practice sets the inclusion of several biological replicates. Whereas this represents multiplying the workload (and costs) and re-doing many of the analyses, it is currently highly valued. On the other hand, the authors already have their analysis pipelines defined, and so the time involved should be much shorter than before.
Single cell QC methods are incomplete as described in Methods. It is key to consider the relative abundance of mitochondrial RNAs when assessing the integrity and validity of cells, and thus a key criterion to select the cells for clustering analysis. The criteria for the selected choice of clustering resolution is also missing.
When first describing tRGs (line 171), orthogonal views of the image z-stacks must be shown to demonstrate the full morphology of these cells. The basal process might have been cut during tissue sectioning. The same applies to images in Fig. 2C, 2D, S3A.
In Figure 3, the authors perform time-lapse imaging to visualize and characterize the cells and lineage that give rise to tRGs. While very nice and a technical challenge that must be properly acknowledged, they unfortunately only obtained a total of three examples, which is clearly insufficient to reach any meaningful conclusion on this respect. These conclusions, while fascinating, are based only on 3 cell divisions. If this is to be taken as a strong argument for the conclusions of the study, the authors must obtain. If the authors want to make a solid statement out of this experimental approach, they must obtain a sufficient amount of additional data, which will depend on the variability of the results they find.
Still regarding the time-lapse results presented in Figure 3, it is unclear why after first division the authors identify the blue cell as IPC, when it has the exact features of tRG: apical process anchored in VZ surface + short basal process. This is applicable to all three examples shown. For example, the authors describe: "the mother IPC of tRG also possessed both an apical endfoot and a short basal fiber (Fig. 3D)". Why is this identified as IPC, when it looks exactly like vRG, NOT as an IPC? The interpretation of IPCs being the mother cells to tRGs must be changed, to those being vRGs. Or else, more convincing data must be provided. In fact, their analyses in Fig 4A contradict their interpretation on tRG mother cells, showing that the transcriptomic trajectory leading to tRGs does not inlcude Eomes+ cells, accumulated in the neurogenic state 2. At the end of this section, the authors indicate: "our data suggest that tRG cells are formed by apical asymmetric division(s) from unique apical IPC with a short basal fiber (Tsunekawa et al, in preparation).". Being as important as this point is, if there is solid supporting data the authors must include it in this study.
Alternative interpretation of time-lapse images (lines 196-197): maybe a tRG can generate one tRG CRYAB+, and one IPC CRYAB-.
Arrows in Fig 5E are shifted between the top and bottom panels. There is no obvious evidence of mitosis visible. This should be unequivocally labeled with anti-PH3 antibodies.
Line 277: "Transcriptomic trajectories were homologous across the two species". What does this refer to? What are these trajectories? Pseudotime? Is this statistically tested?
When comparing tRG cells between ferret and human, the authors indicate a remarkable similarity between the two species as represented by CRYAB, EGR1, and CYR61 expression. As shown in Fig 6E, EGR1 and CYR61 are not expressed selectively in human tRG as they clearly are in ferret tRG. Hence, this argument is not valid.
In the last part, the authors try to identify oRG-like cells in ferret by comparison with their transcriptomes identified in human. For this, they decide to call ferret oRG-like cells those that are near human oRGs in the integrated UMAP, as identified in a previous human study. What was the criterion for this? How much near is "near"? The fact that the selected cells have higher oRG scores is expected and obvious, as these cells were selected precisely based on their proximity in the UMAP. Even more importantly, the identification of oRGs in the human study is not unambiguous. Therefore, the correlate in ferret cells is also non-conclusive as to the identity of such cells.
Discussion is surprisingly short, given the emphasis that the authors place on the importance of their findings. I would suggest extending it for a better coverage of those findings that have the greatest relevance and interest to a wider readership.
In Discussion, the authors state that "ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." Again, this conclusion is purely based on transcriptomic trajectories, which must not be confused with cell lineage. This sentence must be rephrased and toned down accordingly.
In Discussion: "our cross-species analysis highlights the notable role of tRG as progenitors contributing to the formation of the ependyma and white matter". As mentioned above, this is only based on transcriptomic trajectories, it is not demonstrated in this study. In vivo analyses of cell fate are needed to support this conclusion, and a more extensive videomicroscopy analysis is needed to confirm the cell lineage progression suggested by transcriptomes.
Minor issues:
In line 59, the authors state: "cerebral carcinogenesis independently evolved to gain an additional germinal layer (outer SVZ (OSVZ);". Assuming that they mean "cerebral neurogenesis", what is the evidence for this independent evolution? Original publications demonstrating this must be cited.
Lines 60-61, the third key publication reporting the existence of bRG must be cited together with Hansen 2010 and Fietz 2010: Reillo et al., 2011, Cerebral Cortex.
When introducing ferret as an interesting or important animal model, suitable original studies should be cited.
In Figure 2F-H, layer borders should be labeled. The density of CRYAB+ cells in VZ (?) at P5 seems much greater in Fig 2E,F than in Fig. 2B. Clarifying this discrepancy is important to validate the quantification of Fig 2D.
Co-expression of CRYAB and FOXJ1. In Fig 5B this must be demonstrated with merged channels.
Line 247: "near which nuclear line aggregates are observed more frequently (Fig. 2B)". It is very much unclear what the authors refer to. Please, define nuclear line aggregates.
There are a number of typos along the main text and figures, which must be fully checked and corrected. For example, line 59 "cerebral carcinogenesis"; also in Figure S4, Figure 5E. Labeling of graphs in Fig 5C is wrong. The plots present the fraction of CRYAB+ cells that express FOXJ1 (FOXJ1+/CRYAB+ cells), not the reverse.
Significance
This manuscript is of interest for being the first ferret single-cell study, and for identifying and characterizing to a great extent a unique population of cortical progenitor cells that so far had only been observed in human. The study is presented as a resource for studies of ferret cortex development, which as such is clearly of interest to a very limited audience. A more appealing perspective might be if this study in ferret is of interest or of use to the more general community studying cortex development, or even maybe cortex evolution.
Advance: it is, so far, the first study of single cell profiling of the ferret cerebral cortex, a well established and highly valued model of gyrencephalic mammals, and a suitable best-alternative to work in primates. In addition to the technical advance, providing a new resource for work in ferret, it shows for the first time the existence of truncated Radial Glia (tRG) in a non-human cortex, and even more importantly in this model, strengthening even more its value.
This study as is presented will be of most interest to a specialized audience, those directly working with ferret. Nevertheless, it will also be of conceptual interest to the community of cortex development and evolution for the concepts that one can extract on cell type conservation.
My expertise: cerebral cortex development, brain evolution, ferret, cortex folding, neurogenesis, progenitor cell lineage, transcriptomics of developing brain
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Referee #1
Evidence, reproducibility and clarity
Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).
In this manuscript, the authors conduct a series of single-cell transcriptomic analyses and imaging assays in the developing ferret cortex suggesting that (1) ferrets harbor a radial glia (RG) subtype similar to the truncated radial glia (tRG) described previously in humans that may have the potential to (2) produce ependymal and astrogenic lineages which (3) can also be found in the developing human cortex. These findings appear to be an important step in the validation and development of the ferret model towards a tool that can be used to study tRG cell biology, a feat currently difficult due to the inaccessibility of a genetically tractable source of tRG for molecular and cell biology experiments.
Major comments:
- Are the key conclusions convincing?
I found the key conclusions described above and in the authors' abstract convincing. I found the identification of a distinct, tRG-like cell type from the authors' single-cell transcriptomic analysis of the ferret cortex compelling, particularly because (1) the expression of the previously utilized tRG marker gene CRYAB is specific to the tRG-like cluster and (2) the tRG-like cluster marker genes (including CRYAB) are relatively unique to the tRG-like cluster. I found this strengthened by their morphological analyses showing the tRG-characteristic apical endfoot and short basal process in these CRYAB+ cells in the ferret cortex. I found the combination of imaging and bioinformatic analyses showing the increase in FOXJ1 co-expression in CRYAB+ cells to compellingly suggest that CRYAB+ cells can produce FOXJ1+ ependymal cells, and similarly with the authors' analyses to suggest that tRG-like cells can also contribute to SPARCL1+ astrocyte cells. I found that the cluster score analyses compelling suggest that the tRG-like cells in the ferret dataset correlate with the tRG cells annotated in a separate, human developing cortical dataset. I also appreciated the comparison of astroglial, ependymal, and uncommited ferret tRG sub populations from the pseudo time analysis with the clusters generated from the integrated ferret-human dataset in Fig. 7.<br /> - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.
The weakest claim in the paper is lines 202: "...tRG cells are formed by apical asymmetric division(s) from unique apical IPC". From my understanding, the main evidence that the tRG parent cells shown in Fig. 3 are not tRGs are the data from Fig. 2E-G showing the low amounts of CRYAB+ cells co-expressing KI67, TBR2, or OLIG2 in P5 and P10. Especially given that these timepoints are after those used in Fig. 3, I believe further evidence is needed to confirm the cell type identity of tRG parent cells in Fig. 3. Such experiments (isolating IPCs from ferret cortex and growing in vitro to determine progeny cells) may be outside of the scope of this paper, in which case I believe the text can be strengthened with either (1) presenting the data from the cited Tsunekawa et al, in preparation that would suggest this claim or (2) rephrasing these claims to omit the mention of IPCs.
I also believe the claim in Line 365-366 is overstated: "We found that ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." While I believe the transcriptomic comparisons suggest the presence of uncommitted tRG in both the ferret and human datasets, I would appreciate further analyses to confirm the prevalence of astroglial and ependymal tRG in the humans and/or functional analyses before claiming that human tRG cells make ependymal and astrogenic cells. I appreciate the authors' note that "GW25 is...the latest stages experimentally available" (line 376-377), but their comparative approaches could be applied to existing datasets of the human cortex (Herring et al., 2022, PMID: 36318921) that span later developmental ages. Identifying the presence of astroglial and ependymal tRGs in this and/or similar datasets would provide more convincing evidence of the tRGs' developmental potential. If this computational analysis is outside the scope of the paper, I believe paring the certainty of these claims (especially lines 379 - 383) and recognizing the need for further functional analyses would negate the need for deeper mechanistic validation.
I believe the most significant advance for this paper is the potential to use ferret tRG cells to model those of the human brain. However to support this claim (see Lines 83-84), I believe a comparison of the ferret tRG cells with existing cortical organoid datasets (Bhaduri et al., 2020, PMID: 31996853) would be helpful. If cortical organoids currently lack the presence of tRG cell types, that would strengthen the importance of the ferret model and the findings of this paper - otherwise, I feel that the use of the ferret model needs to be justified in light of the greater accesibility and genetic tractability of the cortical organoid system. - Are the data and the methods presented in such a way that they can be reproduced? Yes - Are the experiments adequately replicated and statistical analysis adequate?
I found the total number of tRG-like cells in the ferret dataset quite small (162), but I understand the difficulty with isolating and sequencing rare cell types from primary tissue sources. I believe most of the transcriptomic analyses were conducted with this low n in consideration, but this caveat is even more reason to pare down the wording for the weaker claims mentioned above. - Are prior studies referenced appropriately?
I found it interesting that tRGs persist and even expand in number in postnatal timepoints (Fig. 2C). I'd be interested to know if this is in line with what is known in human developing cortex. If so, it would strengthen the conclusion that ferret tRGs can model that of humans - and if not, this would either be an important finding regarding tRG function or an important caveat in the use of ferret tRGs to model the cell type in humans. - Are the text and figures clear and accurate? Yes - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
For Fig. 2A, I would find it helpful to compare the morphology of GFP+/CRYAB+ cells vs GFP+/CRYAB- cells, with the hypothesis that GFP+/CRYAB- cells will have elongated basal processes. I believe this could be done by finding GFP+/CRYAB- cells in the raw images obtained to generate Fig. 2A (or similar), and showing those cells in an adjacent panel. This side-by-side comparison could provide more support that the CRYAB+ cells from the single-cell analyses are indeed specifically linked to tRG-like morphology.
Significance
This paper primarily presents a technical advance in the field, showing that tRG cells that can model those found in the developing human cortex are found in the developing ferret cortex.
- Place the work in the context of the existing literature (provide references, where appropriate).
- State what audience might be interested in and influenced by the reported findings. Several studies in the human and macaque brain have identified the presence of tRGs (deAzevedo et al., 2003; Nowakowski et al., 2016), but understanding the molecular functions and development of these cells - and many human-specific cell types in the brain - is difficult due to the lack of tractable models of human neurodevelopment. Ferrets, given their layered cortices, may be a potential model system for these cell types, but further analyses to determine their transcriptomic similarity to the developing human cortex and their ability to recapitulate human cell types are required in order to evaluate their use as a model system. By generating a useful resource in the ferret single-cell transcriptomic atlas, this study provides evidence that - at least for the tRG subtypes - ferrets may be useful in dissecting the generation and functional importance of tRG cells. With the caveat that a direct comparison with the use of cortical organoids to study tRG is lacking in this paper (see above), I believe this work can provide useful insight into the field's current search for model systems to functionally interrogate human-specific aspects of cortical development.
- Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Single-cell transcriptomic profiling of primary developing human cortex and cortical organoids
Did not have sufficient expertise in: - Ferret biology
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Reply to the reviewers
Manuscript number: RC-2023-01910
Corresponding author(s): Michael W. Sereda
1. General Statements
Reviewer #1:
In this paper the authors report a direct correlation between PMP22 and PTEN expression levels in the nerve of CMT mutants. In CMT1A Pmp22tg rat nerves, PTEN levels are increased, whereas in Pmp22+/- mutants, a model of the HNPP neuropathy, PTEN levels decrease. Consistent with this, Pmp22tg nerves display lower Akt phosphorylation and, vice versa, Pmp22+/- nerves have higher Akt phosphorylation. The authors lowered PTEN in the transgenic and inhibited mTOR using Rapamycin in the Pmp22+/- to support the functional relevance of the PMP22-PTEN correlation. ... In conclusion, the correlation between PMP22 and PTEN is a potential interesting observation. However, in my opinion, experiments as shown don't support the conclusion that PMP22 controls PTEN expression level and activity, which is suggested at the basis of the pathogenesis of PMP22 dosage-related neuropathies.
We thank Reviewer #1 for this detailed feedback. We appreciate the Reviewer’s assessment that our observation that PMP22 and PTEN are correlated in CMT1A and HNPP is of potential interest. In the revised manuscript we addressed this key point by adding additional quantifications (Figure 1a, d; Figure 5d) and novel Western Blot analyses (Figure 1a, d). Regarding the pathophysiological significance of the correlation, we point out that both the original as well as the partially revised manuscript contain multiple pieces of evidence demonstrating that altered PTEN activity is critical for both PMP22 gene-dosage related neuropathies:
- The inhibition of the PI3K/PTEN/AKT/mTOR axis upstream (LY294002) or downstream (Rapamycin) of decreased PTEN ameliorates myelin defects in an in vitro HNPP model (Figure 2b, c).
- Downstream of PTEN, Rapamycin treatment ameliorates myelin defects, motor behavior and electrophysiology in the HNPP mouse model in vivo (Figure 3c, d, e,____ g, i)
- Targeting of increased PTEN directly by inhibiting its activity pharmacologically (VO-OHpic) in a CMT1A rat model or by depleting it genetically in a CMT1A model leads to ameliorated myelination in vitro (Figure 4b, c; Figure 5f, g).
- The genetic depletion of PTEN in a CMT1A mouse model increases myelination in vivo, albeit not in the long term (Figure 6a, b, c, d). We therefore feel that any additional evidence to show that "PMP22 controls PTEN activity" is not vital for supporting the major claims of the manuscript, i.e. that the observed correlation of PTEN levels with PMP22 gene dosage has relevance for the etiology of PMP22 dosage diseases and and that targeting the PI3K-PTEN-AKT-mTOR axis downstream of PTEN provides a potential pharmacological therapy of HNPP (while directly targeting PTEN ultimately fails to rescue CMT1A). However, we agree that the activity of PTEN on the molecular level is interesting, and such evidence would further strengthen our conclusions. Therefore, in the final revised version, we plan to add further Western Blots and explore possible downstream effects of altered PTEN levels.
Reviewer #2:
This study investigates the modulation, both genetically and pharmacologically, of the PI3K/Akt/mTOR signaling in preclinical animal models for the inherited peripheral neuropathies HNPP and CMT1A. These conditions result from a gene dosage abnormality of the peripheral myelin protein gene PMP22. The exact biological molecular mechanisms remain enigmatic despite it having been over 30 years since the major genetic lesions, the CMT1A duplication and HNPP deletion, were described. With respect to myelin biology one observes focally slowed nerve conduction at pressure palsies and local/segmental hypermyelination in HNPP whereas hypomyelination occurs in CMT1A. The study is nicely conducted, data illustrations very informative, and writing clear and concise. This paper will likely be of great interest to your readers. The authors provide convincing evidence that the HNPP pathobiology is ameliorated by PI3K/Akt/mTOR inhibitors. Interestingly they found radial myelin growth was most affected by this approach and suggest an interesting transdermal approach in injured nerves in the acute prevention of pressure palsies.
We thank Reviewer #2 for this positive evaluation.
Reviewer #3:
*In this paper Sareda and co-workers demonstrate that the PTEN/mTOR pathway is indirectly involved in regulating myelin thickness and wrapping in models of altered PMP22 gene dosage both in vitro and in vivo. Inhibition of this pathway decreases myelin thickness in models of HNPP, while increasing myelin thickness in models of CMT1A. The evidence for these conclusions is complex but reasonably presented, and the conclusions mainly supported by the data. The abstract for this paper, however, presents a somewhat oversimplified conclusion that the PTEN pathway mainly modifies models of HNPP, where the paper clearly demonstrates that models of CMT1A are also affected by this same pathway. This should be clarified. *
We thank Reviewer #3 for the feedback on the manuscript. We agree with the Reviewer that the same pathway (PI3K/Akt/mTOR) also affects CMT1A, but it is of importance for us to highlight that the disease mechanisms are -at least partly- different between HNPP and CMT1A. This is supported by our observation that PTEN reduction in CMT1A only transiently improves myelination in vivo (Figure 6) and the persistent alteration of differentiation markers despite PTEN reduction, which is not observed in HNPP (Figure 7).
2. Description of the planned revisions
Reviewer #1
Regarding the activity of PTEN
Figure 1
- Additional experiments are needed to support the conclusion of Figure 1 that, in the two mutants, Pten levels reversely correlate with PI3K-Akt-mTOR pathway activation, which represents the rationale of all further experiments. For example, it should be shown systematically in both mutants both Akt and ERK phosphorylation levels (Akt at both T308 and S473), and mTOR activity read outs. In the previously published paper (Fledrich et al.) only increased Akt phosphorylation in Pmp22+/- nerves was reported, whereas Pmp22tg analysis was focused on the interdependence between Akt and ERK without exploring mTOR activation, which is relevant here. 2) (Figure 4) A different model, the C61 mouse a Pmp22tg overexpressing PMP22 is used here (rather than the CMT1A rat). This should be explained in the results. Is also this model characterized by increased Pten levels in the nerve? And low Akt-mTOR activation for instance? 3) (Figure 5) How is Akt-mTOR signaling in the double mutant as compared to Pmp22tg? Is that increased at P18? * Response: We fully agree with the Reviewer that further exploration of PTEN downstream effects will add value to the manuscript. We already justified the usage of the C61 mouse model more clearly, added P-S6 staining of wildtype in addition to an improved representation in Figure 5e, and performed extra Western Blot analysis of PTEN expression (described in the next section “Incorporated *revisions”). Moreover, we will further evaluate the downstream signaling components of PTEN and will perform additional Western Blot analyses of peripheral nerves of HNPP mice, CMT1A rats as well as C61 and C61xPTENhKO mice.
Figure S1
- *Figure S1, page 4: what does it mean "in line with this finding we were unable to detect protein-protein...". May be the authors meant: since there is a direct correlation between Pmp22 and Pten expression levels in the mutants, the authors explored the possibility of an interaction between the two. Regarding the co-IPs, in panel a, the co-IP at the endogenous level, the immunoprecipitation efficiency of PMP22 is very low. May be a pull-down experiment using either exogenous purified PMP22 or PTEN and nerve lysates can help to rule out the possibility of an interaction. The experiments in b, c are performed in overexpression in a heterologous system (293 cells). * Response: We agree with the Reviewer that we might have missed a possible interaction between PMP22 and PTEN in the experiments performed so far. Indeed, pull-down experiments may prove helpful to rule out / reveal protein-protein interaction. Therefore, we will use purified PMP22 and perform pull-down experiments using nerve lysates of wildtype and CMT1A rats.
Figure 5
- *Pten Fl/+ Dhh-Cre cultures seem to have axonal fasciculation. * Response____: We thank the Reviewer for this observation. We will systematically inspected all recorded images for features of fasciculation. We will also assess whether fasciculation is a representative feature in cultures derived from any of the genotypes, and if so, whether the genotypes differ in this regard.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Changes in the text are highlighted in green in the revised manuscript
Reviewer #1:
Figure 1
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*Panel a: the decrease of Pten expression should be quantified with at least n=3 taking into account the variability among different samples at the different time points indicated (the same applies in panel b, even if here the increase of Pten expression level in Pmp22tg nerves is more evident). * Response: We agree with the Reviewer that the timeline is not sufficient to demonstrate alteration in PTEN expression in PMP22 gene dosage diseases CMT1A and HNPP. Therefore, we performed new Western Blot experiments evaluating PTEN expression in (i) HNPP mice, (ii) CMT1A rat (iii) C61 mice and (iv) C61xPTENhKO mice with minimum n = 3 biological replicates and performed the respective quantification which is shown in Figure1 (i, ii) and Figure 5 (iii, iv). The results of the Western Blot analysis and quantification show an increase in PTEN abundance in CMT1A rat (Figure 1d) and C61 mice (Figure 5d) while a decrease is observed in HNPP mice (Figure 1a) and PTENhKOxC61 mice (Figure 5d) when compared to wildtype controls.
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*Panel a and b: the statement that Pten is more expressed at P18 at the peak of myelination in wildtype nerves is not supported by the blots as shown. * Response: We agree that this observation is only partly supported by the Western Blot analysis, as seen in the HNPP mouse model, and deleted this part in the results section.
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Figure S1, page 4: what does it mean "in line with this finding we were unable to detect protein-protein...". May be the authors meant: since there is a direct correlation between Pmp22 and Pten expression levels in the mutants, the authors explored the possibility of an interaction between the two. Response: We thank the Reviewer for pointing out the lack of clarity here. We changed the respective sentence accordingly:
“Since there is a direct correlation between PMP22 and PTEN expression levels in the mutants, we explored the possibility of an interaction between the proteins. By immunoprecipitation experiments we were unable to detect protein-protein interaction between PMP22 and PTEN (Figure S1).” (Page 4)
- *Page 4: "Taken together, Pmp22 dosage inversely correlates with the abundance of PTEN...": please revise this statement * Response: We thank the reviewer for spotting this mistake. We changed the sentence accordingly, which now reads:
“Taken together, Pmp22 dosage directly correlates with the abundance of PTEN and presumably the activation level of the PI3K/Akt/mTOR pathway in myelinating Schwann cells (Figure 1i)." (Page 4, Line 23)
Figure 2:
- The aberrant myelin figures displayed are similar to myelin ovoids preceding degeneration rather than myelin outfoldings. It is also strange that these alterations are in the wildtype cultures treated with RAPA, that instead, in this system, has been reported to increase myelination as it improves protein homeostasis (autophagy, quality control, etc). Response: We thank the Reviewer for pointing this out. Indeed, in the way the images have been presented the aberrant myelin profiles can be mistaken for ovoids. However, a close inspection of the TUJ1 channel images revealed continuity of the axons below the aberrant myelin, thereby excluding ovoid formation. In the partially revised manuscript, we now also show the TUJ1 channel individually (Figure 2), so that it can be appreciated that the defects are confined to the myelin. Concerning the incidence of the myelin defects in RAPA treated wildtype cultures, our analysis can have missed a potential amelioration due to the rather high variability in the data.
Figure 3
*Panel c-e: aberrant fibers should be normalized on total number of fibers and on the area, particularly because RAPA is used. *
Response: We agree with the Reviewer that number of tomacula and recurrent loops should be normalized to the total number of fibers on the area. We have quantified the total number of fibers in the whole sciatic nerve and normalized the tomacula and recurrent loops number accordingly. Results show a decrease in both tomacula and recurrent loops after Rapamycin treatment in the HNPP mice (Figure 3c, d, e, f).
Figure 4
The improvement in the number of myelin segments following PTEN inhibition in Pmp22tg co-cultures is very weak. The 500 nM has instead a consistent effect in reducing myelin segments in the wildtype and I think that these results overall don't support the conclusion that myelination is ameliorated by reducing PTEN activity in Pmp22tg co-cultures.
Response: We thank the Reviewer for this important point. We like to emphasize that we treated whole cultures with the PTEN inhibitor and we cannot rule out a (probably) negative effect on axonal PTEN, resulting in only weak improvement of myelination in PMP22tg cultures and strong effects also on the wildtype co-cultures. Therefore, we decided against a treatment of CMT1A models in vivo and further explored the effects of PTEN reduction specifically in Schwann cells using the genetic model as described Figure 5. The Reviewer made clear to us that this is inappropriately explained in the results section and we therefore adapted this in the manuscript on page 6:
“Similarly, the prolonged inhibition of PTEN with VO-OHpic (for 14 days) caused a dosage-dependent reduction in myelinated segments in wildtype co-cultures (Figure 4c, Figure S2). The mechanism is currently unexplained but cannot rule out a negative effect of PTEN inhibition on DRG neurons and myelination.”
Figure 5:
- *A different model, the C61 mouse a Pmp22tg overexpressing PMP22 is used here (rather than the CMT1A rat). This should be explained in the results. Is also this model characterized by increased Pten levels in the nerve? And low Akt-mTOR activation for instance? * Response: We agree with the Reviewer that it has not been clear in the text why we changed here to the C61 mouse model. We clarified this in the Results section which now reads on page 6:
“To reduce Pten function in CMT1A models also in vivo, we applied a genetic approach (Figure 5a). As the genetic tools to specifically target Schwann cells were only available in the mouse and not the rat, we used the C61 mouse model of CMT1A. We reduced PTEN by about 50% selectively in CMT1A Schwann cells by crossbreeding Pmp22 transgenic mice with floxed Pten and Dhh-cre mice, yielding PTENfl/+Dhhcre/+PMP22tg experimental mutants (Figure 5b). Western blot analyses of sciatic nerve lysates confirmed the increase of PTEN in PMP22tg mice and the reduction of PTEN in the double mutants (Figure 5c, d).”
Moreover, regarding the PTEN expression we added Western Blot analysis and quantification in Figure 5c, d showing increased PTEN expression in the C61 mouse model of CMT1A and decreased PTEN in the PTENhKOxC61 double mutants. Further analysis of the downstream signaling is planned (see “planned revision”).
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*PTEN, Akt-mTOR expression/activation levels should be checked biochemically also in this model. And quantified (panel c). * Response: We added an explanation for the use of the C61 mouse model (see point Figure 5.1 above). Moreover, we quantified the Western Blot analysis and added it in Figure 5d. The expression of PTEN was included in the Western Blot analysis (Figure 5c) showing increased PTEN expression also in the C61 mouse model. Further biochemical analysis of the C61 mouse model is planned (see “planned revision”).
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*In panel d overactivation of mTOR (PS6 staining) in Schwann cells is not evident. * Response: We agree with the Reviewer that the way the image was displayed is not sufficient to show P-S6 activation in the double mutants. We have now split the image (Figure 5e) to better visualize the P-S6 staining alone compared to the co-staining with P0 (marker for compact myelin) and DAPI (nuclei). Further, we added staining of wildtype nerve. We hope this way the differences in P-S6 activation can be easier appreciated.
Figure 6:
*G-ratio analysis: which are the mean values (numbers) with SEM in the three groups analyzed wildtype, Pmp22tg and Pmp22tg; Pten fl/+; Dhh-Cre? *
Response: We thank the Reviewer for pointing this out. We added the quantification of the mean g-ratios in Figure 6d, f.
Figure 7:
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*If more fibers are committed to myelinate in the double mutant as compared to the single Pmp22tg at P18 ,particularly, it is unclear why there is no difference in differentiation marker expression in Figure 7 (Oct6 and Hmgcr). * Response: We thank the reviewer for this comment. We do not necessarily expect to see a strong difference in the expression of differentiation markers given the mild increase in myelination in the double mutants. Similarly, we do not observe alterations in the expression of differentiation markers in HNPP mice, while these fibers produce more myelin. Therefore, we concluded that alterations in PTEN-PI3K/Akt/mTOR signaling do not influence differentiation in the mouse models while in the PMP22 overexpressing situation of CMT1A other mechanisms alter differentiation of the Schwann cells. We also note that experiments were performed at postnatal day 18 and we cannot rule out possible alterations in differentiation marker expression at earlier time points in development in the double mutants.
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In conclusion, the correlation between PMP22 and PTEN is a potential interesting observation. However, in my opinion, experiments as shown don't support the conclusion that PMP22 controls PTEN expression level and activity, which is suggested at the basis of the pathogenesis of PMP22 dosage-related neuropathies. Response: Please also see section 1. In order to avoid any overstatement that "PMP22 controls PTEN expression level and activity", in our revised version we have clarified this point and changed the wording in the main text:
"The mechanisms that link the abundance of PMP22 to that of PTEN are still unclear and we here neither show direct nor indirect control of PTEN expression by PMP22." (Page 8)
Reviewer #2:
- Regarding in the Introduction: "...the molecular mechanisms causative for the abnormal myelination remain largely unknown and still no therapy is available." Suggest consider modifying to perhaps: '...no small molecule or pharmacological therapeutic intervention exist.' To say "no therapy" exist is 'myopic' and untrue.
*Suggest adding question mark to end of sentence or changing ‘asked’ to “investigated” for following thought: “Here, we asked whether PI3K/Akt/mTOR signaling provides therefore a therapeutic target to treat the consequences of altered Pmp22 gene-dosage.” *
Rather than attempt to establish PRIORITY perhaps ‘softening’ the INTRODUCTION concluding statement “Our results thus identify a potential pharmacological target for this inherited neuropathy.
[This makes thePI3K/Akt/mTOR pathway a promising target for a preventive treatment of affected nerves also in human patients.] *Does this belong in RESULTS? Or rather DISCUSSION? *
Response: We thank the Reviewer for the suggestions. We changed the sentences accordingly in the manuscript (1.: Page 3, Line 23; 2.: Page 3, Line 26; highlighted in green). Regarding point 3, we are convinced that identifying pharmacological targets for peripheral neuropathies should be given priority. Indeed, the aspect concerning point 4 is already highlighted in the discussion therefore we removed the sentence from the result section.
Reviewer #3:
*The abstract for this paper, however, presents a somewhat oversimplified conclusion that the PTEN pathway mainly modifies models of HNPP, where the paper clearly demonstrates that models of CMT1A are also affected by this same pathway. This should be clarified. *
We agree with the Reviewer that the same pathway (PI3K/Akt/mTOR) also affects CMT1A, but it is of importance for us to highlight that the disease mechanisms are -at least partly- different between HNPP and CMT1A. This is supported by our observation that PTEN reduction in CMT1A only transiently improves myelination in vivo (Figure 6) and the persistent alteration of differentiation markers despite PTEN reduction, which is not observed in HNPP (Figure 7). For clarification we have altered the wording in the abstract which now reads: "In contrast, we found that CMT1A pathogenesis was only transiently ameliorated by altered PI3K/Akt/mTOR signaling, which drives radial but not longitudinal growth of peripheral myelin sheaths".
3. Description of analyses that authors prefer not to carry out
Reviewer #1:
Figure 1:
*Figure 1, Panel e: may be with this experiment the authors aim to suggest that Pten and Pmp22 are unlikely to interact directly or indirectly since Pten is cytosolic and Pmp22 myelin-membrane enriched. However, this myelin purification shows that Pmp22 as P0 expression levels are also abundant in the cytosol, may be also because P18 has been chosen as time point. What about a different type of membrane-cytosol fractionation experiment and/or another time point? *
Response: We want to clarify that in this experiment not myelin and cytosol fractions were separated but myelin and whole sciatic nerve lysate (which is the input before isolation of the myelin fraction, called “lysate”). Therefore, the analysis aimed at showing an enrichment of PMP22 and P0 in the myelin fraction while PTEN and TUJ (as a control) are not, which makes it more unlikely for PTEN and PMP22 to interact directly. This experiment, together with the immunohistochemical analysis in Figure 1h should highlight the location of PMP22 and PTEN in the Schwann cell. Together with the newly suggested experiments of the Reviewer for Figure S1 (see planned Revision point 1) we do not see the need for extra membrane-cytosol fractionations and/ or another timepoint as the more detailed as the improved experiment on protein-protein interaction using nerve lysate (not only cell culture) is the experiment of choice to clarify whether we have a direct interaction or not.
Regarding in vitro Schwann cell- DRG co-culture experiments:
(Figure 2, Figure 4 and Figure 5e)
- *(Figure 2) For this experiment, pulse treatment may be beneficial rather than in continuous. Is Akt-mTOR phosphorylation-signaling increased also in Pmp22+/- co-cultures as in mutant nerves? Is the treatment reducing the overactivation? *
- *(Figure 4) Similarly to Figure 2, is PTEN level increased in Pmp22tg cultures along with Akt-mTOR downregulation? *
- *(Figure 5) Panel e: co-cultures are established using ex vivo Dhh-Cre recombination. The downregulation of Pten in the cultures should be documented. * Response: We agree with Reviewer #1 that a deeper analysis of the co-culture system regarding the downstream signaling of PTEN would increase the value of the experiments. Unfortunately, the experiments were designed in a very small scale with the intention of only evaluating myelin alterations on a histological level and we did have enough tissue to collect cells for deeper protein expression analysis. Moreover, we tried to use the co-culture system as a proof-of-principle experiment in parallel to our in vivo studies which we value more important due to the still quite artificial co-culture setup. We hope that the Reviewer can understand our approach with the focus we set on the in vivo work.
Figure 3:
- *The RAPA treatment seems to increase Pten level in the mutant even above wildtype levels (panel b), which can result in decreased myelin thickness due to downregulation of Akt-mTOR. A different method to normalize expression levels should be used. * Response: Comparing the mean, relative expression levels resulting from our quantification as plotted in the graph (panel b) revealed no increase above wildtype level after Rapamycin treatment in the HNPP mouse. Further, we decided for whole protein staining as the superior approach to loading control because we have observed alterations in the expression of other frequently used “housekeepers” such as GAPDH, Actin and Vinculin in the CMT1A rodent models.
*Panel c-e: Can these data also be reproduced in quadriceps nerves as tomacula are more prominent in these Pmp22+/- nerves showing less variability due to the prevalence of large caliber axons? *
Response: Unfortunately, quadriceps nerves were not collected for histology in the experiment and therefore we cannot redo the quantification. Nevertheless, we agree that the quadriceps nerves have less variability than the sciatic nerve and will definitely include the tissue in our future experiments.
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Referee #3
Evidence, reproducibility and clarity
In this paper Sareda and co-workers demonstrate that the PTEN/mTOR pathway is indirectly involved in regulating myelin thickness and wrapping in models of altered PMP22 gene dosage both in vitro and in vivo. Inhibition of this pathway decreases myelin thickness in models of HNPP, while increasing myelin thickness in models of CMT1A. The evidence for these conclusions is complex but reasonably presented, and the conclusions mainly supported by the data. The abstract for this paper, however, presents a somewhat oversimplified conclusion that the PTEN pathway mainly modifies models of HNPP, where the paper clearly demonstrates that models of CMT1A are also affected by this same pathway. This should be clarified.
Significance
These data are significant, since they would provide new targets for treating inherited neuropathy associated with altered PLP22 gene dosage.
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Referee #2
Evidence, reproducibility and clarity
This study investigates the modulation, both genetically and pharmacologically, of the PI3K/Akt/mTOR signaling in preclinical animal models for the inherited peripheral neuropathies HNPP and CMT1A. These conditions result from a gene dosage abnormality of the peripheral myelin protein gene PMP22. The exact biological molecular mechanisms remain enigmatic despite it having been over 30 years since the major genetic lesions, the CMT1A duplication and HNPP deletion, were described. With respect to myelin biology one observes focally slowed nerve conduction at pressure palsies and local/segmental hypermyelination in HNPP whereas hypomyelination occurs in CMT1A.
The study is nicely conducted, data illustrations very informative, and writing clear and concise. This paper will likely be of great interest to your readers. A few things the authors may want to consider:
- Regarding in the Introduction: "...the molecular mechanisms causative for the abnormal myelination remain largely unknown and still no therapy is available." Suggest consider modifying to perhaps: '...no small molecule or pharmacological therapeutic intervention exist.' To say "no therapy" exist is 'myopic' and untrue.
- Suggest adding question mark to end of sentence or changing 'asked' to "investigated" for following thought: "Here, we asked whether PI3K/Akt/mTOR signaling provides therefore a therapeutic target to treat the consequences of altered Pmp22 gene-dosage."
- Rather than attempt to establish PRIORITY perhaps 'softening' the INTRODUCTION concluding statement "Our results thus identify a potential pharmacological target for this inherited neuropathy.
- [This makes thePI3K/Akt/mTOR pathway a promising target for a preventive treatment of affected nerves also in human patients.] Does this belong in RESULTS? Or rather DISCUSSION?
Significance
The authors provide convincing evidence that the HNPP pathobiology is ameliorated by PI3K/Akt/mTOR inhibitors. Interestingly they found radial myelin growth was most affected by this approach and suggest an interesting transdermal approach in injured nerves in the acute prevention of pressure palsies.
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Referee #1
Evidence, reproducibility and clarity
In this paper the authors report a direct correlation between PMP22 and PTEN expression levels in the nerve of CMT mutants. In CMT1A Pmp22tg rat nerves, PTEN levels are increased, whereas in Pmp22+/- mutants, a model of the HNPP neuropathy, PTEN levels decrease. Consistent with this, Pmp22tg nerves display lower Akt phosphorylation and, viceversa, Pmp22+/- nerves have higher Akt phosphorylation. The authors lowered PTEN in the transgenic and inhibited mTOR using Rapamycin in the Pmp22+/- to support the functional relevance of the PMP22-PTEN correlation.
I have major concerns on the data as shown, which, in my opinion, don't support the main conclusion of this paper. In more detail:
Figure 1 Panel a: the decrease of Pten expression should be quantified with at least n=3 taking into account the variability among different samples at the different time points indicated (the same applies in panel b, even if here the increase of Pten expression level in Pmp22tg nerves is more evident) Panel a and b: the statement that Pten is more expressed at P18 at the peak of myelination in wildtype nerves is not supported by the blots as shown
Figure S1, page 4: what does it mean "in line with this finding we were unable to detect protein-protein...". May be the authors meant: since there is a direct correlation between Pmp22 and Pten expression levels in the mutants, the authors explored the possibility of an interaction between the two. Regarding the co-IPs, in panel a, the co-IP at the endogenous level, the immunoprecipitation efficiency of PMP22 is very low. May be a pull-down experiment using either exogenous purified PMP22 or PTEN and nerve lysates can help to rule out the possibility of an interaction. The experiments in b, c are performed in overexpression in a heterologous system (293 cells).
Panel e: may be with this experiment the authors aim to suggest that Pten and Pmp22 are unlikely to interact directly or indirectly since Pten is cytosolic and Pmp22 myelin-membrane enriched. However, this myelin purification shows that Pmp22 as P0 expression levels are also abundant in the cytosol, may be also because P18 has been chosen as time point. What about a different type of membrane-cytosol fractionation experiment and/or another time point?
Page 4: "Taken together, Pmp22 dosage inversely correlates with the abundance of PTEN...": please revise this statement
Additional experiments are needed to support the conclusion of Figure 1 that, in the two mutants, Pten levels reversely correlate with PI3K-Akt-mTOR pathway activation, which represents the rationale of all further experiments. For example, it should be shown systematically in both mutants both Akt and ERK phosphorylation levels (Akt at both T308 and S473), and mTOR activity read outs. In the previously published paper (Fledrich et al.) only increased Akt phosphorylation in Pmp22+/- nerves was reported, whereas Pmp22tg analysis was focused on the interdependence between Akt and ERK without exploring mTOR activation, which is relevant here.
Figure 2 The aberrant myelin figures displayed are similar to myelin ovoids preceding degeneration rather than myelin outfoldings. It is also strange that these alterations are in the wildtype cultures treated with RAPA, that instead, in this system, has been reported to increase myelination as it improves protein homeostasis (autophagy, quality control, etc). Also for this experiment, pulse treatment may be beneficial rather than in continuous. Is Akt-mTOR phosphorylation-signaling increased also in Pmp22+/- co-cultures as in mutant nerves? Is the treatment reducing the overactivation?
Figure 3 The RAPA treatment seems to increase Pten level in the mutant even above wildtype levels (panel b), which can result in decreased myelin thickness due to downregulation of Akt-mTOR. A different method to normalize expression levels should be used. Panel c-e: aberrant fibers should be normalized on total number of fibers and on the area, particularly because RAPA is used. Can these data also be reproduced in quadriceps nerves as tomacula are more prominent in these Pmp22+/- nerves showing less variability due to the prevalence of large caliber axons?
Figure 4 A different model, the C61 mouse a Pmp22tg overexpressing PMP22 is used here (rather than the CMT1A rat). This should be explained in the results. Is also this model characterized by increased Pten levels in the nerve? And low Akt-mTOR activation for instance?
The improvement in the number of myelin segments following PTEN inhibition in Pmp22tg co-cultures is very weak.. The 500 nM has instead a consistent effect in reducing myelin segments in the wildtype and I think that these results overall don't support the conclusion that myelination is ameliorated by reducing PTEN activity in Pmp22tg co-cultures. Similarly to Figure 2, is PTEN level increased in Pmp22tg cultures along with Akt-mTOR downregulation?
Figure 5 As for Figure 4, the use of the mouse transgenic instead of the CMT1A rat should be specified and PTEN, Akt-mTOR expression/activation levels should be checked biochemically also in this model. And quantified (panel c). In panel d overactivation of mTOR (PS6 staining) in Schwann cells is not evident. Panel e: co-cultures are established using ex vivo Dhh-Cre recombination. The downregulation of Pten in the cultures should be documented. Pten Fl/+ Dhh-Cre cultures seem to have axonal fasciculation.
Figure 6 G-ratio analysis: which are the mean values (numbers) with SEM in the three groups analyzed wildtype, Pmp22tg and Pmp22tg; Pten fl/+; Dhh-Cre? How is Akt-mTOR signaling in the double mutant as compared to Pmp22tg? Is that increased at P18? If more fibers are committed to myelinate in the double mutant as compared to the single Pmp22tg at P18 ,particularly, it is unclear why there is no difference in differentiation marker expression in Figure 7 (Oct6 and Hmgcr).
Significance
In conclusion, the correlation between PMP22 and PTEN is a potential interesting observation. However, in my opinion, experiments as shown don't support the conclusion that PMP22 controls PTEN expression level and activity, which is suggested at the basis of the pathogenesis of PMP22 dosage-related neuropathies.
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Reply to the reviewers
Reviewer 1:
- *
I am not an expert in algal ultrastructure or TEM, and I principally found this manuscript informative and well-written. I can only make minor recommendations, although I urge the Editors to supplement this review with others from specialists within the field.
We thank the reviewer for their positive comment on the manuscript as well as for the interesting following discussion points.
To date, to my knowledge, the Pentapharsodinium chloroplast has not been characterised at a molecular level. The placement of the nuclear lineage within the Peridiniales as a close relative or Durinskia spp. and other dinoflagellates with diatom endosymbionts raises the question of whether Pentapharsodinium possesses a peridinin-containing chloroplast, per most other dinoflagellates, or possesses a chloroplast of an alternative endosymbiotic derivation, although I suppose the single chloroplast would be typical for peridinin-containing chloroplasts from the Peridiniales. __Can the authors make any inference on this from their data? __
In the course of this study, we did not perform molecular characterization of the chloroplast. Therefore, formal answer to this question would not be possible based on our data.
However, we have evidence that suggest that the chloroplast from P. tyrrhenica does not come here from a diatom endosymbiont.
1/ In the suggested cases of diatom endosymbiont origin of the chloroplast, eg in the genus Durinskia, Yamada et al., 2019 show that Durinskia capensis or Durinskia kwazulunatalensis can present diatom organelles such as chloroplast, mitochondria, nucleus, or their remnants for a certain amount of time after uptake. The detailed analysis of our vEM data does not show these diatom organelles, thus ruling out the hypothesis.
2/ Additionally, the eyespot of dinoflagellate species possessing diatom association seems to be characteristic to these cells (Horiguchi., 2007, Hoppenrath et al., 2017). In our case, the eyespot of P. tyrrhenica corresponds to a distinct type (type IA, thought to be in peridinin dino-chloroplast according to Hoppenrath., 2017), that varies from the one observed in Durinksia baltica, or other dinoflagellates species possessing these diatom associations (Horiguchi., 2007, Hoppenrath et al., 2017).
Therefore, the chloroplast of our cell does not seem to be acquired from a diatom. However, as we don’t have molecular evidence at this stage, and are not able to perform such analyses, we prefer not to address this question in the manuscript.
While I appreciate that this is a study of a single cell only, I would prefer some more extensive evidence that the partial chromosome unfolding identified correlates to transcriptional activity. The nucleolus is surrounded by a layer of heterochromatin and perhaps the filamentous structures involved are transcriptionally quiescent. Were the authors able to take any preliminary images of cells harvested mid-day or exposed to higher light intensities, and do they see greater chromatin unfolding in this case? Similarly I would be curious if cells visualised later in the day possess multiple rather than single chloroplasts.
Our description is here based on one microorganism of very low abundance, and we do not have data for it across conditions. Therefore, we would not be comfortable inferring too many details in this paper and will thus modify the text in order be more careful about the potential role of these fibres and their putative association to transcription. We are planning to address this interesting point in the future, in a more biologically focused paper in preparation. Nevertheless, similar structures have been described protruding in the nucleoplasm in other species (Soyer., 1981, Bhaud et al., 2000, Decelle et al., 2021) and have been suggested to be associated to RNA transcription (Sigee., 1983). We will make sure to cite and discuss more literature on this point.
Line 39: should be "dinoflagellate biology" <br /> Line 131: "a far red signal" <br /> Line 196: should be "number of chloroplasts"
We thank the reviewer, the errors will be corrected in the revised version.
Line 200: by curiosity, how does the measured chloroplast volume compare to those computed in vEM studies of Symbiodinium (c.f., Uwizeye 2021)
The measured chloroplast volume in our cell differs to those computed in Symbiodinium in Uwizeye et al., 2021. Indeed, in Uwizeye et al., 2021, the chloroplast represents 30% of the cell volume. This is more than in our cell for which the chloroplast represents only 9.5% of the cell volume. However, these two cells are different species and come from different growth conditions (culture VS environment). These factors potentially contribute to morphometric variations as the environment possesses different types and amounts of nutrients and light resources.
Line 221: how does the number of observed chromosomes compare to estimated chromosome numbers in dinoflagellates from karyotyping or whole-genome sequencing?
To our knowledge, there is a wide variability in terms of number of chromosomes observed between dinoflagellate species. Bhaud et al (2000) reports the presence of between 4 and 200 chromosomes depending on the species. Unfortunately, we did not find information concerning the number of chromosomes for this species to compare with our analysis. However, we are planning in the revised version to discuss how our workflow is complementary to other methods such as genomics, transcriptomics and light microscopy towards better understanding of marine organism.
Line 240: does the eyespot show any proximity to the mitochondria, as per the hybrid chloroplast/ mitochondrial-derived eyespots found in Warnowiacean dinoflagellates?
In our study the eyespot is composed of pigment globules, organized as a sheet, located inside the chloroplast and facing towards the theca. This type of arrangement seems to be distinct to the chloroplast/mitochondria-derived eyespot described in Warnowiaceae (Colley and Nilson., 2016). Additionally, for the revision of this paper we will further segment the mitochondria to investigate any potential proximity to the eyespot. We will also put the eyespot of P. tyrrhenica in context with other types described in the literature.
Reviewer #1 (Significance (Required)):
The application of vEM to environmental algal samples has to my knowledge not been attempted previously. If these approaches could be scaled up to a multi-cell approach and is not completely destructive to the cells, it could provide a fascinating way to connect algal morphology in the wild to other culture-free methods to understand algal biology (e.g., meta-barcoding).
We would like to address the interesting comment concerning the possibility to combine vEM along with other molecular tools to study organisms from a culture free method. While FIB-SEM is a destructive method, it could be used in combination with other methods and thus synergize towards the characterization of the environmental samples. We will add a few sentences in the discussion, as a forward look, as it represents indeed an important axis of our future research, where molecular taxonomy (e.g. metabarcoding) will be correlated to morphological characterisations. We believe such approaches will be useful beyond the study of micro-algae, and will make this point clear in the discussion.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This is avery important and interesting contribution to the ultrastructural analysis of one scientific species among many others living together in a rich sample as a marine microplankton. In addition the work also shows the possibility to obtain fundamental volumetric informations on the various structures and organelles. The work was well planned and executed and certainly represents a tremendous effort of the members of the group. It is clearly written, explaining each detail of the methodology necessary to the understanding of the whole work.
Reviewer #2 (Significance (Required)):
This a phantatis piece of scientific work where the authros were able to use the moderns three dimensional reconstruction technique possible using high resolution scanning electron to reconstruct one specific cell in a large population of heterogenous cells. The identification of one specific cell based on fluorescence images detected in a light microscopy was very important to present one new methodology to observed such types of cells. In addition to a detailed description of the strucutres and organelles found they were able to determne the area/volume occupies by each of them in cells. <br /> Therefore I strong recommend the acceptance of the manuscript as it is.
We thank the reviewer for their appreciation of our work.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
# Summary --
The authors present a workflow to characterize a microorganism, dinoflagellate cell, from environmental samples by vEM. The workflow enables the identification of specific taxa of interest in a heterogenous environmental sample and allows correlative fluorescence and FIB-SEM acquisition. I see no major flaws with this study. The authors present a proof of principle pipeline for utilizing CLEM to explore the ultrastructure of microorganisms.
# Major comments --
What is not clear, is how highly reproducible this workflow is. For example, what is the time required for each sample? How manual vs automated is each step? While, the workflow is important and sound, it would be helpful for the reader to understand a little more about the variability and throughput. It doesn't need to be exhaustive, but further characterization of the workflow would significantly improve the impact of the manuscript and should be included. As a reader, it would be tremendously helpful to clearly state what is part of the preparation/imaging workflow and what is an application example of the workflow. If it is intended that the post-processing is also part of the workflow, there should be significantly more details provided into the segmentation and analyses processes.
We thank the reviewers for pointing this out. This workflow has been applied to several blocks and repeated imaging of a subset of species could be achieved with 100% success. We will show this in a follow up paper. Moreover, the same method has been applied for targeted FIB-SEM acquisition of samples expressing fluorescent proteins (Ronchi et al., 2021). Therefore, we believe our workflow is highly reproducible and robust. We are planning to address the concern on technical variability in our revised version, together with a better explanation of time scales and automated VS manual aspect of the steps that are used for this workflow. We will also add information concerning the segmentation.
# Minor comments --
As this study describes a workflow that was developed for identification and imaging of microorganisms, it would be highly beneficial to the reader to have a figure that shows each of the steps, end to end.
We thank the reviewer for this suggestion. We plan to add a supplementary figure that describes the workflow step by step.
It would also be helpful to add annotation labels and a scale bar to supplemental video 2.
We thank the reviewer for spotting this missing information. We plan to add annotation labels as well as a scale bar to the video S2.
Do the nonphotosynthetic species also have nuclear autofluorescence or is this just a trait of a subset of the photosynthetic species?
We thank the reviewer for this question. From our analysis, we can’t conclude that it is a trait of some photosynthetic species compared to non-photosynthetic ones. However, heterogeneous autofluorescence profiles, even though unexplained, represent a valuable tool to discriminate between cell types. We would like to further address this point in the manuscript that such fluorescence profiles, even though unexplained, can contribute to the identification of specific cell types.
Reviewer #3 (Significance (Required)):
# General assessment --
The authors present an important, yet missing, workflow for characterizing microorganisms from environmental samples using vEM and CLEM. A strength of the study is that it enables identification and selection of taxa in heterogenous samples. Using correlative, confocal imaging of both auto-fluorescence and transmitted light, the authors show that specific taxa can be identified and selected for according to the organism's photosynthetic and morphological properties.
We thank the reviewer for their positive comments.
To improve upon this, it could be useful for the authors to provide a list or table of potential organisms that could be selected for in this manner to exemplify the use cases of this protocol.
We thank the reviewer for their suggestion. We plan to add a supplementary figure with a gallery of different cells and their identification.
# Advance --
This study, while a proof of concept, is also one of the first examples of using vEM on environmental studies. The author's also present the potential value add of using CLEM, not just for selection purposes, but also more comprehensive identification and mapping of subcellular structures. While the workflow itself is incremental (and important), the application is quite novel.
# Audience --
Because the authors' study combines a vEM workflow and microorganism characterization, the potential audience is broad reaching. The workflow itself could be adapted to many different systems - beyond microorganisms - making it of use to several biological fields.
We thank the reviewer for their positive comments.
# Expertise --
vEM, CLEM, FIB-SEM, membrane trafficking, cell biology, tissue cell biology, machine learning
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Referee #3
Evidence, reproducibility and clarity
Summary
The authors present a workflow to characterize a microorganism, dinoflagellate cell, from environmental samples by vEM. The workflow enables the identification of specific taxa of interest in a heterogenous environmental sample and allows correlative fluorescence and FIB-SEM acquisition. I see no major flaws with this study. The authors present a proof of principle pipeline for utilizing CLEM to explore the ultrastructure of microorganisms.
Major comments
What is not clear, is how highly reproducible this workflow is. For example, what is the time required for each sample? How manual vs automated is each step? While, the workflow is important and sound, it would be helpful for the reader to understand a little more about the variability and throughput. It doesn't need to be exhaustive, but further characterization of the workflow would significantly improve the impact of the manuscript and should be included.
As a reader, it would be tremendously helpful to clearly state what is part of the preparation/imaging workflow and what is an application example of the workflow. If it is intended that the post-processing is also part of the workflow, there should be significantly more details provided into the segmentation and analyses processes.
Minor comments
As this study describes a workflow that was developed for identification and imaging of microorganisms, it would be highly beneficial to the reader to have a figure that shows each of the steps, end to end.
It would also be helpful to add annotation labels and a scale bar to supplemental video 2.
Do the nonphotosynthetic species also have nuclear autofluorescence or is this just a trait of a subset of the photosynthetic species?
Significance
General assessment
The authors present an important, yet missing, workflow for characterizing microorganisms from environmental samples using vEM and CLEM. A strength of the study is that it enables identification and selection of taxa in heterogenous samples. Using correlative, confocal imaging of both auto-fluorescence and transmitted light, the authors show that specific taxa can be identified and selected for according to the organism's photosynthetic and morphological properties. To improve upon this, it could be useful for the authors to provide a list or table of potential organisms that could be selected for in this manner to exemplify the use cases of this protocol.
Advance
This study, while a proof of concept, is also one of the first examples of using vEM on environmental studies. The author's also present the potential value add of using CLEM, not just for selection purposes, but also more comprehensive identification and mapping of subcellular structures. While the workflow itself is incremental (and important), the application is quite novel.
Audience
Because the authors' study combines a vEM workflow and microorganism characterization, the potential audience is broad reaching. The workflow itself could be adapted to many different systems - beyond microorganisms - making it of use to several biological fields.
Expertise
vEM, CLEM, FIB-SEM, membrane trafficking, cell biology, tissue cell biology, machine learning
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Referee #2
Evidence, reproducibility and clarity
This is avery important and interesting contribution to the ultrastructural analysis of one determoned species among many others living together in a rich sample as a marine microplankton. In addition the work also shows the possibility to obtain fundamental volumetric informations on the various structures and organelles. The work was well planned and executed and certainly represents a tremendous effort of the members of the group. It is clearly written, explaining each detail of the methodology necessary to the understanding of the whole work.
Significance
This a phantatis piece of scientiic work where the authros were able to use the moderns three dimensional reconstruction technique possible using high resolution scanning electron to reconstruct one specific cell in a large population of heterogenous cells. The identification of one specific cell based on fluorescence images detected in a light microscopy was very important to present one new methodology to observed such types of cells. In addition to a detailed description of the strucutres and organelles found they were able to determne the area/volume occupies by each of them in cells. Therefore I strong recommend the acceptance of the manuscript as it is.
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Referee #1
Evidence, reproducibility and clarity
I am not an expert in algal ultrastructure or TEM, and I principally found this manuscript informative and well-written. I can only make minor recommendations, although I urge the Editors to supplement this review with others from specialists within the field.
To date, to my knowledge, the Pentapharsodinium chloroplast has not been characterised at a molecular level. The placement of the nuclear lineage within the Peridiniales as a close relative or Durinskia spp. and other dinoflagellates with diatom endosymbionts raises the question of whether Pentapharsodinium possesses a peridinin-containing chloroplast, per most other dinoflagellates, or possesses a chloroplast of an alternative endosymbiotic derivation, although I suppose the single chloroplast would be typical for peridinin-containing chloroplasts from the Peridiniales. Can the authors make any inference on this from their data?
While I appreciate that this is a study of a single cell only, I would prefer some more extensive evidence that the partial chromosome unfolding identified correlates to transcriptional activity. The nucleolus is surrounded by a layer of heterochromatin and perhaps the filamentous structures involved are transcriptionally quiescent. Were the authors able to take any preliminary images of cells harvested mid-day or exposed to higher light intensities, and do they see greater chromatin unfolding in this case? Similarly I would be curious if cells visualised later in the day possess multiple rather than single chloroplasts.
Finally, I have a few (very) small grammatical corrections:
Line 39: should be "dinoflagellate biology"
Line 131: "a far red signal"
Line 196: should be "number of chloroplasts"
Line 200: by curiosity, how does the measured chloroplast volume compare to those computed in vEM studies of Symbiodinium (c.f., Uwizeye 2021)
Line 221: how does the number of observed chromosomes compare to estimated chromosome numbers in dinoflagellates from karyotyping or whole-genome sequencing?
Line 240: does the eyespot show any proximity to the mitochondria, as per the hybrid chloroplast/ mitochondrial-derived eyespots found in Warnowiacean dinoflagellates?
Significance
The application of vEM to environmental algal samples has to my knowledge not been attempted previously. If these approaches could be scaled up to a multi-cell approach and is not completely destructive to the cells, it could provide a fascinating way to connect algal morphology in the wild to other culture-free methods to understand algal biology (e.g., meta-barcoding).
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
The authors of this study utilize a novel nanobody-based technique to specify the location of the SPT complex to either the peripheral or nuclear membrane-associated endoplasmic reticulum membranes. Considering the potential importance of sub-ER compartmentalization on metabolic enzymes of the ER, this is a novel and useful approach. The studies are, with the minor exceptions noted below, comprehensive and very well executed and documented. The authors have combined genetic, proteomic, lipidomic, and flux experimental approaches to test whether sub-ER compartmentalization affects the function and regulation of the SPT complex. The results are, for the most part, negative, although there does seem to be some effect on the overall activity of the SPT complex as measured with flux analysis. Overall, while the authors do not detect dramatic effects on SPT complex localization, the technical advance using tethered nanobodies to direct complex localization, and the complementary approaches to testing SPT function and regulation, will be useful to workers in the sphingolipid field.
Minor points:
The results with YPK1-linker-CAAX are confusing. This construct does not result in Orm2 phosphorylation with heat shock, whereas endogenous YPK1 does. Yet it can support viability even without Orm deletion. In other words, this tethered construct appears functional in viability assays, but not in a biochemical assay.This discrepancy is not discussed by the authors. The manuscript would be improved by a discussion by the authors that addresses this issue. It is not clear why the figure legend to Figure 2 suggests that Ypk1 regulates Orms mainly in the peripheral ER. Considering that WT Ypk1 is more efficient than CAAX tethered YPK1, this statement does not seem supported. Perhaps the authors can elaborate on how they came to this conclusion.
The figures depicting Orm phosphorylation (Figure 1e, f Figure 2d,e, Figure 6 b,c) should be improved. The resolution of two forms is not sufficient in Figure1 and 2. The use of Phos-Tag might solve this issue. It would be helpful to the reader to include arrows that indicate the phosphorylated and unphosphorylated forms of Orm. Quantitation of these gels is essential.
Lines 318 and 319. Figure 6e and 6f are referred to. The correct assignment is 6f and 6g.
Referees cross-commenting
I agree with Reviewer #2's assessment that some of the conclusions are over stated. While Reviewer #2 is correct that the advances in this manuscript are modest, this is principally because expected differences in the function and regulation of the SPT in different ER sub-domains did not materialize. This may be disappointing, but is still important to document
Significance
This is a very well performed study, utilizing a variety of approaches to test whether localization of the SPT complex impacts on it activity and regulation. With very minor exceptions, it is well executed and documented.
The advances reported here are two-fold. First, the authors introduce a novel approach using nanobodies that are tethered to distinct regions of the yeast endoplasmic reticulum to localize intact and unmodified complexes to distinct locations. This could be a very useful tool in other contexts to examine the role of subcellular compartmentalization in the function of enzymes and signaling components. This targeting system is well characterized in this study. The second advance, utilizing this targeting system, is that localization of the SPT complex to distinct subcompartments of the ER has minimal effects on regulation, and observable, but relatively minor effects on SPT function in terms of sphingolipid production. While a positive result would have been more exciting, negative results can be equally informative.
This study will be of interest to workers in the signaling and metabolic fields that may utilize this unique targeting strategy. It will also be of interest to the sphingolipid community.
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Referee #2
Evidence, reproducibility and clarity
This manuscript uses a combination of immunoblotting, microscopy, and MS-based lipidomics to study sphingolipid synthesis in S. cerevisiae. The authors recently published a paper demonstrating that exogenous serine taken from the medium is preferentially used to generate LCBs. Given multiple levels of regulation of the SPT complex, the authors postulate that SPT in different sub-compartments of the ER could be differently regulated or at least have variable activities. Using a nanobody capture approach, they can restrict SPT activity to the peripheral ER and nuclear ER. Using this model, they investigate the role of Orm phosphorylation and SPT activity in response to 5-min heat shock. Phosphorylation does not seem to be a key element of the regulation. Ultimately, this is a collection of experiments without a clear story. In the end, the only take-home message I can find is that peripheral SPT is able to use exogenous heavy Serine as a substrate better than nuclear SPT.
Page 4. "We had previously demonstrated that increased de novo LCB biosynthesis is directly dependent on the uptake of exogenous serine through the general amino acid permease Gnp1. Consistent with our previous findings, deletion of GNP1 resulted in a blunted heat shock response, while deletion of the endogenous serine biosynthesis pathway (ser2) had no effect on LCB biosynthesis". This claim is too strong. If they feel this strongly, the authors should test a gnp1 agp1 double mutant. The alternative explanation is that to support the rapid increase in SPT activity, the Lcb1-Lcb2 enzyme use both a pre-existing cytoplasmic pool of serine and exogenous serine.
If cells are grown for 1 generation with heavy serine to label the cytoplasmic pool of serine and then cells as shifted to serine-free media and heat shock is induced is there any difference in LCB synthesis between allSPT, nSPT and pSPT?
It would be worth re-doing some of these experiments in rtn1 rtn2 yop1 yeast triple mutant (Stefan...Emr Cell 2011) or the delta super-tether mutant (Quon...Menon PLOS Biology 2016) both of which have substantially less cortical ER.
The authors make strong claims that are not supported by the data. Further, the presentation of the data is not optimal, and much of the data is qualitative, not quantitative. Too much of the data is presented as fold-change and i believe that the base-line LCB levels may change. The raw data should be included in a supplement excel file.
The blots of FLAG-tagged Orm1 and Orm2 are a critical part of this manuscript, but the data is not compelling in most figures. There is a lack of quantitation and replication. On the surface I agree with the authors that the phosphorylation is hard to align with the stimulation, but a more rigorous analysis is needed. Additionally, does the expression of an Orm2 mutant without the Ypk1 phosphorylation sites prevent the heat shock-induced increase in LCBs?
If this remains in the manuscript, the difference between ypk2 Ypk1-CaaX and ypk2 Ypk1-103aa-CaaX should be better highlighted in the main document. However, this whole course of experiments is problematic, and the authors' narrative changes to accommodate the findings; the arguments aren't internally consistent. Ypk is essential to regulate Orms. Ypk1-linker-CaaX can regulate Orms, Ypk1-linker can't increase Orm2 phosphorylation. Furthermore, a single prenyl group is not sufficient to restrict the localization of a protein. If there are other targeting motifs in addition to the last 4 amino acids this should be indicated. Ultimately, this is all negative data. Without a better interrogation of Orm phosphorylation it seems to have little value.
The "Orm proteins mediate SPT upregulation after heat shock" should be re-worded. The orm1 orm2 mutant already has a 25-fold increase in LCBs, and heat shock is unable to stimulate SPT activity any further. It suggests that heat shock is directly inhibiting Orm proteins which in turn removes the feedback inhibition on the SPT.
Figure 7b - the Y-axis scale needs to be corrected. A break should be inserted to alert the reviews of the scale's narrow range.
Is GFP-Orm1 and Orm2 functional? Both are more abundant in the nER than the peripheral.
Most papers I have read use "SL" as the abbreviation for sphingolipids, not SP. Consistency would be nice for readers.
Significance
The work appears technically sound but there are issues with the presentation of the data. Lack of raw data for the lipidomics, lack of quantitation.
The findings here are rather modest and much of the data is inconclusive or suggests certain pathways aren't involved rather than defining a clear mechanism.
This is an incremental finding that supports the 2020 PLOS genetics paper from the same authors. The target audience for this would be people interested in sphingolipid metabolism in yeast.
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Referee #1
Evidence, reproducibility and clarity
This is a nice paper showing that the intracellular location of the SPOTS complex affects SL de-novos synthesis. The experiments are well designed including a comprehensive set of controls. I just have a few minor points: It is hard for me to see the differences in ORM phosphorylation on the blots. Does phosphorylation refer to the more intense upper band (Fig 1e+f)? Given the general variability of WBs it might be better to demonstrate the differences in ORM phosphorylation with a more specific and quantitative method? e.g by a phosphoprotein stain or (better) by targeted (phospho)proteomics.
Significance
Unfortunately, the discussion focusses mostly on technical aspects of the method. However, the biological observations concerning the regulation of the SL metabolism itself are interesting and relevant too. They should be discussed in more detail, which might also be of interest to a more general readership. Concerning the factors involved in regulating SL de-novo synthesis, the authors did not mention CERT which also contributes to the regulation of SL de-novo formation (PMID: 36976648) and connects to SacI which is a component of the SPOTS complex.<br /> Why does yeast have two ORM isoforms and to which extend are they redundant? The authors see functional difference between the two ORM isoforms, which could be discussed in more depth. It might also be interesting to interlink the findings to the mammalian system, which is based on three ORMDL isoforms, and appear not to be regulated by phosphorylation. Another aspect that could be discussed in this contet is the observation that mammalian SPT appears preferentially located at MEM contact sites, which indicates a special role of SL de-novo synthesis at this location (PMID: 34785538). However, overall this is a well done paper.
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Reply to the reviewers
Reply to Reviewers
We thank both Reviewers for their comments which we believe could greatly improve the manuscript by adding more functional data. Part of the revision process has been already carried out and part of it is ongoing as detailed in the following sections.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Comments to the Authors In this manuscript, Girolamo et al., describe the differences in molecular signature and biological features of MuSC populations between extraocular (EOM) and limb (Tibialis anterior, TA) muscles. Comprehensive approaches including scRNA-seq, bioinformatics, and live-cell imaging reveal that EOM MuSCs is more proliferative in vitro and express ECM components at high levels as well as non-myogenic markers such as Foxc1 and Pdgfrb. The transcription factor network described in this study will characterize the identity of EOM MuSCs, providing insights into stem cell-based therapies for muscle diseases.
Major comments 1. In Figure 1A, EOM-MuSCs appear to form myotubes as efficiently as TA-MuSCs. If so, why myotube formation is not affected in EOM-MuSCs even with lower Myog expression compared to TA-MuSCs? The authors should discuss about this point.
We thank the reviewer for raising this point. Actually, EOM myoblasts show a delay in activation of Myog that allows their sustained expansion, however Myog activation per se is not impaired. This is reflected by the presence of the EOM Differentiating cluster in our sc-RNAseq analysis which converges towards the TA Differentiating cluster and shows co-expression of Myog and canonical differentiation markers such as Myh3 and Troponins (Figure 2C, D). Accordingly, Stuelsatz et al. (Dev Biol 2015) showed efficient and robust differentiation of EOM myoblasts in vitro, while a higher fraction of self-renewed (Pax7+) cells was observed in long term cultures.
To formally address this point, we propose to:
- Isolate EOM and TA MuSCs from Tg:Pax7-nGFP;MyogtdTom mice, plate them at low density and quantify the tdTom+ cell proportions in short vs longer term cultures (D3 vs D5, D8).
- Re-isolate the tdTom+ mononucleated fraction at D4-5 upon activation to:
- plate EOM and TA myoblasts at high density and assess their fusion index. Here, we will evaluated fusion independent of proliferation history and Myog activation.
- perform qRT-PCR for Myomixer/Myomaker on the tdTom+ mononucleated fraction to assess the extent of fusion potential.
- In Figure 2, activated EOM-MuSCs express Myod1e at lower levels compared to TA-MuSCs. Are MyoD protein levels are also lower in EOM-MuSCs?
We have performed quantitation of the intensity of fluorescence for Myod at D3.5 in culture. In agreement with the sc-RNAseq in vitro activated dataset, we have noted lower Myod protein levels in EOM MuSCs. This data is now included as part of Suppl Fig 2A.
In Figure 7E-G: Does the Foxc1 knockdown change the myogenic potential, especially on myotube formation? In addition to FOXC1 and Myog, the mRNA levels of Pax7 and Myod1 would be better to be provided.
Previously, we used siRNA for short term silencing. As siRNA are diluted upon cell division, we now use lentivirus-mediated KD, which provides more consistent results. To address this point, we propose to use lentivirus carrying 3 different shRNAs for Foxc1 and address expression levels of Pax7, Myod, Myog together with EdU detection and assessment of myotube formation. Given that cell density influences myotube formation, we will pre-amplify the cells, replate them at high density, and silence Foxc1 concomitantly with induction of differentiation. These 3 shRNAs have been already validated in vitro and induce a massive reduction in EOM cell numbers. This data is now included as part of __Figure 7E-L. __
Please state a reasonable explanation for the physiological role of high amounts of ECM produced by EOM-MuSCs.
We regret that this explanation did not come across clearly on the manuscript. Previous studies have demonstrated a greater cellular output of cranial MuSCs in clonal assays in vitro (Ono Dev Biol 2010, Stuelsatz et al. Dev Biol 2015; Randolph et al. Stem Cells 2015) and better engraftment capacity in vivo (Stuelsatz et al. Dev Biol 2015). On the other hand, it has been shown that fetal MuSCs, which produce high amounts of extracellular matrix (ECM) cell autonomously, expand more efficiently and contribute more to muscle repair than the adult counterparts (Tierney et al. Cell Reports 2016). Finally, while in vitro expanded MuSCs show a reduced engraftment potential (Montarras et al. Science 2005, Ikemoto et al. Mol Therapy 2007), recapitulation of the endogenous niche ex vivo, allows maintenance of an undifferentiated proliferative state and their capacity to support regeneration in vivo (Ishii et al. Stem Cell Reports 2017). Therefore, we hypothesize that in vitro activated EOM MuSCs secrete high amounts of ECM to self-autonomously maintain stemness when removed from their niche. Alternatively, EOM MuSCs might contribute to connective tissue cells postnatally in vivo as we described previously in the embryo (Grimaldi et al. elife 2022). Secretion of ECM and expression of ECM-related regulons when activated in vivo might recapitulate this process.
To address these hypotheses, we propose to:
- Culture EOM and TA MuSCs, generate EOM and TA decellularized ECM (dECM) and test the proliferation/differentiation potentials of MuSCs on the dECM versus that on wells coated with Matrigel or Fibronectin alone. Shall this experiment not give conclusive results, we propose to assess the proliferation/differentiation potential of TA MuSCs on dishes coated with proteins present in the EOM ECM such as Sparc, Bgn, Mgp, Fbn1, Fn1, for which recombinant proteins exist.
- Assess the expression levels of EOM-specific ECM proteins and TF regulons identified in vitro expressed in EOM MuSCs activated in vivo. To do so we are trying to optimize an EOM injury protocol based on previous observations highlighting the sensitivity of EOMs to anesthetics (Carlson et al. Arch Opthal 1985).
- Use Pax7CreERT2:R26tdTom:PdgfraH2BGFP reporter mice to determine whether EOM MuSCs can give rise to muscle connective tissue postnatally. Minor comments :
On p7, the 3rd line: "marker" is redundant.
This has been corrected.
On p9: what is meant by "force-directed environment"? Is this the aggregation of regulons and targets based on interaction strength determined by the algorithmic arrangement used by pySCENIC?
A force-directed graph is a type of visualization technique where nodes are positioned based on the principles of physics that assign forces among the set of edges and the set of nodes. Spring like attractive forces are used to attract pairs of edges towards each other (connected nodes) while repulsive forces, like those of electrically charged particles, are used to separate all pairs of nodes. In the equilibrium state for this system, the edges tend to have uniform length (because of the spring forces), and nodes that are not connected by an edge tend to be drawn further apart (because of the electrical repulsion). This results in a layout that visually represents the relationships between the nodes, where each node (circle) is an active transcription factor and each edge (distance between nodes) is an inferred regulation between 2 transcription factors.
The text has been changed accordingly.
On p11: add space between (Sambasivan et al., 20009a)and.
Corrected
On p11: 1.7 fold increase → 2.7 fold?
Corrected
On p13: please describe the GRN abbreviation since it is the first used here and not clarified beforehand.
Corrected to gene regulatory network (GRN)
On p15: add space between (Vallejo et al., 2018)Other.
Corrected
On p16: add "a" after "Klf4 is" → Klf4 is a pioneer...
Corrected
In Fig1: it would be better to show the quantification of EdU incorporation, especially at D5 for highlighting the difference between EOM and TA
This has been done and now included Figure 1B.
In Fig1: MF20 staining seemingly describes larger myotubes in EOM compared with TA at D5. Is this most likely due to the higher number of cells to start with in EOM rather than having more fusion ability?
Indeed, as discussed in the Main Comments (question 1), the fusogenic ability of EOM an TA MuSCs will be addressed by plating the same number of cells at high density.
In Fig 2E,F: some font is very faint in color and hard to read in printed format e.g. "TP53 regulates transcription of". May want to change color. In addition, the bottom edge of the Figure is slightly cut off.
We have enhanced the color of some words so all terms are properly seen. The figure has been adjusted in order for the bottom edge to be seen.
In Fig 4D: COLVI should be changed to COLIV.
Corrected
In Fig 5B: Myod1 is redundant.
Actually Myod as regulon (Myod1_(+)) in Fig 5B is not redundant. It is written twice as it is a regulon of both EOM Diff and TA Diff.
In Fig 6D; please specify "Amp". Is Hey1 a myogenic marker?
Amp stands for Activated/Amplified cells. We have changed this to Act to keep consistency.
Hey1 is a bHLH transcription factor that is required in a cell-autonomous manner for maintenance of MuSCs (Noguchi et al. Development 2019). This information has now been added to the text.
In Fig 7F; there are Ctrl and control- please unify them.
Corrected
In Fig7C,D: When does Foxc1 start to be expressed in EOM progenitors in the embryo? If the authors tested, please mention it.
Foxc1 is a DEG and top regulon of EOM progenitors in the early embryo (E11.5, Grimaldi et al. elife 2022). We will formally address this point by immunostaining on tissue sections of E10.5, E12.5 and E14.5 embryos.
In Supp Fig 7A: graphs are lacking color-coded legends.
Corrected
In Supp Fig 7C: Font is very small and illegible in a paper format.
Corrected. For networks it is sometimes difficult to get bigger font size.
In Supp Fig 7F: what are the dynamics of PDGFβ+ cell populations in successive passages in culture? If the authors tested please mention it.
We have been that Pdgfrb expression increases upon passages (Fig 6D). However, this experiment does not tell us whether there is a higher fraction of PDGFRβ+ cells or a similar fraction compared to Day 3 but expressing higher levels of the protein. To distinguish these possibilities, we propose to assess the PDGFRβ+ fraction by FACS upon passages in culture.
In Supp Fig 8 A: Are the colors reversed for EOM? In Fig 8 E, EOM progenitor and differentiation stream are light blue and yellow respectively but in Supp 8A the colors are flipped and don't seem to match the Map laid out in Fig 8E.
The scvelo pipeline will be re-run to correct this error on the graphical output.
Some references are listed as redundant.
Corrected
Reviewer #1 (Significance (Required)):
Skeletal muscle stem cells (MuSCs) play an indispensable role in muscle regeneration in adults. MuSCs are distributed in all muscles throughout the body and their function and molecular properties are diverse among muscles. Extraocular muscles (EOMs) are known to be preferentially spared in muscular dystrophies and during aging. In addition, EOM-derived MuSCs are highly transplantable compared to those of limb muscles. However, intrinsic regulators of EOM MuSCs have not been fully characterized yet. This study by Girolamo et al. shows the differences in molecular signature and biological features of MuSC populations between EOM and limb (Tibialis anterior, TA) muscles. Comprehensive approaches including scRNA-seq, bioinformatics, and live-cell imaging revealed that a subset of the EOM-derived MuSC population is highly proliferative and expresses extracellular matrix components at high levels. The analysis also shows that EOM-derived MuSCs have non-myogenic signatures such as Foxc1 and Pdgfrb. A transcriptional factor Foxc1 is described as a pro-mitogenic factor in the cancer field and is known as a driver of endothelial/smooth muscle fate. In this study, the authors find that Foxc1 is expressed in EOM MuSCs but not TA MuSCs. A siRNA-mediated gene silencing study shows that Foxc1 is important for the population expansion of EOM MuSCs. Furthermore, the authors demonstrated that the EOM MuSCs contain a PDGFRβ+ve cell population that is more proliferative and less myogenic compared to a PDGFRβ-ve cell population. Altogether, this study provides new insights into the regional differences in MuSCs and will contribute to the development of stem cell-based therapies for muscle-wasting diseases including muscular dystrophies and age-related sarcopenia.
We appreciate the assessment of the Reviewer noting the new insights that our work provides on muscle stem cell heterogeneity.
Reviewer #2 (Evidence, reproducibility and clarity (Required)): Benavente-Diaz et al. address a question of why do progenitors isolated from extra ocular muscles have higher proliferative and regenerative capacity compared to progenitors from limb muscles. They perform transcriptomic profiling of the two progenitor populations and report which genes and functional groups are differentially expressed. They perform multiple bioinformatic analyses aimed at providing insights into which differentially expressed genes may be master regulators of these differences.
Major comments:
Many conclusions in this paper are based on bioinformatic analyses with little experimental data provided as a confirmation. Overall, to increase significance of the manuscript I would suggest expanding the experimental validation part of the study. By following up on the findings from the bioinformatic analyses and confirming them in vitro this manuscript could move from beyond preliminary and only potentially interesting.
The previous version of our manuscript relied on bioinformatics analysis of single cell transcriptomics and some experimental validations to investigate muscle stem cell heterogeneity. Now, we performed loss of function experiments in EOM cells and gain of function experiments in TA cells using lentiviruses to validate those points mechanistically (Figure 7E-L).
Authors suggest a number of TFs and ECM components as master regulators of progenitor identity. An experiment of a rather limited scope was provided in Figure 7E-F, where effects of silencing TF Foxc1 on EOM progenitor proliferation was assessed. It would be highly beneficial to expand these experiments to include other TFs found in their dataset. Importantly, by overexpressing the proposed master regulators in TA progenitors authors should investigate whether these TFs indeed confer higher proliferative and regenerative capacity. Otherwise, the authors should make it clear that their conclusions about TFs regulating or maintaining EOM progenitors are preliminary and based on bioinformatic analyses.
Indeed, these experiments are necessary to consolidate the bioinformatic analysis and provide further mechanistic insights on this point. We have obtained lentiviruses and optimized gain of function assays in TA cells. Gain of function experiments with Creb3l1 and Dmrta2, two other top regulon transcription factors have been planned besides the experiments already included with Foxc1.
Minor comments:
In Figure 2, authors describe single-cell RNAseq analysis of EOM and TA progenitors. They report a number of markers differentially expressed between these two populations. It would be good to perform higher-resolution subclustering of each these populations to understand whether markers are expressed in all EOM progenitors or whether there is a specific subpopulation that is characterized by Mgp/Bgn/... expression. A paper by Yartseva et al. (Cell Reports 2020) did describe a population of activated satellite cells that express extracellular matrix components. In addition, from the data presented it is not clear whether proposed EOM markers are uniquely expressed or only enriched in EOM progenitors.
Actually, In Figure 2 we are showing what the reviewer is requesting. The heatmap highlights the presence of subpopulations (Progenitor/Differentiating) in both EOM and TA. The 4-way heatmap in Figure 2D is showing the distinctly upregulated genes of each subcluster. Matrix related genes are indeed expressed by a subpopulation of EOM cells, that we identified as "Progenitors". Two of those, Bgn and Mgp, can be seen in Figure 3H. Moreover, in Figure 4B we show the Average Expression and Percentage of cells expressing certain ECM component.
Actinomycin D is commonly used in single cell preparations for RNAseq to mitigate stress gene activation due to isolation procedure (for example see Wu YE et al. Neuron 2017). It is possible that EOM progenitors are particularly responsive to the isolation procedure, which would imply that stress genes are not involved in EOM progenitor maintenance, but that their expression is an artifact of isolation. These experiments should be repeated with actinomycin D to exclude potential artifacts.
While this is a possibility, the likelihood of this is relatively low as activated cells were obtained by quick trypsinization of cells. To formally exclude the problem mentioned by the reviewer we will perform qRT-PCR for EOM markers on activated cells at Day 3 that were fixed in PFA or treated with Actinomycin D prior to trypsinization. Moreover, in Suppl Figure 5G-E, we already used the stress index calculation defined by Machado et al. 2020 and this does not seem to affect our activated dataset.
It is unclear why authors chose to focus on PdgfrB+ population in Figure 7. Was it chosen as a target of Foxc1 or as a marker of proliferative cells? Any other reason? This should be explained as well as significance of this part in connection to the rest of the article.
We decided to focus on Pdgfrb for several reasons stated in the text: 1) it is a component of the matrisome we have validated; 2) it stands out in the Reactome pathways/Molecular function analysis; 3) it is a target of Foxc1; 4) it is a marker of cells with mesenchymal characteristics. We also chose this marker, for which antibodies for FACs exist, as proof of principle validation of the EOM mesenchymal phenotype. Similarly, sc-RNAseq analysis of human muscle, used Cav1 antibodies to isolate a functionally different subpopulation of MuSCs (Barruet elife 2020). To make the flow of the manuscript more consistent this data is now compiled on Figure 4.
Please explain the significance and relevance of the results presented in Figure 8A-C.
These figures highlight the potential of EOM progenitors to specifically regulate matrisome genes with respect to the TA. The 90 top active regulons of the EOM potentially regulate more matrisome genes than the TA, and the ratio (Number of EOM presumptive regulations/ Number of TA presumptive regulations) peaks when looking at the top 5 regulons (a 3-fold difference), and progressively reduces.
Prior studies are (not) referenced appropriately
We have corrected reference duplications.
Figures contain small fonts that are not legible. In particular, networks such as the one in Fig 5C are difficult to read.
The font size, font color and/or size of networks has been changed for Fig 2E-F, 5C-D, 8G-J and Suppl Fig 2A, 7C.
General assessment: Functional differences between EOM and TA progenitors have been previously described, but a deep mechanistic understanding of the underlying molecular pathways is lacking. This manuscript takes a step towards elucidating these molecular pathways.
We now provide more mechanistic data regarding the differential role that Foxc1 plays in extraocular compared to limb myogenic cells. Our revised plan will consolidate and extend these observations.
Advance: Tajbakhsh group recently published a paper Evano et al. Plos Genetics 2020, that also explored the differences between EOM and TA stem cells. Among other things this paper showed that EOM stem cells have intrinsic molecular mechanisms/programs that differentiate them from TA stem cells. In that context, the experimental insights that Benavente-Diaz et al. provide are not novel. Benavente-Diaz et al. would extend the knowledge in the field if they experimentally confirmed that the proposed master regulators indeed determine progenitor proliferation and regeneration capacity.
We respectfully disagree regarding the novelty of our work. In our previous study cited above, we showed that EOM stem cells have in vivo transcriptional differences with those in the limb (bulk RNAseq) and they can adopt the fate of limb cells when transplanted into the limb. Interestingly, full “reprogramming” was not achieved in those experiments pointing to limited plasticity.
Here, we show by scRNAseq and bioinformatic analysis of regulons that Foxc1 and extracellular matrix signature are features that are unique to EOM stem cells. Also, our overall single cell transcriptome analysis and comparative studies show that EOM muscle stem cells have adopted a signature that overlaps with mesenchymal stem cells – a property that has to date not been reported.
Muscle is arguably the best system to investigate stem cell heterogeneity, and our study provides mechanistic insights into the extend of this diversity.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Benaverte-Diaz et al. address a question of why do progenitors isolated from extra ocular muscles have higher proliferative and regenerative capacity compared to progenitors from limb muscles. They perform transcriptomic profiling of the two progenitor populations and report which genes and functional groups are differentially expressed. They perform multiple bioinformatic analyses aimed at providing insights into which differentially expressed genes may be master regulators of these differences.
Major comments:
- Many conclusions in this paper are based on bioinformatic analyses with little experimental data provided as a confirmation. Overall, to increase significance of the manuscript I would suggest expanding the experimental validation part of the study. By following up on the findings from the bioinformatic analyses and confirming them in vitro this manuscript could move from beyond preliminary and only potentially interesting.
- Authors suggest a number of TFs and ECM components as master regulators of progenitor identity. An experiment of a rather limited scope was provided in Figure 7E-F, where effects of silencing TF Foxc1 on EOM progenitor proliferation was assessed. It would be highly beneficial to expand these experiments to include other TFs found in their dataset. Importantly, by overexpressing the proposed master regulators in TA progenitors authors should investigate whether these TFs indeed confer higher proliferative and regenerative capacity. Otherwise, the authors should make it clear that their conclusions about TFs regulating or maintaining EOM progenitors are preliminary and based on bioinformatic analyses.
Minor comments:
- In Figure 2, authors describe single-cell RNAseq analysis of EOM and TA progenitors. They report a number of markers differentially expressed between these two populations. It would be good to perform higher-resolution subclustering of each these populations to understand whether markers are expressed in all EOM progenitors or whether there is a specific subpopulation that is characterized by Mgp/Bgn/... expression. A paper by Yartseva et al. (Cell Reports 2020) did describe a population of activated satellite cells that express extracellular matrix components. In addition, from the data presented it is not clear whether proposed EOM markers are uniquely expressed or only enriched in EOM progenitors.
- Actinomycin D is commonly used in single cell preparations for RNAseq to mitigate stress gene activation due to isolation procedure (for example see Wu YE et al. Neuron 2017). It is possible that EOM progenitors are particularly responsive to the isolation procedure, which would imply that stress genes are not involved in EOM progenitor maintenance, but that their expression is an artifact of isolation. These experiments should be repeated with actinomycin D to exclude potential artifacts.
- It is unclear why authors chose to focus on PdgfrB+ population in Figure 7. Was it chosen as a target of Foxc1 or as a marker of proliferative cells? Any other reason? This should be explained as well as significance of this part in connection to the rest of the article.
- Please explain the significance and relevance of the results presented in Figure 8A-C.
- Prior studies are referenced appropriately
- Figures contain small fonts that are not legible. In particular, networks such as the one in Fig 5C are difficult to read.
Significance
- General assessment: Functional differences between EOM and TA progenitors have been previously described, but a deep mechanistic understanding of the underlying molecular pathways is lacking. This manuscript takes a step towards elucidating these molecular pathways.
- Advance: Tajbakhsh group recently published a paper Evano et al. Plos Genetics 2020, that also explored the differences between EOM and TA stem cells. Among other things this paper showed that EOM stem cells have intrinsic molecular mechanisms/programs that differentiate them from TA stem cells. In that context, the experimental insights that Benaverte-Diaz et al. provide are not novel. Benaverte-Diaz et al. would extend the knowledge in the field if they experimentally confirmed that the proposed master regulators indeed determine progenitor proliferation and regeneration capacity.
- Audience: basic research, translational. This research would be of interest to regenerative therapy field.
My field of expertise: molecular and cellular biology, muscle regeneration. I am not a bioinformatician and I am not able to technically evaluate in silico approaches used in this study.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Girolamo et al., describe the differences in molecular signature and biological features of MuSC populations between extraocular (EOM) and limb (Tibialis anterior, TA) muscles. Comprehensive approaches including scRNA-seq, bioinformatics, and live-cell imaging reveal that EOM MuSCs is more proliferative in vitro and express ECM components at high levels as well as non-myogenic markers such as Foxc1 and Pdgfrb. The transcription factor network described in this study will characterize the identity of EOM MuSCs, providing insights into stem cell-based therapies for muscle diseases.
Major comments
- In Figure 1A, EOM-MuSCs appear to form myotubes as efficiently as TA-MuSCs. If so, why myotube formation is not affected in EOM-MuSCs even with lower Myog expression compared to TA-MuSCs? The authors should discuss about this point.
- In Figure 2, activated EOM-MuSCs express Myod1e at lower levels compared to TA-MuSCs. Are MyoD protein levels are also lower in EOM-MuSCs?
- In Figure 7E-G: Does the Foxc1 knockdown change the myogenic potential, especially on myotube formation? In addition to FOXC1 and Myog, the mRNA levels of Pax7 and Myod1 would be better to be provided.
- The authors mention that Foxc1 maintains EOM MuSCs in a progenitor-like state through matrix remodeling and cooperation with other TFs. However, such functional analysis does not seem to be provided sufficiently. For example: does Foxc1 siRNA lower matrisome and Pdgfrb genes in EOM-MuSCs; does forced expression of Foxc1in TA-MuSCs acquire the EOM-like state? Although the authors can predict the role and function of Foxc1 in the MuSC population based on the pySCENIC and previous studies, the experimental approach by the authors would be more convincing. The reviewer would like to emphasize that these experiments are not entirely necessary in this manuscript.
- Please state a reasonable explanation for the physiological role of high amounts of ECM produced by EOM-MuSCs.
Minor comments
On p7, the 3rd line: "marker" is redundant.
On p9: what is meant by "force-directed environment"? Is this the aggregation of regulons and targets based on interaction strength determined by the algorithmic arrangement used by pySCENIC?
On p11: add space between (Sambasivan et al., 20009a)and.
On p11: 1.7 fold increase → 2.7 fold?
On p13: please describe the GRN abbreviation since it is the first used here and not clarified beforehand.
On p15: add space between (Vallejo et al., 2018)Other.
On p16: add "a" after "Klf4 is" → Klf4 is a pioneer...
In Fig1: it would be better to show the quantification of EdU incorporation, especially at D5 for highlighting the difference between EOM and TA.
In Fig1: MF20 staining seemingly describes larger myotubes in EOM compared with TA at D5. Is this most likely due to the higher number of cells to start with in EOM rather than having more fusion ability?
In Fig 2E,F: some font is very faint in color and hard to read in printed format e.g. "TP53 regulates transcription of". May want to change color. In addition, the bottom edge of the Figure is slightly cut off.
In Fig 4D: COLVI should be changed to COLIV.
In Fig 5B: Myod1 is redundant.
In Fig 6D; please specify "Amp". Is Hey1 a myogenic marker?
In Fig 7F; there are Ctrl and control- please unify them.
In Fig7C,D: When does Foxc1 start to be expressed in EOM progenitors in the embryo? If the authors tested, please mention it.
In Supp Fig 7A: graphs are lacking color-coded legends.
In Supp Fig 7C: Font is very small and illegible in a paper format.
In Supp Fig 7F: what are the dynamics of PDGFβ+ cell populations in successive passages in culture? If the authors tested please mention it.
In Supp Fig 8 A: Are the colors reversed for EOM? In Fig 8 E, EOM progenitor and differentiation stream are light blue and yellow respectively but in Supp 8A the colors are flipped and don't seem to match the Map laid out in Fig 8E.
Some references are listed as redundant.
Significance
Skeletal muscle stem cells (MuSCs) play an indispensable role in muscle regeneration in adults. MuSCs are distributed in all muscles throughout the body and their function and molecular properties are diverse among muscles. Extraocular muscles (EOMs) are known to be preferentially spared in muscular dystrophies and during aging. In addition, EOM-derived MuSCs are highly transplantable compared to those of limb muscles. However, intrinsic regulators of EOM MuSCs have not been fully characterized yet. This study by Girolamo et al. shows the differences in molecular signature and biological features of MuSC populations between EOM and limb (Tibialis anterior, TA) muscles. Comprehensive approaches including scRNA-seq, bioinformatics, and live-cell imaging revealed that a subset of the EOM-derived MuSC population is highly proliferative and expresses extracellular matrix components at high levels. The analysis also shows that EOM-derived MuSCs have non-myogenic signatures such as Foxc1 and Pdgfrb. A transcriptional factor Foxc1 is described as a pro-mitogenic factor in the cancer field and is known as a driver of endothelial/smooth muscle fate. In this study, the authors find that Foxc1 is expressed in EOM MuSCs but not TA MuSCs. A siRNA-mediated gene silencing study shows that Foxc1 is important for the population expansion of EOM MuSCs. Furthermore, the authors demonstrated that the EOM MuSCs contain a PDGFRβ+ve cell population that is more proliferative and less myogenic compared to a PDGFRβ-ve cell population. Altogether, this study provides new insights into the regional differences in MuSCs and will contribute to the development of stem cell-based therapies for muscle-wasting diseases including muscular dystrophies and age-related sarcopenia.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Major points:
- Although the role of mitofusin on mitochondrial morphology has been established by others and comprehensively assessed in the present study, the authors should determine the functional outcome from the genetic manipulations on Mfn2 and Mfn1. As observed by increased glucose uptake, one could hypothesize an impairment in mitochondrial oxidative phosphorylation, leading the cells to rely uniquely or heavily on glycolysis as a fuel. Also, as mentioned by the authors in the discussion, ROS play a fundamental role in adipogenesis, and, therefore, mitochondrial ROS emission and/or cellular redox balance should also be assessed. I believe these two experiments will add insightful information to the current dataset.
__Thank you for these suggestions. Whilst we agree with the general premise of this point, unfortunately quantifying oxidative phosphorylation and ROS production with sufficient precision to detect relatively subtle changes remains very challenging. We have attempted these experiments but they require considerable optimisation (particularly using adipocytes). Preliminary studies done in MEFs (Cover letter figure 1) suggest that under some stimuli there may be higher ROS in Mfn1 and Mfn2 knock-out lines. However this preliminary data would require further optimisation and repetition in adipocytes, which is more challenging. __
For now, we have amended the Discussion to specify that these experiments are of particular interest.
Cover letter figure 1. Levels of reactive oxygen species (ROS) in mouse embryonic fibroblasts measured by flow cytometry for fluorometric dyes CellROX (total cellular ROS), D2-HDCFA (total cellular ROS), and MitoSOX (mitochondrial ROS). Levels are expressed relative to wild-type. MEFs were treated with antimycin A (or media only) for 20minutes prior to incubation with the ROS dyes, then washed three times before assayed. AntA, Antimycin; CR, CellROX; M1, Mfn1-/- MEFs; M2, Mfn2-/- MEFs; MS, MitoSOX; WT, wild-type.
The insulin effect on glucose uptake does not allow to conclude any impairment in insulin responsivity. The fold change of glucose uptake mediated by insulin was roughly 1.2 in undifferentiated adipocytes, 2.3 in differentiated WT, and 2.5 in Mfn1KO differentiated adipocytes. The absolute increase in glucose uptake could be a compensatory mechanism due to impairment in mitochondrial bioenergetics (see point #1), given that the cells can still respond to insulin. Measuring Akt phosphorylation levels following insulin treatment would help solve this issue.
__As requested, we have assessed the effect of insulin treatment on Ser 473 phosphorylation of Akt2 (Pkb) in wild-type and knock-out MEFs differentiated into adipocytes (Fig 2D). Mfn1_-/-_ MEFs show an increase in Akt phosphorylation relative to the other cell lines. They also have higher expression of insulin receptor and Glut4, consistent with their degree of adipogenic differentiation. __
We agree that impaired mitochondrial bioenergetics could account for the observations in perturbed glucose uptake in the knockout cell lines (especially Mfn2-null) and have therefore amended the text throughout to reflect this.
Usually, working with clonal transgenic cells lines has the limitations that the cells might behave differently in terms of adipogenic potential over passages. A transient loss of function in the same cells would solve this concern. Also, introducing the patient mutations might be closer to the human situation than working with KO mouse fibroblasts.
__We agree with this potential concern, which is why we conducted knock-down studies in 3T3-L1 cells in addition to the work in knockout MEFs. These data were concordant with what we observed in the KO MEFs so we don’t think it is necessary to conduct repeat KD experiments in WT MEFs. __
In our previous study we observed that human fibroblasts with biallelic MFN2-R707W mutations did not have any obvious phenotype (____https://elifesciences.org/articles/23813____). We have separate work studying these mutations in vivo where we provide further characterisation of murine adipocytes harbouring Mfn2-R707W; this work is now published here: https://elifesciences.org/articles/82283
Minor points:
- Although the authors mention in the introduction that the differentiation of adipocytes is followed by an increase in mitochondrial mass, it would be interesting the determine the expression profile of mfn1 and mfn2 during the differentiation process.
We have found that there is an increase in markers of mitochondrial fusion (Mfn1 & Mfn2) as well as fission (Fis1) throughout differentiation of 3T3-L1s. ____We have included this data in the manuscript (Supplementary Figure ____6A ).
The authors should discuss other models, even though pre-clinical, of mitochondrial dysfunction that results in lipodystrophy but with different metabolic outcomes. To cite a few but not only PMID: 29588285; PMID: 21368114; PMID: 31925461.
Thank you for this suggestion. We have added a section on this in the introduction.
It would be interesting to discuss the role of Mfn1/2 in the context of cold-induced adipogenesis, given the prominent role of mitochondrial dynamics, as mentioned by the authors in the reference list, on cold-induced adaptative thermogenesis (Mahdaviane et al. 2017; Boutant et al. 2017).
Thank you for this suggestion. We have added a section on this in the introduction.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
- In Fig.2A, the authors report "increased lipid accumulation in Mfn1-/- MEFs, but not in Mfn2-/- MEFs". While the overall content might be similar, the pattern of lipid accumulation seems to be different. Indeed, differences in lipid droplet morphology have been observed in Mfn2 KO MEFs upon oleate treatment (McFie et al., 2016). The manuscript would benefit from having quantifications of lipid droplet size and number.
Thank you for highlighting this. We have quantified lipid droplet size and, consistent with McFie et al have found increased size in Mfn2 knock-down. This data is now included in Supplementary Figure 6B.
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Following the above point, McFie et al. also reported that Mfn1/Mfn2 double KO MEFs could differentiate into adipocytes. The authors should discuss these opposing observations. The contrasting observation may be due to acquired clonal differences in MEF lines. We have attempted ‘double’ knock down (of both Mfn1 and Mfn2 concurrently) in 3T3-L1 cells however this was essentially lethal and also did not generate any cells capable of differentiation. We have added a section in the Discussion regarding this point.
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In relation to the effects of Mitofusin deletions on glucose uptake, the authors mention that Mfn2 KO MEFs show impaired insulin stimulated glucose uptake. The interpretation of the result is not straight forward, as basal glucose uptake is highly increased in Mfn2 KO MEFs. Maybe there is simply a treshold for maximal glucose uptake capacity in MEF-derived adipocytes. In any of these cases, the authors might want to check GLUT1 levels, in line of their suggestion that the increased basal glucose uptake might be related to higher GLUT1. Alternatively, the authors might also want to check elements of the insulin signaling path, in case there are alterations that could explain the phenomenon.
As mentioned above in response to reviewer 1, we have now ____performed immunoblots to quantify some components of the insulin signalling cascade (Fig 2D). We observed lower expression of both Glut1 and Glut4 in the Mfn2-/- cells. Mfn2-/- cells did demonstrate some Akt phosphorylation but considerably less than Mfn1-/- cells. These results are now included in the revised manuscript (Figure 2D).
- In line with the above point, one would have wished that mitochondrial biology was better characterized in the different MEF models. While mitochondrial shape analyses are provided, some information on, at least, mitochondrial respiratory capacity, glucose oxidation and/or fatty acid oxidation rates, would be important. This would allow for a more solid discussion on why Mfn2 KO MEFs display such high basal glucose uptake rates.
We have responded to a similar suggestion from Reviewer 1, above.
- In relation to the experiments in MEFs, one should never forget that WT, Mfn1 and Mfn2 KO MEFs derive from different mice. Hence, the phenotypes could be related to trait variabilities in the origin mice themselves, and not just the gene deletion. To control for this aspect, the authors could simply re-introduced Mfn1 or Mfn2 in their respective MEFs and evaluate if their alterations are normalized.
__Yes one could try this but we have addressed this general concern by replicating the impact of Mfn1/2 KD in 3T3L1 cells so are not inclined to pursue this at this time. __
- Transcriptomic analyses reveals a decrease in adipogenic gene expression in Mfn2 KO MEFs. However, lipid accumulation is comparable to WT MEFs is normal. This could be due to defects in lipolytic capacity, leading to similar lipid accumulation despite lower adipogenic capacity. This could be tested by evaluating the adrenergic response of these cells (e.g.: glycerol release).
Thank you for this suggestion. We have commented in the Discussion to explain that we have not fully characterised this mechanism.
- The experiments in 3T3-L1 would also benefit from some gene expression analyses to evaluate if Mfn1 depletion leads to acceleration and/or magnification of the differentiation stages. In relation to this, 3T3-L1 cells could be used to monitor Mfn1 and Mfn2 through differentiation, which in itself would be valuable information.
We have performed a protein-level time course for markers of mitochondrial fusion (Mfn1 & Mfn2) as well as fission (Fis1) throughout differentiation of 3T3-L1s. We have included this data in the manuscript (Supplementary Figure 6A). We think that changes in protein expression are more relevant than changes in mRNA so have not included gene expression changes at this time.
CROSS-CONSULTATION COMMENTS The comments from the three independent reviewers are extremely well aligned and agree that improving the following aspects could largely benefit the manuscript:
- A better metabolic characterisation of the models used
- Provide measurements in relation to mitochondrial bioenergetics and ROS production – we have attempted this but the data is not very clear in our view and warrants further optimisation which we are not inclined to pursue currently. - Explorations of insulin signaling - done thank-you.
- Improve the validation and significance of the cellular models used, following the different suggestions from the three reviewers. Most notably, considering the introduction of human Mfn2 mutation forms – we have published a separate manuscript on follow up work on the human MFN2 variant as mentioned above.
A number of additional comments are raised, all of which are very reasonable and, in my opinion, should not be difficult to address. I think we can all agree that a mechanistic underpinning of the observations would give a larger degree of novelty to the work. Also, none of us would like the revision's quality to be constraint by a tight deadline. I would therefore be totally OK to extend the timeframe for the revision beyond the original 3 months proposed.
Reviewer #2 (Significance (Required)):
This is an interesting and well-crafted manuscript. Mice deficient for Mfn2 or Mfn1 have been reported by different laboratories, yet most of them fail to explore the effects on early adipogenesis. The study is limited to cultured cells, but this is well acknowledged by the authors Given the existence of human mutations in the mitofusin-2 gene that largely alter fat mass distribution, this work provides new clues on how these mutations might impact adipose tissue.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Mann et al. The objective of this study is to determine the extent to which mitofusins (Mfn1 and Mfn2) have redundant functions and assess their contributions to adipocyte differentiation. While a point mutation in the Mfn2 gene has been associated with severe adipose tissue dysfunction and lipodystrophy, no disease phenotypes have been linked to mutations in Mfn1. To address these objectives, the authors sought to characterize how adipocyte differentiation and function is affected in Mfn1, Mfn2 or double knockout adipocytes in two distinct in vitro models. Their findings indicate divergent effects of Mfn1 and Mfn2 on adipocyte differentiation and function despite similar alterations to mitochondrial morphology. Loss of Mfn1 promotes adipogenesis while Mfn2 decreases it. The authors conclude that these findings are indicative of non-redundant functions in Mfn1 and Mfn2.
Major comments: The observation that Mfn1 KO/KD leads to increased adipogenesis in vitro is somehow novel and, perhaps, surprising, as the author say. However, the molecular understanding underlying this phenotype remains unexplored. The analyses performed are mainly descriptive and don't dig deeper into the identification of the molecular mechanism. They do hypothesize that ROS production may be responsible for the observed effects, but that's how far they go.
The authors do highlight the limitations of this work, but these limitations need careful consideration, for not addressing them seriously limits the novelty of this study, especially not testing these conditions in human cells. The current version of this work seems too preliminary to suggest useful experiments that could strengthen the study, since future analyses could take many different directions.
Yes, we accept that the findings are rather preliminary but our initial efforts suggest that precisely elucidating the underlying mechanism/s is likely to be more difficult and complicated than alluded to by the reviewers. We would therefor prefer to share our initial observations so that others can also attempt to clarify the underlying mechanisms.
A few unanswered questions that the authors might consider are: What is the difference between the Arg707Trp mutation and the KO/KD? Mfn1 and 2 deletions lead to fragmented mitochondria, but opposite adipogenic potentials. What other mitochondrial defects can explain it? Are organelle contact site disrupted only with Mfn2? How does Mfn1 and 2 KO/KD affect mitochondrial proteome? What does mitochondrial bioenergetics look like? How is ROS production affected? Is the increased glucose uptake (basal) a compensatory mechanism for mitochondrial dysfunction? Thank you for these suggestions. We acknowledge that this work is largely descriptive in nature. These are all questions that should be addressed to improve mechanistic understanding of our observations.
__The difference between p.Arg707Trp and KO/KD is challenging to address because in the non-adipose cell lines studied so far (human and mouse fibroblasts) there has been no evidence of perturbation of the mitochondrial network. __
As discussed above, we have done preliminary studies into ROS production but are unable to provide a complete characterisation at this time. Similarly, we have not been able to perform bioenergetic studies (e.g. Seahorse, Oxyboros) that would provide more insight into differences between Mfn1 and Mfn2 KO cell lines.
CROSS-CONSULTATION COMMENTS I agree the work is interesting, but is too preliminary and merely descriptive. the experiments suggested will significantly improve the manuscript. However, I don't think they will take only three months to be completed. This work needs a significant amount of work including the study of the mechanism, at least an idea of what the mechanism could be, to be considered novel.
We accept this limitation and have responded to this general point above.
Reviewer #3 (Significance (Required)):
Understanding how mitochondrial dynamics affect adipogenic differentiation is critical to better understand how metabolism impact cell signaling, cell fate and function.
Strengths: this work reveals an interesting phenotype for Mfn1 and Mfn2 mutant preadipocytes. Weaknesses: this work is merely descriptive and preliminary to provide a clear understanding of the observed phenotypes
Advance: Although the performed experiments are accurate, well designed, and well controlled, the fact that Mfn1 and 2 have distinct functions and cannot compensate for one another was already clear based on the embryonic lethality of either Mfn1 and Mfn2 KO mice as well as the Mfn2 mutation in humans that leads to a pathological condition.In the current verison, this work minimally contributes to advancing the field.
Audience: an extensively revised version of this work including deeper phenotyping of thier models and human cell work would be of interest for sceintists studying mitchondrial biology, adipose tissue, metabolic diseases, and human genetic diseases.
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Referee #3
Evidence, reproducibility and clarity
Mann et al. The objective of this study is to determine the extent to which mitofusins (Mfn1 and Mfn2) have redundant functions and assess their contributions to adipocyte differentiation. While a point mutation in the Mfn2 gene has been associated with severe adipose tissue dysfunction and lipodystrophy, no disease phenotypes have been linked to mutations in Mfn1. To address these objectives, the authors sought to characterize how adipocyte differentiation and function is affected in Mfn1, Mfn2 or double knockout adipocytes in two distinct in vitro models. Their findings indicate divergent effects of Mfn1 and Mfn2 on adipocyte differentiation and function despite similar alterations to mitochondrial morphology. Loss of Mfn1 promotes adipogenesis while Mfn2 decreases it. The authors conclude that these findings are indicative of non-redundant functions in Mfn1 and Mfn2.
Major comments:
The observation that Mfn1 KO/KD leads to increased adipogenesis in vitro is somehow novel and, perhaps, surprising, as the author say. However, the molecular understanding underlying this phenotype remains unexplored. The analyses performed are mainly descriptive and don't dig deeper into the identification of the molecular mechanism. They do hypothesize that ROS production may be responsible for the observed effects, but that's how far they go.
The authors do highlight the limitations of this work, but these limitations need careful consideration, for not addressing them seriously limits the novelty of this study, especially not testing these conditions in human cells.<br /> The current version of this work seems too preliminary to suggest useful experiments that could strengthen the study, since future analyses could take many different directions. A few unanswered questions that the authors might consider are: What is the difference between the Arg707Trp mutation and the KO/KD? Mfn1 and 2 deletions lead to fragmented mitochondria, but opposite adipogenic potentials. What other mitochondrial defects can explain it? Are organelle contact site disrupted only with Mfn2? How does Mfn1 and 2 KO/KD affect mitochondrial proteome? What does mitochondrial bioenergetics look like? How is ROS production affected? Is the increased glucose uptake (basal) a compensatory mechanism for mitochondrial dysfunction?
Referees cross-commenting
I agree the work is interesting, but is too preliminary and merely descriptive. the experiments suggested will significantly improve the manuscript. However, I don't think they will take only three months to be completed. This work needs a significant amount of work including the study of the mechanism, at least an idea of what the mechanism could be, to be considered novel.
Significance
Understanding how mitochondrial dynamics affect adipogenic differentiation is critical to better understand how metabolism impact cell signaling, cell fate and function.
Strenghts: this work reveals an interesting phenotype for Mfn1 and Mfn2 mutant preadipocytes. Weaknesses: this work is merely descriptive and preliminary to provide a clear understanding of the observed phenotypes
Advance: Although the performed experiments are accurate, well designed, and well controlled, the fact that Mfn1 and 2 have distinct functions and cannot compensate for one another was already clear based on the embryonic lethality of either Mfn1 and Mfn2 KO mice as well as the Mfn2 mutation in humans that leads to a pathological condition.In the current verison, this work minimally contributes to advancing the field.
Audience: an extensively revised version of this work including deeper phenotyping of thier models and human cell work would be of interest for sceintists studying mitchondrial biology, adipose tissue, metabolic diseases, and human genetic diseases.
Reviewer expertise: adipose tissue function, metabolic disorders, mitochondrial bioenergetics.
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Referee #2
Evidence, reproducibility and clarity
The work by Mann and colleagues explores the adipogenic potential of MEF cells derived from Mfn1, Mfn2, Mfn1/Mfn2 and OPA1 KO mice. Two of these cell lines (Mfn1/Mfn2 KO MEFs and OPA1 MEFs) failed to differentiate, so only Mfn1 and Mfn2 were considered for most of the work. The experiments revealed that Mfn1 deletion lead to a faster and more prominent differentation of MEFs into adipocytes, which did not occur on Mfn2 KO MEFs. In contrast Mfn2 KO MEFs showed some signs of impaired adipogenesis, including lower GLUT4 gene expression and reduced insulin-stimulated glucose transport. Most of these observations were verified using a second cell model, in which Mfn1 or Mfn2 were knocked-down in 3T3-L1 adipocytes. This led the authors to conclude that Mfn1, but not Mfn2, enhances adipogenesis.
The manuscript is very well written and the experiments are proficiently designed. One might have wished confirmation of these findings in primary adipocyte cultures or in model organisms, but this limitation is duly acknowledged by the authors. The methods are well described and should allow other labs to easily reproduce the experiment. A few suggestions that could improve the manuscript can be found below.
- In Fig.2A, the authors report "increased lipid accumulation in Mfn1-/- MEFs, but not in Mfn2-/- MEFs". While the overall content might be similar, the pattern of lipid accumulation seems to be different. Indeed, differences in lipid droplet morphology have been observed in Mfn2 KO MEFs upon oleate treatment (McFie et al., 2016). The manuscript would benefit from having quantifications of lipid droplet size and number.
- Following the above point, McFie et al. also reported that Mfn1/Mfn2 double KO MEFs could differentiate into adipocytes. The authors should discuss these opposing observations.
- In relation to the effects of Mitofusin deletions on glucose uptake, the authors mention that Mfn2 KO MEFs show impaired insulin stimulated glucose uptake. The interpretation of the result is not straight forward, as basal glucose uptake is highly increased in Mfn2 KO MEFs. Maybe there is simply a treshold for maximal glucose uptake capacity in MEF-derived adipocytes. In any of these cases, the authors might want to check GLUT1 levels, in line of their suggestion that the increased basal glucose uptake might be related to higher GLUT1. Alternatively, the authors might also want to check elements of the insulin signaling path, in case there are alterations that could explain the phenomenon.
- In line with the above point, one would have wished that mitochondrial biology was better characterized in the different MEF models. While mitochondrial shape analyses are provided, some information on, at least, mitochondrial respiratory capacity, glucose oxidation and/or fatty acid oxidation rates, would be important. This would allow for a more solid discussion on why Mfn2 KO MEFs display such high basal glucose uptake rates.
- In relation to the experiments in MEFs, one should never forget that WT, Mfn1 and Mfn2 KO MEFs derive from different mice. Hence, the phenotypes could be related to trait variabilities in the origin mice themselves, and not just the gene deletion. To control for this aspect, the authors could simply re-introduced Mfn1 or Mfn2 in their respective MEFs and evaluate if their alterations are normalized.
- Transcriptomic analyses reveals a decrease in adipogenic gene expression in Mfn2 KO MEFs. However, lipid accumulation is comparable to WT MEFs is normal. This could be due to defects in lipolytic capacity, leading to similar lipid accumulation despite lower adipogenic capacity. This could be tested by evaluating the adrenergic response of these cells (e.g.: glycerol release).
- The experiments in 3T3-L1 would also benefit from some gene expression analyses to evaluate if Mfn1 depletion leads to acceleration and/or magnification of the differentiation stages. In relation to this, 3T3-L1 cells could be used to monitor Mfn1 and Mfn2 through differentiation, which in itself would be valuable information.
Referees cross-commenting
The comments from the three independent reviewers are extremely well aligned and agree that improving the following aspects could largely benefit the manuscript:
- A better metabolic characterisation of the models used
- Provide measurements in relation to mitochondrial bioenergetics and ROS production
- Explorations of insulin signaling
- Improve the validation and significance of the cellular models used, following the different suggestions from the three reviewers. Most notably, considering the introduction of human Mfn2 mutation forms
A number of additional comments are raised, all of which are very reasonable and, in my opinion, should not be difficult to address. I think we can all agree that a mechanistic underpinning of the observations would give a larger degree of novelty to the work. Also, none of us would like the revision's quality to be constraint by a tight deadline. I would therefore be totally OK to extend the timeframe for the revision beyond the original 3 months proposed.
Significance
This is an interesting and well-crafted manuscript. Mice deficient for Mfn2 or Mfn1 have been reported by different laboratories, yet most of them fail to explore the effects on early adipogenesis. The study is limited to cultured cells, but this is well acknowledged by the authors Given the existence of human mutations in the mitofusin-2 gene that largely alter fat mass distribution, this work provides new clues on how these mutations might impact adipose tissue.
-
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Referee #1
Evidence, reproducibility and clarity
Mann et al. described the effects of Mfn1 and Mfn2 deletion on adipogenesis. The authors describe a surprisingly pro-adipogenic effect of Mfn1 deletion despite massive mitochondrial fragmentation whilst conversely, loss of Mfn2 led to mitochondria fragmentation and impairment of adipogenesis. Overall, the research is well-designed and properly presented. Besides the lack of in vivo data (which is difficult due to the lack of a specific preadipocyte Cre), as acknowledged by the authors as a limitation, the study would benefit from a few experimental data in order to make the conclusions more robust. Please, find my comments in a point-by-point manner, which I hope will be useful for the authors.
Major points:
- Although the role of mitofusin on mitochondrial morphology has been established by others and comprehensively assessed in the present study, the authors should determine the functional outcome from the genetic manipulations on Mfn2 and Mfn1. As observed by increased glucose uptake, one could hypothesize an impairment in mitochondrial oxidative phosphorylation, leading the cells to rely uniquely or heavily on glycolysis as a fuel. Also, as mentioned by the authors in the discussion, ROS play a fundamental role in adipogenesis, and, therefore, mitochondrial ROS emission and/or cellular redox balance should also be assessed. I believe these two experiments will add insightful information to the current dataset.
- The insulin effect on glucose uptake does not allow to conclude any impairment in insulin responsivity. The fold change of glucose uptake mediated by insulin was roughly 1.2 in undifferentiated adipocytes, 2.3 in differentiated WT, and 2.5 in Mfn1KO differentiated adipocytes. The absolute increase in glucose uptake could be a compensatory mechanism due to impairment in mitochondrial bioenergetics (see point #1), given that the cells can still respond to insulin. Measuring Akt phosphorylation levels following insulin treatment would help solve this issue.
- Usually, working with clonal transgenic cells lines has the limitations that the cells might behave differently in terms of adipogenic potential over passages. A transient loss of function in the same cells would solve this concern. Also, introducing the patient mutations might be closer to the human situation than working with KO mouse fibroblasts.
Minor points:
- Although the authors mention in the introduction that the differentiation of adipocytes is followed by an increase in mitochondrial mass, it would be interesting the determine the expression profile of mfn1 and mfn2 during the differentiation process.
- The authors should discuss other models, even though pre-clinical, of mitochondrial dysfunction that results in lipodystrophy but with different metabolic outcomes. To cite a few but not only PMID: 29588285; PMID: 21368114; PMID: 31925461.
- It would be interesting to discuss the role of Mfn1/2 in the context of cold-induced adipogenesis, given the prominent role of mitochondrial dynamics, as mentioned by the authors in the reference list, on cold-induced adaptative thermogenesis (Mahdaviane et al. 2017; Boutant et al. 2017).
Referees cross-commenting
I agree with the statement of reviewer #2. I agree with reviewer #3, this is not the first paper on Mfns in adipocytes, so the novelty is limited but TMO sufficient for publication. Also, I tend to first look at what is there, not what is not there, and to my opinion, based on quality control measures, this work has merit.
Significance
See above
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Reply to the reviewers
Reply to reviewers.
We deeply thank the reviewers for the time spent on evaluating our manuscript as well as providing comments and suggestions to improve our study.
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
*In this manuscript Lebdy et al. describe a new role of GNL3 in DNA replication. They show that GNL3 controls replication fork stability in response to replication stress and they propose this is due to the regulation of ORC2 and the licensing of origins of replication. Their data suggest that GNL3 regulates the sub nuclear localization of ORC2 to limit the number of licensed origins of replication and to prevent resection of DNA at stalled forks in the presence of replication stress.
While many of the points of the manuscript are proven and well supported by the results, there are some experiments that could improve the quality and impact of the manuscript. The main issue is that the connection between the role of GNL3 in controlling ORC2, the firing of new origins and the protection of replication forks is not clearly established. At the moment the model relies on mainly correlative data. In order to further substantiate the model, we propose to address some of the following issues:*
- *The authors indicate that RPA and RAD51 accumulation at stalled forks is not affected by GNL3 depletion. These data should be included and other proteins should be analysed. In addition, the role of helicases could be explored through the depletion of the main helicases involved in the remodelling of the forks. * Response: As asked by the reviewer we will add the fractionation experiments that show that the level of RAD51 and RPA on chromatin is not affected by GNL3 depletion. So far, the other proteins we checked (RIF1 and BRCA1), both involved in nascent strand protection, did not show clear differences. Therefore, we concluded that depletion of GNL3 does not seem to affect the recruitment of major proteins required for protection of nascent DNA. Of course, we cannot exclude that other proteins may be affected by GNL3 depletion, but testing all the possible candidates would be time consuming with a very low chance of success. In addition, fractionation experiments are possibly not quantitative enough to uncover small differences and may be not that informative. Thus it remains possible that RPA exhaustion may be the cause of resection in absence of GNL3 as suggested by the work conducted in Lukas’ lab (Toledo et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24267891/). To test this hypothesis, we will analyze if resection in absence of GNL3 is still occurring in a well-characterized cell line that overexpress the three RPA subunits that we obtained from Lukas’ lab.
To our knowledge not many helicases have been shown to be involved in remodeling of stalled forks. The best example is RECQ1, however we feel that testing RECQ1 involvement in resection upon GNL3 depletion will complicate our story without adding much regarding the mechanism. We hope the reviewer understands our concern.
- The proposed model implies that GNL3 depletion leads to increased origin licensing. FThe authors should address if the primary effect of GNL3 depletion is on origin firing by using CDC7 inhibition in the absence of stress (Rodríguez-Acebes et al., JBC 2018). *
__Response: __This is an excellent point raised by the reviewer. To test if the primary effect of GNL3 depletion in on origin firing we will test if the defect in replication fork progression is dependent on CDC7 using DNA fibers experiments and CDC7 inhibitor.
- A way to prove that origin firing mediates the effect of GNL3 on fork protection would be to reduce the number of available origins. The depletion of MCM complexes has been shown to limit the number of back-up origins that are licensed and leads to sensitivity to replication stress (Ibarra et al., PNAS 2008). If GNL3 depletion results in increased number of origins, this effect should be prevented by the partial depletion of MCM complexes. *
__Response: __This is also an excellent point. We will test if MCM depletion decreases resection upon GNL3 depletion and treatment with HU. In addition, we will integrate in the manuscript experiments that we have done recently that show that treatment with roscovitine, a CDK inhibitor that impairs origin firing, decreases the level of resection observed in absence of GNL3. We think this experiment strengthens the results obtained with CDC7 inhibitors.
*Alternatively, the authors could try to modulate the depletion of GNL3. Origin licensing takes place in the G1 phase and thus the depletion of GNL3 by siRNA could affect the following S phase. Using an inducible degron for GNL3 depletion would allow to deplete GNL3 in G1 or S phase specifically. If the model is correct, the removal of GNL3 in S phase should not affect fork protection but removing GNL3 in the previous G2/M phase should reduce the number of licensed origins and lead to impaired fork protection. *
__Response: __This is obviously a good point given the fact that GNL3 deletion is not viable (see responses to reviewer 2). We tried to develop an auxin induced degron of GNL3, but we could not obtain homozygous clones, meaning that our clones had always an untagged GNL3 allele. Since GNL3 is essential its tagging may impair its function, explaining why we could not obtain homozygous clones. However, we are planning to optimize the design using other degrons system (for instance Halo-tag) to address the role of GNL3 specifically during S-phase. But we think this is above the scope of the present study.
*In addition to the connection GNL3-origin firing-fork protection, it is unclear how the lack of GNL3 in the nucleolus and the change in the sub nuclear localization of ORC2 controls origin firing and resection. The strong interaction observed between GNL3-dB and ORC2, and the subsequent change in ORC2 localization does not explain how origin licensing can be affected. In this sense, the authors could address: *
- *Does the depletion of GNL3 and the expression of GNL3-dB affect the formation of the ORC complex, its subnuclear localization or its binding to chromatin? The authors have not explored if the interaction of GNL3 with ORC2 is established in the context of the ORC complex. An IF showing NOP1 with PLA data from GNL3-dB and ORC2 is needed to analyse how the expression of increasing amounts of GNL3-dB affects ORC2. * __Response: __We tested if GNL3 depletion impacts ORC2 and ORC1 recruitment on chromatin, but we could not observe significant differences. No clear differences were observed upon GNL3-dB expression either. One reason for this may be due to the excess of ORC complex on the chromatin, in addition chromatin fractionation is likely not sensitive enough to observe small differences. We think that quantitative ChIP-seq of ORC2 or other ORC subunits upon GNL3 depletion is required to visualize such differences, but this is above the scope of the study, and this constitutes the following of this project. We also tried to look at subnuclear localization of ORC2 using immunofluorescence, but the signal was not specific enough to observe differences. We think that the increased interaction (PLA) of ORC2 with GNL3-dB (Figure 5E) demonstrates a change in ORC2 subnuclear localization. To confirm this, we will perform the excellent experiment proposed by the reviewer to test if increasing level of GNL3-dB affects its interaction with ORC2 using PLA.
We do not think that the interaction between ORC2 and GNL3 is established in the context of the ORC complex since only ORC2 (and not the other ORC) was significantly enriched in the GNL3 Bio-ID experiment. The full list of proteins from the Bio-ID experiment (Figure 4A) will be provided in the revised version. Therefore, we think that either GNL3 regulates ORC2 subnuclear localization that in turns impact the ORC complex or GNL3 regulates ORC2-specific functions. More and more evidences show that ORC2 plays roles possibly independently of the ORC complex (see Huang et al. 2016 https://doi.org/10.1016/j.celrep.2016.02.091 or Richards et al. 2022 https://doi.org/10.1016/j.celrep.2022.111590 for instance). Future work should uncover how these ORC2 functions may regulate origins activity.
*In order to confirm if the mislocalization of ORC2 by the expression of GNL3-dB increases origin firing and mediates the effects on fork protection the authors could check DNA resection levels inhibiting CDC7 in high GNL3-dB conditions. Also, the levels of MCM2, phosphor-MCM2, CDC45, have not been analysed upon expression of GNL3-dB. *
__Response: __This is a good point; we will test if the resection observed upon expression of GNL3-dB is dependent on origin firing using CDC7 inhibitor. We have not measured the level of the cited proteins but instead we performed DNA combing to measure Global Instant Fork Density. We now show that expression of GNL3-WT suppresses the increased origin firing observed upon GNL3 depletion, in contrast expression of GNL3-dB does not suppress it. This important result indicates that origin firing is increased upon GNL3-dB expression, providing a link between aberrant localization and increased firing. These data will be part of the revised version of the manuscript.
The data in the paper suggest that GNL3 may affect the role of ORC2 in centromeres. Since depletion of GNL3 leads to increased levels of gH2AX, it would be interesting to address if this damage is due to incomplete replication in centromeres by analysing the co-localization of g*H2AX and centromeric markers both in unstressed conditions and upon the induction of replication stress. *
__Response: __This is indeed and interesting comment, however since it has been previously shown that gH2AX signal is rather strong upon GNL3 depletion (see Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/) we do not think that co-localization experiments with CENP-A for instance will be informative given the high number of gH2AX foci.
*Minor points: *
- In the initial esiRNA screen the basal levels of g*H2AX should also be shown. * Response: Our negative control is the transfection of an esiRNAs that targets EGFP (a gene that is not expressed in the tested cell line). This esiRNAs is ranked at the end of the list and therefore constitutes the basal level of gH2AX signal. In any case it is well-established that GNL3 depletion increases gH2AX signal (see Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/).
*Figure EV1B: I think the rank needs another RS mark to see better the effect of each esiRNA on DNA lesions (high variability in all the conditions showed). *
__Response: __We understand this issue, but we cannot repeat this set of experiments for technical reasons (reagents and cost mainly). Anyway, we believe that the role of GNL3 is response to replication stress is extensively addressed by other experiments of this manuscript.
*Figure 1C and Figure EV1D/E: the quantification of the pCHK1/CHK1 levels could be included to show that there are no changes in phosphorylation upon GNL3 depletion. *
Response: it is a good point; we will put quantification in the revised version.
*In the first section of the results, at the end Figure 4B is incorrectly called for. *
__Response: __Thanks for the comment, we will modify accordingly.
The levels of GLN3 expression in 293 cells should be already included in section GNL3 interacts with ORC2.
__Response: __We will add a figure that shows the level of expression in 293 cells.
The full MS data needs to be included for both GNL3 and ORC2.
__Response: __This will be integrated in the revised version.
Figure 4B should be improved, since there is a faint band in the IgG mouse control.
__Response: __it is true that the figure is not perfect, but we believed that our Bio-ID and PLA experiments fully demonstrate the interaction between GNL3 and ORC2.
__Reviewer #1 (Significance (Required)): __
*The work is nicely written, the figures are well presented and the experiments have the necessary controls. It provides relevant information to understand how replication stress is controlled and linked to replication fork protection through origin firing. These results are relevant to the field, linking GNL3 to origin firing and with potential to help understand the role of GNL3 in cancer. They provide new information and can give rise to new studies in the future. Many of the conclusions of the manuscript are well supported. Additional support for some of the main claims would strengthen the results and also increase the impact providing a bigger conceptual advance by performing some of the suggested experiments. *
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
*This manuscript explores the role of GNL3/nucleostemin in DNA replication and specifically in the response of DNA replication to DNA damage. GNL3 is a predominantly nucleolar protein, previously characterised as a GTP-binding protein and shown to be necessary for effective recruitment of the RAD51 recombinase to DNA breaks. The entry point for this report is a mini screen, based on proteins identified previously by the authors to associate with replication forks by iPOND, for factors that increase gamma-H2Ax (an indicator of DNA damage) after treatment with the Top1 inhibitor camptothecin (CPT). In this mini-screen GNL3 emerged as the top hit.
The authors put forward the hypothesis that GNL3 is able to sequester the replication licensing factor ORC2 in the nucleolus and that failure of this mechanism leads to excessive origin firing and DNA resection following CPT treatment.*
- The model put forward is interesting, but currently rather confusing. However, for the reasons upon which I expand below, I do not believe that the data provide a compelling mechanistic explanation for the effects that are reported and I am left not being certain about some of the links that are made between the various parts of the study, even though individual observations appear to be of good quality. *
*Specific points: *
*The knockdown of GNL3 is very incomplete. In this regard, the complementation experiments are welcome and important. However, is it an essential protein? Can it be simply deleted with CRISPR-Cas9?
*__Response: __There are obviously variations between experiments but overall, the depletion of GNL3 using siRNA seems good in our opinion. Deletion of GNL3/nucleostemin leads to embryonic lethality in mouse (Beekman et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000755/ ; Zhu et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000763/). ES cells deleted for GNL3 can be obtain but do not proliferate probably because of their inability to enter in S-phase (Beekman et al. 2006. https://pubmed.ncbi.nlm.nih.gov/17000755/). We wanted to test if it was the case in our cellular model and we tried to delete it using CRISPR-Cas9. We managed to obtain few clones deleted for GNL3, but they grow really poorly prevented us to do experiments. To bypass this, and as suggested by the reviewer 1, we tried to make an auxin-induced degron of GNL3. Unfortunately, we did not manage to obtain homozygous clones, only heterozygous. One possibility could be that the tagging induced a partial loss of function of GNL3, and since GNL3 is essential, it may explain why we did not obtain homozygous clones. We may also want to use alternative degron systems such as Halo-Tag, but we believe this is out of the scope of the study.
__ __*Global instant fork density is not quite the same as actually measuring origin firing. Ideally, it would be good to see some more direct evidence of addition origin firing e.g. by EdU-seq (Macheret & Halazonetis Nature 2018) but this would be quite a significant additional undertaking. However, given the authors have performed DNA combing with DNA counterstain, they should be able to provide accurate measurements of origin density and inter-origin distance. *
__Response: __As indicated by the reviewer EdU-seq would need a lot of development since we are not using this approach in our team. In addition, this method can detect replication origins only if performed in the beginning of S-phase, meaning that only the early firing origins will be detected and not the others. GIFD measurement is actually directly linked with origin firing since it is counting the forks to duplicate the genome. The measurements of IODs have at least two main limitations: (1) there is a bias for short IODs due to the length of analyzed fibers and (2) it focuses only on origins within a cluster not globally. Overall, we believe that GIFD is the method of choice to measures origins firing. In addition, these experiments have been done by the lab of Etienne Schwob (see acknowledgments), a leader in the field.
*'Replication stress' is induced with CPT. This term is frequently used to describe events that lead to helicase-polymerase uncoupling (e.g. O'Connor Mol Cell 2015) but that is not the case with CPT, which causes fork collapse and breaks. Are similar effects seen with e.g. UV or cisplatin? Additionally, a clear statement of the authors definition of replication stress would be welcome. *
__Response: __We will better define the term ‘replication stress’ in the revised version of the manuscript. It should be understood, in our case, that any impediment that leads to replication fork stalling and measurable by DNA combing or Chk1 phosphorylation. We have not performed experiments using UV and cisplatin.
*It is really not clear how the authors explain the link between potential changes in origin firing and resection. i.e. What is the relationship between global origin firing and resection at a particular fork, presumably broken by encounter with a CPT-arrested TOP1 complex. What is the link mechanistically? This link needs elaborating experimentally or clearly explaining based on prior literature. *
- *__Response: __Most of our results on resection has been performed with hydroxyurea, but it is true that we saw resection in absence of GNL3 in response to CPT. Treatment with HU or CPT reduces fork speed and activates additional replication origins (see Ge et al. 2007 https://pubmed.ncbi.nlm.nih.gov/18079179/ for HU or Hayakawa et al. 2021 https://pubmed.ncbi.nlm.nih.gov/34818230/ for CPT ). When GNL3 is depleted, more forks are active, meaning more targets for HU and CPT. In addition, it is likely that the firing of additional origins in response to HU and CPT is stronger in absence of GNL3. Because of this we believe that factors required to protect stalled forks may be exhausted explaining why resection is observed. This is inspired by the work of Lukas’ lab (Toledo et al. 2013 https://pubmed.ncbi.nlm.nih.gov/24267891/) and is described in the figure 6. One obvious candidate that may be exhausted is RPA, to test this we will check if resection upon GNL3 depletion and treatment with HU is still occurring in cell lines provided by Lukas’ lab that overexpress RPA complex (described in Toledo et al.). We will explain our model more carefully in the revised version.
*Related to this, I remain unconvinced that the experiments in Figure 3 show that the effects of ATRi and Wee1i on origin firing and on resection are contingent on each other. I do not believe that the authors have adequately supported the statement (end of pg 9) 'We conclude that the enhanced resection observed upon GNL3 depletion is a consequence of increased origin firing.' The link between origin firing and resection needs really needs further substantiation and / or explanation.
*__Response: __Our rational was the following. Inhibition of ATR or WEE1 increase replication origin firing, a situation that may be like the one observed for GNL3 depletion. In Toledo et al, they show that inhibition of WEE1 or ATR induces exhaustion of RPA. This exhaustion is reduced in presence of CDC7 inhibitor, roscovitine (a CDK inhibitor that inhibits origin firing) or depletion of CDC45, indicating that this is due to excessive origin activation. In our case we show that the resection observed upon WEE1 or ATR inhibition is reduced upon treatment with CDC7 inhibitor. We conclude that excessive replication origin firing induces DNA resection. Since we observed the same thing upon GNL3 depletion (but not upon BRCA1 depletion) we conclude that excessive origin firing favors DNA resection likely through exhaustion of RPA. As indicated above we will test this hypothesis by overexpressing RPA. In addition, we now show that treatment with roscovitine decreases resection upon GNL3 depletion (this will be part of the revised manuscript), an experiment that we believe confirms that excessive replication origins firing is responsible for resection upon GNL3 depletion. As suggested by reviewer 1, we will also test if depletion of MCM also reduces resection observed in absence of GNL3.
*It is not clear whether the binding of ORC2 to GNL3 also sequesters other components of the origin recognition complex? Does loss of the ability of GNL3 to bind ORC2 actually lead to more ORC bound to chromatin? How does GNL3 contribute to regulation of origin firing under normal conditions? Is it a quantitatively significant sink for ORC2 and what regulates ORC2 release? *
Response: The results of GNL3 Bio-ID were extremely clear, we could not significantly detect any other ORC subunits than ORC2 (these data were not present in the manuscript but will be added in the revised version), therefore we believe that GNL3 may sequester/regulate only ORC2. We tried to see if GNL3 depletion was changing the binding of ORC1 and ORC2 to the chromatin, but we could not see any difference, one possibility may be that small differences are not detectable by chromatin fractionation. We believe that ChIP-seq or ORC2 or other ORC subunits in absence of GNL3 is required but this it out of the scope of the study. GNL3 may regulates the stability of the ORC complex on chromatin via ORC2 but GNL3 may also regulates other ORC2 functions, at centromeres for instance. It has been shown indeed that ORC2 plays roles possibly independently of the ORC complex (see Huang et al. 2016 https://doi.org/10.1016/j.celrep.2016.02.091 or Richards et al. 2022 https://doi.org/10.1016/j.celrep.2022.111590 for instance). How exactly this is affecting origin firing is still mysterious. This is something we are planning to address in the future.
We do not know if it is a quantitatively sink for ORC2 or how this is regulated, however we believe that the ability of GNL3 to accumulate in the nucleolus may sequester ORC2. Consistent with this, we show that a mutant of GNL3 (GNL3-dB) that diffuses in the nucleoplasm interacts more with ORC2 in the nucleoplasm suggesting a release. As suggested by reviewer 1 we will now test if the interaction between ORC2 and GNL3-dB is dependent on the level of expression of GNL3-dB. In addition, we now show that expression of GNL3-dB increases replication origin firing like GNL3 depletion (data that will be added in the revised version), suggesting that regulation of ORC2 is the major cause of increased firing upon GNL3 depletion.
*Minor points: *
*All blots should include size markers *
__Response: __We will add them
*Some use of language is not sufficiently precise. For instance: ** - the meaning of 'DNA lesions' at the end of the first paragraph of the introduction needs to be more explicit. *
* - the approach to measurement of these 'lesions' (monitoring gamma-H2Ax) needs to be spelled out explicitly, e.g. line 4 of the last paragraph of the introduction. *
*
- 'we observed that the interaction between GNL3-dB and ORC2 was stronger' ... I do not see how number of foci indicates necessarily the strength of an interaction. *
* - in many places throughout 'replication origins firing' should be 'replication origin firing' (or 'firing of replication origins'). *
__Response: __We will correct these language mistakes.
__Reviewer #2 (Significance (Required)): __
The model put forward here has the potential to shed light on an important facet of the cellular response to DNA damage, namely the control of origin firing in response to replication stress that will certainly be of interest to the DNA repair / replication community and possibly more widely. The roles of GNL3 are poorly understood and this study could improve this state of affairs. However, the gaps in the mechanism outlined above and somewhat confusing conclusions do limit the ability of the paper to achieve this at present.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
*In this study, Lebdy et al propose a new mechanism to regulate the resection of nascent DNA at stalled replication forks. The central element of this mechanism is nucleolar protein GNL3, whose downregulation with siRNA stimulates DNA resection in the presence of stress induced by HU (Figure 1). Resection depends on the activity of nucleases MRE11 and CtIP, and can be rescued by reintroducing exogenous GNL3 protein in the cells (Figure 1G). GNL3 downregulation decreases fork speed and increases origin activity, without any strong effect on replication timing (Figure 2). Inhibition of Dbf4-dependent kinase CDC7 (a known origin-activating factor) also restricts fork resection (Figure 3). GNL3 interacts with ORC2, one of the subunits of the origin recognition complex, preferentially in nucleolar structures (Figure 4). A mutant version of GNL3 (GNL3-dB) that is not sufficiently retained in the nucleoli fails to prevent fork resection as the WT protein (Figure 5). In the final model, the authors propose that GNL3 controls the levels of origin activity (and indirectly, stalled fork resection) by maintaining a fraction of ORC2 in the nucleoli (Figure 6). *
This model is interesting and provocative, but it also relies on a significant degree of speculation. The authors are not trying to "oversell" their observations, because the Discussion section entertains different interpretations and possibilities, and the model itself contains several interrogative statements (e.g. "ORC2-dependent?"; "exhaustion of factors?").
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While the article is honest about its own limitations, the major concern remains about its highly speculative nature. I have some questions and suggestions for the authors to consider that could contribute to test (and hopefully support) their model. *
-
*If GNL3 downregulation induces an excess of licensed origins and mild replicative stress resulting in some G2/M accumulation (Figure 2), what is the consequence of longer-term GNL3 ablation? Do the cells adapt, or do they accumulate signs of chromosomal instability? (micronuclei, chromosome breaks and fusions, etc) * __Response: __This is an important point also raised by Reviewer 2: deletion of GNL3 leads to embryonic lethality in mouse and ES cells deleted for GNL3 do not proliferate and fail to enter into S-phase. Consistent with this, the clones deleted for GNL3 that we obtained using CRISPR-Cas9 grow poorly, thus preventing us to do experiments. To our knowledge micronuclei and chromosome breaks have never been analyzed upon transient depletion of GNL3 using siRNA. However, it is well established that depletion of GNL3 induces phosphorylation of H2A.X) and the formation of ATR, RPA32 and 53BP1 foci due to S-phase arrest (Lin et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24610951/ ; Meng et al. 2013. https://pubmed.ncbi.nlm.nih.gov/23798389/). DNA lesions have also been visualized by comet assay (Lin et al. 2019. https://pubmed.ncbi.nlm.nih.gov/30692636/). Consistent with this we observed a weak increased of DNA double-strand breaks upon GNL3 depletion using pulse-field gel electrophoresis as well as mitotic DNA synthesis (MiDAS). We can integrate this data in the revised version of the manuscript if required. To sum up, it is clear that GNL3 depletion is inducing problems during S-phase that may lead to possible genomic rearrangements.
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The model relies on the link between origin activity and stalled fork resection that is almost exclusively based on the results obtained with CDC7i (Figure 3). But CDC7 has other targets besides pre-RC components at the origins, such as Exo1 (from the Weinreich lab, cited in the study), MERIT40 and PDS5B (from the Jallepalli lab, also cited). The effect of CDC7i could be exerted through these factors, which are linked to fork stability and DNA resection. The loss of BRCA1 (Figure 3F) could somehow entail the loss of control over these factors. Could the authors check the possible participation of these proteins?*
__Response: __It is true that CDC7 has other targets than pre-RC components. We therefore decided to inhibit origin firing using roscovitine, a broad CDK inhibitor, a strategy previously used in Lukas lab (Toledo et al. 2013. https://pubmed.ncbi.nlm.nih.gov/24267891/). We observed that treatment with roscovitine decreased significantly resection observed upon GNL3 depletion, confirming the link between origin activity and stalled fork resection. This will be integrated in the revised version of the manuscript. As asked by Reviewer 1, we will also perform depletion of MCM to strength our model.
Exo1 is indeed a target of CDC7 as shown by the Weinreich lab (Sasi et al. 2018. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111017/) however the authors do not formally demonstrate that Exo1 phosphorylation is required for its activity. We observed that depletion of Exo1 significantly reduced resection upon GNL3 depletion (data that will be added in the revised version), indicating that the effect of CDC7 inhibitor could be exerted via the control of Exo1. This is why our BRCA1 control is important, it is well stablished that Exo1 is required for nascent strand degradation upon BRCA1 depletion (Lemaçon et al. 2017. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643552/) but CDC7 inhibition has no effect on resection upon BRCA1 depletion suggesting that resection by Exo1 may not be regulated by CDC7 in our context.
As stated by the reviewer MERIT40 and PDS5B are targets of DDK kinases (Jones et al. 2021 https://doi-org.insb.bib.cnrs.fr/10.1016/j.molcel.2021.01.004) and seem to be required for protection of nascent DNA and in response to HU. However, little is known about the role(s) of these proteins and we think that adding them will complicate message. We hope the reviewer understands this.
The model also relies on the fact that GNL3-dB mutant (not retained in the nucleoli) is not sufficient to counteract fork resection induced by HU (Figure 5G). The authors should test directly whether GNL3-dB induces extra origin activation, using their available DNA fibers-based technique.
__Response: __This is an excellent point. We have now GIFD (Global Instant Fork Density) data that shows that the number of active forks is increased upon dB GNL3-dB expression. It demonstrates that when GNL3 is no longer retained in the nucleolus more origins are active. These data will be integrated in the revised version of the manuscript, and we believe further support the regulation of ORC2 by GNL3.
*Finally, the model implies an exquisite regulation of the amount of ORC2 protein, which could influence the number of active origins and the extent of fork resection in case of stress. In this scenario, one could predict that ORC2 ectopic expression would have similar, or even stronger effects, than GNL3 downregulation. Is this the case? *
__Response: __We completely agree with this prediction. However, we are afraid that overexpression of ORC2 may have indirect effects due to the many described functions of ORC2, therefore it may be difficult to interpret the data. We will give a try anyway.
*Even if the connection between origins and fork resection could be firmly established, the molecular link between them remains enigmatic. The authors hint (as "data not shown") that it is neither mediated by RPA nor RAD51. Unfortunately, the reader is left without a clear hypothesis about this point. *
__Response: __We will add data that show that RPA and RAD51 recruitment is not affected by GNL3 depletion. However, the sensitivity of chromatin fractionation approach may be too weak to detect low differences. Based on the work of Lukas Lab (Toledo et al. 2013 https://pubmed.ncbi.nlm.nih.gov/24267891/) one possible mechanism may be exhaustion of the pool of RPA. This may link the excessive activation of origins observed upon GNL3 depletion and resection. To test this, we will check if resection upon GNL3 depletion and treatment with HU is still occurring in cell lines that overexpress RPA complex (described in Toledo et al.) that we obtained from Lukas’ lab.
__ __ **Referees cross-commenting**
__ __In addition to each reviewer's more specific comments, the three reviews share a main criticism: the lack of mechanistic information about the proposed link between origin activity and resection of nascent DNA at stalled forks.
__Reviewer #3 (Significance (Required)): __
In principle, this study would appeal to the readership interested in fundamental mechanisms of DNA replication and the cellular responses to replicative stress.
For the reasons outlined in the previous section, I believe that in its current version the study is not strong enough to provide a new paradigm about origins being regulated by partial ORC2 sequestering at nucleoli. The other potentially interesting advance is the connection between frequency of origin activity and the extent of nascent DNA resection at stalled forks, but the molecular link between both remains unknown.
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Referee #3
Evidence, reproducibility and clarity
In this study, Lebdy et al propose a new mechanism to regulate the resection of nascent DNA at stalled replication forks. The central element of this mechanism is nucleolar protein GNL3, whose downregulation with siRNA stimulates DNA resection in the presence of stress induced by HU (Figure 1). Resection depends on the activity of nucleases MRE11 and CtIP, and can be rescued by reintroducing exogenous GNL3 protein in the cells (Figure 1G). GNL3 downregulation decreases fork speed and increases origin activity, without any strong effect on replication timing (Figure 2). Inhibition of Dbf4-dependent kinase CDC7 (a known origin-activating factor) also restricts fork resection (Figure 3). GNL3 interacts with ORC2, one of the subunits of the origin recognition complex, preferentially in nucleolar structures (Figure 4). A mutant version of GNL3 (GNL3-dB) that is not sufficiently retained in the nucleoli fails to prevent fork resection as the WT protein (Figure 5). In the final model, the authors propose that GNL3 controls the levels of origin activity (and indirectly, stalled fork resection) by maintaining a fraction of ORC2 in the nucleoli (Figure 6).
This model is interesting and provocative, but it also relies on a significant degree of speculation. The authors are not trying to "oversell" their observations, because the Discussion section entertains different interpretations and possibilities, and the model itself contains several interrogative statements (e.g. "ORC2-dependent?"; "exhaustion of factors?").
While the article is honest about its own limitations, the major concern remains about its highly speculative nature. I have some questions and suggestions for the authors to consider that could contribute to test (and hopefully support) their model.
- If GNL3 downregulation induces an excess of licensed origins and mild replicative stress resulting in some G2/M accumulation (Figure 2), what is the consequence of longer-term GNL3 ablation? Do the cells adapt, or do they accumulate signs of chromosomal instability? (micronuclei, chromosome breaks and fusions, etc)
- The model relies on the link between origin activity and stalled fork resection that is almost exclusively based on the results obtained with CDC7i (Figure 3). But CDC7 has other targets besides pre-RC components at the origins, such as Exo1 (from the Weinreich lab, cited in the study), MERIT40 and PDS5B (from the Jallepalli lab, also cited). The effect of CDC7i could be exerted through these factors, which are linked to fork stability and DNA resection. The loss of BRCA1 (Figure 3F) could somehow entail the loss of control over these factors. Could the authors check the possible participation of these proteins?
- The model also relies on the fact that GNL3-dB mutant (not retained in the nucleoli) is not sufficient to counteract fork resection induced by HU (Figure 5G). The authors should test directly whether GNL3-dB induces extra origin activation, using their available DNA fibers-based technique.
- Finally, the model implies an exquisite regulation of the amount of ORC2 protein, which could influence the number of active origins and the extent of fork resection in case of stress. In this scenario, one could predict that ORC2 ectopic expression would have similar, or even stronger effects, than GNL3 downregulation. Is this the case?
- Even if the connection between origins and fork resection could be firmly established, the molecular link between them remains enigmatic. The authors hint (as "data not shown") that it is neither mediated by RPA nor RAD51. Unfortunately, the reader is left without a clear hypothesis about this point.
Referees cross-commenting
In addition to each reviewer's more specific comments, the three reviews share a main criticism: the lack of mechanistic information about the proposed link between origin activity and resection of nascent DNA at stalled forks.
Significance
In principle, this study would appeal to the readership interested in fundamental mechanisms of DNA replication and the cellular responses to replicative stress.
For the reasons outlined in the previous section, I believe that in its current version the study is not strong enough to provide a new paradigm about origins being regulated by partial ORC2 sequestering at nucleoli. The other potentially interesting advance is the connection between frequency of origin activity and the extent of nascent DNA resection at stalled forks, but the molecular link between both remains unknown.
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Referee #2
Evidence, reproducibility and clarity
This manuscript explores the role of GNL3/nucleostemin in DNA replication and specifically in the response of DNA replication to DNA damage. GNL3 is a predominantly nucleolar protein, previously characterised as a GTP-binding protein and shown to be necessary for effective recruitment of the RAD51 recombinase to DNA breaks. The entry point for this report is a mini screen, based on proteins identified previously by the authors to associate with replication forks by iPOND, for factors that increase gamma-H2Ax (an indicator of DNA damage) after treatment with the Top1 inhibitor camptothecin (CPT). In this mini-screen GNL3 emerged as the top hit.
The authors put forward the hypothesis that GNL3 is able to sequester the replication licensing factor ORC2 in the nucleolus and that failure of this mechanism leads to excessive origin firing and DNA resection following CPT treatment.
The model put forward is interesting, but currently rather confusing. However, for the reasons upon which I expand below, I do not believe that the data provide a compelling mechanistic explanation for the effects that are reported and I am left not being certain about some of the links that are made between the various parts of the study, even though individual observations appear to be of good quality.
Specific points:
The knockdown of GNL3 is very incomplete. In this regard, the complementation experiments are welcome and important. However, is it an essential protein? Can it be simply deleted with CRISPR-Cas9?
Global instant fork density is not quite the same as actually measuring origin firing. Ideally, it would be good to see some more direct evidence of addition origin firing e.g. by EdU-seq (Macheret & Halazonetis Nature 2018) but this would be quite a significant additional undertaking. However, given the authors have performed DNA combing with DNA counterstain, they should be able to provide accurate measurements of origin density and inter-origin distance.
'Replication stress' is induced with CPT. This term is frequently used to describe events that lead to helicase-polymerase uncoupling (e.g. O'Connor Mol Cell 2015) but that is not the case with CPT, which causes fork collapse and breaks. Are similar effects seen with e.g. UV or cisplatin? Additionally, a clear statement of the authors definition of replication stress would be welcome.
It is really not clear how the authors explain the link between potential changes in origin firing and resection. i.e. What is the relationship between global origin firing and resection at a particular fork, presumably broken by encounter with a CPT-arrested TOP1 complex. What is the link mechanistically? This link needs elaborating experimentally or clearly explaining based on prior literature.
Related to this, I remain unconvinced that the experiments in Figure 3 show that the effects of ATRi and Wee1i on origin firing and on resection are contingent on each other. I do not believe that the authors have adequately supported the statement (end of pg 9) 'We conclude that the enhanced resection observed upon GNL3 depletion is a consequence of increased origin firing.' The link between origin firing and resection needs really needs further substantiation and / or explanation.
It is not clear whether the binding of ORC2 to GNL3 also sequesters other components of the origin recognition complex? Does loss of the ability of GNL3 to bind ORC2 actually lead to more ORC bound to chromatin? How does GNL3 contribute to regulation of origin firing under normal conditions? Is it a quantitatively significant sink for ORC2 and what regulates ORC2 release?
Minor points:
All blots should include size markers
Some use of language is not sufficiently precise. For instance:
- the meaning of 'DNA lesions' at the end of the first paragraph of the introduction needs to be more explicit.
- the approach to measurement of these 'lesions' (monitoring gamma-H2Ax) needs to be spelled out explicitly, e.g. line 4 of the last paragraph of the introduction.
- 'we observed that the interaction between GNL3-dB and ORC2 was stronger' ... I do not see how number of foci indicates necessarily the strength of an interaction.
- in many places throughout 'replication origins firing' should be 'replication origin firing' (or 'firing of replication origins').
Significance
The model put forward here has the potential to shed light on an important facet of the cellular response to DNA damage, namely the control of origin firing in response to replication stress that will certainly be of interest to the DNA repair / replication community and possibly more widely. The roles of GNL3 are poorly understood and this study could improve this state of affairs. However, the gaps in the mechanism outlined above and somewhat confusing conclusions do limit the ability of the paper to achieve this at present.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript Lebdy et al. describe a new role of GNL3 in DNA replication. They show that GNL3 controls replication fork stability in response to replication stress and they propose this is due to the regulation of ORC2 and the licensing of origins of replication. Their data suggest that GNL3 regulates the sub nuclear localization of ORC2 to limit the number of licensed origins of replication and to prevent resection of DNA at stalled forks in the presence of replication stress.
While many of the points of the manuscript are proven and well supported by the results, there are some experiments that could improve the quality and impact of the manuscript. The main issue is that the connection between the role of GNL3 in controlling ORC2, the firing of new origins and the protection of replication forks is not clearly established. At the moment the model relies on mainly correlative data. In order to further substantiate the model, we propose to address some of the following issues:
- The authors indicate that RPA and RAD51 accumulation at stalled forks is not affected by GNL3 depletion. These data should be included and other proteins should be analysed. In addition, the role of helicases could be explored through the depletion of the main helicases involved in the remodelling of the forks.
- The proposed model implies that GNL3 depletion leads to increased origin licensing. FThe authors should address if the primary effect of GNL3 depletion is on origin firing by using CDC7 inhibition in the absence of stress (Rodríguez-Acebes et al., JBC 2018).
- A way to prove that origin firing mediates the effect of GNL3 on fork protection would be to reduce the number of available origins. The depletion of MCM complexes has been shown to limit the number of back-up origins that are licensed and leads to sensitivity to replication stress (Ibarra et al., PNAS 2008). If GNL3 depletion results in increased number of origins, this effect should be prevented by the partial depletion of MCM complexes.
- Alternatively, the authors could try to modulate the depletion of GNL3. Origin licensing takes place in the G1 phase and thus the depletion of GNL3 by siRNA could affect the following S phase. Using an inducible degron for GNL3 depletion would allow to deplete GNL3 in G1 or S phase specifically. If the model is correct, the removal of GNL3 in S phase should not affect fork protection but removing GNL3 in the previous G2/M phase should reduce the number of licensed origins and lead to impaired fork protection.
In addition to the connection GNL3-origin firing-fork protection, it is unclear how the lack of GNL3 in the nucleolus and the change in the sub nuclear localization of ORC2 controls origin firing and resection. The strong interaction observed between GNL3-dB and ORC2, and the subsequent change in ORC2 localization does not explain how origin licensing can be affected. In this sense, the authors could address:
- Does the depletion of GNL3 and the expression of GNL3-dB affect the formation of the ORC complex, its subnuclear localization or its binding to chromatin? The authors have not explored if the interaction of GNL3 with ORC2 is established in the context of the ORC complex. An IF showing NOP1 with PLA data from GNL3-dB and ORC2 is needed to analyse how the expression of increasing amounts of GNL3-dB affects ORC2.
- In order to confirm if the mislocalization of ORC2 by the expression of GNL3-dB increases origin firing and mediates the effects on fork protection the authors could check DNA resection levels inhibiting CDC7 in high GNL3-dB conditions. Also, the levels of MCM2, phosphor-MCM2, CDC45, have not been analysed upon expression of GNL3-dB.
- The data in the paper suggest that GNL3 may affect the role of ORC2 in centromeres. Since depletion of GNL3 leads to increased levels of H2AX, it would be interesting to address if this damage is due to incomplete replication in centromeres by analysing the co-localization of H2AX and centromeric markers both in unstressed conditions and upon the induction of replication stress.
Minor points:
- In the initial esiRNA screen the basal levels of H2AX should also be shown.
- Figure EV1B: I think the rank needs another RS mark to see better the effect of each esiRNA on DNA lesions (high variability in all the conditions showed).
- Figure 1C and Figure EV1D/E: the quantification of the pCHK1/CHK1 levels could be included to show that there are no changes in phosphorylation upon GNL3 depletion.
- In the first section of the results, at the end Figure 4B is incorrectly called for.
- The levels of GLN3 expression in 293 cells should be already included in section GNL3 interacts with ORC2.
- The full MS data needs to be included for both GNL3 and ORC2.
- Figure 4B should be improved, since there is a faint band in the IgG mouse control.
Significance
The work is nicely written, the figures are well presented and the experiments have the necessary controls. It provides relevant information to understand how replication stress is controlled and linked to replication fork protection through origin firing. These results are relevant to the field, linking GNL3 to origin firing and with potential to help understand the role of GNL3 in cancer. They provide new information and can give rise to new studies in the future. Many of the conclusions of the manuscript are well supported. Additional support for some of the main claims would strengthen the results and also increase the impact providing a bigger conceptual advance by performing some of the suggested experiments.
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Reply to the reviewers
1. General Statements [optional]
We are grateful for the thoughtful comments and suggestions from the reviewers which we feel have resulted in a manuscript that is both clearer to the reader and more rigorous. We have addressed the suggested revisions point by point below:
2. Point-by-point description of the revisions
Reviewer #1 Major comments:
Figure 2E. This shows the residence time of Cse4 without Ndc10 association. How does this compare to the residence time on mutant CEN3 (Supplemental Figure 1). It looks like Cse4 still binds to CEN3 with some specificity even in the absence of Ndc10. Does this suggest that Cse4 has some intrinsic ability to recognize CEN3? Alternatively, Ndc10 is still required for Cse4 binding but is below detection in the Cse4-alone residence lifetimes. Ideally, the authors would compare this with Cse4 binding in an Ndc10 mutant.
We thank the reviewer for this interesting question. As suggested, we analyzed Cse4 behavior on the mutant CDEIIImut CEN3 DNA, which does not stably recruit Ndc10 (Figure 1C), using the real-time colocalization assay. Although overall Cse4 associations were reduced, we still observed transient interactions, consistent with the possibility that Cse4 has some intrinsic ability to recognize CEN3. Kaplan-Meier analysis of the lifetimes of Cse4 colocalizations on CDEIIImut CEN3 DNA were significantly reduced when compared to CEN DNA (Figure EV1G, H). We have added these points to the text (p. 10, lines 29-31 and p. 11, lines 1-8).
Figure 3 explores the very interesting relationship between Scm3 dynamics and Cse4 binding but I feel that the data is not fully flushed out. A key finding is that Cse4 can potentially bind to CEN DNA prior to engaging with Scm3 to be incorporated. This predicts that Cse4 should bind first and then colocalize with Scm3. Can this be detected in the timing of the traces? How often does Scm3 bind to already prebound Cse4 and does this correlate with Cse4 residing longer?
We performed new and more rigorous analyses of the data to address these questions in the revised manuscript. After our analysis to calculate ternaryScm3 off-rates, we analyzed the relative timing of these ternary residences and found that indeed Cse4 can bind to CEN DNA prior to Scm3 and these events do correlate with Cse4 residing longer. Complete analysis of the binding order of Cse4 and Scm3 ternary residences revealed that Scm3 bound to CEN3 DNA prior to Cse4 more often than Cse4 preceding Scm3 (46% vs 34% of ternary residences) with the remaining 20% arriving simultaneously (Figure EV2E). Despite a difference in prevalence, the median lifetimes of both Scm3-Firstand Scm3-Last contexts were similar to each other (Figure EV2F) and both were significantly stabilized when compared to non-ternary residences. These results highlight a potential mechanism where Scm3 catalyzes stable Cse4 incorporation at centromeric DNA instead of delivering it to the centromere regardless of the order of arrival. These data are now reported and discussed in the revision (p. 12, line 11-p.13, line 10).
A related and perhaps even more interesting point is whether Scm3 is involved in "loading" of Cse4. If so, then one would expect that once Cse4 is assembled into nucleosomes it should be stable, even if Scm3 leaves. Can the authors extract this from the data? Alternatively, it is possible that Scm3 remains associated to Cse4 to maintain the nucleosome which would imply a more extended role for Scm3 apart from assembly alone. It would be interesting if information on this can be extracted from the data.
Using our updated analysis of ternaryScm3 Cse4 residences, feel we have addressed this possibility indirectly in a couple of ways. First, we found that in instances where ternary Scm3/Cse4 complexes are formed, Scm3 co-occupied the CEN DNA an average of 56.0% of the total Cse4 residence time, which is distinctly shorter than the 84% of the total Cse4 residence that was occupied by Ndc10 in Cse4/Ndc10 ternary residences. These findings are consistent with a Scm3-turnover mechanism that occurs post ternary complex formation with Cse4 as we found that Cse4 off-rates were significantly reduced after Scm3 association (Figure 3D).
Second, further analysis of Scm3 residence behavior revealed that there was no significant stabilization of Scm3 after ternary Cse4/Scm3 complex formation vs non-ternary Scm3 residences found in either off-rates (33 hr-1 vs 32 hr-1, Figure EV2C) or median lifetimes (45 s vs 52 s, Figure EV2D). These results indicate that Scm3 association is not stabilized like Cse4 after ternary complex formation and points to a potential catalytic role in Cse4 nucleosome formation, leaving a stable Cse4 nucleosome after turnover. We reported these findings in the revision results section (p. 12, lines 11-16) as well as briefly within the discussion.
Even in the presence of Scm3 and CCAN components, Cse4 appears to have a limited lifetime in the in vitro assay compared to in vivo stability. The authors should speculate on whether activities exist in their extract that actively disassembles nucleosomes. Perhaps ATP could be depleted to inactivate remodellers?
This is an excellent suggestion that we addressed with a new experiment. We performed an endpoint localization experiment with lysate containing fluorescently labeled Ndc10 and Cse4 and then removed the lysate and incubated the slide for 24hr in imaging buffer at RT. Strikingly, the proteins were maintained at the CEN DNA with a high protein total colocalization (~75% retention) (shown in Figure EV1B, C). These data suggest that the lysates may contain negative regulatory factors and we have added this point in the revised text (p. 9 lines 6-25).
We were not able to address whether the removal of ATP stabilized the proteins because we previously found that ATP depletion of the lysates completely abrogates kinetochore assembly in extracts. We will need to eventually dissect the role of remodelers in future work using a different approach.
For Figure 6, it is not clear why AT-track mutants of CDEII are labeled as genetically stable and genetically unstable. This is confusing as the "genetically stable" show a more than 10-fold increase in chromosome loss rates. Perhaps these can be renamed into "unstable" and "very unstable" or "weak" and "strong" mutants, just to make clear that these classes are both poorer than wild type.
We had deferred to the nomenclature used in the previous study (Baker and Rogers, 2005) which initially characterized these mutants. To avoid this confusion, we have renamed these mutants “unstable” and “very unstable” as suggested to make it clearer that none of these synthetic mutants fully recapitulate WT CEN3 behavior.
Finally, it would be wonderful to include data to assess whether a full Cse4 nucleosome is assembled or a partial nucleosome e.g. just Cse4/H4 tetrasomes. This could be done by tracking the accumulation of H2A or H2B at the CEN3. This would give further insight into what step Scm3 catalyses.
This is a very interesting suggestion that we were not able to directly address. Epitope tagging of these histone proteins in Saccharomyces cerevisiae with endogenous fluorophores has proved challenging due to gene duplication, overall sensitivity to histone levels within the cells and disruption of histone function by epitope tagging. We hope to find a workable method to address this in the future to address this question directly.
Minor comments:
Typo on page 5, line 1 "nucleosom" missing an e.
We have corrected this in the revised text.
Kaplan-Meyer should be spelled Kaplan-Meier
We have corrected this in the revised text.
The term "censored" is mentioned across many figures but comes up just ones in the methods where it is not clearly explained. Perhaps this could be clarified in the legend.
We have now provided a clear explanation of the term “censored” in the text on p. 28, lines 25-27. It has also been added to the figure legends and reported in the Statistical tests section of the methods section to address this point.
The abstract states that Cse4 can arrive at the centromere without its chaperone. More likely, Cse4 is in complex with other chaperones that may allow it to bind. Perhaps the abstract can be modified to read "Cse4 can arrive at the centromere without its dedicated centromere-specific chaperone Scm3..."
We updated the abstract to reflect this point in the revised text.
Related to this point, the discussion states the possibility that Cse4 can initially bind to CEN3 via other more general chaperones. However, it should be acknowledged that transient Cse4 binding in their assay may simply occur through mass action due to high concentrations of CEN3 DNA. In vivo, this transient binding may not be that relevant.
We acknowledge this potential caveat in the discussion section (p. 20 lines 15-18), although we feel this is somewhat unlikely due to our observation of significantly reduced Cse4 binding on CDEIIImut DNA despite DNA concentrations being similar to previous assays (Figure EV3A). We speculate that some of this transient behavior is at least in part driven by two major factors: negative regulatory factors within our cellular extracts that counter nucleosome formation (as explored in Figure EV1B-C) and photostability of the endogenous fluorophores used within the study (Figure EV1D-E). These points were highlighted within the second paragraph of page 9.
Reviewer #2 Major comments:
- Figure S1A-D seem like some of the most compelling data in the paper to bolster the rigor of their experimental setup. There appears to be plenty of space to include these data in the main figure set in Figure 1 after panel D. The authors would be well served to consider moving S1A-D somewhere in the main figure set.
We appreciate the helpful feedback on the importance of the date found in Supplemental Figure 1 and have now incorporated it into Figure 1 within the main text as suggested.
The authors conclude that Ndc10 recruits HJURP(Smc3) to the yeast point centromeres. If this is the case, can the TIRFM assay measure ternary residence lifetimes complexes between Ndc10/HJURP(Smc3)/CenDNA?
We made the conclusion that Ndc10 recruits Scm3 based on previous publications showing this requirement in vivo. We have now attempted to address this in our assay indirectly by monitoring Scm3 behavior on the CDEIIImut CEN3 DNA that lacks Ndc10. Surprisingly, we found that Scm3 interacted similarly with CDEIIImut CEN3 DNA and actually showed an increase in median lifetimes vs. CEN3 DNA (Figure EV2B), suggesting its intrinsic DNA-binding activity may be the primary driver of its CEN DNA binding and that stable Cse4 association is required for its turnover. These data suggest that Ndc10 is not driving Scm3 interaction (or targeting) to CEN3. We are grateful to the reviewer for pointing this out and have adjusted our conclusions in the revised manuscript (p. 12, line 26-p.13 line 10).
Throughout the manuscript short- and long- term residence lifespans are mentioned, referencing the figures containing lines with lengths depicting residence times. This is a qualitative reference to short and long residences. Can the authors provide a graph for short-term ( 300 s) residence life-spans for, CENPA alone, CENPA/Ndc10, and CENPA/HJURP on CEN3 DNA? Or some figure similar to Figure 3C, but reporting the proportion of short-term vs long-term residence?
We typically used Kaplan-Meier survival function estimates to compare binding behavior but agree that quantification of residences within these contexts may be easier for the reader to follow. We have therefore quantified short-term ( 300 s) as suggested and added them as a panel (F) to Figure EV1 and as panel (E) in Figure 3.
The choice of CCAN components for analysis in Fig. 5 is interesting, but many readers may be curious why Mif2 wasn't selected for disruption, since it has such a cozy placement with CENP-A and CEN DNA. Can choice to not include Mif2 mutants/degrons be mentioned/justified in the text (unless, even better yet, they choose to address Mif2 role directly in new experimentation)?
We relied on structural models to choose CCAN proteins that are in close proximity to the DNA. Because Mif2 is not in these structures, we did not include it in our studies. We have explained this in the revised text (p. 16 lines 14-17) and agree it is an interesting future area of study.
Minor comments:
- Are these whole cell extracts (WCE) DNA-free? I'm curious if there is any competition from endogenous DNA from the yeast cellular extract.
The extracts are not DNA-free so it is likely there is some competition from endogenous DNA. We have avoided enzymatically removing the DNA since the TIRF assay depends on the integrity of DNA.
In relation to Mif2 and comment #4 above, do the authors make any connection to their results with synthetic nucleosome sequences not being conducive to yeast centromere formation with the prior observation (Allu et al 2019) using recombinant components that the human version of Mif2 more easily saturates its binding sites on CENP-A nucleosomes when they are assembled with natural centromere DNA rather than the Widom 601 sequence?
We did not speculate on the role of Mif2 and stability of synthetic nucleosome sequences. This is an interesting point but the differences between the yeast and human systems combined with the fact we have not yet started to study Mif2 made it seem too premature to include in this manuscript.
Providing a gel (or other measure) of the DNA templates (750, 250, and 80 bp) used in TIRFM assay would be nice to show to confirm the designed size of the pre-tethered DNA.
We agree this is a helpful control and we have now included it as a panel in Figure EV5B.
- Some of the references to figures/figure panels in the main text do not match the figures. (discussion, pg 16, paragraph 1 & 2;pg 18, paragraph 1).
We have updated references mentioned to reflect to the correct figure in both sections of the discussion.
Reviewer #3 Major comments: -->Statistics are highly recommended for all the data in the ms.
We have included log-rank analysis to instances where two survival functions were plotted together where appropriate. P-values for all these analyses were reported in the appropriate figure legends.
- At what rate is data collected in the TIRFM setup. For clarity for the reader, it is important to provide imaging details for time-lapse. What is the impact of photobleaching on the stability of the fluorophore signal? Please clarify.
This is a helpful suggestion and we have now included imaging details (like time intervals for each channel) when the real-time assay is introduced in the results section (p. 8, lines 10-12). We have also provided additional details for the photobleaching estimates and how these might censor data in turn (p. 9, lines 14-25). Although photobleaching is a primary limitation of the time-lapse assays, we point out that it is appropriate to compare protein behavior under identical imaging parameters within differing contexts. We also noted that we typically compared time-lapse behavior (which is affected by photobleaching) with endpoint assays to ensure consistent behavior.
- The power of single-molecule technique is precisely that such data can be made quantitative. Indeed, the Kaplan-Meyer graphs do show nice quantitative results. Unfortunately, in the text few quantitative measurements are reported. In fact, the Kaplan-Meyer graphs can be interpreted in a quantitative manner such as probability of residency time. Although in most cases the statistical significance between two conditions can be expected, this is not formally calculated and shown. What is the 50% survival time of Cse4 alone or with Ndc10, for instance? This manuscript would greatly benefit from a quantitative approach to the data, including a summary table of the results of the various conditions tested. Please add this important table.
We initially put the quantitative data in the figure legends but omitted it from the main text for simplicity but appreciate the Reviewer’s point. We note that we performed log-rank tests on all Kaplan-Meier analyses that are plotted on the same graph to provide statistical differences where applicable and have included all P-values in the figure legends. In response to the suggestion, we have now also included a table (Table 1) that contains the median survival time for all proteins tested as well as the median survival times for the differing contexts tested for quick reference and easier comparison for the reader.
- This reviewer would expect that the endpoint (90 min) would roughly reflect the occupancy results from time-lapse (45 min) experiments. Based on the data presented in Figures 1, 2, S1-3, this does not appear to be the case. 50% of Cse4-GFP and 70% Ndc10-mCherry colocalized with CEN3 DNA in the endpoint experiment, whereas in Fig 2C, ~30 and ~52 traces were positive for Cse4-GFP and Ndc10-mCherry, resp. with the former having drastically lower residency survival compared to Ndc10-mCherry. If indeed, 50% of Cse4-GFP makes it to the endpoint, about 50% of all traces should reach the end of the 45 minutes time-lapse window. Only about 1/3 of all positive Cse4-GFP traces can be seen at the end of the 45 min window. Could this be due to technical issues of photostability of GFP? Why does the colocalization of Cse4 signal with the DNA signal disappear so readily? Are Cse4 so unstable? Is the same true for canonical H3 nucleosomes? This unlikely true for nucleosomes in cells.
This is a valid concern, and we appreciate the thoughtful critique. The inconsistency noted between the initial endpoint colocalization and those reported later in the paper is mainly due to the difference between yeast strains carrying Cse4 tagged alone in comparison to multiple kinetochore proteins with tags that exhibit mild genetic interactions. This point is now explained in the revised text (p. 8, line 29-p. 9. line 3).
Photostability is also a factor in the live imaging experiments compared to the endpoint localization assays. However, our photobleaching estimates suggest that the Cse4 lifetimes are not limited by photobleaching (Figure EV1D, E) so we do not believe that accounts for the differences between experiments and it is mainly the presence of multiple epitope tags.
In regard to why Cse4 is not more stable, Reviewer 1 had the same question so we performed an experiment to address whether the lysate contains negative regulatory factors. We found that Cse4 is stable once the lysate is removed (Figure EV1B, C), consistent with the idea that there are factors that disrupt it in the lysate. We discuss these potential reasons for transient association in the revised text (p. 9, lines 4-25).
It should also be noted that there are clear differences in nucleosome formation in reconstitutions and within our extracts, as evident by the Widom-601 DNA data (Figure 6D). This was not necessarily unexpected, as extracts are a much more complex medium, but we are encouraged by the fact that at least once formed, these Cse4-containing particles on CEN DNA are perhaps more stable than their reconstituted counterparts that seem to be so far unsuitable for structural studies.
Along the same lines, in Suppl Fig 3 there is a disconnect between residency survival and endpoint colocalization on either CEN3, CEN7, or CEN9. What could be the underlying mechanism between the discordance of endpoint results and time-lapse results? Could this be the result of experimental differences?
We are grateful that this discrepancy was highlighted to us, as upon closer examination we discovered that endpoint colocalization analysis had not been correctly updated in the figure to include data from equivalent genetic backgrounds as the CEN3 and CEN9 assays. Updating the figure in Appendix Figure S2 to include this data remedied this discrepancy.
- What fraction of particles show colocalization between Cse4-GFP and Ndc10-mCherry? What fraction of occupancy time show colocalization between Cse4-GFP and Ndc10-mCherry? Altogether, understanding the limitation and benefits of endpoint analysis and time-lapse analysis in this particular experimental set-up is important to be able to interpret the results. Please clarify these points.
We have now added particle numbers to all survival estimate plots which makes it much easier for the reader to interpret the proportion of Cse4 residences that are ternary vs. non-ternary in instances where off-rates were quantified and Kaplan-Meier analysis was performed on the resulting lifetimes. We determined that for ternary Cse4-Ndc10 residences, Cse4 and Ndc10 co-occupied the CEN DNA an average of ~84% of the total Cse4 residence.
- Page 9, third sentence of third paragraph it is stated that the "results suggests that Scm3 helps promote more stable binding of Cse4 ...". This is indeed one possible explanation of the results, and this possibility is tested by overexpressing Psh1 or Scm3 by endpoint colocalization analysis. 1) Taking the concerns regarding the endpoint vs time-lapse results into account, wouldn't it be more accurate to compare either time-lapse results against each other or endpoint results? 2) Alternatively, more stable Cse4 particles are able to recruit Scm3, simply because of the increased binding opportunity of a more stable particle. In this scenario, just the residency time of Cse4 alone is the predicting factor in likelihood to associate with Scm3. To test the latter possibility, Cse4 stability would need to be altered. Please consider this experiment- should be relatively easy with the right mutant of either CSE4 or CDEII (see Luger or Wu papers).
We appreciate the points raised here and addressed both as follows. For point (1) we altered the text in the third paragraph of the section, The conserved chaperone Scm3HJURP is a limiting cofactor that promotes stable association Cse4CENP-A with the centromere, to make it more clear to the reader that in the experiments presented in Figure EV4, endpoint analysis results were only compared to each other, and likewise time-lapse experiments were only compared to each other for each genetic background. While the results were consistent between experiments, we did not directly compare results from one to the results of another, but instead we used both assays to characterize Cse4CENP-A behavior more completely in differing contexts.
To test the alternative hypothesis proposed in point (2), we sought to avoid potential selection bias by utilizing off-rate analysis, which enabled us to separate the portions of Cse4 residences that occurred prior to ternary association with either Ndc10 or Scm3. This unbiased approach allowed us to compare Cse4 residence lifetimes pre and post ternary association and we found that there were still significant differences in off-rates and median lifetimes of the associated ternary and non-ternary residences using this updated analysis. We thank the reviewer for helping to guide us towards this more robust analysis.
Based on the recommendation in point (2), we also sought to directly compare the behavior of Cse4 and Scm3 on the “Very Unstable” CDEII mutants described in the section, DNA-composition of centromeres contributes to genetic stability through Cse4CENP-A recruitment. In this case, equivalent extracts were used and Cse4 stability was altered directly via the DNA template. When the off rates of ternaryScm3 Cse4 residences were compared, we found a significant increase in off-rates of Cse4 on the “Very Unstable” CDEII mutant CEN DNA (Appendix Figure S3B) compared to WT CEN DNA. If the alternative hypothesis proposed in point (2) were true, we would expect this reduction in median lifetime to correlate with a lower proportion of Cse4-Scm3 ternary association but quantification yielded proportions that, while varied, were not on average lower than the proportion of Cse4-Scm4 association on CEN3 DNA (.23 vs .31, Appendix Figure S3A). This finding, taken together with the fact that it would be difficult for us to propose an alternative hypothesis that explains the results outlined in Figure EV4, supports our hypothesis that Scm3 helps promote more stable binding of Cse4 and that this stabilization is directly influenced by DNA sequence composition.
- In Figure 1C and Supplemental Figure 5B, there appears to be foci that CEN3-ATTO-647 positive, but Cse4-GFP negative and visa verse. It seems logical that there are DNA molecules that didn't reconstitute Cse4 nucleosomes. But how can there be Cse4-GFP positive foci without a positive DNA signal? Is it possible that unlabeled DNA make it onto the flow chamber? If so, can these unlabeled DNA be visualized by Sytox Orange for instance to confirm that no spurious Cse4 deposition occurred? Please clarify.
Because it is unlikely that random associations will colocalize with the labeled DNA based on control assays (Supp. Figure 1C) that show this occurs rarely (
- On page 10, at the end of the first paragraph, growth phenotype of pGAL-SCM3 and pGAL-PSH1 mutants were tested. On GAL plates, pGAL-PSH1 shows reduced growth, but not pGAL-SCM3. Wouldn't a more accurate conclusion be that Psh1 is limiting stable centromeric nucleosome formation, instead of Scm3?
The growth defects on galactose don’t necessarily mean that a factor is limiting in cells. Instead, they report on whether changing the relative amounts of the complex lead to phenotypes in cells that could be the result of many causes that would require characterization of the phenotypes to understand. In this case, we presume that Psh1 titrates Scm3 away from Cse4 to prevent nucleosome formation in vivo. However, we have not directly tested this so we just concluded that the relative levels of the complexes are important for cell growth.
- In the section where DNA was tethered at either one or both ends, an important control is missing. How does such a set-up impact nucleosome formation in general. Can H3 nucleosomes form on random DNA that is either tethered at one or both ends? Does this potentially affect the unwrapping potential/topology of AT-tract DNA? Please comment.
This is an interesting point and one that we hope to explore further in the future but was beyond the scope of this paper. We suspect that restrictions via tethering would also limit canonical nucleosome formation on random DNA. We envision that unwrapping may be affected as well and hope to explore this via other, potentially better suited techniques like optical tweezers.
Minor comments + Censored data points are not explained in the text.
A brief explanation of censorship was added to the figure legends and we have now provided a clear explanation of the term “censored” in the text on p. 28, lines 25-27. It has also been reported in the Statistical tests section of the methods section to address this point.
- Number of particles tested should be reported in the main and supplemental figures, not just the legends for those readers who first skim the manuscript before deciding to read it.
We add these values to all Kaplan-Meier plots in all figures (Main, Expanded View, and Appendix)
- Typo on page 5, first line: "nucleosom" should be "nucleosome".
We fixed this in the text.
- Typo on page 9, second line: sentence is missing something "... is required for Scm3-dependent ..."
We fixed this in the text.
- It is unclear how the difference in Supplemental figure 5D was calculated.
We included log-rank test generated P-values as well as description in the figure legend of EV4.
- Figure 4C: why are there more Ndc10-mCherry foci observed in double tethered constructs vs single tethered constructs?
There can be variances in DNA density between slides, particularly with non-dye labeled DNA template. We updated figure panel C to include a representative image with similar Ndc10 density.
- For the display of individual traces as shown in Fig 2B, 3A, 4E, and 5E, it might be more informative if the z-normalized signal and the binary read-out are shown in separate windows to better appreciate how the z-normalized signal was interpretated.
Due to spacing limits within figures we attempted to accommodate this by reducing the thickness of the binary read-out and ensured that the raw data traces were overlaid for easier interpretation by the reader.
- Page 17, fifth line of the second paragraph, it is stated that a conserved feature of centromeres is their AT-richness. This is most certainly true for the majority of species studied thus far, but bovine centromeres for instance are about 54% GC rich. Indeed, Melters et al 2013 Genome Biol showed that in certain clades centromeres can be comprised of GC-rich sequences. It might be worthwhile to nuance this statement.
We have updated the text to reflect that AT-rich DNA is widely conserved but not a universal feature of centromeres.
- Page 17, last paragraph. Work by Karolin Luger and Carl Wu is cited in relationship to AT-rich DNA being unfavorable for canonical nucleosome deposition. A citation is missing here: Stormberg & Lyubchenko 2022 IJMS 23(19): 11385. Also, the first person to show that AT-tracts affect nucleosome positioning are Andrew Travers and Drew. This landmark work should be cited.
We thank the Reviewer for noticing this and have added the appropriate citations.
- Page 18, 9th line from the top, it is stated that yeast centromeres are sensitive to negative genetic drift. This reviewer is of the understanding that genetic drift is a statistical fluctuation of allele frequency, which can result in either gain or loss of specific alleles. Population size is a major factor in the potential power of genetic drift. The smaller a population, the greater the effect. Budding yeast is found large numbers, which would mean that drift only has limited predicted impact. Maybe the authors meant to use the term purifying selection?
We appreciate this clarification and agree with the reviewer, we have updated the manuscript to cite purifying selection and not genetic drift as at centromeres.
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Referee #3
Evidence, reproducibility and clarity
The manuscript "Single molecule visualization of native centromeric nucleosome formation reveals coordinated deposition by kinetochore proteins and centromere DNA sequence" by Popchock and colleagues describes a new high-throughput single-molecule technique that combines both in vitro and in vivo sample sources. Budding yeast centromeres are genetically defined centromeres, which makes them ideal for studying short DNA segments at the single-molecule level. By flowing in whole cell lysates, Cse4 nucleosomes can form under near physiological conditions. Two analytical experiments were performed: endpoint and time-lapse. In the former case, nucleosomes were allowed to form within 90 minutes and the latter case, nucleosome formation was tracked for up to 45 minutes. In addition, well described genetic mutants were used to assess the stability of Cse4 nucleosomes, as well as different DNA sequences (this we particularly liked- well done). Overall, this is a very interesting technique with potential to be useful for studying any DNA-based effect, ranging from DNA repair to kinetochore assembly. This is strong and impactful work, and the potential this kind of microscopy has for solving kinetic problems in the field. We think it's worthy of publication after revising technical and experimental concerns that would elevate the ms significantly.
Major comments:
Statistics are highly recommended for all the data in the ms.
- At what rate is data collected in the TIRFM setup. For clarity for the reader, it is important to provide imaging details for time-lapse. What is the impact of photobleaching on the stability of the fluorophore signal? Please clarify.
- The power of single-molecule technique is precisely that such data can be made quantitative. Indeed, the Kaplan-Meyer graphs do show nice quantitative results. Unfortunately, in the text few quantitative measurements are reported. In fact, the Kaplan-Meyer graphs can be interpreted in a quantitative manner such as probability of residency time. Although in most cases the statistical significance between two conditions can be expected, this is not formally calculated and shown. What is the 50% survival time of Cse4 alone or with Ndc10, for instance? This manuscript would greatly benefit from a quantitative approach to the data, including a summary table of the results of the various conditions tested. Please add this important table.
- This reviewer would expect that the endpoint (90 min) would roughly reflect the occupancy results from time-lapse (45 min) experiments. Based on the data presented in Figures 1, 2, S1-3, this does not appear to be the case. 50% of Cse4-GFP and 70% Ndc10-mCherry colocalized with CEN3 DNA in the endpoint experiment, whereas in Fig 2C, ~30 and ~52 traces were positive for Cse4-GFP and Ndc10-mCherry, resp. with the former having drastically lower residency survival compared to Ndc10-mCherry. If indeed, 50% of Cse4-GFP makes it to the endpoint, about 50% of all traces should reach the end of the 45 minutes time-lapse window. Only about 1/3 of all positive Cse4-GFP traces can be seen at the end of the 45 min window. Could this be due to technical issues of photostability of GFP? Why does the colocalization of Cse4 signal with the DNA signal disappear so readily? Are Cse4 so unstable? Is the same true for canonical H3 nucleosomes? This unlikely true for nucleosomes in cells. Along the same lines, in Suppl Fig 3 there is a disconnect between residency survival and endpoint colocalization on either CEN3, CEN7, or CEN9. What could be the underlying mechanism between the discordance of endpoint results and time-lapse results? Could this be the result of experimental differences?
- What fraction of particles show colocalization between Cse4-GFP and Ndc10-mCherry? What fraction of occupancy time show colocalization between Cse4-GFP and Ndc10-mCherry? Altogether, understanding the limitation and benefits of endpoint analysis and time-lapse analysis in this particular experimental set-up is important to be able to interpret the results. Please clarify these points.
- Page 9, third sentence of third paragraph it is stated that the "results suggests that Scm3 helps promote more stable binding of Cse4 ...". This is indeed one possible explanation of the results, and this possibility is tested by overexpressing Psh1 or Scm3 by endpoint colocalization analysis. 1) Taking the concerns regarding the endpoint vs time-lapse results into account, wouldn't it be more accurate to compare either time-lapse results against each other or endpoint results? 2) Alternatively, more stable Cse4 particles are able to recruit Scm3, simply because of the increased binding opportunity of a more stable particle. In this scenario, just the residency time of Cse4 alone is the predicting factor in likelihood to associate with Scm3. To test the latter possibility, Cse4 stability would need to be altered. Please consider this experiment- should be relatively easy with the right mutant of either CSE4 or CDEII (see Luger or Wu papers).
- In Figure 1C and Supplemental Figure 5B, there appears to be foci that CEN3-ATTO-647 positive, but Cse4-GFP negative and visa verse. It seems logical that there are DNA molecules that didn't reconstitute Cse4 nucleosomes. But how can there be Cse4-GFP positive foci without a positive DNA signal? Is it possible that unlabeled DNA make it onto the flow chamber? If so, can these unlabeled DNA be visualized by Sytox Orange for instance to confirm that no spurious Cse4 deposition occurred? Please clarify.
- On page 10, at the end of the first paragraph, growth phenotype of pGAL-SCM3 and pGAL-PSH1 mutants were tested. On GAL plates, pGAL-PSH1 shows reduced growth, but not pGAL-SCM3. Wouldn't a more accurate conclusion be that Psh1 is limiting stable centromeric nucleosome formation, instead of Scm3?
- In the section where DNA was tethered at either one or both ends, an important control is missing. How does such a set-up impact nucleosome formation in general. Can H3 nucleosomes form on random DNA that is either tethered at one or both ends? Does this potentially affect the unwrapping potential/topology of AT-tract DNA? Please comment.
Minor comments
- Censored data points are not explained in the text.
- Number of particles tested should be reported in the main and supplemental figures, not just the legends for those readers who first skim the manuscript before deciding to read it.
- Typo on page 5, first line: "nucleosom" should be "nucleosome".
- Typo on page 9, second line: sentence is missing something "... is required for Scm3-dependent ..."
- It is unclear how the difference in Supplemental figure 5D was calculated.
- Figure 4C: why are there more Ndc10-mCherry foci observed in double tethered constructs vs single tethered constructs?
- For the display of individual traces as shown in Fig 2B, 3A, 4E, and 5E, it might be more informative if the z-normalized signal and the binary read-out are shown in separate windows to better appreciate how the z-normalized signal was interpretated.
- Page 17, fifth line of the second paragraph, it is stated that a conserved feature of centromeres is their AT-richness. This is most certainly true for the majority of species studied thus far, but bovine centromeres for instance are about 54% GC rich. Indeed, Melters et al 2013 Genome Biol showed that in certain clades centromeres can be comprised of GC-rich sequences. It might be worthwhile to nuance this statement.
- Page 17, last paragraph. Work by Karolin Luger and Carl Wu is cited in relationship to AT-rich DNA being unfavorable for canonical nucleosome deposition. A citation is missing here: Stormberg & Lyubchenko 2022 IJMS 23(19): 11385. Also, the first person to show that AT-tracts affect nucleosome positioning are Andrew Travers and Drew. This landmark work should be cited.
- Page 18, 9th line from the top, it is stated that yeast centromeres are sensitive to negative genetic drift. This reviewer is of the understanding that genetic drift is a statistical fluctuation of allele frequency, which can result in either gain or loss of specific alleles. Population size is a major factor in the potential power of genetic drift. The smaller a population, the greater the effect. Budding yeast is found large numbers, which would mean that drift only has limited predicted impact. Maybe the authors meant to use the term purifying selection?
Significance
This study developed an in vitro imaging technique that allows native proteins from whole cell lysates to associate with a specific DNA sequence that is fixed to a surface. By labeling proteins with specific fluorophore-tags colocalization provides insightful proximity data. By creating mutants, the assembly or disassembly of protein complexes on native or mutated DNAs can therefore be tracked in real time. In a way, this is a huge leap forward from gel shift EMSA assays that have traditionally been used to do comparative kinetics in biochemistry.
This makes this technique ideal for studying DNA binding complexes, and potentially, even RNA-binding complexes. This study shows both the importance of using genetic mutants, as well as testing the effects of the fixed DNA sequence. As many individual fixed DNA molecules can be tracked at one, it allows for high-throughput analysis, similar to powerful DNA curtain work from Eric Greene's lab. Overall, this is a promising new single-molecule technique that combines in vitro and ex vivo sample sources, and will likely appeal to a broad range of molecular and biophysics scientists.
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Referee #2
Evidence, reproducibility and clarity
Centromeres drive chromosome inheritance from one cell generation to the next, so understanding their nature is of utmost importance in biology. The assembly of centromeric chromatin is of outstanding interest since defects in this step impair faithful genetic inheritance. Popchock et al investigate the molecular mechanisms of Cse4(CENPA) deposition and stabilization on a native budding yeast centromeric DNA. Using TIRMF (Total Internal Reflection Fluorescent Microscopy) enabled single molecule visualization of de novo Cse4(CENPA) nucleosome formation from a yeast cellular extract. The centromeric DNA used in this study was derived from PCR amplification of plasmids containing the native yeast point centromere from chromosome 3 (CEN3 DNA) sequences containing the CDEI, CDEII, CDEII, flanking pericentromeric DNA and linker DNA totaling ~750bp. In the PCR preparation, the CEN3 DNA contains a 5'-fluorescent dye and a biotinylated 3'-end, and was tethered to a functionalized (streptavidin) slide for TIRFM. The yeast extract was extracted from cells arrested in mitosis. This system is a novel application of a single molecule to study Cse4(CENPA) formation on centromeric DNA.
Using this system, the authors observed coordinated Cse4(CENPA) deposition on the CEN3 DNA, reporting inherent, transient colocalization of Cse4(CENPA) with CEN3 DNA. Stable Cse4(CENPA) colocalization on the CEN3 DNA is correlated with the Cse4(CENPA) chaperone Smc3(HJURP), and an ability for nucleosome formation on the CEN3 DNA. Further stabilization of Cse4(CENPA) was shown to depend on the DNA binding CCAN protein chl4(CENPN) and okp1(CENPQ) which dimerizes with the DNA/CENPA binding Ame1(CENPU).
Using this single molecule system they also demonstrated a role for the A/T run (>4) content in the CDEII as specifically important for Cse4(CENPA) deposition on CEN3 DNA. Cse4(CENPA) colocalization was preferred on the native CDEII sequence, relative to mutant CDEII sequences with similar A/T content but variable homopolymeric runs.
Major comments:
- Figure S1A-D seem like some of the most compelling data in the paper to bolster the rigor of their experimental setup. There appears to be plenty of space to include these data in the main figure set in Figure 1 after panel D. The authors would be well served to consider moving S1A-D somewhere in the main figure set.
- The authors conclude that Ndc10 recruits HJURP(Smc3) to the yeast point centromeres. If this is the case, can the TIRFM assay measure ternary residence lifetimes complexes between Ndc10/HJURP(Smc3)/CenDNA?
- Throughout the manuscript short- and long- term residence lifespans are mentioned, referencing the figures containing lines with lengths depicting residence times. This is a qualitative reference to short and long residences. Can the authors provide a graph for short-term (<120 s) and long-term (> 300 s) residence life-spans for, CENPA alone, CENPA/Ndc10, and CENPA/HJURP on CEN3 DNA? Or some figure similar to Figure 3C, but reporting the proportion of short-term vs long-term residence?
- The choice of CCAN components for analysis in Fig. 5 is interesting, but many readers may be curious why Mif2 wasn't selected for disruption, since it has such a cozy placement with CENP-A and CEN DNA. Can choice to not include Mif2 mutants/degrons be mentioned/justified in the text (unless, even better yet, they choose to address Mif2 role directly in new experimentation)?
Minor comments:
- Are these whole cell extracts (WCE) DNA-free? I'm curious if there is any competition from endogenous DNA from the yeast cellular extract.
- In relation to Mif2 and comment #4 above, do the authors make any connection to their results with synthetic nucleosome sequences not being conducive to yeast centromere formation with the prior observation (Allu et al 2019) using recombinant components that the human version of Mif2 more easily saturates its binding sites on CENP-A nucleosomes when they are assembled with natural centromere DNA rather than the Widom 601 sequence?
- Providing a gel (or other measure) of the DNA templates (750, 250, and 80 bp) used in TIRFM assay would be nice to show to confirm the designed size of the pre-tethered DNA.
- Some of the references to figures/figure panels in the main text do not match the figures. (discussion, pg 16, paragraph 1 & 2;pg 18, paragraph 1).
Significance
This work demonstrates the dynamics of Cse4(CENPA) coordination with Smc3(HJURP) to form nucleosomes on point centromeric DNA, and the necessity for homopolymeric A/T runs. It is a truly impressive piece of work that makes sense of findings from prior genetics experiments. Then it extends the understanding and clarifies the role of both centromere proteins and DNA sequence. The quantitative and powerful single molecule-based experimentation, the high importance of the subject matter, and its connection to studies using yeast genetics, will make this work, upon modest improvements (see section A of this review), of outstanding interest to an extremely broad audience of biologist.
My relevant expertise keywords: centromeres, nucleosomes, biochemical reconstitution, chromosome engineering
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Referee #1
Evidence, reproducibility and clarity
Summary
This work presents a novel experimental setup to explore native centromeric nucleosome formation at a single molecule level with a high degree of temporal resolution. It combines TIRF microscopy with an immobilized fluorescent yeast CEN3 DNA sequence. Binding of centromere proteins was probed by using whole-cell lysates produced from various mutant yeast strains expressing fluorescently tagged centromere proteins. This system was used to investigate the recruitment of Cse4 (yeast CENP-A), the histone H3 variant which defines centromeres, to the native yeast CDE3 sequence. Consistent with previous studies, the authors identified a requirement for Ndc10, which binds the CDE3 sequence with high efficiency, in Cse4 recruitment. Colocalization of Ndc10 with Cse4 significantly increases the stability of the Cse4-CDE3 interaction, and Ndc10 binding predominantly precedes Cse4 recruitment. Secondly, they investigated the impact of Scm3 (yeast equivalent of HJURP) on Cse4 stability and found that Scm3 is not required for transient short-term associations between the DNA and Cse4 but is needed for longer more stable interactions between Cse4 & the CDE3 sequence. Overexpression of Scm3 & its negative regulator Psh1 also showed that Scm3 availability is the rate-limiting factor in stable Cse4 associations with DNA. However, these interactions were abolished when the CDE3 DNA sequence was topologically restricted by tethering both ends to the streptavidin-coated coverslip suggesting that the formation of a nucleosome is essential for stable binding of Cse4 to CEN3. The sequence-specific DNA binding kinetochore proteins ChI4 & Okp1 were also found to improve the lifetime of Cse4-CDE3 interactions suggesting a stabilizing interaction. Finally, the impact of A/T runs on nucleosome formation was examined using a library of scrambled CDEII sequences, with approximately native levels As & Ts but shorter runs of the 2 nucleotides. Previous studies found a correlation between the length of AT runs and the incidence of chromosome missegregation. They now extend this correlation to a decreased ability of DNA to stably recruit Cse4. Finally, experiments with other sequences including the Wisdom601 sequence suggest that A/T runs along with CBF3 binding inhibit the recruitment of histone H3 to the CDEII sequence which is overcome by the interactions between Cse4 and Scm3.
Assessment
This is an insightful, well-executed study of a high technical standard. While several observations confirm or further validate previous findings in vivo (such as the requirement for Ndc10 and Scm3 for Cse4 assembly), this work adds a better understanding of the dynamics of Cse4 requirement and the role of Scm3 in assembly and the role of CCAN components in stabilizing Cse4. Figures are well laid out and methods are clearly described. In general, the claims are supported by the data. The development of their single-molecule setup to assay Cse4 nucleosome assembly is a promising tool for future work and for this reason work reporting. In short, I believe this paper contains exciting developments in the understanding of the specific mechanism and temporal dynamics of the formation of a Cse4 nucleosome on its native DNA and the interactions which underpin its stability in vitro and in vivo.
Major comments
I have several comments and suggestions for further analysis of the data. I also have suggestions for additional experiments, but I would stress that none of these are essential for publication.
Figure 2E. This shows the residence time of Cse4 without Ndc10 association. How does this compare to the residence time on mutant CEN3 (Supplemental Figure 1). It looks like Cse4 still binds to CEN3 with some specificity even in the absence of Ndc10. Does this suggest that Cse4 has some intrinsic ability to recognize CEN3? Alternatively, Ndc10 is still required for Cse4 binding but is below detection in the Cse4-alone residence lifetimes. Ideally, the authors would compare this with Cse4 binding in an Ndc10 mutant.
Figure 3 explores the very interesting relationship between Scm3 dynamics and Cse4 binding but I feel that the data is not fully flushed out. A key finding is that Cse4 can potentially bind to CEN DNA prior to engaging with Scm3 to be incorporated. This predicts that Cse4 should bind first and then colocalize with Scm3. Can this be detected in the timing of the traces? How often does Scm3 bind to already prebound Cse4 and does this correlate with Cse4 residing longer?
A related and perhaps even more interesting point is whether Scm3 is involved in "loading" of Cse4. If so, then one would expect that once Cse4 is assembled into nucleosomes it should be stable, even if Scm3 leaves. Can the authors extract this from the data? Alternatively, it is possible that Scm3 remains associated to Cse4 to maintain the nucleosome which would imply a more extended role for Scm3 apart from assembly alone. It would be interesting if information on this can be extracted from the data.
Even in the presence of Scm3 and CCAN components, Cse4 appears to have a limited lifetime in the in vitro assay compared to in vivo stability. The authors should speculate on whether activities exist in their extract that actively disassembles nucleosomes. Perhaps ATP could be depleted to inactivate remodellers?
For Figure 6, it is not clear why AT-track mutants of CDEII are labeled as genetically stable and genetically unstable. This is confusing as the "genetically stable" show a more than 10-fold increase in chromosome loss rates. Perhaps these can be renamed into "unstable" and "very unstable" or "weak" and "strong" mutants, just to make clear that these classes are both poorer than wild type.
Finally, it would be wonderful to include data to assess whether a full Cse4 nucleosome is assembled or a partial nucleosome e.g. just Cse4/H4 tetrasomes. This could be done by tracking the accumulation of H2A or H2B at the CEN3. This would give further insight into what step Scm3 catalyses.
Minor comments:
Typo on page 5, line 1 "nucleosom" missing an e.
Kaplan-Meyer should be spelled Kaplan-Meier
The term "censored" is mentioned across many figures but comes up just ones in the methods where it is not clearly explained. Perhaps this could be clarified in the legend.
The abstract states that Cse4 can arrive at the centromere without its chaperone. More likely, Cse4 is in complex with other chaperones that may allow it to bind. Perhaps the abstract can be modified to read "Cse4 can arrive at the centromere without its dedicated centromere-specific chaperone Scm3..."
Related to this point, the discussion states the possibility that Cse4 can initially bind to CEN3 via other more general chaperones. However, it should be acknowledged that transient Cse4 binding in their assay may simply occur through mass action due to high concentrations of CEN3 DNA. In vivo, this transient binding may not be that relevant.
Significance
This paper offers new insight into the assembly of yeast centromeres with a focus on the role of the Chaperone Scm3 in the assembly of the centromere-specific histone Cse4. This is still a poorly understood process and the authors offer an elegant in vitro system to study this and have presented new insights. For this reason, this is study is of interest to a broad readership in the area of mitosis and chromosome structure. The advance is strong at the technical level but also new insight is provided particularly in the role of Scm3 and the nature of centromeric DNA in centromeric chromatin assembly. Overall a strong, high-quality paper.
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Referee #3
Evidence, reproducibility and clarity
Summary
In this manuscript, Wang and colleagues demonstrate that a single systemic injection of a high dose of Akkermansia muciniphila (A.m.) lysate drives a rapid pancytopenia followed by prolonged anaemia and hepatosplenomegaly with late-onset extramedullary hematopoiesis (EMH). The latter, as well as the splenomegaly, were likely mediated through activation of pattern recognition receptors and IL-1R signalling pathways. This was demonstrated through the partial and full phenotype reversal in Tlr2;4-/- and MyD88;Trif-/- mice, respectively. Moreover, the phenotype was partially reversed following IL-1R antagonism. After performing multiplex protein assays and flow cytometry, the authors conclude that EMH, in this model, is mediated by IL-1a produced within the spleen by local monocytes and DC.
Overall, the manuscript by Wang et al. is quite well presented, the experiments are mostly well controlled, the methods are well reported, and the data fit a clearly defined story with clinical relevance. Nevertheless, there are several major concerns that if addressed would greatly increase the strength of the authors conclusions.
Major comments
- The "two wave" hypothesis of hematopoiesis - first in the bone marrow (BM) and then in the spleen - is interesting. However, although an early wave of BM hematopoiesis would make sense, under these circumstances, I don't think the data are strong enough to support this hypothesis as they stand. For example, although the frequency of LSK cells increase, the numbers of most LSK subsets decrease. Given the decrease in the absolute number of BM cells 1d after A.m. injection, isn't it possible that the LSK cells are only proportionally increased relative to the remaining Lin- cells? What happens to the absolute number of LSK cells following A.m. injection?
Also, describing "two distinct waves of HSPC increase in the A.m.-injected spleen" (Fig 2 & S2 titles) and describing a "first wave" of HSPC expansion in the BM (lines 396, 399, 402 etc.) is misleading for the following reasons: (i) the data strongly support a single wave of increasing HSPC in the spleen, peaking at d14, and (ii) there is no evidence HSPC are increased in the BM until d56, although there does appear to be an early increase in MPP. The language should be changed accordingly. 2. The flow cytometry panel is not comprehensive enough to fully characterize the mature hematopoietic cell populations to the levels that are claimed here. For example, it is a stretch to assume that all B220- CD3- CD11c- cells are DC (splenic NK cells, eosinophils, monocytes and red pulp macrophages, for example, can express CD11c, particularly following inflammatory insult), or that CD11b+ F4/80+ SSC-hi cells are eosinophils, especially when eosinophils should be F4/80-lo are not known to express Ly6C in the spleen (For reference, see Immgen). These gating issues may explain the conspicuous absence of macrophages (should be F4/80+CD11b+Ly6C- and would also have a higher SSC than monocytes) in the plots. The B cell gate will also contain PDC, which express B220 (but can be easily excluded using Ly6C and CD11c). With respect to assessing the mature leukocyte populations in the spleen, relabelling the gates (CD11c+ cells instead of DC, F4/80+ myeloid cells instead of eosinophils) would suffice, however, these issues become a problem when trying to identify which cell populations express IL-1a.
Due to the limited antibody panel used here, there is not enough evidence to suggest that DC and monocytes are producing IL-1a. Moreover, the histograms showing the changes in expression of IL-1a on the "DC" and "Mo" are not very convincing. How does the IL-1a staining look on a dot plot? Is there good separation between positive and negative? These plots need to be included. What happens if you gate on the IL-1a+ cells first, then phenotype them?
Macrophages and splenic stromal cells are also likely candidates for IL-1a production. To assess which cell types are the true source of IL-1a, the authors need to repeat these experiments (namely, injecting A.m. and assessing IL-1a expression by leukocytes (and ideally also mesenchymal cells)) at d1 and d14, using a more comprehensive panel. Consider adding MHCII, CD64, Siglec F and CD24 to help differentiate between DC, MF, eosinophils and monocytes. CD45+ vs CD45- could be used as a minimum to assess the expression of IL-1a on leukocytes vs. stroma.
OPTIONAL: The mechanism could be better defined using bone marrow chimeras to assess the different contribution of TLR2/4 signalling and IL-1R signalling on the hematopoietic vs. mesenchymal cell compartments. 3. From these experiments, it is difficult to fully rule out a contribution from the adaptive immune system to the splenomegaly phenotype due to the marked difference in the size of BALB/c and MSTRG spleens at steady state. The authors should show the differences in spleen weight and total cell number as a % increase from control. The no of HSPC should also be normalized per gram of tissue weight or represented as a fold change compared to the relevant control groups. 4. When using fluorescent imaging to compare the abundance of HSPC and other cell populations in the spleen, the authors should provide absolute quantification from multiple FOV and multiple mice. 5. Finally, although the experiments are adequately replicated, the stats are not always appropriate. For example, a t-test shouldn't be used when there are >2 groups, or for a time course. This needs to be amended.
Minor comments
- Line 82-83: I'm fairly certain monocytes and inflammatory Ly6Chi cells are the same thing.
- Line 83-84: "IL-1a is crucial for sustaining inflammatory responses, recruiting myeloid cells to infected tissue and inducing hematopoietic stem and progenitor cell (HSPC) mobilization and expansion both in vitro and in vivo" - I don't believe IL-1a has been shown to be crucial for either, even if it has been shown to play a role. If I am mistaken, please reference with a manuscript showing relevant phenotypes using KO mice.
- Line 214: "Thus, we decided to use 200ug of lysate for the rest of all experiments." - is this what was usen for Figures 1A-C? This is not mentioned anywhere.
- Line 227: "containing both non-hematopoietic cells and immature HSPCs" Please reference Fig. 1H here. Otherwise, it is unclear how you identified the "HSPC and other cell types" in Fig. 1G.
- Figure S2A is described in text before Supp 1I-O and Fig S1H is not referenced in text at all.
- It would be interesting to include what happens to hepatomegaly in MSTRG, Tlr2;4-/- and MyD88;Trif-/- mice.
- Please define WBM. Presumably whole bone marrow?
- Notably, CCL2 is increased in spleen lysate, BM lysate and serum. Given is role in myeloid cell mobilization from the BM, I would expect its role in the phenotype described here to at least be discussed.
- HSPC LT gate includes MPP1, and should be labelled as such.
Significance
General assessment: The manuscript provided by Wang et al. describes, for the first time, a prolonged anaemia and hepatosplenomegaly with late-onset extramedullary hematopoiesis following a single systemic injection of A.m. lysate. The EMH phenotype appears robust and the data implicating TLR-signalling and IL-1a production are compelling. The work has clinical relevance as it increases our understanding of the factors driving EMH.
There are two key limitations that let this study down. Firstly, the lack of depth in the flow cytometry panel used for immunophenotyping means it is not at all clear which cell types are producing IL-1a. Secondly, the authors use an enormous dose of bacterial lysate that is well above physiological levels, even following a loss of barrier integrity (e.g., in patients with IBD). This makes me question the biological relevance of the study, particularly with respect to Akkermansia translocation.
Advance: With some improvement, this study will advance the field, in general. Previous work has looked at EMH following LPS injection, or live E. coli infection, however; the authors are able to demonstrate a distinct Akkermansia-specific effect that differs to that of LPS, membrane components of a different gram-negative bacteria, B. theta. The advancements implicate IL-1a in the modulation of EMH, for the first time, providing some mechanistic insight into this phenomenon.
Audience: This work will likely be of interest to basic researchers interested in EMH. It may also be of interest to clinical researchers of pathologies where EMH is a known complication, such as rheumatoid arthritis and cancer. The impact of the work will depend on whether or not EMH contributes to pathogenesis, or is an epiphenomenon. To my knowledge, this has not been fully established, although this is not my area of research.
I am a basic researcher with expertise in immunology focused on host-microbe interactions, both within the intestine and at distal tissues. I have knowledge of BM hematopoiesis and the microbial factors that influence if although my knowledge on extramedullary hematopoiesis is limited.
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Referee #2
Evidence, reproducibility and clarity
In this study, Wang et al reported single injection of Akkermansia muciniphila (A.m.) induces two waves of extramedullary hematopoiesis (EMH), and demonstrated the mechanism of "second wave" was contributed by IL-1α secretion in spleen DCs and Ly6C+ monocytes. It is an important work on understanding infection-induced EMH. However, several major concerns about claims in this manuscript need to be addressed.
- The authors demonstrated that A.m.-induced EMH were alleviated by knockout of Tlr2;4 or Myd88-Trif, or even IL-1R inhibition. EMH in the spleen is a mechanism by which the hematopoietic system responds to stresses. Therefore, whether inhibition of EMH by these ways can affect normal hematopoiesis in mice? Do mice have pancytopenia? Will the function of HSC in bone marrow be affected?
- In Fig.S1E, why did the WBC and PLT recover quickly after the first day, while the RBC took 14 days to recover? Are WBC and RBC regulated by two waves of EMH, respectively?
- The authors should show absolute numbers of each cell type, not just the percentage of immunophenotypically defined cells. For example, in Fig.1G, I, 2B.
- In transplantation assays, whole BM cells or splenocytes was use. However, the proportion of HSCs in BM and spleen were all changed post A.m injection, which could affect the outcome of chimerism rate after transplantation. Transplantations should be done on by transplanting sorted fresh immunophenotypic HSCs.
- The concentration of IFN-γ in both spleen and serum were increased continuously from D1 to 14. Would IFN-γ cause the second wave of EMH? Relevant assays for exclusion are necessary.
Significance
It is an important work on understanding infection-induced EMH.
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Referee #1
Evidence, reproducibility and clarity
Wang et al. examined the mechanisms of splenic extramedullary hematopoiesis upon systemic injection of Akkermansia muciniphila in mice. They showed that components of this mucin-degrading bacterium mobilize bone marrow hematopoietic cells and induce splenomegaly by MYD88/TRIF-dependent innate immune signaling pathways. Activation of TLRs and release of interleukin 1-alpha from splenic cells were then responsible for the expansion and differentiation of functional hematopoietic progenitors in the spleen. Genetic deletion of TLR2 and 4 restrained splenomegaly, while, pharmacological inhibition of IL1 receptor abrogated splenomegaly and extramedullary hematopoiesis suggesting their cooperation in the observed phenotype. It has been widely accepted that splenomegaly arises as a consequence of inflammation and that TLRs are major drivers of this process in the context of bacterial or viral infections. Here, the novelty relies on the potential circuit with the IL1alpha-IL1R axis as an additional driver of splenic extramedullary hematopoiesis. Although the results summarized above indicate that both TLRs and IL1 individually participate to some extent in splenomegaly after Akkermansia muciniphila administration, they fail to demonstrate that they concertedly do so in the spleen. In fact, the blockade of IL1R has a more profound impact.
Significance
My concerns are the following: - The authors mentioned that a specific lipid from Akkermansia muciniphila is able to trigger a non-canonical TLR2-TLR1 heterodimer to release inflammatory cytokines and regulate human immune response. Why TLR1 was not considered in the experimental strategy? - IL1alpha is up-regulated in several splenic cells (in particular in macrophages, Fig. S4F). To demonstrate a critical involvement of dendritic cells or monocytes, depletion studies or conditional mice models should be evaluated. What about megakaryocytes? Why were excluded from the analyses? - Interleukin 1alpha KO mice model should be also evaluated. - The paper is very dense and not easy to read and follow. English editing is required.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The manuscript presents a detailed numerical model of blood flow in a region of the zebrafish vasculature.
The results section is quite intense and detailed. it is difficult to understand what the authors are after. I think a rewrite would beneficial. The authors present simulations for a wild type and a couple of phenotypes. For each of these they speculate on the possible adaptation mechanism leading to the discussed phenotype, as preservation of constant wall shear stress. However, the comparison between experiments and numerical simulations is really elusive as the conclusions on those mechanisms. Overall we suggest a rewrite with clearer organisation in a way that the reader is not overflown with useless details.
We thank the reviewer for the advice on the general writing standard and data organization. We have reanalyzed experiment data and interpreted the findings more conservatively for application into the simulation models. As a result, some conclusions to the results sections have changed. Accordingly, we have done a major revision of the entire Results, Discussion and Models and Methods sections in the paper to articulate these reinterpretations while removing superfluous details that may obfuscate the data.
It is not always clear what info of the experiments are used in the simulations on top of the anatomy. Our understanding is that the pressure boundary conditions are set to match the red blood cel velocity observed in experiments. Is this always the case for the three phenotypes and which vessels ?
We thank the reviewer for the question. Only WT and Marcksl1 KO have been matched for peak velocities in the CA, CV and ISVs between experiments and simulations. WT results were compared to both the experimental reference of 27 embryos in Table 3 and also to the current experiment pool of WT (5 embryos) in Table 6. Marcksl1 KO simulation models 1, 2 and 3 were compared against the average level seen in the low and moderate perfusion Marcksl1 KO phenotypes (8 embryos) from the experiment (Table 5 and Table 6). Additionally, we also have represented the similar level of RBC hematocrit in the CA for WT model to WT experiment data from the reference cited in Table 3.
In addition to the velocity comparisons, we now use the experimentally observed trend of decreased flow rate in the CA of Marcksl1 KO experiment data to assess the model boundary conditions amongst Marcksl1 KO models 1, 2 and 3 that best reflect the experimental observations:
Page 11, lines 1 to 20
The Marcksl1 OE cannot be matched because we do not have the experiment data for that, the same goes for PlxnD1 where we have no experiment flow data. These two networks represent more conceptual discussions, particularly in PlxnD1 case where we have explicitly stated in the new discussion section:
Page 15, lines 24 to 34
There are about 7 inlets and outlets where to impose pressure boundary conditions. Can the author comment on the uniqueness of this problem?
Can different combination of pressure boundary condition leading to the same result ? In how many points/vessels is the measured velocity matched ?
We thank the reviewer on this insightful concern. Indeed, the uniqueness of flow and pressure field can be a problem without careful consideration. We have tried to address this to some extent, because CA, CV are connected by the ISV and DLAV network, to match flow velocity in all regions, the pressure distribution ought to be unique to the particular setting we employed.
As shown in table 3, average systolic peak flow velocities in the entire CA and CV encompassing the 5 ISV segment domain is matched between the simulation and the population-averaged experimental data from the experimental reference (27 fish sampled in the cited reference) for the same regions in WT network. Average systolic peak flow velocities for the 10 ISVs in the simulation were matched against WT experiment population-averaged systolic peak flow velocities in arterial and venous ISVs in the same caudal region.
Additionally, we also compared the flow velocities to the experiment conducted within this study (5 WT, and embryos). This comparison data is shown in Table 6. Admittedly the discrepancy was large for CV and ISVs regions likely due to a smaller data set sampled in this study and biological variations that happen from one experiment to another. We have acknowledged this deficiency in the revised discussion section:
Page 15, lines 3 to 9
The argument that similar beating frequency in the WT and GATA1 MO suggest pressure does not change is not clear. If the heart was a volumetric pump it would impose the same flow rate, not the same pressure. It would be more useful to measure the cardiac output in terms of flow rate in the Dorsal Aorta. Previous measurements by Vermot suggested the latter would not change much in gata1 MO. It could be that the cardiac output is the same but the vasculature network is different in a way that the shear stress remain the same. It does not look like this was checked by the authors.
We thank the reviewer for this insight. In accordance with the reviewer’s suspicion, we have estimated the flow rates in the CA of gata1 MO injected embryos and found the level to be similar to WT. This supports the reviewer’s opinion that the heart rate similarity indicates cardiac output similarity and not arterial pressure similarity as we previously put forward. Furthermore, we have checked that the gata1 morphants do in fact present reduced ISV diameters. In light of this reinterpretation, we performed an additional zero hematocrit model (model 3 in section 2.1). We have consequently rewritten the entire section on how RBC hematocrit modulates hemodynamics in a microvascular network:
Page 6, line 18 to page 8 line 10.
Additionaly, it would be useful to provide an effective viscosity for the different vessels, and an effective hydraulic impedance relating DP and Q to interpret the results.
We have followed the reviewer’s advice and have analyzed for vessel hydraulic impedance and effective viscosity in all the network models presented. This is included in the main figures and discussion. The vessel impedances are discussed for the various models in these following parts of the manuscript:
Page 9, lines 20 to 29
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Is the hydraulic impedance of the vessels kept constant in the smooth-geometry model? This needs clarification
The SGM diameters have been determined based on geometric averages and not impedance equivalency. The reason why we did this is because the impedance will not be known until the CFD is performed for the WT network. This is because without a pressure distribution (which cannot be determined experimentally) we cannot calculate vessel impedance since only flow can be measured and both flow and pressure are requirements to impedance calculation. Our intention with the SGM is to highlight how geometric averaging of morphological characteristics lead to incorrect flow and stress predictions. However, we understand the reviewer’s sensibility and have revised the entire section of the SGM results. We have now discussed three SGM models with varying degrees of geometry simplification. The SGM1 in the revised manuscript matches WT network impedance in the ISVs by including both axial variation in lumen diameter of the WT network and the elliptical fit representation of cross-sectional skewness seen in WT ISV lumens. SGM 2 has representation of axial variation but not luminal skewness and SGM3 has only geometric average similarity to WT ISVs. The new findings and discussion can be found in the revised manuscript here:
Page 8, line 19 to page 9 line 36.
As mentioned by the authors they propose a very complex and time expensive simulation. However the results they report are kind of intuitive. Given the availability of the experimental results, would it be useful to use a simpler red blood cell model in the future, to make their simulation more practical? Or clarify when such demanding simulations can add something new?
We agree that the intuition feedback depends on the expertise of the investigator. The boundary condition selection is intuitive from the experimental findings and key data like pressures in the network cannot be measured. Furthermore, population-averaged flow data does not always match the flow-to-geometry situations that vary from sample to sample, thus demonstrated by the high margin of prediction discrepancy for flow velocities in table 6. We have discussed these challenges and our recommendations for improvement in the Discussion section:
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On the topic of RBC model simplification, we agree with the reviewer that our work suggests the methodology would benefit from a further coarse-graining approach to the RBC phase. Accordingly, we discussed the possibility of using a low-dimensional RBC model already published in literature:
Page 14, lines 13 to 17
The authors should check their references as this is not the first time work has been done on the topic. Would be good to have a check in the work of Freund JB and colleagues, as well as Dickinson and colleagues and Franco and colleagues to discuss how the work compares. There may be interesting work in modelling cardiac flow forces in the embryo too.
Thank you for referring us to other publications that are related to our study. To our knowledge and after performing publication search on these authors, we find that although Dickinson and colleagues performed experiments to examine the effects of perturbed blood flow on vessel remodelling (Udan et al., 2013), they did not perform any numerical modelling to calculate hemodynamic forces such as WSS and luminal pressure. Instead, changes in vessel morphogenetic process were only correlated with blood flow velocity. In our study, we attempt to quantitatively correlate WSS and pressure distributions within a vascular network. Franco and colleagues (Bernabeu et al., 2014) developed PoINet to model haemodynamic forces in mouse retina model of angiogenesis. From what we understand, PoINet is different from our 3D CFD model by 1) not having red blood cells incorporated in their model and as such, the blood viscosity prediction is modelled using shear-rate dependent formulation and not through red blood cell hematocrit, 2) cross sections of blood vessels are assumed to be circular and therefore have no irregularity and 3) live imaging of blood flow is difficult in mouse retina therefore preventing accurate boundary conditions for the model.
We have included the reference to work of Franco and colleagues:
Page 14, line 28 to line 31
Page 9, lines 12 to 14
Freund JB indeed has had extensive work on RBC and cellular flow in microvessels. We have included a reference of his work in:
Page 14, lines 22 to 25.
Reviewer #1 (Significance (Required)):
The authors discuss the applicability of a detailed numerical model of blood flow in a region of the zebrafish vasculature.
We are not expert in the lattice boltzmann method used here, but the results are what it would be expected from a physical stand point, and together with the information from the method section, we do not have major concerns about the numerics.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary: The authors report corroborating numerical-experimental studies on the relationship between morphological alterations (e.g. vessel lumen dilation/constriction, network mispatterning) and hemodynamical changes (e.g. variation in flow rate, pressure, wall shear stress) in the vascular network of zebrafish trunk circulation. Various physiological or pathological adaptation scenarios were proposed and tested, with a range of simulation and experiment models. Where I found it a solid piece of work supported by abundant data, certain aspects need to be clarified/enhanced to improve the scientific rigor and potential impact of the manuscript. Below are my detailed comments in the hope of helping the authors improve the manuscript's quality.
Major comments:
- Cellular blood flow in vascular networks has been extensively studied in recent years by existing computational models (some of which were published open-source) with similar methods and features to the one proposed by the present work. Can the authors be more explicit about the original contributions of the current model, and provide evidence accordingly (e.g. Github repository or code resources)
The RBC model is essentially the model developed by Fedosov and colleagues (Fedosov, et al., 2010). Likewise, the LBM solver for fluid flow calculation is not. Following the reviewer’s advice, we have removed the details of these non-novel aspects of the methodology and placed them in sections E and F of supplementary material instead. The new Models and methods now show condensed descriptions of the three numerical solvers used and the addition of a grid independence matrix discussion section:
Page 17, line 8 to page 20, line 33.
Crucial details for the simulation setup and model configuration are missing. What were the exact boundary conditions (e.g. inlet and outlet pressures) and initial conditions (e.g. feeding hematocrit of RBCs), and how the numerical-experimental validation process of "to match the velocities of various segments of the network by iteratively altering the pressure inputs ..." as stated on page 13 (lines 1-2) was performed for simulations in this work?
We apologize for the vagueness of our description on how numerical to experimental validations were performed. As replied to reviewer 1 for a similar clarification, we have indicated in Table 3 how average systolic peak flow velocities in the entire CA and CV encompassing the 5 ISV segment domain were matched between the simulation and the population-averaged experimental data for the same regions in WT network. Average systolic peak flow velocities for the 10 ISVs in the simulation were matched against WT experiment population-averaged systolic peak flow velocities in arterial and venous ISVs in the same caudal region.
With regards to what iterative alterations of pressure inputs mean, we monitored the average systolic peak velocities and hematocrit levels in CA, CV and ISVs in intervals of 5 cardiac cycle intervals before manually correcting the pressure input levels to better match average systolic peak velocities in these vessels from the experiment averages. Since we are using population averaged flow data, we do not expect their levels to match the levels in a particular fish-specific geometry, the degree of discrepancy between experiment averages and the model predictions of systolic velocities can be large (Table 6). Admittedly, this is one of the weaknesses of our approach and this limitation is stated in the Discussion section:.
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As RBC flow typically requires roughly 5 cardiac cycles of flow to reach flow development this process of iterative correction typically takes place over 10 to 20 cardiac cycles. We understand that validation may be a subject of keen interest to readers, hence we have now briefly described the solution initialization and flow development protocol in our modeling approach here:
Page 6, lines 5 to 8
What lattice resolution was used for the flow solver and was the RBC membrane mesh chosen accordingly? Were there any sensitivity analysis (regarding pressure input) or grid-independence study (regarding lattice resolution)
We originally decided on the grid (∆X) and time (∆T) discretization resolutions (0.5 µm and 0.5 µs) based on the acceptable computing turnaround time for each model within our scale of resources. We have now included a section on the grid independence matrix in Models and Methods:
Page 19, line 20 to page 20, line 33
Details of the statistical tests (type of tests used, assessment of data normality, sample size etc.) should be given in the figure caption where applicable (e.g. Fig. 3C, Figs. 7-9).
We apologize for the lack of clarity. All statistical tests used have now been mentioned at least once in each section of results and also in Figure captions wherever significance bars are displayed.
The regression models should also be used with caution, e.g. in Fig. 4B, why should data from two different fish types, namely Gata1 MO and WT, be grouped to fit a linear regression model?
We understand the reviewer’s concern that two population data sets should not be carelessly pooled together for regression analysis without adequate justification. In this case we are utilizing gata1 morpholino injection as a means to alter hematocrit level. There is no reported side-effect as to the best of our knowledge, only hematocrit and possibly hemodynamics and morphological response related to hematocrit level should be affected. Moreover, we have mislabelled the companion set to the gata1 morpholino as WT, the data is in fact data from control morphants and not WT. This change has been applied to Fig. 3 graphs and Table 4 and results section:
Page 7, lines 3 to 16
Finally, as we want to generate a continuum range of varying hematocrit for embryos of the same developmental age. In this regard, we think that within the scope of our intentions and well-accepted usage of gata1 morpholino as a hematocrit reduction protocol it is reasonable to pool the two data sets together for regression analysis.
4.I found the data presented in Fig. 7 insufficient to confidently exclude the numerical models 2, 3 but favor model 1 as the adaptation scenario for the Marcksl1KO case. The first question is, how are the threshold RBC perfusion levels determined to categorize the experimented Marcksl1KO fishes into four groups, i.e. "high", "moderate", "low", "zero"? The authors also need to justify why the "high", "moderate", "low" groups can be mapped to the three modelling scenarios (namely models 1, 2, 3) is it just because "a qualitative match with the experimental trend of ascending CA blood velocity" (Fig. 7F)?
We thank the reviewer for his interpretation of our results. Firstly, we apologize for generating the confusion but we are not trying to map simulation models 1, 2 and 3 to high moderate and low groups respectively in Fig. 7. The high, moderate and low categorizations of experimental Marcksl1 KO phenotypes are based on RBC flux levels observed experimentally. We are trying to ascertain which Marcksl KO phenotype the models 1, 2 and 3 fit, if they do fit the experiment trend at all.
Second, in Fig. 7C, it is shown that no significant difference exists between the "high" group and the WT in their average ISV diameter, then what is defining that group as Marcksl1KO type ?
We apologize for the confusion generated. High flow phenotype is similar to WT flow, the diameter is also similar to WT. In Marcksl1 KO mutants we don’t always see clear phenotyping and often a range is presented from mutant to mutant. Hence the high group is essentially morphometrically and hemodynamically similar to WT, the only reason we know it is a mutant because we have genotyped the zebrafish (marcksl1a-/-;marcksl1b1-/-).
Third, a central assumption here is using heart rate as a measure of the pressure drop in different fish individuals (Fig. 7D). Can't two fishes with similar heart rate have distinct pressure drops in the trunk due to difference in network architecture and topology, vice versa?
We agree with the reviewer’s opinion and now feel that our initial proposition was naïve. After addressing the interpretation of heart rate similarity in the gata1 morphants with more convincing CA flow rate estimations, we now believe that heart rates might not be useful indicators of flow or pressure levels in the network. Instead, cardiac output in the form of CA flow rate as reviewer 1 has suggested might be a better indicator. As the reanalysis has dismantled the earlier interpretation, and found that based on the flow rate estimation for the CA, Marcksl1 KO networks have reduced blood flow rates in the CA.
Page 11, lines 9 to 20
This finding has been incorporated into the consideration of flow adaptation scenarios predicted by the simulation models accordingly in the revised manuscript:
Page 12, line 1 to page 13, line 10
Fourth, the authors should explain why a power-law fit (note that it is not "exponential" as stated on page 10, line 3) should be adopted for the regression analysis in Figs. 7E-v,vi (a useful reference may be Joseph et al. eLife 2019: 10.7554/eLife.45077).
We thank the reviewer for the useful reference and the careless mislabeling of regression curve used. This figure has been redone and a linear regression is instead used that does not attempt to imply any physical law for a power or exponential fitting.
Change made: Fig. 7C
Minor comments:
- The state of art of cell-resolved blood flow models employed to simulate microcirculatory hemodynamics is not accurately described in the introduction (page 4). More recent works should be cited and critically reviewed to present a fair view on the novelty of the computational model developed herein.
We apologize that the models were mentioned in a passing manner. However ,the need for brevity in introduction somewhat limits their expansion. We have instead gave more direct discussion on similar studies and their relevance to our present work in the Discussion section:
Page 14, lines 13 to 31
It is unclear what "realistic representation of local topologies in the network" (page 7, lines 28-31) means as a claim of novelty. If it means vessel "diameter variation", this geometric feature has been modeled by the works the author referenced (namely Roustaei et al. 2022, Zhou et al. 2021). If it means something else, for example, unsmooth or non-circular vessel surface (or "irregularity of the local endothelium surface" as mentioned on page 5, line 2), then strangely the effects of such features are actually not described in the manuscript.
We apologize for not meeting the expectation of novelty as claimed. We see value in the SGM study matrix have now generated data on three SGM scenarios. The SGM1 in the revised manuscript matches WT network impedance in the ISVs by including both axial variation in lumen diameter of the WT network and the elliptical fit representation of cross-sectional skewness seen in WT ISV lumens. SGM 2 has representation of axial variation but not luminal skewness and SGM3 has only geometric average similarity to WT ISVs. Essentially the comparison between SGM1 and SGM2 highlights the role of luminal cross-sectional shape skewness while SGM2 to SGM3 highlights the role of axial variation in luminal diameter. With this new SGM data set, we think we can better qualify the aspiration of demonstrating how vessel shape “irregularities” can alter network hemodynamics. The new findings and discussion can be found in the revised manuscript here:
Page 8, line 19 to page 9 line 36.
Why should Fig. 8 contain data from Marcksl1KO model 2? The scenario underlying model 2 was rejected earlier in the manuscript (see point 6 above), and the Marcksl1KO model 2 data are not mentioned in the text when describing the results of Fig. 8, either.
We have reanalyzed the experiment trend and rewritten the outcome of this results section. In summary, both models 1 and model 2 meet the trend of flow rate reduction (with respect to WT levels) in the CA observed in the experiment. Hence, model 2 inclusion is relevant to the WSS analysis. The changes pertaining to this can be found here:
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It is a dense article with loads of data, which is an advantage but only if appropriately streamlined. More subheadings should be considered, especially for section 2.3 (for which the current subsections appear mistaken, 2.3.1 followed by 2.4.2) The manuscript could also benefit from restructuring through optimal combination of simulation visualizations and quantitative analyses. For example, in Fig. 6, not all simulation snapshots are needed here (it is difficult to visually compare the changes between different cases), whereas some quantification in the form of histograms or boxplots will be handy for the readers to note the variation of WSS magnitudes and ranges.
Thank you for the advice, we removed the unnecessary graphical plots and refer to simulation videos in supplementary data instead for such cases. The bad indexing of results subsections has been fixed, while new subsections have been made for better directional narrative to the paper. These changes are colored in red throughout the revised results section:
Page 4, line 37 to page 13 line 39
Related to point 8, the authors could also consider integrating or synthesizing the analyses for individual aISVs and vISVs presented in various figures. Current descriptions for the ISV data appear scattered with frequent exceptions to the summarized trends or relationships. Some minor formatting issues should also be addressed, e.g. the confusing color codes in Figs. 9D-i, E-i.
Thank you for the advice, we have now pooled aISVs together into one group and vISVs into another, instead of discussing data trends on each of the 10 ISVs.
The mispattening case presented in the end of the results section (section "2.4.2") is interesting but appears loosely connected to the preceding contents. Also, it seems not even mentioned in the discussion section.
We agree that the mispatterning case has been only tangentially relevant to the rest of the manuscript. We have linked the topic thematically by network alterations transforming network flows. It is also now included in the discussion section here:
Page 15, lines 30 to 34
Finally, apart from the effect of topological features on local blood flow, the authors should consider the global flow redistribution arising from the network structure (useful refs. Include Chang et al. PLOS Computational Biology 2017: 10.1371/journal.pcbi.1005892; Meigel et al. Physical Review Letters 2019: 10.1103/PhysRevLett.123.228103; Schmid et al. eLife 2021: 10.7554/eLife.60208).
Thank you for the additional references. These are solid pieces of work that have been added to the discussion here:
Page 16, lines 3 to 10
**Referees cross-commenting**
This review report resonates with mine from an experimental perspective and I agree with all points made regarding issues of the current manuscript that the authors need to address with a revised version.
Reviewer #2 (Significance (Required)):
Significance: The particular merit of the work lies in its comprehensiveness of design and abundance of data, which will be of great interest to both the computational and experimental communities in this research field. However, some crucial details (especially with respect to the modelling aspects) are missing, thus hampering the scientific rigor and potential impact of the work. Furthermore, certain justifying statements appear speculative and inconclusive to explain the obtained data, especially regarding the effect of boundary conditions and systemic parameters. The citation of references (some not cited, some cited already but not properly discussed) also needs to be enhanced with engaging discussions to better bridge the findings of the current work (e.g. RBC partitioning in vascular network, effect of WSS on vasculature morphogenesis) with recent works on this research topic.
References
Fedosov DA, Caswell B, Karniadakis GE. 2010. A Multiscale Red Blood Cell Model with Accurate Mechanics, Rheology, and Dynamics. Biophys J 98:2215–2225. doi:10.1016/j.bpj.2010.02.002
Freund JB, Goetz JG, Hill KL, Vermot J. 2012. Fluid flows and forces in development: functions, features and biophysical principles. Dev Camb Engl 139:1229–45. doi:10.1242/dev.073593
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Referee #2
Evidence, reproducibility and clarity
Summary: The authors report corroborating numerical-experimental studies on the relationship between morphological alterations (e.g. vessel lumen dilation/constriction, network mispatterning) and hemodynamical changes (e.g. variation in flow rate, pressure, wall shear stress) in the vascular network of zebrafish trunk circulation. Various physiological or pathological adaptation scenarios were proposed and tested, with a range of simulation and experiment models. Where I found it a solid piece of work supported by abundant data, certain aspects need to be clarified/enhanced to improve the scientific rigor and potential impact of the manuscript. Below are my detailed comments in the hope of helping the authors improve the manuscript's quality.
Major comments:
- Cellular blood flow in vascular networks has been extensively studied in recent years by existing computational models (some of which were published open-source) with similar methods and features to the one proposed by the present work. Can the authors be more explicit about the original contributions of the current model, and provide evidence accordingly (e.g. Github repository or code resources)?
- Crucial details for the simulation setup and model configuration are missing. What were the exact boundary conditions (e.g. inlet and outlet pressures) and initial conditions (e.g. feeding hematocrit of RBCs), and how the numerical-experimental validation process of "to match the velocities of various segments of the network by iteratively altering the pressure inputs ..." as stated on page 13 (lines 1-2) was performed for simulations in this work? What lattice resolution was used for the flow solver and was the RBC membrane mesh chosen accordingly? Were there any sensitivity analysis (regarding pressure input) or grid-independence study (regarding lattice resolution)?
- Details of the statistical tests (type of tests used, assessment of data normality, sample size etc.) should be given in the figure caption where applicable (e.g. Fig. 3C, Figs. 7-9). The regression models should also be used with caution, e.g. in Fig. 4B, why should data from two different fish types, namely Gata1 MO and WT, be grouped to fit a linear regression model? 4.I found the data presented in Fig. 7 insufficient to confidently exclude the numerical models 2, 3 but favor model 1 as the adaptation scenario for the Marcksl1KO case. The first question is, how are the threshold RBC perfusion levels determined to categorize the experimented Marcksl1KO fishes into four groups, i.e. "high", "moderate", "low", "zero"? The authors also need to justify why the "high", "moderate", "low" groups can be mapped to the three modelling scenarios (namely models 1, 2, 3); is it just because "a qualitative match with the experimental trend of ascending CA blood velocity" (Fig. 7F)? Second, in Fig. 7C, it is shown that no significant difference exists between the "high" group and the WT in their average ISV diameter, then what is defining that group as Marcksl1KO type? Third, a central assumption here is using heart rate as a measure of the pressure drop in different fish individuals (Fig. 7D). Can't two fishes with similar heart rate have distinct pressure drops in the trunk due to difference in network architecture and topology, vice versa? Fourth, the authors should explain why a power-law fit (note that it is not "exponential" as stated on page 10, line 3) should be adopted for the regression analysis in Figs. 7E-v,vi (a useful reference may be Joseph et al. eLife 2019: 10.7554/eLife.45077).
Minor comments:
- The state of art of cell-resolved blood flow models employed to simulate microcirculatory hemodynamics is not accurately described in the introduction (page 4). More recent works should be cited and critically reviewed to present a fair view on the novelty of the computational model developed herein.
- It is unclear what "realistic representation of local topologies in the network" (page 7, lines 28-31) means as a claim of novelty. If it means vessel "diameter variation", this geometric feature has been modeled by the works the author referenced (namely Roustaei et al. 2022, Zhou et al. 2021). If it means something else, for example, unsmooth or non-circular vessel surface (or "irregularity of the local endothelium surface" as mentioned on page 5, line 2), then strangely the effects of such features are actually not described in the manuscript.
- Why should Fig. 8 contain data from Marcksl1KO model 2? The scenario underlying model 2 was rejected earlier in the manuscript (see point 6 above), and the Marcksl1KO model 2 data are not mentioned in the text when describing the results of Fig. 8, either.
- It is a dense article with loads of data, which is an advantage but only if appropriately streamlined. More subheadings should be considered, especially for section 2.3 (for which the current subsections appear mistaken, 2.3.1 followed by 2.4.2). The manuscript could also benefit from restructuring through optimal combination of simulation visualizations and quantitative analyses. For example, in Fig. 6, not all simulation snapshots are needed here (it is difficult to visually compare the changes between different cases), whereas some quantification in the form of histograms or boxplots will be handy for the readers to note the variation of WSS magnitudes and ranges.
- Related to point 8, the authors could also consider integrating or synthesizing the analyses for individual aISVs and vISVs presented in various figures. Current descriptions for the ISV data appear scattered with frequent exceptions to the summarized trends or relationships. Some minor formatting issues should also be addressed, e.g. the confusing color codes in Figs. 9D-i, E-i.
- The mispattening case presented in the end of the results section (section "2.4.2") is interesting but appears loosely connected to the preceding contents. Also, it seems not even mentioned in the discussion section.
- Finally, apart from the effect of topological features on local blood flow, the authors should consider the global flow redistribution arising from the network structure (useful refs. Include Chang et al. PLOS Computational Biology 2017: 10.1371/journal.pcbi.1005892; Meigel et al. Physical Review Letters 2019: 10.1103/PhysRevLett.123.228103; Schmid et al. eLife 2021: 10.7554/eLife.60208).
Referees cross-commenting
This review report resonates with mine from an experimental perspective and I agree with all points made regarding issues of the current manuscript that the authors need to address with a revised version.
Significance
The particular merit of the work lies in its comprehensiveness of design and abundance of data, which will be of great interest to both the computational and experimental communities in this research field. However, some crucial details (especially with respect to the modelling aspects) are missing, thus hampering the scientific rigor and potential impact of the work. Furthermore, certain justifying statements appear speculative and inconclusive to explain the obtained data, especially regarding the effect of boundary conditions and systemic parameters. The citation of references (some not cited, some cited already but not properly discussed) also needs to be enhanced with engaging discussions to better bridge the findings of the current work (e.g. RBC partitioning in vascular network, effect of WSS on vasculature morphogenesis) with recent works on this research topic.
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Referee #1
Evidence, reproducibility and clarity
The manuscript presents a detailed numerical model of blood flow in a region of the zebrafish vasculature.
The results section is quite intense and detailed. it is difficult to understand what the authors are after. I think a rewrite would beneficial. The authors present simulations for a wild type and a couple of phenotypes. For each of these they speculate on the possible adaptation mechanism leading to the discussed phenotype, as preservation of constant wall shear stress. However, the comparison between experiments and numerical simulations is really elusive as the conclusions on those mechanisms. Overall we suggest a rewrite with clearer organisation in a way that the reader is not overflown with useless details.
It is not always clear what info of the experiments are used in the simulations on top of the anatomy. Our understanding is that the pressure boundary conditions are set to match the red blood cel velocity observed in experiments. Is this always the case for the three phenotypes and which vessels ? There are about 7 inlets and outlets where to impose pressure boundary conditions. Can the author comment on the uniqueness of this problem? Can different combination of pressure boundary condition leading to the same result ? In how many points/vessels is the measured velocity matched ?
The argument that similar beating frequency in the WT and GATA1 MO suggest pressure does not change is not clear. If the heart was a volumetric pump it would impose the same flow rate, not the same pressure. It would be more useful to measure the cardiac output in terms of flow rate in the Dorsal Aorta. Previous measurements by Vermot suggested the latter would not change much in gata1 MO. It could be that the cardiac output is the same but the vasculature network is different in a way that the shear stress remain the same. It does not look like this was checked by the authors.
Additionaly, it would be useful to provide an effective viscosity for the different vessels, and an effective hydraulic impedance relating DP and Q to interpret the results.
Is the hydraulic impedance of the vessels kept constant in the smooth-geometry model? This needs clarification
As mentioned by the authors they propose a very complex and time expensive simulation. However the results they report are kind of intuitive. Given the availability of the experimental results, would it be useful to use a simpler red blood cell model in the future, to make their simulation more practical? Or clarify when such demanding simulations can add something new?
The authors should check their references as this is not the first time work has been done on the topic. Would be good to have a check in the work of Freund JB and colleagues, as well as Dickinson and colleagues and Franco and colleagues to discuss how the work compares. There may be interesting work in modelling cardiac flow forces in the embryo too.
Significance
The authors discuss the applicability of a detailed numerical model of blood flow in a region of the zebrafish vasculature.
We are not expert in the lattice boltzmann method used here, but the results are what it would be expected from a physical stand point, and together with the information from the method section, we do not have major concerns about the numerics.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The current study uses 3D organotypic rafts to culture primary keratinocytes from Foreskin, Tonsil and Cervix. Further the authors looked at the transcriptomic profiles of each tissue types to study similarities and differences depending on the tissue of origin as well as show the similarity in the tissue specific gene signatures and the ex-vivo samples (data from GTEx). As mentioned by authors Skin and Cervix keratinocytes have been previously cultured on collagen rafts however extending it to Tonsil provides resource and possibility of growing more tissue specific epithelial cells in 3D.
Major comments 1. As the papers focus is to culture epithelial/ epidermal cells on 3D rafts, methods section needs more details about the raft composition, preparation, fibroblast embedding what was the plate size used for raft preparation and culturing of cells on those rafts. What culture media was used for epithelial raft cultures?
We have a detailed published protocol that highlight these details. However, we will expand on some of these details in the manuscript
Results: Figure 1, authors show IF staining's for COL17A1 as marker for basal cells and cornulin for differentiated layers. However, it is important to show how many cells in the basal layer are proliferative? (or how many layers of proliferative cells are present in different epithelia analysed here?) after 14 days majority of cells might already start losing their stemness potential (maybe staining for at least ki67 if staining for basal stem cell marker not possible? Along with loricrin or Involucrin might be good idea).
We will stain for ki67 as suggested. However, based on published data using these raft cultures, we do not expect that many cells will be positive.
This is also important as from supp fig 3 you can see F1 has higher expression of Loricrin, filaggrin etc as compared to all other samples indicating higher diff in this sample. Also, if authors can comment on what was the passage of cells used? And have they observed any difference in the re-epithelization in early passage versus late passage of keratinocytes?
We will expand on this is the updated manuscript. Importantly, we grow these cells in a rho-kinase inhibitor that ‘conditionally’ immortalizes these cells as described (DOI: 10.1172/JCI42297).
It is interesting to see Tonsillar 3D epithelia recapitulate the crypt and surface epithelia and authors also show this with gene expression profile, if possible (Optional), can authors show staining for crypt specific and surface specific markers.
We agree that this is an important control. This will be included.
For all the Supplementary tables where only Ensembl ids are represented, please add gene Id column alongside (it is easier to get biological context from gene id for the reader rather than looking up Ensembl ids). Rename the file names to include the Supplementary file 1, 2, 3?
Since there is 1-to-1 conversion for Ensembl to Gene Id, we elected to not include these. The online app does try tp accommodate this as much as possible. We propose to include two versions of each table. 1 with Ensemble ids only and one with both IDs.
Its excellent to see that in vitro tissue signature matched the in vivo tissue samples (Figure 8) but it will be interesting to show the gene expression differences if found any between the in vitro and in vivo samples that will give insight on the changes as result of in vitro system.
Since the in vivo data will be a mixture of epithelial cells and stroma, these comparisons are not straightforward. However, we are currently examining the use of existing scRNA-Seq data to begin addressing these concerns. This data will be included in the next revision.
Minor comments
- Abstract: Give sample number (n?) and brief results about the genes that had tissue specific expression pattens.
- Gene names needs to be in Italics throughout.
- Introduction: page 5 line 9, authors claim that they based on comparisons they can "identify potential therapeutic targets for various disease" I think this statement either needs experimental evidence or statement / claim needs to be modified.
- Data submission to GEO???
- Typo (page 15, line 16 should be "HFK-down", same on page 23 "ectocervix", "endocervix", "uterus", so on, please correct, comma needs to be placed after "
- Page 24 last line is the heatmap referred here Fig 9B?
- Fig. 1 legends please indicate what F1, F2, F3, C1--- T1--- represent. Fig 1C Please add axis range/ values for protein atlas data as well.
- Can authors comment in discussion how was current 3D cervix cells on raft method different from Meyers, C., 1996 3D system?
All these ‘minor’ comments will be addressed.
Reviewer #1 (Significance (Required)):
This article does extend and validate the 3D raft culture method to different epithelial tissues in addition to Skin and cervix. This will be useful for the researchers using co culture systems and interested in understanding epithelial cell and immune cell interactions or host pathogen interacts etc
Describe your expertise: establishing and maintaining primary skin and oral keratinocyte cultures on feeders and 3D cultures on DEDs, Organoid cultures from oral keratinocytes, Oral cancer biology, Histopathology, transcriptomics study, Immuno-oncology.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary Jackson et al.'s manuscript describes an experiment that directly compares 3D organotypic assays created with primary human epithelial cells from foreskin, cervix and tonsil using histological and bulk RNA sequencing approaches. The authors convincingly show the retention of site-specific histological and transcriptomic differences between the stratified epithelial tissues in culture. Differentially expressed genes are identified and pathway analyses suggest genes that might be involved in the different differentiation processes between these tissue sites and differential regulation of ECM and immune pathways. Differentially expressed genes are used to develop a classifier for tissue identification, which is tested using GTEx data.
Major Comments • The interferon stimulated genes of B cells and macrophages (from Mostafavi et al., 2016) are likely to be very different from those in epithelial cells, so the analysis presented in Figure 9 seems like a stretch to me.
We will include caveats to this interpretation. We are planning stimulation experiments of each tissue to compare IFN responses. However, depending on the outcomes, these may end up being outside of the scope of the current manuscript.
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OPTIONAL: Further data comparing the nature and magnitude of the interferon responses of the three epithelia would improve interest in the manuscript but are not necessary for publication of the current dataset.
See above
Minor Comments • Details of n numbers and what each point represents should be added to Figure 1C. Are these points measurements from 25 um intervals of just one raft per donor? What are 'fields of view' here? Are measurements from one section or from multiple sections per raft? • Page 12 - provide a figure/panel citation for the "micrograph derived from a tonsillectomy" that is suggested for comparison. • In Figure 1 - Figure Supplement 1, how representative of the whole raft are these images? Does the extent of stratification change near to the edge of the collagen gel, for example? How well matched for location within a raft are the images shown? • Page 24 - clarify uses of the phrase "down-regulated in tonsils". Presumably this section refers to tonsil epithelium in 3D organotypic rafts.
Typos • Page 3 - "the cervix is lined with stratified squamous epithelia", should be epithelium. • "J.G. Rheinwald" in in text references. • Page 6 - 'or' not 'and' in first sentence of primary cell culture section.
All these ‘minor’ comments and typos will be addressed.
Reviewer #2 (Significance (Required)):
This highly descriptive study provides a detailed analysis of a bulk RNA sequencing experiment comparing foreskin, cervix and tonsil 3D organotypic rafts. Retained histological and transcriptional differences between epithelial tissues of different origins in organotypic assays are well documented in the literature (e.g., parmoplantar vs non-parmoplantar skin, PMID: 36732947; airway tract, PMID: 32526206) so the observed differences between these three very distinct anatomical tissues are unsurprising overall. The data have been made available via SRA and a shiny web app and are likely to be of interest and use to other researchers working on these tissues in culture. The experiment was performed in matched cell culture conditions so replicates are well-controlled, if limited in number (n=3).
We appreciate this feedback. We agree this is a descriptive study. Nonetheless, we believe there is value in formally demonstrating differences and similarities between these tissues. The provided references will be included to expand our discussion.
I am an epithelial cell biologist specializing in human cell culture models. I do not have sufficient computational background to comment in detail on the RNA sequencing methods or analysis within the manuscript.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This is very carefully analysed and written study describing the transcriptional differences between in vitro models of epithelia derived from cervix, foreskin and tonsil tissues. Importantly, they compare the findings to in vivo samples using publicly available data. The findings are significant and will be of interest to the scientific community. I cannot fault the analysis pathways or the conclusions, and the manuscript is a pleasure to read. I recommend it is accepted for publication as is.
Reviewer #3 (Significance (Required)):
This is an important study that is highly significant for researchers interested in epithelia tissue and infection. The data are clearly presented and the analysis is thorough. The authors state that they will make the data publicly available. This will be an important resource for the community.
We appreciate the kind words
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Referee #3
Evidence, reproducibility and clarity
This is very carefully analysed and written study describing the transcriptional differences between in vitro models of epithelia derived from cervix, foreskin and tonsil tissues. Importantly, they compare the findings to in vivo samples using publicly available data. The findings are significant and will be of interest to the scientific community. I cannot fault the analysis pathways or the conclusions, and the manuscript is a pleasure to read. I recommend it is accepted for publication as is.
Significance
This is an important study that is highly significant for researchers interested in epithelia tissue and infection. The data are clearly presented and the analysis is thorough. The authors state that they will make the data publicly available. This will be an important resource for the community.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
Summary
Jackson et al.'s manuscript describes an experiment that directly compares 3D organotypic assays created with primary human epithelial cells from foreskin, cervix and tonsil using histological and bulk RNA sequencing approaches. The authors convincingly show the retention of site-specific histological and transcriptomic differences between the stratified epithelial tissues in culture. Differentially expressed genes are identified and pathway analyses suggest genes that might be involved in the different differentiation processes between these tissue sites and differential regulation of ECM and immune pathways. Differentially expressed genes are used to develop a classifier for tissue identification, which is tested using GTEx data.
Major Comments
- The interferon stimulated genes of B cells and macrophages (from Mostafavi et al., 2016) are likely to be very different from those in epithelial cells, so the analysis presented in Figure 9 seems like a stretch to me.
- OPTIONAL: Further data comparing the nature and magnitude of the interferon responses of the three epithelia would improve interest in the manuscript but are not necessary for publication of the current dataset.
Minor Comments
- Details of n numbers and what each point represents should be added to Figure 1C. Are these points measurements from 25 um intervals of just one raft per donor? What are 'fields of view' here? Are measurements from one section or from multiple sections per raft?
- Page 12 - provide a figure/panel citation for the "micrograph derived from a tonsillectomy" that is suggested for comparison.
- In Figure 1 - Figure Supplement 1, how representative of the whole raft are these images? Does the extent of stratification change near to the edge of the collagen gel, for example? How well matched for location within a raft are the images shown?
- Page 24 - clarify uses of the phrase "down-regulated in tonsils". Presumably this section refers to tonsil epithelium in 3D organotypic rafts.
Typos
- Page 3 - "the cervix is lined with stratified squamous epithelia", should be epithelium.
- "J.G. Rheinwald" in in text references.
- Page 6 - 'or' not 'and' in first sentence of primary cell culture section.
Significance
This highly descriptive study provides a detailed analysis of a bulk RNA sequencing experiment comparing foreskin, cervix and tonsil 3D organotypic rafts. Retained histological and transcriptional differences between epithelial tissues of different origins in organotypic assays are well documented in the literature (e.g., parmoplantar vs non-parmoplantar skin, PMID: 36732947; airway tract, PMID: 32526206) so the observed differences between these three very distinct anatomical tissues are unsurprising overall. The data have been made available via SRA and a shiny web app and are likely to be of interest and use to other researchers working on these tissues in culture. The experiment was performed in matched cell culture conditions so replicates are well-controlled, if limited in number (n=3).
I am an epithelial cell biologist specializing in human cell culture models. I do not have sufficient computational background to comment in detail on the RNA sequencing methods or analysis within the manuscript.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #1
Evidence, reproducibility and clarity
The current study uses 3D organotypic rafts to culture primary keratinocytes from Foreskin, Tonsil and Cervix. Further the authors looked at the transcriptomic profiles of each tissue types to study similarities and differences depending on the tissue of origin as well as show the similarity in the tissue specific gene signatures and the ex-vivo samples (data from GTEx). As mentioned by authors Skin and Cervix keratinocytes have been previously cultured on collagen rafts however extending it to Tonsil provides resource and possibility of growing more tissue specific epithelial cells in 3D.
Major comments
- As the papers focus is to culture epithelial/ epidermal cells on 3D rafts, methods section needs more details about the raft composition, preparation, fibroblast embedding what was the plate size used for raft preparation and culturing of cells on those rafts. What culture media was used for epithelial raft cultures?
- Results: Figure 1, authors show IF staining's for COL17A1 as marker for basal cells and cornulin for differentiated layers. However, it is important to show how many cells in the basal layer are proliferative? (or how many layers of proliferative cells are present in different epithelia analysed here?) after 14 days majority of cells might already start losing their stemness potential (maybe staining for at least ki67 if staining for basal stem cell marker not possible? Along with loricrin or Involucrin might be good idea). This is also important as from supp fig 3 you can see F1 has higher expression of Loricrin, filaggrin etc as compared to all other samples indicating higher diff in this sample. Also, if authors can comment on what was the passage of cells used? And have they observed any difference in the re-epithelization in early passage versus late passage of keratinocytes?
- It is interesting to see Tonsillar 3D epithelia recapitulate the crypt and surface epithelia and authors also show this with gene expression profile, if possible (Optional), can authors show staining for crypt specific and surface specific markers.
- For all the Supplementary tables where only Ensembl ids are represented, please add gene Id column alongside (it is easier to get biological context from gene id for the reader rather than looking up Ensembl ids). Rename the file names to include the Supplementary file 1, 2, 3?
- Its excellent to see that in vitro tissue signature matched the in vivo tissue samples (Figure 8) but it will be interesting to show the gene expression differences if found any between the in vitro and in vivo samples that will give insight on the changes as result of in vitro system.
Minor comments
- Abstract: Give sample number (n?) and brief results about the genes that had tissue specific expression pattens.
- Gene names needs to be in Italics throughout.
- Introduction: page 5 line 9, authors claim that they based on comparisons they can "identify potential therapeutic targets for various disease" I think this statement either needs experimental evidence or statement / claim needs to be modified.
- Data submission to GEO???
- Typo (page 15, line 16 should be "HFK-down", same on page 23 "ectocervix", "endocervix", "uterus", so on, please correct, comma needs to be placed after "
- Page 24 last line is the heatmap referred here Fig 9B?
- Fig. 1 legends please indicate what F1, F2, F3, C1--- T1--- represent. Fig 1C Please add axis range/ values for protein atlas data as well.
- Can authors comment in discussion how was current 3D cervix cells on raft method different from Meyers, C., 1996 3D system?
Significance
This article does extend and validate the 3D raft culture method to different epithelial tissues in addition to Skin and cervix. This will be useful for the researchers using co culture systems and interested in understanding epithelial cell and immune cell interactions or host pathogen interacts etc
Describe your expertise: establishing and maintaining primary skin and oral keratinocyte cultures on feeders and 3D cultures on DEDs, Organoid cultures from oral keratinocytes, Oral cancer biology, Histopathology, transcriptomics study, Immuno-oncology.
-